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物种敏感度分布的非参数核密度估计模型
引用本文:王颖,冯承莲,黄文贤,刘跃丹,马燕,张瑞卿,吴丰昌.物种敏感度分布的非参数核密度估计模型[J].生态毒理学报,2015,10(1):215-224.
作者姓名:王颖  冯承莲  黄文贤  刘跃丹  马燕  张瑞卿  吴丰昌
作者单位:1. 北京师范大学水科学研究院,北京100875;中国环境科学研究院环境基准与风险评估国家重点实验室,北京100012;2. 中国环境科学研究院环境基准与风险评估国家重点实验室,北京,100012;3. 北京师范大学数学科学学院,北京,100875;4. 环境保护部华南环境科学研究所广东省水与大气污染防治重点实验室,广州,510065;5. 中国环境科学研究院环境基准与风险评估国家重点实验室,北京100012;青岛理工大学环境与市政工程学院生物环保与绿色化工研究中心,青岛266033;6. 内蒙古大学环境与资源学院,呼和浩特,010021
基金项目:环保公益项目(201309060;201409037) ;国家自然科学基金重点项目(41130743)
摘    要:针对目前物种敏感度分布参数方法建模所存在的缺点,首次提出基于非参数核密度估计方法的物种敏感度分布模型,并提出相应的最优窗宽和检验方法。选用无机汞作为案例研究对象,利用非参数核密度估计方法和3种传统参数模型分别推导了保护我国水生生物的无机汞的急性水质基准值。结果表明,非参数核密度估计方法在推导无机汞水质基准中的稳健性和精确度都大大优于传统参数模型,能够更好地构建物种敏感度分布曲线。该方法的提出丰富了水质基准的理论方法学,为更好地保护水生生物提供了有力的支撑。

关 键 词:无机汞  淡水水生生物  水质基准  非参数核密度估计  物种敏感度分布
收稿时间:2014/5/27 0:00:00
修稿时间:7/4/2014 12:00:00 AM

Non-Parametric Kernel Density Estimation of Developing Species Sensitivity Distributions
Wang Ying,Feng Chenglian,Huang Wenxian,Liu Yuedan,Ma Yan,Zhang Ruiqing and Wu Fengchang.Non-Parametric Kernel Density Estimation of Developing Species Sensitivity Distributions[J].Asian Journal of Ecotoxicology,2015,10(1):215-224.
Authors:Wang Ying  Feng Chenglian  Huang Wenxian  Liu Yuedan  Ma Yan  Zhang Ruiqing and Wu Fengchang
Institution:1. College of Water Sciences, Beijing Normal University, Beijing 100875, China 2. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China;State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China;School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;The Key Laboratory of Water and Air Pollution Control of Guangdong Province, Environmental Simulation and Pollution Control Research Center, South China Institute of Environmental Sciences, the Ministry of Environment Protection of PRC, Guangzhou 510065, China;1. College of Water Sciences, Beijing Normal University, Beijing 100875, China 2. Research Center of Environmental Biology and Green Chemistry, School of Environmental and Municipal Engineering, Qingdao Technological University, Qingdao 266033, China;College of Environment and Resources, Inner Mongolia University, Huhhot 010021, China;State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China
Abstract:To address the inadequacies associated with parametric density estimations for species sensitivity distributions, we developed a new probabilistic model based on non-parametric kernel density estimation and proposed related optimal bandwidths and testing methods as well. With inorganic mercury as the target compound, the non-parametric kernel density estimation method and three conventional parametric density estimation methods were used to derive acute water quality criteria for protection of aquatic species in China. The results demonstrated that the new probabilistic model was superior over the conventional parametric density estimations in deriving water quality criteria for inorganic mercury, as well as in constructing species sensitivity distribution. The proposed method has enriched the methodological foundation for water quality criteria and provided solid support for protection of aquatic organisms.
Keywords:inorganic mercury  freshwater organisms  water quality criteria  non-parametric kernel density estimation  species sensitivity distribution
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