基于遥感数据和GWR 模型的成都PM2.5 浓度时空分布特征研究

摘要/Abstract

摘要： 利用 MODIS 021KM 数据反演成都地区 2018 年逐日 AOD 数据, 并结合 PM2.5 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 PM2.5 浓度。结果表明: (1) 和多元线性回归模型相比, GWR 模型反演的 PM2.5 浓度的 R2、 ERMS 和 EMA 分别为 0.884、 7.8704 µg·m−3 和 6.1566 µg·m−3 , 都优于多元线性回归的 0.808、 9.7098 µg·m−3 和 7.6081 µg·m−3, 说明该模型能有效估算成都地区 2018 年 PM2.5 浓度。 (2) 成都地区 PM2.5 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 67.38 µg·m−3, 7 月最低为 28.31 µg·m−3; PM2.5 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 PM2.5 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 PM2.5 浓度低值区, 中部地区为高值区, 东部的简阳市和金堂县为 PM2.5 浓度次高值区。

关键词: 遥感, 地理加权回归, 中分辨率成像光谱仪, 多元线性回归, PM2.5

Abstract: Using MODIS L1B021KM data to obtain daily AOD data of Chengdu area, China, in 2018 firstly, then combined with PM2.5 ground monitoring data and meteorological data, a geographically weighted regression (GWR) model is constructed to obtain the monthly PM2.5 concentration of Chengdu. The results show that: (1) Compared with the multiple linear regression model, GWR model has higher credibility in the inversion of PM2.5 concentration in Chengdu area in 2018, which is specifically reflected in that R2, ERMS and EMA are 0.884, 7.8704 µg·m−3 and 6.1566 µg·m−3, respectively for GWR model in the inversion of PM2.5 concentration, which are better than 0.808, 9.7098 µg·m−3 and 7.6081 µg·m−3 of multiple linear regression. (2) On a monthly scale, PM2.5 concentration in Chengdu shows a characteristic of first decreasing and then increasing. The highest concentration reaches 67.38 µg·m−3 in February, and the lowest reaches 28.31 µg·m−3 in July. The seasonal variation of PM2.5 concentration is characterized by increasing in summer, autumn, spring and winter. (3) The spatial distribution of PM2.5 concentration in Chengdu generally presents a characteristics of “high in the middle and low on both sides”. The western region is a low-value PM2.5 concentration area, the central region is a high-value area, and Jianyang City and Jintang County in the east are the second-highest PM2.5 concentration areas.

Key words: remote sensing, geographically weighted regression, moderate-resolution imaging spectroradiometer, multiple linear regression, PM2.5

中图分类号:

P427.2

贾宏亮, 罗俊, 肖东升∗. 基于遥感数据和GWR 模型的成都PM2.5 浓度时空分布特征研究[J]. 大气与环境光学学报, 2021, 16(6): 529-540.

JIA Hongliang, LUO Jun, XIAO Dongsheng∗. Temporal and Spatial Distribution Characteristics of PM2.5 in Chengdu Area Based on Remote Sensing Data and GWR Model[J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 529-540.

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