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Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data
Authors
A Notario
A Saiz-Lopez
+83 more
APK Tai
AW Strawa
B Gao
BL MR Boys
BV Bhaskar
C Liousse
C Yang
C Zhang
CA Pope
CJ Paciorek
CQ LY Lin
DA Chu
DC Carslaw
DE Abbey
DK Deshmukh
DP Edwards
DYH Pui
F Costabile
F Yang
G Lin
G Wang
GX Li
Hong-Lei Yang
HS Bian
HS Kim
J Schwartz
J Tao
J Tian
J Wang
J Wang
JG Watson
JH Seinfeld
JJ Cao
Jun-Huan Peng
JY Xin
K Zhang
K Zhang
KF Ho
L Glasser
L Tao
LF Li
LWA Chen
M Khodeir
M Schaap
M Sorek-Hamer
ML Bell
N Kumar
P Glantz
P Gupta
Qian Sun
Qinghua Sun
R Federal
R Federal
RA Moyeed
RB Schlesinger
RBA Koelemeijer
RM Hoff
S Wood
SC Dogruparmak
SS Lim
W Song
X Chi
XF Hu
Y Huang
Y Liu
Y Liu
Y Liu
Y Wang
Yi-Rong Song
YL Sun
YM Guo
Yong-Ze Song
YP DJ Lu
YS Wang
Yuan Li
Z Hu
Z Li
Z Ma
Z Meng
Z Sun
Z Wang
ZW Yan
ZX Shen
Publication date
1 January 2015
Publisher
'Public Library of Science (PLoS)'
Doi
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on
PubMed
Abstract
© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5
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