Speciation modeling of bioavailability has increasingly been used for environmental risk assessment (ERA). Heavy metal pollution is the most prevalent environmental pollution issue globally, and metal bioavailability is strongly affected by its chemical speciation. Dissolved organic matter (DOM) in freshwater will bind heavy metals thereby reducing bioavailability. While speciation modeling has been shown to be quite effective and is validated for use in ERA, there is an increasing body of literature reporting problems with the accuracy of metal-DOM binding in speciation models. In this study, we address this issue for a regional-scale field area (Lake Tai, with 2,400 km² surface area and a watershed of 36,000 km²) where speciation models in common use are not highly accurate, and we tested alternative approaches to predict metal-DOM speciation/bioavailability for lead (Pb) in this first trial work. We tested five site-specific approaches to quantify Pb-DOM binding that involve varying assumptions about conditional stability constants, binding capacities, and different components in DOM, and we compare these to what we call a one-size-fits-all approach that is commonly in use. We compare model results to results for bioavailable Pb measured using a whole-cell bioreporter, which has been validated against speciation models and is extremely rapid compared to many biological methods. The results show that all of the site-specific approaches we use provide more accurate estimates of bioavailability than the default model tested, however, the variation of the conditional stability constant on a site-specific basis is the most important consideration. By quantitative metrics, up to an order of magnitude improvement in model accuracy results from modeling active DOM as a single organic ligand type with site-specific variations in Pb-DOM conditional stability constants. Because the biological method is rapid and parameters for site-specific tailoring of the model may be obtained via high-throughput analysis, the approach that we report here in this first regional-scale freshwater demonstration shows excellent potential for practical use in streamlined ERA.

生物利用度的物种模型已经越来越多地用于环境风险评估（ERA）。重金属污染是全球最普遍的环境污染问题，金属生物利用度受其化学形态的强烈影响。淡水中溶解的有机物（DOM）会结合重金属，从而降低生物利用度。尽管物种模型已被证明是非常有效的，并且已被验证可用于ERA，但越来越多的文献报道了物种模型中金属-DOM结合的准确性问题。在这项研究中，我们针对区域规模的田间区域（太湖，具有2,400 km ^2的表面积和36,000 km ²的分水岭），但通常使用的物种模型并不十分准确，因此我们在此首次试验工作中测试了替代方法来预测铅的金属DOM物种/生物利用度（Pb）。我们测试了五种特定于站点的方法来量化Pb-DOM绑定，这些方法涉及对条件稳定性常数，绑定能力和DOM中不同组件的各种假设，并将这些与我们所谓的“一刀切”的方法进行了比较。通常使用。我们将模型结果与使用全细胞生物报告仪测量的可生物利用铅的结果进行比较，该报告已针对物种模型进行了验证，并且与许多生物方法相比非常快速。结果表明，与所测试的默认模型相比，我们使用的所有针对具体地点的方法都可以提供更准确的生物利用度估算值，最重要的考虑因素是基于特定地点的条件稳定性常数的变化。通过定量指标，将活动DOM建模为单个有机配体类型，在Pb-DOM条件稳定性常数中具有特定位置的变化，从而使模型精度提高了一个数量级。由于生物学方法是快速的，并且可以通过高通量分析来获得模型的特定于位置的参数，因此我们在此首次区域规模的淡水演示中在此报告的方法显示出在简化的ERA中的实际应用的巨大潜力。通过将活动DOM建模为单个有机配体类型，并在Pb-DOM条件稳定性常数中进行特定位置的变化，可将模型精度提高多达一个数量级。由于生物学方法是快速的，并且可以通过高通量分析来获得模型的特定于位置的参数，因此我们在此首次区域规模的淡水演示中在此报告的方法显示出在简化的ERA中的实际应用的巨大潜力。通过将活动DOM建模为单个有机配体类型，并在Pb-DOM条件稳定性常数中进行特定位置的变化，可将模型精度提高多达一个数量级。由于生物学方法是快速的，并且可以通过高通量分析来获得模型的特定于位置的参数，因此，我们在此首次区域规模的淡水演示中在此报告的方法显示出在简化的ERA中的实际应用的巨大潜力。