Land use regression model established using Light Gradient Boosting Machine incorporating the WRF/CMAQ model for highly accurate spatiotemporal PM2.5 estimation in the central region of Thailand-Reference-Cited by-同舟云学术

Land use regression model established using Light Gradient Boosting Machine incorporating the WRF/CMAQ model for highly accurate spatiotemporal PM2.5 estimation in the central region of Thailand

Published:2023-03 Issue: Volume:297 Page:119595
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Thongthammachart Tin^ORCID,Shimadera Hikari^ORCID,Araki Shin^ORCID,Matsuo Tomohito,Kondo Akira

Funder

Environmental Restoration and Conservation Agency

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference73 articles.

1. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework;Akita;Environ. Sci. Technol.,2014

2. Long range transport of southeast asian PM2.5 pollution to northern Thailand during high biomass burning episodes;Amnuaylojaroen;Sustainability,2020

3. Estimating historical PM2.5 exposures for three decades (1987–2016) in Japan using measurements of associated air pollutants and land use regression;Araki;Environ. Pollut.,2020

4. Estimating monthly concentrations of ambient key air pollutants in Japan during 2010–2015 for a national-scale birth cohort;Araki;Environ. Pollut.,2021

5. Land use regression modeling for fine particulate matters in Bangkok, Thailand, using time-variant predictors: effects of seasonal factors, open biomass burning, and traffic-related factors;Chalermpong;Atmos. Environ.,2021

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