基于MGWR模型的中国城市PM<sub>2.5</sub>影响因素空间异质性

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (916 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Based on the 2018 annual average PM_2.5 concentration data, natural and socio-economic data of 297 prefecture-level cities in China, the multi-scale geographically weighted regression (MGWR) model was used to analyze the spatial scale and the spatial heterogeneity of the influencing factors on PM_2.5 concentration. MGWR model was tested and analyzed suitable for studying influencing factors of PM_2.5 concentration in prefecture-level cities in China. In terms of the spatial scale, per capita GDP and technical support level had the largest effect scale, followed by relative humidity, residential land proportion, population density and wind speed. After that, precipitation, secondary industry proportion, vegetation cover, temperature and energy intensity had the most limited effect scale. In terms of the impact, relative humidity, population density and residential land proportion were all positive. Secondary industry proportion and energy intensity were mainly positive, accounting for 70.71% and 64.98% of the total sample respectively. Wind speed and temperature had both positive and negative effects, showing polarization in space. The positive effect account for 49.83% and 57.91% of the total samples, respectively. Precipitation and vegetation cover were mainly negative effects, accounting for 91.58% and 69.70% of the total samples respectively. Per capita GDP and technical support level were all negative effects. The results showed that the influence of various factors on PM_2.5 concentration in Chinese cities has varied spatial heterogeneity.

Key words： PM_2.5 multi-scale geographically weighted regression (MGWR) model spatial heterogeneity China

Received: 18 November 2020

PACS:

X513

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHOU Zhi-ling
	CHENG Xian-fu

Cite this article:

ZHOU Zhi-ling,CHENG Xian-fu. Spatial heterogeneity of influencing factors of PM_2.5 in Chinese cities based on MGWR model[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(6): 2552-2561.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I6/2552

