Development of land-use regression models to estimate particle mass and number concentrations in Taichung, Taiwan

Ta Yuan Chang, Ching Chih Tsai, Chang Fu Wu, Li Te Chang, Kai Jen Chuang, Hsiao Chi Chuang, Li Hao Young

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號118303
期刊Atmospheric Environment
252
DOIs
出版狀態已發佈 - 五月 1 2021

ASJC Scopus subject areas

  • 環境科學 (全部)
  • 大氣科學

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