Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Point-surface fusion of station measurements and satellite observations
  for mapping PM2.5 distribution in China: methods and assessment   Fine particulate matter (PM2.5) is associated with adverse human health
effects, and China is currently suffering from serious PM2.5 pollution. To
obtain spatially continuous ground-level PM2.5 concentrations, several models
established by point-surface fusion of ground station and satellite
observations have been developed. However, how well do these models perform at
national scale in China? Is there space to improve the estimation accuracy of
PM2.5 concentration? The contribution of this study is threefold. Firstly,
taking advantage of the newly established national monitoring network, we
develop a national-scale generalized regression neural network (GRNN) model to
estimate PM2.5 concentrations. Secondly, different assessment experiments are
undertaken in time and space, to comprehensively evaluate and compare the
performance of the widely used models. Finally, to map the yearly and seasonal
mean distribution of PM2.5 concentrations in China, a pixel-based merging
strategy is proposed. The results indicate that the conventional models (linear
regression, multiple linear regression, and semi-empirical model) do not
perform well at national scale, with cross-validation R values of 0.488~0.552
and RMSEs of 30.80~31.51 {\mu}g/m3, respectively. In contrast, the more
advanced models (geographically weighted regression, back-propagation neural
network, and GRNN) have great advantages in PM2.5 estimation, with R values
ranging from 0.610 to 0.816 and RMSEs from 20.93 to 28.68 {\mu}g/m3,
respectively. In particular, the proposed GRNN model obtains the best
performance. Furthermore, the mapped PM2.5 distribution retrieved from 3-km
MODIS aerosol optical depth (AOD) products, agrees quite well with the station
measurements. The results also show that our study has the capacity to provide
reasonable information for the global monitoring of PM2.5 pollution in China.

24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
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The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
1
The categories of this paper are: cs