LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction
Journal
Science of the Total Environment
Journal Volume
514
Pages
178-184
Date Issued
2015
Author(s)
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.
SDGs
Publisher
Elsevier B.V.
Type
journal article