基于遥感数据和GWR模型的成都PM_(2.5)浓度时空分布特征研究

Temporal and Spatial Distribution Characteristics of PM_(2.5) in Chengdu Area Based on Remote Sensing Data and GWR Model

利用MODIS 021KM数据反演成都地区2018年逐日AOD数据,并结合PM_(2.5)地面监测数据以及气象数据构建地理加权回归(GWR)模型得到成都地区逐月PM_(2.5)浓度。结果表明:(1)和多元线性回归模型相比,GWR模型反演的PM_(2.5)浓度的R2、ERMS和EMA分别为0.884、7.8704μg·m^(−3)和6.1566μg·m^(−3),都优于多元线性回归的0.808、9.7098μg·m^(−3)和7.6081μg·m^(−3),说明该模型能有效估算成都地区2018年PM_(2.5)浓度。(2)成都地区PM_(2.5)浓度在月尺度上呈现出先降低、后升高的变化特征。2月最高为67.38μg·m^(−3),7月最低为28.31μg·m^(−3);PM_(2.5)浓度季节变化特征为夏季、秋季、春季、冬季依次递增。(3)成都地区PM_(2.5)浓度空间分布总体上呈现“中间高、两边低”的特征。西部地区为PM_(2.5)浓度低值区,中部地区为高值区,东部的简阳市和金堂县为PM_(2.5)浓度次高值区。

Using MODIS L1B021KM data to obtain daily AOD data of Chengdu area,China,in 2018 firstly,then combined with PM_(2.5) ground monitoring data and meteorological data,a geographically weighted regression(GWR)model is constructed to obtain the monthly PM_(2.5) concentration of Chengdu.The results show that:(1)Compared with the multiple linear regression model,GWR model has higher credibility in the inversion of PM_(2.5) concentration in Chengdu area in 2018,which is specifically reflected in that R2,ERMS and EMA are 0.884,7.8704μg·m^(−3) and 6.1566μg·m^(−3),respectively for GWR model in the inversion of PM_(2.5) concentration,which are better than 0.808,9.7098μg·m^(−3) and 7.6081μg·m^(−3) of multiple linear regression.(2)On a monthly scale,PM_(2.5) concentration in Chengdu shows a characteristic of first decreasing and then increasing.The highest concentration reaches 67.38μg·m^(−3) in February,and the lowest reaches 28.31μg·m^(−3) in July.The seasonal variation of PM_(2.5) concentration is characterized by increasing in summer,autumn,spring and winter.(3)The spatial distribution of PM_(2.5) concentration in Chengdu generally presents a characteristics of“high in the middle and low on both sides”.The western region is a low-value PM_(2.5) concentration area,the central region is a high-value area,and Jianyang City and Jintang County in the east are the second-highest PM_(2.5) concentration areas.