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

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摘要基于全国297个地级市2018年PM_2.5浓度数据、自然与社会经济数据，采用多尺度地理加权回归（MGWR）模型分析了各影响因素对PM_2.5浓度的作用尺度与影响效果的空间异质性.结果表明，MGWR模型适用于中国地级市PM_2.5浓度影响因素研究.在作用尺度上，人均GDP、技术支持水平作用尺度最大，其次是相对湿度、居民地比重、人口密度与风速，降水量、第二产业比重、植被覆盖状况、温度与能源消费强度作用尺度最为局限.在影响效果上，相对湿度、人口密度与居民地比重全部为正向作用；第二产业比重和能源消费强度主要为正向作用，分别占总样本的70.71%与64.98%；风速、温度既存在正向作用也存在负向作用，空间上呈两极分化，其中正向作用分别占总样本的49.83%与57.91%；降水量、植被覆盖状况主要为负向作用，分别占总样本的91.58%与69.70%；人均GDP、技术支持水平全部为负向作用.研究结果表明各因素对于中国城市PM_2.5浓度的影响均存在着不同程度的空间异质性.

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关键词 ： PM_2.5, 多尺度地理加权回归(MGWR)模型, 空间异质性, 中国

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

收稿日期: 2020-11-18

PACS:

X513

基金资助:国家自然科学基金资助项目（41271516）

通讯作者: 程先富,教授,xianfucheng@sina.com E-mail: xianfucheng@sina.com

作者简介: 周志凌(1996-),男,江苏南京人,安徽师范大学硕士研究生,主要从事大气污染研究.发表论文1篇.

引用本文:

周志凌, 程先富. 基于MGWR模型的中国城市PM_2.5影响因素空间异质性[J]. 中国环境科学, 2021, 41(6): 2552-2561. ZHOU Zhi-ling, CHENG Xian-fu. Spatial heterogeneity of influencing factors of PM_2.5 in Chinese cities based on MGWR model. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(6): 2552-2561.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2021/V41/I6/2552

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