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2. Improving the Interest of High-School Students toward Chemistry by Crime Scene Investigation

Author

Basso, A., Chiorri, C., Bracco, F., Carnasciali, M. M., Alloisio, M., and Grotti, M.

Abstract

Improving the interest of high-school students towards chemistry (and science in general) is one of the goals of the Italian Ministry of Education. To this aim, we designed a context-based activity that actively involved students in six different laboratory experiences interconnected by a case study of the murder of Miss Scarlet, from the famous game Clue. Key points of the activity were: the interest aroused by the subject of crime scene investigation; the direct involvement of the students in all stages of the work (from the realization of the experiments to the resolution of the case); the use of a multidisciplinary approach for addressing a complex scientific problem; the work in chemical laboratories with modern instrumentation; the team work and the supervision by young tutors. To verify the hypothesis that such a multidisciplinary activity could foster the interest for the discipline, an evaluation was performed using a self-report questionnaire designed to assess changes in the situational interest raised by the internship. It was found that the activity significantly increased interest and attitude toward chemistry, mainly for students with lower scores in pleasure for the study of chemistry, self-efficacy and self-concept in chemistry

Published
2018
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8. From model selection to maps: A completely design-based data-driven inference for mapping forest resources

Author

Di Biase, R, Fattorini, L, Franceschi, S, Grotti, M, Puletti, N, Corona, P, Di Biase, Rosa Maria, Fattorini, Lorenzo, Franceschi, Sara, Grotti, Mirko, Puletti, Nicola, Corona, Piermaria, Di Biase, R, Fattorini, L, Franceschi, S, Grotti, M, Puletti, N, Corona, P, Di Biase, Rosa Maria, Fattorini, Lorenzo, Franceschi, Sara, Grotti, Mirko, Puletti, Nicola, and Corona, Piermaria

Abstract

A completely data-driven, design-based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least-squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave-one-out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described.

Published
2022

19. Correction to: Progress on bringing together raptor collections in Europe for contaminant research and monitoring in relation to chemicals regulation (Environmental Science and Pollution Research, (2019), 26, 20, (20132-20136), 10.1007/s11356-019-05340-6)

Author

Movalli, P. Duke, G. Ramello, G. Dekker, R. Vrezec, A. Shore, R.F. García-Fernández, A. Wernham, C. Krone, O. Alygizakis, N. Badry, A. Barbagli, F. Biesmeijer, K. Boano, G. Bond, A.L. Choresh, Y. Christensen, J.B. Cincinelli, A. Danielsson, S. Dias, A. Dietz, R. Eens, M. Espín, S. Eulaers, I. Frahnert, S. Fuiz, T.I. Gkotsis, G. Glowacka, N. Gómez-Ramírez, P. Grotti, M. Guiraud, M. Hosner, P. Johansson, U. Jaspers, V.L.B. Kamminga, P. Koschorreck, J. Knopf, B. Kubin, E. Brutto, S.L. Lourenco, R. Martellini, T. Martínez-López, E. Mateo, R. Nika, M.-C. Nikolopoulou, V. Osborn, D. Pauwels, O. Pavia, M. Pereira, M.G. Rüdel, H. Sanchez-Virosta, P. Slobodnik, J. Sonne, C. Thomaidis, N. Töpfer, T. Treu, G. Väinölä, R. Valkama, J. van der Mije, S. Vangeluwe, D. Warren, B.H. Woog, F.

Subjects
GeneralLiterature_INTRODUCTORYANDSURVEY, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, ComputerApplications_COMPUTERSINOTHERSYSTEMS, GeneralLiterature_MISCELLANEOUS
Abstract

The correct affiliation of Sabrina Lo Brutto is shown in this paper. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Published
2019

23. Correction to: Progress on bringing together raptor collections in Europe for contaminant research and monitoring in relation to chemicals regulation (Environmental Science and Pollution Research, (2019), 26, 20, (20132-20136), 10.1007/s11356-019-05340-6)

Author

Movalli, P., Duke, G., Ramello, G., Dekker, R., Vrezec, A., Shore, R.F., García-Fernández, A., Wernham, C., Krone, O., Alygizakis, N., Badry, A., Barbagli, F., Biesmeijer, K., Boano, G., Bond, A.L., Choresh, Y., Christensen, J.B., Cincinelli, A., Danielsson, S., Dias, A., Dietz, R., Eens, M., Espín, S., Eulaers, I., Frahnert, S., Fuiz, T.I., Gkotsis, G., Glowacka, N., Gómez-Ramírez, P., Grotti, M., Guiraud, M., Hosner, P., Johansson, U., Jaspers, V.L.B., Kamminga, P., Koschorreck, J., Knopf, B., Kubin, E., Brutto, S.L., Lourenco, R., Martellini, T., Martínez-López, E., Mateo, R., Nika, M.-C., Nikolopoulou, V., Osborn, D., Pauwels, O., Pavia, M., Pereira, M.G., Rüdel, H., Sanchez-Virosta, P., Slobodnik, J., Sonne, C., Thomaidis, N., Töpfer, T., Treu, G., Väinölä, R., Valkama, J., Mije, S. van der, Vangeluwe, D., Warren, B.H., Woog, F., and Publica

Abstract

The correct affiliation of Sabrina Lo Brutto is shown in this paper.

