ArticleToxics2026
Water Quality Criteria and Ecological Risk Assessment of Fluoride for the Protection of Water Organisms in Surface Water.
Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
The widespread occurrence of fluoride pollution in water bodies and its toxic effects on aquatic organisms have raised significant environmental concerns; however, studies on water quality criteria for fluoride remain relatively limited. This study aimed to derive such criteria and assess the ecological risks of fluoride in China's surface waters, for the reference of readers. Acute and chronic toxicity data were collected globally, covering 34 species (14 families, 4 phyla) and 7 species (5 families, 3 phyla), respectively. Using species sensitivity distribution (SSD) methods, the short-term water quality criterion (SWQC) and long-term water quality criterion (LWQC) were derived as 17.47 mg/L and 3.334 mg/L. Ecological risk assessment based on the risk quotient (RQ) identified several high-risk areas among 32 major river and lake basins, with RQ values of 6.326 (Xihe River), 1.953 (Ebinur Lake), 1.368 (Chagan Lake), and 1.158 (Shahe River). At the provincial level, Guangxi Zhuang Autonomous Region showed as no risk (RQ = 0.0001140), while other regions were classified as moderate or low risk. This study achieved its objectives of deriving water quality criteria for fluoride and conducting an ecological risk assessment for surface waters in China. It also highlights current limitations, including insufficient fluoride toxicity data and the frequent oversight of key indicators in existing assessments. Future research could focus on improving water quality criteria derivation and risk assessment methods through integrated predictive modeling and expanded toxicity datasets.
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