Field assessment of the effects of land-cover type and pattern on PM10 and PM2.5 concentrations in a microscale environment
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The microscale environment is a very important human-scale outdoor spatial unit. Aimed at investigating the effects of microscale land-cover type and pattern on levels of PM10 and PM2.5, we monitored PM10 and PM2.5 concentrations among different land-cover type and pattern sites through field measurements, during four seasons (December 2015 to November 2016) in Beijing, China. Differences of daily PM10 and PM2.5 concentrations among seven typical land-cover types, and correlations between daily two-sized PM levels and various microscale land-cover patterns as explained by landscape metrics were analyzed. Results show that concentrations of the two-sized particles had stable daytime and seasonal trends. During the four seasons, there were various differences in daily PM10 and PM2.5 levels among the seven land-cover types. Overall, bare soil always had the highest daily PM10 level, whereas high canopy density vegetation and water bodies had low levels. Maximum PM2.5 levels were always found in high canopy density vegetation. Moderate canopy density vegetation and water bodies had lower concentrations. Correlations between different landscape metrics and daily levels of two-sized PM varied by season. Metrics reflecting the dominance and distribution of land-cover classifications had closer relationships with particle concentrations in the microscale environment. The patterns of pavement along with low and moderate canopy density vegetation had a greater impact on PM10 level. The responses of PM2.5 level to patterns of building and low and moderate canopy density vegetation were sensitive. Reasonable design of land-cover structure would be conducive to ameliorate air particle concentrations in the microscale environment.
KeywordsMicroscale environment PM10 PM2.5 Land-cover type Land-cover pattern
We thank Yu Cao, Jing Han, Yu Cai, Rui Jing, Jia Guo, and Shimingyue Qi for their help with monitoring data processing.
This work was financially supported by the Special Fund for Beijing Common Construction Project.
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