TY - JOUR
T1 - Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan
AU - Chang, Ta Yuan
AU - Tsai, Ching Chih
AU - Wu, Chang Fu
AU - Chang, Li Te
AU - Chuang, Kai Jen
AU - Chuang, Hsiao Chi
AU - Young, Li Hao
N1 - Funding Information:
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.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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/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 PM10, PM2.5, and PM1 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.
AB - 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/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 PM10, PM2.5, and PM1 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.
KW - Land use regression
KW - Particle mass concentration
KW - Particle number concentration
KW - Particulate matter
KW - Validity
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U2 - 10.1016/j.atmosenv.2021.118303
DO - 10.1016/j.atmosenv.2021.118303
M3 - Article
AN - SCOPUS:85101937659
SN - 1352-2310
VL - 252
JO - Atmospheric Environment
JF - Atmospheric Environment
M1 - 118303
ER -