Department of Civil Engineering, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8552, Japan.
Aquat Toxicol. 2013 May 15;132-133:151-6. doi: 10.1016/j.aquatox.2013.02.007. Epub 2013 Feb 22.
Knowledge about which predictors of metal exposure are best to model the impacts of metal mixtures on river macroinvertebrates remains uncertain. A new predictor based on the amount of metals binding to humic acid, which is assumed to be a proxy of non-specific biotic ligand sites, has been proposed. The amount can be calculated using Windermere Humic Aqueous Model (WHAM), which we will refer to as the WHAM-HA approach. Here, we tested the hypothesis that the predictor based on the WHAM-HA approach provided a better estimate of metal effects observed in microcosm experiments than three other measures: total metal concentrations, free metal ion concentrations, and the cumulative criterion unit (CCU) which is a measure of the ratios of measured metal concentrations relative to the U.S. Environmental Protection Agency hardness adjusted criterion values. For this evaluation, we used nine macroinvertebrate metrics of abundance and richness obtained from microcosm experiments conducted with metal mixtures (Zn alone, Zn+Cd, and Zn+Cd+Cu). For each of the four predictors, we performed multiple linear regression with variables corresponding to the three metal concentrations or CCU and selected the best model based on Akaike's information criterion corrected for small sample sizes. For all of the macroinvertebrate metrics affected by metals, the WHAM-HA approach was selected as the best among the four predictors, followed by the model with total metal concentration. In most of best models, Zn and Cu or Cu alone was responsible for reductions in invertebrate metrics, even though the highest concentrations of Cd exceeded 100 times the hardness-adjusted criterion value. Either of the models with free metal ion concentration and CCU was the third ranked model. Our results suggest that the estimated amount of metals binding to humic acid is a better predictor for the effects on macroinvertebrate richness and abundance observed in microcosm experiments than total or free ion concentrations of metals and CCU.
关于哪些预测因子最适合模拟金属混合物对河流大型无脊椎动物的影响，人们的认识仍存在不确定性。目前已经提出了一种基于金属与腐殖酸结合量的新预测因子，该预测因子被认为是非特异性生物配体位点的替代物。该结合量可以使用温德米尔腐殖酸水溶液模型（WHAM）进行计算，我们将其称为 WHAM-HA 方法。在这里，我们检验了一个假设，即基于 WHAM-HA 方法的预测因子比其他三种方法（总金属浓度、游离金属离子浓度和累积标准单位（CCU））更好地估计了微宇宙实验中观察到的金属效应。CCU 是一种衡量实测金属浓度与美国环境保护署硬度调整标准值之比的指标。为此，我们使用了 9 种从金属混合物微宇宙实验中获得的丰度和丰富度的大型无脊椎动物指标（单独的 Zn、Zn+Cd 和 Zn+Cd+Cu）。对于这四种预测因子中的每一种，我们都进行了多元线性回归，其中变量对应于三种金属浓度或 CCU，并根据小样本量修正的 Akaike 信息准则选择最佳模型。对于所有受金属影响的大型无脊椎动物指标，WHAM-HA 方法被选为四种预测因子中最好的方法，其次是总金属浓度模型。在大多数最佳模型中，Zn 和 Cu 或单独的 Cu 是导致无脊椎动物指标下降的原因，尽管 Cd 的最高浓度超过了硬度调整标准值的 100 倍。游离金属离子浓度和 CCU 模型中的任一个都是排名第三的模型。我们的研究结果表明，与金属的总浓度或游离离子浓度和 CCU 相比，腐殖酸结合金属的估计量是更好的预测因子，可以预测微宇宙实验中对大型无脊椎动物丰富度和丰度的影响。
