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This work presents the on-line coupled meteorology–chemistry transport model BOLCHEM, based on the hydrostatic meteorological BOLAM model, the gas chemistry module SAPRC90, and the aerosol dynamic module AERO3. It includes parameterizations to describe natural source emissions, dry and wet removal processes, as well as the transport and dispersion of air pollutants. The equations for different processes are solved on the same grid during the same integration step, by means of a time-split scheme. This paper describes the model and its performance at horizontal resolution of 0.2 ∘ × 0.2 ∘ over Europe and 0.1 ∘ × 0.1 ∘ in a nested configuration over Italy, for one year run (December 2009–November 2010). The model has been evaluated against the AIRBASE data of the European Environmental Agency. The basic statistics for higher resolution simulations of O3, NO2 and particulate matter concentrations (PM 2.5 and PM10) have been compared with those from Copernicus Atmosphere Monitoring Service (CAMS) ensemble median. In summer, for O3 we found a correlation coefficient R of 0.72 and mean bias of 2.15 over European domain and a correlation coefficient R of 0.67 and mean bias of 2.36 over Italian domain. PM10 and PM 2.5 are better reproduced in the winter, the latter with a correlation coefficient R of 0.66 and the mean bias MB of 0.35 over Italian domain. [ABSTRACT FROM AUTHOR]

In this paper, an operational forecasting and daily assessment system of air quality is presented. This new system is thought of as a Copernicus-CAMS downstream national service, able to develop and implement a service for air quality forecasting and monitoring in the Italian domain, running every day on the National territory. The system is being developed on behalf of a cooperation between Agenzia Spaziale Italiana (ASI) and Sistema Nazionale Protezione Ambiente (SNPA). SNPA is the network between Istituto Superiore per la Protezione e Ricerca Ambientale (ISPRA) and the Regional Environmental Agencies (ARPAs). The objective of the cooperation is to provide full operation service in terms of continuity, sustainability, and availability of the air quality forecast and evaluation services at the national level. The system forecasts and analyzes air quality throughout Italy, with a focus on Italian regions, for the principal pollutants: Particulate matter with diameter smaller than 10 μm (PM10), ozone (O3), and nitrogen dioxide (NO2). It includes a Chemical Transport Model (CTM) nested with the Copernicus Atmosphere Monitoring Service (CAMS) global model and data from the air quality monitoring stations in Italy. The system, under public control and based on open software, is now under testing. To date, it is able to deliver free open data, which is available to environmental agencies and citizens. The data are delivered both as maps and graphs, and as numerical data, useful for providing boundary conditions to local–high resolution-air quality models or for developing customized services. In this work, a downscaling application to a regional nested domain highlights how the new air quality forecasting system gains better results than the Copernicus-CAMS system. [ABSTRACT FROM AUTHOR]

Due to some harmful effects on humans and the environment, particulate matter (PM) has recently become among the most studied atmospheric pollutants. Given the growing sensitivity to the problem and, since production and accumulation phenomena involving both primary and secondary P M 10 fractions are complex and non-linear, environmental authorities need tools to assess their plans designed to improve the air quality as requested from environmental laws. Multi-criteria decision analysis (MCDA) can be applied to support decision makers, by processing quantitative opinions provided by pools of experts, especially when different views on social aspects should be considered. The results obtained through this approach, however, can be highly dependent on the subjectivity of experts. To partially overcome these challenges, this paper suggests a two-step methodology in which an MCDA is fed with the solution of a multi-objective analysis (MOA). The methodology has been applied to a test case in northern Italy and the results show that this approach is a viable solution for the inclusion of subjective criteria in decision making, while reducing the impact of uncertain expert opinions for data that can be computed through the MOA. [ABSTRACT FROM AUTHOR]