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\frontmatter

\title{Vehicular emissions inventory with VEIN}
\author{Sergio Ibarra-Espinosa}
\date{2018-07-06}

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\chapter*{Preface}\label{preface}


This book is about the process for estimating vehicular emissions. This
process is complex and \emph{can} be dificult. It is required a big
amount of information related to traffic, emissions factors and then
process the outpus for the desired purpose. In this book this process is
addressed with the \textbf{VEIN} \citep{gmd-2017-193} model, which is an
R package available at \url{https://CRAN.R-project.org/package=vein}\}.

\section*{Purpose}\label{purpose}


I wrote this book to provide instructions to all possible users who want
to estimate vehicular emissions using VEIN. VEIN provides several
function which are related to each other reading different traffic
sources and emission factors for different pollutants. However,
incorporating all the functions with input/output (I/O) process can be
difficult. Moreover, communicating the recommended practices and
instructions to a increasingly broader audience can be more difficult.
Therefore, the purpose of this book is to reach the maximum amount of
interested people in a simple way. The language chosen is engligh
because it is the global language.

\section*{Structure}\label{structure}


Chapter \ref{intro} covers the introduction to this book including the
installation of R packages and dependent libraries to several operative
systems. Chapter \ref{basic} covers basics commands using R. Chapter
\ref{st} presents the function \emph{inventory} to produce an structure
of directories and scripts for runnin vein. Chapter \ref{traffic} covers
the required traffic data for inputting into VEIN including
differenttypes and sources of information. Chapter \ref{ef} includes the
emission factors that are included into the package and also, examples
for inputing and creating new emission factors. Chapter \ref{est}
presents the estimations functions and tips. Chapter \ref{post} includes
the functions to do post-estimations to generate emission data-bases and
also emissions at each street. Chapter \ref{he} and \ref{ep} provides
applications into air quality modeling, some studies with to investigte
health effects and lastly, the use of VEIN as a tool for environmental
planning.

\section*{About the author}\label{about-the-author}


Sergio Ibarra Espinosa is a chilean Environmental and Loss Prevention
Engineer from the Technologycal Metropolitan University (UTEM) where
study the health effects of air pollution using R in year 2007. Then
started working at the National Center for Environment (CENMA) in Chile
focused on emissions from vehicles, airports, biomass burning and
minning industry. Then obtained a MSc. in Environmental Planning and
Managment from the University of Chile, with a scholarship form CENMA.
Then obtained a PhD. in Meteorology at the Institute of Astronomy,
Geophysics and Atmospheric Sciences (IAG) from the University of São
Paulo (USP) with scholarship from CAPEs during the first year and Becas
Chile for the last three years. During his PhD. he learned programming
with R and developed the R package VEIN.

\mainmatter

\chapter{Introduction}\label{intro}

\section{Definitions and sources of
information}\label{definitions-and-sources-of-information}

Developing emissions inventories is a complex task. Therefore, before
entering into the details of the functions, it is good to provide some
initial definitions.

A good starting point is the book \emph{``The Art of Emissions
Inventorying''} \citep{pulles2010art}. This book provides general
description of what is an emissions inventory. One of the early sources
of information about emissions inventories and emissions factors is the
Environmental Protection Agency (U.S.EPA) and its compilation of
emission factors AP42
(\url{https://www.epa.gov/air-emissions-factors-and-quantification}). A
third source of information are the European EMEP/EEA air pollutant
emission inventory guidebook
(\url{https://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook/}).

An emissions inventory is the compilation of all mass emitted into the
atmosphere by activities in a defined area during a lapse of time. The
Eq. (\eqref{eq:efGERAL} shows the general form \citep{guia}:

\begin{equation}
Emission_{pollutant}=\sum_{activities} Activity reate \cdot Emission factor_{activity, pollutant}
\label{eq:efGERAL}
\end{equation}

There are two main tpes of inventories based on the application, one
type is for policy application and the other for scientific application
\citep{pulles2010art}.

\begin{itemize}
\tightlist
\item
  Inventories for policy application include the National Greenhouse Gas
  Inventory for the parties under the United Nations Framework on
  Climate Change \citep{unfccc}.
\item
  Emissions inventories with scientific applications include the
  Emission Database for Global Atmospheric Research EDGAR \citep{edgar}.
\end{itemize}

Emissions inventories can be also multi medi ainventories, such as the
Pollutant Release and Transfer Registers (PRTR) which include pollutants
released to air, water and soil and transferred off-site for treatment
or disposal
(\url{http://www.oecd.org/chemicalsafety/pollutant-release-transfer-register/}).
However, the type of inventory covered in this book cover only the
emissions released into the atmosphere.

\subsection{Approaches}\label{approaches}

It is necessary show some definitions of vehicular emissions inventories
approaches, which in this case comes from the European Emissions
Guidelines \citep{NtziachristosSamaras2016}:

\begin{itemize}
\tightlist
\item
  \textbf{Top-down} inventories are uses input activity traffic data as
  fleet statistics, fuel consumption, representative speeds and country
  balances. The emissions factors uses representative speeds. This
  inventory are also know as \emph{static}.
\item
  \textbf{Bottom-up} Input activity traffic data comes from traffic
  counts, traffic simulations, vehicle composition, speed recordings and
  length of the raods. The emission factors are speed or/and
  acceleration functions. These inventories are also know as
  \emph{dynamic}.
\item
  \textbf{Reconciliation} Both approaches can be reconciliated in urban
  emissions inventories with comparison of total mileage, cold start
  mileage or emission factors in order that the comparison with the
  total fuel consumption on the study area is satisfactory. This means
  that bottom-up and top-down vehicular emissions must match the fuel
  sales over a region of study.
\end{itemize}

\section{Installation}\label{installation}

The VEIN r-package can be installed from Comprehensive R Archive Network
\href{https://cran.r-project.org/}{CRAN} directly:

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{# CRAN}
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"vein"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

VEIN can be also installed from github:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(devtools)}
\KeywordTok{install_github}\NormalTok{(}\StringTok{"ibarraespinosa/vein"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

The github installation requires that the \emph{devtools} package
\citep{devtools} must be installes, including its dependencies.

\section{Required R packages and
dependencies}\label{required-r-packages-and-dependencies}

In order to use the VE0IN package it is necesary to download other
r-packages and its dependencies. The current dependencies of VEIN are:

\begin{itemize}
\tightlist
\item
  \textbf{sp}. This is a package that provides clases and methods for
  spatial data \citep{sp} considering points, lines, polygons and grids.
\item
  \textbf{rgeos}. It is an interface to the ``geometry Engine - Open
  Source (`GEOS') using the C `API' for topology operations on
  geometries'' \citep{rgeos}. Depends on the library GEOS
  (\url{http://trac.osgeo.org/geos}).
\item
  \textbf{rgdal}. Provides bindings to the `Geospatial' Data Abstraction
  Library (`GDAL') (\textgreater{}= 1.6.3) and access to
  projection/transformation operations from the `PROJ.4' library
  \citep{rgdal}. Depends on the library GDAL
  (\url{http://www.gdal.org/}) and PROJ (\url{http://proj4.org/}).
\item
  \textbf{raster}. It is a package that provides tools for analyses and
  modeling of geographic data \citep{raster}.
\item
  \textbf{units}. It i a package dor measurement units in R
  \citep{units}. This package depends on the library udunits2
  (\url{http://www.unidata.ucar.edu/software/udunits/}). I
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\KeywordTok{c}\NormalTok{(}\StringTok{"sp"}\NormalTok{, }\StringTok{"rgeos"}\NormalTok{, }\StringTok{"rgdal"}\NormalTok{, }\StringTok{"raster"}\NormalTok{, }\StringTok{"units"}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

In order to install these package it is necessary to install the
dependencies into each operational system. The instructions are:

\textbf{Ubuntu}

\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{sudo}\NormalTok{ add-apt-repository ppa:ubuntugis/ubuntugis-unstable}
\FunctionTok{sudo}\NormalTok{ apt-get update}
\FunctionTok{sudo}\NormalTok{ apt-get install libudunits2-dev libgdal-dev libgeos-dev l}
\ExtensionTok{ibproj-dev} 
\end{Highlighting}
\end{Shaded}

\textbf{Fedora}

\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{sudo}\NormalTok{ dnf install gdal-devel proj-devel proj-epsg proj-nad }
\ExtensionTok{geos-devel}\NormalTok{ udunits2-devel}
\end{Highlighting}
\end{Shaded}

\textbf{Debian and other}

Dockerfiles from \href{https://github.com/rocker-org/geospatial}{rocker
geospatial}.

\textbf{Windows} Via
\href{https://cran.r-project.org/bin/windows/Rtools/}{Rtools}.

\textbf{MAC OS}

\begin{Shaded}
\begin{Highlighting}[]
\ExtensionTok{brew}\NormalTok{ unlink gdal}
\ExtensionTok{brew}\NormalTok{ tap osgeo/osgeo4mac }\KeywordTok{&&} \ExtensionTok{brew}\NormalTok{ tap --repair}
\ExtensionTok{brew}\NormalTok{ install proj}
\ExtensionTok{brew}\NormalTok{ install geos}
\ExtensionTok{brew}\NormalTok{ install udunits}
\ExtensionTok{brew}\NormalTok{ install gdal2 --with-armadillo --with-complete }
\ExtensionTok{--with-libkml}\NormalTok{ --with-unsupported}
\ExtensionTok{brew}\NormalTok{ link --force gdal2}
\end{Highlighting}
\end{Shaded}

The new version of VEIN wil replace the spatial r-packages dependencies
of raster, rgeos and rgdal by the new \textbf{sf} \citep{sf} package and
the units \citep{units} package.

sf is a package that provides simple features access for R \citep{sf},
which can be installed in this way:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{install.packages}\NormalTok{(}\StringTok{"sf"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

or via devtools

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(devtools)}
\KeywordTok{install_github}\NormalTok{(}\StringTok{"r-spatial/sf"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\section{Data inside the VEIN model}\label{data-inside-the-vein-model}

There are several datasets available inside the model:

\subsection{Emission factors from
CETESB}\label{emission-factors-from-cetesb}

\begin{itemize}
\tightlist
\item
  \textbf{fe2015}: Emission factors from Environmental Agency of Sao
  Paulo (CETESB). Pollutants included: ``CH4'', ``CO'', ``CO2'', ``HC'',
  ``N2O'', ``NMHC'', ``NOx'' and ``PM''. The type of vehicles included
  are Passenger Cars (PC) and
\end{itemize}

usage:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein, }\DataTypeTok{quietly =}\NormalTok{ T)}
\KeywordTok{data}\NormalTok{(fe2015)}
\end{Highlighting}
\end{Shaded}

\begin{table}

\caption{\label{tab:unnamed-chunk-11}Emission factors from CETESB in VEIN model}
\centering
\begin{tabular}[t]{rrlrr}
\toprule
Age & Year & Pollutant & PC\_G & LT\\
\midrule
1 & 2015 & CO & 0.171 & 0.027\\
2 & 2014 & CO & 0.216 & 0.012\\
3 & 2013 & CO & 0.237 & 0.012\\
4 & 2012 & CO & 0.273 & 0.005\\
5 & 2011 & CO & 0.275 & 0.379\\
6 & 2010 & CO & 0.204 & 0.416\\
\bottomrule
\end{tabular}
\end{table}

\subsection{Mileage functions of Brazilian
Fleet}\label{mileage-functions-of-brazilian-fleet}

\begin{itemize}
\tightlist
\item
  \textbf{fkm}: List of functions of mileage in km fro Brazilian fleet.
  Includes mileage functions based on more than 2 million recordings of
  vehicles \citep{BruniBales2013}. It consists in a list of age
  functions. The type of vehicles are Passneger Cars (PC), Light
  Commercial Vehicles (LCV), Small Bus (SBUS), Trucks, Articulated
  Trucks (ATRUCKS), Motorcycles (MOTO) and Light vehicles' (LDV). The
  fuels are Gasoline using 25\% of ethanol (E25), Ethanol 100\% (E100)
  and Diesel with 5\% of biodiesel (B5). There are also vehicles with
  flex engines which can run either with E25, E100 or any mixture in
  between \citep{2005-01-4130}. The Fig. (\ref{fig:mil} shows the
  mileage of PC using fuel E25.
\end{itemize}

usage:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein, }\DataTypeTok{quietly =}\NormalTok{ T)}
\KeywordTok{data}\NormalTok{(fkm)}
\KeywordTok{names}\NormalTok{(fkm)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "KM_PC_E25"     "KM_PC_E100"    "KM_PC_FLEX"    "KM_LCV_E25"   
##  [5] "KM_LCV_FLEX"   "KM_PC_B5"      "KM_TRUCKS_B5"  "KM_BUS_B5"    
##  [9] "KM_LCV_B5"     "KM_SBUS_B5"    "KM_ATRUCKS_B5" "KM_MOTO_E25"  
## [13] "KM_LDV_GNV"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{age <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{50}
\NormalTok{mileage <-}\StringTok{ }\NormalTok{fkm[[}\StringTok{"KM_PC_E25"}\NormalTok{]](}\DecValTok{1}\OperatorTok{:}\DecValTok{50}\NormalTok{) }
\KeywordTok{par}\NormalTok{(}\DataTypeTok{mar =} \KeywordTok{c}\NormalTok{(}\DecValTok{4}\NormalTok{, }\DecValTok{4}\NormalTok{, }\FloatTok{.1}\NormalTok{, }\FloatTok{.1}\NormalTok{))}
\KeywordTok{plot}\NormalTok{(}\DataTypeTok{y =}\NormalTok{ mileage, }\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{pch =} \DecValTok{19}\NormalTok{, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/mil-1} 

}

\caption{Mileage of PC using E25}\label{fig:mil}
\end{figure}

\begin{itemize}
\tightlist
\item
  \textbf{net}: Road network of the west part of Sao Paulo city. It
  consistes in a SpatialLinesDataFrame (class of sp) with the attributes
  ldv (light duty vehicles, \(1 \cdot h^{-1}\)), hdv (heavy duty
  vehicles, \(1 \cdot h^{-1}\)), ps (peak speed, \(km \cdot h^{-1}\)),
  ffs (free flow speed, \(km \cdot h^{-1}\)), tstreet (type of street),
  lanes (number of lanes), capacity (capacity fo vehicles at each link,
  1/h) and tmin (time for travelling each link, min). The Fig.
  (\ref{traffic} shows more details.
\end{itemize}

\subsection{Temporal factors for Passenger
Cars}\label{temporal-factors-for-passenger-cars}

Temporal factors (TF) are marix of jourly traffic data normalized for
the hour of interest, normally the morning rush hour 08:00-09:00 in
local time (LT) \citep{gmd-2017-193}. This dataset cover 168 hours of
one week and it is based on traffic counts of toll stations near the
center of the city of SãoPaulo, Brazil. The Fig. (\ref{fig:tf} shows the
temporal factors for PC.

usage:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein, }\DataTypeTok{quietly =}\NormalTok{ T)}
\KeywordTok{data}\NormalTok{(pc_profile)}
\NormalTok{tf <-}\StringTok{ }\KeywordTok{unlist}\NormalTok{(pc_profile)}
\NormalTok{hours <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{168}
\KeywordTok{par}\NormalTok{(}\DataTypeTok{mar =} \KeywordTok{c}\NormalTok{(}\DecValTok{4}\NormalTok{, }\DecValTok{4}\NormalTok{, }\FloatTok{.1}\NormalTok{, }\FloatTok{.1}\NormalTok{))}
\KeywordTok{plot}\NormalTok{(}\DataTypeTok{y =}\NormalTok{ tf, }\DataTypeTok{x =}\NormalTok{ hours, }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/tf-1} 

}

\caption{Temporal Factors for PC}\label{fig:tf}
\end{figure}

\subsection{Profile for cold starts of Passenger
Cars}\label{profile-for-cold-starts-of-passenger-cars}

This is a profile with the hourly percentage of cold starts of Passenger
Cars. This data covers 24 hours of a working day and it is based cold
start recordings during the implementation of the International Vehicle
Emissions (IVE) model \citep{Davisetal2005} in São Paulo \citep{ivesp}.
The Fig. (\ref{fig:cold} shows the cold-start profile.

usage:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein, }\DataTypeTok{quietly =}\NormalTok{ T)}
\KeywordTok{data}\NormalTok{(pc_cold)}
\NormalTok{cold <-}\StringTok{ }\KeywordTok{unlist}\NormalTok{(pc_cold)}
\NormalTok{hours <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{24}
\KeywordTok{par}\NormalTok{(}\DataTypeTok{mar =} \KeywordTok{c}\NormalTok{(}\DecValTok{4}\NormalTok{, }\DecValTok{4}\NormalTok{, }\FloatTok{.1}\NormalTok{, }\FloatTok{.1}\NormalTok{))}
\KeywordTok{plot}\NormalTok{(}\DataTypeTok{y =}\NormalTok{ cold, }\DataTypeTok{x =}\NormalTok{ hours, }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/cold-1} 

}

\caption{Cold-starts profile for PC}\label{fig:cold}
\end{figure}

\chapter{Basics of R}\label{basic}

\section{Brief history}\label{brief-history}

R is a programming language developed by Ross Ihaka and Robert Genleman
in 1993 \citep{ihaka1996r}. A good resource about R is the book
programming for data science \citep{peng2015r}. R can be explained as a
free version of S-PLUS programming language
(\url{http://www.solutionmetrics.com.au/products/splus/default.html}).
It is free and recognized a GNU spftware
(\url{https://www.gnu.org/software/software.html}) with GNU General
Public License (GPLV2-GPLV3) license. This feature allowed that many
developers started improving and adding code over the years.

R includes several statistical functions, therefore, user who just want
to use the base capabilities of R are not forced to learn R-programming.
However, the evolution from user to developer in R has been facilitated
with numerous publications such as the book ``R packages: organize,
test, document, and share your code'' \citep{wickham2015r}, or ``The art
of R programming: A tour of statistical software design''
\citep{matloff2011art}.

\subsection{Installation}\label{installation-1}

R can be installed in different OS including Linux, Windows, Mac and
Solaris. The webpage \url{https://cran.r-project.org/} shows how to
install R in any platform. A popular Integrated development environment
(IDE) is \textbf{RStudio} \url{https://www.rstudio.com/}, which contains
many useful integrated options. However, R can be run on the terminal
and use any text editor to write scripts.

\section{Using R}\label{using-r}

If we type at the R promt

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{x <-}\StringTok{ }\DecValTok{1}   \CommentTok{# Not printing  results}
\NormalTok{x        }\CommentTok{# Print results}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{print}\NormalTok{(x) }\CommentTok{# Explicit print x}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

Rhe integer 1 was \emph{assigned} (\textless{}-) to x, then writing x or
print(x) will show the value of x. Instead of numbers, can be assigned
other expressions such as strings, dates or other objects. R includes
several statistical functions. For instance, if we type `pi' at the
terminal, it will print the pi number.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{pi}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3.141593
\end{verbatim}

\subsection{R objects}\label{r-objects}

There are five basic classes (or atomic classes).

\begin{itemize}
\tightlist
\item
  character.
\item
  numeric.
\item
  integer.
\item
  complex.
\item
  logical (TRUE/FALSE or just T or F).
\end{itemize}

Objects of the same class can be grouped in \textbf{vectors}. Vectors
are created with writting \textbf{c()} with the elements of the vector
inside the parenthesis, separated by colon. In fact, \textbf{c} is a
builtin function to create vectors. In this case, the vector v1 contain
a sequence of three integers, from 1 to 3. The resulting class is
numeric.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{v1 <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{3}
\NormalTok{v2 <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{, }\DecValTok{2}\NormalTok{, }\DecValTok{3}\NormalTok{)}
\KeywordTok{identical}\NormalTok{(v1, v2)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] FALSE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{v1}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1 2 3
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{class}\NormalTok{(v1)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "integer"
\end{verbatim}

With the operator \texttt{{[}} we can get specific elements of your
vectors. In the following code it is also used the function
\texttt{length}, which simply returns the length of an object.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{v1[}\DecValTok{1}\NormalTok{]          }\CommentTok{# first element of v1}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{v1[}\KeywordTok{length}\NormalTok{(v1)] }\CommentTok{# last element element of v1}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3
\end{verbatim}

If we create a vector with numbers and letter, the resulting vector will
be ``character'' converting the numbers in characters.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{v2 <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{,}\StringTok{"a"}\NormalTok{)}
\NormalTok{v2}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "1" "2" "3" "a"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{class}\NormalTok{(v2)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "character"
\end{verbatim}

Objects can be converted to other classes with the ``as.*" functions.
For instance, If we convert the vector v2 to numeric, it will recognize
the numbers adding an NA into the position of the character.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{as.numeric}\NormalTok{(v2)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Warning: NAs introduzidos por coerção
\end{verbatim}

\begin{verbatim}
## [1]  1  2  3 NA
\end{verbatim}

\subsection{Factors}\label{factors}

Factors represents categorical data. A typical example is a character
vector of days of the week. The function \texttt{factor} creates
factors, to see help type \texttt{?factor}. The following example will
use this function with the arguments \texttt{x} and \texttt{levels}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dow <-}\StringTok{ }\KeywordTok{factor}\NormalTok{(}\DataTypeTok{x =} \KeywordTok{c}\NormalTok{(}\StringTok{"Wednesday"}\NormalTok{, }\StringTok{"Monday"}\NormalTok{, }\StringTok{"Thursday"}\NormalTok{, }\StringTok{"Friday"}\NormalTok{),}
              \DataTypeTok{levels =}  \KeywordTok{c}\NormalTok{(}\StringTok{"Monday"}\NormalTok{, }\StringTok{"Tuesday"}\NormalTok{, }\StringTok{"Wednesday"}\NormalTok{, }\StringTok{"Thursday"}\NormalTok{,}
                          \StringTok{"Friday"}\NormalTok{))}
\NormalTok{dow}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] Wednesday Monday    Thursday  Friday   
## Levels: Monday Tuesday Wednesday Thursday Friday
\end{verbatim}

\subsection{Missing Values}\label{missing-values}

Missing values in data-sets are a typical source of headache in r users.
Fortunatly, R counts with several tools to avoid these headaches. These
tools are fhe functions \texttt{is.na} to check for NA (not available)
and \texttt{is.nan} (not a number). This functions returns logical
values.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{n <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{, }\OtherTok{NA}\NormalTok{)}
\KeywordTok{is.na}\NormalTok{(n)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] FALSE  TRUE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{n <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{, }\OtherTok{NaN}\NormalTok{)}
\KeywordTok{is.na}\NormalTok{(n)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] FALSE  TRUE
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{is.nan}\NormalTok{(n)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] FALSE  TRUE
\end{verbatim}

\subsection{Matrices}\label{matrices}

Matrices are structure with rows and columns. They can be created using
the \texttt{matrix} function and also, using vectors. Remember, if you
want to know more about any function you just have to type ? and
function, for instance:\texttt{?matrix} will open the help documentation
for the function \texttt{matrix}. Let's create a matrix using vectors:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{12}  \CommentTok{#numeric vector}
\NormalTok{(m <-}\StringTok{ }\KeywordTok{matrix}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ a, }\DataTypeTok{nrow =} \DecValTok{3}\NormalTok{, }\DataTypeTok{ncol =} \DecValTok{4}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
\end{verbatim}

We can check the dimensions of our matrix \texttt{m} with \texttt{dim},
which are 3 and 4.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{dim}\NormalTok{(m)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3 4
\end{verbatim}

In order to get the elements of matrix \texttt{m} we can use
\texttt{{[}} operator:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{m[}\DecValTok{1}\NormalTok{, }\DecValTok{1}\NormalTok{] }\CommentTok{# first element}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{m[, }\DecValTok{1}\NormalTok{]  }\CommentTok{# firs column}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1 2 3
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{m[}\DecValTok{1}\NormalTok{, ]  }\CommentTok{# firs row}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1]  1  4  7 10
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{m[}\DecValTok{3}\NormalTok{,}\DecValTok{4}\NormalTok{]  }\CommentTok{# last element}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 12
\end{verbatim}

\subsection{Arrays}\label{arrays}

Arrays are like matrices inside other matrices. In fact, a matrix is a
2-dimensional array. Let's create an array create an array using the
same vector a and same dimensiones of m, 3 and 4. \texttt{array} has
three arguments, \texttt{data}, \texttt{dim} and \texttt{dimnames}. In
th argument \texttt{dim} let's add the number 2, resulting in an array
of two matrices identical to \texttt{m}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{(a <-}\StringTok{ }\KeywordTok{array}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ a, }\DataTypeTok{dim =} \KeywordTok{c}\NormalTok{(}\DecValTok{3}\NormalTok{,}\DecValTok{4}\NormalTok{,}\DecValTok{2}\NormalTok{)))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
\end{verbatim}

We can subset elements from array also with the \texttt{{[}} operator.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a[}\DecValTok{1}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{1}\NormalTok{] }\CommentTok{# first element}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{dim}\NormalTok{(a)     }\CommentTok{# dimensions}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3 4 2
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a[}\DecValTok{3}\NormalTok{, }\DecValTok{4}\NormalTok{, }\DecValTok{2}\NormalTok{] }\CommentTok{# last element}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 12
\end{verbatim}

\subsection{Lists}\label{lists}

List are objects that can contain elements of different classes. I like
to call lists as bags. In a bag you can put almost anything. Also,
inside the bag, you can put other bags with different things. This means
that you can have a list of lists of any R object. Lists can be created
with the \texttt{list} function. Let's create an empty list. Then we
will use the vecctor \texttt{a} to create another list.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{3}          \CommentTok{# vector of three elements}
\NormalTok{l1 <-}\StringTok{ }\KeywordTok{list}\NormalTok{()      }\CommentTok{# empty list}
\NormalTok{l1 <-}\StringTok{ }\KeywordTok{list}\NormalTok{(a)     }\CommentTok{# vector of three elements}
\KeywordTok{length}\NormalTok{(l1)        }\CommentTok{# length 1}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{l1 <-}\StringTok{ }\KeywordTok{as.list}\NormalTok{(a)  }\CommentTok{# vector a as list}
\KeywordTok{length}\NormalTok{(l1)        }\CommentTok{# three elements }
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3
\end{verbatim}

As mentioned, the list can have lists inside.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{l1 <-}\StringTok{ }\KeywordTok{list}\NormalTok{(}\KeywordTok{list}\NormalTok{(a),                    }\CommentTok{# three numeric elements}
            \KeywordTok{list}\NormalTok{(}\OtherTok{TRUE}\NormalTok{, }\OtherTok{TRUE}\NormalTok{, }\OtherTok{FALSE}\NormalTok{))   }\CommentTok{# three logical elements}
\KeywordTok{length}\NormalTok{(l1)                             }\CommentTok{# length 2}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 2
\end{verbatim}

\subsection{Data-Frames}\label{data-frames}

``Data-frames are used to store tabular data in R'' \citep{peng2015r}.
You can think in data-frames as spreadsheet-like objects. They are
similar with matrix and you can have a matrix and a data-frame with the
same dimensions. Basically, you have rows and columns, columns can have
different classes, and the columns usually have a one-word name.

As this type of object is very used by scientists, there are R packages
created to work with this type of ojects. Below some of them:

\begin{itemize}
\tightlist
\item
  \texttt{data.frame}: This is not a package, for a class and function
  from the \texttt{base}. With this functionyou can create data-frames.
\item
  \texttt{data-table} \citep{datatable}: \texttt{data.tables} objects
  has the class \texttt{data.table} which inherits from the class
  \texttt{data-frame}, which means that they share common
  characteristics. However, the big difference with \texttt{data-table}
  is the efficiency with memory. It includes function such as
  \texttt{fread} for reading millions of observations in seconds.
\item
  \texttt{tidyr}\citep{tidyr}: Sometimes your data is in long format and
  you need it in wide format, and viceversa. This package does the job.
\item
  \texttt{readxl} \citep{readxl}: Good package for importing Excel and
  LibreOffice files. I recommend this packages for newbies.
\item
  \texttt{sf} \citep{sf}: This package presents the class \texttt{sf}
  and introduce the list-column of spatial geometry.
\end{itemize}

Let's create a data-frame.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{3}
\NormalTok{b <-}\StringTok{ }\DecValTok{3}\OperatorTok{:}\DecValTok{5}
\NormalTok{(ab <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(a, b))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##   a b
## 1 1 3
## 2 2 4
## 3 3 5
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{class}\NormalTok{(ab)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "data.frame"
\end{verbatim}

\subsection{Names}\label{names}

In R you can put names in almost everything. Below examples for
\texttt{vectors}, \texttt{matrix} and \texttt{data.frames}.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{names}\NormalTok{(ab)                }\CommentTok{# original names}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "a" "b"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{names}\NormalTok{(ab) <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"c"}\NormalTok{, }\StringTok{"d"}\NormalTok{)}
\KeywordTok{names}\NormalTok{(ab)                }\CommentTok{# new names}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "c" "d"
\end{verbatim}

\section{Inputting data into R}\label{inputting-data-into-r}

Your data can be in different formats. In this sections i will show you
how you can input text and comma separated values files, which is a very
common task. Let's say that you uses an spreadsheet program such as
Microsoft Excel or LibreOffice. Then you might want to export your data
into R.

\begin{itemize}
\tightlist
\item
  Your first check if the first row of your data has name or not. If
  they do have names, this means that they have a header.
\item
  Then click on `Save as' and search text file with extension .txt or
  CSV.
\item
  Then you your file with a note bloc, gedit or other tool to check if
  the decimal character is point `.' and the separator character is
  comma `,' or semicolon `;' or other character. This depends on the
  regional confugarion of your spreadsheet software.
\item
  If your file has extension `.txt', use \texttt{read.table} and if is
  `.csv', use \texttt{read.csv}.
\end{itemize}

Once you check, you can import your `.txt' or `.csv' files in R:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{a <-}\StringTok{ }\KeywordTok{read.csv}\NormalTok{(}\StringTok{"path/to/file.csv"}\NormalTok{, }\DataTypeTok{sep =} \StringTok{","}\NormalTok{, }\DataTypeTok{header =}\NormalTok{ T)}
\NormalTok{a <-}\StringTok{ }\KeywordTok{read.tablesv}\NormalTok{(}\StringTok{"path/to/file.txt"}\NormalTok{, }\DataTypeTok{sep =} \StringTok{","}\NormalTok{, }\DataTypeTok{header =}\NormalTok{ T)}
\end{Highlighting}
\end{Shaded}

\chapter{Structuring an Emissions Inventory}\label{st}

The description file of vein says:

Elaboration of vehicular emissions inventories, consisting in four
stages,

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  pre-processing activity data,
\item
  preparing emissions factors,
\item
  estimating the emissions and
\item
  post-processing of emissions in maps and databases.
\end{enumerate}

This means that vein provide functions to be used in each of the stages.
However, in order follow these stages it is needed a structure of
directories with scripts with vein functions. Therefore, it was created
the function \texttt{inventory}, to create this structure.

\section{Overview of emissions inventorying using
VEIN}\label{veinstructure}

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{figuras/dia1} 

}

\caption{Structuring an emissions inventories with VEIN}\label{fig:diavein}
\end{figure}

\section{\texorpdfstring{The \texttt{inventory}
function}{The inventory function}}\label{the-inventory-function}

The inventory function creates creates a structure of directories and
scripts to run vein. This structure is a suggestion and the user can use
any other. The following code shows the arguments of the function. This
is done using the base function \texttt{args} with \texttt{inventory}.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(inventory)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (name, vehcomp = c(PC = 1, LCV = 1, HGV = 1, BUS = 1, 
##     MC = 1), scripts = TRUE, show.dir = TRUE, show.scripts = FALSE, 
##     clear = TRUE, rush.hour = FALSE) 
## NULL
\end{verbatim}

The arguments are \texttt{name}, \texttt{vehcomp}, \texttt{scripts},
\texttt{show.dir}, \texttt{show.scripts} and \texttt{clear}.

\begin{itemize}
\tightlist
\item
  The argument \texttt{name}
\end{itemize}

name is a word to be used as main directory where directories and
scripts are stored in. It consists in \emph{one} word with ascii
characters. It is also recommended to be used without any special
character or accents, for instance:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{name =}\StringTok{ }\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{)}
\KeywordTok{inventory}\NormalTok{(}\DataTypeTok{name =}\NormalTok{ name, }\DataTypeTok{show.dir =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{show.scripts =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{itemize}
\tightlist
\item
  The argument \texttt{vehcomp}
\end{itemize}

This argument is very important and comes from \emph{Vehicular
Composition}, which is the classification of the fleet by type of use,
type of fuel, size of engine and gross weight, based on definitions of
\citet{Corvalanetal2002}. There is also the \emph{technological
composition} of the fleet which relates the technological modifications
of the car in order to accomplish the emissions standards. However, in
vein uses a distribution by age of use as \ref{traffic} shows.

The vehicular composition \texttt{vehcomp} has 5 types of vehicles:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Passenger Carss (PC).
\item
  Light Commercial Vehicles (LCV).
\item
  Heavy Good Vehicles or trucks (HGV).
\item
  Buses (BUS).
\item
  Motorcycles (MC).
\end{enumerate}

The default value of this argument is:
\texttt{c(PC\ =\ 1,\ LCV\ =\ 1,\ HGV\ =\ 1,\ BUS\ =\ 1,\ MC\ =\ 1)},
which means that there are 1 types of PC, 1 of LCV, 1 of trucks, 1 of
buses and 1 types of motorcycles. This vehicular composition is only for
ilutrasting that the user can chnge these values according its own data.
Appendix B shows the vehicular composition from the vehicular emissions
inventory of the Environmental Agency of São Paulo, Brazil
\citep{CETESB2015}. In Brazil, the fuel used in vehicles is blended with
ethanol with and biodiesel. The user can use \textbf{any} vehicular
composition that representes its fleet with up-to 99 type of vehicles
per category. For instance, if there are 4 types of PC in a fleet,
\texttt{PC\ =\ 4}, and each one has a age of use distribution.

\begin{itemize}
\tightlist
\item
  The argument \texttt{show.dir}
\end{itemize}

This is a logical argument to decide if the output of the function will
return print the new directories or not. The use is:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{name =}\StringTok{ }\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{)}
\KeywordTok{inventory}\NormalTok{(}\DataTypeTok{name =}\NormalTok{ name, }\DataTypeTok{show.dir =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{show.scripts =} \OtherTok{FALSE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## files at /tmp/RtmpXTmmRT/YourCity
\end{verbatim}

\begin{verbatim}
## Directories:
##  [1] "/tmp/RtmpXTmmRT/YourCity"             
##  [2] "/tmp/RtmpXTmmRT/YourCity/ef"          
##  [3] "/tmp/RtmpXTmmRT/YourCity/emi"         
##  [4] "/tmp/RtmpXTmmRT/YourCity/emi/BUS_01"  
##  [5] "/tmp/RtmpXTmmRT/YourCity/emi/HGV_01"  
##  [6] "/tmp/RtmpXTmmRT/YourCity/emi/LCV_01"  
##  [7] "/tmp/RtmpXTmmRT/YourCity/emi/MC_01"   
##  [8] "/tmp/RtmpXTmmRT/YourCity/emi/PC_01"   
##  [9] "/tmp/RtmpXTmmRT/YourCity/est"         
## [10] "/tmp/RtmpXTmmRT/YourCity/images"      
## [11] "/tmp/RtmpXTmmRT/YourCity/network"     
## [12] "/tmp/RtmpXTmmRT/YourCity/post"        
## [13] "/tmp/RtmpXTmmRT/YourCity/post/df"     
## [14] "/tmp/RtmpXTmmRT/YourCity/post/grids"  
## [15] "/tmp/RtmpXTmmRT/YourCity/post/streets"
## [16] "/tmp/RtmpXTmmRT/YourCity/profiles"    
## [17] "/tmp/RtmpXTmmRT/YourCity/veh"
\end{verbatim}

\texttt{inventory} creates the direcrory ``YourCity'' and the sub
directories: daily, ef, emi, est, images, network and veh.

\begin{itemize}
\tightlist
\item
  \textbf{daily}: it is for storing the profiles saved as .csv files.
  For instance, \texttt{data(pc\_profile)} is a matrix than could be
  saved as .csv.
\item
  \textbf{ef}: it is for storing the emission factors data-frame,
  similar to \texttt{data(fe2015)} but including one column for each of
  the categories of the vehicular composition. For instance, if PC = 5,
  there should be 5 columns with emission factors in this file. If LCV =
  5, another 5 columns should be present, and so on.
\item
  \textbf{emi}: Directory with subdirectories matching the vehicular
  composition for saving the estimates. It is suggested to use .rds
  extension instead of .rda.
\item
  \textbf{est}: Directory with subdirectories matching the vehicular
  composition for for storing the scripts named \texttt{input.R}.
\item
  \textbf{images}: Directory for saving images.
\item
  \textbf{network}: Directory for saving the road network with the
  required attributes. This file will include the vehicular flow per
  street to be used by functions \texttt{age\_ldv}, \texttt{age\_hdv},
  \texttt{age\_moto} or \texttt{my\_ldv}.
\item
  \textbf{post}: Directory for storing the processed emissions. It
  includes the directories \textbf{df} for emissions by age of use, hou
  and other parameters, \textbf{streets} for storing the total emissions
  by streets and \textbf{grids} for storing total gridded emissions by
  pollutant.
\item
  \textbf{veh}: Directory for storing the distribution by age of use of
  each category of the vehicular composition. Those are data-frames with
  number of columns with the age distribution and the number of rows as
  the number of streets. The class of these objects is ``Vehicles''.
\end{itemize}

devt - The argument \texttt{scripts}

This argument adds scripts into the directories. The default is TRUE.
The type of scrits create are:

\begin{itemize}
\item
  main.R: Adds the \texttt{setwd("YourCity")}, \texttt{library(vein)},
  \texttt{source(traffic.R)}, some comments and a loop for source all
  inputs. It is recommended not using the loop till it is certain that
  all scripts are correct.
\item
  traffic.R: Includes two lines with exampmles of how to use the
  \texttt{age\_ldv} function and saving the output in the directory veh.
\item
  input.R: Each file has the scripts for reading the network, vehicles,
  emission factors and estimating emissions.
\item
  The argument \texttt{show.scripts}
\end{itemize}

This argument prints the scripts created:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{name =}\StringTok{ }\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{)}
\KeywordTok{inventory}\NormalTok{(}\DataTypeTok{name =}\NormalTok{ name, }\DataTypeTok{show.dir =} \OtherTok{FALSE}\NormalTok{, }\DataTypeTok{show.scripts =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## files at /tmp/RtmpXTmmRT/YourCity
\end{verbatim}

\begin{verbatim}
## Scripts:
## [1] "est/BUS_01_input.R" "est/HGV_01_input.R" "est/LCV_01_input.R"
## [4] "est/MC_01_input.R"  "est/PC_01_input.R"  "main.R"            
## [7] "post.R"             "traffic.R"
\end{verbatim}

\begin{itemize}
\tightlist
\item
  The argument \texttt{clear}
\end{itemize}

This is a logical argument that deletes recursively when TRUE, or not
when FALSE, the directory and created another one. Default is TRUE.

