期刊
BUILDING AND ENVIRONMENT
卷 156, 期 -, 页码 33-45出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2019.03.007
关键词
Green space; Fine particulate matter; Neighborhood; Measured data
资金
- General program of Chinese National Natural Science Foundation [51778254]
- Key program of Chinese National Natural Science Foundation [51538004]
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.
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