Intensive short-term sampling with long-term consequences: characterizing pollutant transport with implications for...

Riverine sampling of pollutants is commonly used to understand pollutants' transport pathways, relationships with hydrology, and overall presence in a waterbody. However, temporal gaps between sample collection introduce errors to these efforts, and guidance prescribing sampling frequency remains sparse. The magnitude of error often depends on a contaminant's transport mechanisms and local hydrologic conditions, making the creation of comprehensive sampling guidance difficult. This study analyzed a unique dataset that measured 18 analytes, including pesticides, nutrients, and pathogens, in three Iowa rivers for 90 consecutive days (May 4-August 1, 2000). This dataset provided a novel opportunity to relate pollutants to local hydrology and quantify errors associated with recurring sampling. Pesticide concentrations followed the spring flush phenomenon, where values were greatest during high streamflow in May and June but often depleted by July. Fecal coliform and total phosphorus (TP) also coincided with high flow, but unlike pesticides, their concentrations never diminished. Nitrate exhibited more complex behavior; concentrations were diluted during high flows and then increased as streamflow receded. Autocorrelations were significant for nitrate and atrazine in larger rivers but negligible for fecal coliform and TP. Loads were calculated for four pollutants with minimal non-detects (atrazine, fecal coliform, nitrate, and TP). We simulated intermittent sampling by selecting evenly spaced subsets of measured values to estimate loads, which were compared to the loads calculated using every daily sample to quantify error. This method typically overestimated nitrate loads but underestimated other pollutants, and errors often decreased in larger watersheds. Nitrate generally had the lowest error, while fecal coliform had the highest. We used these results to approximate the sampling frequency needed to bind errors within a certain threshold.