The following chapters show the use of other vein functions inside an
structure of directories and scripts with the name
\texttt{inventory("YourCity")}.

\chapter{Traffic data}\label{traffic}

Traffic data is read in VEIN as spatial information. In other words,
traffic data must in any vectorial line spatial format with the drivers
provided by the library \textbf{GDAL}
(\url{http://www.gdal.org/ogr_formats.html}) called by the packages
\textbf{rgdal} or \textbf{sf}. This means that traffic data must in in
any GDAL spatial format. The Eq. \eqref{eq:flow} show how traffic data is
treated into vein.

\begin{equation}
F^*_{i,j,k} = Q_{i} \cdot VC_{i,j} \cdot Age_{j,k}
\label{eq:flow}
\end{equation}

where \(F^*_{i,j,k}\) is the vehicular flow at street link \(i\) for
vehicle type \(j\) by age of use \(k\). \(j\) defines the vehicular
composition according to their type of use, type of fuel, size of engine
and gross weight, based on definitions of \citep{Corvalanetal2002}.
\(Q_i\) is the traffic flow at street link \(i\). \(VC_{i,j}\) is the
fraction of vehicles varying according to the type of vehicles \(j\) in
the composition for street link \(i\). \(Age_{j,k}\) is the age
distribution by vehicular composition \(j\) and age of use \(k\). This
Equation shows that \(VC\) splits the total vehicular flow \(Q\) to
identify the vehicular fraction, which varies according to the type of
fuel, size of motor and gross weight. For example, if \(Q\) is light
duty vehicles (LDV) and it is known that 5\% of the \(Q\) are passenger
cars (PC), with engine lesser than 1400 cc, \(VC\) is 0.05. This
characterization of the fleet depends on the amount and quality of the
available information. VEIN then multiplies the traffic with \(Age\) to
obtain the amount of each type of vehicle by age of use.

\section{Sources of traffic data}\label{sources-of-traffic-data}

\subsection{Travel demand outputs}\label{travel-demand-outputs}

VEIN was developepd according to the available traffic data in São Pauo,
Brazil. In this case, the available data was a 4-stage macroscopic
travel model for morning rush hour and hourly traffic counts for morning
and evening rush hours. The travel model is based on data from an
Origin-Destination-Survey (ODS) \citep{ODS} which started in the decade
of 1950 in São Paulo. A classic reference of a 4-stage modeling
transport is \citep{OrtuzarWillumsen2002}. The 4 stages of the traffic
modeling includes characterization of trip attractions and productions
by zone in some regions, distribution of these trips, preferred mode of
transport for traveling and finally the allocation of the trips at each
mode, in this case into the road network. The ODS is made every 10 years
by Metro (\url{http://www.metro.sp.gov.br/}), which is the underground
company, and they perform a smaller update of ODS after 5 years. The
information gathered in the ODS is massive with the participation of
thousands of commuters. It helps to identify characteristics of the
trips inside MASP. CET uses the information from ODS and performs the
traffic simulation. In this case, it is a macro or strategic traffic
simulation which represents the equilibrium between offer and supply of
transportation at maximum load of the road network, that is, at the rush
hour, which is from 08:00 to 09:00 Local Time (LT).

VEIN incorporates an extract of a traffic model simulation for the west
part of São Paulo named \emph{net}. The Fig. (\ref{fig:ldv} shows the
traffic of Light Duty Vehicles (LDV). This data covers the surrounding
area of the University of São Paulo. The information obtained from CET
consists in:

\begin{itemize}
\tightlist
\item
  \emph{ldv}: Light Duty Vehicles (1/h).
\item
  \emph{hdv}: Heavy Duty Vehicles (1/h).
\item
  \emph{lkm}: Length of the link (km).
\item
  \emph{ps}: Peak Speed (km/h).
\item
  \emph{ffs}: Free Flow Speed (km/h).
\item
  \emph{tstreet}: Type of street.
\item
  \emph{lanes}: Number of lanes per link.
\item
  \emph{capacity}: Capacity of vehicles in each link (1/h).
\item
  \emph{tmin}: Time for travelling each link (min).
\end{itemize}

The following scripts show how to load this data in R. This data has the
class ``SpatialLinesDataFrame'' from the package \citep{sp}. This data
was converted into an spatial feature ``sf'' object \citep{sf} because
it consists in a data-frame with a list-column of geometry with the
spatial attributes and it is easier to handle than an object class of
sp. As entioned, future versions of VEIN will migrate to sf.

The user must call the library vein first, then load the data net. The
object net has the class ``SpatialLinesDataFrame''. This data can be
seen loaded in R as:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{library}\NormalTok{(sp)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{class}\NormalTok{(net)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "SpatialLinesDataFrame"
## attr(,"package")
## [1] "sp"
\end{verbatim}

The Fig. (\ref{fig:ldv}) shows that traffic is concentrated in mains
streets such as motorway Marginal Pinheiros, located at the north east
part of the area.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\NormalTok{net}\OperatorTok{$}\NormalTok{ldv <-}\StringTok{ }\KeywordTok{as.numeric}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv)}
\KeywordTok{spplot}\NormalTok{(net, }\StringTok{"ldv"}\NormalTok{, }\DataTypeTok{scales =} \KeywordTok{list}\NormalTok{(}\DataTypeTok{draw =}\NormalTok{ T),}
       \DataTypeTok{col.regions =} \KeywordTok{rev}\NormalTok{(}\KeywordTok{bpy.colors}\NormalTok{(}\DecValTok{16}\NormalTok{)))}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/ldv-1} 

}

\caption{LDV at 08:00-09:00 (LT) in west São Paulo}\label{fig:ldv}
\end{figure}

\subsection{Inteprolation of traffic
counts}\label{inteprolation-of-traffic-counts}

Travel model simulation are not always available and the user interested
in estimating vehicular emisisons with a bottom-up approach would have
to look for alternatives to obtain traffic data at street level. These
alternatives covers interpolating traffic data for different temporal
resolutions. For instance, \citet{requia} developed a vehicular
bottom-up inventory using traffic counts and a geostatistic method
called Global Moran's I test \citep{anselin}. It measures the spatial
autocorrelation. However, this technique requires that the observed
values are normal, which is not the case for count data.

Other authors were instested in predicting Anual Average Daily Traffic
(AADT) or only ADT. \citet{lowry2014spatial} presented a new method for
predicting AADT based on the concept of origin-destination centrality.
The idea is to obtain predictor variables directly from the road
network. He identified origin and destination zones and added
multiplication factors based on the land-use characteristics.

\citet{zhao2001contributing} tested several multiple stepwise
regressions incorporating land-use characteristics. They considered
several variables: number of lanes, classificatiion of road type,
employment numbers and access to expressway with correlations between
0.66 and 0.82. \citet{lam2000estimation} compared neural networks and
regressions for a dataset of 13 locations. Kriging methods were used in
AADT interpolation by \citet{eom2006improving}, who predicted AADT for
non-motorway streets. Also, \citet{selby2013spatial} compared Kriging
and geographically weighted regressions in the prediction of AADT.

Another approach is to perform a spatial regression based on the
distributio on the oberved data. As the data is counts of traffic, a
poisson, quasi-poisson or negative binomial regressions can be used
\citep{zeileis2008regression}. Recently, \citet{Ibarraetal2017a}
compared quasi-posson and negative binomial regresions predicting hourly
traffic data, with better results with quasi-poisson approach and
correlation of 0.72.

Newer approaches involves using GPS data from smart phones and cars.

\subsection{Generating traffic flows from Origin-Destination-Surveys
(ODS)}\label{generating-traffic-flows-from-origin-destination-surveys-ods}

The origin destination survey (ODS) is an important tool which
quantifies the amount of trips generated in the study area. ODS studies
started decades ago. The oldest ODS paper on Google Scholar is entitles
``FUNCTIONS OF A HIGHWAY-PLANNING SURVEY DEPARTMENT'' by W. J. Sapp in
1938 at the TWENTY-FOURTH ANNUAL ROAD SCHOOL \citep{sapp1938runctions}.
In this paper it is discussed the importance information for the trffic
engineer, and Sapp even mentios: ``The engineer must have the supporting
data available to substantiate his decisions.''

Other study of 1942 shows talks about the importance of traffic counts,
disucssing the elaboration of an origin destination survey where 8000
Boy Scouts of America participated counting traffic. The method for
analyzing the data consisted in countign the vehicles identifying the
licence plate in order to determine the origin (the first time the
vehicle was recorded), the route of vehicles, the destination and the
approximate time for the trip.

After those pioneer studies many many other studies went deeper into the
subject. And after that, with the irruption of new technologies, new
software, use of smarthphones with GPS, satellites and big technological
centers own by companies such as Google (\url{http://www.google.com}),
new ways for characterizing trips were developed. In this section i will
briefly mention the Google Distance Matrix
(\url{https://developers.google.com/maps/documentation/distance-matrix/}),
the pgRouting library \citep{patrushev2007shortest} for postGIS and will
expand with two R packages, googleway \citep{googleway} and dodgr
\citep{dodgr}.

The ODS will provide us the matrix of pairs of zones origin and
destinations by mode of transport. Then, we can use any of the mentiones
softwares to find the shortest path between each zone. The table
\ref{tab:ods} shows an extraction of the OD matrix for the Metropolitan
Area of São Paulo (MASP) in 2007 for motorized individual trips. The fig
@ref(fig.zod) shows a map with the location of the zones OD.

\begin{table}

\caption{\label{tab:ods}Matrix OD for motorized indivitual trips between 06:30 and 08:30 MASP 2007}
\centering
\begin{tabular}[t]{lcccccccccc}
\toprule
  & V101 & V102 & V103 & V104 & V105 & V106 & V107 & V108 & V109 & V110\\
\midrule
101 & 42 & 0 & 0 & 83 & 0 & 84 & 42 & 0 & 0 & 318\\
102 & 49 & 0 & 10 & 0 & 0 & 20 & 0 & 10 & 0 & 0\\
103 & 0 & 10 & 0 & 19 & 0 & 81 & 17 & 0 & 0 & 54\\
104 & 0 & 35 & 86 & 40 & 0 & 170 & 40 & 35 & 35 & 0\\
105 & 0 & 0 & 0 & 13 & 0 & 0 & 0 & 0 & 0 & 0\\
\addlinespace
106 & 94 & 0 & 72 & 87 & 24 & 196 & 15 & 0 & 0 & 27\\
107 & 0 & 0 & 13 & 64 & 13 & 28 & 44 & 0 & 0 & 0\\
108 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3525 & 885 & 0\\
109 & 0 & 0 & 0 & 0 & 0 & 206 & 0 & 3916 & 975 & 0\\
110 & 954 & 0 & 18 & 954 & 0 & 0 & 0 & 652 & 939 & 2928\\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{veinbook_files/figure-latex/zod-1} 

}

\caption{Zones OD for MASP 2007}\label{fig:zod}
\end{figure}

Now, as we know the the trips between each pair of OD zones, we can find
the routes that connectes them. A used algorithm is the shortest path,
which connects two nodes minimizing the total travel time. There are
several algorithms, including \citet{dijkstra1959note}.

\subsubsection{Google Distance Matrix
API}\label{google-distance-matrix-api}

This we servise allow to ``retrieve duration and distance values based
on the recommended route between start and end points.'' It returns
durations and distance, it is possible to choose modes of transport and
even to choose between current or historial times. The first step is to
get a KEY.

If you browser

\begin{Shaded}
\begin{Highlighting}[]
\ExtensionTok{https}\NormalTok{://maps.googleapis.com/maps/api/distancematrix/json?units=imperial}\KeywordTok{&}\VariableTok{origins=}\NormalTok{Washington,DC}\KeywordTok{&}\VariableTok{destinations=}\NormalTok{New+York+City,NY}\KeywordTok{&}\VariableTok{key=}\NormalTok{YOUR_API_KEY}
\end{Highlighting}
\end{Shaded}

and replace YOUR\_API\_KEY you will see (my brower is in portuguese):

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{\{}
  \StringTok{"destination_addresses"} \BuiltInTok{:}\NormalTok{ [ }\StringTok{"Nova York, NY, EUA"}\NormalTok{ ],}
  \StringTok{"origin_addresses"} \BuiltInTok{:}\NormalTok{ [ }\StringTok{"Washington, D.C., Distrito de Columbia, EUA"}\NormalTok{ ],}
  \StringTok{"rows"} \BuiltInTok{:}\NormalTok{ [}
    \KeywordTok{\{}
      \StringTok{"elements"} \BuiltInTok{:}\NormalTok{ [}
        \KeywordTok{\{}
          \StringTok{"distance"} \BuiltInTok{:}\NormalTok{ \{}
            \StringTok{"text"} \BuiltInTok{:} \StringTok{"225 mi"}\NormalTok{,}
            \StringTok{"value"} \BuiltInTok{:}\NormalTok{ 361972}
          \KeywordTok{\}}\NormalTok{,}
          \StringTok{"duration"} \BuiltInTok{:}\NormalTok{ \{}
            \StringTok{"text"} \BuiltInTok{:} \StringTok{"3 horas 53 minutos"}\NormalTok{,}
            \StringTok{"value"} \BuiltInTok{:}\NormalTok{ 13964}
          \KeywordTok{\}}\NormalTok{,}
          \StringTok{"status"} \BuiltInTok{:} \StringTok{"OK"}
        \KeywordTok{\}}
\NormalTok{        ]}
\NormalTok{    \}}
\NormalTok{    ],}
  \StringTok{"status"} \BuiltInTok{:} \StringTok{"OK"}
\NormalTok{\}}
\end{Highlighting}
\end{Shaded}

The modes of transport covered are \textbf{driving} using the road
network, \textbf{walking} via pedestrian paths \& sidewalks,
\textbf{bicycling} via bicycle paths and \textbf{transit} via public
transit routes. For more information read the documentation.

\subsubsection{pgRouting for postGIS}\label{pgrouting-for-postgis}

pgRouting (\url{http://pgrouting.org/}) is an open source routing
library for postGIS. It provides many routing algorithms and it can be
run via QGIS (\url{https://www.qgis.org}). This library is very
extensive. A good resource is the book ``pgRouting: A Practical Guide''
(\url{http://locatepress.com/pgrouting}).

\subsubsection{The R package googleway}\label{the-r-package-googleway}

googleway \citep{googleway} R package allows to access Goolge Maps API
\url{https://developers.google.com/maps/}. The API functions are:

\begin{itemize}
\tightlist
\item
  Directions - \texttt{google\_directions()}
\item
  Distance Matrix - \texttt{google\_distance()}
\item
  Elevation - \texttt{google\_elevation()}
\item
  Geocoding - \texttt{google\_geocode()}
\item
  Reverse Geocoding - \texttt{google\_reverse\_geocode()}
\item
  Places - \texttt{google\_places()}
\item
  Place Details - \texttt{google\_place\_details()}
\item
  Time zone - \texttt{google\_timezone()}
\item
  Roads - \texttt{google\_snapToRoads()} and
  \texttt{google\_nearestRoads()}
\end{itemize}

This package allows to plot over Google Maps.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(googleway)}
\NormalTok{## not specifying the api will add the key as your 'default'}
\NormalTok{key <-}\StringTok{ "my_api_key"}
\KeywordTok{set_key}\NormalTok{(}\DataTypeTok{key =}\NormalTok{ key)}
\KeywordTok{google_keys}\NormalTok{()}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Google API keys
##  -  default : my_api_key 
##  -  map :  
##  -  directions :  
##  -  distance :  
##  -  elevation :  
##  -  geocode :  
##  -  places :  
##  -  place_autocomplete :  
##  -  place_details :  
##  -  reverse_geocode :  
##  -  roads :  
##  -  streetview :  
##  -  timezone :
\end{verbatim}

From zone Luz 7, coordinates long -46.63461 and lat -23.53137 to zone 8
Bom Retiro coordinates -46.64482 -23.52204 there are 133 LDV trips
between 6:30 and 08:30. We first create the data-frame. This data is
based on OD from São Paulo.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{mydf <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{region =} \DecValTok{1}\NormalTok{,}
                   \DataTypeTok{from_lat =} \FloatTok{-23.53137}\NormalTok{,}
                   \DataTypeTok{from_long =} \FloatTok{-46.634613}\NormalTok{,}
                   \DataTypeTok{to_lat =} \FloatTok{-23.52204}\NormalTok{,}
                   \DataTypeTok{to_long =} \FloatTok{-46.64482}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

Then we use the package \texttt{googleway} to create the points between
origin and destination and maptools to transform points to lines.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(maptools, }\DataTypeTok{quietly =}\NormalTok{ T)}
\KeywordTok{library}\NormalTok{(googleway)}
\NormalTok{foo <-}\StringTok{ }\KeywordTok{google_directions}\NormalTok{(}\DataTypeTok{origin =} \KeywordTok{unlist}\NormalTok{(mydf[}\DecValTok{1}\NormalTok{, }\DecValTok{2}\OperatorTok{:}\DecValTok{3}\NormalTok{]),}
                         \DataTypeTok{destination =} \KeywordTok{unlist}\NormalTok{(mydf[}\DecValTok{1}\NormalTok{, }\DecValTok{4}\OperatorTok{:}\DecValTok{5}\NormalTok{]),}
                         \DataTypeTok{key =}\NormalTok{ mykey,}
                         \DataTypeTok{mode =} \StringTok{"driving"}\NormalTok{,}
                         \DataTypeTok{simplify =} \OtherTok{TRUE}\NormalTok{)}
\NormalTok{pl <-}\StringTok{ }\KeywordTok{decode_pl}\NormalTok{(foo}\OperatorTok{$}\NormalTok{routes}\OperatorTok{$}\NormalTok{overview_polyline}\OperatorTok{$}\NormalTok{points)}
\NormalTok{df <-}\StringTok{ }\NormalTok{sf}\OperatorTok{::}\KeywordTok{st_as_sf}\NormalTok{(pl, }\DataTypeTok{coords =} \KeywordTok{c}\NormalTok{(}\StringTok{"lon"}\NormalTok{, }\StringTok{"lat"}\NormalTok{), }\DataTypeTok{crs =} \DecValTok{4326}\NormalTok{)}
\NormalTok{streets <-}\StringTok{ }\KeywordTok{list}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ pl}\OperatorTok{$}\NormalTok{lon, }\DataTypeTok{y =}\NormalTok{ pl}\OperatorTok{$}\NormalTok{lat)}
\NormalTok{streets <-}\StringTok{ }\KeywordTok{map2SpatialLines}\NormalTok{(streets)}
\KeywordTok{plot}\NormalTok{(streets, }\DataTypeTok{axes =}\NormalTok{ T, }\DataTypeTok{main=} \StringTok{"Route of 133 LDV trips"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/fgw-1.pdf}
\caption{\label{fig:fgw}Driving route between zones `Luz' and `Bom Retiro'
in São Paulo}
\end{figure}

\subsubsection{The R package dodgr}\label{the-r-package-dodgr}

dpdgr\citep{dodgr} is a new R package with great capbilities. It
calculates the distance on dual-weight graphs using priority-queue
shorest paths. In order to calculate the traffic flows it is necessary
the matrix OD for the desired mode of transport and the coorodinates of
centroids of the OD zones.

The function \texttt{dodgr\_streetnet} uses the osmdata r package to
download street network data from Open Street Map \citep{osm} for the
points of the centroids of zones OD. Depending on the spatial extent of
the data, the resulting data can be large. The function
\texttt{weight\_streetnet} weight the lines from OSM road network
according to a specific profile of traffic: ``psv'' for Public Service
Vehicle. ``bicycle'', nd others. The weights are profile values for the
OSM type of roads based on this webpage:
\url{https://www.routino.org/xml/routino-profiles.xml}. The function
\texttt{dodgr\_vertices} extract the vertices pf thegraph including the
coordinates. Finally, the function \texttt{dodgr\_flowmap} reads the
graph and plot the flows. Belows s an example for São Paulo using the
data of ODS the urban uderground public transportation system
\citep{ODS}.

\begin{figure}

{\centering \includegraphics[width=4.82in]{figuras/dodgr} 

}

\caption{Daily trips of Light Duty Vehicles in São Paulo}\label{fig:rdodgr}
\end{figure}

\subsection{Top-down approach}\label{top-down-approach}

VEIN was designed with traffic flow at street leve on mind, however, it
is possible that user might be inclined for doing for using a top-down
approach. Here some possible causes:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Bottom-up approach can demand more computatonal resources for a
  country or a continent.
\item
  Another possibility is that the emissions inventory is going to be
  used solely for air quality modellign purposes, where the proportion
  of grid spacing and street is such that spatial detail would be lose,
  for instance with resolution of 10 km.
\item
  Also, it is possible that there is no way of obtaining traffic
  information at street level.
\item
  Limitated resources, time, funding, human resources, etc.
\item
  Lastly, due to simplism. It is also possible that the objective simply
  the objective is to estimate an emissions inventory using a top-down
  approach.
\end{enumerate}

Under these circumstances a top-down approach would be better suited for
some users. As VEIN counts toolkit for estimating emissions, it is
reasonable to use all VEIN resources with a top-down approach.

In this case, the user must follow some considerations:

\begin{itemize}
\tightlist
\item
  Use \texttt{inventory} functions in the same way as shown on section
  \ref{st}.
\item
  Create a network, but instead use a road network with spatial lines,
  use spatial polygons. The Spatial polygon might represent some area
  where the amount of vehicles is known.
\item
  The \texttt{age} functions shown in following sections, show that it
  is possible to apply age distributions to each street, in this case,
  to each area.
\end{itemize}

The Fig. \ref{fig:ftd} shows the area of each minucipality of São Paulo
State. This area could represent a type vehicles or aven average age of
use. In São Paulo state there are 645 municipalities, which could
represent a \emph{network} of only 645 \emph{streets}, which implies
that emissions estimations will be very fast.

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/ftd-1.pdf}
\caption{\label{fig:ftd}Area of municipalityes of São Paulo State Brazil}
\end{figure}

\section{Main functions}\label{main-functions}

The Fig.(\ref{fig:diavein}) shows a complete diagram with the steps for
elaborating an emissions inventory. Also, the function
\texttt{inventory} shown on sub-section (\ref{veinstructure}) shows the
functions to structure an inventory. This and the following subsection
of application shows respective the part of the diagram
Fig.(\ref{fig:diatraffic}) and how to run vein storing the results in
the directories shown on Fig. (\ref{st}).

The first element that the user must have is the traffic at street
level. This is shown as a green circle with the word `traffic' on Fig.
(\ref{fig:diatraffic}). The user must use any \texttt{age} function
which produces objects of class \texttt{Vehicles}. The function
\texttt{netspeed} produces an object of class \texttt{Speed}. This
objects are required by the \texttt{emis} function.

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{figuras/dia4} 

}

\caption{Structuring an emissions inventories with VEIN}\label{fig:diatraffic}
\end{figure}

\subsection{\texorpdfstring{Expanding traffic data with the function
\texttt{temp\_fact}}{Expanding traffic data with the function temp\_fact}}\label{expanding-traffic-data-with-the-function-temp_fact}

Traffic data must be temporally extrapolated because it is usually
available only for the morning rush hour. Traffic data can be estimated
from short period traffic count datasets, then expanded to represent
longer timespan, such as Annual Average Daily Traffic (AADT;
\citep[lam2000estimation]{wang2009forecasting}). The next step is to
extrapolate the vehicular flow at street link \(i\), vehicle type \(j\),
and age of use \(k\), to obtain the vehicular flow for hour of the week
\(l\) (\(F_{i,j,k,l}\); see equation \eqref{eq:flowx}.

\begin{equation}
F_{i,j,k,l} = F^*_{i,j,k} \cdot TF_{j,l}
\label{eq:flowx}
\end{equation}

where \(TF_{j,l}\) are the temporal factors varying according to each
hour of \(l\) and type of vehicle \(j\). For instance, \(TF\) is defined
as a matrix with 24 lines and numbers of columns to each day considered,
from Monday to Sunday. In order to expand traffic to other hours, \(TF\)
matrices must be normalized to the hour that represents the traffic
data. It means that \(TF\) values at morning peak hour must be 1 and the
respective proportion must be assigned to the other hours. For example,
\(TF\) values can be obtained from automatic traffic count stations.

The function \texttt{temp\_fact} return \(F_{i,j,k,l}\) as an object
with class \texttt{Vehicles}. The arguments are:

\begin{itemize}
\tightlist
\item
  \texttt{q} is a data-frame of traffic flow to each hour (veh/h) at
  each street.
\item
  \texttt{pro} s a matrix or data-frame to extrapolate .
\end{itemize}

\subsection{\texorpdfstring{Calculating speed at other hours with the
function
\texttt{netspeed}}{Calculating speed at other hours with the function netspeed}}\label{calculating-speed-at-other-hours-with-the-function-netspeed}

The average speed of traffic flow is very important and it must be
determined for each link and hour. Once the vehicular flow is identified
for each hour, the average speed is then identified for each hour. This
was accomplished by employing curves from the Bureau of Public Roads
(BPR; \citep{bpr}), as shown in Eq. \eqref{eq:bpr}. The process involves
calculating speed by dividing the length of road by the time. The time
is calculated using the total traffic expanded to each street link \(i\)
and hour \(l\).

\begin{equation}
T_{i,l} = To_i \cdot \left(1 +\alpha \cdot \left(\dfrac{Q_{i,l}}{C_i}\right)^\beta \right)
\label{eq:bpr}
\end{equation}

The function \texttt{netspeed} do this calculations. The arguments of
this function are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(netspeed)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (q = 1, ps, ffs, cap, lkm, alpha = 0.15, beta = 4, net, 
##     scheme = FALSE, distance = "km", time = "h", isList) 
## NULL
\end{verbatim}

\texttt{netspeed} allows basically two types of speeds data-frame which
depends on the availability od data by the user. If the user has
\texttt{q}, \texttt{ps}, \texttt{ffs} and \texttt{cap} can use the
argument \texttt{scheme\ =\ FALSE}, or when the user only has
\texttt{ps} and \texttt{ffs} and use the argument
\texttt{scheme\ =\ TRUE}.

\begin{itemize}
\tightlist
\item
  \texttt{q} is a data-frame of traffic flow to each hour (veh/h) at
  each street.
\item
  \texttt{ps}is the Peak speed (km/h) at each street.
\item
  \texttt{ffs} Free flow speed (km/h) at each street.
\item
  \texttt{cap} Capacity of link (veh/h) at each street.
\item
  \texttt{lkm} Distance of link (km) of each street.
\item
  \texttt{alpha} Parameter of \citet{bpr} curves.
\item
  \texttt{beta} Parameter of \citet{bpr} curves.
\item
  \texttt{scheme} Logical to create a Speed data-frame with 24 hours and
  a default profile. It needs \texttt{ffs} and \texttt{ps} at each
  street:
\end{itemize}

\begin{table}

\caption{\label{tab:unnamed-chunk-44}speeds for scheme = T}
\centering
\begin{tabular}[t]{cc}
\toprule
Period & Speed\\
\midrule
00:00-06:00 & ffs (Free flow speed)\\
06:00-07:00 & average between ffs and ps\\
07:00-10:00 & ps (Peakspeed)\\
10:00-17:00 & average between ffs and ps\\
17:00-20:00 & ps (Peak speed)\\
\addlinespace
20:00-22:00 & average between ffs and ps\\
22:00-00:00 & ffs (Free flow speed)\\
\bottomrule
\end{tabular}
\end{table}

\subsection{\texorpdfstring{Distribution of vehicles by age of use with
the functions \texttt{age\_ldv}, \texttt{age\_hdv} and
\texttt{age\_moto}}{Distribution of vehicles by age of use with the functions age\_ldv, age\_hdv and age\_moto}}\label{distribution-of-vehicles-by-age-of-use-with-the-functions-age_ldv-age_hdv-and-age_moto}

These functions reads traffic data at each street and returns an object
of class \texttt{Vehicles}, which is a data-frame with the number of
vehicles at each street and the number of columns represent the amount
of vehicles at each age. The functions \texttt{age\_moto} and
\texttt{age\_hdv} are identical. These functions apply survival
equations into the fleet from \citet{MMA2011}. The arguments are:

\begin{itemize}
\tightlist
\item
  \texttt{x}numerical vector of vehicles at each street.
\item
  \texttt{name} word of vehicle assigned to columns of dataframe.
\item
  \texttt{a} parameter of survival equation. The default value for
  \texttt{age\_ldv} is 1.698 and for \texttt{age\_hdv} and
  \texttt{age\_moto} is 0.2.
\item
  \texttt{b} parameter of survival equation. The default value for
  \texttt{age\_ldv} is -0.2 and for \texttt{age\_hdv} and
  \texttt{age\_moto} is 17.
\item
  \texttt{agemin} age of newest vehicles for that category. The default
  value is 1, however it can be bigger than one when it is a vehicle
  that is not circulating more than year ago.
\item
  \texttt{agemax} age of oldest vehicles for that category. Teh default
  value is 50 assuming that the oldest vehicle in circulation has 50
  years of use. However, new type of vehicles will be newer.
\item
  \texttt{k} multiplication factor. This factor helps to split the
  traffic \texttt{x} in the vehicular composition.
\item
  \texttt{bystreet} when TRUE it is expecting that \texttt{a} and
  \texttt{b} are numeric vectors with length equal to \texttt{x}. In
  other words, the values of \texttt{a} and \texttt{b} varies in each
  street.
\item
  \texttt{message} message with average age and total number of
  vehicles.
\end{itemize}

\subsection{\texorpdfstring{The function
\texttt{my\_age}}{The function my\_age}}\label{the-function-my_age}

These functions also reads traffic data at each street and returns an
object of class \texttt{Vehicles}, which is a data-frame with the number
of vehicles at each street and the number of columns represent the
amount of vehicles at each age. However, it is based on data the user
has and not by parameters. Therefore, using this function with own data
should produce more representative results. The arguments are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(my_age)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (x, y, name = "veh", k = 1, net, message = TRUE) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{x} numerical vector of vehicles at each street.
\item
  \texttt{y} Age dustribution of vehicles. This parameter can be anual
  sales or anual registry for the category of vehicle.
\item
  \texttt{name} of vehicle assigned to columns of dataframe.
\item
  \texttt{k} multiplication factor. This factor helps to split the
  traffic \texttt{x} in the vehicular composition.
\item
  \texttt{message} message with average age and total numer of vehicles.
\end{itemize}

\subsection{\texorpdfstring{The class
\texttt{Vehicles}}{The class Vehicles}}\label{the-class-vehicles}

\texttt{Vehicles} is a class in vein, shown in on Fig.
(\ref{fig:diatraffic}). This class includes the methods \texttt{print},
\texttt{plot} and \texttt{summary}. This means that, \texttt{Vehicles}
objects presents customized versions of \texttt{print}, \texttt{plot}
and \texttt{summary} in order to make easier to the user to use vein.
The figure @ref(fig.plotpc) shows a simple plot of a \texttt{Vehicles}
object, followed by the \texttt{head} of this object. The plot shows the
sum of each type of vehicle by age of use, with a vertical red line
indicating the average age, 11.17 years of use.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\NormalTok{PC_E25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"PC_E25"}\NormalTok{, }\DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{37.25}\NormalTok{, }\DataTypeTok{message =}\NormalTok{ F)}
\KeywordTok{plot}\NormalTok{(PC_E25)}
\end{Highlighting}
\end{Shaded}

\includegraphics{veinbook_files/figure-latex/plotpc-1.pdf}

\begin{verbatim}
## 
## Average =  11.17
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{head}\NormalTok{(PC_E25[, }\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{]) }\CommentTok{# The first 4 columns}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Result for Vehicles 
##               V1             V2             V3             V4
## 1 1761.92888 1/h 2968.78285 1/h 4404.60761 1/h 5877.97812 1/h
## 2  591.76508 1/h  997.10155 1/h 1479.34063 1/h 1974.18989 1/h
## 3  240.18939 1/h  404.70994 1/h  600.44421 1/h  801.29679 1/h
## 4  341.44967 1/h  575.32964 1/h  853.58258 1/h 1139.11162 1/h
## 5   22.27726 1/h   37.53633 1/h   55.69044 1/h   74.31926 1/h
## 6 1163.27810 1/h 1960.07916 1/h 2908.05358 1/h 3880.81682 1/h
\end{verbatim}

\subsection{Other traffic functions}\label{other-traffic-functions}

Another function is \texttt{adt} which calculates average daily traffic
(ADT) from hourly traffic data. This function reads numeric vectors with
the amount of vehicles at each street expand the traffic with temporal
factors for each type of vehicle. The arguemtns are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(adt)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (pc, lcv, hgv, bus, mc, p_pc, p_lcv, p_hgv, p_bus, p_mc, 
##     expanded = FALSE) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{pc} numeric vector for passenger cars
\item
  \texttt{lcv} numeric vector for light commercial vehicles
\item
  \texttt{hgv} numeric vector for heavy good vehicles or trucks
\item
  \texttt{bus} numeric vector for bus
\item
  \texttt{mc} numeric vector for motorcycles
\item
  \texttt{p\_pc} data-frame profile for passenger cars
\item
  \texttt{p\_lcv} data-frame profile for light commercial vehicles
\item
  \texttt{p\_hgv} data-frame profile for heavy good vehicles or trucks
\item
  \texttt{p\_bus} data-frame profile for bus
\item
  \texttt{p\_mc} data-frame profile for motorcycles
\end{itemize}

\section{Vehicular composition}\label{vehicular-composition}

\section{Application}\label{application}

As mentioned above, the application consists in using the travel demand
model for west part of São Paulo city, present in vein. This script
reads traffic data and expand applying an age function. It will be used
the \texttt{inventory} function with the default vehicular composition,
shown on section (@red(st)). Please, use the scripts in the extra
material of this book. The base year is 2015 and the extended example is
available in ppendix \ref{ap1}.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{inventory}\NormalTok{(}\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{))}
\KeywordTok{setwd}\NormalTok{(}\StringTok{"YourCity"}\NormalTok{)}
\KeywordTok{data}\NormalTok{(net) }\CommentTok{# Loads the traffic demand simulation for west São Paulo}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(pc_profile)}
\NormalTok{pc_week <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, pc_profile)}
\NormalTok{speed <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pc_week, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm)}
\KeywordTok{saveRDS}\NormalTok{(net, }\DataTypeTok{file =} \StringTok{"network/net.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(speed, }\DataTypeTok{file =} \StringTok{"network/speed.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\chapter{Emission Factors}\label{ef}

An emission factor is the amount of mass of pollutant generated by
activities \citep{pulles2010art}. In the case of vehicles, the emission
factors are generally known as the mass of pollutant per traveled
distance \(g \cdot km^{-1}\) in metric units. The distance unit could be
different, like miles for instance. The type of units depends on
measurement used when developing the emission factors.

