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Journal Abstract Search

165 related items for PubMed ID: 37442931

  • 1. Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand.
    Wongnakae P, Chitchum P, Sripramong R, Phosri A.
    Environ Sci Pollut Res Int; 2023 Aug; 30(38):88905-88917. PubMed ID: 37442931
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  • 2. Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.
    Paciorek CJ, Liu Y, HEI Health Review Committee.
    Res Rep Health Eff Inst; 2012 May; (167):5-83; discussion 85-91. PubMed ID: 22838153
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  • 3. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015.
    Jung CR, Hwang BF, Chen WT.
    Environ Pollut; 2018 Jun; 237():1000-1010. PubMed ID: 29157969
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  • 5. Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach.
    Li X, Zhang X.
    Environ Pollut; 2019 Jun; 249():735-749. PubMed ID: 30933771
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  • 12. Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models.
    You W, Zang Z, Pan X, Zhang L, Chen D.
    Sci Total Environ; 2015 Feb 01; 505():1156-65. PubMed ID: 25466686
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  • 14. Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis.
    Aneja VP, Pillai PR, Isherwood A, Morgan P, Aneja SP.
    J Air Waste Manag Assoc; 2017 Apr 01; 67(4):421-430. PubMed ID: 27731783
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