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Development and use of interspecies correlation estimation models in China for potential application in water quality criteria

机译:中国种间相关性估计模型的开发和使用在水质标准中的潜在应用

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摘要

Establishment of numerical water quality criteria (WQC) has brought increasing interest in China. However, toxicity data to develop robust WQC values (number of toxicity data >= 8) of contaminants based solely on endemic and indigenous species are insufficient. In this study, interspecies correlation estimation (ICE) models were developed using a combination of North American ICE models supplemented with China-specific species to resolve this problem. A total of 207 significant surrogate-predicted models (p < 0.05, F-test) were derived: 119, 66 and 22 models for vertebrates, invertebrates and plant surrogate species, respectively. Model cross-validation success rate (>= 80%), mean square error (MSE, <= 0.54), R-2 (>= 0.78) and taxonomic distance (<= 4, within the same class) were selected as guiding criteria to screen the resulted ICE models. The differences of 5th percentile hazard concentrations (HC5s) for 6 chemicals (2,4-dichlorophenol, triclosan, tetrabromobisphenol A, nitrobenzene, perfluorooctane sulfonate and octabromodiphenyl ether) calculated from ICE-based and measured toxicity-based SSDs were within 3-fold among models. Although the number of derived ICE models was not comprehensive and continues to be improved, they can already be used in the development of WQC targeting protection of aquatic life and environmental risk assessments for chemicals lacking toxicity data. (C) 2019 Elsevier Ltd. All rights reserved.
机译:建立水质数字标准(WQC)在中国引起了越来越多的兴趣。但是,仅基于地方和本地物种无法建立可靠的污染物WQC值(毒性数据> = 8)的毒性数据是不够的。在这项研究中,种间相关性估计(ICE)模型是通过使用北美ICE模型与中国特有物种的补充组合来解决的。总共得出了207个重要的替代模型预测模型(p <0.05,F检验):分别为脊椎动物,无脊椎动物和植物替代物种的119、66和22模型。选择模型交叉验证成功率(> = 80%),均方差(MSE,<= 0.54),R-2(> = 0.78)和分类距离(<= 4,在同一类别中)作为指导标准筛选生成的ICE模型。根据基于ICE的固态硬盘和基于毒性的固态硬盘计算出的6种化学品(2,4-二氯苯酚,三氯生,四溴双酚A,硝基苯,全氟辛烷磺酸盐和八溴二苯醚)的第五个百分点的危险浓度(HC5s)差异在3倍之内楷模。尽管衍生的ICE模型数量不全面,并且仍在不断改进,但它们已经可以用于针对缺乏毒性数据的化学品的WQC开发,以保护水生生物和环境风险评估为目标。 (C)2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Chemosphere》 |2020年第2期|124848.1-124848.8|共8页
  • 作者单位

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment State Environm Protect Key Lab Ecol Effect & Risk Beijing 100012 Peoples R China;

    Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment State Environm Protect Key Lab Ecol Effect & Risk Beijing 100012 Peoples R China|Nanchang Univ Minist Educ Key Lab Poyang Lake Environm & Resource Utilizat Nanchang 330047 Jiangxi Peoples R China;

    Procter & Gamble Co Global Prod Stewardship 8700 Mason Montgomery Rd Mason OH 45040 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Interspecies correlation estimations; Water quality criteria; Ecological risk assessment; Aquatic species;

    机译:种间相关性估计;水质标准;生态风险评估;水生物种;

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