There are hot and cold exhaust emisions. \emph{Hot} exhaust emissions
are produced by a vehicle when its engine and exhaust after-treatment
system are at their normal operational temperatures and \emph{Cold}
during the vehicle warm-up phase \citep{trlef}.

The emission factors comes from measurement of pollutants emitted by the
sources. In the case of vehicles, the dynanometer measurements the
emission factors are the mass of pollutant collected during the distance
traveled by a vehicle following a specific driving cycle. The Federal
Test Procedure (FTP) is a test to certify the vehicle emissions under
the driving cycles such as FTP-75. This driving cycle has a duration of
1874 \(s\) for a distance of 17783 \(m\) and average speed of 34.12
\(km \cdot h^{-1}\) \citep{tim}.

In the literature it is possible to find different type of emission
factors. For instance, in europe, during the development of the project
Assesment of Reliability of Transport Emission Models and Inventory
Systems (ARTEMIS), it was developed the \emph{traffic situation model}.
This concept consists in the combination of parameters that affects the
vehicle kinematics and engine operation: type of road, sinuosity and
gradient, trafffic conditions or level of service (free-flow, heavy,
saturated and stop and go) and average speed by speed limit of the road
\citep{artemis}. Measurenets of vehicular emissions under each traffic
situation led to emission factors by traffic situation, used in the
Handbook of Emission Factors (HBEFA, www.hbefa.net).

There is another approach based on 1 Hz measurement of emissions. Here,
the pollutants are measured on real live traffic driving. Emission
factors can be derived here using mainly two approaches. One approach is
applying the Vehicular Specific Power (VSP) \citep{jimenez1999vehicle}.
This methods is used in the MOtor Vehicle Emission Simulator (MOVES)
\citep{koupal2003design} and the International Vehicle Emissions model
(IVE) \citep{Davisetal2005}. Another approach is to apply the Passenger
car and Heavy-duty Emission Model (PHEM), which is based on
instantaneous engine power demand and engine speed developed during the
Artemis project \citep[\citet{artemis}]{tim}.

Perhaps the most used approach to obtain emission factors are the speed
functions. As the emissions tests on driving cycles are valid for only
one average speed, measuring emissions of different driving cycles at
different average speeds, allow to obtain point clouds where a
regressions is applied, obtaining an emission factor as a speed
function. The literature shows experiments in diffferent parts of the
world, such as in Chile \citep{Corvalanetal2002} and Europe
\citep{ntziachristos2000speed}. However, probably the most used source
of emission factors as speed functions are the European emission
guidelines developed by the European Monitoring and Evaluation Programme
(EMEP, \url{http://www.emep.int/}) and published by the European
Environment Agency (EEA,
\url{https://www.eea.europa.eu/themes/air/emep-eea-air-pollutant-emission-inventory-guidebook/emep})
\citep{NtziachristosSamaras2016}. These emission factors are included in
the VEIN model.

Another aspect important in vehicular emissions is the degradation of
the emissions of aged vehicles. Over the time, different part of the
vehicles suffer deterioration, increasing the emissions. Therefore,
approachas have been developed to account of this effect in inventories.
One approach consists in determining functions that increment the
emissions by accumulated mileage, such as studies of
\citet{CorvalanVargas2003}.

In Brazil, the emission factors are reported by the Environmental
Protection Agency of São Paulo \citet{CETESB2015}, in their vehicular
emissions inventory report. Light vehicles emission factors comes from
average measurements of the FTP75 driving cycle, motorcycle emission
factors comes from average measurements of World Motorcycle Test Cycle
(WMTC) and HDV with from the European Stationary Cycle (ETC).

As the VEIN model was developed in Brazil, but user of other parts of
the world may be intereted in using the model, the approach in VEIN has
to allow flexibility to the user. Therefore, the emission factors
approach in VEIN consists in three options, as shown on Fig.
(\ref{fig:diaef}). The first approach consists in local emissions
factors by age of use, such as the one from \citet{CETESB2015}, denoted
as local\_ef in the green circle. The second approach consists in
selecting speed functions from \citet{NtziachristosSamaras2016},
availabe in VEIN and denoted as the grey box as speed\_ef. The third
approach conists in incorporate speed variation from the speed function
into the local emission factors by age of use. In any case, VEIN count
with function to account of the degradation of emissions due to
accumulated mileage denotes in grey box as emis\_det. These emissions
are entered into the estimation emission function.

The following sections show how to do these calculation in VEIN, step by
step.

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{figuras/dia5} 

}

\caption{Structuring an emissions inventories with VEIN}\label{fig:diaef}
\end{figure}

\section{\texorpdfstring{Speed functions \texttt{ef\_ldv\_speed} and
\texttt{ef\_hdv\_speed}}{Speed functions ef\_ldv\_speed and ef\_hdv\_speed}}\label{speedef}

The estimation of emissions in VEIn is shown the equation \eqref{eq:emi}:

\begin{equation}
EH_{i,j,k,l,m} =F_{i,j,k,l} \cdot L_i \cdot EF(V_{i,l})_{j,k,m} \cdot DF_{j,k}
\label{eq:emi}
\end{equation}

Where \(EH_{i,j,k,l,m}\) is the emissions for each street link \(i\),
vehicle category from composition \(k\), hour \(l\) and pollutant \(m\),
where \(F_{i,j,k,l}\) is the vehicular flow calculated in Eq. 1. \(L_i\)
is the length of the street link \(i\). \(EF(V_{i,l})_{j,k,m}\) is the
emission factor of each pollutant \(m\). \(DF_{j,k}\) is the
deterioration factor for vehicle of type \(j\) and age of use \(k\),
explained in detial in section \ref{det}.

These function return speed functions as \(f(V)\). The functions comes
from the EMEP/EEA emissions guidelines \citep{NtziachristosSamaras2016}
and are available in PDF reports. The equations and their parameter were
translated to spreedsheets and loaded internally in VEIN. Therefore, the
user only must enter the required parameters obtaining the desired
function. The functions respect the assumptions and speed limits.

\subsection{\texorpdfstring{Emission factors of LDV depending on speed
with the function
\texttt{ef\_ldv\_speed}}{Emission factors of LDV depending on speed with the function ef\_ldv\_speed}}\label{emission-factors-of-ldv-depending-on-speed-with-the-function-ef_ldv_speed}

The arguments for \texttt{ef\_ldv\_speed} are:

\begin{itemize}
\tightlist
\item
  \texttt{v}: Category vehicle: ``PC'', ``LCV'', ``Motorcycle'' or
  ``Moped''.
\item
  \texttt{t}: Sub-category of of vehicle: PC: ``ECE\_1501'',
  ``ECE\_1502'', ``ECE\_1503'', ``ECE\_1504'' ,
  ``IMPROVED\_CONVENTIONAL'', ``OPEN\_LOOP'', ``ALL'', ``2S'' or ``4S''.
  LCV: ``4S'', Motorcycle: ``2S'' or ``4S''. Moped: ``2S'' or ``4S''.
\item
  \texttt{cc}: Size of engine in cc: PC: ``\textless{}=1400'',
  ``\textgreater{}1400'', ``1400\_2000'', ``\textgreater{}2000'',
  ``\textless{}=800'', ``\textless{}=2000''. Motorcycle:
  ``\textgreater{}=50'' (for ``2S''), ``\textless{}=250'', ``250\_750'',
  ``\textgreater{}=750''. Moped: ``\textless{}=50''. LCV :
  ``\textless{}3.5'' for gross weight.
\item
  \texttt{f}: Type of fuel: ``G'', ``D'', ``LPG'' or ``FH'' (Full
  Hybrid: starts by electric motor).
\item
  \texttt{eu}: Euro standard: ``PRE'', ``I'', ``II'', ``III'',
  ``III+DPF'', ``IV'', ``V'', ``VI'' or ``VIc''.
\item
  \texttt{p}: Pollutant: ``CO'', ``FC'', ``NOx'', ``HC'' or ``PM''.
\item
  \texttt{k}: Multiplication factor.
\item
  \texttt{show.equation}: Option to see or not the equation parameters.
\end{itemize}

It is very important that the user enter the right value into the
arguments because the final category must match. If the user enters
values that do not match, the resulting speed function will be wrong.
There are 146 matchs. The probably most used combinations are:

\begin{table}

\caption{\label{tab:unnamed-chunk-49}Most used combinations for emission factors for LDV}
\centering
\begin{tabular}[t]{ll}
\toprule
Argument & Value\\
\midrule
v & PC LCV Motorcycles\\
t & 4S\\
cc & <=1400 1400\_2000 >2000\\
f & G D\\
eu & PRE I II III IV V VI\\
p & CO NOx HC FC PM\\
\bottomrule
\end{tabular}
\end{table}

The returning speed function represents how a type of vehicle emits
pollutants on average. Fig. (\ref{fig:figef}) shows the emissions of CO
by Passenger Cars with with technologies Pre Euro and Euro I from
\citet{NtziachristosSamaras2016}. Higher emissions occure at low average
speeds and older vehicles with technologies pre Euro emit more than
vehicles with Euro I. Even more, the average emissions of Pre Euro are
29.31 \(g \cdot km^{-1}\) and Euro I 2.61 \(g \cdot km^{-1}\), meaning
that, on average, a vehicle Pre euro is 14 times than a vehicle with
euro I.

The Fig. (\ref{fig:figef}) shows the plot using the package ggplot2
\citep{ggplot2}. This package requires that the data must be in long
format.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{library}\NormalTok{(ggplot2)}
\NormalTok{ef1 <-}\StringTok{ }\KeywordTok{ef_ldv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =} \StringTok{"G"}\NormalTok{,}
                    \DataTypeTok{eu =} \StringTok{"PRE"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{, }\DataTypeTok{show.equation =}\NormalTok{ F)}
\NormalTok{ef2 <-}\StringTok{ }\KeywordTok{ef_ldv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =} \StringTok{"G"}\NormalTok{,}
                    \DataTypeTok{eu =} \StringTok{"I"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{, }\DataTypeTok{show.equation =}\NormalTok{ F)}
\NormalTok{V <-}\StringTok{ }\DecValTok{0}\OperatorTok{:}\DecValTok{110}
\NormalTok{df <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{EF =} \KeywordTok{c}\NormalTok{(}\KeywordTok{ef1}\NormalTok{(V), }\KeywordTok{ef2}\NormalTok{(V)))}
\NormalTok{df}\OperatorTok{$}\NormalTok{V =}\StringTok{ }\NormalTok{V}
\NormalTok{df}\OperatorTok{$}\NormalTok{Euro <-}\StringTok{ }\KeywordTok{factor}\NormalTok{(}\KeywordTok{c}\NormalTok{(}\KeywordTok{rep}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\DecValTok{111}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\DecValTok{111}\NormalTok{)),}
                  \DataTypeTok{levels =} \KeywordTok{c}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\StringTok{"I"}\NormalTok{))}
\KeywordTok{ggplot}\NormalTok{(df, }\KeywordTok{aes}\NormalTok{(V, EF, }\DataTypeTok{colour =}\NormalTok{ Euro)) }\OperatorTok{+}\StringTok{ }\KeywordTok{geom_point}\NormalTok{(}\DataTypeTok{size =} \DecValTok{3}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{geom_line}\NormalTok{() }\OperatorTok{+}\StringTok{ }\KeywordTok{theme_bw}\NormalTok{()}\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{y =} \StringTok{"CO[g/km]"}\NormalTok{, }\DataTypeTok{x =} \StringTok{"[km/h]"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/figef-1} 

}

\caption{Emission factor as a speed function}\label{fig:figef}
\end{figure}

\subsection{\texorpdfstring{Emission factors of HDV depending on speed
with the function
\texttt{ef\_hdv\_speed}}{Emission factors of HDV depending on speed with the function ef\_hdv\_speed}}\label{emission-factors-of-hdv-depending-on-speed-with-the-function-ef_hdv_speed}

The arguments for \texttt{ef\_hdv\_speed} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(ef_hdv_speed)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (v, t, g, eu, x, gr = 0, l = 0.5, p, k = 1, show.equation = FALSE) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{v} :Category vehicle: ``Coach'', ``Trucks'' or ``Ubus''
\item
  \texttt{t} Sub-category of of vehicle: ``3Axes'', ``Artic'', ``Midi'',
  ``RT,''Std" and ``TT''
\item
  \texttt{g} Gross weight of each category: ``\textless{}=18'',
  ``\textgreater{}18'', ``\textless{}=15'', ``\textgreater{}15 \&
  \textless{}=18'', ``\textless{}=7.5'', - ``\textgreater{}7.5 \&
  \textless{}=12'', ``\textgreater{}12 \& \textless{}=14'',
  ``\textgreater{}14 \& \textless{}=20'', ``\textgreater{}20 \&
  \textless{}=26'', ``\textgreater{}26 \& \textless{}=28'',
  ``\textgreater{}28 \& \textless{}=32'', ``\textgreater{}32'',
  ``\textgreater{}20 \& \textless{}=28'', ``\textgreater{}28 \&
  \textless{}=34'', ``\textgreater{}34 \& \textless{}=40'',
  ``\textgreater{}40 \& \textless{}=50'' or ``\textgreater{}50 \&
  \textless{}=60''
\item
  `eu Euro emission standard: ``PRE'', ``I'', ``II'', ``III'', ``IV''
  and ``V''
\item
  `gr Gradient or slope of road: -0.06, -0.04, -0.02, 0.00, 0.02. 0.04
  or 0.06
\item
  \texttt{l} Load of the vehicle: 0.0, 0.5 or 1.0
\item
  \texttt{p} Pollutant: ``CO'', ``FC'', ``NOx'' or ``HC''
\item
  \texttt{k} Multiplication factor
\item
  \texttt{show.equation} Option to see or not the equation parameters
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{V <-}\StringTok{ }\DecValTok{0}\OperatorTok{:}\DecValTok{130}
\NormalTok{ef1 <-}\StringTok{ }\KeywordTok{ef_hdv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"Trucks"}\NormalTok{,}\DataTypeTok{t =} \StringTok{"RT"}\NormalTok{, }\DataTypeTok{g =} \StringTok{"<=7.5"}\NormalTok{,}
                    \DataTypeTok{e =} \StringTok{"II"}\NormalTok{, }\DataTypeTok{gr =} \DecValTok{0}\NormalTok{,}
\DataTypeTok{l =} \FloatTok{0.5}\NormalTok{, }\DataTypeTok{p =} \StringTok{"HC"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{)}
\NormalTok{ef2 <-}\StringTok{ }\KeywordTok{ef_hdv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"Trucks"}\NormalTok{,}\DataTypeTok{t =} \StringTok{"RT"}\NormalTok{, }\DataTypeTok{g =} \StringTok{"<=7.5"}\NormalTok{,}
                    \DataTypeTok{e =} \StringTok{"II"}\NormalTok{, }\DataTypeTok{gr =} \FloatTok{0.06}\NormalTok{,}
\DataTypeTok{l =} \FloatTok{0.5}\NormalTok{, }\DataTypeTok{p =} \StringTok{"HC"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{)}
\KeywordTok{plot}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{130}\NormalTok{, }\KeywordTok{ef1}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{130}\NormalTok{), }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{,}
     \DataTypeTok{ylab =} \StringTok{"HC [g/km]"}\NormalTok{, }\DataTypeTok{xlab =} \StringTok{"Speed [km/h]"}\NormalTok{)}
\KeywordTok{lines}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{130}\NormalTok{, }\KeywordTok{ef2}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{130}\NormalTok{), }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{col =} \StringTok{"blue"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\includegraphics{veinbook_files/figure-latex/unnamed-chunk-51-1.pdf}

\section{\texorpdfstring{Local emission factors by age of use. The
functions \texttt{EmissionFactors} and
\texttt{EmissionFactorsList}}{Local emission factors by age of use. The functions EmissionFactors and EmissionFactorsList}}\label{localef}

The functions \texttt{EmissionFactors} and \texttt{EmissionFactorsList}
creates the classes with the same names. The class
\texttt{EmissionFactors} is also a data-frame with numeric columns with
units of \(g \cdot km^{-1}\). This class is not used in critical steps
of VEIN, and it has a purpose of only creating data-frame with numeric
columns with units. The this function is applied to a numeric vector, it
just convert it to \texttt{units} adding units to it.

In the case of \texttt{EmissionFactorsList}, this class is a list of
functions \(f(V)\). This is usefull because the \texttt{emis} function,
seen in detail on chapter \ref{est}, requires that the emission factors
are functions of Velocity \(f(V)\), regardless \(f(V)\) is constant or
not. When this function is applied to a data-frame or to a matrix, it
returns a list and each columns of the data-frame or matrix is another
list. Also, each row of the original data-frame is converted to a
\(f(V)\).

The arguements or \texttt{EmissionFactorsList} and
\texttt{EmissionFactors} are:

-\texttt{x}: Object with class ``data.frame'', ``matrix'' or ``numeric''

The following lines of ode show examples using \texttt{EmissionFactors}.
Firstly, it is loaded the dataset of emission factors \emph{fe2015} for
CO and the vehicles PC using gasoline PC\_G and Light Trucks LT. The
class of fe2015 is data-frame. The subset of the data is named DF\_CO.
When we see the first 6 elemens of the data-frame are numbers without
units.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(fe2015) }\CommentTok{#CETESB Emission factors}
\NormalTok{DF_CO <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant}\OperatorTok{==}\StringTok{"CO"}\NormalTok{, }\KeywordTok{c}\NormalTok{(}\StringTok{"PC_G"}\NormalTok{, }\StringTok{"LT"}\NormalTok{)]}
\KeywordTok{head}\NormalTok{(DF_CO)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##    PC_G    LT
## 1 0.171 0.027
## 2 0.216 0.012
## 3 0.237 0.012
## 4 0.273 0.005
## 5 0.275 0.379
## 6 0.204 0.416
\end{verbatim}

The object EF\_CO has class\texttt{EmissionFactors} and ``data-frame''
and each observation has the units \(g \cdot km^{-1}\).

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EF_CO <-}\StringTok{ }\KeywordTok{EmissionFactors}\NormalTok{(DF_CO)}
\KeywordTok{head}\NormalTok{(EF_CO)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## EmissionFactors:
##         PC_G         LT
## 1 0.171 g/km 0.027 g/km
## 2 0.216 g/km 0.012 g/km
## 3 0.237 g/km 0.012 g/km
## 4 0.273 g/km 0.005 g/km
## 5 0.275 g/km 0.379 g/km
## 6 0.204 g/km 0.416 g/km
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{class}\NormalTok{(EF_CO)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "EmissionFactors" "data.frame"
\end{verbatim}

The plot of an object with class \texttt{EmissionFactors} shows maximum
9 emission factors. Fig. (\ref{fig:efco}) shows the emission factors of
both categories, where newer vehicles emit less and older vehicles emit
more.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(EF_CO, }\DataTypeTok{xlab =} \StringTok{"Age of use"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/efco-1} 

}

\caption{Emission factor of PC and LT by age of use}\label{fig:efco}
\end{figure}

Regarding the class \texttt{EmissionFactorsList}, the \texttt{print} and
\texttt{summary} methods are very simply, ony showing a description of
the object. The example of the following lines of code also uses the
same set of Emission Factors from \citet{CETESB2015}. The resulting
object, EF\_COL, has class \texttt{EmissionFactorsList} and
\texttt{list}. The print of the object shows only a description of the
first and last member of the list object.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EFL_CO <-}\StringTok{ }\KeywordTok{EmissionFactorsList}\NormalTok{(DF_CO)}
\KeywordTok{class}\NormalTok{(EFL_CO)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "EmissionFactorsList" "data.frame"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EFL_CO}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This EmissionFactorsList has  2  lists
## First has  36  functions
## Last has  36  functions
\end{verbatim}

Indeed, we want to see the content of the \texttt{EmissionFactorsList}
we would have to extract the elements of the list. EFL\_CO has two list,
each one for PC and LT. Then, each list has 36 functions, the command
\texttt{class(EFL\_CO{[}{[}1{]}{]}{[}{[}1{]}{]})} shows the object is a
functon \(f(V)\).

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EFL_CO[[}\DecValTok{1}\NormalTok{]][[}\DecValTok{1}\NormalTok{]]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (V) 
## x[j, i]
## <environment: 0x556e4f98c528>
\end{verbatim}

Also, as the speed functions are constant, there is no need to add
values of V.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EFL_CO[[}\DecValTok{1}\NormalTok{]][[}\DecValTok{1}\NormalTok{]](}\DecValTok{100}\NormalTok{) }\CommentTok{# constant speed function}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 0.171
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{EFL_CO[[}\DecValTok{1}\NormalTok{]][[}\DecValTok{1}\NormalTok{]](}\DecValTok{0}\NormalTok{)   }\CommentTok{# constant speed function}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 0.171
\end{verbatim}

Finally, after extracting the value, Fig. (\ref{fig:efco2}) shows the
same figure as Fig. (\ref{fig:efco})

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{efco1 <-}\StringTok{ }\KeywordTok{sapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{36}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) EFL_CO[[}\DecValTok{1}\NormalTok{]][[i]]())}
\NormalTok{efco2 <-}\StringTok{ }\KeywordTok{sapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{36}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) EFL_CO[[}\DecValTok{2}\NormalTok{]][[i]]())}
\NormalTok{df <-}\StringTok{ }\KeywordTok{EmissionFactors}\NormalTok{(}\KeywordTok{cbind}\NormalTok{(efco1, efco2))}
\KeywordTok{plot}\NormalTok{(df, }\DataTypeTok{xlab =} \StringTok{"Age of use"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/efco2-1} 

}

\caption{Emission factor of PC and LT}\label{fig:efco2}
\end{figure}

\section{\texorpdfstring{Incorporating speed variation with the
functions \texttt{ef\_ldv\_scaled} and
\texttt{ef\_hdv\_scaled}}{Incorporating speed variation with the functions ef\_ldv\_scaled and ef\_hdv\_scaled}}\label{scale}

The functions on section \ref{speedef} and \ref{localef} showed the
speed and local emission factors respectivly. However, the user might
have local and constant emission factors depending only on age of use,
but also, outputs from a traffic demand model which includes speed.
Under those circumnstances, it would be interesting to investigate the
effects of the average speed of traffic flow. VEIN includes a function
to transform a constant emission factor by age of use into a speed
dependent function.

This functions are called \texttt{ef\_ldv\_scaled} and
\texttt{ef\_hdv\_scaled}. The concept is explained in detail in my Ph.D
thesis \citep{ibarrathesis}. The idea is comes from revisitng the nature
of the local emission factors. The Brazilian emission factors consist of
average measurements emissions certification tests which use the driving
cycles Federal Test Procedure FTP-75 for LDV, World Motorcycle Test
Cycle (WMTC) for motorcycles, and also the European Stationary Cycle
(ESC) for HDV \citep{CETESB2015}. The average speed of FTP-75 and WMTC
are known, being 34.2 \(km \cdot h^{-1}\) and 54.7 \(km \cdot h^{-1}\)
based to the reference report on driving cycles from the Transport and
Research Laboratory (TRL) \citep{tim}. In the case of ESC, the user
might search on literature a way to relate this cycle with an average
speed.

Coming back to the \citet{CETESB2015} emission factors, these are annual
average values of emissions for the different technologies to accomplish
Brazilian emission standards, which are valid only for a specific
average speed.If two vehicles have similar technology but different
emission factors, one constant and other speed function, it would be
possble to relate them. These functions use the speed to relate both
emission factors. The condition to transform a contant emission factor
into a speed dependent function is that, the new speed dependent
function must be the same value at the speed which is representative the
local factor, and the values at other speeds are the incorporation of
the speed variation. The equation \eqref{eq:efscaled} shows the procedure:

\begin{equation}
EF_{scaled}(V_{i,l})_{j,k,m}=EF(V_{i,l})_{j,k,m} \cdot \frac{EFlocal_{j,k,m}}{{EF(Vdc_{i,l})_{j,k,m}}}
\label{eq:efscaled}
\end{equation}

Where \(EF_{scaled}(V_{i,l})_{j,k,m}\) is the scaled emission factor and
\(EF(V_{i,l})_{j,k,m}\) is the Copert emission factor for each street
link \(i\), vehicle from composition \(k\), hour \(l\) and pollutant
\(m\). \(EFlocal_{j,k,m}\) represents the constant emission factor (not
as speed functions). \(EF(Vdc_{i,l})_{j,k,m}\) are Copert emission
factors with average speed value of the respective driving cycle for the
vehicular category \(j\).

The functions \texttt{ef\_ldv\_scaled} and \texttt{ef\_hdv}\_scaled`
returns scaled emissions factors automatically. The arguments are:

\begin{itemize}
\tightlist
\item
  \texttt{df}: Data-frame with emission factors (Deprecated in 0.3.10).
\item
  \texttt{dfcol}: Numeric vector with the emission factors constant by
  age of use. These emission factors are the target to incorporate peed
  variation.
\item
  \texttt{SDC}: A number with the speed of the driving cycle of the
  emission factors from the argumetn \texttt{dfcol}. The resulting speed
  functions will return the same values of dfcol at the speed
  \texttt{SDC}. The default is 34.12 ``km/h''.
\item
  \texttt{v}. Category vehicle: ``PC'', ``LCV'', ``Motorcycle'' or
  ``Moped''.
\item
  \texttt{t}. Character; sub-category of of vehicle: PC: ``ECE\_1501'',
  ``ECE\_1502'', ``ECE\_1503'', ``ECE\_1504'' ,
  ``IMPROVED\_CONVENTIONAL'', ``OPEN\_LOOP'', ``ALL'', ``2S'' or ``4S''.
  LCV: ``4S'', Motorcycle: ``2S'' or ``4S''. Moped: ``2S'' or ``4S''.
  The default is \texttt{4S}.
\item
  \texttt{cc}. Character; size of engine in cc: PC:
  ``\textless{}=1400'', ``\textgreater{}1400'', ``1400\_2000'',
  ``\textgreater{}2000'', ``\textless{}=800'', ``\textless{}=2000''.
  Motorcycle: ``\textgreater{}=50'' (for ``2S''), ``\textless{}=250'',
  ``250\_750'', ``\textgreater{}=750''. Moped: ``\textless{}=50''. LCV :
  ``\textless{}3.5'' for gross weight.
\item
  \texttt{f}: Character; type of fuel: ``G'', ``D'', ``LPG'' or ``FH''
  (Full Hybrid: starts by electric motor).
\item
  \texttt{eu}: Character; euro standard: ``PRE'', ``I'', ``II'',
  ``III'', ``III+DPF'', ``IV'', ``V'', ``VI'' or ``VIc''.
\item
  \texttt{p}: Character; pollutant: ``CO'', ``FC'', ``NOx'', ``HC'' or
  ``PM''.
\end{itemize}

And the argument of \texttt{ef\_hdv\_scaled} are:

\section{Cold Starts}\label{cold-starts}

Cold start emissions are produced during engine startup, when the engine
and/or catalytic converter system has not reached its normal operational
temperature. Sev- eral studies have shown the significant impact for
these types of emissions
\citep[\citet{Weilenmannetal2009}]{chenetal2011}. Under this condition
emissions will be higher, and if the atmospheric temperature decreases,
cold start emissions will increase regardless of whether the catalyst
has reached its optimum temperature for functioning \citep{artemis}.
VEIN also considers cold start emissions using the approach outlined in
Copert \citep{NtziachristosSamaras2016}, as shown in equation
\eqref{eq:cold}.

\begin{equation}
EC_{i,j,k,l,m} =\beta_{j} \cdot F_{i,j,k,l} \cdot L_i \cdot EF(V_{i,l})_{j,k,m} \cdot DF_{j.k} \cdot \left(EF_{\mbox{cold}}(ta_n,V_{i,l})_{j,k,m}-1\right)
\label{eq:cold}
\end{equation}

This approach adds two terms to Eq. \ref{eq:emi}. The first term
\(EF_{cold}(ta_n,V_{i,l})_{j,k,m}-1\) is the emission factors for cold
start conditions at each street link \(i\), vehicle category from
composition \(k\), hour \(l\) and pollutant \(m\) and monthly average
temperature \(n\). \citep{NtziachristosSamaras2016} suggest using
monthly average temperature. This is an important aspect that will be
reviewed in future versions of VEIN.

The second term \(\beta_j\) is defined as the fraction of mileage driven
with a cold engine/catalyst \citep{NtziachristosSamaras2016}. The VEIN
model incorporates a dataset of cold starts recorded during the
implementation of the International Vehicle Emissions (IVE) model
\citep{Davisetal2005} in São Paulo \citep{ivesp}, which provides the
hourly mileage driven with cold start conditions.

The function for cold start emission factors is \texttt{ef\_ldv\_cold}
and their arguments are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(ef_ldv_cold)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (v = "LDV", ta, cc, f, eu, p, k = 1, show.equation = FALSE) 
## NULL
\end{verbatim}

where:

\begin{itemize}
\tightlist
\item
  \texttt{v}: Category vehicle: ``LDV''.
\item
  \texttt{ta}: Ambient temperature. Monthly men can be used.
\item
  \texttt{cc}: Size of engine in cc: ``\textless{}=1400'',
  ``1400\_2000'' or ``\textgreater{}2000''.
\item
  \texttt{f}: Type of fuel: ``G'', ``D'' or ``LPG''.
\item
  \texttt{eu}: Euro standard: ``PRE'', ``I'', ``II'', ``III'', ``IV'',
  ``V'', ``VI'' or ``VIc''.
\item
  \texttt{p}: Pollutant: ``CO'', ``FC'', ``NOx'', ``HC'' or ``PM''.
\item
  \texttt{k}: Multiplication factor.
\item
  \texttt{show.equation}: Option to see or not the equation parameters.
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{ef1 <-}\StringTok{ }\KeywordTok{ef_ldv_cold}\NormalTok{(}\DataTypeTok{ta =} \DecValTok{-5}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =}\StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"I"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{)}
\NormalTok{ef2 <-}\StringTok{ }\KeywordTok{ef_ldv_cold}\NormalTok{(}\DataTypeTok{ta =} \DecValTok{0}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =}\StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"I"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{)}
\NormalTok{ef3 <-}\StringTok{ }\KeywordTok{ef_ldv_cold}\NormalTok{(}\DataTypeTok{ta =} \DecValTok{5}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =}\StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"I"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{)}
\NormalTok{ef4 <-}\StringTok{ }\KeywordTok{ef_ldv_cold}\NormalTok{(}\DataTypeTok{ta =} \DecValTok{10}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =}\StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"I"}\NormalTok{, }\DataTypeTok{p =} \StringTok{"CO"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/efcold-1} 

}

\caption{Cold Emission factor of PC CO (g/km)}\label{fig:efcold}
\end{figure}

There is another function called \texttt{ef\_ldv\_cold\_list} which will
be deprecated soon.

\section{Deterioration}\label{det}

By default, VEIN includes deterioration factors from Copert
\citep{NtziachristosSamaras2016}. However, it is possible to include
other sources, such as from \citep{CorvalanVargas2003} or included
deteriorated local emission factors. This aspesct is faced in vein with
the function \texttt{emis\_det}. This function returns deteriorated
factors, as numeric vectors, which must be multiplied with the emission
factors of vehicles with 0 mileage.

The arguments of this functions are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(emis_det)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (po, cc, eu, km) 
## NULL
\end{verbatim}

where:

\begin{itemize}
\tightlist
\item
  \texttt{p}: Pollutant.
\item
  \texttt{cc}: Size of engine in cc.
\item
  \texttt{eu}: Euro standard: ``PRE'', ``I'', ``II'', ``III'', ``III'',
  ``IV'', ``V'', ``VI''.
\item
  \texttt{km}: mileage in km. VEIN includes a dataset of mileage
  function named ``fkm''.
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{data}\NormalTok{(fe2015)}
\KeywordTok{data}\NormalTok{(fkm)}
\NormalTok{pckma <-}\StringTok{ }\KeywordTok{cumsum}\NormalTok{(fkm}\OperatorTok{$}\KeywordTok{KM_PC_E25}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{24}\NormalTok{)) }\CommentTok{#only vehicles with catalizer}
\NormalTok{x1 <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "CO"}\NormalTok{, ]}
\NormalTok{x1E3 <-}\StringTok{ }\KeywordTok{subset}\NormalTok{(x1, x1}\OperatorTok{$}\NormalTok{Euro_LDV }\OperatorTok{%in%}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"III"}\NormalTok{, }\StringTok{"IV"}\NormalTok{, }\StringTok{"V"}\NormalTok{))}\OperatorTok{$}\NormalTok{PC_G}
\NormalTok{x1E1 <-}\StringTok{ }\KeywordTok{subset}\NormalTok{(x1, x1}\OperatorTok{$}\NormalTok{Euro_LDV }\OperatorTok{%in%}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\StringTok{"II"}\NormalTok{))}\OperatorTok{$}\NormalTok{PC_G}
\NormalTok{det_E3 <-}\StringTok{ }\KeywordTok{emis_det}\NormalTok{(}\DataTypeTok{po =} \StringTok{"CO"}\NormalTok{, }\DataTypeTok{cc =} \DecValTok{1000}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"III"}\NormalTok{,}
                   \DataTypeTok{km =}\NormalTok{ pckma[}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(x1E3)])}
\NormalTok{det_E1 <-}\StringTok{ }\KeywordTok{emis_det}\NormalTok{(}\DataTypeTok{po =} \StringTok{"CO"}\NormalTok{, }\DataTypeTok{cc =} \DecValTok{1000}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"III"}\NormalTok{,}
                   \DataTypeTok{km =}\NormalTok{ pckma[(}\KeywordTok{length}\NormalTok{(x1E3) }\OperatorTok{+}\StringTok{ }\DecValTok{1}\NormalTok{)}\OperatorTok{:}\DecValTok{23}\NormalTok{ ])}
\NormalTok{original_EF <-}\StringTok{ }\NormalTok{x1}\OperatorTok{$}\NormalTok{PC}
\NormalTok{deteriorated_EF <-}\StringTok{ }\NormalTok{x1}\OperatorTok{$}\NormalTok{PC }\OperatorTok{*}\StringTok{ }\KeywordTok{c}\NormalTok{(det_E3, det_E1, }\KeywordTok{rep}\NormalTok{(}\DecValTok{1}\NormalTok{, }\DecValTok{13}\NormalTok{))}
\KeywordTok{plot}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\OperatorTok{:}\DecValTok{36}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ original_EF, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{15}\NormalTok{, }\DataTypeTok{col =} \StringTok{"blue"}\NormalTok{,}
     \DataTypeTok{ylab =} \StringTok{"CO[g/km]"}\NormalTok{, }\DataTypeTok{xlab =} \StringTok{"Age of use"}\NormalTok{)}
\CommentTok{#     main = "Deteriorated (red) and not-deteriorated (blue) EF CO")}
\KeywordTok{lines}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\OperatorTok{:}\DecValTok{36}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ deteriorated_EF,  }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{col =} \StringTok{"red"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{veinbook_files/figure-latex/efDET-1} 

}

\caption{Deteriorated (red) and not-deteriorated (blue) EF CO}\label{fig:efDET}
\end{figure}

\section{\texorpdfstring{Evaporative emissions
\texttt{ef\_evap}}{Evaporative emissions ef\_evap}}\label{evaporative-emissions-ef_evap}

Evaporative emissions are important sources of hydrocarbons and these
emissions are produced by vaporization of fuel due to variations in
ambient temperatures
\citep[\citet{Andradeetal2017}]{MelliosNtziachristos2016}. There are
three types of emissions: \textbf{diurnal emissions}, due to increases
in atmospheric temperature, which lead to thermal expansion of vapor
fuel inside the tank; \textbf{running losses}, when the fuel evaporates
inside the tank due to normal operation of the vehicle; and \textbf{hot
soak} emissions, which occur when the hot engine is turned off. These
methods implemented in VEIN were sourced from the evaporative emissions
methods of Copert Tier 2 \citep{MelliosNtziachristos2016}. VEIN it is
also possible to use local emission factors. Here I'm showing both
approaches:

\begin{enumerate}
\def\labelenumi{\alph{enumi})}
\tightlist
\item
  Tier 2 copert approach
\end{enumerate}

\begin{equation}
EV_{j,k} =\sum_{s}D_{s} \cdot \sum_{j}F_{j} \cdot (HS_{j,k}+de_{j,k}+RL_{j,k}) 
\label{eq:ev1}
\end{equation}

where \(EV_{j}\) are the volatile organic compounds (VOC) evaporative
emissions due to each type of vehicle \(j\). \(D_s\) is the ``seasonal
days'' or number of days when the mean monthly temperature is within a
determined range: {[}-5\(^{\circ}\),10\(^{\circ}\)C{]},
{[}0\(^{\circ}\), 15\(^{\circ}\)C{]}, {[}10\(^{\circ}\),
25\(^{\circ}\)C{]} and {[}20\(^{\circ}\), 35\(^{\circ}\)C{]}.
\(F_{j,k}\) is the number of vehicles according to the same type \(j\)
and age of use \(k\). \(HS_{j,k}\), \(de_{j,k}\) and \(RL_{j,k}\) are
average hot/warm soak, diurnal and running losses evaporative emissions
(\(\mathrm{g \cdot day^{-1}}\)), respectively, according to the vehicle
type \(j\) and age of use \(k\). \(HS_{j,k}\) and \(RL_{j,k}\) are
obtained using equations also sourced from
\citep{MelliosNtziachristos2016}:

\begin{equation}
HS_{j,k} = x_{j,k} \cdot (c \cdot (p \cdot e_{shc}+(1-p) \cdot e_{swc})+(1-c) \cdot e_{shfi} )
\label{eq:ev2}
\end{equation}

where \(x\) are the number of trips per day for the vehicular type \(j\)
and age of use \(k\). \(c\) is the fraction of vehicles with fuel return
systems. \(p\) is the fraction of trips finished with hot engine, for
example, an engine that has reached its normal operating temperature and
the catalyst has reached its light-off temperature
\citep{NtziachristosSamaras2016}. The light-off temperature is the
temperature at the point when catalytic reactions occur inside a
catalytic converter. \(e_{shc}\) and \(e_{swc}\) are average hot-soak
and warm-soak emission factors for gasoline vehicles with carburettor or
fuel return systems (\(\mathrm{g \cdot parking^{-1}}\)). \(e_{shfi}\) is
the average hot-soak emission factors for gasoline vehicles equipped
with fuel injection and non-return fuel systems
(\(\mathrm{g \cdot parking^{-1}}\)). The Eq. \eqref{eq:ev3} shows the
procedure.

\begin{equation}
RL_{j,k} = x_{j,k} \cdot (c \cdot (p \cdot e_{rhc}+(1-p) \cdot e_{rwc})+(1-c) \cdot e_{rhfi} )
\label{eq:ev3}
\end{equation}

\(x\) and \(p\) have the same meanings of Eq. \eqref{eq:ev2}. \(e_{rhc}\)
and \(e_{rwc}\) are average hot and warm running losses emission factors
for gasoline vehicles with carburettor or fuel return systems
(\(\mathrm{g \cdot trip^{-1}}\)) and \(e_{rhfi}\) are average hot
running losses emission factors for gasoline vehicles equipped with fuel
injection and non-return fuel systems (\(\mathrm{g \cdot trip^{-1}}\)).
It is recommended to estimate the number of trips per day
\citep{MelliosNtziachristos2016}, \(x\), as the division between the
mileage and 365 times the length of trip:
\(x = \frac{mileage_j}{(365 \cdot ltrip)}\). However, the mileage of a
vehicle is not constant throughout the years. Therefore, VEIN
incorporates a dataset of equations to estimate mileage of different
types of vehicles by age of use \citep{BruniBales2013}.

