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Journal Abstract Search

605 related items for PubMed ID: 29702402

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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. Spatiotemporal patterns of PM10 concentrations over China during 2005-2016: A satellite-based estimation using the random forests approach.
    Chen G, Wang Y, Li S, Cao W, Ren H, Knibbs LD, Abramson MJ, Guo Y.
    Environ Pollut; 2018 Nov; 242(Pt A):605-613. PubMed ID: 30014938
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  • 5. Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5.
    Xu Y, Ho HC, Wong MS, Deng C, Shi Y, Chan TC, Knudby A.
    Environ Pollut; 2018 Nov; 242(Pt B):1417-1426. PubMed ID: 30142557
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  • 6. 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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  • 8. Predicting monthly high-resolution PM2.5 concentrations with random forest model in the North China Plain.
    Huang K, Xiao Q, Meng X, Geng G, Wang Y, Lyapustin A, Gu D, Liu Y.
    Environ Pollut; 2018 Nov; 242(Pt A):675-683. PubMed ID: 30025341
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  • 9. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information.
    Chen G, Knibbs LD, Zhang W, Li S, Cao W, Guo J, Ren H, Wang B, Wang H, Williams G, Hamm NAS, Guo Y.
    Environ Pollut; 2018 Feb; 233():1086-1094. PubMed ID: 29033176
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  • 10. Estimating ground-level PM2.5 in China using satellite remote sensing.
    Ma Z, Hu X, Huang L, Bi J, Liu Y.
    Environ Sci Technol; 2014 Jul 01; 48(13):7436-44. PubMed ID: 24901806
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  • 14. A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels.
    Zhang R, Di B, Luo Y, Deng X, Grieneisen ML, Wang Z, Yao G, Zhan Y.
    Environ Pollut; 2018 Dec 01; 243(Pt B):998-1007. PubMed ID: 30248607
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  • 15. Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach.
    Chen CC, Wang YR, Yeh HY, Lin TH, Huang CS, Wu CF.
    Environ Pollut; 2021 Dec 15; 291():118159. PubMed ID: 34543952
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