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Nanoparticles (NP), aerosols under 100 nm of nominal diameter, might be responsible of multiple diseases (cardiovascular, nervous, respiratory, cancer, etc.) in mid- and long-term exposed workers in industrial layouts. These particles in suspension are not just originated from nanomaterials manufacturing, but also accidentally by plenty of industrial high-energetic and machining processes, such as atmospheric plasma spraying, welding or ceramic tiles firing, representing a potential danger for manufacturers from a wide variety of sectors. This paper will present a simulation tool of Incidental NanoParticles’ (INP) concentration in industrial environments that is currently under development within the Life Nanohealth European project to assess occupational health to external and internal hygiene risk prevention services. The concentration model has been implemented through a Modelica library and evaluated with a case study with real work conditions in an industrial plant with different processes, materials, ventilation systems and sources’ nature. Obtained results show simulations that are coherent with the field campaigns’ data, reaching a prediction precision over the error of the used sensors and a high correlation between measurements and model., This research is part of a LIFE-funded project (LIFE20 ENV/ES/000187). The authors acknowledge SALONI (https://saloni.com/) for their committed cooperation. The first author gratefully acknowledges the Universitat Politècnica de Catalunya for the financia., Postprint (published version)

Air quality monitoring sensor networks focusing on air pollution measure pollutants that are regulated by the authorities, such as CO, NO2, NO, SO2, O3, and particulate matter (PM10, PM2.5). However, there are other pollutants, such as black carbon (BC), which are not regulated, have a major impact on health, and are rarely measured. One solution is to use proxies, which consist of creating a mathematical model that infers the measurement of the pollutant from indirect measurements of other pollutants. In this paper, we propose a robust machine learning proxy (RMLP) framework for estimating BC based on nonlinear machine learning methods, calibrating the low-cost sensors (LCSs), and adding robustness against noise and data missing in the LCS. We show the impact of LCS data aggregation, denoising and missing imputation on BC estimation, and how the concentrations estimated by the BC proxy approximate the values obtained by a reference instrument with an accurate BC sensor., This work is supported by projects H2020 FIRE-RES, PID 2019-107910RB-I00, CEX 2018-000794-S, 2017 SGR41, 2021 SGR01059, and the support of Sec. d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu., Peer Reviewed, Postprint (author's final draft)

[ES] La trata de seres humanos con fines de explotación sexual, se presenta como una forma de moderna esclavitud que afecta a millones de mujeres y niñas en el mundo. Tras numerosas reformas, el Código Penal español, responde a esta realidad criminológica a través de un tipo penal autónomo, art. 177 bis CP, diferenciado finalmente del fenómeno de la inmigración clandestina. El trabajo pretende analizar, el tipo penal, los factores estructurales que inciden en la vulnerabilidad de las víctimas de trata sexual y la necesaria perspectiva de género que requiere la materia.

Background: This study presents information on the evolution of severe cases of SARS-CoV-2 infection that required hospitalization since the beginning of vaccination in Spain. The objective was to know the impact of vaccination against COVID-19 on the hospitalization of patients with SARS-CoV-2 infection, hospital mortality and readmissions for this cause, and to describe the characteristics of vaccinated patients who required admission. Methods: A retrospective, observational epidemiological study was conducted of all patients admitted with SARS-CoV-2 infection confirmed by a diagnostic test for active infection (PDIA) in a tertiary hospital, from January 2021 to June 2021. The incidence of admissions was calculated based on the vaccination status of the patients and age groups at different times according to the progress of the strategy of vaccination COVID-19. Results: Between December 27, 2020 and June 30, 2021, 1,308 patients with positive PDIA were admitted to the University Hospital of Salamanca, of which 1,167 (89.2%) were not vaccinated, 129 (9.9%) had received one dose of vaccine and 12 (0.9%) were fully vaccinated. Of the latter, none were admitted to the ICU and 2 died. Conclusions: Vaccination against COVID-19 has contributed to the decrease in hospitalizations, since February 2021, of older and institutionalized people. Fully vaccinated people have a lower risk of admission to the ICU and death. These data, together with the information available on recent cases of new SARS-CoV-2 infections in unvaccinated young people, are in favor of achieving high vaccination coverage of the entire population in the shortest possible time., Fundamentos: Este estudio presenta información sobre la evolución de los casos graves de infección SARS-CoV-2 que requirieron hospitalización desde el inicio de la vacunación en España. El objetivo fue conocer el impacto de la vacunación frente a COVID-19 sobre la hospitalización de pacientes con infección SARS-CoV-2, la mortalidad intrahospitalaria y los reingresos por esta causa, y describir las características de los pacientes vacunados que precisaron ingreso. Métodos: Se realizó un estudio epidemiológico observacional retrospectivo, de todos los pacientes ingresados con infección por SARS-CoV-2 confirmada mediante una prueba de diagnóstico de infección activa (PDIA) en un hospital de tercer nivel, de enero de 2021 a junio de 2021. Se calculó la incidencia de ingresos en función del estado vacunal de los pacientes y grupos de edad en diferentes momentos según el avance de la campaña de vacunación. Resultados: Entre el 27 de diciembre de 2020 y el 30 de junio de 2021 ingresaron en el Hospital Universitario de Salamanca 1.308 pacientes con PDIA positiva, de los cuales 1.167 (89,2%) no estaban vacunados, 129 (9,9%) habían recibido una dosis de vacuna y 12 (0,9%) estaban completamente vacunados. De estos últimos, ninguno ingresó en UCI y 2 fallecieron. Conclusiones: La vacunación frente a COVID-19 ha contribuido al descenso de las hospitalizaciones desde el mes de febrero de 2021, sobre todo en personas mayores e institucionalizadas. Las personas completamente vacunadas parecen tener menor riesgo de ingreso en UCI y fallecimiento. Estos datos, junto con la información disponible de los casos recientes de nuevas infecciones por SARS-CoV-2 en personas jóvenes no vacunadas, están a favor de conseguir una cobertura vacunal elevada de toda la población en el menor tiempo posible.

Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018–2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R2 = 0.828) with a relatively low error (RMSE = 0.48 µg/m3). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R2 = 0.633; RMSE = 1.19 µg/m3) due to the lack of N data and PM2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas., The authors would like to acknowledge the support from the Generalitat de Catalunya (Dept. Medi Ambient) by providing the air quality data. This work was partly supported by H2020 project RI-URBANS (H2020-LC-GD-2020-6, reference 101036245), the Spanish Ministry of Science and Innovation (projects CEX2018-000794-S and PID2019- 107910RB-I00), Academy of Finland via flagship on Atmosphere and Climate Competence Center (ACCC, project number 337549) and by AGAUR (project 2017 SGR41 and 2017 SGR 990). It was carried out in the framework of a joint collaboration between IDAEA-CSIC and University of Barcelona (Physics Faculty)., Peer Reviewed, Postprint (published version)