The function for evaporative emission factors is \texttt{ef\_evap} and
it arguments:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(ef_evap)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (ef, v, cc, dt, ca, k = 1, show = FALSE) 
## NULL
\end{verbatim}

where:

\begin{itemize}
\item
  \texttt{ef}: Name of evaporative emission factor as \emph{eshotc}
  (\(g \cdot proced^-1\)): mean hot-soak with carburator, \emph{eswarmc}
  (\(g \cdot proced^-1\)): mean cold and warm-soak with carburator,
  eshotfi: mean hot-soak with fuel injection, \emph{erhotc}
  (\(g \cdot trip^-1\)): mean hot running losses with carburator,
  \emph{erwarmc} (\(g \cdot trip^-1\)) mean cold and warm running
  losses, \emph{erhotfi} (\(g \cdot trip^-1\)) mean hot running losses
  with fuel injection.
\item
  \texttt{v}: Type of vehicles, ``PC'', ``Motorcycles'',
  ``Motorcycles\_2S'' and ``Moped''
\item
  \texttt{cc}: Size of engine in cc. PC ``\textless{}=1400'',
  ``1400\_2000'' and ``2000'' Motorcycles\_2S: ``\textless{}=50''.
  Motorcyces: ``\textgreater{}50'', ``\textless{}250'', ``250\_750'' and
  ``\textgreater{}750''
\item
  \texttt{dt}: Average daily temperature variation: ``-5\_10'',
  ``0\_15'', ``10\_25'' and ``20\_35''
\item
  \texttt{ca}: Size of canister: ``no'' meaning no canister, ``small'',
  ``medium'' and ``large''
\item
  \texttt{k}: multiplication factor
\item
  \texttt{show}: when TRUE shows row of table with respective emission
  factor.
\end{itemize}

Example:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{library}\NormalTok{(ggplot2)}
\KeywordTok{library}\NormalTok{(cptcity)}
\NormalTok{dt <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"-5_10"}\NormalTok{, }\StringTok{"0_15"}\NormalTok{, }\StringTok{"10_25"}\NormalTok{, }\StringTok{"20_35"}\NormalTok{)}
\NormalTok{ef <-}\StringTok{ }\KeywordTok{sapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{ef_evap}\NormalTok{(}\DataTypeTok{ef =} \StringTok{"erhotc"}\NormalTok{,}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{,}
                \DataTypeTok{dt =}\NormalTok{ dt[i], }\DataTypeTok{ca =} \StringTok{"no"}\NormalTok{,}
                \DataTypeTok{show =} \OtherTok{TRUE}\NormalTok{)}
\NormalTok{\})}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##        ef  units veh     cc    dt canister    g
## 28 erhotc g/trip  PC <=1400 -5_10       no 1.67
##        ef  units veh     cc   dt canister    g
## 21 erhotc g/trip  PC <=1400 0_15       no 2.39
##        ef  units veh     cc    dt canister    g
## 14 erhotc g/trip  PC <=1400 10_25       no 3.25
##       ef  units veh     cc    dt canister    g
## 7 erhotc g/trip  PC <=1400 20_35       no 5.42
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{ef =}\NormalTok{ ef, }\DataTypeTok{temp =} \KeywordTok{c}\NormalTok{(}\DecValTok{0}\NormalTok{, }\DecValTok{10}\NormalTok{, }\DecValTok{20}\NormalTok{, }\DecValTok{30}\NormalTok{))}
\KeywordTok{source}\NormalTok{(}\StringTok{"theme_black.R"}\NormalTok{)}
\KeywordTok{ggplot}\NormalTok{(df, }\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ temp, }\DataTypeTok{y =}\NormalTok{ ef)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{geom_bar}\NormalTok{(}\DataTypeTok{stat =} \StringTok{"identity"}\NormalTok{, }\DataTypeTok{fill =} \KeywordTok{cpt}\NormalTok{(}\DataTypeTok{n =} \DecValTok{4}\NormalTok{), }\DataTypeTok{col =} \StringTok{"white"}\NormalTok{) }\OperatorTok{+}\StringTok{ }
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{x =} \StringTok{"Temperature[C]"}\NormalTok{, }\DataTypeTok{y =} \StringTok{"RL Evaporative NMHC [g/trip]"}\NormalTok{) }\OperatorTok{+}\StringTok{ }
\StringTok{  }\KeywordTok{theme_black}\NormalTok{()}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{veinbook_files/figure-latex/rlevap-1} 

}

\caption{Running losses evaporative emission actors (g/trip)}\label{fig:rlevap}
\end{figure}

\begin{enumerate}
\def\labelenumi{\alph{enumi})}
\setcounter{enumi}{1}
\tightlist
\item
  Alternative approach
\end{enumerate}

The evaporative emission factors mentioned before are suitable for
top-down estimations. That approach also requires knowledge of the
`procedure' and `trips'. In this section it is introduced an alternative
which consists in transform the emission factors into \(g \cdot km^-1\)
and then do a bottom-up estimation.

In order to do that, we need to follows assumptions. First, we consider
that the number of procedures is equal to trips. Then estimates how many
trips per day are produced each day and then estiamtes how many
kilometers are driven each day and convert th emission factors as shown
on Eq. \eqref{eq:efev2}.

\begin{equation}
EF_{ev}(\frac{g}{km})=EF(_{ev})(\frac{g}{trip}) \cdot A(\frac{trips}{day}) \cdot B(\frac{km}{day})^{-1}
\label{eq:efev2}
\end{equation}

Clearly, we need to know A \((\frac{trips}{day})\) and
B\((\frac{km}{day})^{-1}\). Diurnal evaporative emissions factors are
expressed as \((\frac{g}{day})\). To conbvert to \((\frac{g}{km})\) we
only need the kilometers driven each day \(\frac{km}{day}^{-1}\).

Now, it will be shown an example with the evaporative emission factors
from \citet{CETESB2015}. These emission factors are based on the
methodology Tier 2 from -@\citet{MelliosNtziachristos2016}. If we
consider evaporative emission factors of Passenger Cars using Gasoline
(blended with 25\% of Ethanol) \citep{CETESB2015} and 4.6
\((\frac{trips}{day})\) \citep{ibarrathesis}.

\begin{verbatim}
##   year ed_20_35 eshot_20_35 erhot_20_35
## 1 2015     0.06        0.09        0.03
## 2 2014     0.10        0.10        0.04
## 3 2013     0.12        0.13        0.05
## 4 2012     0.19        0.16        0.06
## 5 2011     0.19        0.17        0.14
## 6 2010     0.08        0.08        0.06
\end{verbatim}

The variable ed\_20\_35 are diurnal evaporative emission factors in
\(g \cdot day^{-1}\) and eshot\_20\_35 and erhot\_20\_35
\(g \cdot trip^{-1}\).

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(fkm)}
\NormalTok{evap}\OperatorTok{$}\NormalTok{ed_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm <-}\StringTok{ }\NormalTok{evap}\OperatorTok{$}\NormalTok{ed_}\DecValTok{20}\NormalTok{_}\DecValTok{35} \OperatorTok{/}\StringTok{ }\NormalTok{fkm}\OperatorTok{$}\KeywordTok{KM_PC_E25}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{)}\OperatorTok{/}\DecValTok{365}
\NormalTok{evap}\OperatorTok{$}\NormalTok{eshot_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm <-}\StringTok{ }\NormalTok{evap}\OperatorTok{$}\NormalTok{eshot_}\DecValTok{20}\NormalTok{_}\DecValTok{35} \OperatorTok{*}\StringTok{ }\FloatTok{4.6}\OperatorTok{/}\StringTok{ }\NormalTok{fkm}\OperatorTok{$}\KeywordTok{KM_PC_E25}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{)}\OperatorTok{/}\DecValTok{365}
\NormalTok{evap}\OperatorTok{$}\NormalTok{erhot_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm <-}\StringTok{ }\NormalTok{evap}\OperatorTok{$}\NormalTok{erhot_}\DecValTok{20}\NormalTok{_}\DecValTok{35}  \OperatorTok{*}\StringTok{ }\FloatTok{4.6}\OperatorTok{/}\StringTok{ }\NormalTok{fkm}\OperatorTok{$}\KeywordTok{KM_PC_E25}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{)}\OperatorTok{/}\DecValTok{365}
\KeywordTok{plot}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ evap}\OperatorTok{$}\NormalTok{eshot_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm, }\DataTypeTok{col =} \StringTok{"blue"}\NormalTok{, }
     \DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{,}
     \DataTypeTok{xlab =} \StringTok{"Age of use"}\NormalTok{, }\DataTypeTok{ylab =} \StringTok{"EV NMHC [g/km]"}\NormalTok{)}
\KeywordTok{lines}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ evap}\OperatorTok{$}\NormalTok{ed_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm, }\DataTypeTok{col =} \StringTok{"red"}\NormalTok{, }
      \DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{15}\NormalTok{)}
\KeywordTok{lines}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\OperatorTok{:}\DecValTok{41}\NormalTok{, }\DataTypeTok{y =}\NormalTok{ evap}\OperatorTok{$}\NormalTok{erhot_}\DecValTok{20}\NormalTok{_}\DecValTok{35}\NormalTok{_gkm, }\DataTypeTok{col =} \StringTok{"green"}\NormalTok{, }
      \DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{17}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{veinbook_files/figure-latex/efEVAP-1} 

}

\caption{Hot Soak (blue), Diurnal (green) and Running Losses (red) EF (g/km)}\label{fig:efEVAP}
\end{figure}

\section{\texorpdfstring{Emission factors of wear
\texttt{ef\_wear}}{Emission factors of wear ef\_wear}}\label{emission-factors-of-wear-ef_wear}

Wear emissions are very important because they contribute with an
important fraction of PM from tyre and break wear. VEIN includes these
functions from \citet{NtziachristosBoulter2009} which incudewear
emissions factors form tyre, break and road abrassion.

The arguments are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(ef_wear)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (wear, type, pol = "TSP", speed, load = 0.5, axle = 2) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{wear}: Character; type of wear: ``tyre'', ``break'' and
  ``road'' type: Character; type of vehicle: ``2W'', ``PC'', ``LCV'',
  'HDV"
\item
  \texttt{pol}: Character; pollutant: ``TSP'', ``PM10'', ``PM2.5'',
  ``PM1'' and ``PM0.1''
\item
  \texttt{speed}: List of speeds
\item
  \texttt{load}: Load of the HDV
\item
  \texttt{axle}: Number of axle of the HDV
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\NormalTok{?ef_wear}
\end{Highlighting}
\end{Shaded}

\section{Emission factors from tunnel
studies}\label{emission-factors-from-tunnel-studies}

With VEIN you can choose three type of emissions factors, speed
functions, local emission factors or scaled factors. In this section i
will show how to expand the use of local emission factors considering
tunnel emission factors \citep{perez2014emission}, from the
International Vehicle Emissions (IVE) Model \citep{Davisetal2005}
applied in Colombia \citep{gonzalez2017relative}.

\citet{perez2014emission} measured air pollutant concentrations in two
tunnels in São Paulo city, one with predominant use of LDV vehicles and
another with more HDV. The resulting emission factors for the fleet are
shown on Table \ref{tab:eftun}.

\begin{table}

\caption{\label{tab:eftun}Emission factors from tunnels in São Paulo 2014}
\centering
\begin{tabular}[t]{lrr}
\toprule
  & CO & NOx\\
\midrule
LDV & 5.8 g/km & 0.3 g/km\\
HDV & 3.6 g/km & 9.2 g/km\\
\bottomrule
\end{tabular}
\end{table}

If we want to compare with the \citet{CETESB2015} emission factors, we
would have to weight these factors with the fleet. The weight mean
emission factor of CO and Passenger Cars (PC) is 1.40
\(g \cdot km^{-1}\) and for Light Trucks (LT) is 0.60
\(g \cdot km^{-1}\). In the case of NOx, Passenger Cars (PC) is 0.16
\(g \cdot km^{-1}\) and for Light Trucks (LT) is 3.24
\(g \cdot km^{-1}\). The emission factors from the example are for
vehicles with 0 kilometers without deterioration. Anyways, tunnel
emission factors are higher that the weighted emission factors from
CETESB.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(fe2015)}
\NormalTok{fecoPC <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "CO"}\NormalTok{, }\StringTok{"PC_G"}\NormalTok{]}
\NormalTok{fecoLT <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "CO"}\NormalTok{, }\StringTok{"LT"}\NormalTok{]}
\NormalTok{fenoxPC <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "NOx"}\NormalTok{, }\StringTok{"PC_G"}\NormalTok{]}
\NormalTok{fenoxLT <-}\StringTok{ }\NormalTok{fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "NOx"}\NormalTok{, }\StringTok{"LT"}\NormalTok{]}
\NormalTok{PC <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\NormalTok{, }\DataTypeTok{name =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{36}\NormalTok{, }\DataTypeTok{message =} \OtherTok{FALSE}\NormalTok{)}
\NormalTok{LT <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =} \DecValTok{1}\NormalTok{, }\DataTypeTok{name =} \StringTok{"LT"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{36}\NormalTok{, }\DataTypeTok{message =} \OtherTok{FALSE}\NormalTok{)}
\KeywordTok{weighted.mean}\NormalTok{(fecoPC, PC)  }\CommentTok{# PC CO}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1.40355
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{weighted.mean}\NormalTok{(fecoLT, LT)  }\CommentTok{# LT CO}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 0.5971173
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{weighted.mean}\NormalTok{(fenoxPC, PC) }\CommentTok{# PC NOx }
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 0.1699646
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{weighted.mean}\NormalTok{(fenoxLT, LT) }\CommentTok{# LT NOx}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 3.241522
\end{verbatim}

If the user want to use tunnel emission factors from
\citet{perez2014emission} in VEIN, the user would have to use the same
value for all respective categories of the vehicular composition. For
instance, after running \texttt{inventory}, include tunnel emission
factors in each \texttt{*input.R}.

\section{Emission factors from International Vehicle Emissions
(IVE)}\label{emission-factors-from-international-vehicle-emissions-ive}

The IVE models applies the vehicle specific power (VSP) methodology to
predict emissions \citep{jimenez1999vehicle}. The formlula is shown on
Eq. \eqref{eq:vsp}.

\begin{equation}
VSP = v \cdot (1.1 \cdot a + 9.81(atan(sin(grade))) + 0.132) + 0.000302v^3
\label{eq:vsp}
\end{equation}

where \(v\) is velocity \((m \cdot s^{-1})\), \(a\) is the second by
second acceleration \((m \cdot s^{-2})\) and grade the second by second
change in height defined as \(\frac{(h_{t=0} - h_{t=-1})}{v}\); being
\(h\) as altitude \(m\). This method requires that the user measures the
activity Global Position System (GPS) recordings second by second and/or
emissions measurements with Portable Emissions Measurement Systems
(PEMS).

\citet{gonzalez2017relative} measure the vehicular activity in the city
of Manizales, Colombia. In htis case, there were not PEMS measurements,
but IVE allows to produce emission factors from the GPS recordings based
on Mobile EPA emissions
model\citep[\citet{Davisetal2005}]{arbor2003user}.

The resulting emission factors varies for each type of vehicle and
includes several pollutants. If the user wants to use IVE emission
factors in vein would have to add them in each \texttt{*input.R}, after
running \texttt{inventory}.

\chapter{Estimation of emissions}\label{est}

The emissions estimation process was shown on Eq. \eqref{eq:emi} on
chapter \ref{ef}. In this equation, it was shown that how the emissions
are obtained by multiplying the traffifc flow with the distance that
each car travels (length of the road if it is a road network), and
emissions and deterioration factors.

In this chapter I will show how VEIN interprets this and other emission
equations to estimate emissions. At the end of each section The Fig.
\ref{fig:diaemis} showsa diagram with the estimation process. The pink
boxes hows VEIN classes, the circle data and the grey boxes functions.
The object \texttt{Vehicles} is a class consisting in a matrix of
vehicles with untis \(1 \cdot h^{-1}\), as shown on chapter
\ref{traffic}, however, \emph{the units in \texttt{Vehicles} is not a
requirement for estimating emissions}. The object of class
\texttt{Speed} is a matrix of speeds with the number of columns as the
calculated speeds at different hours at each row. This object can be
present or not, depending on the type of emission factor considered, for
instant, emission factors not depending on speed. The object class of
\texttt{EmissionFactorsList} are list of functions depending on speed as
\(f(V)\). Each function can depend on speed or not as shown on chapter
\ref{ef}, section \ref{localef}. Lastly, the object lkm is the length of
the road expressed in \(lkm\).

The function \texttt{emis} reads the inputs and estimates the emissions
producing an object with class \texttt{EmissionsArray} which has 4
dimensions, number of streets, ages of use of vehicle, number of hours
and number of days.

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{figuras/dia6} 

}

\caption{Emissions estimation process with VEIN}\label{fig:diaemis}
\end{figure}

The estimation process is done with the emissions functions. The
emissions estimations functions are:

\begin{itemize}
\tightlist
\item
  \texttt{emis} for hot emissions,
\item
  \texttt{emis\_cold} for cold start emissions,
\item
  \texttt{emis\_evap} for evaporative emissions,
\item
  \texttt{emis\_paved} for resuspension emissions on paved roads, and
\item
  \texttt{emis\_wear} for wear of breakes, tyres and roads.
\end{itemize}

These functions perform internal checks for ensuring that the length lkm
has the unit of \(km\). This means that, if \texttt{lkm} has units
different than \(km\), the function will not run. These function runs
internally \texttt{lapply} which are faster \texttt{for} written in C.
Despite that these functions are fast, i will try to make them faster
with some RCPP implementation in future.

\section{\texorpdfstring{The \texttt{emis}
function}{The emis function}}\label{the-emis-function}

The arguments of \texttt{emis} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (veh, lkm, ef, speed = 34, agemax = ifelse(is.data.frame(veh), 
##     ncol(veh), ncol(veh[[1]])), profile, hour = nrow(profile), 
##     day = ncol(profile), array = T) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{veh}: ``Vehicles'' data-frame or list of ``Vehicles''
  data-frame. Each data-frame has number of columns matching the age
  distribution of that type of vehicle. The number of rows is equal to
  the number of streets link.
\item
  \texttt{lkm}: Length of each link that must in \(km\). As consequence,
  emission factors must be in \(g \cdot km^{-1}\).
\item
  \texttt{ef}: ``EmissionFactorsList''. A list of emission factors as
  speed functions. Each element has the form \(f(V)\). The implicit unit
  is \(g \cdot km^{-1}\).
\item
  \texttt{speed}: ``Speed'' object. A Speed data-frame with number of
  columns as hours.
\item
  \texttt{agemax}: Age of oldest vehicles of the vehicles veh. The
  information of this argument can be obtained from the \texttt{veh}
  argument Therefore, this argument will be deprecated.
\item
  \texttt{profile}: Numerical or dataframe or matrix with nrows equal to
  the hours and cols to each day. This is traffic data normalized to the
  hour of the input traffic data.
\item
  \texttt{hour}: Number of considered hours in estimation. As this
  information can be derived form the argument \texttt{profile}, the
  default value is the number of rows of the matrix profile.
\item
  \texttt{day}: Number of considered days in estimation. As this
  information can be derived form the argument \texttt{profile}, the
  default value is the number of coluns of the matrix profile.
\item
  \texttt{array}: When FALSE produces a dataframe of the estimation.
  When TRUE expects a profile as a dataframe producing an array with
  dimensions (streets x columns x hours x days)
\end{itemize}

\section{\texorpdfstring{The \texttt{emis\_cold}
function}{The emis\_cold function}}\label{the-emis_cold-function}

The arguments of \texttt{emis\_cold} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_cold)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (veh, lkm, ef, efcold, beta, speed = 34, agemax = if (!inherits(x = veh, 
##     what = "list")) {
##     ncol(veh)
## } else {
##     ncol(veh[[1]])
## }, profile, hour = nrow(profile), day = ncol(profile), array = TRUE) 
## NULL
\end{verbatim}

The arguments are similar with \texttt{emis} with the difference that it
is added two arguments:

\begin{itemize}
\tightlist
\item
  \texttt{efcold}: List of functions of cold start emission factors of
  vehicular categories. Technically, each function is also of the form
  \(f(V)\).
\item
  \texttt{beta}: Datraframe with the hourly cold-start distribution to
  each day of the period. Number of rows are hours and columns are days.
  It represents the fraction of mileage driven under cold-start
  conditions.
\end{itemize}

The equation for estimating cold-start emissions is on Eq.
\eqref{eq:cold}.

\section{\texorpdfstring{The \texttt{emis\_evap}
function}{The emis\_evap function}}\label{the-emis_evap-function}

The estimation of evaporative emissions applies Tier 2 from
\citet{MelliosNtziachristos2016}. The approach followed in VEIN was very
simply, consisting in only adding elements of a data-frame.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_evap)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (veh, name, size, fuel, aged, nd4, nd3, nd2, nd1, hs_nd4, 
##     hs_nd3, hs_nd2, hs_nd1, rl_nd4, rl_nd3, rl_nd2, rl_nd1, d_nd4, 
##     d_nd3, d_nd2, d_nd1) 
## NULL
\end{verbatim}

\begin{itemize}
\tightlist
\item
  \texttt{veh}: this are the number of vehicles at each age of use and
  for each street. It is a \texttt{Vehicles} object.
\item
  \texttt{name}: Character indicating the name of the vehicle.
\item
  \texttt{size}: Character indicating the size of the vehicle.
\item
  \texttt{fuel}: Character indicating the fuel of the vehicle.
\item
  \texttt{age}: Numeric vector with the age distribution, for instance,
  1:40 for vehicles between 1 and 40 years of use.
\item
  \texttt{nd4}: Number of days of your perdiof of study with monthly
  average temperature between 20 and 35 celcius degrees. If the period
  of time is a year, this is the annual number of days under that
  condition.
\item
  \texttt{nd3}: Number of days of your perdiof of study with monthly
  average temperature between 10 and 25 celcius degrees.
\item
  \texttt{nd2}: Number of days of your perdiof of study with monthly
  average temperature between 0 and 15 celcius degrees.
\item
  \texttt{nd1}: Number of days of your perdiof of study with monthly
  average temperature between -5 and 10 celcius degrees.
\item
  \texttt{hs\_nd4}: Number of average daily hot-soak evaporative
  emissions for days with temperature between 20 and 35 celcius degrees
\item
  \texttt{hs\_nd3}: Number of average daily hot-soak evaporative
  emissions for days with temperature between 10 and 25 celcius degrees
\item
  \texttt{hs\_nd2}: Number of average daily hot-soak evaporative
  emissions for days with temperature between 0 and 15 celcius degrees
\item
  \texttt{hs\_nd1}: Number of average daily hot-soak evaporative
  emissions for days with temperature between -5 and 10 celcius degrees
\item
  \texttt{rl\_nd4}: Number of average daily running losses evaporative
  emissions for days with temperature between 20 and 35 celcius degrees
\item
  \texttt{rl\_nd3}: Number of average daily running losses evaporative
  emissions for days with temperature between 10 and 25 celcius degrees
\item
  \texttt{rl\_nd2}: Number of average daily running losses evaporative
  emissions for days with temperature between 0 and 15 celcius degrees
\item
  \texttt{rl\_nd1}: Number of average daily running losses evaporative
  emissions for days with temperature between -5 and 10 celcius degrees
\item
  \texttt{d\_nd4}: Number of average daily diurnal evaporative emissions
  for days with temperature between 20 and 35 celcius degrees
\item
  \texttt{d\_nd3}: Number of average daily diurnal evaporative emissions
  for days with temperature between 10 and 25 celcius degrees
\item
  \texttt{d\_nd2}: Number of average daily diurnal evaporative emissions
  for days with temperature between 0 and 15 celcius degrees
\item
  \texttt{d\_nd1}: Number of average daily diurnal evaporative emissions
  for days with temperature between -5 and 10 celcius degrees
\end{itemize}

\section{\texorpdfstring{The \texttt{emis\_paved}
function}{The emis\_paved function}}\label{the-emis_paved-function}

Evaporative emissions are emissions of resuspended dust due to traffic
circulating ober paved or non-paved roads. These emissions can be an
important source of mass particulate matter, and several cities programs
that aims to improve air quality by diminishing these emissions
\citep{AMATO20103070}. However, these emissions are not considered by in
the European emissions guidelines, where their focus is on primary
particles and not those resulting from the resuspension of previously
deposited material \citep{NtziachristosBoulter2009}.

The method adopted in VEIN comes from the USEPA \citep{paved} which
presents equations for annual, daily and hourly estimations:

\begin{equation}
EF_{paved}=k \cdot sL^{0.91} \cdot W^{1.02}
\label{eq:res1}
\end{equation}

Where

\begin{itemize}
\tightlist
\item
  \(EF_{paved}\) is the emission factor of particulate matter.
\item
  \(k\) particle size splitter.
\item
  \(sL\) road surface silt loading \(g \cdot m^{-2}\).
\item
  \(W\) average weight.
\end{itemize}

Equation \eqref{eq:res1} can be extrapolated to consider natural
mitigation of rainy periods of time with daily basis:

\begin{equation}
EF_{anual} = EF_{paved} \cdot (1 - \frac{P}{4 \cdot N})
\label{eq:res2}
\end{equation}

Where

\begin{itemize}
\tightlist
\item
  \(EF_{anual}\) anual or long term emission factor.
\item
  \(P\) number of days with accumulated rain above 0.254 mm.
\item
  \(N\) total number of days.
\end{itemize}

or hourly

\begin{equation}
EF_{hourly} = EF_{paved} \cdot (1 - \frac{1.2 \cdot P}{N})
\label{eq:res2}
\end{equation}

Where

\begin{itemize}
\tightlist
\item
  \(EF_{annual}\) annual or long term emission factor.
\item
  \(P\) number of hours with accumulated rain above 0.254 mm.
\item
  \(N\) total number of hours. For instance, 8760 for 1 year.
\end{itemize}

The function \texttt{emis\_paved} has the arguments:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_paved)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (veh, lkm, k = 0.62, sL1 = 0.6, sL2 = 0.2, sL3 = 0.06, 
##     sL4 = 0.03, W) 
## NULL
\end{verbatim}

Where,

-\texttt{veh}: It is an array with dimenssions number of streets x hours
of day x days of week. - \texttt{lkm}: Length of each link. -
\texttt{k}: K\_PM30 = 3.23, K\_PM15 = 0.77, K\_PM10 = 0.62 and K\_PM2.5
= 0.15. - \texttt{sL1}: Silt loading (g/m2) for roads with ADT
\textless{}= 500. - \texttt{sL2}: Silt loading (g/m2) for roads with ADT
\textgreater{} 500 and \textless{}= 5000. - \texttt{sL3}: Silt loading
(g/m2) for roads with ADT \textgreater{} 5000 and \textless{}= 1000. -
\texttt{sL4}: Silt loading (g/m2) for roads with ADT \textgreater{}
10000. - \texttt{W}: array of dimensions of veh. It consists in the
hourly averaged weight of traffic fleet in each road.

For instance, Lets create an array of vehicles. The funcion \texttt{adt}
calculates the amount pf vehicles at all hours. Lets use the
\texttt{data(net)} asumming no buses. Let's calculate the total traffic
for only 24 hours with the same profile. For simplicity, let's assume
that all vehicle are expressed in \emph{vehicle equivalent} and it is a
dry period.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{vkpc  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.75}\NormalTok{, }\DecValTok{1}\NormalTok{, }\KeywordTok{matrix}\NormalTok{(profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{]))}
\NormalTok{vklcv  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.1}\NormalTok{, }\DecValTok{1}\NormalTok{, }\KeywordTok{matrix}\NormalTok{(profiles}\OperatorTok{$}\NormalTok{LCV_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{]))}
\NormalTok{vkmc  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.15}\NormalTok{, }\DecValTok{1}\NormalTok{, }\KeywordTok{matrix}\NormalTok{(profiles}\OperatorTok{$}\NormalTok{MC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{]))}
\NormalTok{vkhgv  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv, }\DecValTok{1}\NormalTok{, }\KeywordTok{matrix}\NormalTok{(profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{]))}
\NormalTok{vk <-}\StringTok{ }\NormalTok{vkpc }\OperatorTok{+}\StringTok{ }\NormalTok{vklcv }\OperatorTok{+}\StringTok{ }\NormalTok{vkmc }\OperatorTok{+}\StringTok{ }\NormalTok{vkhgv}
\KeywordTok{dim}\NormalTok{(vk)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1505   24
\end{verbatim}

\texttt{W} is the average hourly fleet weight. It ahs the same
dimensions of \texttt{adt2}. Let's assume an individual weight of PC is
1, LCV 1.5, HGV = 10 and MC 0.5. This example is a simplification,
\texttt{adt} should be calculated with full characterization of the
fleet.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{wpc  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.75}\NormalTok{, }\DecValTok{1}\NormalTok{, profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\DecValTok{1}
\NormalTok{wlcv  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.1}\NormalTok{, }\DecValTok{1}\NormalTok{, profiles}\OperatorTok{$}\NormalTok{LCV_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\FloatTok{1.5}
\NormalTok{wmc  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{*}\FloatTok{0.15}\NormalTok{, }\DecValTok{1}\NormalTok{, profiles}\OperatorTok{$}\NormalTok{MC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\FloatTok{0.5}
\NormalTok{whgv  <-}\StringTok{ }\KeywordTok{vkm}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv, }\DecValTok{1}\NormalTok{, profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\DecValTok{10}
\NormalTok{W <-}\StringTok{ }\NormalTok{(wpc }\OperatorTok{+}\StringTok{ }\NormalTok{wlcv }\OperatorTok{+}\StringTok{ }\NormalTok{wmc }\OperatorTok{+}\StringTok{ }\NormalTok{whgv)}\OperatorTok{/}\NormalTok{vk}
\NormalTok{W[}\KeywordTok{is.na}\NormalTok{(W)] <-}\StringTok{ }\DecValTok{0}
\KeywordTok{dim}\NormalTok{(W)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] 1505   24
\end{verbatim}

and now we estimate the emissions with the default values form PM10.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emi <-}\StringTok{ }\KeywordTok{emis_paved}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ vk, }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{W =}\NormalTok{ W)}
\NormalTok{emi[}\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Result for Emissions 
##              h1            h2            h3            h4
## 1 28.492378 g/h 41.326236 g/h 27.987685 g/h 30.978253 g/h
## 2 48.704190 g/h 31.257192 g/h 27.139666 g/h 34.919757 g/h
## 3  4.360828 g/h  2.327479 g/h  1.576257 g/h  1.744684 g/h
## 4 10.371056 g/h  5.535283 g/h  3.748702 g/h  4.149262 g/h
\end{verbatim}

\section{\texorpdfstring{The \texttt{emis\_wear}
function}{The emis\_wear function}}\label{ew}

Wear emissions can be very important, in fact, these are second most
important source of particulate matter in Europe \citep{eear}. These
methods in VEIN comes from \citet{NtziachristosBoulter2009} which
include wear of ``tyre'', ``break'' and ``road''.

The arguments of the function \texttt{emis\_wear} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_wear)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (veh, lkm, ef, what = "tyre", speed, agemax = ncol(veh), 
##     profile, hour = nrow(profile), day = ncol(profile)) 
## NULL
\end{verbatim}

Where,

\begin{itemize}
\tightlist
\item
  \texttt{veh}: Object of class ``Vehicles''.
\item
  \texttt{lkm}: Length of the road.
\item
  \texttt{ef}: list of emission factor functions class
  ``EmissionFactorsList'', length equals to hours.
\item
  \texttt{agemax}: Age of oldest vehicles for that category
\item
  \texttt{profile}: Dataframe or matrix with nrows equal to hours and
  ncol as days of the week
\item
  \texttt{hour}: Number of considered hours in estimation, with default
  value as number of rows of profile.
\item
  \texttt{day}: Number of considered days in estimation, with default
  value as number of columns of profile.
\end{itemize}

Let's estimate wear emissions!

First, we get the emission factor with the \texttt{ef\_wear} function:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{pro <-}\StringTok{ }\NormalTok{profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{] }\CommentTok{# 24 hours}
\NormalTok{pc_week <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, pro)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pc_week, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\NormalTok{ef <-}\StringTok{ }\KeywordTok{ef_wear}\NormalTok{(}\DataTypeTok{wear =} \StringTok{"tyre"}\NormalTok{, }\DataTypeTok{type =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{pol =} \StringTok{"PM10"}\NormalTok{, }
                            \DataTypeTok{speed =}\NormalTok{ df)}
\end{Highlighting}
\end{Shaded}

Then we estimate emissions

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emi <-}\StringTok{ }\KeywordTok{emis_wear}\NormalTok{(}\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"VEH"}\NormalTok{), }\DataTypeTok{speed =}\NormalTok{ df,}
                 \DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ ef, }\DataTypeTok{profile =}\NormalTok{  pro)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of VEH is 11.17
\end{verbatim}

\begin{verbatim}
## Number of VEH is 1946.95 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(emi, }\DataTypeTok{pollutant =} \StringTok{"PM"}\NormalTok{, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{)}
\NormalTok{df[}\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Result for Emissions 
##              V1            V2             V3             V4
## 1 2.0689049 g/h 1.0160833 g/h 0.56468672 g/h 0.52486176 g/h
## 2 0.7614073 g/h 0.3743085 g/h 0.20803274 g/h 0.19336135 g/h
## 3 0.1116298 g/h 0.0548773 g/h 0.03049965 g/h 0.02834868 g/h
## 4 0.2768287 g/h 0.1360872 g/h 0.07563435 g/h 0.07030028 g/h
\end{verbatim}

\chapter{\texorpdfstring{Post estimation with
\texttt{emis\_post}}{Post estimation with emis\_post}}\label{post}

Once the emissions are estimated we obtained \texttt{EmissionsArray}
objects, which as mentioned before, they are arrays with the dimensions
streets x age distribution of vehicles x hours x days.

\begin{figure}

{\centering \includegraphics[width=1\linewidth]{figuras/dia7} 

}

\caption{Post-emissions}\label{fig:demispost}
\end{figure}

Now the function \texttt{emis\_post} reads the \texttt{EmissionsArray}
and and convert it to data-frames that are easier to manage.

