LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction

Jui-Huna Lee; Chang-Fu Wu; Gerard Hoek; Kees de Hoogh; Rob Beelen; Bert Brunekreef; Chang-Chuan Chan

doi:10.1016/j.scitotenv.2015.01.091

LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction

Sci Total Environ. 2015 May 1:514:178-84. doi: 10.1016/j.scitotenv.2015.01.091. Epub 2015 Feb 5.

Authors

Jui-Huna Lee¹, Chang-Fu Wu¹, Gerard Hoek², Kees de Hoogh³, Rob Beelen², Bert Brunekreef⁴, Chang-Chuan Chan⁵

Affiliations

¹ Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan.
² Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands.
³ Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland; MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
⁴ Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
⁵ Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan. Electronic address: ccchan@ntu.edu.tw.

PMID: 25659316
DOI: 10.1016/j.scitotenv.2015.01.091

Abstract

Traffic intensity, length of road, and proximity to roads are the most common traffic indicators in the land use regression (LUR) models for particulate matter in ESCAPE study areas in Europe. This study explored what local variables can improve the performance of LUR models in an Asian metropolis with high densities of roads and strong activities of industry, commerce and construction. By following the ESCAPE procedure, we derived LUR models of PM₂.₅, PM₂.₅ absorbance, PM₁₀, and PMcoarse (PM₂.₅-₁₀) in Taipei. The overall annual average concentrations of PM₂.₅, PM₁₀, and PMcoarse were 26.0 ± 5.6, 48.6 ± 5.9, and 23.3 ± 3.1 μg/m(3), respectively, and the absorption coefficient of PM₂.₅ was 2.0 ± 0.4 × 10(-5)m(-1). Our LUR models yielded R(2) values of 95%, 96%, 87%, and 65% for PM₂.₅, PM₂.₅ absorbance, PM₁₀, and PMcoarse, respectively. PM₂.₅ levels were increased by local traffic variables, industrial, construction, and residential land-use variables and decreased by rivers; while PM₂.₅ absorbance levels were increased by local traffic variables, industrial, and commercial land-use variables in the models. PMcoarse levels were increased by elevated highways. Road area explained more variance than road length by increasing the incremental value of 27% and 6% adjusted R(2) for PM₂.₅ and PM₁₀ models, respectively. In the PM₂.₅ absorbance model, road area and transportation facility explain 29% more variance than road length. In the PMcoarse model, industrial and new local variables instead of road length improved the incremental value of adjusted R(2) from 39% to 60%. We concluded that road area can better explain the spatial distribution of PM₂.₅ and PM₂.₅ absorbance concentrations than road length. By incorporating road area and other new local variables, the performance of each PM LUR model was improved. The results suggest that road area is a better indicator of traffic intensity rather than road length in a city with high density of road network and traffic.

Keywords: Elevated highway; GIS; Land use regression; Long-term exposure; Particulate matter; Road area.

Publication types

Research Support, Non-U.S. Gov't