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Journal Abstract Search

358 related items for PubMed ID: 32480141

  • 21. 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
    [Abstract] [Full Text] [Related]

  • 22. Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China.
    She Q, Choi M, Belle JH, Xiao Q, Bi J, Huang K, Meng X, Geng G, Kim J, He K, Liu M, Liu Y.
    Chemosphere; 2020 Jan; 239():124678. PubMed ID: 31494323
    [Abstract] [Full Text] [Related]

  • 23. 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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  • 24. Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: a case study of Beijing, China.
    Wu Y, Lin S, Shi K, Ye Z, Fang Y.
    Environ Sci Pollut Res Int; 2022 Jun 01; 29(30):45821-45836. PubMed ID: 35150424
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  • 25. Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data.
    He Q, Gu Y, Zhang M.
    Environ Int; 2020 Apr 01; 137():105536. PubMed ID: 32036122
    [Abstract] [Full Text] [Related]

  • 26. New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data.
    Yan X, Zang Z, Luo N, Jiang Y, Li Z.
    Environ Int; 2020 Nov 01; 144():106060. PubMed ID: 32920497
    [Abstract] [Full Text] [Related]

  • 27. A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda.
    Coker ES, Amegah AK, Mwebaze E, Ssematimba J, Bainomugisha E.
    Environ Res; 2021 Aug 01; 199():111352. PubMed ID: 34043968
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  • 29. 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 01; 616-617():1589-1598. PubMed ID: 29055576
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  • 30. Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016.
    Meng X, Liu C, Zhang L, Wang W, Stowell J, Kan H, Liu Y.
    Remote Sens Environ; 2021 Feb 01; 253():. PubMed ID: 34548700
    [Abstract] [Full Text] [Related]

  • 31. 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 01; 254(Pt B):113047. PubMed ID: 31465903
    [Abstract] [Full Text] [Related]

  • 32. An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China.
    Chen B, You S, Ye Y, Fu Y, Ye Z, Deng J, Wang K, Hong Y.
    Sci Total Environ; 2021 May 10; 768():144724. PubMed ID: 33434807
    [Abstract] [Full Text] [Related]

  • 33. Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance.
    Yang L, Xu H, Yu S.
    J Environ Manage; 2020 Oct 15; 272():111061. PubMed ID: 32669259
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  • 35. Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms.
    Chen B, Song Z, Pan F, Huang Y.
    Sci Total Environ; 2022 Jan 20; 805():150338. PubMed ID: 34537706
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  • 40. Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China.
    Zang L, Mao F, Guo J, Gong W, Wang W, Pan Z.
    Environ Pollut; 2018 Oct 20; 241():654-663. PubMed ID: 29902748
    [Abstract] [Full Text] [Related]

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