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Journal Abstract Search

709 related items for PubMed ID: 31739136

  • 1. The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique.
    Lin YC, Chi WJ, Lin YQ.
    Environ Int; 2020 Jan; 134():105305. PubMed ID: 31739136
    [Abstract] [Full Text] [Related]

  • 2. Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors.
    Ho CC, Chen LJ, Hwang JS.
    Environ Pollut; 2020 Sep; 264():114810. PubMed ID: 32559863
    [Abstract] [Full Text] [Related]

  • 3. Spatially adaptive calibrations of airbox PM2.5 data.
    Tzeng S, Lai CW, Huang HC.
    Biometrics; 2023 Dec; 79(4):3637-3649. PubMed ID: 36594650
    [Abstract] [Full Text] [Related]

  • 4. 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]

  • 5. Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors.
    Chen PC, Lin YT.
    Environ Pollut; 2022 Jan 01; 292(Pt B):118401. PubMed ID: 34695517
    [Abstract] [Full Text] [Related]

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  • 8. Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA.
    Huang R, Lal R, Qin M, Hu Y, Russell AG, Odman MT, Afrin S, Garcia-Menendez F, O'Neill SM.
    J Air Waste Manag Assoc; 2021 Jul 01; 71(7):815-829. PubMed ID: 33914671
    [Abstract] [Full Text] [Related]

  • 9. An efficient spatiotemporal data calibration approach for the low-cost PM2.5 sensing network: A case study in Taiwan.
    Lee CH, Wang YB, Yu HL.
    Environ Int; 2019 Sep 01; 130():104838. PubMed ID: 31203027
    [Abstract] [Full Text] [Related]

  • 10. Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.
    Levy JI, Clougherty JE, Baxter LK, Houseman EA, Paciorek CJ, HEI Health Review Committee.
    Res Rep Health Eff Inst; 2010 Dec 01; (152):5-80; discussion 81-91. PubMed ID: 21409949
    [Abstract] [Full Text] [Related]

  • 11. Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting.
    Shih DH, To TH, Nguyen LSP, Wu TW, You WT.
    Int J Environ Res Public Health; 2021 Jul 02; 18(13):. PubMed ID: 34281023
    [Abstract] [Full Text] [Related]

  • 12. Using a land use regression model with machine learning to estimate ground level PM2.5.
    Wong PY, Lee HY, Chen YC, Zeng YT, Chern YR, Chen NT, Candice Lung SC, Su HJ, Wu CD.
    Environ Pollut; 2021 May 15; 277():116846. PubMed ID: 33735646
    [Abstract] [Full Text] [Related]

  • 13. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network.
    Considine EM, Reid CE, Ogletree MR, Dye T.
    Environ Pollut; 2021 Jan 01; 268(Pt B):115833. PubMed ID: 33120139
    [Abstract] [Full Text] [Related]

  • 14. Air quality warning system based on a localized PM2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection.
    Balram D, Lian KY, Sebastian N.
    Ecotoxicol Environ Saf; 2019 Oct 30; 182():109386. PubMed ID: 31255868
    [Abstract] [Full Text] [Related]

  • 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 30; 237():1000-1010. PubMed ID: 29157969
    [Abstract] [Full Text] [Related]

  • 16. Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study.
    Lu Y, Giuliano G, Habre R.
    Environ Res; 2021 Apr 30; 195():110653. PubMed ID: 33476665
    [Abstract] [Full Text] [Related]

  • 17. Temporal and spatial statistical analysis of ambient air quality of Assam (India).
    Bhunia GS, Ding D.
    J Air Waste Manag Assoc; 2020 Aug 30; 70(8):775-794. PubMed ID: 32442037
    [Abstract] [Full Text] [Related]

  • 18. Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques.
    Lee YM, Lin GY, Le TC, Hong GH, Aggarwal SG, Yu JY, Tsai CJ.
    Environ Res; 2024 Mar 01; 244():117906. PubMed ID: 38101720
    [Abstract] [Full Text] [Related]

  • 19. Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations.
    Cao R, Li B, Wang Z, Peng ZR, Tao S, Lou S.
    Environ Pollut; 2020 Sep 01; 264():114549. PubMed ID: 32408078
    [Abstract] [Full Text] [Related]

  • 20. Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China.
    Liang F, Gao M, Xiao Q, Carmichael GR, Pan X, Liu Y.
    Environ Res; 2017 Oct 01; 158():54-60. PubMed ID: 28599195
    [Abstract] [Full Text] [Related]

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