基于遥感技术的成都市PM2.5浓度反演与分析

Inversion and Analysis of PM Concentration in Chengdu by Remote Sensing Technology

成都市是中国四大雾霾高发地之一,科学分析其PM2.5分布特征与影响因素对减轻环境污染具有重要意义。PM2.5监测方法主要以监测站点为主,但是受站点数量、密度的影响,难以在大范围开展研究。因此,使用2018年成都市逐日MODIS L1B影像数据,基于暗像元(dense dark vegetation,DDV)法反演大气气溶胶光学厚度(aerosol optical depth,AOD),使用能见度数据和空气相对湿度数据分别对AOD进行垂直订正和湿度校正,通过构建一元线性回归模型估算成都市PM2.5浓度并分析其分布特征。结果表明:(1)湿度校正后的AOD与PM2.5间的决定系数(R2=0.716)优于初始决定系数(R2=0.275)和仅进行垂直订正获得的决定系数(R2=0.634)。依照PM2.5-AOD模型进行月尺度精度验证,决定系数R2为0.708,P值小于0.001。(2)PM2.5在空间上呈现从西北部向东南部逐渐递增的趋势,在季节上具有冬季浓度(58.8μg/m3)>春季浓度(51.0μg/m3)>秋季浓度(39.9μg/m3)>夏季浓度(32.2μg/m3)的特征。

Chengdu is one of the four places with high incidence of smog in China.Scientific analysis on PM2.5 distribution characteristics and its influencing factors is significant to reduce environmental pollution.Monitoring methods of PM2.5 are mainly based on monitoring stations,but due to the impact of the number and density of stations,it is difficult to carry out large-scale research.Therefore,we use daily MODIS L1 B image data in Chengdu in 2018 to invert the atmospheric aerosol optical depth(AOD)with dense dark vegetation(DDV)method.The visibility data and air relative humidity data are used to perform the vertical correction and humidity correction.And the one-dimensional linear regression model is constructed to estimate the PM2.5 concentration and analyze its distribution features.The results show that:(1)The determination coefficient between AOD after humidity correction and PM2.5(R2=0.716)is better than the initial determination coefficient(R2=0.275)and the determination coefficient obtained only by vertical correction(R2=0.634).The monthly scale accuracy is verified by the PM2.5-AOD model.The determination coefficient R2 is 0.708 and P value is less than 0.001.(2)The PM2.5 concentration increases gradually from northwest to southeast in space,and it has the characteristics of concentration in Winter(58.8μg/m3)>concentration in Spring(51.0μg/m3)>concentration in Autumn(39.9μg/m3)>concentration in Summer(32.2μg/m3).