A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data

被引：34

作者：

Bagheri, Hossein ^{[1
]}

机构：

[1] Univ Isfahan, Fac Civil Engn & Transportat, Esfahan, Iran

来源：

ADVANCES IN SPACE RESEARCH | 2022年 / 69卷 / 09期

基金：

美国国家航空航天局;

关键词：

Abstract; MAIAC; MODIS; AOD; Machine learning; Deep learning; PM2; 5; Regression; AEROSOL OPTICAL DEPTH; PARTICULATE AIR-POLLUTION; USE REGRESSION-MODELS; METEOROLOGICAL PARAMETERS; SPATIOTEMPORAL TRENDS; NEURAL-NETWORK; MODIS; LAND; INTERPOLATION; ALGORITHM;

D O I：

10.1016/j.asr.2022.02.032

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This paper investigates the possibility of high resolution mapping of PM2.5 concentration over Tehran city using high resolution satellite AOD (MAIAC) retrievals. For this purpose, a framework including three main stages, data preprocessing; regression modeling; and model deployment was proposed. The output of the framework was a machine learning model trained to predict PM2.5 from MAIAC AOD retrievals and meteorological data. The results of model testing revealed the efficiency and capability of the developed framework for high resolution mapping of PM2.5, which was not realized in former investigations performed over the city. Thus, this study, for the first time, realized daily, 1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better than 9.0 m3lg. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

引用

页码：3333 / 3349

页数：17

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