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Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM 10 , PM 2.5 , NO 2 , and O 3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds). [ABSTRACT FROM AUTHOR]

Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of Ensemble Model Output Statistics (EMOS) with a spatio-temporal interpolation process performed through the Stochastic Partial Differential Equation-Integrated Nested Laplace Approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use-case is provided, scaling down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2 and O3) at daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role played by conditioning variables to improve the forecast capabilities of ensemble predictions, thus allowing a net improvement of the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the predictive model performance shows a significant improvement of the performance of the raw ensemble forecast, with an almost zero bias, significantly reduced root mean square errors and correlations almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is to be used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the forecast quality was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp, and much better calibrated than the raw ensemble forecast. Finally, we present some additional outcomes based on a set of gridded (4 km x 4 km) daily maps covering the whole Italian country, for the detection of areas where pollution peaks forecasts (exceedances of the regulatory thresholds) occur. [ABSTRACT FROM AUTHOR]

A chemical characterization of PM10 collected at hydrofoil exhaust stacks was performed conducting two on-board measuring campaigns, with the aim of assessing the ship emission impact on PM10 collected in the coastal area of Naples (Southern Italy) and providing information about the characteristics of this important PM emission source. Samples were analysed determining the contribution of different chemical parameters to PM10's mass, which consisted of polycyclic aromatic hydrocarbons (PAHs) (0.10 ± 0.12%), total carbon (61.9% ± 20.0%, with 40.4% of organic carbon, OC, and 21.5% of elemental carbon, EC) and elemental fraction (0.44% ± 1.00%). Differences in terms of composition and chemical parameter profiles were observed between samples collected during offshore navigation (Off) and samples collected during shunting operations (SO), the latter of higher concern on a local scale. For SO samples, lower contributions of OC and EC were observed (39.7% and 19.6% respectively) compared to Off samples (41.5% and 24.2%), and an increase in terms of elements (from 0.32 to 0.51%) and PAHs (from 0.06 to 0.12%) concentrations was observed. In addition, enrichment factors (EFs) for some elements such as V, Zn, Cd, Cu, Ag and Hg as well as PAHs profile varied significantly between SO and Off. Data presented here were compared with data on chemical composition of PM10 sampled in a tunnel, in a background site and in an urban site in the city of Naples. Results indicated that shipping activities contributed significantly to the emission of V and, in some extent, Zn and Cd; in addition, PAH profiles indicated a greater contribution to urban PM10 from vehicular traffic than shipping emissions. These results can significantly contribute to the correct evaluation of the influence of shipping emission on PM10 generation in urban coastal areas and can be a useful reference for similar studies. The coastal area of Naples is an important example of the coexistence of residential, touristic and natural areas with pollutants emission sources including, among the others, shipping emissions. In this and similar contexts, it is important to distinguish the contribution of each emission source to clearly define environmental control policies. [ABSTRACT FROM AUTHOR]