Published
2019

24. Progress on bringing together raptor collections in Europe for contaminant research and monitoring in relation to chemicals regulation.

Author

Movalli, P, Duke, G, Ramello, G, Dekker, R, Vrezec, A, Shore, RF, García-Fernández, A, Wernham, C, Krone, O, Alygizakis, N, Badry, A, Barbagli, F, Biesmeijer, K, Boano, G, Bond, AL, Choresh, Y, Christensen, JB, Cincinelli, A, Danielsson, S, Dias, A, Dietz, R, Eens, M, Espín, S, Eulaers, I, Frahnert, S, Fuiz, TI, Gkotsis, G, Glowacka, N, Gómez-Ramírez, P, Grotti, M, Guiraud, M, Hosner, P, Johansson, U, Jaspers, VLB, Kamminga, P, Koschorreck, J, Knopf, B, Kubin, E, LoBrutto, S, Lourenco, R, Martellini, T, Martínez-López, E, Mateo, R, Nika, M-C, Nikolopoulou, V, Osborn, D, Pauwels, O, Pavia, M, Pereira, MG, Rüdel, H, Sanchez-Virosta, P, Slobodnik, J, Sonne, C, Thomaidis, N, Töpfer, T, Treu, G, Väinölä, R, Valkama, J, van der Mije, S, Vangeluwe, D, Warren, BH, Woog, F, Movalli, P, Duke, G, Ramello, G, Dekker, R, Vrezec, A, Shore, RF, García-Fernández, A, Wernham, C, Krone, O, Alygizakis, N, Badry, A, Barbagli, F, Biesmeijer, K, Boano, G, Bond, AL, Choresh, Y, Christensen, JB, Cincinelli, A, Danielsson, S, Dias, A, Dietz, R, Eens, M, Espín, S, Eulaers, I, Frahnert, S, Fuiz, TI, Gkotsis, G, Glowacka, N, Gómez-Ramírez, P, Grotti, M, Guiraud, M, Hosner, P, Johansson, U, Jaspers, VLB, Kamminga, P, Koschorreck, J, Knopf, B, Kubin, E, LoBrutto, S, Lourenco, R, Martellini, T, Martínez-López, E, Mateo, R, Nika, M-C, Nikolopoulou, V, Osborn, D, Pauwels, O, Pavia, M, Pereira, MG, Rüdel, H, Sanchez-Virosta, P, Slobodnik, J, Sonne, C, Thomaidis, N, Töpfer, T, Treu, G, Väinölä, R, Valkama, J, van der Mije, S, Vangeluwe, D, Warren, BH, and Woog, F

Published
2019

30. Chlorophyll-a in Antarctic Landfast Sea Ice: A First Synthesis of Historical Ice Core Data

Author

Meiners, Klaus M., Vancoppenolle, Martin, Carnat, G., Castellani, Giulia, Delille, B., Delille, D., Dieckmann, Gerhard, Flores, Hauke, Fripiat, François, Grotti, M., Lange, Benjamin, Lannuzel, Delphine, Martin, A., McMinn, Andrew, Nomura, Daiki, Peeken, Ilka, Rivaro, P., Ryan, K.G., Stefels, J., Swadling, K. M., Thomas, D. N., Tison, J. L., van der Merwe, P., van Leeuwe, M. A., Weldrick, C., Yang, E.J., Meiners, Klaus M., Vancoppenolle, Martin, Carnat, G., Castellani, Giulia, Delille, B., Delille, D., Dieckmann, Gerhard, Flores, Hauke, Fripiat, François, Grotti, M., Lange, Benjamin, Lannuzel, Delphine, Martin, A., McMinn, Andrew, Nomura, Daiki, Peeken, Ilka, Rivaro, P., Ryan, K.G., Stefels, J., Swadling, K. M., Thomas, D. N., Tison, J. L., van der Merwe, P., van Leeuwe, M. A., Weldrick, C., and Yang, E.J.

Abstract

Historical sea ice core chlorophyll-a (Chla) data are used to describe the seasonal, regional, and vertical distribution of ice algal biomass in Antarctic landfast sea ice. The analyses are based on the Antarctic Fast Ice Algae Chlorophyll-a data set, a compilation of currently available sea ice Chla data from landfast sea ice cores collected at circum-Antarctic nearshore locations between 1970 and 2015. Ice cores were typically sampled from thermodynamically grown first-year ice and have thin snow depths (mean=0.0520.097m). The data set comprises 888 ice cores, including 404 full vertical profile cores. Integrated ice algal Chla biomass (range: <0.1-219.9mg/m(2), median=4.4mg/m(2), interquartile range=9.9mg/m(2)) peaks in late spring and shows elevated levels in autumn. The seasonal Chla development is consistent with the current understanding of physical drivers of ice algal biomass, including the seasonal cycle of irradiance and surface temperatures driving landfast sea ice growth and melt. Landfast ice regions with reported platelet ice formation show maximum ice algal biomass. Ice algal communities in the lowermost third of the ice cores dominate integrated Chla concentrations during most of the year, but internal and surface communities are important, particularly in winter. Through comparison of biomass estimates based on different sea ice sampling strategies, that is, analysis of full cores versus bottom-ice section sampling, we identify biases in common sampling approaches and provide recommendations for future survey programs: for example, the need to sample fast ice over its entire thickness and to measure auxiliary physicochemical parameters. Plain Language Summary Antarctic sea ice is a key driver of physical, chemical, and biological processes in the Southern Ocean. Importantly, sea ice serves as a substrate for microscopic algae which grow in the bottom, interior, and surface layers of the ice. These algae are considered an important food source for A

