Data fusion systems 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 assimilation strategy for fusing the CTM output with monitoring data in order to improve air quality assessment and forecasting in the Emilia-Romagna region, Italy. We employ dynamic linear modeling 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. We apply our strategy to particulate matter (PM10) concentrations during winter 2013.
|Titolo della pubblicazione ospite||Air Pollution Modeling and its Applications XXIV|
|Numero di pagine||5|
|Stato di pubblicazione||Pubblicato - 2016|
- Kalman filter
- numerical model