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

Intensive short-term sampling with long-term consequences: characterizing pollutant transport with implications for developing monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intensive short-term sampling with long-term consequences: characterizing pollutant transport with implications for developing monitoring Elliot Anderson, Keith Schilling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3919178/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract Riverine sampling of pollutants is commonly used to understand pollutants’ transport pathways, relationships with hydrology, and overall presence in a waterbody. However, gaps between sample collection introduce errors to these efforts, and guidance prescribing sampling frequency remains sparse. The magnitude of error often depends on the type of contaminant and watershed size, 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 actual loads 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. Grab sampling monitoring pollutants concentrations loads rivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Routine grab sampling of rivers and streams is commonly used to provide valuable information about a watershed’s environmental health and water quality. Many agencies organize sampling programs that collect environmental data to help inform decision-makers on watershed management issues. Sometimes, these programs are constructed to assess the local aquatic health (Ahn & Kim, 2017) or the potential presence of pollutants (Geissen et al., 2015). Other times, they aim to determine riverine loadings of various constituents (Johnes, 2007; Littlewood, 1995). Monitoring programs often strive to provide new insights into the behavior of waterborne constituents in the environment (Domagalski & Johnson, 2011; Wang et al., 2021)—hoping to learn about the contaminants’ persistence or transport mechanisms (Barber et al., 2013; Kronvang & Bruhn, 1996). For any grab sampling protocol, the frequency of sample collection is a key consideration. A balance must be struck between the costs associated with sampling and the necessary resolution to provide the desired information. However, developing guidelines on sampling frequency has remained a work in progress (Schleppi et al., 2006; Strobl & Robillard, 2008), as a wide variety of factors can influence the dynamics of waterborne constituents (Brauer et al., 2009). Study objectives, watershed hydrology, and constituent behavior should all inform riverine sampling considerations (A. S. Jones et al., 2012). Universal recommendations are difficult because many waterborne pollutants exhibit vastly different behavior and are transported to streams under various flow conditions through distinct environmental pathways (Tong et al., 2022; Zhang et al., 2022). Some constituents are characterized by abrupt changes in concentration, whereas others are more gradual (Godsey et al., 2009). Some constituents are ubiquitous, while others are largely absent from waterbodies and only detected occasionally under specific circumstances (Kolpin et al., 2021). Many demonstrate strong seasonality (C. S. Jones, Schilling, et al., 2018), where concentrations are reliably higher during particular parts of the year (Lin et al., 2019). Consequently, monitoring programs comprised of periodic sampling at fixed intervals (e.g., monthly, weekly) are not well formulated with the aforementioned considerations in mind (Brauer et al., 2009; Robertson & Roerish, 1999). Resource limitations are almost always the primary concern in these instances. Higher-resolution sampling is often infeasible due to the logistics and analytical procedures required to produce quality datasets (Kronvang & Bruhn, 1996). Insufficient sampling frequency introduces errors in water quality assessments (Neilson & Chapra, 2003) and load estimates (Coynel et al., 2004), and increasing the sampling frequency has been found to consistently improve these studies (Johnes, 2007; Rozemeijer et al., 2010). High-resolution measurements can be obtained for some analytes using in-situ sensors. Streamflow, for instance, is repeatedly measured throughout the United States using stream gauges owned and operated by the United States Geological Survey (USGS). The 15-minute frequency of these observations provides real-time flow estimates that are much more thorough than any obtained by periodic measurements (Hirsch & Costa, 2004). Certain constituents, such as nitrate (C. S. Jones, Schilling, et al., 2018) or dissolved oxygen (Kim et al., 2021), can also be measured on-site using sensor technology, and these high-resolution datasets can supplement periodic samples. The concentrations of other pollutants, such as salinity or total