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Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies

Author

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  • Ronan Hart

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
    These authors contributed equally to this work and should be considered the joint first author.)

  • Lu Liang

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
    These authors contributed equally to this work and should be considered the joint first author.)

  • Pinliang Dong

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA)

Abstract

Fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM 2.5 ) is highly variable in space and time. In this study, the dynamics of PM 2.5 concentrations were mapped at high spatio-temporal resolutions using bicycle-based, mobile measures on a university campus. Significant diurnal and daily variations were revealed over the two-week survey, with the PM 2.5 concentration peaking during the evening rush hours. A range of predictor variables that have been proven useful in estimating the pollution level was derived from Geographic Information System, high-resolution airborne images, and Light Detection and Ranging (LiDAR) datasets. Considering the complex interplay among landscape, wind, and air pollution, variables influencing the PM 2.5 dynamics were quantified under a new wind wedge-based system that incorporates wind effects. Panel data analysis models identified eight natural and built environment variables as the most significant determinants of local-scale air quality (including four meteorological factors, distance to major roads, vegetation footprint, and building and vegetation height). The higher significance level of variables calculated using the wind wedge system as compared to the conventional circular buffer highlights the importance of incorporating the relative position of emission sources and receptors in modeling.

Suggested Citation

  • Ronan Hart & Lu Liang & Pinliang Dong, 2020. "Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4914-:d:381669
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    References listed on IDEAS

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    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    2. Zhang, Chen & Ni, Zhiwei & Ni, Liping, 2015. "Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 114-123.
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    2. Humphreys, Brad R. & Ruseski, Jane E., 2023. "Air quality and employee performance in teams: Evidence from the NFL," Economics & Human Biology, Elsevier, vol. 51(C).
    3. Ekaterina Alekhanova & Kate Foreman & Maya Papineau & Reid Stevens, 2023. "One Size Does Not Fit All: Co-Benefits of Congestion Pricing in the San Francisco Bay Area," Carleton Economic Papers 23-07, Carleton University, Department of Economics.

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