Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors

Chi Chang Ho, Ling Jyh Chen, Jing Shiang Hwang

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM2.5 data in Taiwan. We developed models for estimating ground-level PM2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM2.5 data from the Airbox sensors (AB-PM2.5) was used to predict daily mean PM2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM2.5 at all the grids in the previous year. The modeling–predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R2 increased from 0.40 to 0.87 when the AB-PM2.5 data were included as a nonlinear component in the model, indicating that AB-PM2.5 is a significant predictor of ground-level PM2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM2.5 concentrations were smaller than 5 μg/m3 and the R2 of the CV models of 0.79–0.88 during 2006–2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM2.5 for cohorts of small areas in health impact assessment studies.

原文英語
文章編號114810
期刊Environmental Pollution
264
DOIs
出版狀態已發佈 - 2020 九月
對外發佈Yes

ASJC Scopus subject areas

  • Toxicology
  • Pollution
  • Health, Toxicology and Mutagenesis

指紋 深入研究「Estimating ground-level PM<sub>2.5</sub> levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors」主題。共同形成了獨特的指紋。

引用此