Ek Caroline, Faxneld Suzanne, Nyberg Elisabeth, Rolff Carl, Karlson Agnes M L
Department of Ecology, Environment and Plant Science, SE-106 91, Stockholm University, Stockholm, Sweden.
Department of Environmental Research and Monitoring, Swedish Museum of Natural History, P.O. 50007, SE-104 05 Stockholm, Sweden.
Sci Total Environ. 2021 Mar 25;762:143913. doi: 10.1016/j.scitotenv.2020.143913. Epub 2020 Dec 9.
To improve the statistical power of detecting changes in contaminant concentrations over time, it is critical to reduce both the within- and between-year variability by adjusting the data for relevant confounding variables. In this study, we present a method for handling multiple confounding variables in contaminant monitoring. We evaluate the highly variable temporal trends of Polycyclic Aromatic Hydrocarbons (PAHs) in blue mussels from the central Baltic Sea during the period 1987-2016 (data from 25 years during this period) using various regression analyses. As potential explanatory variables related to PAH exposure, we use mussel size and retrospective analyses of mussel δN and δC (representing large scale biogeochemical changes as a result of e.g. eutrophication and terrestrial inputs). Environmental data from concurrent monitoring programmes (seasonal data on Chlorophyll-a, salinity and temperature in the water column, bioturbation of sediment dwelling fauna) were included as variables related to feeding conditions. The concentrations of high-molecular-weight and low-molecular-weight PAHs in blue mussel were statistically linked to different combinations of environmental variables. Adjustment using these predictors decreased the coefficient of variation in all 15 PAHs tested and improved the statistical power to detect changes. Moreover, the adjustment also resulted in a significant downward trend for fluoranthene that could not be detected initially. For another PAH, benzo(g,h,i)perylene, adjustment which reduced variation resulted in the loss of an apparent downward trend over time. Hence, our study highlights the importance of using auxilliary data to reduce variability caused by environmental factors with general effects on physiology when assessing contaminant time trends. Furthermore, it illustrates the importance of extensive and well designed monitoring programmes to provide relevant data.
为提高检测污染物浓度随时间变化的统计效能，通过对相关混杂变量进行数据调整来降低年内和年间变异性至关重要。在本研究中，我们提出了一种处理污染物监测中多个混杂变量的方法。我们使用各种回归分析方法，评估了1987 - 2016年期间波罗的海中部蓝贻贝中多环芳烃（PAHs）高度可变的时间趋势（该期间25年的数据）。作为与PAH暴露相关的潜在解释变量，我们使用贻贝大小以及对贻贝δN和δC的回顾性分析（代表例如富营养化和陆地输入导致的大规模生物地球化学变化）。来自同期监测计划的环境数据（水柱中叶绿素-a、盐度和温度的季节性数据，底栖动物的生物扰动）作为与摄食条件相关的变量纳入。蓝贻贝中高分子量和低分子量PAHs的浓度与环境变量的不同组合存在统计学关联。使用这些预测变量进行调整降低了所有15种测试PAHs的变异系数，并提高了检测变化的统计效能。此外，调整还导致荧蒽出现了最初未检测到的显著下降趋势。对于另一种PAH，苯并(g,h,i)苝，减少变异的调整导致其随时间的明显下降趋势消失。因此，我们的研究强调了在评估污染物时间趋势时，使用辅助数据来减少由对生理有普遍影响的环境因素引起的变异性的重要性。此外，它还说明了广泛且设计良好的监测计划以提供相关数据的重要性。
