LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction
Introduction
Land use regression (LUR) has been widely used and has rapidly become an important approach to predict long-term average pollutant concentration at an intra-urban scale. Recently, the European Study of Cohorts for Air Pollution Effects (ESCAPE) project described the development and performance of LUR models of 20 European study areas for particulate matter (PM) (Eeftens et al., 2012a). Based on the exposures estimated from the LUR models, this project illustrated an association between mortality and average annual exposure to fine particles (Beelen et al., 2014). Several studies have also developed LUR models for assessing intra-urban contrast of PM in North America (Henderson et al., 2007, Moore et al., 2007, Ross et al., 2007). The above-mentioned European and North American LUR models yielded a predictive capacity (as R2) ranging between 35% and 94% for PM2.5, 39% and 97% for PM2.5 absorbance (i.e., soot), 50% and 90% for PM10, and 32% and 81% for PMcoarse (PM10–2.5). To estimate the concentrations of air pollutants at any point in the area, air pollution data and predictor variables are required. The selection of variables hence plays an important role in developing LUR models. Most study areas in Europe and North America, such as the Netherlands, Munich, London, Los Angeles, and New York City, share the same characteristics. However, cities in rapidly developing countries, such as Taipei, Taiwan, usually have different land use attributes from those in developed countries. One of such variable is the traffic-related predictor of road length, which was suitably used in several LUR studies (Hoek et al., 2008) as a good indicator of traffic intensity in European and North American cities, but may not be good enough in traffic-jammed cities like Taipei. Under such circumstances, road area can be an alternative to road length as a better land use variable for traffic intensity in LUR models in Asian cities with crowded roads. To our knowledge, no research has yet focused on the role of road area as a surrogate for traffic intensity when traffic flow data was not available. Elevated highways, which have been commonly constructed to increase road capacity across the city and soothe the traffic jams in city centers in Asian cities, were seldom considered in previous LUR models. Intra-city variation in air pollution can be better described by including elevated highways in the LUR models as one previous study reported that overpass structures could affect the distribution of air pollutants (Tong et al., 2011).
The continuous construction of buildings and infrastructures to meet residents' living demands is another common feature in rapidly urbanized cities. Previous LUR models have overlooked spatial variation in particulate air pollution arising from fugitive emissions of such construction activities, which are even more important in Asian cities. The mix of residential, commercial, and industrial areas in one small city district is not uncommon in Asian cities, where zoning policies are not as strictly enforced as in most European and North American cities. The individual contribution of residential, commercial, and industrial areas to spatial variation in PM needs to be adequately considered when they are included in LUR models.
Rivers and green lands in cities, by contrast, can alleviate air pollution in nearby areas by restricting or diluting pollution emissions. River, a component of the green land variable in the ESCAPE LUR models, can also be treated as an independent variable as it can cover a larger area in some urban environments and its air pollution dilution effects can be different, compared to urban green land.
This study aimed at developing LUR models by following the ESCAPE modeling procedures to characterize the spatial distribution of PM2.5, PM2.5 absorbance, PM10, and PMcoarse. We focused on the role of specific variables that are important and are associated with emissions of PM in the Taipei metropolis but which have rarely been used in European and North American LUR studies. Further, we investigated whether the performance of PM LUR models in Taipei can be equivalent to those of European cities in the ESCAPE project by incorporating specific new variables. Results of this study could provide a higher spatial resolution of estimation of exposure to PM for future health studies in Taipei, Taiwan.
Section snippets
Study area
The Taipei metropolis lies north of Taiwan Island with the total size of the study area accounting for about 800 km2 and with a population of 6 million. It falls within the Tamsui River Basin, which is considered as the heartland of Taiwan (the detailed description of current traffic and land use status is presented in Appendix A). Table 1 shows the land use characteristics of Taipei in 2010. The inhabitable area is only about half of the study domain area, while other areas are conservation
PM measurements
Table 2 summarizes the PM10, and PMcoarse, PM2.5 concentrations and the PM2.5 absorbance measured from 2009–2010 at 9 street sites and 11 urban background sites in Taipei. The annual averages of PM2.5, PM10, and PMcoarse mass concentrations for the 20 sampling sites were 26.0, 48.6 and 23.3 μg/m3, respectively, and the absorption coefficient of PM2.5 (PM2.5 absorbance) was 2.0 × 10− 5 m− 1. As expected, the PM concentrations measured at street sites were higher than those at urban background sites.
Discussion
Our study has developed LUR models for the Taipei study area by following the same model development and validation procedures as applied in the European ESCAPE project. With extra-potential localized predictor variables, including road area, industry, commerce, construction, transportation facility, river, length of elevated highway and major road, and proximity to road, moderate to great explained variance was obtained for PM10, PMcoarse, PM2.5, and PM2.5 absorbance. Explained variance of the
Conclusions
In conclusion, the ESCAPE LUR modeling approach can be applied to Asian cities with high densities of roads and significant industrial, commerce and construction activities to develop LUR models for PM2.5, PM2.5 absorbance, PM10, and PMcoarse. This study has shown that incorporating local variables improves the performance of LUR models in Taipei. The introducing of road area data as a traffic variable has gained the greatest advancement of model performance for PM2.5, PM2.5 absorbance, and PM10
Funding source
This research was supported by the project NSC97-2923-I002-001-MY4 of the National Science Council of Taiwan.
Financial disclosure
Authors have no financial relationships relevant to this article to disclose.
Conflict of interest
Authors have no conflicts of interest to disclose.
Acknowledgments
This research was supported by the project NSC97-2923-I002-001-MY4 of the National Science Council of Taiwan. The results of this research have received scientific contribution from the European Community's Seventh Framework Program (FP/2007-2011) under the following grant agreement number: 211250.
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