Published
2018

31. Chlorophyll-a in Antarctic Landfast Sea Ice : A First Synthesis of Historical Ice Core Data

Author

Meiners, K. M., Vancoppenolle, M., Carnat, G., Castellani, G., Delille, B., Delille, D., Dieckmann, G. S., Flores, H., Fripiat, F., Grotti, M., Lange, B. A., Lannuzel, D., Martin, A., McMinn, A., Nomura, D., Peeken, I., Rivaro, P., Ryan, K. G., Stefels, J., Swadling, K. M., Thomas, D. N., Tison, J.-L., van der Merwe, P., van Leeuwe, M. A., Weldrick, C., Yang, E. J., Meiners, K. M., Vancoppenolle, M., Carnat, G., Castellani, G., Delille, B., Delille, D., Dieckmann, G. S., Flores, H., Fripiat, F., Grotti, M., Lange, B. A., Lannuzel, D., Martin, A., McMinn, A., Nomura, D., Peeken, I., Rivaro, P., Ryan, K. G., Stefels, J., Swadling, K. M., Thomas, D. N., Tison, J.-L., van der Merwe, P., van Leeuwe, M. A., Weldrick, C., and Yang, E. J.

Abstract

Historical sea ice core chlorophyll‐a (Chla) data are used to describe the seasonal, regional, and vertical distribution of ice algal biomass in Antarctic landfast sea ice. The analyses are based on the Antarctic Fast Ice Algae Chlorophyll‐a data set, a compilation of currently available sea ice Chla data from landfast sea ice cores collected at circum‐Antarctic nearshore locations between 1970 and 2015. Ice cores were typically sampled from thermodynamically grown first‐year ice and have thin snow depths (mean = 0.052 ± 0.097 m). The data set comprises 888 ice cores, including 404 full vertical profile cores. Integrated ice algal Chla biomass (range: <0.1–219.9 mg/m2, median = 4.4 mg/m2, interquartile range = 9.9 mg/m2) peaks in late spring and shows elevated levels in autumn. The seasonal Chla development is consistent with the current understanding of physical drivers of ice algal biomass, including the seasonal cycle of irradiance and surface temperatures driving landfast sea ice growth and melt. Landfast ice regions with reported platelet ice formation show maximum ice algal biomass. Ice algal communities in the lowermost third of the ice cores dominate integrated Chla concentrations during most of the year, but internal and surface communities are important, particularly in winter. Through comparison of biomass estimates based on different sea ice sampling strategies, that is, analysis of full cores versus bottom‐ice section sampling, we identify biases in common sampling approaches and provide recommendations for future survey programs: for example, the need to sample fast ice over its entire thickness and to measure auxiliary physicochemical parameters.

Published
2018

32. A deep learning approachfor automatic mapping of poplar plantations using Sentinel-2 imagery

Author

D’Amico, G., Francini, S., Giannetti, F., Vangi, E., Travaglini, D., Chianucci, F., Mattioli, W., Grotti, M., Puletti, N., Corona, P., and Chirici, G.

Abstract

ABSTRACTPoplars are one of the most widespread fast-growing tree species used for forest plantations. Owing to their distinct features (fast growth and short rotation) and the dependency on the timber price market, poplar plantations are characterized by large inter-annual fluctuations in their extent and distribution. Therefore, monitoring poplar plantations requires a frequent update of information – not feasible by National Forest Inventories due to their periodicity – achievable by remote sensing systems applications. In particular, the new Sentinel-2 mission, with a revisiting period of 5 days, represents a potentially efficient tool for meeting this need.In this paper, we present a deep learning approach for mapping poplar plantations using Sentinel-2 time series. A reference dataset of poplar plantations was available for a large study area of more than 46,000 km2in Northern Italy and served as training and testing data. Two classification methods were compared: (1) a fully connected neural network (also called multilayer perceptron), and (2) a traditional logistic regression. The performance of the two approaches was estimated through bootstrapping procedure with a confidence interval of 99%. Results indicated for deep learning an omission error rate of 2.77%±2.76%, showing improvements compared to logistic regression, omission error rate = 8.91%±4.79%.

Published
2021
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33. Chlorophyll‐ a in Antarctic Landfast Sea Ice: A First Synthesis of Historical Ice Core Data

Author

Meiners, K. M., primary, Vancoppenolle, M., additional, Carnat, G., additional, Castellani, G., additional, Delille, B., additional, Delille, D., additional, Dieckmann, G. S., additional, Flores, H., additional, Fripiat, F., additional, Grotti, M., additional, Lange, B. A., additional, Lannuzel, D., additional, Martin, A., additional, McMinn, A., additional, Nomura, D., additional, Peeken, I., additional, Rivaro, P., additional, Ryan, K. G., additional, Stefels, J., additional, Swadling, K. M., additional, Thomas, D. N., additional, Tison, J.‐L., additional, Merwe, P., additional, Leeuwe, M. A., additional, Weldrick, C., additional, and Yang, E. J., additional

