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Journal Abstract Search

324 related items for PubMed ID: 31785527

  • 1. Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01° × 0.01° spatial resolution.
    Zhao C, Wang Q, Ban J, Liu Z, Zhang Y, Ma R, Li S, Li T.
    Environ Int; 2020 Jan; 134():105297. PubMed ID: 31785527
    [Abstract] [Full Text] [Related]

  • 2. Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model.
    He Q, Huang B.
    Environ Pollut; 2018 May; 236():1027-1037. PubMed ID: 29455919
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  • 3. [High-resolution Estimation of Spatio-temporal Variation in PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region].
    Yang XH, Song CJ, Fan LH, Zhang LY, Wei Q, Li FX, Wang LY, Wang W.
    Huan Jing Ke Xue; 2021 Sep 08; 42(9):4083-4094. PubMed ID: 34414707
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  • 4. 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 08; 249():735-749. PubMed ID: 30933771
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  • 6. The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations.
    Yang Q, Yuan Q, Yue L, Li T, Shen H, Zhang L.
    Environ Pollut; 2019 May 08; 248():526-535. PubMed ID: 30831349
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  • 9. Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China.
    Gao J, Wang K, Wang Y, Liu S, Zhu C, Hao J, Liu H, Hua S, Tian H.
    Environ Pollut; 2018 Feb 08; 233():714-724. PubMed ID: 29126093
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  • 11. Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region.
    Xue W, Zhang J, Hu X, Yang Z, Wei J.
    Int J Environ Res Public Health; 2022 Jul 12; 19(14):. PubMed ID: 35886364
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  • 14. Improvement in hourly PM2.5 estimations for the Beijing-Tianjin-Hebei region by introducing an aerosol modeling product from MASINGAR.
    Zhang Y, Wang W, Ma Y, Wu L, Xu W, Li J.
    Environ Pollut; 2020 Sep 12; 264():114691. PubMed ID: 32388304
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  • 15. 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 12; 237():1000-1010. PubMed ID: 29157969
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  • 16. Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China.
    Wang S, Gao J, Guo L, Nie X, Xiao X.
    Int J Environ Res Public Health; 2022 Jan 30; 19(3):. PubMed ID: 35162629
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  • 19. 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 30; 144():106060. PubMed ID: 32920497
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