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• W2921344994 abstract "Airborne particulate matter (PM) has been a major threat to air quality and public health in major cities in China for more than a decade. Green space has been deemed to be effective in mitigating PM pollution; however, few studies have examined its effectiveness at the neighborhood scale. In this study, the authors probe the contributions from different landscape components in the green space (i.e., tree, grass), as well as the spatial scale of planning on fine PM (PM2.5) concentrations in urban neighborhoods. PM2.5 data including 37 samples from five megacities were collected from the National Environmental Monitoring Centre in China. Results showed that, neighborhood green space greatly contributed to the spatial variation in PM2.5. The total green space coverage, tree coverage, and grass coverage were all negatively correlated with PM2.5 concentration (p < 0.05). The higher green space coverage the site had, the lower the daily mean, daily minimum, and daily maximum of PM2.5 concentration were there. Tree coverage, in particular, was effective in reducing the PM2.5 concentrations, and, more importantly, its effectiveness was more significant with the higher ambient PM2.5 level. According to the examination on the effect of spatial scale, the capability for a neighborhood green space to attenuate PM2.5 pollution would be vanished when its size smaller than 200 m, and would be maximized when its size within 400–500 m. These results will contribute to the evidence-based design and management of green space to mitigating urban PM pollution." @default.
• W2921344994 created "2019-03-22" @default.
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• W2921344994 date "2019-06-01" @default.
• W2921344994 modified "2023-10-16" @default.
• W2921344994 title "Effects of neighborhood green space on PM2.5 mitigation: Evidence from five megacities in China" @default.
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• W2921344994 doi "https://doi.org/10.1016/j.buildenv.2019.03.007" @default.
• W2921344994 hasPublicationYear "2019" @default.
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