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Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing

Journal of Environmental Sciences 2016年第10期28卷 161-168页

作者： Qian Yin Jinfeng Wang Maogui Hu hoting wong LREIS Institute of Geographic Sciences and Nature Resources Research Chinese Academy of Sciences Beijing 100101 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China

When investigating the impact of air pollution on health, particulate matter less than 2.5μm in aerodynamic diameter （PM2.5） is considered more harrnful than particulates of other sizes. Therefore, studies of PM2.5 have attracted more attention. Beijing, the capital of China, is notorious for its serious air pollution problem, an issue which has been of great concern to the residents, government, and related institutes for decades. However, in China, significantly less time has been devoted to observing PM2.5 than for PM10. Especially before 2013, the density of the PM2.5 ground observation network was relatively low, and the distribution of observation stations was uneven. One solution is to estimate PM2.5 concentrations from the existing data on PM10. In the present study, by analyzing the relationship between the concentrations of PM2.5 and PM10, and the meteorological conditions for each season in Beijing from 2008 to 2014, a U-shaped relationship was found between the daily maximum wind speed and the daily PM concentration, including both PM2.5 and PM10. That is, the relationship between wind speed and PM concentration is not a simple positive or negative correlation in these wind directions; their relationship has a complex effect, with higher PM at low and high wind than for moderate winds. Additionally, in contrast to previous studies, we found that the PM2.5/PM10 ratio is proportional to the mean relative humidity （MRH）. According to this relationship, for each season we established a multiple nonlinear regression （MNLR） model to estimate the PM2.5 concentrations of the missing periods.

关键词： PM10 concentrationPM2.5 concentration estimationWind speedWind direction

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