Great Lakes Institute for Environmental Research, University of Windsor , Ontario, N9B3P4, Canada.
College of Environmental Science & Forestry, State University of New York , Syracuse, New York 13035, United States.
Environ Sci Technol. 2016 Oct 18;50(20):11103-11111. doi: 10.1021/acs.est.6b03169. Epub 2016 Sep 30.
Accurate predictions on the bioaccumulation of persistent organic pollutants (POPs) are critical for hazard and ecosystem health assessments. Aquatic systems are influenced by multiple stressors including climate change and species invasions and it is important to be able to predict variability in POP concentrations in changing environments. Current steady state bioaccumulation models simplify POP bioaccumulation dynamics, assuming that pollutant uptake and elimination processes become balanced over an organism's lifespan. These models do not consider the complexity of dynamic variables such as temperature and growth rates which are known to have the potential to regulate bioaccumulation in aquatic organisms. We contrast a steady state (SS) bioaccumulation model with a dynamic nonsteady state (NSS) model and a no elimination (NE) model. We demonstrate that both the NSS and the NE models are superior at predicting both average concentrations as well as variation in POPs among individuals. This comparison demonstrates that temporal drivers, such as environmental fluctuations in temperature, growth dynamics, and modified food-web structure strongly determine contaminant concentrations and variability in a changing environment. These results support the recommendation of the future development of more dynamic, nonsteady state bioaccumulation models to predict hazard and risk assessments in the Anthropocene.
准确预测持久性有机污染物 (POPs) 的生物积累对于危害和生态系统健康评估至关重要。水生系统受到多种胁迫因素的影响,包括气候变化和物种入侵,因此能够预测变化环境中 POP 浓度的变异性非常重要。目前的稳态生物积累模型简化了 POP 生物积累动态,假设污染物的吸收和消除过程在生物体的寿命内达到平衡。这些模型没有考虑到温度和生长速率等动态变量的复杂性,已知这些变量有可能调节水生生物的生物积累。我们将稳态 (SS) 生物积累模型与动态非稳态 (NSS) 模型和无消除 (NE) 模型进行对比。我们证明,NSS 和 NE 模型在预测 POP 个体的平均浓度和变异性方面都更具优势。这一比较表明,时间驱动因素,如温度、生长动态和修改后的食物网结构等环境波动,强烈决定了污染物浓度和变化环境中的变异性。这些结果支持未来开发更具动态性、非稳态生物积累模型的建议,以预测人类世的危害和风险评估。