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Showing results for xavier fleet
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Possible influence of shipping emissions on metals in size-segregated particulate matter in Guanabara Bay (Rio de Janeiro, Brazil).
Silveira RS, Corrêa SM, de M Neto N. Silveira RS, et al. Environ Monit Assess. 2022 Sep 26;194(11):828. doi: 10.1007/s10661-022-10517-7. Environ Monit Assess. 2022. PMID: 36156154 Free PMC article.
In the world of growing maritime fleets, ships powered by fossil fuels are being widely used that are responsible for atmospheric emissions such as particulate matter (PM). ...
In the world of growing maritime fleets, ships powered by fossil fuels are being widely used that are responsible for atmospheric emi …
Inflammatory bowel disease integral care units: Evaluation of a nationwide quality certification programme. The GETECCU experience.
Barreiro-de Acosta M, Gutiérrez A, Zabana Y, Beltrán B, Calvet X, Chaparro M, Domènech E, Esteve M, Panés J, Gisbert JP, Nos P; on behalf GETECCU. Barreiro-de Acosta M, et al. United European Gastroenterol J. 2021 Sep;9(7):766-772. doi: 10.1002/ueg2.12105. Epub 2021 Jun 5. United European Gastroenterol J. 2021. PMID: 34089303 Free PMC article.
An empirical model to predict road dust emissions based on pavement and traffic characteristics.
Padoan E, Ajmone-Marsan F, Querol X, Amato F. Padoan E, et al. Environ Pollut. 2018 Jun;237:713-720. doi: 10.1016/j.envpol.2017.10.115. Epub 2017 Nov 9. Environ Pollut. 2018. PMID: 29128243 Free article.
The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external …
The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pave …
Trends in Targeted Therapy Usage in Inflammatory Bowel Disease: TRENDY Study of ENEIDA.
Gómez-Labrador C, Ricart E, Iborra M, Iglesias E, Martín-Arranz MD, de Castro L, De Francisco R, García-Alonso FJ, Sanahuja A, Gargallo-Puyuelo CJ, Mesonero F, Casanova MJ, Mañosa M, Rivero M, Calvo M, Sierra-Ausin M, González-Muñoza C, Calvet X, García-López S, Guardiola J, Arias García L, Márquez-Mosquera L, Gutiérrez A, Zabana Y, Navarro-Llavat M, Lorente Poyatos R, Piqueras M, Torrealba L, Bermejo F, Ponferrada-Díaz Á, Pérez-Calle JL, Acosta MB, Tejido C, Cabriada JL, Marín-Jiménez I, Roncero Ó, Ber Y, Fernández-Salazar L, Camps Aler B, Lucendo AJ, Llaó J, Bujanda L, Muñoz Villafranca C, Domènech E, Chaparro M, Gisbert JP. Gómez-Labrador C, et al. Pharmaceutics. 2024 May 8;16(5):629. doi: 10.3390/pharmaceutics16050629. Pharmaceutics. 2024. PMID: 38794292 Free PMC article.
On the Coverage of Bus-Based Mobile Sensing.
Cruz Caminha PH, de Souza Couto R, Maciel Kosmalski Costa LH, Fladenmuller A, Dias de Amorim M. Cruz Caminha PH, et al. Sensors (Basel). 2018 Jun 20;18(6):1976. doi: 10.3390/s18061976. Sensors (Basel). 2018. PMID: 29925791 Free PMC article.
We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among other findings, we observe that the fleet of buses covers approximately 5655 km of streets (approximately 47% of the streets) and sh …
We evaluate the model in a real scenario based on a large-scale dataset of more than 5700 buses in the city of Rio de Janeiro, Brazil. Among …