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.
ASJC Scopus subject areas
- Health, Toxicology and Mutagenesis