Quantitative estimation of the PM2.5 removal capacity and influencing factors of urban green infrastructure

Kongming Li; Chunlin Li; Yuanman Hu; Zaiping Xiong; Yongheng Wang

doi:10.1016/j.scitotenv.2023.161476

Quantitative estimation of the PM_2.5 removal capacity and influencing factors of urban green infrastructure

Sci Total Environ. 2023 Apr 1:867:161476. doi: 10.1016/j.scitotenv.2023.161476. Epub 2023 Jan 10.

Authors

Kongming Li¹, Chunlin Li², Yuanman Hu³, Zaiping Xiong⁴, Yongheng Wang⁵

Affiliations

¹ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; College of the Environment and Ecology, Xiamen University, Xiamen 361102, China. Electronic address: likongming@stu.xmu.edu.cn.
² CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. Electronic address: lichunlin@iae.ac.cn.
³ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. Electronic address: huym@iae.ac.cn.
⁴ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. Electronic address: zaipingx@iae.ac.cn.
⁵ CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; College of Geography and Environment, Shandong Normal University, Jinan 250358, China.

PMID: 36634767
DOI: 10.1016/j.scitotenv.2023.161476

Abstract

Long-term exposure to PM_2.5 (fine particulate matter with an aerodynamic diameter <2.5 μm) could cause great harm to human health and sustainable development. It remains a challenge to estimate the long-term PM_2.5 removal capacity of nature-based green infrastructure in urban areas. In this paper, the annual PM_2.5 removal capacity of urban green infrastructure (UGI) from 2000 to 2019 in Shenyang was estimated based on the PM2.5 dry deposition model. The spatial heterogeneity of annual PM_2.5 removal capacity were detected Sen-MK test and local spatial autocorrelations analysis. Then the effects of landscape patterns and socioeconomic variables on PM_2.5 removal capacity were explored based on linear regression model. The results illustrated that the PM_2.5 removal capacity of UGI increased significantly from 2000 to 2019 in Shenyang, with the amount of PM_2.5 removal, PM_2.5 removal flux and removal rate increasing by 20.64 Mg/a, 0.0258 g/m²/a, and 0.377 %/a, respectively. The PM_2.5 removal capacity of UGI exhibited spatial heterogeneity in the study area. Specifically, the regions experiencing the increase in PM_2.5 removal capacity of UGI accounted for majority of the old urban area of Shenyang City during the study period; the lower PM_2.5 removal capacity clustered in the center urban area, in which high density impervious surfaces distributed, while the higher PM_2.5 removal capacity mainly gathered in the area with large scale green space; PM_2.5 removal capacity were significantly higher in urban functional zones with a high proportion of green spaces. The landscape metrics representing fragmentation and shape complexity positively affected the annual PM_2.5 removal flux and removal rate, while the aggregation metrics had significantly negative correlations with the PM_2.5 removal flux and removal rate. Moreover, it was also found that population density and GDP negatively affected the PM_2.5 removal capacity of UGI. This study provides a methodological reference and some new insights for future urban landscape planning and air pollution purification.

Keywords: PM(2.5); Removal capacity; Removal flux; Removal rate; Urban green infrastructure.