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

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 PM 2.5 , PM 2.5 absorbance, PM 10 , and PM coarse (PM 2.5–10 ) in Taipei. The overall annual average concentrations of PM 2.5 , PM 10 , and PM coarse were 26.0 ± 5.6, 48.6 ± 5.9, and 23.3 ± 3.1 μg/m 3 , respectively, and the absorption coefficient of PM 2.5 was 2.0 ± 0.4 × 10 − 5 m − 1 . Our LUR models yielded R 2 values of 95%, 96%, 87%, and 65% for PM 2.5 , PM 2.5 absorbance, PM 10 , and PM coarse , respectively. PM 2.5 levels were increased by local traffic variables, industrial, construction, and residential land-use variables and decreased by rivers; while PM 2.5 absorbance levels were increased by local traffic variables, industrial, and commercial land-use variables in the models. PM coarse 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 PM 2.5 and PM 10 models, respectively. In the PM 2.5 absorbance model, road area and transportation facility explain 29% more variance than road length. In the PM coarse 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 PM 2.5 and PM 2.5 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. [ABSTRACT FROM AUTHOR]