Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan. (1st May 2021) - Chang, Ta-Yuan; Tsai, Ching-Chih; Wu, Chang-Fu; Chang, Li-Te; Chuang, Kai-Jen; Chuang, Hsiao-Chi; Young, Li-Hao
Abstract: Land-use regression (LUR) models have been used to estimate particle mass concentration (PMC), but few studies apply it to predict particle number concentration (PNC) at different sizes. This study aimed to determine both PMC and PNC throughout one year to establish predictive models in Taichung, Taiwan. The annual averages of PM10, PM2.5, and PM1 were 71 ± 46 μg/m 3, 44 ± 35 μg/m 3, and 32 ± 28 μg/m 3, respectively. The PNC at size ranges of <0.5 μm, 0.5–1 μm, 1–2.5 μm, 2.5–10 μm, and ≥10 μm were 715098 ± 664879 counts/L, 29053 ± 30615 counts/L, 1009 ± 659 counts/L, 647 ± 347 counts/L, and 3 ± 3 counts/L, respectively. The model-explained variance (R 2 ) values of PM10, PM2.5, and PM1 were 0.42, 0.53, and 0.51, respectively. The magnitude of the R 2 values ranged from 0.31 to 0.50 for the PNC with the highest R 2 between 0.5 and 1 μm. The differences between the model R 2 and the leave-one-out cross-validation R 2 ranged from 4% to 8% for PMC and from 3% to 10% for PNC. This study developed LUR models with moderate performance to estimate PMC and PNC at different sizes in an Asian metropolis. The built LUR models may be improved by combining with other open data to increase the predictive capacity. Graphical abstract: Image 1 Highlights: Both particle mass and number concentrations are measured over one year. The model explained variance (R 2 ) is 0.53 for PM2.5 and 0.51 for PM1 . The magnitude of R 2 ranged from 0.31 to 0.50 for particle number concentrations. The built model has the highest R 2 for particle number concentrations at 0.5–1 μm. Models with moderate performance are developed to estimate particle concentrations.