Developing Street-Level PM_2.5 and PM₁₀ Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors

Shi, Yuan ORCID: 0000-0003-4011-8735, Lau, Kevin Ka-Lun and Ng, Edward (2016) Developing Street-Level PM_2.5 and PM₁₀ Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 50 (15). pp. 8178-8187.

Text EST-2016-Developing Street-Level PM2.5 and PM10 Land Use Regression Models.pdf - Author Accepted Manuscript Download (795kB) | Preview

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 PM2.5 and PM10 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 PM2.5 and PM10 as dependent variables. Approximately, 10% increases in the model adjusted R(2) 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 R(2) of the final LUR models of PM2.5 and PM10 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 and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jul 2022 14:56
Last Modified:	09 Oct 2023 09:52
DOI:	10.1021/acs.est.6b01807
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3159428