Identifying urban haze islands and extracting their spatial features. (August 2020)

• Record Type: Journal Article
• Authors: Zhu, Lei; Huang, Qingxu; Ren, Qiang; Yue, Huanbi; Jiao, Chentai; He, Chunyang
• Is Part Of: Ecological indicators. Volume 115(2020)
• Publication Date: 2020-08
• Subjects: PM2.5 -- Urban-rural gradient -- Modified sigmoid function -- Urban sustainability -- China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.106385
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: A new method was developed to identify urban haze islands. The new method can accurately and effectively delineate urban haze islands. Two-thirds of Chinese cities had urban haze islands. The new method can be used to depict other urban-rural gradients. Abstract: Air pollution has great impacts on both human health and economic development. The difference in air pollution concentrations between urban and rural areas (urban haze island) is a widespread phenomenon. However, a method that can effectively depict the features of this phenomenon remains lacking. This study developed a method based on the concentric ring approach and modified sigmoid function to identify urban haze islands and extract three features: the background value of urban haze islands (BUHI), the intensity of urban haze islands (IUHI) and the extent of urban haze islands (EUHI). Using this method, we analyzed the PM2.5 concentration data from 346 Chinese cities in 2016 and examined the spatial characteristics and driving factors of the three features of urban haze islands. The results showed that about two-thirds (218/346) of Chinese cities had a significant urban haze island phenomenon, with an average BUHI of 36.8 μg/m 3, an IUHI of 11.3 μg/m 3, and an EUHI of 7.7 km. The developed method can successfully capture the variations of PM2.5 concentration, and the RMSE of the fitting curve was 0.46 μg/m 3 . On the national scale, natural factors had a greater impact (with the standardized regression coefficients varying from −0.74 ~ 0.80) on the BUHI and IUHI than socioeconomic factors (-0.32 ~ 0.32), while socioeconomic factors had a greater impact (0.29 ~ 0.38) on the EUHI than natural factors (−0.23 ~ 0.17). This study proposes a new and simple method to identify urban haze islands with high efficiency and wide applicability. Our results can be helpful for understanding urban-rural environmental differences and providing guidance for policymakers.