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首页> 外文期刊>Environmental toxicology and chemistry >EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL, APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA
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EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL, APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA

机译：评估适用于美国91佛蒙特州和新罕布什尔州湖泊和池塘的基于过程的汞暴露模型（区域汞循环模型）的区域预测能力

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Regulatory agencies must develop fish consumption advisories for many lakes and rivers with limited resources. Process-based mathematical models are potentially valuable tools for developing regional fish advisories. The regional mercury cycling model (R-MCM) specifically was designed to model a series of lakes for a given region with site-specific data and parameterization for each application. In this paper, we explore the feasibility of R-MCM application to develop regional fish advisories from existing data by testing model performance across 91 Vermont ([VT], USA) and New Hampshire ([NH], USA) lakes. We use a progressive method of parameter refinement ranging from simple defaults specified by the model to site-specific parameterization to evaluate potential improvements in model prediction. Model applications and parameter refinement tiers are based on Regional Environmental Monitoring Assessment Program (REMAP) data. Results show that R-MCM generally underpredicts water column methylmercury and total mercury concentrations and overpredicts sediment methylmercury concentrations. Default level input parameterization produced the largest amount of random scatter in model forecasted values. Using site-specific values for the default level char-acteristics reduced this variability but did not improve overall model performance. By separating the observed and predicted data by lake characteristics, we identify some overall trends in bias and fit, but are unable to identify systematic biases in model performance by lake type. This analysis suggests that process-based models like R-MCM cannot be used for a priori predictive applications at the regional scale at this time. Further, this work reinforces the need for additional research on the transport and transformation of mercury to elucidate parameterization useable in a modeling framework to help refine predictive capabilities of process-based models.
机译：监管机构必须为资源有限的许多湖泊和河流制定鱼类消费咨询。基于过程的数学模型是发展区域鱼类咨询的潜在有价值的工具。区域汞循环模型（R-MCM）专门用于对给定区域的一系列湖泊进行建模，并具有针对每个应用的特定地点数据和参数设置。在本文中，我们通过测试91个佛蒙特州（[VT]，美国）和新罕布什尔州（[NH]，美国）的模型性能，探索了R-MCM应用从现有数据发展区域鱼类咨询的可行性。我们使用渐进的参数细化方法，从模型指定的简单默认值到特定于站点的参数设置，以评估模型预测中的潜在改进。模型应用程序和参数细化层基于区域环境监测评估计划（REMAP）数据。结果表明，R-MCM通常会低估水柱中的甲基汞和总汞浓度，而高估沉积物中的甲基汞浓度。默认级别的输入参数设置在模型预测值中产生了最大的随机散布量。使用特定于站点的值作为默认级别的特性可减少这种差异，但不会提高整体模型性能。通过将湖泊数据的观测数据和预测数据分开，我们可以确定偏差和拟合的总体趋势，但无法根据湖泊类型识别模型性能的系统偏差。该分析表明，目前无法将基于过程的模型（例如R-MCM）用于区域规模的先验预测应用。此外，这项工作还需要对汞的传输和转化进行更多研究，以阐明可用于建模框架的参数化，以帮助改进基于过程的模型的预测能力。

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