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

Ronan Hart, Lu Liang and Pinliang Dong
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Ronan Hart: Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
Lu Liang: Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
Pinliang Dong: Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA

IJERPH, 2020, vol. 17, issue 14, 1-18

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.

Keywords: particulate pollution; landscape pattern; mobile monitoring; GIS; Lidar (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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