Integrated Stochastic Environmental Risk Assessment of the Harbour Area Treatment Scheme (HATS) in Hong Kong

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• 作者：K.W. Choi ; Joseph H.W. Lee ; Kevin W.H. Kwok ; Kenneth M.Y. Leung
• 刊名：Environmental Science & Technology
• 出版年：2009
• 出版时间：May 15, 2009
• 年：2009
• 卷：43
• 期：10
• 页码：3705-3711
• 全文大小：202K
• 年卷期：v.43,no.10(May 15, 2009)
• ISSN：1520-5851

文摘

Submarine ocean outfalls are commonly used for the disposal of partially treated effluents in coastal cities. Typically, the greatest environmental risk caused by toxic substances occurs in the near field of the outfall discharge. The ecological impact of the effluent varies greatly under different discharge and environmental conditions that are characterized by both regular and stochastic variations. For a comprehensive environmental risk assessment of a coastal discharge, it is necessary to determine both the likelihood and severity of the adverse effects on the biological community. We present the first integrated stochastic (Monte Carlo) environmental risk assessment of a major coastal sewage outfall discharge - the Stonecutters Island outfall of the Harbour Area Treatment Scheme (HATS) in Hong Kong. Unionized ammonia (NH₃) is used as the target pollutant. To accurately envisage the ambient concentrations of NH₃, a Lagrangian jet model (JETLAG/VISJET) is used to analyze pollutant concentrations in the near field of the outfall. The environmental conditions are simulated from 3D hydrodynamic model simulations over a 4 month period for typical wet and dry seasons. Statistical characteristics of the effluent discharge and receiving water temperature are derived from field data. The probability distribution of predicted exposure concentrations (EC) is generated from this integrated simulation. A species sensitivity distribution, which represents a statistical distribution of threshold sublethal effects levels or benchmark concentrations (BC) for various marine organisms is constructed using available chronic toxicity data. The environmental risk of NH₃ on the marine community is characterized by computing statistical distributions of Hazard Quotient (HQ = EC/BC) using Monte Carlo simulation. It is found that the probability of HQ > 1 for HATS Stage 1 (1.6 million m³/day sewage treated with chemically enhanced primary treatment) is around 0.11 for wet season but just about 0.06 for the dry season. The risk increases by about 10% to 0.08−0.13 with additional sewage loads of 0.8 million m³/day at the same level of treatment (HATS Stage 2A). With an upgrade to secondary treatment (HATS Stage 2B), the probability will be reduced to 0.03−0.05. Compared to the use of “worst case” scenarios or point pollution threshold estimates, the present method offers a more holistic ecological assessment, and is much less sensitive to arbitrary choice of model parameters. The present risk assessment approach can be readily extended to the accurate determination of mixing zones based on statistical evaluation of ecological risks.