Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia-Reference-Cited by-同舟云学术

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Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Published:2022-08 Issue: Volume:306 Page:119425
ISSN:0269-7491
Container-title:Environmental Pollution
language:en
Short-container-title:Environmental Pollution

Author:

Park Seohui^ORCID,Im Jungho^ORCID,Kim Jhoon,Kim Sang-Min^ORCID

Funder

Korea Environmental Industry and Technology Institute

National Research Foundation of Korea

Ministry of Environment

National Institute of Environmental Research

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

Health, Toxicology and Mutagenesis,Pollution,Toxicology,General Medicine

Reference45 articles.

1. Comparison of satellite-based PM2. 5 estimation from aerosol optical depth and top-of-atmosphere reflectance;Bai;Aerosol Air Qual. Res.,2021

2. Random forests;Breiman;Mach. Learn.,2001

3. Estimating PM2. 5 concentration from satellite derived aerosol optical depth and meteorological variables using a combination model;Chelani;Atmos. Pollut. Res.,2019

4. A machine learning method to estimate PM2. 5 concentrations across China with remote sensing, meteorological and land use information;Chen;Sci. Total Environ.,2018

5. Spatiotemporal patterns of PM10 concentrations over China during 2005–2016: a satellite-based estimation using the random forests approach;Chen;Environ. Pollut.,2018

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1. Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning;Atmospheric Environment;2024-10

2. State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods;Environment, Development and Sustainability;2024-04-02

3. Two-step carbon storage estimation in urban human settlements using airborne LiDAR and Sentinel-2 data based on machine learning;Urban Forestry & Urban Greening;2024-04

4. A similarity distance-based space-time random forest model for estimating PM2.5 concentrations over China;Atmospheric Environment;2023-11

5. Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning;Atmospheric Research;2023-07

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