phosphorus (TP), can be approximated using surrogacy models (McCleskey et al., 2023; Schilling et al., 2017; Viviano et al., 2014). Although these models are imperfect and introduce some errors during implementation, they are typically preferable to infrequent grab sampling (Leigh et al., 2019; Villa et al., 2019). However, these in-situ sensors and surrogate relationships do not exist for other problematic pollutants. Many pathogens, nutrient forms, synthetic chemicals, and emerging contaminants cannot be monitored using field-deployed sensors. For these constituents, water samples must be collected on-site, transported to a laboratory, and then quantified via analytical chemistry procedures (Richardson & Kimura, 2019). Producing high-resolution datasets for these constituents thus requires significant labor efforts, and daily timeseries for such pollutants remain rare, especially those containing several co-occurring constituents (Alilou et al., 2018; Gulati et al., 2014; Skeffington et al., 2015; Torres et al., 2022). The State of Iowa is plagued by water quality challenges associated with nutrients (C. S. Jones et al., 2019; C. S. Jones, Nielsen, et al., 2018; Schilling et al., 2020), pathogens (Brendel & Soupir, 2017; Givens et al., 2016), sediment (C. S. Jones & Schilling, 2011; Streeter et al., 2021), and pesticides (Evelsizer & Skopec, 2018; Kolpin et al., 2010). Because of these water quality risks, the Iowa Department of Natural Resources (IDNR) oversees a comprehensive river sampling program to monitor these pollutants, consisting of monthly grab samples in 60 locations (Schilling et al., 2017). However, the extent to which monthly grab sampling can adequately characterize agricultural pollutant concentrations in Iowa rivers is not well understood. In this study, we used a unique, high-temporal resolution water quality dataset from three Iowa rivers to: 1) assess short-term patterns of transport for 18 different analytes in a typical 90-day hydrologic season and 2) develop insights into how best to characterize pollutant concentrations and understand load estimation errors over longer timeframes when periodic grab sampling is conducted at variable frequency. Contaminant transport in Iowa is complex (Kalkhoff et al., 2000; Liang et al., 2021), and it is imperative that watershed managers know how well their sampling programs are capturing pollutant concentrations and loading patterns before nonpoint source reduction strategies are implemented. Methods Site and Data Description The IDNR collected grab samples from three Iowa rivers for 90 consecutive days from May 4 to August 1, 2000. The three rivers (Old Mans Creek, English River, and Iowa River) are all located in eastern Iowa (Figure 1). Grab samples were collected at one fixed location along each river that aligned with a USGS stream gauge (Table 1). Samples were typically gathered each day between 7:00 am – 1:00 pm. The three rivers share similar hydrologic, geologic, and land use characteristics (Prior, 1991; Schilling et al., 2015). The smallest basin is Old Mans Creek (521 km 2 ), which is equivalent to one HUC10, followed by the English River (1,487 km 2 ), which encompasses four HUC10s. These two watersheds lie entirely within the Southern Iowa Drift Plain—a landform region characterized by rolling hills, well-developed surface drained, and loess soils over glacial till. The Iowa River basin is the largest (7,236 km 2 ) and contains two HUC08s. While this watershed’s northern area extends into landscapes made up of poorly drained plains, the basin’s southern portion resides in the Southern Iowa Drift Plain. All three watersheds are dominated by row crop agriculture and contain significant subsurface tile drainage networks. Additionally, the three rivers are largely free-flowing and contain no major impoundments upstream of the sampling sites. Eighteen analytes were measured in each grab sample (Table 2), thereby producing daily timeseries for each analyte in all three rivers. Ten of these analytes were conventional pesticides associated with weed and pest suppression on row crop fields (Curwin et al., 2002; Kolpin et al., 1998; Schnoebelen, 2003). Four were nutrients: inorganic nitrogen (nitrate), ammonia, TP, and orthophosphate (OP). Inorganic nitrogen is often simply referred to as nitrate because other inorganic nitrogen forms are highly unstable in Iowa surface waters and only present in small quantities (Hatfield et al., 2009). Fecal coliform, a standard indicator pathogen, was also measured. Nutrients and pathogens have long plagued Iowa’s water resources (C. S. Jones, Schilling, et al., 2018; Pandey & Soupir, 2014) and are considered widespread pollutants that impair waters throughout the world (Cabral, 2010; de Souza et al., 2020; Smith & Schindler, 2009). Three final analytes included water temperature, dissolved oxygen (DO), and turbidity, all common field measurements for surface waters. Daily concentrations for the 15 chemical pollutants were quantified, and all water quality data are publicly available in the EPA STORET database. Table 2 summarizes the units and detection limits associated with the IDNR’s analytical methods, which are detailed in the Supplemental Materials. Non-detection limits reflect standard protocols for surface water sampling in the early 2000s. Many of these limits have decreased in recent years with improvements in analytical chemistry. Non-detects have historically been common (>50% of samples) for some pollutants, such as ammonia or certain pesticides, in Iowa surface waters. For others, such as fecal coliform, nitrate, and TP, non-detects are rare (<5% of samples). Infrequently, issues with collection logistics resulted in missing data points, but only eight of the 54 timeseries had any missing values, with no timeseries containing more than seven. A USGS gauge adjacent to each sampling site continuously monitored streamflow over the project’s 90 days. Mean daily values were aggregated to create streamflow records coincident with the water quality timeseries. Figure 2 displays these streamflow records alongside daily precipitation observed at a nearby rain gauge (Iowa City Municipal Airport). A total of 337.3 mm of rainfall occurred between May 4 – August 1, 2000, which was typical precipitation (~55 th percentile) for the time of year. Eight days saw significant rainfall (>10 mm), leading to several high-flow events at each site. Due to its larger watershed area, streamflow in the Iowa River was typically higher and less flashy than the other locations. Statistical Analysis Streamflow data were plotted alongside the IDNR data to visualize the interplay between contaminant levels and river hydrology (Figure 3, Supplemental Materials). Specifically, we were interested in how concentrations behaved during high flows, as many pollutants are mobilized following wet weather (Hooda et al., 2000; Ryden et al., 1974) at certain times of the year (Ai et al., 2015; Lin et al., 2019). We calculated and summarized the descriptive statistics for every analyte (Supplemental Materials), including the number of non-detects. We also calculated temporal autocorrelation values for each daily timeseries and created correlograms denoting whether these values were statistically significant (Figure 5, Supplemental Materials). Statistically significant values can reveal the length of time over which it is possible to infer the concentration of an analyte based on a previous measurement (Feng et al., 2013). To investigate seasonal behavior, we categorized data points by their collection month (May, June, or July) and then created boxplots of their concentrations (Figure 4). The sampling schedule resulted in approximately 30 observations per month. Non-detects were set to half their detection limit for parts of the analysis that required non-censored values, such as calculating the mean or autocorrelation. Likewise, the few missing data points (<1% of samples) were estimated using linear interpolation. Error Quantification of Periodic Sampling Four of the pollutants analyzed contained minimal non-detects (<5%): atrazine, fecal coliform, nitrate, and TP. Non-censored data allowed for a more robust analysis of these four pollutants (Kayhanian et al., 2002; Olsen et al., 2012) and enabled us to accurately quantify loads for these parameters in all three rivers (Robertson & Roerish, 1999). Daily loads for the 90-day period were calculated for atrazine, fecal coliform, nitrate, and TP by multiplying daily concentrations by daily streamflow measured at the coincident USGS gauge. The high-resolution monitoring record was later subsampled to evaluate how intermittent grab sampling would impact load estimations. Evenly spaced subsets of samples were selected from the full 90-day datasets to represent regular intervals of grab sampling. Timeseries were divided into consistent increments beginning with the first and last data points, and subsets of samples (ranging from two to 90) were selected based on these increments. Linear interpolation was then used to estimate concentrations not included within these subsets, and loads were calculated based on these interpolated concentrations (Figure 6). For example, when two data points were used to estimate loads, we simply interpolated daily concentrations based on the sampling start and end dates. When four data points were used, the four points were evenly spaced throughout the timeseries—always including the start and end dates—and then interpolated. Beginning with two samples, this process