These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

180 related items for PubMed ID: 34537706

  • 1. 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
    [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 20; (167):5-83; discussion 85-91. PubMed ID: 22838153
    [Abstract] [Full Text] [Related]

  • 3. 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 20; 249():735-749. PubMed ID: 30933771
    [Abstract] [Full Text] [Related]

  • 4. 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 20; 242(Pt B):1417-1426. PubMed ID: 30142557
    [Abstract] [Full Text] [Related]

  • 5. Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China.
    Song Z, Chen B, Huang J.
    Environ Pollut; 2022 Mar 15; 297():118826. PubMed ID: 35016979
    [Abstract] [Full Text] [Related]

  • 6. 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 15; 123():345-357. PubMed ID: 30562706
    [Abstract] [Full Text] [Related]

  • 7. 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 15; 248():526-535. PubMed ID: 30831349
    [Abstract] [Full Text] [Related]

  • 8. 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 15; 236():1027-1037. PubMed ID: 29455919
    [Abstract] [Full Text] [Related]

  • 9. Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China.
    Han Y, Wu Y, Wang T, Zhuang B, Li S, Zhao K.
    Sci Total Environ; 2015 Nov 01; 532():195-207. PubMed ID: 26071961
    [Abstract] [Full Text] [Related]

  • 10. 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 01; 195():110735. PubMed ID: 33460631
    [Abstract] [Full Text] [Related]

  • 11. 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 01; 141():105801. PubMed ID: 32480141
    [Abstract] [Full Text] [Related]

  • 12. The empirical correlations between PM2.5, PM10 and AOD in the Beijing metropolitan region and the PM2.5, PM10 distributions retrieved by MODIS.
    Kong L, Xin J, Zhang W, Wang Y.
    Environ Pollut; 2016 Sep 01; 216():350-360. PubMed ID: 27294786
    [Abstract] [Full Text] [Related]

  • 13. Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China.
    Xu X, Zhang C.
    PLoS One; 2020 Sep 01; 15(10):e0240430. PubMed ID: 33048987
    [Abstract] [Full Text] [Related]

  • 14. Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.
    Zhang K, Lin J, Li Y, Sun Y, Tong W, Li F, Chien LC, Yang Y, Su WC, Tian H, Fu P, Qiao F, Romeiko XX, Lin S, Luo S, Craft E.
    J Expo Sci Environ Epidemiol; 2024 Sep 01; 34(5):814-820. PubMed ID: 38561475
    [Abstract] [Full Text] [Related]

  • 15. 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 01; 242(Pt A):675-683. PubMed ID: 30025341
    [Abstract] [Full Text] [Related]

  • 16. 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 01; 134():105297. PubMed ID: 31785527
    [Abstract] [Full Text] [Related]

  • 17.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 18. 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 01; 237():1000-1010. PubMed ID: 29157969
    [Abstract] [Full Text] [Related]

  • 19. 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
    [Abstract] [Full Text] [Related]

  • 20. 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 01; 221():94-104. PubMed ID: 27889085
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 9.