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Volume 19
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10.3390/ijerph19010321
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Article

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

by
Yuan Shi
1,*,
Alexis Kai-Hon Lau
2,3,4,
Edward Ng
1,5,6,
Hung-Chak Ho
7 and
Muhammad Bilal
8
1
Institute of Future Cities (IOFC), The Chinese University of Hong Kong, Hong Kong, China
2
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
3
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
4
Institute for the Environment, The Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong, China
5
School of Architecture, The Chinese University of Hong Kong, Hong Kong, China
6
Institute of Environment, Energy and Sustainability (IEES), The Chinese University of Hong Kong, Hong Kong, China
7
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
8
Lab of Environmental Remote Sensing (LERS), School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(1), 321; https://doi.org/10.3390/ijerph19010321
Submission received: 18 November 2021 / Revised: 24 December 2021 / Accepted: 28 December 2021 / Published: 29 December 2021
(This article belongs to the Special Issue Urban Microclimate Design: Pollutant Dispersion and Ventilation)
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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 PM2.5 (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)-PM2.5 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-PM2.5 relationship when compared with previous studies (R2¯ 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 PM2.5 spatial estimation. The resultant PM2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.
Keywords: PM2.5; spatial variability; geographic information system; multiscale; multi-source datasets PM2.5; spatial variability; geographic information system; multiscale; multi-source datasets

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MDPI and ACS Style

Shi, Y.; Lau, A.K.-H.; Ng, E.; Ho, H.-C.; Bilal, M. A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets. Int. J. Environ. Res. Public Health 2022, 19, 321. https://doi.org/10.3390/ijerph19010321

AMA Style

Shi Y, Lau AK-H, Ng E, Ho H-C, Bilal M. A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets. International Journal of Environmental Research and Public Health. 2022; 19(1):321. https://doi.org/10.3390/ijerph19010321

Chicago/Turabian Style

Shi, Yuan, Alexis Kai-Hon Lau, Edward Ng, Hung-Chak Ho, and Muhammad Bilal. 2022. "A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets" International Journal of Environmental Research and Public Health 19, no. 1: 321. https://doi.org/10.3390/ijerph19010321

APA Style

Shi, Y., Lau, A. K.-H., Ng, E., Ho, H.-C., & Bilal, M. (2022). A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets. International Journal of Environmental Research and Public Health, 19(1), 321. https://doi.org/10.3390/ijerph19010321

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