A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM_2.5 Concentration by Integrating Multisource Datasets

Shi, Yuan ORCID: 0000-0003-4011-8735, Lau, Alexis Kai-Hon, Ng, Edward, Ho, Hung-Chak and Bilal, Muhammad (2022) A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM_2.5 Concentration by Integrating Multisource Datasets. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 19 (1). 321-.

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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 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 (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 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.

Item Type:	Article
Uncontrolled Keywords:	PM2.5, spatial variability, geographic information system, multiscale, multi-source datasets
Divisions:	Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jul 2022 14:21
Last Modified:	10 Feb 2024 02:06
DOI:	10.3390/ijerph19010321
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3159235