[1]	杨新兴,冯丽华,尉鹏.大气颗粒物PM2.5及其危害[J]. 前沿科学, 2012,6(1):22-31. Yang X X, Feng L H, Wei P. Air particulate matter PM2.5 in Beijing and its harm[J]. Frontier Science, 2012,6(1):22-31.
[2]	Zhao X, Zhou W, Han L, et al. Spatiotemporal variation in PM2.5 concentrations and their relationship with socioeconomic factors in China's major cities[J]. Environment International, 2019,133:105145.
[3]	Wang J, Lu X, Yan Y, et al. Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis[J]. Science of the Total Environment, 2020,724:138134.
[4]	郑保利,梁流涛,李明明.1998~2016年中国地级以上城市PM2.5污染时空格局[J]. 中国环境科学, 2019,39(5):1909-1919. Zheng B L, Liang L T, Li M M. Analysis of temporal and spatial patterns of PM2.5 in prefecture-level cities of China from 1998 to 2016[J]. China Environmental Science, 2019,39(5):1909-1919.
[5]	刘曰庆,孙希华,孙宇祥,等.1998~2016年中国大气PM2.5污染浓度空间格局演化——基于339个城市的实证研究[J]. 长江流域资源与环境, 2020,29(5):1163-1173. Liu Y Q, Sun X H, Sun Y X, et al. Evolution of spatial pattern of atmospheric PM2.5 pollution concentration in China from 1998 to 2016:Based on an empirical study of 339 cities[J]. Resources and Environment in the Yangtze Basin, 2020,29(5):1163-1173.
[6]	Tian S, Liu Y, Wang J, et al. Chemical compositions and source analysis of PM2.5 during autumn and winter in a heavily polluted city in China[J]. Atmosphere, 2020,11(4):336.
[7]	Yu Y, He S, Wu X, et al. PM2.5 elements at an urban site in Yangtze River Delta, China:High time-resolved measurement and the application in source apportionment[J]. Environmental Pollution, 2019,253:1089-1099.
[8]	曾鹏,辛存林,于奭,等.典型西南工业城市春冬季PM2.5来源与潜在源区分析——以柳州市为例[J]. 中国环境科学, 2020,40(9):3781-3790. Zeng P, Xin C L, Yu S, et al. Analysis of PM2.5 sources and potential source areas of typical industrial city in spring and winter:A case study in Liuzhou[J]. China Environmental Science, 2020,40(9):3781-3790.
[9]	Huang Y, Deng T, Li Z, et al. Numerical simulations for the sources apportionment and control strategies of PM2.5 over Pearl River Delta, China, part I:Inventory and PM2.5 sources apportionment[J]. Science of the Total Environment, 2018,634:1631-1644.
[10]	张云伟,王晴茹,陈嘉,等.城市街谷内PM2.5浓度时空变化及影响因素分析[J]. 中国环境科学, 2016,36(10):2944-2949. Zhang Y W, Wang Q R, Chen J, et al. Temporal and spatial characteristics of PM2.5 concentration in urban street canyons and analysis on the affecting factors[J]. China Environmental Science, 2016,36(10):2944-2949.
[11]	Wang P, Cao J, Tie X, et al. Impact of meteorological parameters and gaseous pollutants on PM2.5 and PM10 mass concentrations during 2010 in Xian, China[J]. Aerosol and Air Quality Research, 2015,15(5):1844-1854.
[12]	Yang Q, Yuan Q, Li T, et al. The Relationships between PM2.5 and meteorological factors in China:Seasonal and regional variations[J]. Multidisciplinary Digital Publishing Institute, 2017,14(12):1510-1529.
[13]	何映月,张松林.2000~2016年中国PM2.5时空格局[J]. 中国环境科学, 2020,40(8):3284-3293. He Y Y, Zhang S L. Spatiotemporal pattern of PM2.5 from 2000 to 2016 in China[J]. China Environmental Science, 2020,40(8):3284-3293.
[14]	王嫣然,张学霞,赵静瑶,等.2013~2014年北京地区PM2.5时空分布规律及其与植被覆盖度关系的研究[J]. 生态环境学报, 2016,25(1):103-111. Wang Y R, Zhang X X, Zhao J Y, et al. Temporal and spatial distribution of PM2.5 and its relationship with vegetation coverage in Beijing during the period of 2013~2014[J]. Ecology and Environmental Sciences, 2016,25(1):103-111.
[15]	贺祥,林振山,刘会玉,等.基于灰色关联模型对江苏省PM2.5浓度影响因素的分析[J]. 地理学报, 2016,71(7):1119-1129. He X, Lin Z S, Liu H Y, et al. Analysis of the driving factors of PM2.5 in Jiangsu province based on grey correlation model[J]. Acta Geographica Sinica, 2016,71(7):1119-1129.
[16]	许珊,邹滨,宫俊霞.2001~2015年中国城镇化与PM2.5浓度时空关联特征[J]. 中国环境科学, 2019,39(2):469-477. Xu S, Zou B, Gong J X. Analysis of the spatial-temporal association between urbanization and PM2.5 concentration during 2001~2015 period in Mainland China[J]. China Environmental Science, 2019, 39(2):469-477.
[17]	Liu C, Henderson B H, Wang D, et al. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in city of Shanghai, China[J]. Science of the Total Environment, 2016, 565:607-615.
[18]	刘海猛,方创琳,黄解军,等.京津冀城市群大气污染的时空特征与影响因素解析[J]. 地理学报, 2018,73(1):177-191. Liu H M, Fang C L, Huang J J, et al. The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration[J]. Acta Geographica Sinica, 2018,73(1):177-191.
[19]	王少剑,高爽,陈静.基于GWR模型的中国城市雾霾污染影响因素的空间异质性研究[J]. 地理研究, 2020,39(3):651-668. Wang S J, Gao S, Chen J. Spatial heterogeneity of driving factors of urban haze pollution in China based on GWR model[J]. Geographical Research, 2020,39(3):651-668.