Published
2018
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34. Size distribution and ion composition of aerosol collected at Ny-Ålesund in the spring–summer field campaign 2013

Author

Giardi, Fabio, Becagli, Silvia, Traversi, Rita, Frosini, Daniele, Severi, Mirko, Caiazzo, Laura, Ancillotti, Claudia, Cappelletti, D., Moroni, B., Grotti, M., Bazzano, A., Lupi, A., Mazzola, M., Vitale, V., Abollino, O., Ferrero, L., Bolzacchini, E., Viola, A., and Udisti, Roberto

Subjects
Aerosol, Arctic, Ion chromatography, PM10, Size distribution, 2300, Agricultural and Biological Sciences (all), Earth and Planetary Sciences (all)
Published
2016

35. Climate and Environment

Author

Udisti, R, Becagli, S, Frosini, D, Ghedini, C, Rugi, F, Severi, M, Traversi, R, Zanini, R, Calzolai, G, Chiari, M, Lucarelli, F, Nava, S, Ardini, F, Grotti, M, Vione, D, Malandrino, M, BOLZACCHINI, EZIO, FERRERO, LUCA, PERRONE, MARIA GRAZIA, SANGIORGI, GIORGIA MAURA LUISA, Francesconi, S, Giannarelli, S, Cappelletti, D, Moroni, B, Ceccato, D, Mittner, P, Sartori, P., Ciciotti, T, Braico, P, Liberatori, E, Mazari Villanova, L, Sparapani, R, Udisti, R, Becagli, S, Frosini, D, Ghedini, C, Rugi, F, Severi, M, Traversi, R, Zanini, R, Calzolai, G, Chiari, M, Lucarelli, F, Nava, S, Ardini, F, Grotti, M, Vione, D, Malandrino, M, Bolzacchini, E, Ferrero, L, Perrone, M, Sangiorgi, G, Francesconi, S, Giannarelli, S, Cappelletti, D, Moroni, B, Ceccato, D, Mittner, P, and Sartori, P

Subjects
CHIM/12 - CHIMICA DELL'AMBIENTE E DEI BENI CULTURALI, Ny-Alesund, Aerosol sampling, vertical profiles, tethered balloon, SEM-EDS measurements
Abstract

The proceedings contain 36 papers. The topics discussed include: real time monitoring of urban particulate matter on a mobile platform; aspects about air pollution prediction on urban environment; water pollution detection; Raman probe of pollutants in water: measurement process; sensor network for environment monitoring: water quality case study; the use of a multiwavelength lidar to detect aerosol layers in the atmosphere; an artificial neural network approach to investigate cavitating flow regime at different temperatures; spectral characterization of solar cells for indoor wireless sensor nodes; microscale dielectric barrier discharge plasma actuators: experimental characterization; evaluation of the relation between building materials and indoor radon pollution; and a smart robotic arm for automatic sorting of objects with different tags.

Published
2013

36. Source assessment of atmospheric lead measured at Ny-Ålesund, Svalbard

Author

Bazzano, A., Ardini, F., Grotti, M., Becagli, S., Traversi, R., Udisti, R., Malandrino, Mery, and Cappelletti, D.

Subjects
Atmospheric particulate, Lead isotopic ratios, Heavy metals, The Arctic, Atmospheric particulate, Chemical tracers, Heavy metals, The Arctic, Chemical tracers, Lead isotopic ratios
Published
2015

