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Shuang Liu, Geography
Faculty Mentors: Professor Tao Tang, Geography and Planning and Professor Wenji Zhao, Capital Normal University
The North China Plain has been suffering from severe PM2.5 pollution in recent years. A series of measures have been taken to improve atmospheric conditions. An accurate assessment of the spatiotemporal characteristics of PM2.5 levels is crucial to design effective air pollution control policy and obtain the trend of PM2.5 levels. At present, the research on estimating PM2.5 concentrations has focused on urban agglomeration in plain areas and has largely ignored mountainous areas. In this research, the mountainous region of Mentougou in Beijing was selected as the study area as it has 98.5% of its area covered by mountains. Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD and ground-based PM2.5 measurements were used to estimate PM2.5 concentrations in Mentougou for 2014-2017 at 1km resolution through a stepwise regression model. According to the estimation results, we analysed the spatiotemporal characteristics of PM2.5 and key influence factors. Annual PM2.5 concentrations decreased by 15.69% from 2014 to 2017. Average PM2.5 concentrations in winter decreased by 33.64% from 2014 to 2017. This proves that the adjustment of energy structure in winter has achieved significant results for improving the atmospheric environment. Moreover, the PM2.5 level is the highest in winter, while it is the lowest in summer. The PM2.5 level is higher in east area, while it is lower in west area. This spatial distribution pattern is mainly affected by terrain. The long-term PM2.5 prediction filled the gaps left by ground monitors, which would support relevant decision-making and studies.
Liu, Shuang, "Long-Term Spatiotemporal Trends of PM2.5 in Mountainous Areas based MAIAC" (2020). Physical Geography and Sciences. 22nd Annual Student Research and Creativity Conference. SUNY Buffalo State.