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http://dx.doi.org/10.1177/00048674221103481 | DOI Listing |
Mar Pollut Bull
January 2025
Institut des sciences de la mer, Université du Québec à Rimouski, Rimouski, Québec G5L 3A1, Canada. Electronic address:
Microplastic contamination in the St. Lawrence River and Estuary (SLRE), Canada, poses potential risks to aquatic species. However, limited understanding of microplastic contamination in benthic fish, potentially more vulnerable than pelagic species, impedes effective risk assessment in this crucial ecosystem.
View Article and Find Full Text PDFSci Total Environ
January 2025
Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
International mass gathering events, such as the Olympic and Paralympic Games, face the risk of cross-border transmission of infectious diseases. We previously reported that wastewater-based epidemiology (WBE), which has attracted attention as a COVID-19 surveillance tool, was implemented in the Tokyo 2020 Olympic and Paralympic Village to gain a comprehensive understanding of COVID-19 incidence in the village. In the present study, we explored the quantitative association of wastewater viral load and clinically confirmed cases in various areas of the village.
View Article and Find Full Text PDFSci Total Environ
January 2025
Unitat mixta d'Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra 08193, Catalonia. Spain; IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra 08193, Catalonia. Spain. Electronic address:
Campylobacter spp. and Salmonella spp. are the leading cause of human enteric infections in the European Union.
View Article and Find Full Text PDFEcotoxicol Environ Saf
January 2025
Guangzhou Ecological and Environmental Monitoring Center of Guangdong Province, Guangzhou 510030, China.
The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rapid prediction of antibiotics. Many scholars have shown the effectiveness of machine learning in water quality prediction, however, its effectiveness in predicting antibiotic concentrations in the aquatic environment remains inconclusive.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Academy of Sciences, Guangzhou 510650, China.
Arsenic pollution and its associated health risks have raised widespread concern. Under anaerobic conditions, arsenic mobility and toxicity increase when arsenate [As(V)] is reduced to arsenite [As(III)] by microbes through the cytoplasmic and dissimilatory pathways. However, the relative importance of these two pathways in the environment remains unclear, restricting our ability to effectively predict and regulate the environmental behavior of arsenic.
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