An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements

Ming Yeng Lin, Yi Xin Guo, Yu Cheng Chen, Wei Ting Chen, Li Hao Young, Kuo Jung Lee, Zhu You Wu, Perng Jy Tsai

研究成果: Article同行評審

15 引文 斯高帕斯(Scopus)

摘要

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R2) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement.

原文English
頁(從 - 到)1139-1148
頁數10
期刊Science of the Total Environment
636
DOIs
出版狀態Published - 2018 9月 15

All Science Journal Classification (ASJC) codes

  • 環境工程
  • 環境化學
  • 廢物管理和處置
  • 污染

指紋

深入研究「An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements」主題。共同形成了獨特的指紋。

引用此