The ecological risks posed by widespread organophosphorus pesticide (OPs) pollution in the surface waters of China remain unclear. In this study, species sensitivity distribution (SSD) parametric statistical approaches were coupled with fully acute and chronic toxicity data to fit the sensitivity distributions of different aquatic species to five typical OPs: dimethoate, malathion, parathion-methyl, trichlorfon, and dichlorvos. Crustaceans exhibit the highest sensitivity to OPs, whereas algae are the least sensitive. The acute hazardous concentrations that affected 5 % of the species (HC) were 0.112, 0.001, 0.001, 0.001, and 0.001 mg/L for dimethoate, malathion, parathion-methyl, trichlorfon, and dichlorvos, respectively, whereas their chronic HC values were 0.004, 0.004, 0.053, 0.001, and 0.0005 mg/L, respectively. Hence, dichlorvos is highly toxic and poses greater risk to non-target aquatic species. The evaluation data revealed varying geographical distribution characteristics of the ecological risks from OPs in 15 freshwater aquatic systems across different regions of China. Dichlorvos posed the highest risk in the basins of Zhejiang and Guangdong Provinces, with the highest chronic Risk Quotient (RQ) and Hazard Index (HI) at 9.34 and 9.92, respectively. This is much higher than what was collected and evaluated for foreign rivers (the highest chronic RQ and HI in foreign rivers were 1.65 and 2.24, respectively). Thus, dichlorvos in the surface waters of China poses a substantial ecological risk to aquatic organisms, and may endanger human health.
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http://dx.doi.org/10.1016/j.scitotenv.2023.169805 | DOI Listing |
Environ Manage
January 2025
Department of Engineering, Reykjavik University, Reykjavík, Iceland.
This research assesses heavy metal contamination within the riparian zone of the Danro River, a tributary of the Ganges River basin in India, particularly impacted by sand mining activities. The study conducted analyses on major and trace elements in soil samples, focusing on those identified as ecologically hazardous by the Water Framework Directive of India. Utilizing a combination of indices (Enrichment Factor, Pollution Load Index, and Index of geo-accumulation) and statistical techniques such as Principal Component Analysis (PCA), the investigation aimed to evaluate contamination severity, ecological risks, and pollution sources.
View Article and Find Full Text PDFConserv Biol
January 2025
Chair of Wildlife Ecology and Management, Albert Ludwigs University of Freiburg, Freiburg, Germany.
Survival and cause-specific mortality rates are vital for evidence-based population forecasting and conservation, particularly for large carnivores, whose populations are often vulnerable to human-caused mortalities. It is therefore important to know the relationship between anthropogenic and natural mortality causes to evaluate whether they are additive or compensatory. Further, the relation between survival and environmental covariates could reveal whether specific landscape characteristics influence demographic performance.
View Article and Find Full Text PDFBiol Rev Camb Philos Soc
January 2025
Laboratório de Ecologia e Conservação, Departamento de Engenharia Ambiental, Universidade Federal do Paraná, Av. Cel. Francisco H. dos Santos 100, Curitiba, 81531-980, Brazil.
Non-native species can be major drivers of ecosystem alteration, especially through changes in trophic interactions. Successful non-native species have been predicted to have greater resource use efficiency relative to trophically analogous native species (the Resource Consumption Hypothesis), but rigorous evidence remains equivocal. Here, we tested this proposition quantitatively in a global meta-analysis of comparative functional response studies.
View Article and Find Full Text PDFLancet Reg Health West Pac
January 2025
State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Science, Beijing, PR China.
Background: As natural reservoirs of diverse pathogens, small mammals are considered a key interface for guarding public health due to their wide geographic distribution, high density and frequent interaction with humans.
Methods: All formally recorded natural occurrences of small mammals (Order: Rodentia, Eulipotyphla, Lagomorpha, and Scandentia) and their associated microbial infections in China were searched in the English and Chinese literature spanning from 1950 to 2021 and geolocated. Machine learning models were applied to determine ecological drivers for the distributions of 45 major small mammal species and two common rodent-borne diseases (RBDs), and model-predicted potential risk locations were mapped.
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