38. Proposal for an MRPC system with high-precision timing in the LVD structure

Author

Abbrescia, M, Agafonova, N. Yu, Aglietta, M., Agostinelli, A., Aiola, S., Akindinov, A., Alici, A., Amati, E., Amerigo, S., Angelotti, A. M., Antolini, R., Antonioli, P., Arcelli, S., Asta, A., Atalmi, A., Attanasi, A., Avanzini, C., Baldini Ferroli, R., Bari, G., Bartolucci, S., Belligan, N., Bellini, F., Bencivenni, G., Bessi, C., Boi, M., Bologna, M., Bonardi, A., Bonetti, C., Borromeo, A., Bossini, E., Boyarkin, V. V., Bragagnolo, A., Bressan, E., Brunelli, C., Brunetti, G., Brunos, G., Burato, M., Burzi, S., Calemasi, D., Calvari, F., Cannas, A., Cara Romeo, G., Cardillo, A., Cari, A., Carli, M., Cassinadri, A., Celati, F., Celano, M., Chiaranda, L., Chiavassa, A., Chinnici, L., Cicalo, C., Cifarelli, L., Civili, I., Coccetti, F., Coccia, E., Cocuzza, S., Corti, M., Dambrosio, G., D'Amico, C., De Angelo, N., DE CARO, Annalisa, DE GRUTTOLA, Daniele, Del Chiaro, R., Del Corda, S., Del Corral, F., DE PASQUALE, Salvatore, Deplano, S., Di Giovanni, A., D'Incecco, M., Dispensa, E., Di Carlo, L., Di Crescenzo, E., Di Pietro, F., Di Staso, G., Di Vittorio, P., Dondarini, I., Dreucci, M., Evangelista, M. R., Evans, L., Fabbri, F. L., Falchi, G., Fanti, B., Fantini, A., Fantini, G., Ferrari, S., Finelli, S., Folino, R., Forni, E., Forte, P., Frolov, V., Fulgione, W., FUSCO GIRARD, Mario, La Teana, A., Londrillo, L., Lorenzo, A. R., Lupo, F., Gatti, S., Gaias, A., Galante, L., Galeotti, P., Gallone, M. G., Garbini, M., Gargano, A., Garrisi, M., Gemme, G., Ghia, P. L., Giacconi, P., Giusti, P., Gnesi, I., Guidi, S., Grishuk, Yu, Grotti, M., Guerzoni, B., Gustavino, C., Gustavino, S., Hatzifotiadou, D., Imperio, T., Indelicato, S., Jung, H., Kemp, E., Kim, D. W., Kim, M., Kim, J., Kiselev, S., Kuznetsov, V. V., Kuznetsov, V. A., La Rocca, P., Lee, S., Li, S., Libertini, L., Librizzi, F., Lo Mastro, A., Lucchesi, D., Maccari, A., Maggiora, A., Maggisano, M. P., Magistrale, G., Magli, G., Malguin, A. S., Mal'Kevich, D., Mattina, G., Margotti, A., Maroi, R., Marrone, S., Massai, M., Mastrodomenico, M., Matarese, A., Mazzone, A., Mazzotta, U., Melley, S., Memme, A., Miozzi, S., Moro, R., Murgia, M., Nania, R., Natali, G., Natucci, B., Nedosekin, A., Noferini, F., Nurisso, N., Nuti, S., Paccapelo, A. M., Paccoia, A., Pagano, Paola, Palombella, L., Panareo, M., Paoletti, R., Paravano, A., Pascale, R., Perasso, L., Perrone, D., Persiani, R., Persico, M. C., Pesci, A., Pettini, F., Piazzola, G., Pilo, F., Piragino, G., Pizzigoni, G., Pless, I. A., Porcu, M., Poggi, F., Polverino, E., Porta, A., Pozzanghera, R., Prati, A., Preghenella, R., Pulze, M., Quattromini, O., Ranieri, G., Rebagnati, S., Regano, A., Riggi, F., Righini, G., Rioli, A., Romano, F., Ronchini, L., Rossi, L., Rossi, R., Rossino, M., Rubino, M. G., Ruggeri, A., Russo, G., Ryasny, V. G., Ryazhskaya, O. G., Sartorelli, G., Scapparone, E., Schina, R., Scioli, G., Scribano, A., Selvi, M., Senes, E., Serci, S., Siddi, E., Sivori, G., Spampinato, G., Spandre, G., Squarcia, S., Stirparo, G., Stragiotti, A., Strizzai, L., Taiuti, M., Tarantino, F., Tosello, F., Troiano, C., Urbani, S., Usai, C., Verboso, F., Veseli, A., Vigna, A., Vigorito, C., Voloshin, K., Votano, L., Wenninger, H., Williams, M. C. S., Yakushev, V. F., Zaccaro, M., Zampolli, C., Zarri, I., Ziccardi, A., Zichichi, A., Zoffoli, G., Zouyevski, R., Zucchini, A., Abbrescia, M., Agafonova, N. Y., Aglietta, M., Agostinelli, A., Aiola, S., Akindinov, A., Alici, A., Amati, E., Amerigo, S., Angelotti, A. M., Antolini, R., Antonioli, P., Arcelli, S., Asta, A., Atalmi, A., Attanasi, A., Avanzini, C., Baldini Ferroli, R., Bari, G., Bartolucci, S., Belligan, N., Bellini, F., Bencivenni, G., Bessi, C., Boi, M., Bologna, M., Bonardi, A., Bonetti, C., Borromeo, A., Bossini, E., Boyarkin, V. V., Bragagnolo, A., Bressan, E., Brunelli, C., Brunetti, G., Brunos, G., Burato, M., Burzi, S., Calemasi, D., Calvari, F., Cannas, A., Cara Romeo, G., Cardillo, A., Cari, A., Carli, M., Cassinadri, A., Celati, F., Celano, M., Chiaranda, L., Chiavassa, A., Chinnici, L., Cicalo, C., Cifarelli, L., Civili, I., Coccetti, F., Coccia, E., Cocuzza, S., Corti, M., Dambrosio, G., D'Amico, C., De Angelo, N., De Caro, A., De Gruttola, D., Del Chiaro, R., Del Corda, S., Del Corral, F., De Pasquale, S., Deplano, S., Di Giovanni, A., D'Incecco, M., Dispensa, E., Di Carlo, L., Di Crescenzo, E., Di Pietro, F., Di Staso, G., Di Vittorio, P., Dondarini, I., Dreucci, M., Evangelista, M. R., Evans, L., Fabbri, F. L., Falchi, G., Fanti, B., Fantini, A., Fantini, G., Ferrari, S., Finelli, S., Folino, R., Forni, E., Forte, P., Frolov, V., Fulgione, W., Fusco Girard, M., La Teana, A., Londrillo, L., Lorenzo, A. R., Lupo, F., Gatti, S., Gaias, A., Galante, L., Galeotti, P., Gallone, M. G., Garbini, M., Gargano, A., Garrisi, M., Gemme, G., Ghia, P. L., Giacconi, P., Giusti, P., Gnesi, I., Guidi, S., Grishuk, Y., Grotti, M., Guerzoni, B., Gustavino, C., Gustavino, S., Hatzifotiadou, D., Imperio, T., Indelicato, S., Jung, H., Kemp, E., Kim, D. W., Kim, M., Kim, J., Kiselev, S., Kuznetsov, V. V., Kuznetsov, V. A., La Rocca, P., Lee, S., Li, S., Libertini, L., Librizzi, F., Lo Mastro, A., Lucchesi, D., Maccari, A., Maggiora, A., Maggisano, M. P., Magistrale, G., Magli, G., Malguin, A. S., Mal'Kevich, D., Mattina, G., Margotti, A., Maroi, R., Marrone, S., Massai, M., Mastrodomenico, M., Matarese, A., Mazzone, A., Mazzotta, U., Melley, S., Memme, A., Miozzi, S., Moro, R., Murgia, M., Nania, R., Natali, G., Natucci, B., Nedosekin, A., Noferini, F., Nurisso, N., Nuti, S., Paccapelo, A. M., Paccoia, A., Pagano, P., Palombella, L., Panareo, Marco, Paoletti, R., Paravano, A., Pascale, R., Perasso, L., Perrone, D., Persiani, R., Persico, M. C., Pesci, A., Pettini, F., Piazzola, G., Pilo, F., Piragino, G., Pizzigoni, G., Pless, I. A., Porcu, M., Poggi, F., Polverino, E., Porta, A., Pozzanghera, R., Prati, A., Preghenella, R., Pulze, M., Quattromini, O., Ranieri, G., Rebagnati, S., Regano, A., Riggi, F., Righini, G., Rioli, A., Romano, F., Ronchini, L., Rossi, L., Rossi, R., Rossino, M., Rubino, M. G., Ruggeri, A., Russo, G., Ryasny, V. G., Ryazhskaya, O. G., Sartorelli, G., Scapparone, E., Schina, R., Scioli, G., Scribano, A., Selvi, M., Senes, E., Serci, S., Siddi, E., Sivori, G., Spampinato, G., Spandre, G., Squarcia, S., Stirparo, G., Stragiotti, A., Strizzai, L., Taiuti, M., Tarantino, F., Tosello, F., Troiano, C., Urbani, S., Usai, C., Verboso, F., Veseli, A., Vigna, A., Vigorito, C., Voloshin, K., Votano, L., Wenninger, H., Williams, M. C. S., Yakushev, V. F., Zaccaro, M., Zampolli, C., Zarri, I., Ziccardi, A., Zichichi, A., Zoffoli, G., Zouyevski, R., and Zucchini, A.