was repeated until all 90 measurements were included in the load calculation. This method simulates several intervals used in typical grab sampling protocols (Madrid & Zayas, 2007; Schleppi et al., 2006). A subset of size four corresponds to monthly sampling, i.e., analytes are collected and measured every 30 days. A subset of size 13 corresponds to weekly sampling (~every seven days). A subset of size 90 uses every day and is equivalent to the original timeseries. We utilized the %Error metric to quantify errors from the load estimates constructed using sample subsets. %Error is a unitless metric that enables comparison of errors among the four pollutants, even when they are present at various scales in aquatic environments. %Error was found for each load estimate, and it was formally defined as Results Daily timeseries were plotted for all 18 analytes, and various statistics were calculated. The Supplemental Materials contain a complete collection of plots and statistics produced in this study. Pollutants’ Relationships to Streamflow For many pollutants, there was a strong relationship of concentration to streamflow (Figure 3, Supplemental Materials). For pesticides, high concentrations coincided with streamflow events in May and June. Although the highest concentrations did not necessarily co-occur with the highest streamflow values, a runoff component was always present during pesticide spikes. During low flows, pesticide levels were minimal, and non-detects dominated the datasets. By late June and July, pesticide levels were consistently low, regardless of streamflow. Fecal coliform and TP also correlated with streamflow, with their highest values coinciding with peak flows. However, unlike pesticides, their presence did not diminish in June or July, and wet weather events routinely triggered high levels of fecal coliform, TP, and turbidity in all three months. Fecal coliform proved very dynamic, with concentrations often increasing by several orders of magnitude during high flows. On the other hand, nitrate behavior was markedly different, and concentrations were typically diluted during high-flow events (Figure 3). After initial dilution, concentrations typically rebounded and increased while flows receded. Following several events in May and June, nitrate concentrations increased well above their pre-event levels (e.g., June 1 event in the English River, Figure 3). The rising nitrate levels coincided with the falling limbs of the hydrograph rather than hydrograph peaks. After June 15, nitrate concentrations generally declined. High flows continued to dilute nitrate concentrations, but nitrate levels largely returned to their pre-event concentrations during this period of decline rather than exceeding them as they did in May and early June. Patterns of ammonia, OP, and streamflow were more difficult to ascertain. Ammonia and OP levels were typically low but had occasional concentration spikes lasting one or two days. Sometimes, these high-nutrient days coincided with peak flows, but other times, they arose with no discernable change in hydrology. Spikes in ammonia and OP were equally present across May, June, and July. The remaining analytes (DO and temperature) were less related to streamflow. Some dilution of DO may have occurred during peak flows at the Old Mans Creek and the English River sites, but the numerous fluctuations in DO made it challenging to draw definitive conclusions about the relation to streamflow. Water temperature and river hydrology appeared completely independent. Statistical Properties of Pollutants The percentage of measurements below the detection limit varied tremendously among the 18 analytes (Table 2), although the rates of non-detections were consistent across the three sampling locations. Two pesticides (butylate and trifluralin) were not detected at all, whereas others contained over 90% non-detects (2-Chloro-6, alachlor, cyanazine, metribuzin). Atrazine was unique among pesticides in that it contained no non-detects. The abundance of samples below the detection limit was more mixed for the remaining pesticides: 2-Chloro-4 (7%), acetochlor (66%), and metolachlor (30%). Pesticides generally had a lower percentage of non-detects in May, followed by greater frequency of non-detects in June and July. Similarly, ammonia was frequently non-detected in the river water samples (66%), with June containing the lowest percentage (49%). Fewer OP samples were non-detects (33%), but DO, fecal coliform, nitrate, temperature, and turbidity datasets contained zero non-detects, and values below the detection limit were also rare for TP (3% of samples). Descriptive statistics were calculated for all 18 analytes (Supplemental Materials), but specific focus was given to the four pollutants