In this work, we describe and implement a data assimilation approach for PM10pollution data in Northern Italy. This was done by combining the best available information from observations and chemical transport models. Specifically, by (1) incorporating PM10surface daily concentrations and model results from the CAMS (Copernicus Atmosphere Monitoring Service) ensemble; and (2) spreading the forecast corrections from the observation locations to the entire gridded domain covered by model forecasts by means of a data regularization approach. Results were verified against independent PM10observations measured at 169 stations by local Environmental Protection Agencies. Twelve months of observations were matched in time and space, from January to December 2017, with air pollution model results. The studied domain encompassed the Po Valley, one of the most polluted areas in Europe, and that still does not meet the air quality criteria for the annual average concentration and the maximum number of exceedances allowed for the particulate matter. Raw model data were found to be affected by a bias with a strong seasonal dependency: a large negative bias in winter and a small bias in the summer months. The data assimilation approach, embedded into a Bayesian hierarchical approach, was able to drastically reduce the bias. Furthermore, an advanced computational approach, based on the variational Bayes method coupled with the minimization of the Kullback–Leibler divergence to approximate the optimal solution, made it possible to cost-effectively assimilate data throughout the period under consideration. By using stratified cross-validation to test the accuracy of our predictions, we found high out-of-sample R2(=0.83) and an average decrease of about two-thirds of the root mean square error. Assimilated data were used to produce daily resolved cumulative population exposures. The Po Valley, in relation to the interim targets (ITs) defined by the World Health Organization, accomplishes the IT-2 level, that is to say that the average annual concentration is lower than 50 μg/m3, but it is still very far from the IT-3 level, corresponding to an average annual concentration of less than 30 μg/m3. Moreover, most of the Po Valley still has a high number of days in which the average daily concentration is higher than 50 μg/m3, well above the maximum limit of 35 days established by European and Italian legislation. Our results demonstrate that PM10can be reproduced reliably using this assimilation approach, combining different sources of information, so as to perform a thorough diagnosis of air quality over a spatially and temporally uniform area.
Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations
Chianese, E.;Galletti, A.;Giunta, G.;Marcellino, L.;Montella, R.;Riccio, A.
2018-01-01
Abstract
In this work, we describe and implement a data assimilation approach for PM10pollution data in Northern Italy. This was done by combining the best available information from observations and chemical transport models. Specifically, by (1) incorporating PM10surface daily concentrations and model results from the CAMS (Copernicus Atmosphere Monitoring Service) ensemble; and (2) spreading the forecast corrections from the observation locations to the entire gridded domain covered by model forecasts by means of a data regularization approach. Results were verified against independent PM10observations measured at 169 stations by local Environmental Protection Agencies. Twelve months of observations were matched in time and space, from January to December 2017, with air pollution model results. The studied domain encompassed the Po Valley, one of the most polluted areas in Europe, and that still does not meet the air quality criteria for the annual average concentration and the maximum number of exceedances allowed for the particulate matter. Raw model data were found to be affected by a bias with a strong seasonal dependency: a large negative bias in winter and a small bias in the summer months. The data assimilation approach, embedded into a Bayesian hierarchical approach, was able to drastically reduce the bias. Furthermore, an advanced computational approach, based on the variational Bayes method coupled with the minimization of the Kullback–Leibler divergence to approximate the optimal solution, made it possible to cost-effectively assimilate data throughout the period under consideration. By using stratified cross-validation to test the accuracy of our predictions, we found high out-of-sample R2(=0.83) and an average decrease of about two-thirds of the root mean square error. Assimilated data were used to produce daily resolved cumulative population exposures. The Po Valley, in relation to the interim targets (ITs) defined by the World Health Organization, accomplishes the IT-2 level, that is to say that the average annual concentration is lower than 50 μg/m3, but it is still very far from the IT-3 level, corresponding to an average annual concentration of less than 30 μg/m3. Moreover, most of the Po Valley still has a high number of days in which the average daily concentration is higher than 50 μg/m3, well above the maximum limit of 35 days established by European and Italian legislation. Our results demonstrate that PM10can be reproduced reliably using this assimilation approach, combining different sources of information, so as to perform a thorough diagnosis of air quality over a spatially and temporally uniform area.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
