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Journal Abstract Search

174 related items for PubMed ID: 31465903

  • 1. Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements.
    Bai K, Li K, Chang NB, Gao W.
    Environ Pollut; 2019 Nov; 254(Pt B):113047. PubMed ID: 31465903
    [Abstract] [Full Text] [Related]

  • 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
    [Abstract] [Full Text] [Related]

  • 3. Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.
    Xue T, Zheng Y, Tong D, Zheng B, Li X, Zhu T, Zhang Q.
    Environ Int; 2019 Feb; 123():345-357. PubMed ID: 30562706
    [Abstract] [Full Text] [Related]

  • 4. 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
    [Abstract] [Full Text] [Related]

  • 5. Investigating the performance of satellite-based models in estimating the surface PM2.5 over China.
    Dong L, Li S, Yang J, Shi W, Zhang L.
    Chemosphere; 2020 Oct; 256():127051. PubMed ID: 32473467
    [Abstract] [Full Text] [Related]

  • 6. 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; 243(Pt B):998-1007. PubMed ID: 30248607
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  • 9. Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data.
    Bai K, Chang NB, Zhou J, Gao W, Guo J.
    Environ Pollut; 2019 Aug; 251():380-389. PubMed ID: 31096142
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  • 10. Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model.
    Gui K, Che H, Zeng Z, Wang Y, Zhai S, Wang Z, Luo M, Zhang L, Liao T, Zhao H, Li L, Zheng Y, Zhang X.
    Environ Int; 2020 Aug; 141():105801. PubMed ID: 32480141
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  • 12. The comparison of AOD-based and non-AOD prediction models for daily PM2.5 estimation in Guangdong province, China with poor AOD coverage.
    Chen G, Li Y, Zhou Y, Shi C, Guo Y, Liu Y.
    Environ Res; 2021 Apr; 195():110735. PubMed ID: 33460631
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  • 13. 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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  • 14. Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing.
    Guo J, Xia F, Zhang Y, Liu H, Li J, Lou M, He J, Yan Y, Wang F, Min M, Zhai P.
    Environ Pollut; 2017 Feb; 221():94-104. PubMed ID: 27889085
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  • 15. 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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  • 17. Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020.
    He Q, Ye T, Wang W, Luo M, Song Y, Zhang M.
    J Environ Manage; 2023 Sep 15; 342():118145. PubMed ID: 37210817
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  • 19. Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.
    Mhawish A, Banerjee T, Sorek-Hamer M, Bilal M, Lyapustin AI, Chatfield R, Broday DM.
    Environ Sci Technol; 2020 Jul 07; 54(13):7891-7900. PubMed ID: 32490674
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  • 20. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China.
    Liang F, Xiao Q, Wang Y, Lyapustin A, Li G, Gu D, Pan X, Liu Y.
    Sci Total Environ; 2018 Mar 07; 616-617():1589-1598. PubMed ID: 29055576
    [Abstract] [Full Text] [Related]

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