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Anna Costa   Dr.  Institute, Department or Faculty Head 
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Anna Costa published an article in November 2018.
Top co-authors See all
Luca Esposito

346 shared publications

Department of Structures for Engineering and Architecture (DiSt), Polytechnic School - College of Engineering, University of Naples Federico II, Naples, Italy

R. Sousa

310 shared publications

INIAV, I.P., Estação Nacional de Fruticultura Vieira Natividade, 2460-059 Alcobaça, Portugal

Andrea Brunelli

243 shared publications

Department of Thoracic Surgery, Leeds Teaching Hospitals National Health System Trust, Leeds, United Kingdom

A. Vaccari

237 shared publications

Department of Electrical & Computer Engineering and University of Virginia, Charlottesville, VA 22904, USA

Carmen Galassi

198 shared publications

National Research Council of Italy - Institute of Science and Technology for Ceramics (CNR-ISTEC), Faenza, Italy

Publication Record
Distribution of Articles published per year 
(2007 - 2018)
Total number of journals
published in
Publications See all
Article 0 Reads 3 Citations Reduced sediment supply in a fast eroding landscape? A multi-proxy sediment budget of the upper Rhône basin, Central Alp... Laura Stutenbecker, Romain Delunel, Fritz Schlunegger, Tiago... Published: 01 November 2018
Sedimentary Geology, doi: 10.1016/j.sedgeo.2017.12.013
DOI See at publisher website
Article 0 Reads 1 Citation Dip coating of air purifier ceramic honeycombs with photocatalytic TiO2 nanoparticles: A case study for occupational exp... Antti Joonas Koivisto, Kirsten Inga Kling, Ana Sofia Fonseca... Published: 01 July 2018
Science of The Total Environment, doi: 10.1016/j.scitotenv.2018.02.316
DOI See at publisher website ABS Show/hide abstract
Nanoscale TiO2 (nTiO2) is manufactured in high volumes and is of potential concern in occupational health. Here, we measured workers exposure levels while ceramic honeycombs were dip coated with liquid photoactive nanoparticle suspension and dried with an air blade. The measured nTiO2 concentration levels were used to assess process specific emission rates using a convolution theorem and to calculate inhalation dose rates of deposited nTiO2 particles. Dip coating did not result in detectable release of particles but air blade drying released fine-sized TiO2 and nTiO2 particles. nTiO2 was found in pure nTiO2 agglomerates and as individual particles deposited onto background particles. Total particle emission rates were 420 × 109 min−1, 1.33 × 109 μm2 min−1, and 3.5 mg min−1 respirable mass. During a continued repeated process, the average exposure level was 2.5 × 104 cm−3, 30.3 μm2 cm−3, <116 μg m−3 for particulate matter. The TiO2 average exposure level was 4.2 μg m−3, which is well below the maximum recommended exposure limit of 300 μg m−3 for nTiO2 proposed by the US National Institute for Occupational Safety and Health. During an 8-hour exposure, the observed concentrations would result in a lung deposited surface area of 4.3 × 10−3 cm2 g−1 of lung tissue and 13 μg of TiO2 to the trachea-bronchi, and alveolar regions. The dose levels were well below the one hundredth of the no observed effect level (NOEL1/100) of 0.11 cm2 g−1 for granular biodurable particles and a daily no significant risk dose level of 44 μg day−1. These emission rates can be used in a mass flow model to predict the impact of process emissions on personal and environmental exposure levels.
Article 0 Reads 1 Citation Hydroclimatic control on suspended sediment dynamics of a regulated Alpine catchment: a conceptual approach Anna Costa, Daniela Anghileri, Péter Molnar Published: 22 June 2018
Hydrology and Earth System Sciences, doi: 10.5194/hess-22-3421-2018
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We analyse the control of hydroclimatic factors on suspended sediment concentration (SSC) in Alpine catchments by differentiating among the potential contributions of erosion and suspended sediment transport driven by erosive rainfall, defined as liquid precipitation over snow-free surfaces, ice melt from glacierized areas, and snowmelt on hillslopes. We account for the potential impact of hydropower by intercepting sediment fluxes originated in areas diverted to hydropower reservoirs, and by considering the contribution of hydropower releases to SSC. We obtain the hydroclimatic variables from daily gridded datasets of precipitation and temperature, implementing a degree-day model to simulate spatially distributed snow accumulation and snow–ice melt. We estimate hydropower releases by a conceptual approach with a unique virtual reservoir regulated on the basis of a target-volume function, representing normal reservoir operating conditions throughout a hydrological year. An Iterative Input Selection algorithm is used to identify the variables with the highest predictive power for SSC, their explained variance, and characteristic time lags. On this basis, we develop a hydroclimatic multivariate rating curve (HMRC) which accounts for the contributions of the most relevant hydroclimatic input variables mentioned above. We calibrate the HMRC with a gradient-based nonlinear optimization method and we compare its performance with a traditional discharge-based rating curve. We apply the approach in the upper Rhône Basin, a large Swiss Alpine catchment heavily regulated by hydropower. Our results show that the three hydroclimatic processes – erosive rainfall, ice melt, and snowmelt – are significant predictors of mean daily SSC, while hydropower release does not have a significant explanatory power for SSC. The characteristic time lags of the hydroclimatic variables correspond to the typical flow concentration times of the basin. Despite not including discharge, the HMRC performs better than the traditional rating curve in reproducing SSC seasonality, especially during validation at the daily scale. While erosive rainfall determines the daily variability of SSC and extremes, ice melt generates the highest SSC per unit of runoff and represents the largest contribution to total suspended sediment yield. Finally, we show that the HMRC is capable of simulating climate-driven changes in fine sediment dynamics in Alpine catchments. In fact, HMRC can reproduce the changes in SSC in the past 40 years in the Rhône Basin connected to air temperature rise, even though the simulated changes are more gradual than those observed. The approach presented in this paper, based on the analysis of the hydroclimatic control of suspended sediment concentration, allows the exploration of climate-driven changes in fine sediment dynamics in Alpine catchments. The approach can be applied to any Alpine catchment with a pluvio-glacio-nival hydrological regime and...
Article 1 Read 0 Citations Pilot- plant study for the photocatalytic/electrochemical degradation of Rhodamine B Carlo Baldisserri, Simona Ortelli, Magda Blosi, Anna Luisa C... Published: 01 April 2018
Journal of Environmental Chemical Engineering, doi: 10.1016/j.jece.2018.02.008
DOI See at publisher website
Article 0 Reads 0 Citations Coatings made of proteins adsorbed on TiO2 nanoparticles: a new flame retardant approach for cotton fabrics Simona Ortelli, Giulio Malucelli, Fabio Cuttica, Magda Blosi... Published: 15 March 2018
Cellulose, doi: 10.1007/s10570-018-1745-z
DOI See at publisher website
Article 1 Read 0 Citations Hazard Screening Methods for Nanomaterials: A Comparative Study Barry Sheehan, Finbarr Murphy, Martin Mullins, Irini Furxhi,... Published: 25 February 2018
International Journal of Molecular Sciences, doi: 10.3390/ijms19030649
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Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.