with minimal non-detects (atrazine, fecal coliform, nitrate, and TP). Figure 4 contains boxplots displaying the range of values for these four pollutants organized by month and site. Concentrations were similar across the three river sites but varied considerably across the three months. Atrazine values ranged from 0.12 to 37 μg/l, but the maximum values always occurred within May and June. By July, the range of values was much smaller (0.12 – 1.2 μg/l). Each atrazine dataset was positively skewed—concentrations on a few days were far greater than median values. Fecal coliform values were very positively skewed and spanned several orders of magnitude, ranging between 10 and 450,000 MPN/100ml. TP also exhibited positive skew, albeit to a lesser degree than fecal coliform. TP samples ranged from values below the detection limit (<0.1) to 4.7 mg/l. Nitrate concentrations ranged from 0.1 to 14 mg/l, but patterns were much more symmetric. Seasonal patterns were evident for fecal coliform, nitrate, and TP, but patterns for atrazine were different (Figure 4). Concentrations of fecal coliform, nitrate, and TP typically increased from May to June, but there was not a consistent pattern for atrazine. Autocorrelation within the timeseries of the four pollutants was explored (Figure 5). No autocorrelation was present among the fecal coliform samples (values within the 95% confidence interval), and it was minimal for TP. Atrazine displayed significant autocorrelation at lags of one and two days in the English and Iowa Rivers. Autocorrelation was particularly high at the one-day lag in the English River (0.8). Nitrate exhibited the highest degree of autocorrelation at all sites, and it increased along with watershed size. Autocorrelation values remained statistically significant at the Iowa River for lags up to seven days. Errors of Estimated Loads The %Error associated with periodic grab sampling was investigated for atrazine, fecal coliform, nitrate, and TP at the three sites (Figure 7). The expected gradual decrease in %Error was evident, and errors converged to 0 by the time 80 interpolation points were included. However, there was a high degree of variation in the %Error as the number of interpolation points increased (Figure 7). For example, the TP %Error in the English River increased from -47.3% to 4.01% based on an increase from 13 to 14 interpolation points. A rolling mean (of size 5) was included to smooth the %Error values and better assess the overall decrease in error (Supplemental Materials). Despite occasional large fluctuations in %Error, broad patterns in the load estimates were apparent. When interpolating loads for atrazine, fecal coliform, and TP with fewer samples collected, loads tended to be underestimated (negative %Error values). In contrast, interpolated nitrate loads largely overestimated the true values. Nitrate errors were the lowest, but interpolation errors associated with atrazine and TP were comparable. For a given contaminant, estimation errors typically decreased in larger watersheds. Errors were higher in the smallest basin (Old Mans Creek) and lower in the largest (Iowa River). On the other hand, fecal coliform had the greatest errors in load quantification using interpolated values (Figure 7). To provide prescriptive sampling guidelines, we identified the number of interpolation points needed to bring the %Error values within certain thresholds using the rolling means. This number was then divided by the total amount of samples (90) to generalize the results for an arbitrary timeframe. The result was the percentage of days that required sampling to bring estimated loads within a given error threshold (Table 3). Using atrazine in Old Mans Creek as an example, once 12 samples were utilized in calculating the load, %Error values were consistently <40%. Dividing 12 by 90 yields 13.3%—this value is the percentage of samples needed to restrict error within the 40% threshold. Table 3 quantifies the general behavior reflected in our results, but the observed fluctuations in %Error mean that this guidance is not absolute. Any periodic sampling schedule may miss key dates that introduce significant errors to estimates (Henjum et al., 2010). Nitrate required the fewest number of samples to meet the 40% threshold. Monthly sampling (~4% of samples) would bring errors beneath the 40% threshold at each site, and weekly sampling (~14% of samples) would achieve the 5% threshold at the Iowa River. Atrazine and TP required weekly sampling to meet the 40% threshold at Old Mans Creek and the English River and the 20% threshold at the Iowa River. Fecal coliform required the most samples at all sites; weekly sampling would only achieve the 40% error limit in the Iowa River. Subweekly monitoring was needed