Subjects
Physics, Large Hadron Collider, Traverse, Physics::Instrumentation and Detectors, Opera, Detector, Structure (category theory), General Physics and Astronomy, Theoretical physics, Computer engineering, Simple (abstract algebra), High Energy Physics::Experiment, Cosmic muons, Neutrino
Abstract

The purpose of this paper is to present a project in order to verify -without the need of knowing the distance CERN-Gran Sasso- the discovery made by the OPERA Collaboration concerning the speed of the CERN neutrinos. The project consists of two parts. A simple one and a less simple one. Both have the great advantage of being totally independent of the knowledge of the distance, ≃ 732 km, between the two Labs, CERN and LNGS, where the neutrinos are produced and detected, respectively. The "simple" version of this project is based on the high-energy horizontal cosmic muons, which traverse LVD and OPERA detectors, thus allowing to cross-calibrate the timing systems of both experiments in a way which is totally independent of the TOF measurements of CNGS. This component of the project is being studied in collaboration with the OPERA group, as the time stabilities of both experiments are needed. In fact it is since a long time that the two groups are engaged with this problem. In this paper we will present and discuss the "less simple" part which allows to establish, at the highest possible level of accuracy, if (v > c) effects really exist.

Published
2012

39. Size distribution and ion composition of aerosol collected at Ny-Ålesund in the spring–summer field campaign 2013

Author

Giardi, F, Becagli, S, Traversi, R, Frosini, D, Severi, M, Caiazzo, L, Ancillotti, C, Cappelletti, D, Moroni, B, Grotti, M, Bazzano, A, Lupi, A, Mazzola, M, Vitale, V, Abollino, O, Ferrero, L, Bolzacchini, E, Viola, A, Udisti, R, Udisti, R., FERRERO, LUCA, BOLZACCHINI, EZIO, Giardi, F, Becagli, S, Traversi, R, Frosini, D, Severi, M, Caiazzo, L, Ancillotti, C, Cappelletti, D, Moroni, B, Grotti, M, Bazzano, A, Lupi, A, Mazzola, M, Vitale, V, Abollino, O, Ferrero, L, Bolzacchini, E, Viola, A, Udisti, R, Udisti, R., FERRERO, LUCA, and BOLZACCHINI, EZIO

