Long-term exposure to PM (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 removal capacity of nature-based green infrastructure in urban areas. In this paper, the annual PM 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 removal capacity were detected Sen-MK test and local spatial autocorrelations analysis. Then the effects of landscape patterns and socioeconomic variables on PM removal capacity were explored based on linear regression model. The results illustrated that the PM removal capacity of UGI increased significantly from 2000 to 2019 in Shenyang, with the amount of PM removal, PM removal flux and removal rate increasing by 20.64 Mg/a, 0.0258 g/m/a, and 0.377 %/a, respectively. The PM removal capacity of UGI exhibited spatial heterogeneity in the study area. Specifically, the regions experiencing the increase in PM removal capacity of UGI accounted for majority of the old urban area of Shenyang City during the study period; the lower PM removal capacity clustered in the center urban area, in which high density impervious surfaces distributed, while the higher PM removal capacity mainly gathered in the area with large scale green space; PM 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 removal flux and removal rate, while the aggregation metrics had significantly negative correlations with the PM removal flux and removal rate. Moreover, it was also found that population density and GDP negatively affected the PM removal capacity of UGI. This study provides a methodological reference and some new insights for future urban landscape planning and air pollution purification.
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| http://dx.doi.org/10.1016/j.scitotenv.2023.161476 | DOI Listing |
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