Influencing Factors of Long-term Variations on Gridded PM2.5 of Typical Regions in China Based on GAM Model

被引：4

作者：

Nan Y. ^{[1
]}

Zhang Q.-Q. ^{[2
]}

Zhang B.-H. ^{[1
]}

机构：

[1] National Meteorological Center, Beijing

[2] National Satellite Meteorological Center, Beijing

来源：

Huanjing Kexue/Environmental Science | 2020年 / 41卷 / 02期

关键词：

GAM model; Influencing factors; PM[!sub]2.5[!/sub; Spatio-temporal variations; Typical regions;

D O I：

10.13227/j.hjkx.201905090

中图分类号：

学科分类号：

摘要：

This study investigates annually-averaged surface PM2.5 concentrations with the spatial resolution of 0.01°×0.01° to explore spatio-temporal variations and influencing factors of annual PM2.5 over typical regions in China during the period of 1998-2016, applying the generalized additive model (GAM). Regionally-averaged PM2.5 concentrations of five typical regions are ranked from high to low as follows: East China (40.5 μg•m-3)>North China (37.4 μg•m-3)>South China (27.8 μg•m-3)>Northeast China (23.7 μg•m-3)>Sichuan Basin (22.4 μg•m-3). The PM2.5 over Northeast China showed a linear increasing trend, while in other regions, PM2.5 tended to increase from 1998 to 2007 and decrease after 2007. PM2.5 concentrations over typical regions were all stably distributed which clearly exhibited areas with high PM2.5 values. For the single influencing factor GAM model of PM2.5 concentration, all influencing factors passed the significance test. The most influential factors with regard to the variations in the PM2.5 concentration differed among typical regions. In the multiple-influencing-factors-GAM model of PM2.5 concentration, all factors exhibited a non-linear relationship with PM2.5, and they accounted for 87.5%-92% (average 89. 0%) of variations in the PM2.5 concentration, suggesting a good model fit. The most significant influencing factors on PM2.5 concentrations were YEAR and LON-LAT in all typical regions. Meteorological factors have different impacts on PM2.5 concentrations among the typical regions. The three most influential meteorological factors in the five typical regions ranked from high to low are as follows: tp>v10>ssr for Northeast China; temp>tp>msl for North China; temp>tp>ssr for East and Central China; temp>RH>blh for South China; tp>temp>u10 for the Sichuan Basin. Our results demonstrated that the GAM model could quantitatively analyze influencing factors in long-term variations of the regional PM2.5 concentration, which is important for the assessment of PM2.5 pollution. © 2020, Science Press. All right reserved.

引用

页码：499 / 509

页数：10

共 36 条

[1] Chowdhury S., Dey S., Smith K.R., Ambient PM2.5 exposure and expected premature mortality to 2100 in India under climate change scenarios, Nature Communications, 9, (2018)
[2] Zhang L., Li L., Zhang L.J., Et al., Spatial-temporal characteristics of the local PM2.5 pollution episodes in Shenzhen and analysis on background circulation of typical samples, Acta Scientiae Circumstantiae, 38, 1, pp. 41-51, (2018)
[3] Liu J.S., Gu Y., Ma S.S., Et al., Day-night differences and source apportionment of inorganic components of PM2.5 during summer-winter in Changzhou city, Environmental Science, 39, 3, pp. 980-989, (2018)
[4] Duan W.J., Lang J.L., Cheng S.Y., Et al., Air pollutant emission inventory from iron and steel industry in the Beijing-Tianjin-Hebei region and its impact on PM2.5 , Environmental Science, 39, 4, pp. 1445-1454, (2018)
[5] Yang W.T., Yao S.Q., Deng M., Et al., Spatio-temporal distribution characteristics of PM2.5 and spatio-temporal variation characteristics of the relationship between PM2.5 and PM10 in Beijing, Environmental Science, 39, 2, pp. 684-690, (2018)
[6] Lu Z., Wei W., Zhou Y., Et al., Cause and effect evaluation of PM2.5 during three red alerts in Beijing from 2015 to 2016, Environmental Science, 40, 1, pp. 1-10, (2019)
[7] Yu M.F., Zhu Y., Lin C.J., Et al., Effects of air pollution control measures on air quality improvement in Guangzhou, China, Journal of Environmental Management, 244, pp. 127-137, (2019)
[8] Shen L., Jacob D.J., Mickley L.J., Et al., Insignificant effect of climate change on winter haze pollution in Beijing, Atmospheric Chemistry and Physics, 18, 23, pp. 17489-17496, (2018)
[9] Hao J., Sun C., Guo X.Y., Et al., Simulation of the spatio-temporally resolved PM2.5 aerosol mass concentration over the inland plain of the Beijing-Tianjin-Hebei region, Environmental Science, 39, 4, pp. 1455-1465, (2018)
[10] Sahu S.K., Zhang H.L., Guo H., Et al., Health risk associated with potential source regions of PM2.5 in Indian cities, Air Quality, Atmosphere & Health, 12, 3, pp. 327-340, (2019)

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