A review on estimation of particulate matter from satellite-based aerosol optical depth: Data, methods, and challenges

AK Ranjan, AK Patra, AK Gorai - Asia-Pacific Journal of Atmospheric …, 2021 - Springer
Detailed, reliable, and continuous monitoring of aerosol optical depth (AOD) is essential for
air quality management and protection of human health. The satellite-based AOD datasets …

LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion

K Bai, K Li, M Ma, K Li, Z Li, J Guo… - Earth System …, 2021 - essd.copernicus.org
Develo** a big data analytics framework for generating the Long-term Gap-free High-
resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great …

Estimating PM2. 5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017

B Guo, D Zhang, L Pei, Y Su, X Wang, Y Bian… - Science of The Total …, 2021 - Elsevier
Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM 2.5) poses
adverse impacts on public health and the environment. It is still a great challenge to estimate …

[HTML][HTML] Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Y Kang, M Kim, E Kang, D Cho, J Im - ISPRS Journal of Photogrammetry …, 2022 - Elsevier
Abstract Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important
information for air quality research. Both are mainly obtained from satellite data based on a …

A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing …

PSS Kumar, CS Matli - Atmospheric Research, 2024 - Elsevier
Particulate matter (PM), one of the major air pollutants, is generated by variety of natural or
man-made sources, leading to acute and chronic diseases in humans since the last few …

A two-stage machine learning algorithm for retrieving multiple aerosol properties over land: Development and validation

M Cao, M Zhang, X Su, L Wang - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Satellite-based aerosol optical property retrieval over land, especially size-related
parameters, is challenging. This study proposed a novel two-stage machine learning (ML) …

Estimation of particulate matter concentrations in Türkiye using a random forest model based on satellite AOD retrievals

G Tuna Tuygun, T Elbir - Stochastic Environmental Research and Risk …, 2023 - Springer
This study estimates intra-daily PM10 concentrations at 213 inland and coastal monitoring
sites in Türkiye from 2008 to 2019 using satellite-based aerosol optical depth (AOD) from the …

A similarity distance-based space-time random forest model for estimating PM2. 5 concentrations over China

S Guan, X Zhang, W Zhao, Y Duan, S Yang… - Atmospheric …, 2023 - Elsevier
China has experienced persistent fine particulate matter (PM 2.5) pollution for the past few
years, which adversely affects both physical and mental health. The availability of high …

[HTML][HTML] Ozone, nitrogen dioxide, and PM2. 5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical …

B Tang, CO Stanier, GR Carmichael, M Gao - Atmospheric Environment, 2024 - Elsevier
High-resolution multi-component estimates of ground-level air pollutants are necessary for
assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a …

[HTML][HTML] Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

NAFK Zaman, KD Kanniah, DG Kaskaoutis, MT Latif - Applied Sciences, 2021 - mdpi.com
Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due
to extensive forest, agricultural and peat fires. This study aims to estimate the PM2. 5 …