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城市形态与空气质量之间的关系:基于新冠疫情期间中国五大城市群证据的重新审视

Relationships between urban form and air quality: A reconsideration based on evidence from China's five urban agglomerations during the COVID-19 pandemic.

作者信息

Sun Jianing, Zhou Tao, Wang Di

机构信息

School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China.

Research Center for Construction Economy and Management, Chongqing University, Chongqing 400044, China.

出版信息

Land use policy. 2022 Jul;118:106155. doi: 10.1016/j.landusepol.2022.106155. Epub 2022 Apr 15.

Abstract

The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O, NO, PM, PM, SO). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.

摘要

2019年冠状病毒病(COVID-19)的爆发导致城市活动广泛停滞,工业污染和交通污染显著减少。这影响了城市形态对空气质量的影响方式。本研究重新审视了疫情期间中国五个城市群中城市形态对空气质量的影响。采用随机森林算法对城市形态与空气质量的关系进行量化。城市形态通过城市规模、形状、破碎度、紧凑度和蔓延度来描述。空气质量通过空气质量指数(AQI)和六种污染物(一氧化碳、臭氧、一氧化氮、细颗粒物、可吸入颗粒物、二氧化硫)的浓度进行评估。结果表明,城市破碎度是影响空气质量和六种污染物浓度的最重要因素。此外,城市形态与空气质量的关系在不同城市群中有所不同。通过分析影响空气污染的极其重要的指标,京津冀地区的城市形态与空气质量关系较为复杂。在成渝地区和珠江三角洲地区,城市蔓延度和城市紧凑度分别是某些空气污染物的极其重要指标。此外,在中国中部地区和长江三角洲地区,城市形状在某些空气污染物方面排名第一。基于稳健性检验,随机森林模型的性能优于多元线性回归(MLR)模型和极端梯度提升(XGBoost)模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c184/9010237/302353a0485d/gr1_lrg.jpg

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