to hit the 10% threshold for all parameters (except nitrate at the Iowa River). Ensuring errors are within 5% in the smaller watersheds was exceptionally burdensome (>50% of days required). Discussion Pollutant Transport Pathways Pairing streamflow alongside daily pollutant concentrations confirms many of the mechanisms noted by studies of contaminant hydrology. Pesticide concentration patterns were emblematic of the “spring flush” phenomenon (Phillips & Bode, 2004; Spalding et al., 1994; Thurman et al., 1991), which has been previously documented in Iowa streams (Kolpin et al., 2010). Pesticides are mobilized by wet weather during the spring, where they run off agricultural fields into nearby waterbodies. After the first few rainfall events, the mass of pesticides is mostly extinguished on the landscape, and thus, concentrations are not typically found in streams by July. This behavior aligns with the standard agricultural practice, where most pesticides are applied in the spring (Curwin et al., 2002). In this study, pesticides largely demonstrated the “spring flush” phenomenon, but atrazine was unique in that it never diminished below detection limits. Atrazine’s prolific usage and lack of degradation have made it a ubiquitous pollutant in Iowa groundwater (Kolpin et al., 1995). The atrazine timeseries shown herein suggest it is constantly delivered to Iowa streams under low flow conditions but is exacerbated during the “spring flush” in runoff. Fecal coliform and TP were also runoff-driven, but other factors complicate their relationships with streamflow. Wet weather events trigger erosion from agricultural landscapes that carry sediments and pathogens (Harmel et al., 2010) and TP (Mallarino et al., 2002). Both pollutants spike during high streamflow, but the magnitude of these spikes is not always proportional to flow, i.e., the highest flow events do not necessarily produce the highest fecal coliform or TP concentrations. Instead, local and event-specific factors likely confound a simple relationship among streamflow, TP, and fecal coliform. Unlike the “spring flush,” fecal coliform and TP do not decline in the summer. Their ubiquity on Iowa’s landscape has been well documented (Brendel & Soupir, 2017; Schilling et al., 2020), so any storm event has the potential to elevate their concentrations. Nitrate concentration patterns are consistent with subsurface delivery of nitrate to streams with groundwater flow (Schilling et al., 2007) and tile drainage (Schilling et al., 2012). Wet weather events initially dilute nitrate levels due to an influx of runoff with lower nitrate levels (Poor & McDonnell, 2007), but as rainfall permeates the soils, it mobilizes nitrate and transports it through subsurface drainage networks (Sebilo et al., 2013). This subsurface discharge often enters the river network as peak flows recede, resulting in increasing nitrate concentrations (Poor & McDonnell, 2007). This complicated relationship highlights the influence of hysteresis on nitrate transport, which has been observed in many watersheds with the advent of continuous nitrate sensor data (Baker & Showers, 2019; Vaughan et al., 2017). Ammonia contamination appears to be more binary than other nutrient species (e.g., TP and nitrate). It is usually absent from eastern Iowa waters (Garrett, 2012), but rivers occasionally suffer from abrupt concentration spikes. These spikes sometimes coincide with high streamflow, indicating a runoff-driven process observed in similar agricultural conditions where ammonia is applied in manure or fertilizer (Moore Jr et al., 2000). Other times, ammonia spikes during low flow conditions. Elevated ammonia during low flows is often the result of industrial or municipal discharges (Van Damme et al., 2018). Implications for Sampling Programs Most water quality sampling programs have one of two goals: 1) determine the mechanisms that dictate how pollutants are delivered to streams or 2) assess the immediate or long-term presence of contaminants in a waterbody. This study has implications for both goals. The daily timeseries evaluated in this study confirmed the disparate nature of pollutant transport. High-resolution sampling proved valuable in deciphering agricultural pollutants’ transport pathways, which would have been much harder to discern when evaluating periodic sampling data (Godsey et al., 2019). In regions where uncertainty surrounds contaminant sources and transport mechanisms, conducting a short-term, high-resolution sampling study may be used to inform long-term monitoring plans. Knowledge of transport pathways is also crucial for understanding limitations in periodic sampling programs. Monthly sampling routines can be effective at gauging the overall presence of some pollutants that are