Formatted Title:

How Long to Wait for Accurate Trend Assessment? Insights from Case Studies and Statistical Analyses

Background/Objectives:

Estimating temporal trends in environmental contaminant concentrations is essential for effective resource management and policymaking. While trends are often thought of as smooth, broad changes that occur over long periods, the definition of “long periods” can vary depending on the context. For instance, in climate science, a minimum data length of around 20 years is often recommended to accurately assess trends. However, such guidelines are not well-established for environmental contaminant trend analysis, for which shorter datasets may fail to capture the within-year and year-to-year variability. Analyses based on limited data can lead to over- or under-estimation of the true trends, which may lead to misguided management actions. This study aims to determine the minimum number of years of data required to reliably detect trends in contaminant concentrations, considering the variability generally associated with such datasets.

Approach/Activities:

A statistical approach called moving window analysis was used to establish the minimum duration of data collection necessary to reliably detect temporal trends in contaminant concentrations. The moving window analysis involves calculating trends over a range of possible start and end years and comparing these calculated shorter term trends to the known long-term trend. This analysis was used to assess trend stability across different temporal periods. The minimum duration was established as the minimal number of years for the periodic temporal trends to approach the long-term trend. The robustness of the moving window analysis was further confirmed under a range of conditions using a probabilistic approach. The range of conditions evaluated includes sampling frequency, number of samples per event, and within-year variation. Our evaluation is based on a few long-term PCB datasets in fish, including the Hudson River PCBs Superfund Site, as well as datasets simulated to represent a range of conditions generally observed by our team in environmental samples.

Results/Lessons Learned:

The results show that for shorter temporal windows (window length), the trends are highly variable, and it will be premature to use the information to make management decisions on system recovery during these short temporal windows. As the length of temporal window increases, trends across different window periods converge to the long-term trend. The minimal number of years needed for a reliable trend estimate depends significantly on the observed sample variability. Specifically, with the variability typically observed in fish PCB concentrations dataset, data series shorter than eight years tend to show high variability and inconsistent trends, while those spanning eight years or more consistently produce stable and reliable trend estimates. This study demonstrates the importance of sufficient long-term data to ensure the reliability of trend assessments and our analysis provides a valuable framework for long-term monitoring programs like that being implemented for the Upper Hudson River.

Queenie Chang

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How Long to Wait for Accurate Trend Assessment? Insights from Case Studies and Statistical Analyses