Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan
Graphical abstract
Introduction
Particulate matter (PM) emitted from traffic vehicles, industrial operations, power generation, fuel combustion, and other sources has caused ambient air pollution locally and globally. Many epidemiological studies have demonstrated the association between prolonged exposure to PM and adverse health effects in the public, including increased mortality (Beelen et al., 2014; Schwartz et al., 2015), reduced lung function (Hwang et al., 2015), elevated blood pressure (Chang et al., 2015; Chen et al., 2015), diabetes mellitus (Chen et al., 2013), atherosclerosis (Su et al., 2015), cerebrovascular events (Stafoggia et al., 2014), myocardial infarction (Bai et al., 2019), and lung cancer (Raaschou-Nielsen et al., 2013). The International Agency for Research on Cancer (IARC) has classified PM with an aerodynamic diameter less than 2.5 μm (PM2.5) in outdoor air pollution as carcinogenic to humans (Group 1), based on sufficient evidence of carcinogenicity in humans and experimental animals and strong support from mechanistic studies (IARC, 2015; Loomis et al., 2013). In 2010, exposure to ambient PM2.5 was estimated to contribute to 3.2 million premature deaths worldwide, due largely to cardiovascular disease, and 223 000 deaths (approximately 15%) from lung cancer. According to these estimates, more than half of the lung cancer deaths attributable to ambient PM2.5 have been reported in China and other East Asian countries (Lim et al., 2012).
Land-use regression (LUR) has been widely used as an approach to estimate the ambient PM annual average for exposure assessment (Brauer et al., 2003; Eeftens et al., 2012; Hoek et al., 2011; Lee et al., 2015; Moore et al., 2007; Wu et al., 2014). The established LUR models are applied to investigate the association between PM exposure and adverse health effects in the general population (Beelen et al., 2014; Chen et al., 2015; Raaschou-Nielsen et al., 2013; Stafoggia et al., 2014; Su et al., 2015). Unlike other mechanistic models based on comprehensive data collection and physical phenomenon, LUR models empirically rely on statistical methods to combine air pollution measurements at multiple locations with surrounding geospatial features (predictor variables) (Hoek et al., 2008). LUR models also reveal the feasibility of explaining spatial concentration variations in particle mass concentration (PMC) and particle number concentration (PNC) using surrounding land-use features. Most of these LUR models were developed to estimate PMC, such as PM with an aerodynamic diameter less than 10 μm (PM10), coarse particles (PM2.5-10), and PM2.5 (Brauer et al., 2003; Eeftens et al., 2012; Hoek et al., 2011; Lee et al., 2015; Moore et al., 2007; Wu et al., 2014). Some studies have established LUR models for PNC, but they are limited to ultrafine particles (aerodynamic diameter less than 0.1 μm) (Eeftens et al., 2016; Ghassoun et al., 2015; Hoek et al., 2011; Patton et al., 2015; Wolf et al., 2017) due to measured data from the available instrument. To the best of our knowledge, no studies have applied LUR to predict PNC at different sizes.
Because PM may undergo complex physicochemical changes related to mass- and number-based concentrations upon internalization by cells leading to a biological impact and toxicity (Warheit and Brown, 2019), the spatial distribution and sensitivity to primary particle emission are noticeably different between PNC and PMC (Chen et al., 2018), and the adverse health effects caused by PM exposure were found to be different between PMC and PNC (Aguilera et al., 2016; Hennig et al., 2018). It is important to elucidate and estimate PMC and PNC at different sizes for future epidemiological studies. By using the portable and newly developed device to measure PMC and PNC at different sizes simultaneously, this study can provide the exposure estimates to build the LUR models for PMC and PNC. This study aimed to develop LUR models of PMC and PNC at different sizes by using 24-h average measurements of ambient levels encompassing one year for inhabitants living in Central Taiwan.
Section snippets
Study area
This study was conducted in the metropolitan area of Taichung, which was inhabited by 2.7 million people residing in 29 administrative districts in 2014. The study design has been described in detail previously (Chang et al., 2019b; Wang et al., 2016). In brief, a total of 50 monitoring sites for particulate concentrations were established to include 6 fixed stations of noise measurements constructed by the government, and 44 new stations built by this study according to different emission
Sampling results
Table 2 shows the annual averages of PMC and PNC at different sizes. The annual means of PM10, PM2.5, and PM1, as well as PNC sizes of 0.5–1 μm, 1–2.5 μm, 2.5–10 μm, and ≥10 μm, were significantly higher during the cold season than those measured during the warm season, with the exception of the seasonal comparison for the PNC at < 0.5 μm, which was controversially significant (all p values < 0.05). The average value of daily temperature (°C) during the warm season was significantly higher than
Main results
We found significantly seasonal differences in PMC and PNC at different sizes in Taichung, Taiwan. The higher annual levels of PMC and PNC were identified in the cold season than those measured during the warm season except the controversial association for the PNC at < 0.5 μm. The results were consistent with findings of PM10, PM2.5, and ultrafine (10–100 nm) particles in a previous study conducted in the Central Taiwan (Young et al., 2012). The association of higher PNC at <0.5 μm with warm
Conclusion
In summary, we established the LUR models with moderate performance for PMC and PNC at different sizes in an Asian metropolis. Various explained variances of the LUR models were observed between PMC and PNC at different sizes, as well as regional differences in model performance between and within continents. The land-use type and the major emitted stationary source were identified as major variables to predict the PMC. Land-use types, roads (length and width), population, and meteorological
CRediT authorship contribution statement
Ta-Yuan Chang: Conceptualization, Methodology, Formal analysis, Writing – original draft. Ching-Chih Tsai: Data curation, Software, Formal analysis. Chang-Fu Wu: Methodology, Writing – review & editing. Li-Te Chang: Investigation, Writing – review & editing. Kai-Jen Chuang: Data curation, Software. Hsiao-Chi Chuang: Visualization, Writing – review & editing. Li-Hao Young: Supervision, Writing – review & editing, All authors of this paper have read and approved the final manuscript.
Declaration of competing interest
None.
Acknowledgments
We thank the National Science Council, Taiwan (NSC 102-2221-E-039-003), for financial support. We also want to thank Dr. Rob Beelen for his methodology consultation in developing land-use regression models and suggestions for study design.
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