The arguments of \texttt{emis\_post} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_post)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (arra, veh, size, fuel, pollutant, by = "veh", net) 
## NULL
\end{verbatim}

Where,

\begin{itemize}
\tightlist
\item
  \texttt{arra}: \texttt{EmissionsArray} obtained with the \texttt{emis}
  functions. It is an array of emissions 4d: streets x category of
  vehicles x hours x days or 3d: streets x category of vehicles x hours
\item
  \texttt{veh}: Character indicating the type of vehicle , for instance:
  ``PC''.
\item
  \texttt{size}: Character indicating the size or weight, for instance:
  ``\textless{}1400'', ``1400\textless{}cc\textless{}2000'', ``GLP'',
  ``Diesel''.
\item
  \texttt{fuel}: Character indicating fuel, for instance: ``Gasoline'',
  ``E\_25'', ``GLP'', ``Diesel''.
\item
  \texttt{pollutant}: Character indicating the pollutant, for instance
  ``CO'', ``NOx'', etc.
\item
  \texttt{by}: Character indicating type of output, ``veh'' for total
  vehicular category , ``streets\_narrow'' or ``streets\_wide''. This is
  the most important argument of this function. ``streets\_wide''
  returns a dataframe with rows as number of streets and columns the
  hours as days*hours considered, e.g.~168 columns as the hours of a
  whole week and ``streets\_wide repeats the row number of streets by
  hour and day of the week
\end{itemize}

Lets create some objects

\section{Emissions by street}\label{emissions-by-street}

From previous chapters we worked with \texttt{Vehicles},
\texttt{EmissionFactors} and estimate emissions generating
\texttt{EmissionsArray}. Now let's create an \texttt{EmissionsArray}
object.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{library}\NormalTok{(veinreport)}
\KeywordTok{library}\NormalTok{(cptcity)}
\KeywordTok{library}\NormalTok{(ggplot2)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\KeywordTok{data}\NormalTok{(fe2015)}
\NormalTok{PC_G <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"PC"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of PC is 11.17
\end{verbatim}

\begin{verbatim}
## Number of PC is 1946.95 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{# Estimation for 168 hour and local factors}
\NormalTok{pcw <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv, profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{)}
\CommentTok{# June is not holliday in southern hemisphere}
\NormalTok{hdvw <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv, profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2014}\NormalTok{)}
\NormalTok{speed <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pcw }\OperatorTok{+}\StringTok{ }\NormalTok{hdvw, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\NormalTok{lef <-}\StringTok{ }\KeywordTok{EmissionFactorsList}\NormalTok{(fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant}\OperatorTok{==}\StringTok{"CO"}\NormalTok{, }\StringTok{"PC_G"}\NormalTok{])}
\NormalTok{E_CO <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ PC_G,}\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ lef, }\DataTypeTok{speed =}\NormalTok{ speed,}
             \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of columns of 'veh' is different than length of 'ef'
\end{verbatim}

\begin{verbatim}
## adjusting length of ef to the number of colums of 'veh'
\end{verbatim}

\begin{verbatim}
## 187546.38 kg emissions in 24 hours and 7 days
\end{verbatim}

Now that we have our \texttt{EmissionsArray} E\_CO, we can process it
with the function \texttt{emis\_post}. The emissions by street are
obtained with the argument \texttt{by} = ``streets\_wide'':

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_CO_STREETS <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(E_CO, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{)}
\NormalTok{E_CO_STREETS[}\DecValTok{1}\OperatorTok{:}\DecValTok{6}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Result for Emissions 
##               V1             V2            V3
## 1 676.629408 g/h 349.068278 g/h 206.13032 g/h
## 2 259.924802 g/h 134.093349 g/h  79.18423 g/h
## 3  38.107534 g/h  19.659404 g/h  11.60919 g/h
## 4  90.628506 g/h  46.754599 g/h  27.60933 g/h
## 5   3.884417 g/h   2.003943 g/h   1.18336 g/h
## 6 378.775340 g/h 195.407492 g/h 115.39120 g/h
\end{verbatim}

In the version 0.3.17 it was added the capability of returning an
spatial object class `sf' with `LINESTRING':

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_CO_STREETS <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(E_CO, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{, }\DataTypeTok{net =}\NormalTok{ net)}
\KeywordTok{class}\NormalTok{(E_CO_STREETS)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "sf"         "data.frame"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{ggplot}\NormalTok{(E_CO_STREETS) }\OperatorTok{+}\StringTok{ }\KeywordTok{geom_sf}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{colour =} \KeywordTok{as.numeric}\NormalTok{(V9))) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale_color_gradientn}\NormalTok{(}\DataTypeTok{colours =} \KeywordTok{rev}\NormalTok{(}\KeywordTok{cpt}\NormalTok{())) }\OperatorTok{+}\StringTok{ }\KeywordTok{theme_bw}\NormalTok{()}\OperatorTok{+}
\StringTok{  }\KeywordTok{ggtitle}\NormalTok{(}\StringTok{"CO emissions at 08:00 (g/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/pcco-1.pdf}
\caption{\label{fig:pcco}CO emisions of PC (g/h)}
\end{figure}

Now, if we estimate the NOx emissions of Light Trucks we will a slight
different pattern of emissions:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{LT <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv, }\DataTypeTok{name =} \StringTok{"LT"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of LT is 17.12
\end{verbatim}

\begin{verbatim}
## Number of LT is 148.12 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{lef <-}\StringTok{ }\KeywordTok{EmissionFactorsList}\NormalTok{(fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant}\OperatorTok{==}\StringTok{"NOx"}\NormalTok{, }\StringTok{"LT"}\NormalTok{])}
\NormalTok{E_NOx <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ LT, }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ lef, }\DataTypeTok{speed =}\NormalTok{ speed,}
              \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of columns of 'veh' is different than length of 'ef'
\end{verbatim}

\begin{verbatim}
## adjusting length of ef to the number of colums of 'veh'
\end{verbatim}

\begin{verbatim}
## 34029.87 kg emissions in 24 hours and 7 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_NOx_STREETS <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(E_NOx, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{, }\DataTypeTok{net =}\NormalTok{ net)}
\KeywordTok{ggplot}\NormalTok{(E_NOx_STREETS) }\OperatorTok{+}\StringTok{ }\KeywordTok{geom_sf}\NormalTok{(}\KeywordTok{aes}\NormalTok{(}\DataTypeTok{colour =} \KeywordTok{as.numeric}\NormalTok{(V9))) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale_color_gradientn}\NormalTok{(}\DataTypeTok{colours =} \KeywordTok{rev}\NormalTok{(}\KeywordTok{cpt}\NormalTok{())) }\OperatorTok{+}\StringTok{ }\KeywordTok{theme_bw}\NormalTok{()}\OperatorTok{+}
\StringTok{  }\KeywordTok{ggtitle}\NormalTok{(}\StringTok{"NOX emissions at 08:00 (g/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/ltnox-1.pdf}
\caption{\label{fig:ltnox}NOx emisions of LT (g/h)}
\end{figure}

\section{Total emissions in
data-frames}\label{total-emissions-in-data-frames}

In order to obtain the total emissions in data-frame, the same function
\texttt{emis\_post} is used, but in this case, with the argument
\texttt{by} = ``veh''. We also need to add arguments of \texttt{veh} for
the type of vehicle, \texttt{size} the size or gross weight,
\texttt{fuel} and \texttt{pollutant}. The argument \texttt{net} is not
required in this case.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_NOx_DF <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ E_NOx, }\DataTypeTok{veh =} \StringTok{"LT"}\NormalTok{, }\DataTypeTok{size =} \StringTok{"Small"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"B5"}\NormalTok{,}
                      \DataTypeTok{by =} \StringTok{"veh"}\NormalTok{, }\DataTypeTok{pollutant =} \StringTok{"NOx"}\NormalTok{)}
\KeywordTok{head}\NormalTok{(E_NOx_DF)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##     E_NOx            g veh  size fuel pollutant age hour
## 1 E_NOx_1 182.5943 g/h  LT Small   B5       NOx   1    1
## 2 E_NOx_2 163.2499 g/h  LT Small   B5       NOx   2    1
## 3 E_NOx_3 177.5608 g/h  LT Small   B5       NOx   3    1
## 4 E_NOx_4 208.5892 g/h  LT Small   B5       NOx   4    1
## 5 E_NOx_5 765.7001 g/h  LT Small   B5       NOx   5    1
## 6 E_NOx_6 885.6223 g/h  LT Small   B5       NOx   6    1
\end{verbatim}

The resulting object is a data-frame with the name of the input array,
then it has the g/h. Also, the name of the vehicle, the size, fuel,
pollutant, age and hour. This is interesting because we can now what are
the most pollutatn vehicle and at which hour this emissions ocurres.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_NOx_DF[E_NOx_DF}\OperatorTok{$}\NormalTok{g }\OperatorTok{==}\StringTok{ }\KeywordTok{max}\NormalTok{(E_NOx_DF}\OperatorTok{$}\NormalTok{g), ]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##        E_NOx            g veh  size fuel pollutant age hour
## 517 E_NOx_17 29139.51 g/h  LT Small   B5       NOx  17   11
\end{verbatim}

The highest emissions by hour are of Light Trucks of 17 years of use at
hour 11 on local time. However, it is possible to get more interesting
insights of the data. The datra can be aggregated by age, hour or plot
all together as shown on Fig. \ref{fig:noxlt3}. The highest emissions
ocurre between 17 and 27 years of use. This figure also shows the effect
of emissions standards introduced in different years.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{aggregate}\NormalTok{(E_NOx_DF}\OperatorTok{$}\NormalTok{g, }\DataTypeTok{by =} \KeywordTok{list}\NormalTok{(E_NOx_DF}\OperatorTok{$}\NormalTok{hour), sum)}
\KeywordTok{plot}\NormalTok{(df, }\DataTypeTok{type =} \StringTok{"b"}\NormalTok{, }\DataTypeTok{pch =} \DecValTok{16}\NormalTok{, }\DataTypeTok{xlab =} \StringTok{"hour"}\NormalTok{, }\DataTypeTok{ylab =} \StringTok{"NOx (g/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/noxlt1-1.pdf}
\caption{\label{fig:noxlt1}NOx emissions of LT by hour}
\end{figure}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(ggplot2)}
\KeywordTok{library}\NormalTok{(veinreport) }\CommentTok{# theme_black}
\NormalTok{df <-}\StringTok{ }\KeywordTok{aggregate}\NormalTok{(E_NOx_DF}\OperatorTok{$}\NormalTok{g, }\DataTypeTok{by =} \KeywordTok{list}\NormalTok{(E_NOx_DF}\OperatorTok{$}\NormalTok{age), sum)}
\KeywordTok{names}\NormalTok{(df) <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"Age"}\NormalTok{, }\StringTok{"g"}\NormalTok{)}
\KeywordTok{ggplot}\NormalTok{(df, }\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ Age, }\DataTypeTok{y =} \KeywordTok{as.numeric}\NormalTok{(g), }\DataTypeTok{fill =} \KeywordTok{as.numeric}\NormalTok{(g))) }\OperatorTok{+}
\StringTok{  }\KeywordTok{geom_bar}\NormalTok{(}\DataTypeTok{stat =} \StringTok{"identity"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale_fill_gradientn}\NormalTok{(}\DataTypeTok{colours =} \KeywordTok{cpt}\NormalTok{()) }\OperatorTok{+}\StringTok{ }\KeywordTok{theme_black}\NormalTok{() }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{x =} \StringTok{"Age of use"}\NormalTok{, }\DataTypeTok{y =} \StringTok{"NOx (g/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/noxlt2-1.pdf}
\caption{\label{fig:noxlt2}NOx emissions of LT by age}
\end{figure}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(ggplot2)}
\KeywordTok{library}\NormalTok{(veinreport) }\CommentTok{# theme_black}
\KeywordTok{ggplot}\NormalTok{(E_NOx_DF, }\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ age, }\DataTypeTok{y =} \KeywordTok{as.numeric}\NormalTok{(g), }\DataTypeTok{fill =}\NormalTok{ hour)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{geom_bar}\NormalTok{(}\DataTypeTok{stat =} \StringTok{"identity"}\NormalTok{) }\OperatorTok{+}
\StringTok{  }\KeywordTok{scale_fill_gradientn}\NormalTok{(}\DataTypeTok{colours =} \KeywordTok{cpt}\NormalTok{()) }\OperatorTok{+}\StringTok{ }\KeywordTok{theme_black}\NormalTok{() }\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{x =} \StringTok{"Age of use"}\NormalTok{, }\DataTypeTok{y =} \StringTok{"NOx (g/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/noxlt3-1.pdf}
\caption{\label{fig:noxlt3}NOx emissions of LT}
\end{figure}

\section{Merging emissions}\label{merging-emissions}

The function \texttt{inventory} was designed to store the emissions of
each type of vehicle in each directory. Hence, it was necessary to
create a function that read and merge the emissions that are in each
directory, returning a data-frame os a spatial feature data-frame. The
function is \texttt{emis\_merge}. As it can be seen on Fig.
\ref{fig:demispost}, \texttt{emis\_merge} comes after
\texttt{emis\_post} or \texttt{speciate}. The arguments of
\texttt{emis\_merge} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_merge)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (pol = "CO", what = "STREETS.rds", streets = T, net, 
##     path = "emi", crs) 
## NULL
\end{verbatim}

Where,

\begin{itemize}
\tightlist
\item
  \texttt{pol}: Character. Pollutant.
\item
  \texttt{what}: Character. Word to search the emissions names,
  ``STREETS'', ``DF'' or whatever name. It is important to include the
  extension .`rds'
\item
  \texttt{streets}: Logical. If true, emis\_merge will read the street
  emissions created with emis\_post by ``streets\_wide'', returning an
  object with class `sf'. If false, it will read the emissions
  data-frame and rbind them.
\item
  \texttt{net}: `Spatial feature' or `SpatialLinesDataFrame' with the
  streets. It is expected \#`that the number of rows is equal to the
  number of rows of street emissions. If \#' not, the function will
  stop.
\item
  \texttt{path}: Character. Path where emissions are located
\item
  \texttt{crs}: coordinate reference system in numeric format from
  \url{http://spatialreference.org/} to transform/project spatial data
  using sf::st\_transform
\end{itemize}

In order to see how this functions works, let's run the example in the
\texttt{inventory} function. First, lets create a project, or in other
words a directory named ``YourCity'', into the temporary directory. In
this part you can place your project wherever you want with the actual
name of your city. Then run \texttt{inventory} with the default
configuration.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{name =}\StringTok{ }\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{)}
\NormalTok{vein}\OperatorTok{::}\KeywordTok{inventory}\NormalTok{(}\DataTypeTok{name =}\NormalTok{ name)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## files at /tmp/RtmpXTmmRT/YourCity
\end{verbatim}

\begin{verbatim}
## Directories:
##  [1] "/tmp/RtmpXTmmRT/YourCity"             
##  [2] "/tmp/RtmpXTmmRT/YourCity/ef"          
##  [3] "/tmp/RtmpXTmmRT/YourCity/emi"         
##  [4] "/tmp/RtmpXTmmRT/YourCity/emi/BUS_01"  
##  [5] "/tmp/RtmpXTmmRT/YourCity/emi/HGV_01"  
##  [6] "/tmp/RtmpXTmmRT/YourCity/emi/LCV_01"  
##  [7] "/tmp/RtmpXTmmRT/YourCity/emi/MC_01"   
##  [8] "/tmp/RtmpXTmmRT/YourCity/emi/PC_01"   
##  [9] "/tmp/RtmpXTmmRT/YourCity/est"         
## [10] "/tmp/RtmpXTmmRT/YourCity/images"      
## [11] "/tmp/RtmpXTmmRT/YourCity/network"     
## [12] "/tmp/RtmpXTmmRT/YourCity/post"        
## [13] "/tmp/RtmpXTmmRT/YourCity/post/df"     
## [14] "/tmp/RtmpXTmmRT/YourCity/post/grids"  
## [15] "/tmp/RtmpXTmmRT/YourCity/post/streets"
## [16] "/tmp/RtmpXTmmRT/YourCity/profiles"    
## [17] "/tmp/RtmpXTmmRT/YourCity/veh"
\end{verbatim}

In my case, the files are in directory /tmp/RtmpXTmmRT/YourCity. Now, if
you open the file file main.R, you will see:

\begin{center}\includegraphics[width=0.7\linewidth]{figuras/main} \end{center}

This script starts with \texttt{setwd} function, Then the first section
is the network, where user reads and prepares the traffic information.
Then the second section runs a script whichruns the age functions for
the deault vehicular composition of the \texttt{inventory} function. The
third section estimates the emissions by running each input.R file
inside the directory \emph{est} The fourth section, creates a grid and
run the script post.R is shown below:

\begin{center}\includegraphics[width=0.7\linewidth]{figuras/post} \end{center}

The function \texttt{emis\_merge} shown in line 2 is designed to read
the all the files with names CO and the default argument \texttt{what}
is ``STREETS.rds'', for instance, PC\_GASOLINE\_CO\_STREETS.rds. Hence,
this function reads all the files under that condition. The other
argument is \texttt{streets} with defailt value of \texttt{TRUE},
meaning that the function now knows that the resulting object must be an
spatial network, and the \texttt{net} argument is already calling the
net object part of the VEIN data, the function will merge all the
objects summing the emissions of all vehicle street by street. The
resulting object is a spatial ``LINESTRING'' class of \texttt{sf}. This
function assumes that emissons files are data.frames and non tests have
been made to see if this function would read work \texttt{sf} objects.

On the other hand, the line 12 also runs all the emissions files whose
names includes the word CO, but also including the words ``DF.rds'',
which are the files from \texttt{emis\_post} with argument \texttt{by}
equals to ``veh''. In this case, it reads and merge the data-frames and
prints the sum emissions by pollutant.

If the user source he file main.R, it will calculate the emissions and
produce some plots.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{source}\NormalTok{(}\KeywordTok{paste0}\NormalTok{(name, }\StringTok{"/main.R"}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\section{Creating grids}\label{creating-grids}

Once we have processed our emissions array with \texttt{emis\_post}
and/or we have spatial emissions, we can then create grids and allocate
our emissions into the grids. Lets check the function
\texttt{make\_grid} first:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{make_grid)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (spobj, width, height = width, polygon, crs = 4326, 
##     ...) 
## NULL
\end{verbatim}

This function originally used an \texttt{sp} approach. However, now uses
an \texttt{sf} approach for creating the grid. Actually, the arguments
are pretty much the same with the \texttt{sf::st\_make\_grid} with the
exeption that is focused in returning polygons with coordinates at
centroids of each cell. Therefore, the arguments height and polygon are
deprecated. Lets create grid for our net.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{library}\NormalTok{(sf)}
\KeywordTok{data}\NormalTok{(net)}
\NormalTok{net <-}\StringTok{ }\KeywordTok{st_transform}\NormalTok{(}\KeywordTok{st_as_sf}\NormalTok{(net), }\DecValTok{31983}\NormalTok{)}
\NormalTok{g <-}\StringTok{ }\KeywordTok{make_grid}\NormalTok{(}\DataTypeTok{spobj =}\NormalTok{ net, }\DataTypeTok{width =} \DecValTok{500}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of lon points: 23
## Number of lat points: 21
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{class}\NormalTok{(g)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "sf"         "data.frame"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(g}\OperatorTok{$}\NormalTok{geometry, }\DataTypeTok{axes =} \OtherTok{TRUE}\NormalTok{)}
\KeywordTok{plot}\NormalTok{(net}\OperatorTok{$}\NormalTok{geometry, }\DataTypeTok{add =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/grid1-1.pdf}
\caption{\label{fig:grid1}Grid with spacing of 500 mts}
\end{figure}

The class of \texttt{net} is \texttt{SpatialLinesDataFrame}, which is
converted to \texttt{sf}. Then, as the coordinte reference system of net
is WGS84 with epsg 4326, we are transforming to UTM to create a grid
with grid spacing of 500 mts. The class of the grid object, \texttt{g}
is ``sf data.frame''.

\section{\texorpdfstring{Gridding emissions with
\texttt{emis\_grid}}{Gridding emissions with emis\_grid}}\label{gridding-emissions-with-emis_grid}

The function\texttt{emis\_grid} allocates emissions proportionally to
each grid cell. The process is performed by intersection between
geometries and the grid. It means that requires ``sr'' according with
your location for the projection. It is assumed that spobj is a
spatial*DataFrame or an ``sf'' with the pollutants in data. This
function return an object class ``sf''.

Before \texttt{sf} era, i tried to use\texttt{raster::intersect} which
imports \texttt{rgeos} and took \textbf{ages}. Then I started to use
\citet{QGIS} (\url{https://www.qgis.org}), and later the R package
\citet{RQGIS} which takes 10 minutes for allocating emissions of the
mega-city of São Paulo and a grid spacing of 1 km. \emph{10 min is not
bad}. However, when \texttt{sf} appeared and also, included the Spatial
Indexes
(\url{https://www.r-spatial.org/r/2017/06/22/spatial-index.html}), it
changed the game. A new era started. It took \textbf{5.5 seconds}. I
could not believe it. But there also some i could accelerate this
process importing some \texttt{data.table} aggregation functions. Now
\texttt{emis\_grid} can aggegate big spatial extensions. I havent tried
yet at continental scale, but I will try. Anyways, let's go see the
arguments:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{emis_grid)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (spobj, g, sr, type = "lines") 
## NULL
\end{verbatim}

Where,

\begin{itemize}
\tightlist
\item
  \texttt{spobj} : A spatial dataframe of class ``sp'' or ``sf''. When
  class is ``sp'' it is transformed to ``sf''.
\item
  \texttt{g}: A grid with class ``SpatialPolygonsDataFrame'' or ``sf''.
\item
  \texttt{sr}: Spatial reference e.g: 31983. It is required if spobj and
  g are not projected. Please, see \url{http://spatialreference.org/}.
\item
  \texttt{type}: type of geometry: ``lines'' or ``points''.
\end{itemize}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{data}\NormalTok{(net)}
\NormalTok{net}\OperatorTok{@}\NormalTok{data <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{hdv =}\NormalTok{ net[[}\StringTok{"hdv"}\NormalTok{]])}
\NormalTok{g <-}\StringTok{ }\KeywordTok{make_grid}\NormalTok{(}\DataTypeTok{spobj =}\NormalTok{ net, }\DataTypeTok{width =} \DecValTok{1}\OperatorTok{/}\FloatTok{102.47}\OperatorTok{/}\DecValTok{2}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of lon points: 23
## Number of lat points: 19
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{netg <-}\StringTok{ }\KeywordTok{emis_grid}\NormalTok{(net, g)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Sum of street emissions 148118
\end{verbatim}

\begin{verbatim}
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
\end{verbatim}

\begin{verbatim}
## Sum of gridded emissions 148118
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{head}\NormalTok{(netg)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Simple feature collection with 6 features and 2 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -46.8066 ymin: -23.62 xmax: -46.77732 ymax: -23.61512
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##   id      hdv                       geometry
## 1  1 201.8275 POLYGON ((-46.8066 -23.62, ...
## 2  2 200.6097 POLYGON ((-46.80172 -23.62,...
## 3  3 205.6319 POLYGON ((-46.79684 -23.62,...
## 4  4 224.1988 POLYGON ((-46.79196 -23.62,...
## 5  5 770.9490 POLYGON ((-46.78708 -23.62,...
## 6  6   0.0000 POLYGON ((-46.7822 -23.62, ...
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(netg[}\StringTok{"hdv"}\NormalTok{], }\DataTypeTok{axes =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/netgldv-1.pdf}
\caption{\label{fig:netgldv}Spatial grid of HDV traffic 500 mts}
\end{figure}

The example in this case, showed the allocation of traffic. The
resulting object is a \texttt{sf} with the sum of the attributes inside
each grid cell. This means that, when the input are street emissions at
different hours, it will return gridded emissions at each hour:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{E_NOx_STREETS <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(E_NOx, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{, }\DataTypeTok{net =}\NormalTok{ net)}
\NormalTok{NOxg <-}\StringTok{ }\KeywordTok{emis_grid}\NormalTok{(E_NOx_STREETS, g)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Sum of street emissions 34029872.64
\end{verbatim}

\begin{verbatim}
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
\end{verbatim}

\begin{verbatim}
## Sum of gridded emissions 34029872.64
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(NOxg[}\StringTok{"V1"}\NormalTok{], }\DataTypeTok{axes =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/Noxg-1.pdf}
\caption{\label{fig:Noxg}NOx emissions for 00:00 {[}g/h{]}}
\end{figure}

\chapter{Speciation}\label{he}

The speciation for emissions is a very important part that deserves a
only chapter on this book. VEIN initially covered speed functions
emission factors of CO, HC, NOx, PM and Fuel Consumption (FC). But
currently, the the pollutants covered are:

\begin{itemize}
\tightlist
\item
  \textbf{Criteria}: ``CO'', ``NOx'', ``HC'', ``PM'', ``CH4'', ``NMHC'',
  ``CO2'', ``SO2'', ``Pb'', ``FC'' (Fuel Consumption).
\item
  \textbf{Polycyclic Aromatic Hydrocarbons (PAH) and Persisten Organic
  Pollutants (POP)}: ``indeno(1,2,3-cd)pyrene'',
  ``benzo(k)fluoranthene'', ``benzo(b)fluoranthene'',
  ``benzo(ghi)perylene'', ``fluoranthene'', ``benzo(a)pyrene'',
  ``pyrene'', ``perylene'', ``anthanthrene'', ``benzo(b)fluorene'',
  ``benzo(e)pyrene'', ``triphenylene'', ``benzo(j)fluoranthene'',
  ``dibenzo(a,j)anthacene'', ``dibenzo(a,l)pyrene'',
  ``3,6-dimethyl-phenanthrene'', ``benzo(a)anthracene'',
  ``acenaphthylene'', ``acenapthene'', ``fluorene'', ``chrysene'',
  ``phenanthrene'', ``napthalene'', ``anthracene'', ``coronene'',
  ``dibenzo(ah)anthracene''
\item
  \textbf{Dioxins and Furans}: ``PCDD'', ``PCDF'', ``PCB''.
\item
  \textbf{Metals}: ``As'', ``Cd'', ``Cr'', ``Cu'', ``Hg'', ``Ni'',
  ``Pb'', ``Se'', ``Zn''.
\item
  \textbf{NMHC}:
\item
  \emph{ALKANES}: ``ethane'', ``propane'', ``butane'', ``isobutane'',
  ``pentane'', ``isopentane'', ``hexane'', ``heptane'', ``octane'',
  ``TWO\_methylhexane'', ``nonane'', ``TWO\_methylheptane'',
  ``THREE\_methylhexane'', ``decane'', ``THREE\_methylheptane'',
  ``alcanes\_C10\_C12'', ``alkanes\_C13''.
\item
  \emph{CYCLOALKANES}: ``cycloalcanes''.
\item
  \emph{ALKENES}: ``ethylene'', ``propylene'', ``propadiene'',
  ``ONE\_butene'', ``isobutene'', ``TWO\_butene'',
  ``ONE\_3\_butadiene'', ``ONE\_pentene'', ``TWO\_pentene'',
  ``ONE\_hexene'', ``dimethylhexene''.
\item
  \emph{ALKYNES}:``ONE\_butine'', ``propine'', ``acetylene''.
\item
  \emph{ALDEHYDES}: ``formaldehyde'', ``acetaldehyde'', ``acrolein'',
  ``benzaldehyde'', ``crotonaldehyde'', ``methacrolein'',
  ``butyraldehyde'', ``isobutanaldehyde'', ``propionaldehyde'',
  ``hexanal'', ``i\_valeraldehyde'', ``valeraldehyde'',
  ``o\_tolualdehyde'', ``m\_tolualdehyde'', ``p\_tolualdehyde''.
\item
  KETONES: ``acetone'', ``methylethlketone''.
\item
  \emph{AROMATICS}: ``toluene'', ``ethylbenzene'', ``m\_p\_xylene'',
  ``o\_xylene'', ``ONE\_2\_3\_trimethylbenzene'',
  ``ONE\_2\_4\_trimethylbenzene'', ``ONE\_3\_5\_trimethylbenzene'',
  ``styrene'', ``benzene'', ``C9'', ``C10'', ``C13''.
\end{itemize}

Also, some Brazilian emission factors and speciations for WRF-Chem,
mechanisms: ``e\_eth'', ``e\_hc3'', ``e\_hc5'', ``e\_hc8'', ``e\_ol2'',
``e\_olt'', ``e\_oli'', ``e\_iso'', ``e\_tol'', ``e\_xyl'',
``e\_c2h5oh'', ``e\_ald'', ``e\_hcho'', ``e\_ch3oh'', ``e\_ket'',
``E\_SO4i'', ``E\_SO4j'', ``E\_NO3i'', ``E\_NO3j'', ``E\_MP2.5i'',
``E\_MP2.5j'', ``E\_ORGi'', ``E\_ORGj'', ``E\_ECi'', ``E\_ECj'',
``H2O''.

By the end of this book, more emission factors will be added.

Because VEIN has the capabilities to produce species from this
pollutants including volatile organic compunds (VOC) and particulate
matter compounds.

The speciation of emissions in VEIN can be done with three ways, by
selecting the pollutants in ef\_speed* functions, other way explicit and
the other implicit. The explicit way is by usint the function
\texttt{speciate} for the specific available speciation. The implicit
way is by adding a percentage value in the argument \texttt{k} in any of
the functions of emission factors. The implicit way requires that the
user must know the percentage of the species of pollutant.

\section{Speed functions of VOC
species}\label{speed-functions-of-voc-species}

As mentioned before, the speed function emission factors, currently
covers several pollutants. We firstly focus on som PAH and POP. They way
to select them is just:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\NormalTok{pol <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"indeno(1,2,3-cd)pyrene"}\NormalTok{, }\StringTok{"benzo(k)fluoranthene"}\NormalTok{,}
         \StringTok{"benzo(b)fluoranthene"}\NormalTok{, }\StringTok{"benzo(ghi)perylene"}\NormalTok{, }\StringTok{"fluoranthene"}\NormalTok{,}
         \StringTok{"benzo(a)pyrene"}\NormalTok{)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(pol), }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{EmissionFactors}\NormalTok{(}\KeywordTok{ef_ldv_speed}\NormalTok{(}\StringTok{"PC"}\NormalTok{, }\StringTok{"4S"}\NormalTok{, }\StringTok{"<=1400"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"PRE"}\NormalTok{,}
\NormalTok{                               pol[i], }\DataTypeTok{x =} \DecValTok{10}\NormalTok{)(}\DecValTok{10}\NormalTok{))}
\NormalTok{\})}
\KeywordTok{names}\NormalTok{(df) <-}\StringTok{ }\NormalTok{pol}
\NormalTok{df}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## $`indeno(1,2,3-cd)pyrene`
## 1.03e-06 g/km
## 
## $`benzo(k)fluoranthene`
## 3e-07 g/km
## 
## $`benzo(b)fluoranthene`
## 8.8e-07 g/km
## 
## $`benzo(ghi)perylene`
## 2.9e-06 g/km
## 
## $fluoranthene
## 1.822e-05 g/km
## 
## $`benzo(a)pyrene`
## 4.8e-07 g/km
\end{verbatim}

These pollutants comes from \citet{NtziachristosSamaras2016} and in this
case, are not dependent on speed. I used lapply to iterate in each
pollutant at 10 km/h. Now I will show the same for dioxins and furans.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\NormalTok{pol <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"PCDD"}\NormalTok{, }\StringTok{"PCDF"}\NormalTok{, }\StringTok{"PCB"}\NormalTok{)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(pol), }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{EmissionFactors}\NormalTok{(}\KeywordTok{ef_ldv_speed}\NormalTok{(}\StringTok{"PC"}\NormalTok{, }\StringTok{"4S"}\NormalTok{, }\StringTok{"<=1400"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"PRE"}\NormalTok{,}
\NormalTok{                               pol[i], }\DataTypeTok{x =} \DecValTok{10}\NormalTok{)(}\DecValTok{10}\NormalTok{))}
\NormalTok{\})}
\KeywordTok{names}\NormalTok{(df) <-}\StringTok{ }\NormalTok{pol}
\NormalTok{df}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## $PCDD
## 1.03e-11 g/km
## 
## $PCDF
## 2.12e-11 g/km
## 
## $PCB
## 6.4e-12 g/km
\end{verbatim}

In the case of nmhc, the estimation is:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\NormalTok{pol <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"ethane"}\NormalTok{, }\StringTok{"propane"}\NormalTok{, }\StringTok{"butane"}\NormalTok{, }\StringTok{"isobutane"}\NormalTok{, }\StringTok{"pentane"}\NormalTok{)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(pol), }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{EmissionFactors}\NormalTok{(}\KeywordTok{ef_ldv_speed}\NormalTok{(}\StringTok{"PC"}\NormalTok{, }\StringTok{"4S"}\NormalTok{, }\StringTok{"<=1400"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"PRE"}\NormalTok{,}
\NormalTok{                               pol[i], }\DataTypeTok{x =} \DecValTok{10}\NormalTok{)(}\KeywordTok{c}\NormalTok{(}\DecValTok{10}\NormalTok{,}\DecValTok{20}\NormalTok{,}\DecValTok{30}\NormalTok{)))}
\NormalTok{\})}
\KeywordTok{names}\NormalTok{(df) <-}\StringTok{ }\NormalTok{pol}
\NormalTok{df}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## $ethane
## Units: g/km
## [1] 0.09934632 0.06062781 0.04524681
## 
## $propane
## Units: g/km
## [1] 0.02829865 0.01726974 0.01288849
## 
## $butane
## Units: g/km
## [1] 0.1746087 0.1065580 0.0795247
## 
## $isobutane
## Units: g/km
## [1] 0.07767076 0.04739993 0.03537478
## 
## $pentane
## Units: g/km
## [1] 0.10717361 0.06540455 0.04881171
\end{verbatim}

Now the same example for one pollutant and several euro standards.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\NormalTok{euro <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"V"}\NormalTok{, }\StringTok{"IV"}\NormalTok{, }\StringTok{"III"}\NormalTok{, }\StringTok{"II"}\NormalTok{,}\StringTok{"I"}\NormalTok{, }\StringTok{"PRE"}\NormalTok{)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(euro), }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{EmissionFactors}\NormalTok{(}\KeywordTok{ef_ldv_speed}\NormalTok{(}\StringTok{"PC"}\NormalTok{, }\StringTok{"4S"}\NormalTok{, }\StringTok{"<=1400"}\NormalTok{, }\StringTok{"G"}\NormalTok{, euro[i],}
                               \StringTok{"benzene"}\NormalTok{)(}\KeywordTok{c}\NormalTok{(}\DecValTok{10}\NormalTok{, }\DecValTok{30}\NormalTok{, }\DecValTok{50}\NormalTok{)))}
\NormalTok{\})}
\KeywordTok{names}\NormalTok{(df) <-}\StringTok{ }\NormalTok{euro}
\NormalTok{df}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## $V
## Units: g/km
## [1] 0.0003928819 0.0002194495 0.0001597751
## 
## $IV
## Units: g/km
## [1] 0.0004864655 0.0004872061 0.0005276205
## 
## $III
## Units: g/km
## [1] 0.0017465678 0.0008206890 0.0005928019
## 
## $II
## Units: g/km
## [1] 0.013332523 0.004133087 0.002363304
## 
## $I
## Units: g/km
## [1] 0.025656752 0.010921829 0.006979341
## 
## $PRE
## Units: g/km
## [1] 0.4112336 0.1872944 0.1287895
\end{verbatim}

\section{Speciate function}\label{speciate-function}

The arguments of the function \texttt{speciate} are:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(vein}\OperatorTok{::}\NormalTok{speciate)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (x, spec = "bcom", veh, fuel, eu, show = FALSE, list = FALSE, 
##     dx) 
## NULL
\end{verbatim}

Where,

\begin{itemize}
\tightlist
\item
  \texttt{x}: Emissions estimation
\item
  \texttt{spec}: speciation: The speciations are: ``bcom'',
  tyre``,''break``,''road``,''iag``,''nox" and ``nmhc''. `iag' now
  includes a speciation for use of industrial and building paintings.
  ``bcom'' stands for black carbon and organic matter.
\item
  \texttt{veh}: Type of vehicle: When spec is ``bcom'' or ``nox'' veh
  can be ``PC'', ``LCV'', HDV" or ``Motorcycle''. When spec is ``iag''
  veh can take two values depending: when the speciation is for vehicles
  veh accepts ``veh'', eu ``Evaporative'', ``Liquid'' or ``Exhaust'' and
  fuel ``G'', ``E'' or ``D'', when the speciation is for painting, veh
  is ``paint'' fuel or eu can be ``industrial'' or ``building'' when
  spec is ``nmhc'', veh can be ``LDV'' with fuel ``G'' or ``D'' and eu
  ``PRE'', ``I'', ``II'', ``III'', ``IV'', ``V'', or ``VI''. when spec
  is ``nmhc'', veh can be ``HDV'' with fuel ``D'' and eu ``PRE'', ``I'',
  ``II'', ``III'', ``IV'', ``V'', or ``VI''. when spec is ``nmhc'' and
  fuel is ``LPG'', veh and eu must be ``ALL''
\item
  \texttt{fuel}: Fuel. When spec is ``bcom'' fuel can be ``G'' or ``D''.
  When spec is ``iag'' fuel can be ``G'', ``E'' or ``D''. When spec is
  ``nox'' fuel can be ``G'', ``D'', ``LPG'', ``E85'' or ``CNG''. Not
  required for ``tyre'', ``break'' or ``road''. When spec is ``nmhc''
  fuel can be G, D or LPG.
\item
  \texttt{eu}: sEuro emission standard: ``PRE'', ``ECE\_1501'',
  ``ECE\_1502'', ``ECE\_1503'', ``I'', ``II'', ``III'', ``IV'', ``V'',
  ``III-CDFP'',``IV-CDFP'',``V-CDFP'', ``III-ADFP'',
  ``IV-ADFP'',``V-ADFP'' and ``OPEN\_LOOP''. When spec is ``iag'' accept
  the values ``Exhaust'' ``Evaporative'' and ``Liquid''. When spec is
  ``nox'' eu can be ``PRE'', ``I'', ``II'', ``III'', ``IV'', ``V'',
  ``VI'', ``VIc'', ``III-DPF'' or ``III+CRT''. Not required for
  ``tyre'', ``break'' or ``road''
\item
  \texttt{show}: when TRUE shows row of table with respective speciation
\item
  \texttt{list}: when TRUE returns a list with number of elements of the
  list as the number species of pollutants
\end{itemize}

As shown before, there are currently 8 type of speciations. Now, i will
show examples for most of them.