Abstract

During the 2013 Arctic campaign, direct measurements and size-segregated samplings of atmospheric aerosol were carried out from March to September at the Gruvebadet observatory in Ny-Ålesund (78°56′N, 11°56′E; Svalbard Islands). Continuous size distribution measurements (104 size classes) were performed both in the nano- (TSI-SMPS system) and micro-metric (TSI-APS device) range with a resolution of 10 min. Aerosol sampling was carried out on daily basis (PM10 fraction, 00:01–23:59 UTC) and with a 4-day resolution (four-stage cascade impactor). A back-trajectory analysis was performed for specific events to understand transport processes and possible source areas of aerosol reaching Ny-Ålesund. Aerosol samples were analyzed for ion composition (inorganic cations and anions, selected organic anions) by a three-chromatograph system after extraction in ultra-sonic bath. Special attention was spent in identifying and interpreting the seasonal pattern of natural and anthropic chemical markers. Sea spray aerosol was evenly distributed along all the sampling period with maxima related to wind speed. Its size distribution peaks in 1.0–2.5 or 2.5–10 µm, depending on the transport conditions and distance from source areas. Anthropic sulfate dominates the spring aerosol load (Arctic Haze), both in acidic form (H2SO4) and in partially or totally neutralized ammonium salts. Biogenic contributions, marked by methanesulfonic acid, are relatively relevant in late spring–early summer and are distributed in the finest aerosol fraction (<1.0 µm).

Published
2016

40. Sulfate source apportionment in the Ny-Ålesund (Svalbard Islands) Arctic aerosol

Author

Udisti, R, Bazzano, A, Becagli, S, Bolzacchini, E, Caiazzo, L, Cappelletti, D, Ferrero, L, Frosini, D, Giardi, F, Grotti, M, Lupi, A, Malandrino, M, Mazzola, M, Moroni, B, Severi, M, Traversi, R, Viola, A, Vitale, V, Vitale, V., BOLZACCHINI, EZIO, FERRERO, LUCA, Udisti, R, Bazzano, A, Becagli, S, Bolzacchini, E, Caiazzo, L, Cappelletti, D, Ferrero, L, Frosini, D, Giardi, F, Grotti, M, Lupi, A, Malandrino, M, Mazzola, M, Moroni, B, Severi, M, Traversi, R, Viola, A, Vitale, V, Vitale, V., BOLZACCHINI, EZIO, and FERRERO, LUCA

Abstract

Daily PM10 aerosol samples were collected at the Gruvebadet observatory, Ny-Ålesund (Svalbard Islands), during the spring-summer 2014 Italian Arctic Campaign. A total of 136 samples were analysed for ion (inorganic anions and cations, selected organic anions) composition aiming to evaluate the seasonal pattern of sulfate, as a key component of the Arctic haze. Ionic balances indicated a strong sulfate seasonality with mean spring concentration about 1.5 times higher than that measured in summer. The spring and summer aerosol was almost neutral, indicating that ammonia was the major neutralizing agent for atmospheric acidic species. The linear regression between sulfate from potential acidic sources (non-sea salt sulfate and non-crustal sulfate) and ammonium indicated that the mean sulfate/ammonium ratio was intermediate between semi-(NH4HSO4) and complete ((NH4)2SO4) neutralization. Using sea-salt sodium as sea-spray marker, non-sea-salt calcium as crustal marker and methanesulfonic acid as biogenic marker, a detailed source apportionment for sulfate was carried out. The anthropogenic input (calculated as the differences between total sulfate and the sum of sea-salt, crustal and biogenic contributes) was found to be the most relevant contribution to the sulfate budget in the Ny-Ålesund aerosol in summer and, especially, in spring. In this last season, crustal, sea-salt, biogenic and anthropogenic sources accounted for 3.3, 12.0, 11.5 and 74.8 %, respectively.

Published
2016

41. An integrated study of the chemical composition of Antarctic aerosol to investigate natural and anthropogenic sources

Author

Barbaro, E., primary, Zangrando, R., additional, Kirchgeorg, T., additional, Bazzano, A., additional, Illuminati, S., additional, Annibaldi, A., additional, Rella, S., additional, Truzzi, C., additional, Grotti, M., additional, Ceccarini, A., additional, Malitesta, C., additional, Scarponi, G., additional, and Gambaro, A., additional

Published
2016
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42. Iron in sea ice: Review and new insights

Author

Lannuzel, D., primary, Vancoppenolle, M., additional, van der Merwe, P., additional, de Jong, J., additional, Meiners, K.M., additional, Grotti, M., additional, Nishioka, J., additional, and Schoemann, V., additional

Published
2016
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43. Activity and preliminary results from the 2011 and 2012 field seasons art Ny Alesund

Author

Udisti, R., Becagli, S., Frosini, D., Ghedini, C., Rugi, F., Severi, M., Traversi, R., Zanini, R., Calzolai, G., Chiari, M., Lucarelli, F., Nava, S., Ardini, F., Grotti, M., Vione, D., Malandrino, M., Bolzacchini, E., Ferrero, L., Perrone, M. G., Sangiorgi, G., Francesconi, S., Giannarelli, S., Cappelletti, David Michele, Moroni, Beatrice, Ceccato, D., Mittner, P., and Sartori, P.

