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基于物种敏感性分布法的毒死蜱对稻田生态系统生态风险评价

本站小编 Free考研考试/2021-12-30

肖鹏飞1,
林晓雅2,
刘毅华1,3,
赵颖1,
沈坚1,
徐吉洋1,
耿翠敏1,
朱国念1
1. 浙江大学农药与环境毒理研究所, 杭州 310029;
2. 浙江农林大学暨阳学院, 诸暨 311800;
3. 中国林科院亚热带林业研究所, 富阳 311400
作者简介: 肖鹏飞(1984-),男,博士研究生,研究方向为农药生态毒理学,E-mail:xpf6285842@126.com.
基金项目: 青年科学基金项目(31101458);浙江省重点研发项目(No.2015C02G2010084)


中图分类号: X171.5


Application of Species Sensitivity Distribution in Aquatic Ecological Risk Assessment of Chlopyrifos for Paddy Ecosystem

Xiao Pengfei1,
Lin Xiaoya2,
Liu Yihua1,3,
Zhao Ying1,
Shen Jian1,
Xu Jiyang1,
Geng Cuimin1,
Zhu Guonian1
1. Institute of Pesticide & Environmental Toxicology, Zhejiang University, Hangzhou 310029, China;
2. Jiyang College of Zhejiang A&F University, Zhuji 311800, China;
3. Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang 311400, China

CLC number: X171.5

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摘要:生物敏感性分布法(Species Sensitivity Distributions, SSD)是一种基于单物种测试和概率统计学的、较高级的外推风险评估方法。该方法在国内外均被广泛应用于各种污染物风险评价中。本文选取了采用logistic和normal这2种SSD分布模型,分析了国内外毒死蜱对3组水生生物组合的毒性数据;并且获得各自SSD的HCx值。3组毒性数据分别为:浙江稻田水生生物组,长三角地区水生生物组和美国水生生物组。浙江稻田水生物SSD分布的HC5为:0.32 μg·L-1 (logistic模型)和0.35 μg·L-1 (normal模型);HC10为1.50 μg·L-1 (logistic模型)和1.26 μg·L-1 (normal模型);HC20为8.13 μg·L-1 (logistic模型)和5.96 μg·L-1 (normal模型);HC50为145.44 μg·L-1 (logistic模型)和115.74 μg·L-1 (normal模型)。据此判断水稻种植季节,稻田水域毒死蜱对食蚊鱼、鳑鲏、泽蛙蝌蚪、轮虫、常见腹足类和双壳类软体动物以及绝大多数藻类等的风险较小。利用冗余分析研究了生物物种数量、物种组成结构和拟合模型对HCx影响。结果表明:物种组成结构对HCx有较为明显的影响。具体表现为对毒死蜱较为敏感物种数量与HCx存在明显的负相关性;对毒死蜱不敏感的物种则与HCx呈现正相关性。
关键词: 毒死蜱/
稻田湿地水生生物/
物种敏感性分布曲线(SSD)/
生态风险评价

Abstract:The species sensitivity distribution (SSD) has served as a key method for higher-tier ecological effect assessment. In this study, the SSDs of chlorpyrifos toxicity for three aquatic organism datasets were analyzed. The three datasets were Zhejiang paddy aquatic organisms (Zhejiang paddy dataset), Yangtze Delta aquatic organisms (Yangtze dataset) and American aquatic organisms (American dataset). In all cases, the logistic distribution and normal distribution were adopted. Based on each SSD, the hazardous concentration for 5%-50% species (HC5-HC50) of datasets were calculated to assess the chlorpyrifos ecological risk for aquatic ecosystems. The HC5-HC50 values of Zhejiang dataset were: 0.32 μg·L-1(HC5, logistic distribution model) and 0.35 μg·L-1(HC5, normal distribution model); 1.50 μg·L-1(HC10, logistic distribution model) and 1.26 μg·L-1(HC10, normal distribution model); 8.13 μg·L-1(HC20, logistic distribution model) and 5.96 μg·L-1(HC20, normal distribution model); 145.44 μg·L-1(HC50, logistic distribution model) and 115.74 μg·L-1(HC50, normal distribution model). Survey data showed that the maximum chlorpyrifos concentration in waters close to local rice paddies were 3.1-5.5 μg·L-1 during rice cultivating season. On these evidences, there is a little risk of the chlorpyrifos to rotifers, most fish, tadpoles, mollusks and most algae species. As redundancy analysis results showed that the species composition has more influence on HCx. An obvious negative correlation exists between sensitive species number and HCx, and a positive correlation exists between non-sensitive species number and HCx.
Key words:chlopyrifos/
paddy wetland aquatic organism/
species sensitivity distribution/
ecological risk assessment.

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