Characterizing dissolved oxygen conditions in estuarine environments
1997
Dissolved oxygen concentration, which is often measured in estuaries to quantify the results of and stresses associated with eutrophication, can be highly variable with time of day and tidal stage. To assess how well dissolved oxygen conditions are characterized by typical monitoring programs, we conducted Monte Carlo sampling from 16 semi-continuous, 31-day dissolved oxygen records collected from estuaries along the Atlantic and Gulf coasts to mimic three sampling strategies: (1) systematic point-in-time sampling, (2) random point-in-time sampling, and (3) short-term continuous records. These strategies were evaluated for their accuracy in estimating mean oxygen concentration, minimum oxygen concentration, and percent of time below a threshold value of 2 ppm. Mean dissolved oxygen concentration was most accurately estimated in both estuarine regions by random point-in-time sampling, but this strategy required more than ten samplings per month for the estimate to be within 0.5 ppm on 50% of the simulations. Short-term continuous sampling (24-48 h) correctly identified estuaries in the Gulf of Mexico region where dissolved oxygen concentrations of less than 2 ppm were experienced greater than 20% of the time. However, large tidal variations in Atlantic coast estuaries showed this measure to be inaccurate in these estuarine environments. None of the sampling strategies correctly identified month-long oxygen minima within 0.5 ppm for more than 50% of the simulations. This inability to characterize correctly dissolved oxygen conditions could add significant uncertainty to risk assessments, waste load allocation models, and other water quality evaluations that are the basis for developing wastewater treatment strategies and requirements. Perhaps more importantly, the inaccuracy with which conventional sampling procedures characterize minimum dissolved oxygen values suggests that the extent of hypoxia in estuarine waters in being substantially underestimated.
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