Deterministic air quality forecasting models play a key role for regional and local authorities, being key tools to ensure that timely information about actual or near future exceedances of pollutant threshold values are provided to the public, as stated by the EU directive (2008/50/EC). One of the main problems of these models is that they usually underestimate some important pollutants, like PM10, especially in high-concentration areas. For this reason, the forecast of critical episodes (i.e., exceedance of 50 μg/m3 for PM10 concentration daily threshold) has low accuracy. To overcome this issue, several computationally fast techniques have been implemented in the last decade. In this work, two computational fast techniques are introduced, implemented and evaluated. The techniques are based on the off-line correction of the chemical transport model output in the forecasting window, estimated by means of the measurement data up to the beginning of the forecast. In particular, the techniques are based on the estimation of the correction performed as a linear combination of the corrections computed for the days when the measurements are available. The resulting system has been applied to the Lombardy region case (Northern Italy) for daily PM10 forecasting with good results.
Application of data fusion techniques to improve air quality forecast: A case study in the Northern Italy
Carnevale C.
;De Angelis E.;Finzi G.;Volta M.
2020-01-01
Abstract
Deterministic air quality forecasting models play a key role for regional and local authorities, being key tools to ensure that timely information about actual or near future exceedances of pollutant threshold values are provided to the public, as stated by the EU directive (2008/50/EC). One of the main problems of these models is that they usually underestimate some important pollutants, like PM10, especially in high-concentration areas. For this reason, the forecast of critical episodes (i.e., exceedance of 50 μg/m3 for PM10 concentration daily threshold) has low accuracy. To overcome this issue, several computationally fast techniques have been implemented in the last decade. In this work, two computational fast techniques are introduced, implemented and evaluated. The techniques are based on the off-line correction of the chemical transport model output in the forecasting window, estimated by means of the measurement data up to the beginning of the forecast. In particular, the techniques are based on the estimation of the correction performed as a linear combination of the corrections computed for the days when the measurements are available. The resulting system has been applied to the Lombardy region case (Northern Italy) for daily PM10 forecasting with good results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
