A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM 2.5 Concentration by Integrating Multisource Datasets

Yuan Shi, Alexis Kai-Hon Lau, Edward Ng, Hung-Chak Ho and Muhammad Bilal
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Yuan Shi: Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China
Alexis Kai-Hon Lau: Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Edward Ng: Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China
Hung-Chak Ho: Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
Muhammad Bilal: Lab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

IJERPH, 2021, vol. 19, issue 1, 1-16

Abstract: Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM 2.5 studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM 2.5 relationship when compared with previous studies ( R 2 ¯ from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM 2.5 spatial estimation. The resultant PM 2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.

Keywords: PM 2.5; spatial variability; geographic information system; multiscale; multi-source datasets (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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