Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario M1C 1A4, Canada.
Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario M1C 1A4, Canada.
Environ Int. 2019 Sep;130:104821. doi: 10.1016/j.envint.2019.05.015. Epub 2019 Jul 19.
Evaluating the degree of improvement of an impaired freshwater ecosystem resembles the statistical null-hypothesis testing through which the prevailing conditions are compared against a reference state. The pillars of this process involve the robust delineation of what constitutes an achievable reference state; the establishment of threshold values for key environmental variables that act as proxies of the degree of system impairment; and the development of an iterative decision-making process that takes advantage of monitoring data to assess the system-restoration progress and revisit management actions accordingly. Drawing the dichotomy between impaired and non-impaired conditions is a challenging exercise that is surrounded by considerable uncertainty stemming from the variability that natural systems display over time and space, the presence of ecosystem feedback loops (e.g., internal loading) that actively influence the degree of recovery, and our knowledge gaps about biogeochemical processes directly connected to the environmental problem at hand. In this context, we reappraise the idea of probabilistic water quality criteria, whereby the compliance rule stipulates that no more than a stated number of pre-specified water quality extremes should occur within a given number of samples collected over a compliance assessment domain. Our case study is the Bay of Quinte, Ontario, Canada; an embayment lying on the northeastern end of Lake Ontario with a long history of eutrophication problems. Our study explicitly accounts for the covariance among multiple water quality variables and illustrates how we can assess the degree of improvement for a given number of violations of environmental goals and samples collected from the system. The present framework offers a robust way to impartially characterize the degree of restoration success and minimize the influence of the conflicting perspectives among decision makers/stakeholders and conscious (or unconscious) biases pertaining to water quality management.
评估受损淡水生态系统的改善程度类似于统计零假设检验，通过该检验将当前状况与参考状态进行比较。该过程的支柱包括：稳健划定可实现的参考状态；为关键环境变量建立阈值，这些变量作为系统受损程度的代理；以及开发迭代决策过程，利用监测数据评估系统恢复进度，并相应地重新审视管理措施。在受损和未受损条件之间进行划分是一项具有挑战性的工作，存在很大的不确定性，这源于自然系统随时间和空间的变化、生态系统反馈回路（例如内部负荷）的存在，这些反馈回路积极影响恢复程度，以及我们对与当前环境问题直接相关的生物地球化学过程的知识差距。在这种情况下，我们重新评估概率水质标准的概念，即合规规则规定，在合规评估域内收集的给定数量的样本中，不应超过规定数量的预先指定的水质极端值。我们的案例研究是加拿大安大略省的湾口，这是安大略湖东北端的一个港湾，长期以来一直存在富营养化问题。我们的研究明确考虑了多个水质变量之间的协方差，并说明了如何在给定数量的违反环境目标和从系统中收集的样本的情况下，评估改善程度。该框架提供了一种公正地描述恢复成功程度的可靠方法，并最大限度地减少决策者/利益相关者之间存在的冲突观点以及与水质管理相关的有意识（或无意识）偏见的影响。
