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Journal Abstract Search

259 related items for PubMed ID: 32920497

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  • 2. 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; 248():526-535. PubMed ID: 30831349
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  • 5. First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China.
    Liu J, Chen W.
    Sci Total Environ; 2022 May 10; 820():153289. PubMed ID: 35066047
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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 10; 141():105801. PubMed ID: 32480141
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  • 11. 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 10; 134():105297. PubMed ID: 31785527
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  • 12. Investigating the aerosol mass and chemical components characteristics and feedback effects on the meteorological factors in the Beijing-Tianjin-Hebei region, China.
    Zhang H, Cheng S, Li J, Yao S, Wang X.
    Environ Pollut; 2019 Jan 10; 244():495-502. PubMed ID: 30366297
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  • 13. 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 10; (167):5-83; discussion 85-91. PubMed ID: 22838153
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  • 14. Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II.
    Choi H, Park S, Kang Y, Im J, Song S.
    Environ Pollut; 2023 Apr 15; 323():121169. PubMed ID: 36773685
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  • 15. Satellite-based ground PM2.5 estimation using a gradient boosting decision tree.
    Zhang T, He W, Zheng H, Cui Y, Song H, Fu S.
    Chemosphere; 2021 Apr 15; 268():128801. PubMed ID: 33139054
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  • 16. 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 15; 199():111352. PubMed ID: 34043968
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  • 17. 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 15; 242(Pt A):675-683. PubMed ID: 30025341
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  • 18. 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 15; 233():714-724. PubMed ID: 29126093
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