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1. Evaluation of receptor and chemical transport models for PM10 source apportionment

Author

Belis, C.A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J.L., Kuenen, J., Denier van Der Gon, H., Reizer, M., Riffault, V., Alleman, L.Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I., Besombes, J.-L., Bove, M.C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I., Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P.K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., and Yubero, E.

Published
2020
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2. Joint analysis of deposition fluxes and atmospheric concentrations of inorganic nitrogen and sulphur compounds predicted by six chemistry transport models in the frame of the EURODELTAIII project

Author

Vivanco, M.G., Bessagnet, B., Cuvelier, C., Theobald, M.R., Tsyro, S., Pirovano, G., Aulinger, A., Bieser, J., Calori, G., Ciarelli, G., Manders, A., Mircea, M., Aksoyoglu, S., Briganti, G., Cappelletti, A., Colette, A., Couvidat, F., D'Isidoro, M., Kranenburg, R., Meleux, F., Menut, L., Pay, M.T., Rouïl, L., Silibello, C., Thunis, P., and Ung, A.

Published
2017
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8. Toward sustainable aviation industry: assessing the impact of emissions from aircraft trajectories

Author

A. Riccio, D. Di Luccio, E. Bucchignani, Barbero, D., Calori, G, Finardi, S, Nanni, A, Pepe, N, Pozzi, C, Radice, P, Silibello, C., Tinarelli, G, M. Ciaburri, G. Duca, and Russo, R.

Abstract

Aviation is known as one of the industries that contribute to climate change and the most relevant contributor with reference to emissions at high altitude. Aviation industry’s GHGs (Greenhouse Gases) are set to continue to grow, despite technological innovation advancement in this sector, and the aviation industry is highly unlikely to achieve the carbon neutral growth within the next years and it will likely remain an important source of air pollutants. Since 1990, NOx (Nitrogen Oxides) emissions from aviation have doubled and its share has quadrupled (while emissions from other sectors like traffic or industry have been significantly reduced); in the same way, relative contribution of sulphur oxides and carbon monoxide have gone up. In 2015, it accounted for 14% of all EU transport NOx emissions and for 7% of the total EU NOx emissions. In the effort to mitigate the effects of greenhouse gases and air pollution, a lot of improvement is needed to ensure that the aviation industry reduces its carbon footprint and impact on air pollution in this fast-growing economic scenario. For these reasons, an in-depth study of the relationship between environment and aviation, focusing on all aspects of ATM (Air Traffic Management), is needed to reduce the environmental impact of flight operations. For this purpose, an advanced system for studying the impact of air quality at different spatial and temporal scales is proposed to analyze in detail the impact of emissions from the aeronautical sector, both on a local scale, e.g. in TMAs (Terminal Maneuvering Areas), and on a regional/global scale, e.g. along cruise routes beyond the planetary boundary layer and into the stratosphere. In this work, we present a complete system to assess the impact of emissions from the aviation sector, follow their fate in the atmosphere due to dispersion processes, evaluate the production of secondary pollutants (mainly ozone and particulate matter) and evaluate the effects of climatological interest (radiative effects, transport in the stratosphere). For demonstration purposes, it will be shown how these tools can be used for a detailed bottom-up analysis of airport emissions on the local scale; global emissions will be used for an analysis of the effects of the trend of emissions from the aeronautical sector.

Published
2021
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24. Diabetes, prediabetes and cancer mortality

Author

Zhou, X. H., Qiao, Q., Zethelius, B., Pyörälä, K., Söderberg, S., Pajak, A., Stehouwer, C. D. A., Heine, R. J., Jousilahti, P., Ruotolo, G., Nilsson, P. M., Calori, G., Tuomilehto, J., and for the DECODE Study Group

Published
2010
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27. European guide on air pollution source apportionment for particulate matter with source oriented models and their combined use with receptor models

Author

MIRCEA M., CALORI G., PIROVANO G., and BELIS CLAUDIO

Abstract

Particulate matter is one of the most critical atmospheric pollutants, in terms of impact on human health, in Europe and worldwide. Understanding the role of the different sources and processes leading to its formation and transformation/evolution is essential to support the development of abatement strategies. The present guide, developed under FAIRMODE WG3 with the contribution of WG4, provides an overview, illustrates the methodologies and formulates recommendations for the application of air quality source oriented models (SMs) in estimating contributions of sources to particulate matter. It also describes the potential of SM techniques and critically discusses the advantages and the limitations of applying simultaneously receptor models (RM) and source oriented models (SM) in source apportionment (SA) studies. Among the source oriented modelling methods, the document focuses on those using the Eulerian chemistry-transport models (CTMs) since they simulate both primary and secondary particulate matter directly emitted and formed from gas precursors in the atmosphere, respectively., JRC.C.5-Air and Climate

Published
2020

28. Evaluation of receptor and chemical transport models for PM10 source apportionment

Author

Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J. L., Kuenen, J., van Der Gon, H. Denier, Reizer, M., Riffault, V, Alleman, L. Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I, Besombes, J-L, Bove, M. C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I, Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P. K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., Yubero, E., Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J. L., Kuenen, J., van Der Gon, H. Denier, Reizer, M., Riffault, V, Alleman, L. Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I, Besombes, J-L, Bove, M. C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I, Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M. Garcia, Gilardoni, S., Golly, B., Hellebust, S., Hopke, P. K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A., and Yubero, E.

Abstract

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs va

Published
2020

50. A Study of Heavy Metals Pollution in Italy with the Atmospheric Modelling System of the MINNI project

Author

Mircea M., Silibello C., Calori G., Costa M. P., Dirodi G., Radice P., Vitali L., and Zanini G.

Subjects
Heavy metals, air quality modelling, model validation, Environmental sciences, GE1-350
Abstract

This work shows preliminary results for heavy metals concentrations simulated with the atmospheric modelling system (AMS) of the MINNI project. The simulation has been carried out for the year 2005 with a horizontal resolution of 25 km. The simulated concentrations of As, Cd, Ni and Pb have, in general, the highest values in urban and industrial areas and compare well with available measurements.

Published
2013
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