\section{Black carbon and organic
matter}\label{black-carbon-and-organic-matter}

Let's use the data inside the vein and estimate emissions. The vehicles
considered are Light Trucks, assuming all HGV. We are using the 4 stage
estimation with the first part arranging traffic data

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\CommentTok{# 1 Traffic data}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\KeywordTok{data}\NormalTok{(fe2015)}
\NormalTok{lt <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 17.12
\end{verbatim}

\begin{verbatim}
## Number of veh is 1946.95 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{vw <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv, profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{) }\OperatorTok{+}\StringTok{ }
\StringTok{  }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv, profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2014}\NormalTok{)}
\NormalTok{speed <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(vw, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\CommentTok{# 2 Emission factors}
\NormalTok{euro <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"V"}\NormalTok{, }\KeywordTok{rep}\NormalTok{(}\StringTok{"IV"}\NormalTok{, }\DecValTok{3}\NormalTok{),  }
          \KeywordTok{rep}\NormalTok{(}\StringTok{"III"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"II"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\DecValTok{5}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\DecValTok{27}\NormalTok{))}
\NormalTok{lefd <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{50}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) \{}
  \KeywordTok{ef_hdv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"Trucks"}\NormalTok{, }\DataTypeTok{t =} \StringTok{"RT"}\NormalTok{, }\DataTypeTok{g =} \StringTok{">32"}\NormalTok{, }\DataTypeTok{gr =} \DecValTok{0}\NormalTok{,}
               \DataTypeTok{eu =}\NormalTok{ euro[i], }\DataTypeTok{l =} \FloatTok{0.5}\NormalTok{, }\DataTypeTok{p =} \StringTok{"PM"}\NormalTok{,}
               \DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{) \})}
\CommentTok{# 3 Estimate emissions}
\NormalTok{pmd <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ lt, }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ lefd, }\DataTypeTok{speed =}\NormalTok{ speed,}
            \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2014}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## 32714.53 kg emissions in 24 hours and 7 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{# 4 process emissions}
\CommentTok{# Agreggating emissions by age of use}
\NormalTok{df <-}\StringTok{ }\KeywordTok{apply}\NormalTok{(}\DataTypeTok{X =}\NormalTok{ pmd, }\DataTypeTok{MARGIN =} \KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{), }\DataTypeTok{FUN =}\NormalTok{ sum, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{)}
\NormalTok{euro}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "V"   "IV"  "IV"  "IV"  "III" "III" "III" "III" "III" "III" "III"
## [12] "II"  "II"  "II"  "II"  "II"  "II"  "II"  "I"   "I"   "I"   "I"  
## [23] "I"   "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE"
## [34] "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE" "PRE"
## [45] "PRE" "PRE" "PRE" "PRE" "PRE" "PRE"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dfbcom <-}\StringTok{   }\KeywordTok{rbind}\NormalTok{(}\KeywordTok{speciate}\NormalTok{(df[, }\DecValTok{1}\NormalTok{], }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                  \KeywordTok{speciate}\NormalTok{(}\KeywordTok{rowSums}\NormalTok{(df[, }\DecValTok{2}\OperatorTok{:}\DecValTok{4}\NormalTok{]), }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"IV"}\NormalTok{),}
                  \KeywordTok{speciate}\NormalTok{(}\KeywordTok{rowSums}\NormalTok{(df[, }\DecValTok{5}\OperatorTok{:}\DecValTok{11}\NormalTok{]), }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"III"}\NormalTok{),}
                  \KeywordTok{speciate}\NormalTok{(}\KeywordTok{rowSums}\NormalTok{(df[, }\DecValTok{12}\OperatorTok{:}\DecValTok{18}\NormalTok{]), }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"II"}\NormalTok{),}
                  \KeywordTok{speciate}\NormalTok{(}\KeywordTok{rowSums}\NormalTok{(df[, }\DecValTok{19}\OperatorTok{:}\DecValTok{23}\NormalTok{]), }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"I"}\NormalTok{),}
                  \KeywordTok{speciate}\NormalTok{(}\KeywordTok{rowSums}\NormalTok{(df[, }\DecValTok{24}\OperatorTok{:}\DecValTok{50}\NormalTok{]), }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"PRE"}\NormalTok{))}
\NormalTok{dfbcom}\OperatorTok{$}\NormalTok{id <-}\StringTok{ }\DecValTok{1}\OperatorTok{:}\DecValTok{1505} 
\NormalTok{bc <-}\StringTok{ }\KeywordTok{aggregate}\NormalTok{(dfbcom}\OperatorTok{$}\NormalTok{BC, }\DataTypeTok{by =} \KeywordTok{list}\NormalTok{(dfbcom}\OperatorTok{$}\NormalTok{id), sum)}
\NormalTok{om <-}\StringTok{ }\KeywordTok{aggregate}\NormalTok{(dfbcom}\OperatorTok{$}\NormalTok{OM, }\DataTypeTok{by =} \KeywordTok{list}\NormalTok{(dfbcom}\OperatorTok{$}\NormalTok{id), sum)}
\NormalTok{net}\OperatorTok{$}\NormalTok{BC <-}\StringTok{ }\NormalTok{bc[, }\DecValTok{2}\NormalTok{]}
\NormalTok{net}\OperatorTok{$}\NormalTok{OM <-}\StringTok{ }\NormalTok{om[, }\DecValTok{2}\NormalTok{]}
\end{Highlighting}
\end{Shaded}

Now we have spatial objects of Back Carbon

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{sp}\OperatorTok{::}\KeywordTok{spplot}\NormalTok{(net, }\StringTok{"BC"}\NormalTok{, }\DataTypeTok{scales =} \KeywordTok{list}\NormalTok{(}\DataTypeTok{draw =}\NormalTok{ T), }\DataTypeTok{main =} \StringTok{"Black Carbon [g/168h]"}\NormalTok{,}
           \DataTypeTok{col.regions =} \KeywordTok{rev}\NormalTok{(cptcity}\OperatorTok{::}\KeywordTok{cpt}\NormalTok{()))}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/spplotbm-1.pdf}
\caption{\label{fig:spplotbm}Black Carbon {[}g/168h{]}}
\end{figure}

and Organic Matter

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{sp}\OperatorTok{::}\KeywordTok{spplot}\NormalTok{(net, }\StringTok{"OM"}\NormalTok{, }\DataTypeTok{scales =} \KeywordTok{list}\NormalTok{(}\DataTypeTok{draw =}\NormalTok{ T), }\DataTypeTok{main =} \StringTok{"Organic Matter [g/168h]"}\NormalTok{,}
           \DataTypeTok{col.regions =} \KeywordTok{rev}\NormalTok{(cptcity}\OperatorTok{::}\KeywordTok{cpt}\NormalTok{()))}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/spplotom-1.pdf}
\caption{\label{fig:spplotom}Organic Matter {[}g/168h{]}}
\end{figure}

If you are interested in speciate total emissions by age of use not
spatial emissions, you could use \texttt{emis\_post} with
\texttt{by\ =\ "veh"} and then aggregate the emissions by standard. Then
speciate PM by standard:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{dfpm <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ pmd,  }\DataTypeTok{veh =} \StringTok{"LT"}\NormalTok{, }\DataTypeTok{size =} \StringTok{"small"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"D"}\NormalTok{,}
                  \DataTypeTok{pollutant =} \StringTok{"PM"}\NormalTok{, }\DataTypeTok{by =} \StringTok{"veh"}\NormalTok{)}
\NormalTok{dfpm}\OperatorTok{$}\NormalTok{euro <-}\StringTok{ }\NormalTok{euro}
\NormalTok{dfpmeuro <-}\StringTok{ }\KeywordTok{aggregate}\NormalTok{(dfpm}\OperatorTok{$}\NormalTok{g, }\DataTypeTok{by =} \KeywordTok{list}\NormalTok{(dfpm}\OperatorTok{$}\NormalTok{euro), sum)}
\KeywordTok{names}\NormalTok{(dfpmeuro) <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"euro"}\NormalTok{, }\StringTok{"g"}\NormalTok{)}
\NormalTok{bcom <-}\StringTok{ }\KeywordTok{as.data.frame}\NormalTok{(}\KeywordTok{sapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{6}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i)\{}
  \KeywordTok{speciate}\NormalTok{(dfpmeuro}\OperatorTok{$}\NormalTok{g[i], }\StringTok{"bcom"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"D"}\NormalTok{, dfpmeuro}\OperatorTok{$}\NormalTok{euro[i])}
\NormalTok{\}))}
\KeywordTok{names}\NormalTok{(bcom) <-}\StringTok{ }\NormalTok{dfpmeuro}\OperatorTok{$}\NormalTok{euro}
\end{Highlighting}
\end{Shaded}

And the resulting speciation is:

\begin{table}

\caption{\label{tab:unnamed-chunk-94}Black Carbon and Organic Matter [g/168h]}
\centering
\begin{tabular}[t]{l|l|l|l|l|l|l}
\hline
  & I & II & III & IV & PRE & V\\
\hline
BC & 6826527.37503195 & 5313757.92745243 & 3321183.01325297 & 155078.69338865 & 6700527.80930883 & 10369.2555188646\\
\hline
OM & 2730610.95001278 & 1222164.32331406 & 498177.451987945 & 20160.2301405245 & 4690369.46651618 & 20738.5110377293\\
\hline
\end{tabular}
\end{table}

\section{Tyre wear, breaks wear and road
abrassion}\label{tyre-wear-breaks-wear-and-road-abrassion}

Tyre, break and road abrassions comes from
\citet{NtziachristosBoulter2009}. Tyres consist in a complex mixture of
rubber, which after the use starts to degradate. In deed, when the
driving cycle ismore aggresive, higher is tyre mass emitted from tyre,
with tyre wear emission factors higher in HDV than LDV. Break wear emits
mass and with more aggresive driving, more emissions.

The input are wear emissions in Total Suspended Particles (TSP) ,as
explained in section @ref(\#ew). The speciation consists in 60\% PM10,
42\% PM2.5, 6\% PM1 and 4.8\% PM0.1. Now, a simple example for TP
adspeciation:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{pro <-}\StringTok{ }\NormalTok{profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{] }\CommentTok{# 24 hours}
\NormalTok{pc_week <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, pro)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pc_week, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\NormalTok{ef <-}\StringTok{ }\KeywordTok{ef_wear}\NormalTok{(}\DataTypeTok{wear =} \StringTok{"tyre"}\NormalTok{, }\DataTypeTok{type =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{speed =}\NormalTok{ df)}
\NormalTok{emit <-}\StringTok{ }\KeywordTok{emis_wear}\NormalTok{(}\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"VEH"}\NormalTok{), }\DataTypeTok{speed =}\NormalTok{ df,}
                  \DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ ef, }\DataTypeTok{profile =}\NormalTok{  pro)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of VEH is 11.17
\end{verbatim}

\begin{verbatim}
## Number of VEH is 1946.95 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emib <-}\StringTok{ }\KeywordTok{emis_wear}\NormalTok{(}\DataTypeTok{what =} \StringTok{"break"}\NormalTok{, }\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"VEH"}\NormalTok{),}
                  \DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ ef, }\DataTypeTok{profile =}\NormalTok{  pro, }\DataTypeTok{speed =}\NormalTok{ df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of VEH is 11.17
## Number of VEH is 1946.95 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emit}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This EmissionsArray has
##  1505 streets
##  50 vehicle categories
##  24 hours
##  1 days
## [1] 0.0499920404 0.0183982869 0.0026973699 0.0066891571 0.0002749512
## [6] 0.0212774028
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emib}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This EmissionsArray has
##  1505 streets
##  50 vehicle categories
##  24 hours
##  1 days
## [1] 0.0528581237 0.0153135442 0.0022451163 0.0070774117 0.0002288516
## [6] 0.0271699850
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emitp <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ emit, }\DataTypeTok{veh =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{size =} \StringTok{"ALL"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{,}
                   \DataTypeTok{pollutant =} \StringTok{"TSP"}\NormalTok{, }\DataTypeTok{by =} \StringTok{"veh"}\NormalTok{)}
\NormalTok{emibp <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ emib, }\DataTypeTok{veh =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{size =} \StringTok{"ALL"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{,}
                   \DataTypeTok{pollutant =} \StringTok{"TSP"}\NormalTok{, }\DataTypeTok{by =} \StringTok{"veh"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

The estimation covered 24 hours and for simplicity, we are assuming a
year with 365 similar days and dividing by 1 million to have t/y.
Hence,the speciation is:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(ggplot2)}
\NormalTok{tyrew <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(}\DataTypeTok{x =}  \KeywordTok{unclass}\NormalTok{(emitp}\OperatorTok{$}\NormalTok{g), }\DataTypeTok{spec =} \StringTok{"tyre"}\NormalTok{)}
\NormalTok{breakw <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(}\DataTypeTok{x =}  \KeywordTok{unclass}\NormalTok{(emibp}\OperatorTok{$}\NormalTok{g), }\DataTypeTok{spec =} \StringTok{"brake"}\NormalTok{)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{Emis =} \KeywordTok{c}\NormalTok{(}\KeywordTok{sapply}\NormalTok{(tyrew, sum), }\KeywordTok{sapply}\NormalTok{(breakw, sum))}\OperatorTok{*}\DecValTok{365}\OperatorTok{/}\DecValTok{1000000}\NormalTok{)}
\NormalTok{df}\OperatorTok{$}\NormalTok{Pollutant <-}\StringTok{ }\KeywordTok{factor}\NormalTok{(}\DataTypeTok{x =} \KeywordTok{names}\NormalTok{(tyrew), }\DataTypeTok{levels =} \KeywordTok{names}\NormalTok{(tyrew))}
\NormalTok{df}\OperatorTok{$}\NormalTok{Emissions <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\KeywordTok{rep}\NormalTok{(}\StringTok{"Tyre"}\NormalTok{, }\DecValTok{4}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"Brake"}\NormalTok{, }\DecValTok{4}\NormalTok{))}
\KeywordTok{ggplot}\NormalTok{(df, }\KeywordTok{aes}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ Pollutant, }\DataTypeTok{y =}\NormalTok{ Emis, }\DataTypeTok{fill =}\NormalTok{ Emissions)) }\OperatorTok{+}
\StringTok{  }\KeywordTok{geom_bar}\NormalTok{(}\DataTypeTok{stat =} \StringTok{"identity"}\NormalTok{, }\DataTypeTok{position =} \StringTok{"dodge"}\NormalTok{)}\OperatorTok{+}
\StringTok{  }\KeywordTok{labs}\NormalTok{(}\DataTypeTok{x =} \OtherTok{NULL}\NormalTok{, }\DataTypeTok{y =} \StringTok{"[t/y]"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/styre-1.pdf}
\caption{\label{fig:styre}Break and Tyre emissions (t/y)}
\end{figure}

It is interesting to see that break emissions are higher than tyre wear
emissions, and the smalle the diameter, higher the difference, as shows:

\begin{table}

\caption{\label{tab:unnamed-chunk-96}Ratio between break and tyre wear emissions}
\centering
\begin{tabular}[t]{l|r}
\hline
  & x\\
\hline
PM10 & 1.6593167\\
\hline
PM2.5 & 0.9433433\\
\hline
PM1 & 1.6931803\\
\hline
PM0.1 & 1.6931803\\
\hline
\end{tabular}
\end{table}

The procudure can be used with \texttt{emis\_post} with
\texttt{by\ \ =\ streets\_wide} to obtain the spatial speciation.

\section{\texorpdfstring{\(NO\) and
\(NO_2\)}{NO and NO\_2}}\label{no-and-no_2}

The speciation of \(NO_X\) into \(NO\) and \(NO2\) depends on the type
of vehicle, fuel and euro standard. The following example will consists
in comparing a Gasoline passenger car and a Diesel truck.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{propc <-}\StringTok{ }\NormalTok{profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{] }\CommentTok{# 24 hours}
\NormalTok{prolt <-}\StringTok{ }\NormalTok{profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{] }\CommentTok{# 24 hours}
\NormalTok{pweek <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, pro)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pc_week, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\NormalTok{euro <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"V"}\NormalTok{, }\KeywordTok{rep}\NormalTok{(}\StringTok{"IV"}\NormalTok{, }\DecValTok{3}\NormalTok{),  }
          \KeywordTok{rep}\NormalTok{(}\StringTok{"III"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"II"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\DecValTok{5}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\DecValTok{27}\NormalTok{))}
\NormalTok{lefpc <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{50}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) \{}
  \KeywordTok{ef_ldv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{t =} \StringTok{"4S"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =} \StringTok{"G"}\NormalTok{,}
               \DataTypeTok{eu =}\NormalTok{ euro[i], }\DataTypeTok{p =} \StringTok{"NOx"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{) \})}
\NormalTok{leflt <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{50}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) \{}
  \KeywordTok{ef_hdv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"Trucks"}\NormalTok{, }\DataTypeTok{t =} \StringTok{"RT"}\NormalTok{, }\DataTypeTok{g =} \StringTok{">32"}\NormalTok{, }\DataTypeTok{gr =} \DecValTok{0}\NormalTok{,}
               \DataTypeTok{eu =}\NormalTok{ euro[i], }\DataTypeTok{l =} \FloatTok{0.5}\NormalTok{, }\DataTypeTok{p =} \StringTok{"NOx"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{) \})}
\NormalTok{emipc <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv),}\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ lefpc, }\DataTypeTok{speed =}\NormalTok{ df,}
              \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\DecValTok{365}\OperatorTok{/}\DecValTok{1000000}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 11.17
\end{verbatim}

\begin{verbatim}
## Number of veh is 1946.95 * 10^3 veh
\end{verbatim}

\begin{verbatim}
## 4097.62 kg emissions in 24 hours and 1 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emilt <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv),}\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ leflt, }\DataTypeTok{speed =}\NormalTok{ df,}
              \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2014}\NormalTok{[, }\DecValTok{1}\NormalTok{])}\OperatorTok{*}\DecValTok{365}\OperatorTok{/}\DecValTok{1000000}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 11.17
\end{verbatim}

\begin{verbatim}
## Number of veh is 148.12 * 10^3 veh
\end{verbatim}

\begin{verbatim}
## 14612.67 kg emissions in 24 hours and 1 days
\end{verbatim}

Then, once the emissions are estimated, we will speciate the emissions
by euro standard. The estimation covered 24 hours and for simplicity, we
are assuming a year with 365 similar days and dividing by 1 million to
have t/y.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{rowSums}\NormalTok{(}\KeywordTok{apply}\NormalTok{(}\DataTypeTok{X =}\NormalTok{ emipc, }\DataTypeTok{MARGIN =} \KeywordTok{c}\NormalTok{(}\DecValTok{2}\NormalTok{,}\DecValTok{3}\NormalTok{), }\DataTypeTok{FUN =}\NormalTok{ sum, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{))}
\NormalTok{dfpc <-}\StringTok{   }\KeywordTok{rbind}\NormalTok{(}\KeywordTok{speciate}\NormalTok{(df[}\DecValTok{1}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{2}\OperatorTok{:}\DecValTok{4}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{5}\OperatorTok{:}\DecValTok{11}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{12}\OperatorTok{:}\DecValTok{18}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{19}\OperatorTok{:}\DecValTok{23}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{24}\OperatorTok{:}\DecValTok{50}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"PC"}\NormalTok{, }\StringTok{"G"}\NormalTok{, }\StringTok{"V"}\NormalTok{))}
\NormalTok{df <-}\StringTok{ }\KeywordTok{rowSums}\NormalTok{(}\KeywordTok{apply}\NormalTok{(}\DataTypeTok{X =}\NormalTok{ emipc, }\DataTypeTok{MARGIN =} \KeywordTok{c}\NormalTok{(}\DecValTok{2}\NormalTok{,}\DecValTok{3}\NormalTok{), }\DataTypeTok{FUN =}\NormalTok{ sum, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{))}
\NormalTok{dfhdv <-}\StringTok{   }\KeywordTok{rbind}\NormalTok{(}\KeywordTok{speciate}\NormalTok{(df[}\DecValTok{1}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                 \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{2}\OperatorTok{:}\DecValTok{4}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                 \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{5}\OperatorTok{:}\DecValTok{11}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                 \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{12}\OperatorTok{:}\DecValTok{18}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                 \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{19}\OperatorTok{:}\DecValTok{23}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{),}
                 \KeywordTok{speciate}\NormalTok{(df[}\DecValTok{24}\OperatorTok{:}\DecValTok{50}\NormalTok{], }\StringTok{"nox"}\NormalTok{, }\StringTok{"HDV"}\NormalTok{, }\StringTok{"D"}\NormalTok{, }\StringTok{"V"}\NormalTok{))}

\NormalTok{df <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\KeywordTok{rbind}\NormalTok{(}\KeywordTok{sapply}\NormalTok{(dfpc, sum), }\KeywordTok{sapply}\NormalTok{(dfhdv, sum)))}
\KeywordTok{row.names}\NormalTok{(df) <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"PC"}\NormalTok{, }\StringTok{"HGV"}\NormalTok{)}
\NormalTok{knitr}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(df, }\DataTypeTok{caption =} \StringTok{"NO2 and NO speciation (t/y)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{table}

\caption{\label{tab:sno2}NO2 and NO speciation (t/y)}
\centering
\begin{tabular}[t]{l|r|r}
\hline
  & NO2 & NO\\
\hline
PC & 44.8689 & 1450.761\\
\hline
HGV & 179.4756 & 1316.154\\
\hline
\end{tabular}
\end{table}

\section{\texorpdfstring{Volatile organic compounds:
\texttt{nmhc}}{Volatile organic compounds: nmhc}}\label{volatile-organic-compounds-nmhc}

nmhc were shown earluier in this chapter, but they can be also used to
speciate objects, resulting in all species at once. The speciation of
NMHC must be applied to NMHC. However, the emission guidelines of
\citet{NtziachristosSamaras2016} does not show explicit emission factor
of NMHC and the suggeted procedure is substract the \(CH_4\) from the
\(HC\). This was done on VEIN and now, ef\_speed* functions returns NMHC
directly. This makes easier to produce speciation.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{propc <-}\StringTok{ }\NormalTok{profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{[, }\DecValTok{1}\NormalTok{] }\CommentTok{# 24 hours}
\NormalTok{pweek <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, propc)}
\NormalTok{df <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pweek, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{alpha =} \DecValTok{1}\NormalTok{)}
\NormalTok{euro <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"V"}\NormalTok{, }\KeywordTok{rep}\NormalTok{(}\StringTok{"IV"}\NormalTok{, }\DecValTok{3}\NormalTok{),  }
          \KeywordTok{rep}\NormalTok{(}\StringTok{"III"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"II"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\DecValTok{5}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\DecValTok{27}\NormalTok{))}
\NormalTok{lefpc <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{50}\NormalTok{, }\ControlFlowTok{function}\NormalTok{(i) \{}
  \KeywordTok{ef_ldv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{t =} \StringTok{"4S"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =} \StringTok{"G"}\NormalTok{,}
               \DataTypeTok{eu =}\NormalTok{ euro[i], }\DataTypeTok{p =} \StringTok{"NMHC"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{)\})}
\end{Highlighting}
\end{Shaded}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{euro <-}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"V"}\NormalTok{, }\KeywordTok{rep}\NormalTok{(}\StringTok{"IV"}\NormalTok{, }\DecValTok{3}\NormalTok{),  }
          \KeywordTok{rep}\NormalTok{(}\StringTok{"III"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"II"}\NormalTok{, }\DecValTok{7}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"I"}\NormalTok{, }\DecValTok{5}\NormalTok{), }\KeywordTok{rep}\NormalTok{(}\StringTok{"PRE"}\NormalTok{, }\DecValTok{27}\NormalTok{))}
\NormalTok{lefpc <-}\StringTok{ }\KeywordTok{lapply}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(euro), }\ControlFlowTok{function}\NormalTok{(i) \{}
  \KeywordTok{ef_ldv_speed}\NormalTok{(}\DataTypeTok{v =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{t =} \StringTok{"4S"}\NormalTok{, }\DataTypeTok{cc =} \StringTok{"<=1400"}\NormalTok{, }\DataTypeTok{f =} \StringTok{"G"}\NormalTok{,}
               \DataTypeTok{eu =}\NormalTok{ euro[i], }\DataTypeTok{p =} \StringTok{"HC"}\NormalTok{, }\DataTypeTok{show.equation =} \OtherTok{FALSE}\NormalTok{) \})}
\NormalTok{enmhc <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =} \KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv), }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ lefpc, }\DataTypeTok{speed =}\NormalTok{ df,}
              \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2014}\NormalTok{[, }\DecValTok{1}\NormalTok{])}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 11.17
\end{verbatim}

\begin{verbatim}
## Number of veh is 1946.95 * 10^3 veh
\end{verbatim}

\begin{verbatim}
## 3030.65 kg emissions in 24 hours and 1 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df_enmhc <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ enmhc, }\DataTypeTok{veh =} \StringTok{"PC"}\NormalTok{, }\DataTypeTok{size =} \StringTok{"ALL"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{,}
                      \DataTypeTok{pollutant =} \StringTok{"NMHC"}\NormalTok{, }\DataTypeTok{by =} \StringTok{"veh"}\NormalTok{)}
\CommentTok{# assuming all euro I}
\NormalTok{spec <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(}\DataTypeTok{x =} \KeywordTok{as.numeric}\NormalTok{(df_enmhc}\OperatorTok{$}\NormalTok{g), }\DataTypeTok{spec =} \StringTok{"nmhc"}\NormalTok{, }\DataTypeTok{veh =} \StringTok{"LDV"}\NormalTok{,}
                 \DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"I"}\NormalTok{)}
\KeywordTok{names}\NormalTok{(spec)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "ethane"                   "propane"                 
##  [3] "butane"                   "isobutane"               
##  [5] "pentane"                  "isopentane"              
##  [7] "hexane"                   "heptane"                 
##  [9] "octane"                   "TWO_methylhexane"        
## [11] "nonane"                   "TWO_methylheptane"       
## [13] "THREE_methylhexane"       "decane"                  
## [15] "THREE_methylheptane"      "alcanes_C10_C12"         
## [17] "alkanes_C13"              "cycloalcanes"            
## [19] "ethylene"                 "propylene"               
## [21] "propadiene"               "ONE_butene"              
## [23] "isobutene"                "TWO_butene"              
## [25] "ONE_3_butadiene"          "ONE_pentene"             
## [27] "TWO_pentene"              "ONE_hexene"              
## [29] "dimethylhexene"           "ONE_butine"              
## [31] "propine"                  "acetylene"               
## [33] "formaldehyde"             "acetaldehyde"            
## [35] "acrolein"                 "benzaldehyde"            
## [37] "crotonaldehyde"           "methacrolein"            
## [39] "butyraldehyde"            "isobutanaldehyde"        
## [41] "propionaldehyde"          "hexanal"                 
## [43] "i_valeraldehyde"          "valeraldehyde"           
## [45] "o_tolualdehyde"           "m_tolualdehyde"          
## [47] "p_tolualdehyde"           "acetone"                 
## [49] "methylethlketone"         "toluene"                 
## [51] "ethylbenzene"             "m_p_xylene"              
## [53] "o_xylene"                 "ONE_2_3_trimethylbenzene"
## [55] "ONE_2_4_trimethylbenzene" "ONE_3_5_trimethylbenzene"
## [57] "styrene"                  "benzene"                 
## [59] "C9"                       "C10"                     
## [61] "C13"
\end{verbatim}

\section{\texorpdfstring{The speciation
\texttt{iag}}{The speciation iag}}\label{the-speciation-iag}

The Carbon Bond Mechanism Z \citeyearpar{cbmz} is an lumped-structure
mechanism.

Citing:

**``it is currently unfeasible to treat the organic species individually
in a regional or global chemistry model for three major reasons:**

\textbf{- (1) limited computational resources,} \textbf{- (2) lack of
detailed speciated emissions inventories, and } \textbf{- (3) lack of
kinetic and mechanistic information for all the species and their
reaction products.}

\textbf{Thus there exists a need for a condensed mechanism that is
capable of describing the tropospheric hydrocarbon chemistry with
reasonable accuracy, sensitivity, and speed at regional to global
scales. }

The research in this aspect is intense and out of the scope of this
book. However, it is important that the user who intends to performs
atmospheric simulation to understand this why it is important to
speciate the emissions and what represent each group.

A popular air quality model nowedays is the Weather Research and
Forecasting model coupled to Chemistry (WRF-Chem) \citep{Grelletal2005}
\url{https://ruc.noaa.gov/wrf/wrf-chem/and} the \textbf{Emissions Guide}
(\url{https://ruc.noaa.gov/wrf/wrf-chem/Emission_guide.pdf}).

The speciation \texttt{iag} splits the NMHC into the lumped groups for
the mechanism CMB-Z. The name \texttt{iag} comes from the Institute of
Astronomy, Geophysics and Atmospheric Sciences (IAG,
\url{http://www.iag.usp.br/}) from the University of São Paulo (USP).
The Departament of Atmospheric Sciences (DAC) of IAG has measure and
model air quality models for several years. There are several scientific
production such as: \citet{Nogueiraetal2015},
\citet{HOSHYARIPOUR2016365}, \citet{Andradeetal2017},
\citet{Freitasetal2005}, \citet{martins2006emission},
\citet{perez2014emission}, \citet{ulke2001modeling},
\citet{vivanco2006validation}, \citet{boian2012characterization},
\citet{Andrade2012}, \citet{Andradeetal2015}, \citet{VaraVelaetal2015},
\citet{Rafee2015}, \citet{Ibarraetal2017b}. The following figure, shows
an air quality simulation over South Easth Brazil from DAC/IAG/USP.

\begin{figure}
\includegraphics[width=6.81in]{figuras/wrf} \caption{http://www.lapat.iag.usp.br/aerossol/wrf9/index.php}\label{fig:unnamed-chunk-100}
\end{figure}

Some of these papers show measurements in tunnels, fuel, etc. The
speciation \texttt{iag} it is based on these experiments, and the last
versions, covers an update during 2015 so that the exhaust emissions
speciation is more representative of brazilian gasoline. This means
that:

\begin{itemize}
\tightlist
\item
  ** THE SPECIATION \texttt{IAG} IS BASED ON BRAZILIAN MEASUREMENTS**.
\end{itemize}

and the user who wants to applt it outside Brazil, should be ensure,
that the fleet and fuel are compatible. As that will be hard to
accomplish, I can say that this speciation is for and to be used in
Brazil, even more, in São Paulo.

