1. Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan.
- Author
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Chang, Ta-Yuan, Tsai, Ching-Chih, Wu, Chang-Fu, Chang, Li-Te, Chuang, Kai-Jen, Chuang, Hsiao-Chi, and Young, Li-Hao
- Subjects
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REGRESSION analysis , *PREDICTION models , *PARTICULATE matter - 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 PM 10 , PM 2.5 , and PM 1 were 71 ± 46 μg/m3, 44 ± 35 μg/m3, and 32 ± 28 μg/m3, 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 (R2) values of PM 10 , PM 2.5 , and PM 1 were 0.42, 0.53, and 0.51, respectively. The magnitude of the R2 values ranged from 0.31 to 0.50 for the PNC with the highest R2 between 0.5 and 1 μm. The differences between the model R2 and the leave-one-out cross-validation R2 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. [Display omitted] • Both particle mass and number concentrations are measured over one year. • The model explained variance (R2) is 0.53 for PM 2.5 and 0.51 for PM 1. • The magnitude of R2 ranged from 0.31 to 0.50 for particle number concentrations. • The built model has the highest R2 for particle number concentrations at 0.5–1 μm. • Models with moderate performance are developed to estimate particle concentrations. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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