Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, People's Republic of China; Department of Environmental Science, University of Liverpool, Brownlow Hill, Liverpool, L69 7ZX, United Kingdom.
Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, People's Republic of China.
Ecotoxicol Environ Saf. 2020 Oct 1;202:110848. doi: 10.1016/j.ecoenv.2020.110848. Epub 2020 Jun 20.
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 km surface area and a watershed of 36,000 km) 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.
生物有效性形态建模越来越多地被用于环境风险评估 (ERA)。重金属污染是全球最普遍的环境污染问题，而金属的生物有效性受其化学形态强烈影响。淡水中的溶解有机物 (DOM) 会与重金属结合，从而降低其生物有效性。虽然形态建模已被证明非常有效，并已通过 ERA 的验证，但越来越多的文献报告了形态模型中金属-DOM 结合的准确性存在问题。在这项研究中，我们针对一个区域性实地区域（太湖，表面积 2400 平方公里，流域面积 36000 平方公里）解决了这个问题，在该地区常用的形态模型不太准确，我们测试了替代方法来预测该地区铅（Pb）的金属-DOM 形态/生物有效性，这是首次尝试。我们测试了五种特定于地点的方法来量化 Pb-DOM 结合，这些方法涉及到对条件稳定常数、结合容量以及 DOM 中不同成分的不同假设，我们将这些方法与我们所谓的通用方法进行了比较。我们将模型结果与使用全细胞生物传感器测量的生物可利用 Pb 的结果进行了比较，该生物传感器已经过形态模型的验证，并且与许多生物学方法相比非常快速。结果表明，我们使用的所有特定于地点的方法都比测试的默认模型提供了更准确的生物有效性估计，然而，基于地点的条件稳定常数的变化是最重要的考虑因素。通过定量指标，通过将活性 DOM 建模为具有基于地点的 Pb-DOM 条件稳定常数变化的单一有机配体类型，可以将模型的准确性提高多达一个数量级。由于生物方法快速，并且可以通过高通量分析获得针对特定地点定制模型的参数，因此我们在这里报告的这种首次在淡水区域范围内的演示具有在简化 ERA 中实际应用的巨大潜力。
