Institute of Ecology and Evolutionary Biology, China Medical University, Taichung, Taiwan 40402, Republic of China.
Environ Monit Assess. 2012 Jan;184(1):561-72. doi: 10.1007/s10661-011-1988-8. Epub 2011 Apr 15.
Long-term metal exposure risk assessment for aquatic organism is a challenge because the chronic toxicity of chemical is not only determined by the amount of accumulated chemical but also affected by the ability of biological regulation or detoxification of biota. We quantified the arsenic (As) detoxification ability of tilapia and developed a biologically based growth toxicity modeling algorithm by integrating the process of detoxification and active regulations (i.e., the balance between accumulated dose, tissue damage and recovery, and the extent of induced toxic effect) for a life span ecological risk prediction. Results showed that detoxification rate (k (dex)) increased with increasing of waterborne As when the accumulated metal exceeded the internal threshold level of 19.1 μg g( - 1). The k (dex) values were comparable to or even higher than the rates of physiological loss and growth dilution in higher exposure conditions. Model predictions obtained from the proposed growth toxicity model were consistent with the measured growth data. The growth toxicity model was also used to illustrate the health condition and growth trajectories of tilapia from birth to natural death under different exposure scenarios. Results showed that temporal trends of health rates and growth trajectories of exposed fish in different treatments decreased with increasing time and waterborne As, revealing concentration-specific patterns. We suggested that the detoxification rate is critical and should be involved in the risk assessments framework. Our proposed modeling algorithm well characterizes the internal regulation activities and biological response of tilapia under long-term metal stresses.
水生生物长期金属暴露风险评估是一个挑战，因为化学物质的慢性毒性不仅取决于累积化学物质的数量，还受到生物调节或生物群解毒能力的影响。我们量化了罗非鱼的砷（As）解毒能力，并通过整合解毒和主动调节（即累积剂量、组织损伤和恢复以及诱导毒性效应的程度之间的平衡）过程，为寿命期生态风险预测开发了基于生物学的生长毒性建模算法。结果表明，当积累的金属超过 19.1μg g(-1)的内部阈值水平时，水基砷的解毒率（k(dex)）随着水基砷的增加而增加。k(dex)值与在更高暴露条件下的生理损失和生长稀释率相当，甚至更高。从提出的生长毒性模型获得的模型预测与测量的生长数据一致。该生长毒性模型还用于说明在不同暴露场景下，从出生到自然死亡的罗非鱼的健康状况和生长轨迹。结果表明，不同处理中暴露鱼类的健康率和生长轨迹的时间趋势随着时间和水基砷的增加而降低，显示出浓度特异性模式。我们建议解毒率是关键的，应纳入风险评估框架。我们提出的建模算法很好地描述了长期金属胁迫下罗非鱼的内部调节活动和生物学反应。
