Interactive effects of the influcening factors on the changes of PM2.5 concentration
ZHANG Ying1,2, ZHANG Jie1, WANG Shi-gong1, KANG Ping1, ZHANG Jia-xi1, ZHANG Xiao-ling1,3, LI Yun-chao1
1. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Institute of Meteorological Environment and Public Health, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China; 2. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; 3. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
Abstract：To explore the influence characteristics of the interaction effects between meteorological elements and ambient air pollutants on particulate matter with an aerodynamic less than 2.5 (PM2.5), daily air pollutants data and meteorological data during the same period from 2014 to 2020 in Chengdu were collected. Generalized Additive Models (GAMs) were adopted to explore the effects of different factors on PM2.5 concentration of Chengdu.The results of single-factor GAMs showed that the relationship between PM2.5 concentration and daily average temperature (T), relative humidity (RH), wind speed (Wind), precipitation (Prec), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) all were nonlinear no matter in the whole year or in winter. It was also found that CO, NO2, SO2, T, and Wind all had greater impact on PM2.5 concentration. Furthermore, effects of T and O3 on PM2.5 concentration in winter were weaker than that inwhole year. In the multi-factor GAMs, the combined effects of T, RH, SO2, NO2, O3 and CO had significant impacts on the change of PM2.5 concentration.For whole year, the adjusted judgment coefficient (R2) of the multi-factor gams model was 0.759 and the variance explanation rate was 76.42%. For winter, the adjusted R2 of gams model was 0.708 and the variance explanation rate was 72.2%. CO was the most important influencing factor no matter in whole year or in winter. In the interaction GAMs, for the whole year,it was found that the synergetic effect of moderate low T (around 7℃) + high RH + high concentration of CO + high concentration of NO2+ high concentration of SO2 were beneficial to the formation of PM2.5 in Chengdu, which means this condition had a synergistic amplification effect on the formation of PM2.5 concentration. For winter, the coexistence of low wind + high RH + high CO + high NO2 + high SO2 were beneficial to the formation of PM2.5, which condition had a synergistic amplification effect on the formation of PM2.5 concentration. It was found that GAMs model could not only be used to identify the dominant influencing factors of PM2.5 pollution, but also quantitatively analyze the influence of single effect and interaction of influencing factors on the change of PM2.5 concentration, which was great significance for local to prevent and control PM2.5 pollution.
张莹, 张婕, 王式功, 康平, 张家熙, 张小玲, 李运超. 成都市PM2.5浓度变化的影响因素交互作用研究[J]. 中国环境科学, 2021, 41(10): 4518-4528.
ZHANG Ying, ZHANG Jie, WANG Shi-gong, KANG Ping, ZHANG Jia-xi, ZHANG Xiao-ling, LI Yun-chao. Interactive effects of the influcening factors on the changes of PM2.5 concentration. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(10): 4518-4528.
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