成都市PM<sub>2.5</sub>浓度变化的影响因素交互作用研究

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1366 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

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 (PM_2.5), 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 PM_2.5 concentration of Chengdu.The results of single-factor GAMs showed that the relationship between PM_2.5 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 PM_2.5 concentration. Furthermore, effects of T and O₃ on PM_2.5 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 PM_2.5 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 PM_2.5 in Chengdu, which means this condition had a synergistic amplification effect on the formation of PM_2.5 concentration. For winter, the coexistence of low wind + high RH + high CO + high NO₂ + high SO₂ were beneficial to the formation of PM_2.5, which condition had a synergistic amplification effect on the formation of PM_2.5 concentration. It was found that GAMs model could not only be used to identify the dominant influencing factors of PM_2.5 pollution, but also quantitatively analyze the influence of single effect and interaction of influencing factors on the change of PM_2.5 concentration, which was great significance for local to prevent and control PM_2.5 pollution.

Key words： generalized additive models the change of PM_2.5 concentration influencing factors interactive effects

Received: 18 February 2021

PACS:

X513

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHANG Ying
	ZHANG Jie
	WANG Shi-gong
	KANG Ping
	ZHANG Jia-xi
	ZHANG Xiao-ling
	LI Yun-chao

Cite this article:

ZHANG Ying,ZHANG Jie,WANG Shi-gong等. Interactive effects of the influcening factors on the changes of PM_2.5 concentration[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(10): 4518-4528.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I10/4518

[1]	张小曳,孙俊英,王亚强,等.我国雾-霾成因及其治理的思考[J]. 科学通报, 2013,58(13):1178-1187.Zhang X Y, Sun J Y, Wang Y Q, et al. Factors contributing to haze and fog in China (in Chinese)[J]. Chinese Science Bulletin, 2013,58(13):1178-1187.
[2]	Ning G, Wang S, Ma M, et al. Characteristics of air pollution in different zones of Sichuan Basin, China. Science of the Total Environment, 2017,612:975-984.
[3]	刘长焕,邓雪娇,朱彬,等.近10年中国三大经济区太阳总辐射特征及其与O3、PM2.5的关系[J]. 中国环境科学, 2018,38(8):2820-2829.Liu C H, Deng X J, Zhu B, et al. Characteristics of GSR of China's three major economic regions in the past 10years and its relationship with O3and PM2.5[J]. China Environmental Science, 2018,38(8):2820-2829.
[4]	Liao T, Gui K, Jiang W, et al. 2018. Air stagnation and its impact on air quality during winter in Sichuan and Chongqing,southwestern China[J]. Science of The Total Environment, 635:576-585.
[5]	彭爽,康平,张小玲,等.成都市大气颗粒物粒径分布及其对能见度的影响[J]. 环境科学学报, 2020,40(12):4432-4441.Peng S, Kang P, Zhang X L, et al. The size distribution of atmospheric particles in Chengdu and its influence on visibility[J]. Journal of Environmental Science, 2020,40(12):4432-4441
[6]	杨柳,王式功,张莹.成都地区近3a空气污染物变化特征及降水对其影响[J]. 兰州大学学报(自然科学版), 2018,54(6):731-738.Yang L, Wang S G, Zhang Y. Variation characteristics of air pollutants in Chengdu area in recent three years and influence of precipitation on them[J]. Journal of Lanzhou University (NATURAL SCIENCE EDITION), 2018,54(6):731-738
[7]	Wang Y, Hopke P K, Xia X, et al. Source apportionment of airborne particulate matter using inorganic and organic species as tracers[J]. Atmospheric Environment, 2012,55(3):525-532.
[8]	洪也,张莹,马雁军,等.沈阳大气污染物与气象因素对呼吸疾病门诊数的影响[J]. 中国环境科学, 2020,40(9):4077-4090.Hong Y, Zhang Y, Ma Y J, et al. Effects of air pollutants and meteorological factors on the number of respiratory disease clinics in Shenyang[J]. China Environmental Science, 2020,40(9):4077-4090.
[9]	张莹,王式功,贾旭伟,等.气温与PM2.5协同作用对疾病急诊就诊人数的影响[J]. 中国环境科学, 2017,37(8):3175-3182.Zhang Y, Wang S G, Jia X W, et al. Temperature and PM2.5 the impact of synergy on the number of emergency patients[J]. China Environmental Science, 2017,37(8):3175-3182.
[10]	贺祥,林振山.基于GAM模型分析影响因素交互作用对PM2.5浓度变化的影响[J]. 环境科学, 2017,38(1):22-32.He X, Lin Z S. Analysis of influence factors interaction on PM2.5 concentration change based on gam model[J]. Environmental science, 2017,38(1):22-32.
[11]	Qian Z M, He Q C, Lin H M, et al. Association of daily cause specific mortality with ambient particle air pollution in Wuhan, China[J]. Environmental research, 2007,105(3):380-389.
[12]	Dominici F, Mcdermott A, Zeger S L, et al. On the use of generalized additive models in time-series studies of air pollution and health[J]. American Journal of Epidemiology, 2002,156(3):193-203.
[13]	Camalier L, Cox W, Dolwick P. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends[J]. Atmospheric Environment, 2007,41(33):7127-7137.
[14]	Wood S N, Pya N, S Fken B. Smoothing parameter and model selection for general smooth models[J]. Journal of the American Statistical Association, 2016,111(516):1548-1563.
[15]	Zhu Y H, Yang L X, Kawamura K, et al. Contributions and source identification of biogenic and anthropogenic hydrocarbons to secondary organic aerosols at Mt. Tai in 2014[J]. Environmental Pollution, 2017,220:863-872.
[16]	Zhang Y, Wang S, Fan X, et al. A temperature indicator for heavy air pollution risks (TIP)[J]. Science of The Total Environment, 2019,678.