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中国长三角地区空气质量指数的时空规律及其社会经济驱动因素。

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China.

机构信息

School of Public Administration, Zhejiang University of Technology, Hangzhou 310023, China.

Zhejiang Center of Public Opinion and Research, Hangzhou 310023, China.

出版信息

Int J Environ Res Public Health. 2022 Jul 25;19(15):9017. doi: 10.3390/ijerph19159017.

DOI:10.3390/ijerph19159017
PMID:35897387
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9331707/
Abstract

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the "high clustering club" and the "low clustering club" is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

摘要

空气污染对气候、生态环境和人类健康造成了负面影响,已成为当今世界面临的主要挑战。长三角地区是中国经济最发达、人口最集中的地区。识别和量化该地区空气质量的时空特征和影响机制,有助于制定有效的缓解政策。利用 2015 年至 2018 年长三角 39 个城市的空气质量指数(AQI)年度数据,细致揭示了 AQI 的时空规律。此外,使用地理加权回归(GWR)模型来确定不同社会经济变量对 AQI 水平影响的地理异质性。实证结果表明:(1)长三角城市群呈现出西北低、东南高的空气污染格局。验证了 AQI 水平分布的空间相关性。“高聚类俱乐部”和“低聚类俱乐部”的时空规律明显。(2)不同社会经济因素对 AQI 水平表现出明显的地理异质性影响。其中,交通基础设施的影响强度最大,开放程度的影响强度最小。(3)产业结构升级对改善西北空气质量状况的作用大于对东南的作用。交通基础设施对浙江省城市空气污染的影响明显高于对其他城市的影响。技术创新带来的空气质量改善从北到南逐渐减弱。随着城市规模的扩大,空气质量先恶化后改善的规律明显。最后,政府应推动重点产业升级,合理控制新增建设用地规模,加大培育本地绿色创新型企业力度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/287d6b834983/ijerph-19-09017-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/ccb1ffcde347/ijerph-19-09017-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/d5223b546e06/ijerph-19-09017-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/a521ac4933c3/ijerph-19-09017-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/f3199a6f723e/ijerph-19-09017-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/6288c4876e04/ijerph-19-09017-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/f813f1543ded/ijerph-19-09017-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/4b57add4bd4d/ijerph-19-09017-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/ba595b53127f/ijerph-19-09017-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/9152ceed3e0d/ijerph-19-09017-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/287d6b834983/ijerph-19-09017-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/ccb1ffcde347/ijerph-19-09017-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/d5223b546e06/ijerph-19-09017-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/a521ac4933c3/ijerph-19-09017-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/f3199a6f723e/ijerph-19-09017-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/6288c4876e04/ijerph-19-09017-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/f813f1543ded/ijerph-19-09017-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/4b57add4bd4d/ijerph-19-09017-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/ba595b53127f/ijerph-19-09017-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/9152ceed3e0d/ijerph-19-09017-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fa4/9331707/287d6b834983/ijerph-19-09017-g010.jpg

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