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Deriving field-based sediment quality guidelines from the relationship between species density and contaminant level using a novel nonparametric empirical Bayesian approach

  • Environmental Quality Benchmarks for Protecting Aquatic Ecosystems
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Abstract

This paper describes a novel statistical approach to derive ecologically relevant sediment quality guidelines (SQGs) from field data using a nonparametric empirical Bayesian method (NEBM). We made use of the Norwegian Oil Industrial Association database and extracted concurrently obtained data on species density and contaminant levels in sediment samples collected between 1996 and 2001. In brief, effect concentrations (ECs) of each installation (i.e., oil platform) at a given reduction in species density were firstly derived by fitting a logistic-type regression function to the relationship between the species density and the corresponding concentration of a chemical of concern. The estimated ECs were further improved by the NEBM which incorporated information from other installations. The distribution of these improved ECs from all installations was determined nonparametrically by the kernel method, and then used to determine the hazardous concentration (HC) which can be directly linked to the species loss (or the species being protected) in the sediment. This method also enables an accurate estimation of the lower confidence limit of the HC, even when the number of observations was small. To illustrate the effectiveness of this novel technique, barium, cadmium, chromium, copper, mercury, lead, tetrahydrocannabinol, and zinc were chosen as example contaminants. This novel approach can generate ecologically sound SQGs for environmental risk assessment and cost-effectiveness analysis in sediment remediation or mud disposal projects, since sediment quality is closely linked to species density.

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Acknowledgments

This work was supported by a grant from the University Grants Committee of the Hong Kong Special Administrative Region, China (project no. AoE/P-04/04) to the Area of Excellence in Marine Environment Research and Innovative Technology (MERIT). This paper is dedicated to the late Professor John S. Gray for his excellent input and intellectual exchange in the early stage of this project. The authors also thank the Norwegian Oil Industry Association (OLF) for allowing us to use their sediment database.

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong, China

    Gilbert C. S. Lui & W. K. Li

  2. Institute of Biology, University of Oslo, Oslo, Norway

    Anders Bjørgesæter

  3. The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China

    Kenneth M. Y. Leung

Authors
  1. Gilbert C. S. Lui
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  2. W. K. Li
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  3. Anders Bjørgesæter
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  4. Kenneth M. Y. Leung
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Corresponding author

Correspondence to Kenneth M. Y. Leung.

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Responsible editor: Philippe Garrigues

Appendix: Derivation of Eq. (2)

Appendix: Derivation of Eq. (2)

At x = x BG, the species density at installation j, a LS,j , satisfies the following expression

$$ ln\frac{a_{\mathrm{LS},j}}{1-{a}_{\mathrm{LS},j}}={b}_{\mathrm{LS},j}+{c}_{\mathrm{LS},j}{x}_{\mathrm{BG}}, $$

which implies

$$ {a}_{\mathrm{LS},j}=\frac{1}{1+ exp\left(-{b}_{\mathrm{LS},j}-{c}_{\mathrm{LS},j}{x}_{\mathrm{BG}}\right)}. $$

Then, the natural logarithm of EC(γ)LS,j can be obtained by

$$ ln\frac{a_{\mathrm{LS},j}\left(1-\gamma \right)}{1-{a}_{\mathrm{LS},j}\left(1-\gamma \right)}={b}_{\mathrm{LS},j}+{c}_{\mathrm{LS},j} ln\left(\mathrm{EC}{\left(\gamma \right)}_{\mathrm{LS},j}\right). $$

Hence, we can have the following result

$$ ln\left(\mathrm{EC}{\left(\gamma \right)}_{\mathrm{LS},j}\right)=\frac{1}{c_{\mathrm{LS},j}}\left\{ ln\frac{a_{\mathrm{LS},j}\left(1-\gamma \right)}{1-{a}_{\mathrm{LS},j}\left(1-\gamma \right)}-{b}_{\mathrm{LS},j}\right\}. $$

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Lui, G.C.S., Li, W.K., Bjørgesæter, A. et al. Deriving field-based sediment quality guidelines from the relationship between species density and contaminant level using a novel nonparametric empirical Bayesian approach. Environ Sci Pollut Res 21, 177–192 (2014). https://doi.org/10.1007/s11356-013-1889-1

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  • DOI: https://doi.org/10.1007/s11356-013-1889-1

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