Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1139-1148 |
| Number of pages | 10 |
| Journal | Science of the Total Environment |
| Volume | 636 |
| DOIs | |
| Publication status | Published - 2018 Sep 15 |
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All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Waste Management and Disposal
- Pollution
Cite this
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An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements. / Lin, Ming-Yeng; Guo, Yi Xin; Chen, Yu Cheng; Chen, Wei Ting; Young, Li Hao; Lee, Kuo-Jung; Wu, Zhu You; Tsai, Peng-Chi.
In: Science of the Total Environment, Vol. 636, 15.09.2018, p. 1139-1148.Research output: Contribution to journal › Article
TY - JOUR
T1 - An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements
AU - Lin, Ming-Yeng
AU - Guo, Yi Xin
AU - Chen, Yu Cheng
AU - Chen, Wei Ting
AU - Young, Li Hao
AU - Lee, Kuo-Jung
AU - Wu, Zhu You
AU - Tsai, Peng-Chi
PY - 2018/9/15
Y1 - 2018/9/15
N2 - 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.
AB - 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.
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U2 - 10.1016/j.scitotenv.2018.04.248
DO - 10.1016/j.scitotenv.2018.04.248
M3 - Article
VL - 636
SP - 1139
EP - 1148
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
ER -