Particulate Matter Concentration Mapping using MODIS Satellite Images and Regression Model

Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning

(2022) Tianchen Liang et al.   Remote Sensing

Health risk associated with potential source regions of PM2.5 in Indian cities

(2019) Shovan Kumar Sahu et al.   Air Quality Atmosphere and Health

Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region

(2019) Guanghui Yuan et al.   Sustainability

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

(2019) Qingxin Wang et al.   SENSORS

Estimation of health benefits from air-quality improvement using the MODIS AOD dataset in Seoul, Korea

(2019) Daeun Kim et al.   ENVIRONMENTAL RESEARCH

Ambient air pollution during pregnancy and gestational diabetes in New York City

(2019) Seung-Ah Choe et al.   ENVIRONMENTAL RESEARCH

Rapid improvement of PM2.5 pollution and associated health benefits in China during 2013–2017

(2019) Tao Xue et al.   Science China-Earth Sciences

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

(2019) Zhenqun Hua et al.   Remote Sensing

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

(2019) Jie Chen et al.   ENVIRONMENT INTERNATIONAL

Ambient Particulate Air Pollution and Daily Mortality in 652 Cities

(2019) Cong Liu et al.   NEW ENGLAND JOURNAL OF MEDICINE

Review of aerosol optical depth retrieval using visibility data

(2019) Shuang Zhang et al.   EARTH-SCIENCE REVIEWS

Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM 2.5 concentrations in Taiwan from 2005 to 2015

(2018) Chau-Ren Jung et al.   ENVIRONMENTAL POLLUTION

Study of aerosol optical depth using satellite data (MODIS Aqua) over Indian Territory and its relation to particulate matter concentration

(2018) Neha Shaw et al.   ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

(2018) Jing Wei et al.   ATMOSPHERIC ENVIRONMENT

Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city

(2018) Weiran Yuchi et al.   ENVIRONMENTAL POLLUTION

Long-term variations of the PM2.5 concentration identified by MODIS in the tropical rain forest, Southeast Asia

(2018) Yining Ma et al.   ATMOSPHERIC RESEARCH

Household air pollution from various types of rural kitchens and its exposure assessment

(2017) Maninder Kaur Sidhu et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model

(2015) Guannan Geng et al.   REMOTE SENSING OF ENVIRONMENT

A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data

(2014) Itai Kloog et al.   ATMOSPHERIC ENVIRONMENT

Exposure measurement error in PM2.5 health effects studies: A pooled analysis of eight personal exposure validation studies

(2014) Marianthi-Anna Kioumourtzoglou et al.   Environmental Health

Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas

(2014) Gordon Gong et al.   ENVIRONMENTAL RESEARCH

High resolution aerosol data from MODIS satellite for urban air quality studies

(2013) A. Chudnovsky et al.   Open Geosciences

Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

(2010) Yong Zha et al.   TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY

A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements

(2009) Jie Tian et al.   REMOTE SENSING OF ENVIRONMENT