The currently speciation \texttt{iag} is:

\begin{figure}
\includegraphics[width=12.36in]{figuras/iag1} \caption{Speciation for NMHC by type of fuel and process mol/100g (Ibarra-Espinosa, S., 2017)}\label{fig:unnamed-chunk-101}
\end{figure}

The arguments of the function \texttt{speciate} take the following form:

\begin{itemize}
\tightlist
\item
  \texttt{x}: Emissions estimation, it is recommended that x are
  griddded emissions from NMHC.
\item
  \texttt{spec}: \texttt{iag}. Alternatively, you can put any name of
  the following: ``e\_eth'', ``e\_hc3'', ``e\_hc5'', ``e\_hc8'',
  ``e\_ol2'', ``e\_olt'', ``e\_oli'', ``e\_iso'', ``e\_tol'',
  ``e\_xyl'', ``e\_c2h5oh'', ``e\_hcho'', ``e\_ch3oh'', ``e\_ket''
\item
  \texttt{veh}: When spec is ``iag'' veh can take two values depending:
  when the speciation is for vehicles veh accepts ``veh'', eu
  ``Evaporative'', ``Liquid'' or ``Exhaust''
\item
  \texttt{fuel}: ``G'', ``E'' or ``D''.
\item
  \texttt{eu}: ``Exhaust'' ``Evaporative'' or ``Liquid''.
\item
  \texttt{show}: Let this FALSE.
\item
  \texttt{list}: Let this TRUE so that the result is a list, and each
  element of the list a lumped specie.
\end{itemize}

Example:

\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{# Do not run}
\NormalTok{x <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{x =} \KeywordTok{rnorm}\NormalTok{(}\DataTypeTok{n =} \DecValTok{100}\NormalTok{, }\DataTypeTok{mean =} \DecValTok{400}\NormalTok{, }\DataTypeTok{sd =} \DecValTok{2}\NormalTok{))}
\NormalTok{dfa <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(x, }\DataTypeTok{spec =} \StringTok{"e_eth"}\NormalTok{, }\DataTypeTok{veh =} \StringTok{"veh"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{, }\DataTypeTok{eu =} \StringTok{"Exhaust"}\NormalTok{)}
\KeywordTok{head}\NormalTok{(dfa)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##       e_eth
## 1 0.9263431
## 2 0.9253977
## 3 0.9225670
## 4 0.9277312
## 5 0.9276508
## 6 0.9229689
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(x, }\DataTypeTok{spec =} \StringTok{"iag"}\NormalTok{, }\DataTypeTok{veh =} \StringTok{"veh"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{,}
               \DataTypeTok{eu =} \StringTok{"Exhaust"}\NormalTok{, }\DataTypeTok{list =} \OtherTok{TRUE}\NormalTok{)}
\KeywordTok{names}\NormalTok{(df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "e_eth"    "e_hc3"    "e_hc5"    "e_hc8"    "e_ol2"    "e_olt"   
##  [7] "e_oli"    "e_iso"    "e_tol"    "e_xyl"    "e_c2h5oh" "e_ald"   
## [13] "e_hcho"   "e_ch3oh"  "e_ket"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\ControlFlowTok{for}\NormalTok{(i }\ControlFlowTok{in} \DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(df))\{}
  \KeywordTok{print}\NormalTok{(df[[i]][}\DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{, ]) }\CommentTok{# Firt three lines of each element of the list}
\NormalTok{\}}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Units: mol/h
## [1] 0.9263431 0.9253977 0.9225670
## Units: mol/h
## [1] 1.426447 1.424991 1.420632
## Units: mol/h
## [1] 0.5198974 0.5193668 0.5177781
## Units: mol/h
## [1] 0.2508636 0.2506076 0.2498410
## Units: mol/h
## [1] 1.903390 1.901447 1.895631
## Units: mol/h
## [1] 0.7979758 0.7971615 0.7947230
## Units: mol/h
## [1] 0.5290304 0.5284906 0.5268739
## Units: mol/h
## [1] 0.02537627 0.02535037 0.02527282
## Units: mol/h
## [1] 0.4605284 0.4600584 0.4586511
## Units: mol/h
## [1] 0.8773812 0.8764858 0.8738047
## Units: mol/h
## [1] 1.232421 1.231163 1.227397
## Units: mol/h
## [1] 0.3454653 0.3451127 0.3440570
## Units: mol/h
## [1] 0.4505596 0.4500998 0.4487229
## Units: mol/h
## [1] 0 0 0
## Units: mol/h
## [1] 0 0 0
\end{verbatim}

\section{\texorpdfstring{The speciation
\texttt{pmiag}}{The speciation pmiag}}\label{the-speciation-pmiag}

The speciation \texttt{pmiag}is also based on IAG studies and applied
only for PM2.5 emissions. The speciation splits the PM2.5 in percentages
as follow:

\begin{table}

\caption{\label{tab:unnamed-chunk-103}Speciation of PM2.5}
\centering
\begin{tabular}[t]{r|r|r|r|r|r|r|r|r|r|r}
\hline
E\_SO4i & E\_SO4j & E\_NO3i & E\_NO3j & E\_MP2.5i & E\_MP2.5j & E\_ORGi & E\_ORGj & E\_ECi & E\_ECj & H2O\\
\hline
0.0077 & 0.0623 & 0.00247 & 0.01053 & 0.1 & 0.3 & 0.0304 & 0.1296 & 0.056 & 0.024 & 0.277\\
\hline
\end{tabular}
\end{table}

When running this speciation, the only two required arguments are
\texttt{x} and \texttt{spec}. Also, There is a message that this
speciation applies \textbf{only in gridded emissions, because the unit
must be in flux \(g/km^2/h\)}, and internally are transformed to the
required unit \$ \mu g/m\^{}2/s\$ as:

\[ \frac{g}{(km)^2*h}*\frac{10^6 \mu g}{g}*(\frac{km}{1000m})^2*\frac{h}{3600s}\frac{1}{dx^2}\]

dx is the length of the grid cell. Example:

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{data}\NormalTok{(net)}
\NormalTok{pm <-}\StringTok{ }\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{pm =} \KeywordTok{rnorm}\NormalTok{(}\DataTypeTok{n =} \KeywordTok{length}\NormalTok{(net), }\DataTypeTok{mean =} \DecValTok{400}\NormalTok{, }\DataTypeTok{sd =} \DecValTok{2}\NormalTok{))}
\NormalTok{net}\OperatorTok{@}\NormalTok{data <-}\StringTok{ }\NormalTok{pm}
\NormalTok{g <-}\StringTok{ }\KeywordTok{make_grid}\NormalTok{(net, }\DecValTok{1}\OperatorTok{/}\FloatTok{102.47}\OperatorTok{/}\DecValTok{2}\NormalTok{) }\CommentTok{# 500 m}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of lon points: 23
## Number of lat points: 19
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gx <-}\StringTok{ }\KeywordTok{emis_grid}\NormalTok{(net, g)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Sum of street emissions 602048.96
\end{verbatim}

\begin{verbatim}
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
\end{verbatim}

\begin{verbatim}
## Sum of gridded emissions 602048.96
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\KeywordTok{speciate}\NormalTok{(gx, }\DataTypeTok{spec =} \StringTok{"pmiag"}\NormalTok{, }\DataTypeTok{veh =} \StringTok{"veh"}\NormalTok{, }\DataTypeTok{fuel =} \StringTok{"G"}\NormalTok{,}
               \DataTypeTok{eu =} \StringTok{"Exhaust"}\NormalTok{, }\DataTypeTok{list =} \OtherTok{FALSE}\NormalTok{, }\DataTypeTok{dx =} \FloatTok{0.5}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## To be used in emissions grid only, emissions must be in g/(Xkm^2)/h
\end{verbatim}

\begin{verbatim}
## PM.2.5-10 must be calculated as substraction of PM10-PM2.5 to enter this variable into WRF
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{names}\NormalTok{(df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "e_so4i"   "e_so4j"   "e_no3i"   "e_no3j"   "e_pm2.5i" "e_pm2.5j"
##  [7] "e_orgi"   "e_orgj"   "e_eci"    "e_ecj"    "h2o"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{head}\NormalTok{(df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##                e_so4i     e_so4j       e_no3i      e_no3j   e_pm2.5i
## 1 0.001449528 g/m^2/s 0.01172800 0.0004649784 0.001982276 0.01882504
## 2 0.001440782 g/m^2/s 0.01165724 0.0004621729 0.001970316 0.01871145
## 3 0.001476852 g/m^2/s 0.01194907 0.0004737433 0.002019643 0.01917989
## 4 0.002548892 g/m^2/s 0.02062285 0.0008176316 0.003485693 0.03310250
## 5 0.005807641 g/m^2/s 0.04698910 0.0018629706 0.007942138 0.07542391
## 6 0.000000000 g/m^2/s 0.00000000 0.0000000000 0.000000000 0.00000000
##     e_pm2.5j      e_orgi     e_orgj      e_eci       e_ecj        h2o
## 1 0.05647511 0.005722811 0.02439725 0.01054202 0.004518009 0.05214535
## 2 0.05613436 0.005688282 0.02425005 0.01047841 0.004490749 0.05183073
## 3 0.05753968 0.005830687 0.02485714 0.01074074 0.004603174 0.05312830
## 4 0.09930749 0.010063159 0.04290083 0.01853740 0.007944599 0.09169391
## 5 0.22627173 0.022928869 0.09774939 0.04223739 0.018101738 0.20892423
## 6 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 0.00000000
\end{verbatim}

\chapter{\texorpdfstring{Inputs for atmospheric models with
\texttt{eixport}
Package}{Inputs for atmospheric models with eixport Package}}\label{ep}

This last chapter of this book is about generating inputs for
atmospheric models, specifically, \textbf{WRF-Chem}
\citep{Grelletal2005}. As the Pacific Northwest National Laboratory
(PNNL, \url{https://www.pnnl.gov/atmospheric/research/wrf-chem/})
defines:

\textbf{The Weather Research and Forecasting (WRF) model is a next
generation } \textbf{meteorological model being developed
collaboratively among several agencies} \textbf{(NOAA/NCEP, NOAA/ESRL,
NCAR). WRF-Chem is a version of WRF that also} \textbf{simultaneously
simulates the emission, turbulent mixing, transport,}
\textbf{transformation, and fate of trace gases and aerosols.}

Initially, VEIN counted with the function \texttt{emis\_wrf} which
created a data.frame object of hourly gridded emissions with each
pollutant as a column, from the first cell to the last. The data-frame
extendend in long format to each hour. This data.frame was designed to
be used with script Another Asimilation System for WRF (AS4WRF)
\citep{VaraVelaetal2015} written in NCL \citep{ncl}.

This approach was effective and already tested for some Latinamerican
cities \citep{micro}, \citep{icshmo}. However, this function depends on
external software which may not be available. Therefore, me andsome
collegues developed the package \texttt{eixport} \citep{eixport},
\citep{eixport2}, which is an R package to read and export emissions to
atmospheric models. \texttt{eixport} currently covers the models
WRF-Chem \citep{Grelletal2005}, SPM-BRAMS \citep{Freitasetal2005} and
R-LINE \citep{rline}, but only the WRF-Chem interface has been fully
implemented. Therefore, this chapter covers only this model.

The concept of connecting VEIN and Wrf-Chem via eixport is quite simple
with two approaches:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Generating \texttt{GriddedEmissionsArray} for the wrfinput grid and
  inputting this emissions into the wrfchemi input file..
\item
  Generating gridded emissions data-frame for AS4WRF.
\end{enumerate}

\section{\texorpdfstring{WRFChem input with wrfinput and
\texttt{GriddedEmissionsArray}}{WRFChem input with wrfinput and GriddedEmissionsArray}}\label{wrfchem-input-with-wrfinput-and-griddedemissionsarray}

The process for generation WRF-Chem input files with
\texttt{GriddedEmissionsArray} is shown on Fig. \ref{fig:gea}. Pink
boxes are classes, grey boxes are \texttt{vein} functions, green
functions are \texttt{eixport} functions and white boxes, external
objects. Here, the external input file is the \textbf{wrfinputd\_0x}
file, where the \textbf{x} is for the domain. This file is inputted into
the function \texttt{make\_grid} which creates a polygon grid needed by
\texttt{emis\_grid}. The main characteristic of this grid, is that it
has the resolution of the wrfinput file.

The wrfinpiut\_d0x file isalso used by the \texttt{eixport} function
\texttt{wrf\_create} to create a wrfchemi input file with zeroes.

The objects with class \texttt{EmissionsArray} for each type of vehicle
and pollutant are processed by the function \texttt{emis\_post} which
create street emissions. Then, the function \texttt{emis\_merge} merges
all the street emissions files from
\texttt{emis\_post\_\ into\ one\ street\ emissions\ by\ pollutant\ ehich\ is\ inputted\ into\ function}emis\_grid\texttt{to\ create\ a\ polygon\ grid\ of\ emissions,\ class\ Spatial\ Feature}sf\texttt{{[}@sf{]},\ {[}@RJ-2018-009{]}.\ Then,\ the\ spatial\ gridded\ emissions\ are\ inputted\ into\ the\ contructor\ function}GriddedEmissionsArray`
to create an object of that class, and the dimensions of the wrfinput
file, that is, the sanem number of spatial rows and columns.

Finally, the \texttt{GriddedEmissionsArray} objects are inputted into
the wrfchemi file with the \texttt{eixport} function \texttt{wrf\_put}.

\begin{figure}

{\centering \includegraphics[width=0.5\linewidth]{figuras/gea} 

}

\caption{Generation of wrfchem inputs using GriddedEmissionsArray}\label{fig:gea}
\end{figure}

Example

\subsection{Creating a WRF-Chem input
file}\label{creating-a-wrf-chem-input-file}

The emission file will be generated with traffic simulation from the
Traffic Engineering Company (CET) for 2012, which consists in traffic
flow for morning rush hour of Light Duty Vehicles (LDV) and Heavy Good
Vehicles (HGV). The emission factors will be the data \texttt{fe2015}
and the wrfinput comes from the \texttt{eixport} package. Let's go:

\subsubsection{0) Network}\label{network}

The object net has a class \texttt{SpatialLinesDataFrame}, we transform
it into a \texttt{sf}object. The length of the road is the field `lkm'
in the object net, which are in km but has no units. We must add the
right units in order to use \texttt{vein}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{require}\NormalTok{(vein)}
\KeywordTok{require}\NormalTok{(sf)}
\KeywordTok{require}\NormalTok{(units)}
\NormalTok{net <-}\StringTok{ }\KeywordTok{readRDS}\NormalTok{(}\StringTok{"figuras/net.rds"}\NormalTok{)}
\KeywordTok{class}\NormalTok{(net)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "SpatialLinesDataFrame"
## attr(,"package")
## [1] "sp"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{net <-}\StringTok{ }\KeywordTok{st_as_sf}\NormalTok{(net)}
\NormalTok{net[}\DecValTok{1}\NormalTok{, ]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Simple feature collection with 1 feature and 11 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: -46.66878 ymin: -23.63199 xmax: -46.66663 ymax: -23.63095
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##          Id Inode Jnode ldv hdv     ps ffs lanes capacity    lkm via
## 1 8827-9642  8827  9642  60  14 23.661  30     1      400 0.2474   7
##                         geometry
## 1 LINESTRING (-46.66878 -23.6...
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{net}\OperatorTok{$}\NormalTok{lkm <-}\StringTok{ }\KeywordTok{set_units}\NormalTok{(}\KeywordTok{st_length}\NormalTok{(net), km) }\CommentTok{# ensure right units}
\end{Highlighting}
\end{Shaded}

\subsubsection{1) Vehicular composition}\label{vehicular-composition-1}

In this case, the vehicular composition consits in only to vehicles,
Passenger Cars using Gasoline with 25\% of Ethanol and Light Trucks
consuming Diesel with 5\% od biodiesel. Also, the emission factors cover
\(CO\). The temporal distribution will cover only one hour.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{PC_E25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 11.17
\end{verbatim}

\begin{verbatim}
## Number of veh is 24708.77 * 10^3 veh
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{LT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(net}\OperatorTok{$}\NormalTok{hdv)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Average age of veh is 17.12
\end{verbatim}

\begin{verbatim}
## Number of veh is 2809.3 * 10^3 veh
\end{verbatim}

\subsubsection{2) Emission factors}\label{emission-factors}

The emission factors used comes from \citet{CETESB}, which are constant
by age of use. In practice, this data.frame is like an Excel
spreadsheet. Then, the constructor function \texttt{EmissionFactorsList}
convert our numeric vector, which is the column of our data.frame, into
required type of object of the \texttt{emis} function. Parenthesis were
added in order to print the objects in one line.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{data}\NormalTok{(fe2015)}
\NormalTok{(EF_CO_PC_E25 <-}\StringTok{ }\KeywordTok{EmissionFactorsList}\NormalTok{(fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "CO"}\NormalTok{, }\StringTok{"PC_G"}\NormalTok{]))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This EmissionFactorsList has  36  functions
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{(EF_CO_LT_B5 <-}\StringTok{ }\KeywordTok{EmissionFactorsList}\NormalTok{(fe2015[fe2015}\OperatorTok{$}\NormalTok{Pollutant }\OperatorTok{==}\StringTok{ "CO"}\NormalTok{, }\StringTok{"LT"}\NormalTok{]))}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This EmissionFactorsList has  36  functions
\end{verbatim}

\subsubsection{3) Estimation of
emissions}\label{estimation-of-emissions}

The estimation of emissions is shown in the following code chunk in a
simplified way. The emissions array are aggregated.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{data}\NormalTok{(profiles)}
\NormalTok{emi1 <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ PC_E25, }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ EF_CO_PC_E25,}
             \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{PC_JUNE_}\DecValTok{2012}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of columns of 'veh' is different than length of 'ef'
\end{verbatim}

\begin{verbatim}
## adjusting length of ef to the number of colums of 'veh'
\end{verbatim}

\begin{verbatim}
## 1845699.9 kg emissions in 24 hours and 7 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emi2 <-}\StringTok{ }\KeywordTok{emis}\NormalTok{(}\DataTypeTok{veh =}\NormalTok{ LT_B5, }\DataTypeTok{lkm =}\NormalTok{ net}\OperatorTok{$}\NormalTok{lkm, }\DataTypeTok{ef =}\NormalTok{ EF_CO_PC_E25,}
             \DataTypeTok{profile =}\NormalTok{ profiles}\OperatorTok{$}\NormalTok{HGV_JUNE_}\DecValTok{2012}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Number of columns of 'veh' is different than length of 'ef'
\end{verbatim}

\begin{verbatim}
## adjusting length of ef to the number of colums of 'veh'
\end{verbatim}

\begin{verbatim}
## 2222757.56 kg emissions in 24 hours and 7 days
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{emi =}\StringTok{ }\NormalTok{emi1 }\OperatorTok{+}\StringTok{ }\NormalTok{emi2}
\end{Highlighting}
\end{Shaded}

\subsubsection{4) Post-estimations}\label{post-estimations}

The resulting emissions array \texttt{emi} is now processed with
\texttt{emis\_post}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{co_street <-}\StringTok{ }\KeywordTok{emis_post}\NormalTok{(}\DataTypeTok{arra =}\NormalTok{ emi, }\DataTypeTok{by =} \StringTok{"streets_wide"}\NormalTok{, }\DataTypeTok{net =}\NormalTok{ net)}
\end{Highlighting}
\end{Shaded}

\paragraph{\texorpdfstring{4a WRFChem input file with
\texttt{eixport}}{4a WRFChem input file with eixport}}\label{a-wrfchem-input-file-with-eixport}

Now it is time generate the emissions grid. In order to create a wrfchem
input file, we need a wrfinput file. In this case, the wrfinput coversa
wider area of the emissions, located in São Paulo, Brazil. The wrfinput
file is available from the R package \texttt{eixport}.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{wrf <-}\StringTok{ }\KeywordTok{paste}\NormalTok{(}\KeywordTok{system.file}\NormalTok{(}\StringTok{"extdata"}\NormalTok{, }\DataTypeTok{package =} \StringTok{"eixport"}\NormalTok{),}
             \StringTok{"/wrfinput_d02"}\NormalTok{, }\DataTypeTok{sep=}\StringTok{""}\NormalTok{)}
\NormalTok{g  <-}\StringTok{ }\KeywordTok{make_grid}\NormalTok{(wrf)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## path to wrfinput
\end{verbatim}

\begin{verbatim}
## using grid info from: /home/sergio/R/x86_64-pc-linux-gnu-library/3.4/eixport/extdata/wrfinput_d02 
## Number of lat points 51
## Number of lon points 63
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(}\KeywordTok{st_geometry}\NormalTok{(g), }\DataTypeTok{axes =} \OtherTok{TRUE}\NormalTok{)}
\KeywordTok{plot}\NormalTok{(}\KeywordTok{st_geometry}\NormalTok{(co_street), }\DataTypeTok{add =} \OtherTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\includegraphics{veinbook_files/figure-latex/unnamed-chunk-111-1.pdf}

Notice that weh we create grid from the path to wrfinput, there is a
message with \emph{Number of lat points 51} and \emph{Number of lon
points 63}. This information is crytical for later conversions. Now that
we have the street emissions and the grid, we can obtain our gridded
emissions:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gCO <-}\StringTok{ }\KeywordTok{emis_grid}\NormalTok{(}\DataTypeTok{spobj =}\NormalTok{ co_street, }\DataTypeTok{g =}\NormalTok{ g)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Sum of street emissions 4068457459.79
\end{verbatim}

\begin{verbatim}
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
\end{verbatim}

\begin{verbatim}
## Sum of gridded emissions 4068457459.79
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{names}\NormalTok{(gCO)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##   [1] "id"       "V1"       "V2"       "V3"       "V4"       "V5"      
##   [7] "V6"       "V7"       "V8"       "V9"       "V10"      "V11"     
##  [13] "V12"      "V13"      "V14"      "V15"      "V16"      "V17"     
##  [19] "V18"      "V19"      "V20"      "V21"      "V22"      "V23"     
##  [25] "V24"      "V25"      "V26"      "V27"      "V28"      "V29"     
##  [31] "V30"      "V31"      "V32"      "V33"      "V34"      "V35"     
##  [37] "V36"      "V37"      "V38"      "V39"      "V40"      "V41"     
##  [43] "V42"      "V43"      "V44"      "V45"      "V46"      "V47"     
##  [49] "V48"      "V49"      "V50"      "V51"      "V52"      "V53"     
##  [55] "V54"      "V55"      "V56"      "V57"      "V58"      "V59"     
##  [61] "V60"      "V61"      "V62"      "V63"      "V64"      "V65"     
##  [67] "V66"      "V67"      "V68"      "V69"      "V70"      "V71"     
##  [73] "V72"      "V73"      "V74"      "V75"      "V76"      "V77"     
##  [79] "V78"      "V79"      "V80"      "V81"      "V82"      "V83"     
##  [85] "V84"      "V85"      "V86"      "V87"      "V88"      "V89"     
##  [91] "V90"      "V91"      "V92"      "V93"      "V94"      "V95"     
##  [97] "V96"      "V97"      "V98"      "V99"      "V100"     "V101"    
## [103] "V102"     "V103"     "V104"     "V105"     "V106"     "V107"    
## [109] "V108"     "V109"     "V110"     "V111"     "V112"     "V113"    
## [115] "V114"     "V115"     "V116"     "V117"     "V118"     "V119"    
## [121] "V120"     "V121"     "V122"     "V123"     "V124"     "V125"    
## [127] "V126"     "V127"     "V128"     "V129"     "V130"     "V131"    
## [133] "V132"     "V133"     "V134"     "V135"     "V136"     "V137"    
## [139] "V138"     "V139"     "V140"     "V141"     "V142"     "V143"    
## [145] "V144"     "V145"     "V146"     "V147"     "V148"     "V149"    
## [151] "V150"     "V151"     "V152"     "V153"     "V154"     "V155"    
## [157] "V156"     "V157"     "V158"     "V159"     "V160"     "V161"    
## [163] "V162"     "V163"     "V164"     "V165"     "V166"     "V167"    
## [169] "V168"     "geometry"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gCO}\OperatorTok{$}\NormalTok{geometry}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## Geometry set for 3213 features 
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -47.43966 ymin: -24.4031 xmax: -45.57575 ymax: -23.00105
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 5 geometries:
\end{verbatim}

\begin{verbatim}
## POLYGON ((-47.41831 -24.35009, -47.41831 -24.38...
\end{verbatim}

\begin{verbatim}
## POLYGON ((-47.38871 -24.35053, -47.38871 -24.38...
\end{verbatim}

\begin{verbatim}
## POLYGON ((-47.35907 -24.35097, -47.35907 -24.38...
\end{verbatim}

\begin{verbatim}
## POLYGON ((-47.32947 -24.35143, -47.32947 -24.38...
\end{verbatim}

\begin{verbatim}
## POLYGON ((-47.29984 -24.35187, -47.29984 -24.38...
\end{verbatim}

This function requires the package \texttt{lwgeom} when the data is in
latitude and longitude degrees. There are some messages about the total
emissions of street and grid. This was made in order to ensure that
emis\_grid is conservative. The resulting object \texttt{gCO} is an
\texttt{sf} object of POLYGON. In order to plot the emissions, i
selected the emissions above 1, because it is easier to see the plot. If
we plot the gridded emissions, we will see:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gCO}\OperatorTok{$}\NormalTok{V9 <-}\StringTok{ }\NormalTok{gCO}\OperatorTok{$}\NormalTok{V9}\OperatorTok{*}\NormalTok{(}\DecValTok{12} \OperatorTok{+}\StringTok{ }\DecValTok{16}\NormalTok{)}\OperatorTok{^-}\DecValTok{1}
\KeywordTok{plot}\NormalTok{(gCO[gCO}\OperatorTok{$}\NormalTok{V9 }\OperatorTok{>}\DecValTok{1}\NormalTok{, }\StringTok{"V9"}\NormalTok{], }\DataTypeTok{axes =} \OtherTok{TRUE}\NormalTok{,  }\DataTypeTok{nbreaks =} \DecValTok{50}\NormalTok{,}
     \DataTypeTok{pal =}\NormalTok{ cptcity}\OperatorTok{::}\KeywordTok{cpt}\NormalTok{(}\DataTypeTok{colorRampPalette =}\NormalTok{ T),}
     \DataTypeTok{main =} \StringTok{"Emissions of CO at 08:00 (mol/h)"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/GEmi-1.pdf}
\caption{\label{fig:GEmi}Gridded emissions (mol/h)}
\end{figure}

The emissions are mostly distributed into main roads, as expected. The
next step is create the \texttt{GriddedEmissionsArray} that will be
inputted into the \texttt{eixport} functions. The
\texttt{GriddedEmissionsArray} is a contructor function that reads
class, \texttt{SpatialPolygonDataFrame}, \texttt{sf} of POLYGONs, a
\texttt{data.frame} or a \texttt{matrix} to create an Array of the
gridded emissions. The dimensions are lat, lon, mass and time.
Therefore, it is important to know the number of lat and lon points!

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gCOA <-}\StringTok{ }\KeywordTok{GriddedEmissionsArray}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ gCO, }\DataTypeTok{cols =} \DecValTok{63}\NormalTok{, }\DataTypeTok{rows =} \DecValTok{51}\NormalTok{, }\DataTypeTok{times =} \DecValTok{168}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## This GriddedEmissionsArray has:
##  51 lat points
##  63 lon points
##  168 hours
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{plot}\NormalTok{(gCOA, }\DataTypeTok{col =}\NormalTok{ cptcity}\OperatorTok{::}\KeywordTok{cpt}\NormalTok{())}
\end{Highlighting}
\end{Shaded}

\begin{figure}
\centering
\includegraphics{veinbook_files/figure-latex/GEA-1.pdf}
\caption{\label{fig:GEA}GriddedEmissionsArray (mol/h)}
\end{figure}

The image is similar to the plot of the gridded emissions, which means
that it is correct. Now we can input the our new created object
\texttt{gCOA} into the wrfchem input file, but first, we need to create
it. This is done using the \texttt{eixport} functions.

We first load the library \texttt{eixport}. Then load the data
\texttt{emis\_opt} which has the emissions options for WRFchem. In this
case, we are creating an wrfchem input file with 40 pollutants, with 0.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(eixport)}
\KeywordTok{data}\NormalTok{(emis_opt)}
\NormalTok{emis_opt[[}\StringTok{"ecb05_opt1"}\NormalTok{]]}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##  [1] "E_NO2"    "E_XYL"    "E_TOL"    "E_TERP"   "E_SO2"    "E_ORA2"  
##  [7] "E_OLT"    "E_OLI"    "E_OL2"    "E_NO"     "E_NH3"    "E_ISO"   
## [13] "E_HCL"    "E_HCHO"   "E_ETH"    "E_CSL"    "E_CO"     "E_CH3OH" 
## [19] "E_C2H5OH" "E_ALD"    "E_ALDX"   "E_HC3"    "E_HC5"    "E_HC8"   
## [25] "E_KET"    "E_PM25I"  "E_PM25J"  "E_ECI"    "E_ECJ"    "E_ORGI"  
## [31] "E_ORGJ"   "E_SO4I"   "E_SO4J"   "E_NO3I"   "E_NO3J"   "E_SO4C"  
## [37] "E_NO3C"   "E_ORGC"   "E_ECC"    "E_PM10"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{wrf_create}\NormalTok{(}\DataTypeTok{wrfinput_dir =} \KeywordTok{system.file}\NormalTok{(}\StringTok{"extdata"}\NormalTok{, }\DataTypeTok{package =} \StringTok{"eixport"}\NormalTok{),}
           \DataTypeTok{wrfchemi_dir =} \KeywordTok{tempdir}\NormalTok{(),}
           \DataTypeTok{frames_per_auxinput5 =} \DecValTok{168}\NormalTok{,}
           \DataTypeTok{domains =} \DecValTok{2}\NormalTok{,}
           \DataTypeTok{variaveis =}\NormalTok{ emis_opt[[}\StringTok{"ecb05_opt1"}\NormalTok{]],}
           \DataTypeTok{verbose =}\NormalTok{ F)}
\end{Highlighting}
\end{Shaded}

The wrfchemi input file was created on a temporal directory with the
function \texttt{tempdir}. Now we can input our
\texttt{GriddedEmissionsArray} into our new wrfchem input file.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{f <-}\StringTok{ }\KeywordTok{list.files}\NormalTok{(}\DataTypeTok{path =} \KeywordTok{tempdir}\NormalTok{(), }\DataTypeTok{full.names =}\NormalTok{ T, }\DataTypeTok{pattern =} \StringTok{"wrfchemi_d02"}\NormalTok{)}
\NormalTok{f}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## [1] "/tmp/RtmpXTmmRT/wrfchemi_d02_2011-08-01_00:00:00"
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{wrf_put}\NormalTok{(}\DataTypeTok{file =}\NormalTok{ f, }\DataTypeTok{name =} \StringTok{"E_CO"}\NormalTok{, }\DataTypeTok{POL =}\NormalTok{ gCOA)}
\end{Highlighting}
\end{Shaded}

Then, we just check our resulting NetCDF file plotting the emissions we
just put.

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{wrf_plot}\NormalTok{(f, }\StringTok{"E_CO"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{figure}

{\centering \includegraphics[width=0.8\linewidth]{figuras/wrf2} 

}

\caption{Wrfchem input file (NetCDF) (mol/h)}\label{fig:wrf}
\end{figure}

\paragraph{\texorpdfstring{4b WRFChem input file with
\texttt{emis\_wrf}}{4b WRFChem input file with emis\_wrf}}\label{b-wrfchem-input-file-with-emis_wrf}

\texttt{emis\_wrf} creates a \texttt{data.frame} with columns lat, long,
id, pollutants, local time and GMT time. This dataframe has the proper
format to be used with WRF assimilation system: ``ASimilation System 4
WRF (AS4WRF, \citet{angel})

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{args}\NormalTok{(emis_wrf)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## function (sdf, nr = 1, dmyhm, tz, crs = 4326, islist, utc) 
## NULL
\end{verbatim}

where:

\begin{itemize}
\tightlist
\item
  \texttt{sdf}: Gridded emissions, which can be a
  SpatialPolygonsDataFrame, or a list of SpatialPolygonsDataFrame, or a
  sf object of ``POLYGON''. The user must enter a list with 36
  SpatialPolygonsDataFrame with emissions for the mechanism CBM-Z.
\item
  \texttt{nr}: Number of repetitions of the emissions period
\item
  \texttt{dmyhm}: String indicating Day Month Year Hour and Minute in
  the format ``d-m-Y H:M'' e.g.: ``01-05-2014 00:00'' It represents the
  time of the first hour of emissions in Local Time
\item
  \texttt{tz}: Time zone as required in for function as.POSIXct
\item
  \texttt{crs}: Coordinate reference system, e.g: ``+init=epsg:4326''.
  Used to transform the coordinates of the output
\item
  \texttt{islist}: logical value to indicate if sdf is a list or not
\item
  \texttt{utc}: ignored.
\end{itemize}

In this case, we can use our gridded emissions directly here:

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{gCO}\OperatorTok{$}\NormalTok{id <-}\StringTok{ }\OtherTok{NULL}
\NormalTok{df <-}\StringTok{ }\KeywordTok{emis_wrf}\NormalTok{(}\DataTypeTok{sdf =}\NormalTok{ gCO, }\DataTypeTok{dmyhm =} \StringTok{"06-10-2014 00:00"}\NormalTok{,}
               \DataTypeTok{tz =} \StringTok{"America/Sao_Paulo"}\NormalTok{, }\DataTypeTok{islist =} \OtherTok{FALSE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
## You can create wrchem inputs directly with 
## eixport::create and eixport::wrf_put
## https://CRAN.R-project.org/package=eixport
\end{verbatim}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{head}\NormalTok{(df)}
\end{Highlighting}
\end{Shaded}

\begin{verbatim}
##    id      long       lat pollutant    time_lt            time_utc dutch
## 1   1 -47.41831 -24.35009         0 2014-10-06 2014-10-06 03:00:00   600
## 6   2 -47.38871 -24.35053         0 2014-10-06 2014-10-06 03:00:00   600
## 11  3 -47.35907 -24.35097         0 2014-10-06 2014-10-06 03:00:00   600
## 16  4 -47.32947 -24.35143         0 2014-10-06 2014-10-06 03:00:00   600
## 21  5 -47.29984 -24.35187         0 2014-10-06 2014-10-06 03:00:00   600
## 26  6 -47.27024 -24.35230         0 2014-10-06 2014-10-06 03:00:00   600
\end{verbatim}

Then you must export this \texttt{data.frame} into a .txt file. You must
include the number of columns to match your luped species and also take
out the columns time\_lt, time\_utc and dutch. These three columns are
just informative.

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{df <-}\StringTok{ }\NormalTok{df[,}\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{]}
\KeywordTok{write.table}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ df, }\DataTypeTok{file =} \KeywordTok{tempfile}\NormalTok{(), }\DataTypeTok{sep =} \StringTok{"}\CharTok{\textbackslash{}t}\StringTok{"}\NormalTok{,}
            \DataTypeTok{col.names =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{row.names =} \OtherTok{FALSE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

Finally, in order to generate the wrfchem input file, you must have the
resulting .txt file, a wrfinput file with the same grid spacing, the NCL
script AS4WRF and a namelist. AS4WRF and the namelist can be obtained
contacting
\href{mailto:angel.vela@iag.usp.br}{\nolinkurl{angel.vela@iag.usp.br}}
and \href{mailto:alvv1986@gmail.com}{\nolinkurl{alvv1986@gmail.com}}.

\chapter{Quality check and errors}\label{check}

This last chapter of this book presents to aspestcs fthat must be
considered for any practicioner interested in developing emissions
inventories. The first part covers a summary of the EMEP/EEA air
pollutant emission inventory guidebook 2016, Technical guidance to
prepare national emission inventories \citep{guia}, based on the
Intergovernmental Panel on Climate Change (IPCC) 2006 Good Practice
Guidance {[}\citet{change20062006}. The second part consists in advices
and specific considerations for avoinding errors when runnign VEIN in
each part of the inventory.

\section{\texorpdfstring{Guidance from
\citet{guia}}{Guidance from @guia}}\label{guidance-from-guia}

I believe that this part of this capter delivers knowledge that you
really will gain with experience. But even with experience you can get
lost, and returning to this part would save you.

\subsection{Key categories}\label{key-categories}

Key categories are the most important \textbf{source} categories. They
are important because they are responsabile for most of pollution in a
determined region. For instance, it has been described that vehicles are
the most important source of pollution in mega-cities
\citep{molina2004megacities}. Evenmore, in the case of São Paulo, the
official emissions inventory informs that vehicles are responsabiel for
mor than 97 \% of \(CO\), 79 \% of \(HC\), 68 \% of \(NO_X\), and only
29 \% of \(PM\) and 22 \% of \(SO_X\) \citep{CETESB2015}. This shown
another characteristics of key categories: they depend of specific
pollutants.

For another example, consider a city which suffers cold weather with use
bio-mass burning for cooking and heating. The main source of
\(PM_{2.5}\) will be certainly bio-mass burning.

Now consider an industrial region with industrial process that emit too
much \(SO_X\). The key-cateogries might be industrial sources.

And one more example, consider a huge mega-city with electric mobility.
However, the main source of electricity are thermoelectric based on
carbon (the key).

This implies that it should be a nomemclature for categorizing and
naming sources. IPCC, has a nomenclature for instance.

The ideia behind key categories, is that most efforts must be applied in
this categories, obtainging the highest level of detail with less
uncertainty. Hence, the emissions guidelines \citep{guia} proposes three
levels of complexity estimation methods, known as Tier Methods. The
complexity increases from level 1, 2 and 3. The VEIN tier is 3, which is
that the most complex function of vehicular emissions estimations are
incporated, with the exception of evaporative. I need to improve that
part.

\begin{itemize}
\tightlist
\item
  Tier 1: a simpler method which include available activity and
  emissions factors.
\item
  Tier 2: Similar to Tier 1, but includes more specific emission
  factors.
\item
  Tier 3: Most complex methodologies with higher level of detail,
  temporal and spatial.
\end{itemize}

Key categories can be estimated with Tier 2 and 3. However, the
requisites for Tier 3, is reducing the following \textbf{common} sources
of error:

\begin{itemize}
\tightlist
\item
  \textbf{Model error}: improve the representation of reality.
\item
  \textbf{Parameter error}: emission factors, coefficients, etc.
\item
  \textbf{Input error}: the error in activity data. In the case of
  vehicle, traffic flow.
\item
  \textbf{Process error}: error introduced through mistakes in the
  process of compiling the inventory. For complex models, this includes
  errors in the software implementation of the model. This is crytical
  for VEIN. VEIN has no Grafical User Interface and new users (and old,
  or the developer) can make mistakes! more inforation about this in the
  second part of this chapter.
\end{itemize}

\subsection{Data collection}\label{data-collection}

This part consists in the good practices for collecting the important
data. It seems trivial, but it is not. Sometimes, it will be necessary
to generate new data(activity or emission factors), which will be
limitated by the available resources. The methodolocycal principles are:

\begin{itemize}
\tightlist
\item
  Focus on data needed for the key categories.
\item
  Choose procedures for collecting data that will improve the inventory.
\item
  Plan the data collection that lead to a continious improvement.
\item
  Collect data appropiate for each level of complecity (Tier 1, 2 or 3).
\item
  On a regular basis (i mean \textbf{always}), review activities of data
  collection.
\item
  Generate agreements with data suppliers. \textbf{This is critical}.
  Consequently, you will have to keep good relationships with data
  suppliers.
\end{itemize}

Now I will show some guidance for collecting data.

