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用WRF-Chem模型研究驱动东非地面PM2.5浓度季节性变化的机制

Investigating the mechanisms driving the seasonal variations in surface PM2.5 concentrations over East Africa with the WRF-Chem model

  • 摘要: 以往对东非地表PM2.5浓度的研究大多采用短期站点观测分析。本研究将WRF-Chem模拟与站点观测相结合,以研究东非地区地面PM2.5浓度的季节性变化。WRF-Chem模拟时段为2017年4月到9月。结果表明,模拟的AOD在整个模拟时段内与卫星观测一致。模拟结果表征了4月至9月PM2.5浓度的季节性变化,模拟低估了模拟时段内的浓度,这受该地区排放清单不确定性的影响。地表PM2.5浓度的主要成分是OIN和OC,分别占总浓度的80%和15%,主导了浓度的季节变化。与浓度相关的物理和化学过程的贡献分析表明,地表PM2.5浓度的季节性变化是由传输、PBL混合、干湿沉降过程的变化控制的。5月至7月PM2.5浓度的变化是由于风向变化改变了南非的生物质燃烧气溶胶的传输,加强了传输到上层的气溶胶与地表的湍流混合,且5月至7月降雨的减少也降低了湿沉降的贡献。

     

    Abstract: Most previous studies on surface PM2.5 concentrations over East Africa focused on short-term in situ observations. In this study, the WRF-Chem model combined with in situ observations is used to investigate the seasonal variation in surface PM2.5 concentrations over East Africa. WRF-Chem simulations are conducted from April to September 2017. Generally, the simulated AOD is consistent with satellite retrieval throughout the period, and the simulations depicted the seasonal variation in PM2.5 concentrations from April to September but underestimated the concentrations throughout the period due to the uncertainties in local and regional emissions over the region. The composition analysis of surface PM2.5 concentrations revealed that the dominant components were OIN and OC, accounting for 80% and 15% of the total concentrations, respectively, and drove the seasonal variation. The analysis of contributions from multiple physical and chemical processes indicated that the seasonal variation in surface PM2.5 concentrations was controlled by the variation in transport processes, PBL mixing, and dry and wet deposition. The variation in PM2.5 concentrations from May to July is due to wind direction changes that control the transported biomass burning aerosols from southern Africa, enhanced turbulent mixing of transported aerosols at the upper level to the surface and decreased wet deposition from decreased rainfall from May to July.

     

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