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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

薛文博 武卫玲 许艳玲 易爱华 任阵海 王金南

薛文博, 武卫玲, 许艳玲, 易爱华, 任阵海, 王金南. 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[J]. 环境科学研究, 2016, 29(12): 1751-1758.
引用本文: 薛文博, 武卫玲, 许艳玲, 易爱华, 任阵海, 王金南. 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[J]. 环境科学研究, 2016, 29(12): 1751-1758.
XUE Wenbo, WU Weiling, XU Yanling, YI Aihua, REN Zhenhai, WANG Jinnan. Satellite Retrieval of Near-Surface PM2.5 Based on WRF Model and Aerosol Optical Depth[J]. Research of Environmental Sciences, 2016, 29(12): 1751-1758.
Citation: XUE Wenbo, WU Weiling, XU Yanling, YI Aihua, REN Zhenhai, WANG Jinnan. Satellite Retrieval of Near-Surface PM2.5 Based on WRF Model and Aerosol Optical Depth[J]. Research of Environmental Sciences, 2016, 29(12): 1751-1758.

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

基金项目: 国家环境保护公益性行业科研专项(201509001)

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

  • 摘要: 为了反演高分辨率的PM2.5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(PM2.5)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(PM2.5)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(PM2.5)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(PM2.5)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(PM2.5)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(PM2.5)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(PM2.5)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.

     

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  • 收稿日期:  2016-03-22
  • 修回日期:  2016-09-22
  • 刊出日期:  2016-12-25

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