Dynamic data fusion approach for air quality assessment

Abstract

Data fusion procedures are developed to fill the gap between monitoring networks and CTMs. However, they often do not account for temporal dynamics, leading to potential inaccurate air quality assessment and forecasting. We propose a statistical data fusion strategy for combing the CTM output with monitoring data in order to improve air quality assessment and forecasting in the Emilia-Romagna region, Italy. We employ a dynamic linear model to accommodate dependence across time and obtain air pollution assessment and forecasting for the current and next two days. Finally, air pollution forecast maps are provided at high spatial resolution using universal kriging and exploiting the CTM output. We apply our strategy to particulate matter (PM10) concentrations during winter 2013.