Open AccessEstimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran
Author(s) -
Saeed Sotoudeheian,
Mohammad Arhami
Publication year - 2021
Publication title -
journal of environmental health science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
ISSN - 2052-336X
DOI - 10.1007/s40201-020-00509-5
Subject(s) - environmental science , mean squared error , linear regression , artificial neural network , linear model , regression analysis , support vector machine , meteorology , remote sensing , statistics , mathematics , computer science , geography , machine learning
In this study we aimed to develop an optimized prediction model to estimate a fine-resolution grid of ground-level PM 2.5 levels over Tehran. Using remote sensing data to obtain fine-resolution grids of particulate levels in highly polluted environments in areas such as Middle East with the abundance of brightly reflecting deserts is challenging.