Abstract. A new procedure is presented for simultaneous estimation of SO2 and ash abundance in a volcanic plume, using thermal infrared (TIR) MODIS data. Plume altitude and temperature are the only two input parameters required to run the procedure, while surface emissivity, temperature, atmospheric profiles, ash optical properties, and radiative transfer models are not necessary to perform the atmospheric corrections. The procedure gives the most reliable results when the surface under the plume is uniform, for example above the ocean, but still produces fairly good estimates in more challenging and not easily modelled conditions, such as above land or meteorological cloud layers. The developed approach was tested on the Etna volcano. By linearly interpolating the radiances surrounding a detected volcanic plume, the volcanic plume removal (VPR) procedure described here computes the radiances that would have been measured by the sensor in the absence of a plume, and reconstructs a new image without plume. The new image and the original data allow computation of plume transmittance in the TIR-MODIS bands 29, 31, and 32 (8.6, 11.0 and 12.0 μm) by applying a simplified model consisting of a uniform plume at a fixed altitude and temperature. The transmittances are then refined with a polynomial relationship obtained by means of MODTRAN simulations adapted for the geographical region, ash type, and atmospheric profiles. Bands 31 and 32 are SO2 transparent and, from their transmittances, the effective ash particle radius (Re), and aerosol optical depth at 550 nm (AOD550) are computed. A simple relation between the ash transmittances of bands 31 and 29 is demonstrated and used for SO2 columnar content (cs) estimation. Comparing the results of the VPR procedure with MODTRAN simulations for more than 200 000 different cases, the frequency distribution of the differences shows the following: the Re error is less than ±0.5 μm in more than 60% of cases; the AOD550 error is less than ±0.125 in 80% of cases; the cs error is less than ±0.5 g m−2 in more than 60% of considered cases. The VPR procedure was applied in two case studies of recent eruptions occurring at the Mt Etna volcano, Italy, and successfully compared with the results obtained from the established SO2 and ash assessments based on look-up tables (LUTs). Assessment of the sensitivity to the plume altitude uncertainty is also made. The VPR procedure is simple, extremely fast, and can be adapted to other ash types and different volcanoes.

In this work, the new 1 km-resolved Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is employed to characterize seasonal PM10 - AOD correlations over northern Italy. The accuracy of the new dataset is assessed compared to the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data, retrieved at 0.55 μm with spatial resolution of 10 km (MYD04_L2). We focused on evaluating the ability of these two products to characterize both temporal and spatial distributions of aerosols within urban and suburban areas. Ground PM10 measurements were obtained from 73 of the Italian Regional Agency for Environmental Protection (ARPA) monitoring stations, spread across northern Italy, during a three-year period from 2010 to 2012. The Po Valley area (northern Italy) was chosen as the study domain because of its severe urban air pollution, resulting from it having the highest population and industrial manufacturing density in the country, being located in a valley where two surrounding mountain chains favor the stagnation of pollutants. We found that the global correlations between the bin-averaged PM10 and AOD are R2 = 0.83 and R2 = 0.44 for MYD04_L2 and for MAIAC, respectively, suggesting a greater sensitivity of the high-resolution product to small-scale deviations. However, the introduction of Relative Humidity (RH) and Planetary Boundary Layer (PBL) depth corrections allowed for a significant improvement to the bin-averaged PM - AOD correlation, which led to a similar performance: R2 = 0.96 for MODIS and R2 = 0.95 for MAIAC. Furthermore, the introduction of the PBL information in the corrected AOD values was found to be crucial in order to capture the clear seasonal cycle shown by measured PM10 values. The study allowed us to define four seasonal linear correlations that estimate PM10 concentrations satisfactorily from the remotely sensed MAIAC AOD retrieval. Overall, the results show that the high resolution provided by MAIAC retrieval data is much more relevant than the 10 km MODIS data to characterize PM10 in this region of Italy which has a pretty limited geographical domain but a broad variety of land usages and consequent particulate concentrations

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.