[20]	南洋,张倩倩,张碧辉.基于GAM模型分析中国典型区域网格化PM2.5长期变化影响因素[J]. 环境科学, 2020,41(2):499-509. Nan Y, Zhang Q Q, Zhang B H. Influencing factors of long-term variations on gridded PM2.5 of typical regions in China based on GAM model[J]. Environmental Science, 2020,41(2):499-509.
[21]	周敏丹,匡耀求,云国梁.基于地理探测器的广州市大气PM2.5浓度驱动因素分析[J]. 环境科学研究, 2020,33(2):271-279. Zhou M D, Kuang Y Q, Yun G L. Analysis of driving factors of atmospheric PM2.5 concentration in Guangzhou city based on geo-detector[J]. Research of Environmental Sciences, 2020,33(2):271-279.
[22]	李双成,蔡运龙.地理尺度转换若干问题的初步探讨[J]. 地理研究, 2005,24(1):11-18. Li S C, Cai Y L. Some scaling issues of geography[J]. Geographical Research, 2005,24(1):11-18.
[23]	Xie Y, Weng Q. World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery[J]. GIScience and Remote Sensing, 2015,53(2):265-282.
[24]	黄小刚,赵景波,曹军骥,等.长江经济带PM2.5分布格局演变及其影响因素[J]. 环境科学, 2020,41(3):1013-1024. Huang X G, Zhao J B, Cao J J, et al. Evolution of the distribution of PM2.5concentration in the Yangtze River economic belt and its influencing factors[J]. Environmental Science, 2020,41(3):1013-1024.
[25]	Marquardt D W. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation[J]. Technometrics, 1970,12(3):591-612.
[26]	沈体雁,于瀚辰,周麟,等.北京市二手住宅价格影响机制——基于多尺度地理加权回归模型(MGWR)的研究[J]. 经济地理, 2020, 40(3):75-83. Shen T Y, Yu H C, Zhou L, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression:Scale law of spatial heterogeneity[J]. Economic Geography, 2020,40(3):75-83.
[27]	Fotheringham A S, Yang W, Kang W. Multiscale geographically weighted regression (MGWR)[J]. Annals of the American Association of Geographers, 2017,107(6):1247-1265.
[28]	Fotheringham A S, Yue H, Li Z. Examining the influences of air quality in China's cities using multi-scale geographically weighted regression[J]. Transactions in GIS, 2019,23(6):1444-1464.
[29]	Oshan T M, Smith J P, Fotheringham A S. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression[J]. International Journal of Health Geographics, 2020, 19(1):11.
[30]	Yu H, Fotheringham A S, Li Z, et al. Inference in multiscale geographically weighted regression[J]. Geographical Analysis, 2019, 52(1):87-106.
[31]	张玉洽,杨迎春,李杰,等.东南亚生物质燃烧对我国春季PM2.5质量浓度影响的数值模拟[J]. 环境科学研究, 2016,29(7):952-962. Zhang Y Q, Yang Y C, Li J, et al. Modeling the impacts of biomass burning in Southeast Asia on PM2.5 over China in spring[J]. Research of Environmental Sciences, 2016,29(7):952-962.
[32]	张忠地,邵天杰,黄小刚,等.2017年京津冀地区PM2.5污染特征及潜在来源分析[J]. 环境工程, 2020,38(2):99-106,134. Zhang Z D, Shao T J, Huang X G, et al. Characteristics and potential sources of PM2.5 pollution in Beijing-Tianjin-Hebei region in 2017[J]. Environmental Engineering, 2020,38(2):99-106,134.
[33]	Miao Y, Liu S, Guo J, et al. Impacts of meteorological conditions on wintertime PM2.5 pollution in Taiyuan, North China[J]. Environmental Science and Pollution Research, 2018,25(22):21855-21866.
[34]	Cheng Y, He K, Du Z, et al. Humidity plays an important role in the PM2.5 pollution in Beijing[J]. Environmental Pollution, 2015,197:68-75.
[35]	屈超,陈婷婷,刘佳,等.中国典型城市PM2.5浓度时空演绎规律及影响因素分析[J]. 环境科学研究, 2019,32(7):1117-1125. Qu C, Chen T T, Liu J, et al. Spatio-temporal characteristics of PM2.5 and influence factors in typical cities of China[J]. Research of Environmental Sciences, 2019,32(7):1117-1125.
[36]	赵晨曦,王玉杰,王云琦,等.细颗粒物(PM2.5)与植被关系的研究综述[J]. 生态学杂志, 2013,32(8):2203-2210. Zhao C X, Wang Y J, Wang Y Q, et al. Interactions between fine particulate matter (PM2.5) and vegetation:A review[J]. Chinese Journal of Ecology, 2013,32(8):2203-2210.
[37]	丁俊菘,邓宇洋,汪青.中国环境库兹涅茨曲线再检验——基于1998~2016年255个地级市PM2.5数据的实证分析[J]. 干旱区资源与环境, 2020,34(8):1-8. Ding J S, Deng Y Y, Wang Q. Re-examination of Chinese environmental Kuznets curve——Based on PM2.5 data of 255cities[J]. Journal of Arid Land Resources and Environment, 2020,34(8):1-8.
[38]	刘炳杰,彭晓敏,李继红.基于LUR模型的中国PM2.5时空变化分析[J]. 环境科学, 2018,39(12):5296-5307. Liu B J, Peng X M, Li J H. Analysis of the temporal and spatial variation of PM2.5 in China based on the LUR model[J]. Environmental Science, 2018,39(12):5296-5307.
[39]	王振波,梁龙武,王旭静.中国城市群地区PM2.5时空演变格局及其影响因素[J]. 地理学报, 2019,74(12):2614-2630. Wang Z B, Liang L W, Wang X J. Spatio-temporal evolution patterns and influencing factors of PM2.5 in Chinese urban agglomerations[J]. Acta Geographica Sinica, 2019,74(12):2614-2630.