\subsubsection{Use existing data}\label{use-existing-data}

\textbf{Sources of data}

\begin{itemize}
\tightlist
\item
  National statistics agencies provide great source of information.
\item
  Data from the Pollutant Release and Transfer Registers (PRTR),
  sectorial or national experts.
\item
  USA Environmental Protection Agency's (EPA's) AP 42, Compilation of
  Air Pollutant Emission Factors www.epa.gov/ttn/chief/ap42/.
\item
  EMEP/EEA air pollutant emission inventory guidebook
  \url{https://www.eea.europa.eu/publications/emep-eea-guidebook-2016/}
  The International Energy Agency.
\item
  DuckDuckGoGo/Google/Yahoo/Bing.
\end{itemize}

** Specification\textbf{ Once the data is selected, it is important to
develop a }formal specification**:

\begin{itemize}
\tightlist
\item
  Definition of the data (nationa/regional/units/uncertainty)
\item
  Format (comma separated value?).
\item
  Assumptions.
\item
  Frecuency of publication of the data.
\item
  Reference (for citation) of the data.
\item
  Contact name and organization of the data owner.
\item
  Data availability.
\end{itemize}

The data can be national, international or using surrogate. It is better
to use national data, then international and then surrogate. Surrogate
data is alternative data that have correlation with the data that are
replacing. However, surrogate data can be used when there is no better
information, or to obtain an initial estimate of an emission that can
help to prioritise resources. To use surrogate data, it is important to:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Confirm and document the physical relationship between emissions and
  the surrogate activity data.
\item
  Confirm and document a statistically significant correlation between
  emissions and the surrogate activity data.
\item
  Using regression analysis, develop a country-specific factor relating
  emissions to the surrogate data.
\end{enumerate}

** Maintain data supply **

\begin{itemize}
\tightlist
\item
  I can never get tired of saying this, but it is important to have good
  relatinships with the guys who give you data!
\item
  Try to see uncertainties in your data
\item
  Prepare scientific or statistical workshops of the inputs and outputs
  of the inventory.
\item
  Try specific contracts or agreements for regular data supply.
\item
  \emph{Annual/Regular} informal updates of the methods.
\item
  Stablish terms of reference what and when is needed the data for the
  inventory.
\end{itemize}

** Restricted data and confidentiality **

This is so tipycal! I've head so many times ``We can't give you de data
because it is not anonymous and we have no resources to process it for
you''. This is fine and OK to prevent innappropiate use of the data. I
belive that, if possible, arrange a meeting with the head of the
organization, then sign an agreemtn of confidentiality and process the
data yourself. It is important to explain them:

\begin{itemize}
\tightlist
\item
  Use of data.
\item
  Explain what data will be public
\item
  Explain why this data is important.
\item
  Offer cooperation.
\item
  Give credits.
\end{itemize}

An also, be polite and don't be late (and don't miss meetings!).

\subsubsection{Generate new data}\label{generate-new-data}

This is pretty cool. And also, opportunity of science! (and some
frustation, but once you have dont it good, IT IS FANTASTIC).

You might generate new data if you dont have or the existing data does
not work. This part must be done with people with expertice (experience
+ formal knowledge). Also, it is advisable to do this if the category is
\textbf{key} and there are no better options.

Data can be generated by doing measurements. This job must be done by
people with expertice accomplsihing rigosourse protocols. Measurements
are usually epensive, so it is good to make sure the measurements are
done in the correct way.

It is also possible to use models to generate data. For instance, the
output of travel models can be used for input of vehicular emissions.

\subsubsection{Adapt data to use it in
inventory}\label{adapt-data-to-use-it-in-inventory}

Whatever method you chose to get the data, you must ensure it cover your
needs. And it doesnt, you must get/create/transfomr your data and
information to have full fill your needs.

For instance, origin destination surveys (ODS) are made every 10 years
and some cities, such as São Paulo, Brazil. ODS provide many information
about trips characterization in a city and it is possible to generate
travel demand models. However, in order to generate emissions inventory,
this information must be transformed to represent the yearly variation.
In this particular case, I would recommend to calibrate the travle
demand with the yearly fuel sales of the city. This means, estimate
annual fuel consumption with travel demand outputs and compare with
automotive fuel sales for the same city, scaling the traffic flow to
match the fuel sales of a respective year.

The expert judgement about methods, inputs and outputs can be also used
to fill the gaps about information. The goal might be: choise of method
and values of parameters, appropiate mix of technology, the most
appropiate activity data, or the most appropiate way to apply a
methodology.

\subsubsection{Emission factors and measured
emissions}\label{emission-factors-and-measured-emissions}

It is good practice follo these steps: set priorities, get a strategy
for accesing the data and collect and process the data.

It is ok to rely on literature for developing an emissions inventory.
However, it is a good practice for countries to useown, peer-reviewed
publised literature. As mentioned before, good examples for emission
factors include

\begin{itemize}
\tightlist
\item
  EMEP/EEA air pollutant emission inventory guidebook 2009
  \citep{copert}.
\item
  2006 IPCC guidelines for national greenhouse gas inventories
  \citep{change20062006}.
\item
  U.S. EPA: AP42 Compilation of emission factors \citep{paved}.
\item
  International emission factors databases OECD.
\end{itemize}

\subsubsection{Activity data}\label{activity-data}

National Statistics Centers generally provide good information about
activity data. However, nowdays the data is on the cloud. For instance,
more and more GPS devices are available with information that can be
used to produce activity. This also provides a good opportunity to do
research.

\subsection{Time Series conistency}\label{time-series-conistency}

It was already mentiones some aspects regarind consistency in time
series in activity and emission factors. This section convers
consistency of times series of emissions.

The recalculation of timeseries of emissions can be done in several
cases: available data have changed, change in method, a category has
become key, adding new categories, the capacity for the inventory has
increased, new methods are available and correction of errors.

Another case is when it is necessary to track increased or decreases in
emissions due to a technological change and other factors. For instance,
track the impacts of new emissions standards.

** Splicing techniques **

These techniques refers to the combining or joining of ore than one
method to form a complete time series.

\begin{enumerate}
\def\labelenumi{\alph{enumi})}
\tightlist
\item
  \emph{Overlap}: Sometimes, a new method for estimating arrive and this
  method cannot be used to estimate emissions of the initial periodof
  time because the required data was not available. \emph{Overlap} is
  the period of time when the estimation can be done with newer and
  older methods. Based on the relationships of both estimates, a
  proportion of emissions of the new method can be atributed to the
  period of time where it cannot be directly used. The emissions
  associated with the new methods are estimated according the Eq.
  (\eqref{eq:overlap}):
\end{enumerate}

\begin{equation}
y_0=x_0 \cdot \left(\frac{1}{n - m + 1} \cdot \sum_{i=m}^{n} \frac{y_i}{x_i} )\right
)
\label{eq:overlap}
\end{equation}

Where, \(y_0\) is the recalculated emissions estimated using overlap
method. \(x_0\) is the estimate with the older method. \(x_i\) and
\(y_i\) are the estimates using older and newer method duringthe overlap
period of years \(m\).

Plots of the estimates with both methods must be made to check
consistency.

This technique is used when a new method is introduced but data are not
available to apply to the new methods to the early years in the time
series, for instance, with a higher Tier. In this case, it is possible
to develop a time series based on the relationship (or \textbf{overlap})
observed between the two methods during the years when both can be used.
Obviusly, the assumption is that there is a consisten relationship
between both results. The estimates for those years when the new method
cannot be be used are developed by proportionally adjusting the previous
estimates, based on the relationship observed during the period of
overlap.

\begin{enumerate}
\def\labelenumi{\alph{enumi})}
\setcounter{enumi}{1}
\tightlist
\item
  \emph{Surrogate data}: It relates emissions to tunderlying activity or
  oder indicative of data. Changes in these data are used to simulate
  the trend in emissions. For instance, vehicular emissions may be
  related to the trends in vehicle distances travelled, emissions from
  domestic wastewater may be related to population ad industrial
  emissions to the production levels. This can be expressed as shown in
  Eq. (\eqref{eq:surro}):
\end{enumerate}

\begin{equation}
y_0=y_t \cdot \left ( \frac{s_0}{s_t}) \right)
\label{eq:surro}
\end{equation}

Where, \(y\) is the emissions estimate in years \(0\) and \(t\). \(s\)
is the surrogate statistical parameter in years \(0\) and \(t\). A
mentioned example ins \citet{guia} consists in relationship between
measured methane emissions in coal minning and coal production. If the
measured emissions are not available, a relationship can be used with
coal production.

\begin{enumerate}
\def\labelenumi{\alph{enumi})}
\setcounter{enumi}{2}
\item
  \emph{Interpolation}: It referes to directly interpolate emissions
  when it is not possible to apply a surrogate technique. For instance,
  if an emissiones time series is missing years and the tendency is
  clear and stable, then it would be possible to apply an annual
  increment of emissions to interpolate in the missing years.
\item
  \emph{Trend extrapolation}: This technique is similar to
  interpolation, but less is known about the actual trend. This
  technique can be used forward or backward in time. The assumption is
  that the observed trend remains constant. It is not recommended if
  change in trend is not constant over time and using extrapolations
  based on surrogate data is recommended.
\end{enumerate}

It is recommended expert judgement for chossing the appropiate
technique.

\subsection{Uncertainty}\label{uncertainty}

This is a very important part in any emissions inventory and a dedicated
chapter or section should be made in any report/paper.

The 2006 IPCC Guidelines chapter states that estimating the uncertainty
of an inventory is needed \citep{change20062006}. Some approaches
includes:

\begin{itemize}
\tightlist
\item
  A method of determining uncertainties in individual terms in the
  inventory.
\item
  A method of aggregating theuncertainties of individual terms to the
  total inventory. A method of determine the significance of
  year-to-year differences and long-term trends in the inventories
  taking into account uncertainty information.
\item
  An understainding of the likely uses for this information which
  include indentifying areas requiring further research and observations
  and quanitfying the significance of year-to-year and longer-term
  changesin inventories.
\item
  An understanding that other uncertainties mayexist, such as those
  arising from inaccurate definitions that cannot be addresses by
  statistical means.
\end{itemize}

\subsubsection{How to express
uncertainty}\label{how-to-express-uncertainty}

It is recommended to use 95\% confidence interval. This means that:

\begin{itemize}
\tightlist
\item
  there is a 95\% of probability that the actual value of the quantity
  estimates is within the interval defined by the confidence interval.
\item
  The probability of the actual value will be outside the range it is
  5\%.
\end{itemize}

\subsubsection{Calculating incertaity}\label{calculating-incertaity}

The general form for estimating emissions was shown on Eq.
(\eqref{eq:efGERAL}. This equation will be the base for calculating the
uncertainty. This section is applied when measurements are made, for the
case of activity or emission factors. In those case, it is possible
calculate the required confiden intervals.

\subsubsection{Default uncertainty
ranges}\label{default-uncertainty-ranges}

\emph{Activity data}

The following table is taking directly form \citet{guia} and propose
indicative ranges that could be applies in cases where no independed
data are available.

\begin{table}

\caption{\label{tab:taunc}Indicative errros ranges (McGlade and Vidic 2009)}
\centering
\begin{tabular}[t]{l|l|l}
\hline
Source & Error\_Range & Remarks\\
\hline
National official statistics & - & Assumed as 'fixed' data with no uncertainty\\
\hline
Update of last year's statistics using Gross Economic Growth factors & 0 - 2\% & Reasonable\\
\hline
International Energy Agency (IEA) statistics & OECD: 2-3\%, non-OECD 5-10\% & Educatd guess for non-OECD\\
\hline
United Nation data bases & 5-10\% & Similar with IEA\\
\hline
Default values, other sources & 30-100\% & -\\
\hline
\end{tabular}
\end{table}

\emph{Emission factors}

The following table is taking directly form \citet{guia} and propose
rating definitions for emission factors.

\begin{table}

\caption{\label{tab:tarange}Rating definitions (McGlade and Vidic 2009)}
\centering
\begin{tabular}[t]{l|l|l}
\hline
Rating & Definition & Range\_Error\\
\hline
A & An estimate based on a large number of measurements made at a large number of facilities that fully represent the sector & 10 o 30 \%\\
\hline
B & An estimate based on a large number of measurements made at a large number of facilities that represent a large part of the sector & 20 to 60\%\\
\hline
C & An estimate based on a number of measurements made at a small number of representative facilities, or an engineering judgement based on a number of relevant facts & 50 to 200 \%\\
\hline
D & An estimate based on single measurements, or an engineering calculation derived from a number of relevant facts & 100 to 300\%\\
\hline
E & An estimate based on an engineering calculation derived from assumptions only & Order of magnitude\\
\hline
\end{tabular}
\end{table}

In \citet{guia} appears ratings for road transport emission factors that
diffier from the ratings that appear in
\citet{NtziachristosSamaras2016}. To preserve consistenty in this book,
here is presented the precision indicators, Table 4-1 of
\citet{NtziachristosSamaras2016} report.

\begin{table}

\caption{\label{tab:precision}Precision indicators (Ntziachristos and Samaras 2016)}
\centering
\begin{tabular}[t]{l|l|l|l|l|l|l|l|l}
\hline
Category & NOx & CO & NMHC & CH4 & PM & N2O & NH3 & CO2\\
\hline
PC G w/o Catalyst & A & A & A & A & - & C & C & A\\
\hline
PC G w Catalyst & A & A & A & A & - & A & A & A\\
\hline
PC D & A & A & A & A & A & B & B & A\\
\hline
PC LPG & A & A & A & - & - & - & - & A\\
\hline
PC LPG w/o Catalyst & A & A & A & A & D & C & C & A\\
\hline
PC LPG w Catalyst & D & D & D & D & D & D & D & A\\
\hline
PC 2 strokes & B & B & B & D & - & D & D & B\\
\hline
LCV G & B & B & B & C & - & B & B & A\\
\hline
LCV D & B & B & B & C & A & B & B & A\\
\hline
HDV G & D & D & D & C & - & D & D & D\\
\hline
HDV D & A & A & A & D & A & B & B & A\\
\hline
MC cc <50 & A & A & A & B & - & B & B & A\\
\hline
MC cc > 50 2 strokes & A & A & A & B & - & B & B & A\\
\hline
MC cc > 50 4 strokes & A & A & A & B & - & B & B & A\\
\hline
Cold-start PC G Pre Euro & B & B & B & - & - & - & - & B\\
\hline
Cold-start PC G Euros & B & B & B & A & - & - & - & A\\
\hline
Cold-start PC D Pre Euro & C & C & C & - & C & - & - & B\\
\hline
Cold-start PC D Euros & A & A & A & A & A & - & - & A\\
\hline
Cold-start PC LPG & C & C & C & - & - & - & - & B\\
\hline
Cold-start LCV G & D & D & D & - & - & - & - & D\\
\hline
Cold-start LCV F & D & D & D & - & D & - & - & D\\
\hline
\end{tabular}
\end{table}

\subsubsection{Aggregting uncertainties}\label{aggregting-uncertainties}

There are two rules:

\begin{itemize}
\tightlist
\item
  \emph{Rule A}: where uncertain quantities re to be combined by
  \emph{addition}, the standard deviation of the sum will be the square
  root of the sum of the squares of the standard deviations of the
  quantities that are added with the standard deviations all expressed
  in absolute terms, as shown in Eq. \eqref{eq:sumun}:
\end{itemize}

\begin{equation}
U_{total}=\frac{\sqrt{\sum_{i = 1}^{n}(U_i \cdot x_i )^2}}{\sum_{i = 1}^{n}}
\label{eq:sumun}
\end{equation}

Where, \(x\) are the quantities, \(U_i\) are the uncertain quantities
andthe percentage uncertainties (half the 95\% confidence interval)
associated with them, respectivly. \(U_{total}\) is the percentage
uncertinti in the sum of the quantites (half the 95\% confidence
interval divided by the total (i.e.~mean) and expressed as percentage).

\begin{itemize}
\tightlist
\item
  \emph{Rule B}: where uncertain quantities re to be combined by
  \emph{multiplication}, the same as Rule A, except that the standard
  deviations must all be expressed as fractions of the appropiate mean
  values (the rule is appriximate for all random variables), as shown on
  Eq. \eqref{eq:sumul}:
\end{itemize}

\begin{equation}
U_{total}=\sqrt{\sum_{i = 1}^{n}(U_i)^2} 
\label{eq:sumul}
\end{equation}

Where, \(U_i\) are th epercentage quantities (hal the 95\% confidence
interval) associated with each of the quantities. \(U_{total}\) is the
percentage in the product of the quantities (halp the 95\% confidence
itnerval divided by the total and expressed as percentage).

Alternatively, a Monte Carlo simulation can be made.

\subsection{Quality Assurance and Quality
Check}\label{quality-assurance-and-quality-check}

\section{Avoiding errors with VEIN}\label{avoiding-errors-with-vein}

\subsection{Back to basics}\label{back-to-basics}

\subsection{Traffic flow}\label{traffic-flow}

\subsection{Vehicular composition}\label{vehicular-composition-2}

The vehicular composition is a critical part in the emissions
inventories for vehicles. It consists in the oercetnage of each type of
vehicle and technology. Despite that it seems pretty straightforward,
making a mistake in this part of the emissions inventories would have
catastrophic time cost. \textbf{Developing an} \textbf{emissions
inventory is a tricky task, you must take great care in simple}
\textbf{calculations, because if the resutls are not consistent, it can
take LOTS} \textbf{of time, to find the error, usually when the dealines
are dead}.

A few days ago, I was developing an inventory in which my team was
evaluating the impact in air quality of a new type of fuel on the city
of São Paulo. The results were consistens comparison base case with
scenario, however, the magnitud of the emissions are too high. The air
quality simulatiosn showed a clear bias, constant over time. I struggled
to find any error without results. Until I remembered the words of my
former boss \emph{Plot the results, becaust it is} \emph{easier fo find
any mistake}. I did it, and this is was the result.

\subsection{Length}\label{length}

\subsubsection{Are units OK?}\label{are-units-ok}

\subsection{Emission factors}\label{emission-factors-1}

\subsection{Deterioration}\label{deterioration}

\subsection{Fuel evaluation}\label{fuel-evaluation}

\subsection{Emissions estimation}\label{emissions-estimation}

\subsection{Post estimation}\label{post-estimation}

\subsection{Inputs for air quality
models}\label{inputs-for-air-quality-models}

\appendix \addcontentsline{toc}{chapter}{\appendixname}


\chapter*{Appendix A: Fast Example of
VEIN}\label{appendix-a-fast-example-of-vein}


\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{library}\NormalTok{(vein)}
\KeywordTok{inventory}\NormalTok{(}\DataTypeTok{name =} \KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{),}
          \DataTypeTok{show.dir =} \OtherTok{TRUE}\NormalTok{,}
          \DataTypeTok{show.scripts =} \OtherTok{TRUE}\NormalTok{)}
\KeywordTok{source}\NormalTok{(}\KeywordTok{paste0}\NormalTok{(}\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{), }\StringTok{"main.R"}\NormalTok{))}
\end{Highlighting}
\end{Shaded}

\chapter*{Appendix B: Detailed Example of
VEIN}\label{appendix-b-detailed-example-of-vein}


\section{Network}\label{network-1}

\begin{Shaded}
\begin{Highlighting}[]
\KeywordTok{inventory}\NormalTok{(}\KeywordTok{file.path}\NormalTok{(}\KeywordTok{tempdir}\NormalTok{(), }\StringTok{"YourCity"}\NormalTok{))}
\KeywordTok{setwd}\NormalTok{(}\StringTok{"YourCity"}\NormalTok{)}
\KeywordTok{data}\NormalTok{(net) }\CommentTok{# Loads the traffic demand simulation for west São Paulo}
\KeywordTok{data}\NormalTok{(net)}
\KeywordTok{data}\NormalTok{(pc_profile)}
\NormalTok{pc_week <-}\StringTok{ }\KeywordTok{temp_fact}\NormalTok{(net}\OperatorTok{$}\NormalTok{ldv}\OperatorTok{+}\NormalTok{net}\OperatorTok{$}\NormalTok{hdv, pc_profile)}
\NormalTok{speed <-}\StringTok{ }\KeywordTok{netspeed}\NormalTok{(pc_week, net}\OperatorTok{$}\NormalTok{ps, net}\OperatorTok{$}\NormalTok{ffs, net}\OperatorTok{$}\NormalTok{capacity, net}\OperatorTok{$}\NormalTok{lkm)}
\KeywordTok{saveRDS}\NormalTok{(net, }\DataTypeTok{file =} \StringTok{"network/net.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(speed, }\DataTypeTok{file =} \StringTok{"network/speed.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\section{\texorpdfstring{Vehicular composition
\citep{CETESB2015}}{Vehicular composition {[}@CETESB2015{]}}}\label{vehicular-composition-cetesb2015}

The vehicular composition \texttt{vehcomp} has 5 types of vehicles:
Passenger Carss (PC), Light Commercial Vehicles (LCV), Heavy Good
Vehicles or trucks (HGV), Buses (BUS), and Motorcycles (MC). The default
value of this argument is:
\texttt{c(PC\ =\ 4,\ LCV\ =\ 5,\ HGV\ =\ 5,\ BUS\ =\ 3,\ MC\ =\ 9)},
which means that there are 4 types of PC, 5 of LCV, 5 of trucks, 3 of
buses and 9 types of motorcycles. This composition comes from the
vehicular emissions inventory of the Environmental Agency of São Paulo,
Brazil \citep{CETESB2015}. In Brazil, the fuel used in vehicles is
blended with ethanol with and biodiesel. The user can use \textbf{any}
vehicular composition that representes its fleet with up-to 99 type of
vehicles per category. The default composition according Brazilian
emissions inventory is:

\emph{Passenger Cars (PC)}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Passenger Cars using Gasoline blended with 25\% of ethanol (E25).
\item
  Passenger Cars with Flex engine using Gasoline blended with 25\% of
  ethanol (FE25).
\item
  Passenger Cars with Flex engine using 100\% of ethanol (FE100).
\item
  Passenger Cars with engines that uses only ethanol (E100).
\end{enumerate}

\emph{Light Commercial Vehicles (LCV)}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Light Commercial Vehicles with gross weight (GW) less than 3.8 t using
  Gasoline blended with 25\% of ethanol (E25).
\item
  Light Commercial Vehicles with GW less than 3.8 t with Flex engine
  using Gasoline blended with 25\% of ethanol (FE25).
\item
  Light Commercial Vehicles with GW less than 3.8 t with Flex engine
  using 100\% of ethanol (FE100).
\item
  Light Commercial Vehicles with GW less than 3.8 t with engines that
  uses only ethanol (E100).
\item
  Light Commercial Vehicles with GW less than 3.8 t with engines that
  uses only ethanol (E100).
\end{enumerate}

\emph{Heavy Good Vehicles (HGV) or Trucks}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Semi Light Trucks (SLT) with 3.8 t \textless{} GW \textless{} 6 t
  using Diesel blended with 5\% of biodiesel (B5).
\item
  Light Trucks (LT) with 6 t \textless{} GW \textless{} 10 t using
  Diesel blended with 5\% of biodiesel (B5).
\item
  Medium Trucks (MT) with 10 t \textless{} GW \textless{} 15 t using
  Diesel blended with 5\% of biodiesel (B5).
\item
  Semi Heavy Trucks (SHT) with 15 t \textless{} GW on a Rigid Truck (RT)
  and GW \textless{} 40 on an Articulated Truck (AT) using Diesel
  blended with 5\% of bodiesel (B5).
\item
  Heavy Trucks (HT) with 15 t \textless{} GW on a RT and GW
  \textgreater{}= 40 on an AT using Diesel blended with 5\% of bodiesel
  (B5).
\end{enumerate}

\emph{Buses}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Urban Bus (UB) using Diesel blended with 5\% of biodiesel (B5).
\item
  Small Urban Bus (SUB) using Diesel blended with 5\% of biodiesel (B5).
\item
  Motorway Bus (MB) or Coach using Diesel blended with 5\% of biodiesel
  (B5).
\end{enumerate}

\emph{Motorcycles (MC)}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Motorcycles with engine cc \textless{} 150 using Gasoline blended with
  25\% of ethanol (E25).
\item
  Motorcycles with engine 150 \textless{} cc \textless{} 500 using
  Gasoline blended with 25\% of ethanol (E25).
\item
  Motorcycles with engine cc \textgreater{} 500 using Gasoline blended
  with 25\% of ethanol (E25).
\item
  Motorcycles with engine cc \textless{} 150 with Flex engine using
  Gasoline blended with 25\% of ethanol (FE25).
\item
  Motorcycles with engine 150 \textless{} cc \textless{} 500 with Flex
  engine using Gasoline blended with 25\% of ethanol (FE25).
\item
  Motorcycles with engine cc \textgreater{} 500 with Flex engine using
  Gasoline blended with 25\% of ethanol (FE25).
\item
  Motorcycles with engine cc \textless{} 150 with Flex engine using
  100\% of ethanol (FE100).
\item
  Motorcycles with engine 150 \textless{} cc \textless{} 500 with Flex
  engine using 100\% of ethanol (FE100).
\item
  Motorcycles with engine cc \textgreater{} 500 with Flex engine using
  100\% of ethanol (FE100).
\end{enumerate}

\section{Traffic data}\label{traffic-data}

The vehicular composition will split the traffic simulation as shown on
next table. The simulation has traffic for Light Duty Vehicles and Heavy
Good Vehicles, therefore, the vehicular composition must split this
categories and considerations:

\begin{itemize}
\tightlist
\item
  Passenger Cars (PC) = 75\% of Light Duty Vehicles (LDV) of traffic
  simulation.
\item
  Light Commercial Vehicles (LCV) = 10\% of LDV of traffic simulation.
\item
  Motorcycles (MC) = 15\% of LDV on traffic simulation.
\item
  Heavy Good Vehicles (HGV) = 75\% of Heavy Duty Vehicles (HDV).
\item
  Buses = = 25\% of Heavy Duty Vehicles (HDV).
\item
  LCV and PC have vehicles with flex engines capable to run with any
  mixture of gasoline and ethanol \citep{2005-01-4130}.
\item
  Type of fuel consumed consists in gasoline blended with 25\% of
  ethanol (E25).
\item
  The vehicular composition consists in type of vehicles and type of
  fuel. Optionally, the user could have a wider vehicular composition
  considering more sizes, gross weight, etc.
\item
  The percentages in composition apply for each type of vehicle PC, LCV,
  HGV, BUS and MC.
\end{itemize}

\textbf{Passenger Cars}

The vehicular composition consisted in PC using E25, PC with Flex
engines using E25 and E100, and OC with engines for consuming only E100.
The PC with Flex engines entered into Brazilian market in 2003,
therefore, at year 2015 flex vehicles has 13 years of use. On the other
case, PC with engines for E100 went out of the market in 2007,
therefore, the newest PC with E100 engine has 9 years of use.

\begin{table}

\caption{\label{tab:comppc}Vehicular composition of PC for applied in this book}
\centering
\begin{tabular}[t]{cc}
\toprule
Vehicles & Composition\\
\midrule
PC\_E25 & 37.25\%\\
PC\_FE25 & 22.26\%\\
PC\_FE100 & 37.97\%\\
PC\_E100 & 2.44\%\\
\bottomrule
\end{tabular}
\end{table}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{PC_E25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"PC_E25"}\NormalTok{,}
                  \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{37.25}\NormalTok{)}
\NormalTok{PC_FE25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"PC_FE25"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{13}\NormalTok{,}
                   \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{22.26}\NormalTok{)}
\NormalTok{PC_FE100 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"PC_FE100"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{13}\NormalTok{,}
                    \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{37.97}\NormalTok{)}
\NormalTok{PC_E100 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"PC_E100"}\NormalTok{, }\DataTypeTok{agemin =} \DecValTok{9}\NormalTok{,}
                   \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{2.44}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(PC_E25, }\DataTypeTok{file =} \StringTok{"veh/PC_E25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(PC_FE25, }\DataTypeTok{file =} \StringTok{"veh/PC_FE25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(PC_FE100, }\DataTypeTok{file =} \StringTok{"veh/PC_FE100.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(PC_E100, }\DataTypeTok{file =} \StringTok{"veh/PC_E100.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\textbf{Light Commercial Vehicles}

The engine/fuel type affects in the same way to LCV with PC vehicles.
However, this category has a fraction of vehicles being driven with
diesel. In Brazil, all diesle is blended with approximatly 5\% of
biodiesel, then it is named as B5. The categorization of the names in
LCV is similar with PC.

\begin{table}

\caption{\label{tab:complcv}Vehicular composition of LCV for applied in this book}
\centering
\begin{tabular}[t]{cc}
\toprule
Vehicles & Composition\\
\midrule
LCV\_E25 & 39.13\%\\
LCV\_FE25 & 15.21\%\\
LCV\_FE100 & 25.90\%\\
LCV\_E100 & 1.18\%\\
LCV\_B5 & 18.58\%\\
\bottomrule
\end{tabular}
\end{table}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{LCV_E25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"LCV_E25"}\NormalTok{,}
                   \DataTypeTok{k =} \DecValTok{10}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{39.13}\NormalTok{)}
\NormalTok{LCV_FE25 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"LCV_FE25"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{13}\NormalTok{,}
                    \DataTypeTok{k =} \DecValTok{10}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{15.21}\NormalTok{)}
\NormalTok{LCV_FE100 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"LCV_FE100"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{13}\NormalTok{,}
                     \DataTypeTok{k =} \DecValTok{10}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{25.90}\NormalTok{)}
\NormalTok{LCV_E100 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"LCV_E100"}\NormalTok{, }\DataTypeTok{agemin =} \DecValTok{9}\NormalTok{,}
                    \DataTypeTok{k =} \DecValTok{10}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{1.18}\NormalTok{)}
\NormalTok{LCV_B5 <-}\StringTok{ }\KeywordTok{age_ldv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"LCV_B5"}\NormalTok{,}
                  \DataTypeTok{k =} \DecValTok{10}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{18.58}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(LCV_E25, }\DataTypeTok{file =} \StringTok{"veh/LCV_E25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(LCV_FE25, }\DataTypeTok{file =} \StringTok{"veh/LCV_FE25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(LCV_FE100, }\DataTypeTok{file =} \StringTok{"veh/LCV_FE100.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(LCV_E100, }\DataTypeTok{file =} \StringTok{"veh/LCV_E100.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(LCV_B5, }\DataTypeTok{file =} \StringTok{"veh/LCV_B5.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\textbf{Heavy Good Vehicles}

HGV uses diesel which is blended with approximatly 5\% of biodiesel from
sugar- cane.

\begin{table}

\caption{\label{tab:comphgv}Vehicular composition of HGV for applied in this book}
\centering
\begin{tabular}[t]{cc}
\toprule
Vehicles & Composition\\
\midrule
SLT & 8.38\%\\
LT & 25.50\%\\
MT & 15.28\%\\
SHT & 24.98\%\\
HT & 25.85\%\\
\bottomrule
\end{tabular}
\end{table}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{HGV_SLT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv,}\DataTypeTok{name =} \StringTok{"HGV_SLT_B5"}\NormalTok{,}
                      \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{8.38}\NormalTok{)}
\NormalTok{HGV_LT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv, }\DataTypeTok{name =} \StringTok{"HGV_SLT_B5"}\NormalTok{,}
                     \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{25.50}\NormalTok{)}
\NormalTok{HGV_MT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv, }\DataTypeTok{name =} \StringTok{"HGV_SLT_B5"}\NormalTok{,}
                     \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{15.28}\NormalTok{)}
\NormalTok{HGV_SHT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv, }\DataTypeTok{name =} \StringTok{"HGV_SLT_B5"}\NormalTok{, }
                      \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{24.98}\NormalTok{)}
\NormalTok{HGV_HT_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{hdv, }\DataTypeTok{name =} \StringTok{"HGV_SLT_B5"}\NormalTok{,}
                     \DataTypeTok{k =} \DecValTok{75}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{25.85}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_LT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_SLT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_SLT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_LT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_MT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_MT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_SHT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_SHT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_HT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_HT_B5.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\textbf{Buses}

In Brazil there are many type of buses, but in this book we are
focussing in the most abundandt: Urban Buses, Small Urban Buses and
Motoreway Buses ro Coach. According to the Secretary or Urban Mobility
of São Paulo (Sptrans, \url{http://www.sptrans.com.br/}), the fleet has
an average age of use of 5 years and 5 monthts. To achieve this average
age, the agemax of this vehicles is 10 years of use.

\begin{table}

\caption{\label{tab:compbus}Vehicular composition of BUS for applied in this book}
\centering
\begin{tabular}[t]{cc}
\toprule
Vehicles & Composition\\
\midrule
UB & 77.43\%\\
SUB & 9.07\%\\
MB & 13.5\%\\
\bottomrule
\end{tabular}
\end{table}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{UB_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"UB_B5"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{10}\NormalTok{,}
                 \DataTypeTok{k =} \DecValTok{25}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{77.43}\NormalTok{)}
\NormalTok{SUB_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"SUB_B5"}\NormalTok{, }
                  \DataTypeTok{k =} \DecValTok{25}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{9.07}\NormalTok{)}
\NormalTok{MB_B5 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MB_B5"}\NormalTok{,}
                 \DataTypeTok{k =} \DecValTok{25}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{13.5}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_LT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_SLT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_SLT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_LT_B5.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(HGV_MT_B5, }\DataTypeTok{file =} \StringTok{"veh/HGV_MT_B5.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\textbf{Motorcycles}

The vehicular composition consisted in Motorcycles using E25, and
recnetly, in year 2010, entered into the market flex motorcycles, which
can use gasoline E25 or ethano E100. Therefore, the oldest flex MC is 6
years old.

\begin{table}

\caption{\label{tab:compmc}Vehicular composition of MC for applied in this book}
\centering
\begin{tabular}[t]{cc}
\toprule
Vehicles & Composition\\
\midrule
MC\_150\_E25 & 72.97\%\\
MC\_150\_500\_E25 & 11.28\%\\
MC\_500\_E25 & 3.15\%\\
MC\_150\_FE25 & 3.93\%\\
MC\_150\_500\_FE25 & 0.57\%\\
\addlinespace
MC\_500\_FE25 & 0.16\%\\
MC\_150\_FE100 & 6.69\%\\
MC\_150\_500\_FE100 & 0.98\%\\
MC\_500\_FE100 & 0.27\%\\
\bottomrule
\end{tabular}
\end{table}

\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{MC_}\DecValTok{150}\NormalTok{_E25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"MC_150_E25"}\NormalTok{,}
                      \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{72.97}\NormalTok{)}
\NormalTok{MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_E25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_150_500_E25"}\NormalTok{, }
                          \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{11.28}\NormalTok{)}
\NormalTok{MC_}\DecValTok{500}\NormalTok{_E25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_500_E25"}\NormalTok{,}
                      \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{3.15}\NormalTok{)}

\NormalTok{MC_}\DecValTok{150}\NormalTok{_FE25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"MC_150_FE25"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{,}
                       \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{3.93}\NormalTok{)}
\NormalTok{MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_FE25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_150_500_FE25"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{, }\DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{0.57}\NormalTok{)}
\NormalTok{MC_}\DecValTok{500}\NormalTok{_FE25 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_500_FE25"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{,}
                       \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{0.16}\NormalTok{)}

\NormalTok{MC_}\DecValTok{150}\NormalTok{_FE100 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv,}\DataTypeTok{name =} \StringTok{"MC_150_FE100"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{,}
                        \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{6.69}\NormalTok{)}
\NormalTok{MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_FE100 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_150_500_FE100"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{,}
                            \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{0.98}\NormalTok{)}
\NormalTok{MC_}\DecValTok{500}\NormalTok{_FE100 <-}\StringTok{ }\KeywordTok{age_hdv}\NormalTok{(}\DataTypeTok{x =}\NormalTok{ net}\OperatorTok{$}\NormalTok{ldv, }\DataTypeTok{name =} \StringTok{"MC_500_FE100"}\NormalTok{, }\DataTypeTok{agemax =} \DecValTok{6}\NormalTok{,}
                        \DataTypeTok{k =} \DecValTok{15}\OperatorTok{/}\DecValTok{100}\OperatorTok{*}\FloatTok{0.27}\NormalTok{)}

\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_E25, }\DataTypeTok{file =} \StringTok{"veh/MC_150_E25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_E25, }\DataTypeTok{file =} \StringTok{"veh/MC_150_500_E25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{500}\NormalTok{_E25, }\DataTypeTok{file =} \StringTok{"veh/MC_500_E25.rds"}\NormalTok{)}

\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_FE25, }\DataTypeTok{file =} \StringTok{"veh/MC_150_FE25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_FE25, }\DataTypeTok{file =} \StringTok{"veh/MC_150_500_FE25.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{500}\NormalTok{_FE25, }\DataTypeTok{file =} \StringTok{"veh/MC_500_FE25.rds"}\NormalTok{)}

\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_FE100, }\DataTypeTok{file =} \StringTok{"veh/MC_150_FE100.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{150}\NormalTok{_}\DecValTok{500}\NormalTok{_FE100, }\DataTypeTok{file =} \StringTok{"veh/MC_150_500_FE100.rds"}\NormalTok{)}
\KeywordTok{saveRDS}\NormalTok{(MC_}\DecValTok{500}\NormalTok{_FE100, }\DataTypeTok{file =} \StringTok{"veh/MC_500_FE100.rds"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}

\chapter*{Appendix B}\label{appendix-b}


\bibliography{book.bib,packages.bib}

\backmatter
\printindex

\end{document}
