物种敏感度分布的非参数核密度估计模型

王颖; 冯承莲; 黄文贤; 刘跃丹; 马燕; 张瑞卿; 吴丰昌

doi:10.7524/AJE.1673-5897.20140527002

物种敏感度分布的非参数核密度估计模型

1. 北京师范大学水科学研究院, 北京 100875;

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. 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

作者简介: 王颖(1989-),女,博士,研究方向为环境基准与风险评估,E-mail:wy2012bnu@126.com

通讯作者: 吴丰昌, E-mail: wufengchang@vip.skleg.cn

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)

中图分类号: X171.5

Non-Parametric Kernel Density Estimation of Developing Species Sensitivity Distributions

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;

3. School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;

4. The Key Laboratory of Water and Air Pollution Control of Guangdong Province, South China Institute of Environmental Sciences, the Ministry of Environment Protection of PRC, Guangzhou 510065, China;

5. Research Center of Environmental Biology and Green Chemistry, School of Environmental and Municipal Engineering, Qingdao Technological University, Qingdao 266033, China;

6. College of Environment and Resources, Inner Mongolia University, Huhhot 010021, China

Corresponding author: Wu Fengchang, wufengchang@vip.skleg.cn

Fund Project:

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。

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 nonparametric 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.

Key words:

inorganic mercury /
freshwater organisms /
water quality criteria /
non-parametric kernel density estimation /
species sensitivity distribution

物种敏感度分布的非参数核密度估计模型

通讯作者: 吴丰昌, E-mail: wufengchang@vip.skleg.cn

作者简介: 王颖(1989-),女,博士,研究方向为环境基准与风险评估,E-mail:wy2012bnu@126.com
1. 北京师范大学水科学研究院, 北京 100875;

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. 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

收稿日期: 2014-05-27

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)

关键词:

Non-Parametric Kernel Density Estimation of Developing Species Sensitivity Distributions

Corresponding author: Wu Fengchang, wufengchang@vip.skleg.cn

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;

3. School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;

5. Research Center of Environmental Biology and Green Chemistry, School of Environmental and Municipal Engineering, Qingdao Technological University, Qingdao 266033, China;

6. College of Environment and Resources, Inner Mongolia University, Huhhot 010021, China

Received Date: 2014-05-27

Fund Project:

Keywords:

inorganic mercury /
freshwater organisms /
water quality criteria /
non-parametric kernel density estimation /
species sensitivity distribution

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