Interactive effects of the influencing factors on the changes of PM2.5 concentration based on gam model

被引：25

作者：

He X. ^{[1
,2
,3
,4
,5
]}

Lin Z.-S. ^{[1
,3
,4
,5
]}

机构：

[1] College of Geography Science, Nanjing Normal University, Nanjing

[2] Institute of Tourism, Kaili University, Kaili

[3] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing

[4] State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, 210023, Jiangsu Province

[5] Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing

来源：

Lin, Zhen-Shan (linzhenshan@njnu.edu.cn) | 2017年 / Science Press卷 / 38期

关键词：

GAM model; Influencing factors; Interaction; Nanjing City; The change of PM[!sub]2.5[!/sub] concentration;

D O I：

10.13227/j.hjkx.201606061

中图分类号：

学科分类号：

摘要：

In this paper, the generalized additive model (GAM) was introduced to analyze the interactive effects of the influencing factors on the change of PM2.5 concentration during 2013-2015 in Nanjing city. The results showed as follows: PM2.5 and its influencing factors appeared to follow normal distribution. There were strong correlations among the influencing factors, especially among the temperature(TEM), pressure(PRS) and water vapor pressure(VAP). For the single influencing factor GAM models of PM2.5 concentration, all influencing factors passed the significance test. Moreover, the equation fitting degrees of SO2, CO, and NO2 were much higher. In the multiple influencing factors GAM models of PM2.5 concentration, the contribution of the SO2, CO, NO2, O3, precipitation (PRE), wind and relative humidity(RHU) to the change of PM2.5 concentration was 73.9% with significant impacts on the change of PM2.5 concentration. Based on the diagnostic analysis of the effect of multi factors on the change of PM2.5 concentration, there were linear relationship between PM2.5 and SO2, NO2 and wind, and non-linear relationship between PM2.5 and CO, O3, PRE and RHU. The GAM models, which considered the interaction of SO2 respectively with CO, PRE and RHU, the interaction of CO respectively with NO2, O3, PRE, Wind and RHU, and the interaction of NO2 respectively with Wind, PRE and RHU, all passed the significance test(P<0.01 or P<0.05). The interaction of SO2, CO and NO2 respectively with other factors such as meteorological factors had the most important influence on the change of PM2.5 concentration. At last, through the visualized three-dimensional map of the GAM models considering the interaction of the influencing factors on the PM2.5 concentration, the interactive effects of the influencing factors on PM2.5 concentration were quantitatively modeled. Our results demonstrated that GAM could be used to quantitatively analyze the interactive effect of the influencing factors on the change of PM2.5 concentration. Therefore, the research method is innovative and important for PM2.5 pollution and control. © 2017, Science Press. All right reserved.

引用

页码：22 / 32

页数：10

共 33 条

[1] Li L., An J.Y., Yan R.S., Source contribution analysis of the fine particles in Shanghai during a heavy haze episode in December, 2013 based on the particulate matter source apportionment technology, Environmental Science, 36, 10, pp. 3543-3553, (2015)
[2] Gehrig R., Buchmann B., Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data, Atmospheric Environment, 37, 19, pp. 2571-2580, (2003)
[3] Xue W.B., Fu F., Wang J.N., Et al., Numerical study on the characteristics of regional transport of PM2.5 in China, China Environmental Science, 34, 6, pp. 1361-1368, (2014)
[4] Sawant A.A., Na K., Zhu X.N., Et al., Chemical characterization of outdoor PM2.5 and gas-phase compounds in Mira Loma, California, Atmospheric Environment, 38, 33, pp. 5517-5528, (2004)
[5] Gu J.X., The contaminative characters of PM2.5 and method of haze grade estimation in Tianjin, (2010)
[6] Chen J., Gao Y., Zhang Y.P., Et al., Study on the diffusion model and forecast of PM2.5 pollution, Mathematics in Practice and Theory, 44, 15, pp. 16-27, (2014)
[7] Liu J., Yang P., Lv W.S., Et al., Prediction models of PM2.5 mass concentration based on meteorological factors, Journal of Shandong university (Engineering Science), 45, 6, pp. 76-83, (2015)
[8] Dockery D.W., Pope C.A., Xu X.P., Et al., An association between air pollution and mortality in six U.S. cities, New England Journal of Medicine, 329, 24, pp. 1753-1759, (1993)
[9] Yin Y.W., Cheng J.P., Duan Y.S., Et al., Correlation analysis between the PM2.5, PM10 which were taken in the hazy day and the number of outpatient about breathing sections, breathing sections of pediatrics in Shanghai, Environmental Sciences, 32, 7, pp. 1894-1898, (2011)
[10] Xie Y.B., Chen J., Li W., An Assessment of PM2.5 related health risks and impaired values of Beijing residents in a consecutive high-level exposure during heavy haze days, Environmental Science, 35, 1, pp. 1-8, (2014)

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