Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand-Reference-Cited by-同舟云学术

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand

Published:2023-07-13 Issue:38 Volume:30 Page:88905-88917
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Wongnakae Pimchanok,Chitchum Pakkapong,Sripramong Rungduen,Phosri Arthit^ORCID

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-28698-0.pdf

Reference74 articles.

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2. Amnuaylojaroen T, Kreasuwun J (2012) Investigation of fine and coarse particulate matter from burning areas in Chiang Mai, Thailand using the WRF/CALPUFF. Chiang Mai J Sci 39:311–326

3. Bran SH, Macatangay R, Surapipith V, Chotamonsak C, Chantara S, Han Z et al (2022) Surface PM2.5 mass concentrations during the dry season over northern Thailand: sensitivity to model aerosol chemical schemes and the effects on regional meteorology. Atmos Res 277:106303. https://doi.org/10.1016/j.atmosres.2022.106303

4. Brokamp C, Jandarov R, Hossain M, Ryan P (2018) Predicting daily urban fine particulate matter concentrations using a random forest model. Environ Sci Technol 52:4173–4179. https://doi.org/10.1021/acs.est.7b05381

5. Chai J, Song J, Xu Y, Zhang L, Guo B (2022a) Enhancing the applicability of satellite remote sensing for PM2.5 estimation using machine learning models in China. J Sens:7148682. https://doi.org/10.1155/2022/7148682

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