Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors

Shi, Y ORCID: 0000-0003-4011-8735, Lau, KKL and Ng, E (2016) Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors Environmental Science and Technology, 50 (15). pp. 8178-8187. ISSN 0013-936X, 1520-5851

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Abstract

Monitoring street-level particulates is essential to air quality management but challenging in high-density Hong Kong due to limitations in local monitoring network and the complexities of street environment. By employing vehicle-based mobile measurements, land use regression (LUR) models were developed to estimate the spatial variation of PM<inf>2.5</inf> and PM<inf>10</inf> in the downtown area of Hong Kong. Sampling runs were conducted along routes measuring a total of 30 km during a selected measurement period of total 14 days. In total, 321 independent variables were examined to develop LUR models by using stepwise regression with PM<inf>2.5</inf> and PM<inf>10</inf> as dependent variables. Approximately, 10% increases in the model adjusted R² were achieved by integrating urban/building morphology as independent variables into the LUR models. Resultant LUR models show that the most decisive factors on street-level air quality in Hong Kong are frontal area index, an urban/building morphological parameter, and road network line density and traffic volume, two parameters of road traffic. The adjusted R² of the final LUR models of PM<inf>2.5</inf> and PM<inf>10</inf> are 0.633 and 0.707, respectively. These results indicate that urban morphology is more decisive to the street-level air quality in high-density cities than other cities. Air pollution hotspots were also identified based on the LUR mapping.

Item Type:	Article
Uncontrolled Keywords:	Air Pollutants, Air Pollution, Environmental Monitoring, Hong Kong, Particulate Matter
Divisions:	Faculty of Science & Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jul 2022 14:56
Last Modified:	01 Mar 2026 02:53
DOI:	10.1021/acs.est.6b01807
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3159428
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