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Optimization of Sampling Design to Determine the Spatial Distributions of Emerging Contaminants in Estuaries
Citation:
Katz, D., A. Kuhn, J. Sullivan, AND M. Cantwell. Optimization of Sampling Design to Determine the Spatial Distributions of Emerging Contaminants in Estuaries. Coastal and Estuarine Research Federation (CERF) 24th Biennial Conference, Providence, RI, November 05 - 09, 2017.
Impact/Purpose:
While Narragansett Bay has been intensively sampled over the past 50 years, no widely accepted sampling design for spatial distributions of dissolved contaminants exists. Sampling locations have often been chosen based strictly on local knowledge alone. As a result, comparisons between studies would better facilitated if a single approach to statistically based sampling designs were adopted. In an effort to harmonize future sampling efforts to assess spatial distributions of contaminants in Narragansett Bay and similar environments, a number of commonly implemented spatial models and sampling densities were evaluated. After evaluation, a randomized statistically based sampling design was developed and implemented. Results indicated that spatial models based on Empirical Bayesian Kriging performed best and a minimum number of samples per km2 was determined. Future comparisons of datasets both within and external to the studied estuary and would be facilitated by adoption of a sampling design similar to that proposed in this work.
Description:
Narragansett Bay (NB) has been extensively sampled over the last 50 years by various government agencies, academic institutions, and private groups. To date, most spatial research conducted within the estuary has employed deterministic sampling designs. Several studies have used probabilistic sampling designs on portions of the estuary, however a randomized, probabilistic sampling design has seldom been applied to the estuary as a whole. In this study, a sampling design was developed using commonly available software to implement and execute a probabilistic sampling approach for emerging contaminants in the estuary. Existing high density salinity data from the upper portion of Narragansett Bay was used for the sampling design optimization and results from this analysis were applied to whole estuary. After comparing multiple spatial statistical methods, it was determined that empirical bayesian kriging performed best across the estuary. This was evaluated by cross-validation between predictions and measurements primarily using Pearson’s correlation coefficient and root mean square error but also informed by other error statistics. The results from this analysis were used to determine an optimum number of stations (n=67) and sampling density (0.2 stations/km2) across the estuary. Stations were chosen randomly within a randomized, tessellated hexagonal grid and samples were taken to measure a suite of pharmaceutical compounds potentially present in the estuary. Results indicated that the performance of the spatial model was very accurate at predicting spatial distributions with the full set of samples and that only half of the sites were sufficient to develop a spatial model for NB with only a minor decrease in accuracy compared to the full number of stations. The findings demonstrate the successful use of the discussed spatial modeling framework and indicates its potential suitability for use across similar environments.