Published
2013

45. Climate and Environment

Author

Ciciotti, T, Braico, P, Liberatori, E, Mazari Villanova, L, Sparapani, R, Udisti, R, Becagli, S, Frosini, D, Ghedini, C, Rugi, F, Severi, M, Traversi, R, Zanini, R, Calzolai, G, Chiari, M, Lucarelli, F, Nava, S, Ardini, F, Grotti, M, Vione, D, Malandrino, M, Bolzacchini, E, Ferrero, L, Perrone, M, Sangiorgi, G, Francesconi, S, Giannarelli, S, Cappelletti, D, Moroni, B, Ceccato, D, Mittner, P, Sartori, P, BOLZACCHINI, EZIO, FERRERO, LUCA, PERRONE, MARIA GRAZIA, SANGIORGI, GIORGIA MAURA LUISA, Sartori, P., Ciciotti, T, Braico, P, Liberatori, E, Mazari Villanova, L, Sparapani, R, Udisti, R, Becagli, S, Frosini, D, Ghedini, C, Rugi, F, Severi, M, Traversi, R, Zanini, R, Calzolai, G, Chiari, M, Lucarelli, F, Nava, S, Ardini, F, Grotti, M, Vione, D, Malandrino, M, Bolzacchini, E, Ferrero, L, Perrone, M, Sangiorgi, G, Francesconi, S, Giannarelli, S, Cappelletti, D, Moroni, B, Ceccato, D, Mittner, P, Sartori, P, BOLZACCHINI, EZIO, FERRERO, LUCA, PERRONE, MARIA GRAZIA, SANGIORGI, GIORGIA MAURA LUISA, and Sartori, P.

Abstract

The proceedings contain 36 papers. The topics discussed include: real time monitoring of urban particulate matter on a mobile platform; aspects about air pollution prediction on urban environment; water pollution detection; Raman probe of pollutants in water: measurement process; sensor network for environment monitoring: water quality case study; the use of a multiwavelength lidar to detect aerosol layers in the atmosphere; an artificial neural network approach to investigate cavitating flow regime at different temperatures; spectral characterization of solar cells for indoor wireless sensor nodes; microscale dielectric barrier discharge plasma actuators: experimental characterization; evaluation of the relation between building materials and indoor radon pollution; and a smart robotic arm for automatic sorting of objects with different tags.

Published
2013

46. Chlorophyll‐ain Antarctic Landfast Sea Ice: A First Synthesis of Historical Ice Core Data

Author

Meiners, K. M., Vancoppenolle, M., Carnat, G., Castellani, G., Delille, B., Delille, D., Dieckmann, G. S., Flores, H., Fripiat, F., Grotti, M., Lange, B. A., Lannuzel, D., Martin, A., McMinn, A., Nomura, D., Peeken, I., Rivaro, P., Ryan, K. G., Stefels, J., Swadling, K. M., Thomas, D. N., Tison, J.‐L., Merwe, P., Leeuwe, M. A., Weldrick, C., and Yang, E. J.

Abstract

Historical sea ice core chlorophyll‐a(Chla) data are used to describe the seasonal, regional, and vertical distribution of ice algal biomass in Antarctic landfast sea ice. The analyses are based on the Antarctic Fast Ice Algae Chlorophyll‐adata set, a compilation of currently available sea ice Chladata from landfast sea ice cores collected at circum‐Antarctic nearshore locations between 1970 and 2015. Ice cores were typically sampled from thermodynamically grown first‐year ice and have thin snow depths (mean = 0.052 ± 0.097 m). The data set comprises 888 ice cores, including 404 full vertical profile cores. Integrated ice algal Chlabiomass (range: <0.1–219.9 mg/m2, median = 4.4 mg/m2, interquartile range = 9.9 mg/m2) peaks in late spring and shows elevated levels in autumn. The seasonal Chladevelopment is consistent with the current understanding of physical drivers of ice algal biomass, including the seasonal cycle of irradiance and surface temperatures driving landfast sea ice growth and melt. Landfast ice regions with reported platelet ice formation show maximum ice algal biomass. Ice algal communities in the lowermost third of the ice cores dominate integrated Chlaconcentrations during most of the year, but internal and surface communities are important, particularly in winter. Through comparison of biomass estimates based on different sea ice sampling strategies, that is, analysis of full cores versus bottom‐ice section sampling, we identify biases in common sampling approaches and provide recommendations for future survey programs: for example, the need to sample fast ice over its entire thickness and to measure auxiliary physicochemical parameters. Antarctic sea ice is a key driver of physical, chemical, and biological processes in the Southern Ocean. Importantly, sea ice serves as a substrate for microscopic algae which grow in the bottom, interior, and surface layers of the ice. These algae are considered an important food source for Antarctic marine food webs. Using a newly collated database of historical sea ice core chlorophyll‐adata (a proxy for ice algal biomass) from coastal sites, we describe the seasonal and vertical variability of algal biomass in Antarctic landfast sea ice. The seasonal chlorophyll‐adevelopment is consistent with the current understanding of physical drivers of ice algal biomass, including the seasonal cycle of irradiance and surface temperatures driving landfast sea ice growth and melt. Our analyses show that algae in the lowermost third of ice cores drive the annual cycle of integrated biomass, but internal and surface communities are also important. Through comparison of biomass estimates based on different sea ice sampling strategies, that is, analysis of full cores versus bottom‐ice section sampling, we identify biases in common sampling approaches and provide recommendations for future survey programs: for example, the need to sample fast ice over its entire thickness and to measure auxiliary physical parameters, in particular snow‐thickness data. First comprehensive collation of Antarctic landfast sea‐ice core chlorophyll‐adata establishedAlgae in lowermost third of ice cores drive annual cycle of integrated biomass, but internal and surface communities are also importantFull‐profile ice‐core sampling and systematic collection of auxiliary physico‐chemical parameters is recommended for future studies

Published
2018
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