基于WRF模型与气溶胶光学厚度的PM2.5近地面浓度卫星反演

为了反演高分辨率的PM_2.5近地面浓度,利用WRF(中尺度气象模型)模拟的大气相对湿度、风速、边界层高度等气象因子对AOD(气溶胶光学厚度)分别进行订正,以逐步提高AOD与近地面ρ(PM_2.5)间的相关性；分析不同反演模型的统计学特征,优选反演模型,并利用最优模型反演中国中东部地区2014年年均ρ(PM_2.5)的空间分布特征.结果表明:AOD经相对湿度订正后,其与近地面ρ(PM_2.5)的相关性显著提高,相关系数达到0.77；同时引入相对湿度、风速2个气象因子,AOD与近地面ρ(PM_2.5)的相关系数升至0.79(n=145,P<0.01)；同时引入相对湿度、风速和边界层高度3个气象因子,AOD与近地面ρ(PM_2.5)的相关系数进一步升至0.80(n=145,P<0.01).模型反演表明,研究区域内ρ(PM_2.5)年均值大于35 μg/m3的面积高达334.49×104 km2,占研究区域面积的83.2%,并且高污染地区与人口密度高度重合.分析表明,北京、天津、河北、山东及河南等典型重污染省、直辖市分别有96.30%、100%、78.16%、98.86%、100%面积的ρ(PM_2.5)超标,分别约有99.97%、100%、96.41%、98.88%、100%人口生活在空气质量超标地区. 

Satellite Retrieval of Near-Surface PM2.5 Based on WRF Model and Aerosol Optical Depth

By simulating relative humidity, wind speed and boundary layer height with mesoscale meteorological model WRF to revise aerosol optical depth(AOD), the correlation coefficient between AOD and ρ(PM_2.5) was improved gradually. Using the statistical characteristics of different retrieval models and the optimal linear fitting model, the annual ρ(PM_2.5) was retrieved in central and eastern China in 2014. After multiple linear regression analysis with humidity correction, correlation coefficient between AOD and ρ(PM_2.5) was improved to 0.77,and 0.79(n=145, P<0.01) by introducing wind speed.The correlation coefficient was further improved to 0.80(n=145,P<0.01) by introducing wind speed andboundary layer height, respectively. The results showed that 83.2% of the study area, with an area of 334.49×104 km2, exceeded the secondary standard in Ambient Air Quality Standard(GB 3095-2012) with annual ρ(PM_2.5)＞35 μg/m3, and there was a high coincidence between pollution level and population distribution.In typical areas, such as Beijing, Tianjin, Hebei, Shandong and Henan provinces, the ratio was 96.30%, 100%, 78.16%, 98.86% and 100%, corresponding to 99.97%, 100%, 96.41%, 98.88% and 100% of population exposure, respectively.