Literature DB >> 34896421

Ecological risk assessment for difenoconazole in aquatic ecosystems using a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model.

Chao Shen1, Xinglu Pan1, Xiaohu Wu1, Jun Xu1, Fengshou Dong2, Yongquan Zheng1.   

Abstract

Difenoconazole is a typical triazole fungicide that can inhibit demethylation during ergosterol synthesis. Due to its wide use, difenoconazole is frequently detected in surface water, paddy water, agricultural water, and other aquatic environments. Presently, an assessment of the ecological risk posed by difenoconazole in aquatic ecosystems is lacking. Here, a web-based interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) model was first applied to assess the ecological risk of difenoconazole in aquatic environments. Meanwhile, maximum acceptable concentration (MAC), maximum risk-free concentration (MRFC), and risk quotient (RQ) values were used to evaluate the potential risk of difenoconazole to aquatic organisms. Our results showed that an aquatic MAC value of 0.31 μg/L was acceptable for difenoconazole in aquatic environments. Further, the detected concentration of difenoconazole was lower than the MRFC value of 0.09 μg/L indicating no risk to aquatic organisms. Assessment data suggested that difenoconazole exhibited potential risks to eight studied aquatic ecosystems (including surface water, paddy water, and agricultural water) in different countries (RQ > 1), indicating that difenoconazole overuse could cause adverse effects to aquatic organisms in these aquatic ecosystems. Thus, restricted use and rational use of difenoconazole are recommended.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Aquatic ecosystems; Difenoconazole; Ecological risk assessment; Interspecies correlation estimation; Species sensitivity distribution

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Year:  2021        PMID: 34896421     DOI: 10.1016/j.chemosphere.2021.133236

Source DB:  PubMed          Journal:  Chemosphere        ISSN: 0045-6535            Impact factor:   7.086


  1 in total

1.  Design of Association Application System of Face Recognition and Test-Tube Barcode Based on CNN.

Authors:  Zhangning Zhou; He Shi; Xuemin Niu
Journal:  Comput Math Methods Med       Date:  2022-08-24       Impact factor: 2.809

  1 in total

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