slow-moving and normally distributed (Telci et al., 2009), but large uncertainties arise for pollutants that are dynamic and positively skewed (Johnes, 2007). High autocorrelation for nitrate suggests that this pollutant can be reasonably monitored through periodic samples. As watershed size increases, the autocorrelation of nitrate also increases (Benson et al., 2006). Since other hydrologic processes are less dynamic in larger watersheds (Singh, 1997), periodic sampling is often more effective in larger rivers (Madrid & Zayas, 2007) compared to small rivers, such as Old Mans Creek. On the other hand, atrazine, fecal coliform, and TP are subject to sudden shifts in concentration—evidenced by their daily timeseries and minimal autocorrelation. Other studies have noted similar behaviors in Iowa watersheds (Liang et al., 2021). Evaluating their presence cannot realistically be captured through periodic sampling alone. When attempting to comprehensively assess parameters, such as atrazine or TP, it is essential to deploy methods that fill in the gaps between samples. Potential techniques include water quality surrogates (Castrillo & García, 2020; Schilling et al., 2017; Viviano et al., 2014) or flow-based models, such as LOADEST (Runkel et al., 2004) or WRTDS (Hirsch et al., 2010). These models utilized other in-situ measurements to estimate pollutant concentrations on days without sampled data and have proved successful in many studies (Hirsch et al., 2010; A. S. Jones et al., 2011; Rowland et al., 2021). However, constructing these models requires substantial historical data (often 10+ years and 150+ observations; (Hirsch et al., 2010)). They can also suffer from significant errors in short timeframes and are most appropriate when evaluating concentrations over the course of decades rather than specific seasons or events (Lee et al., 2019). Implications for Short-Term Load Estimates For some studies, including calculating wastewater discharge limits or total maximum daily loads (TMDLs), accurate quantification of short-term loads (<5 years) is required. Our analysis of errors associated with interpolated loads provides general prescriptive guidelines on the frequency of sampling needed to cap errors below specific thresholds (Table 3). These guidelines depend upon the pollutant of interest and watershed size. For example, if a project required quantification of nitrate loads in a HUC08 watershed, Table 3’s values for the Iowa River are most relevant, as it is a watershed of a similar size. The 5.6% value for nitrate prescribes the number of days needed to calculate loads within 10% error. If the project timeframe lasts a year, this would involve collecting samples every 20 days (365 days * 5.6% ≈ 20 days). Our interpolation method was bracketed around a project’s start and end date. It required that measurements take place on each of these days, which is a widespread practice of short-term sampling projects (Halliday et al., 2012). Our method of dividing up the 90-day timeframe into regular intervals simulated various fixed-interval sampling schedules. Fixed-interval sampling is a widespread approach among water quality monitoring programs (Behmel et al., 2016). Although some programs include samples collected during high-flow conditions (Lewis, 1996), this approach is more expensive and onerous, as the logistics of event-specific sampling make it difficult to apply at large spatial scales and timeframes (Lopez et al., 2000). Interpolation based on high-flow samples can also introduce errors by overrepresenting rare hydrologic conditions (Cooper & Watts, 2002). Therefore, the analysis included in our study is representative of sampling protocols that are commonly deployed in monitoring programs. As noted earlier, less error is generally associated with load estimates of larger rivers, i.e., fewer samples are needed as watershed size increases (de Almeida et al., 2023; Telci et al., 2009). Dynamic pollutants like fecal coliform are much more difficult to quantify than slow-moving ones like nitrate. However, there is substantial uncertainty endemic to periodic sampling. The errors of load estimation can vary considerably by slightly shifting the sampling frequency (Figure 7). This is mainly due to randomness associated with capturing days with high streamflow. The large water volume on these particular days makes them high-leverage events that greatly impact overall load calculations. A schedule that either hits or misses disproportionate amounts of high-flow days will be subject to large errors. The direction of the errors shown in Figure 7 is also noteworthy and related to analyte skewness. In most cases, errors were positive for nitrate and negative for atrazine, fecal coliform, and TP. This generalized phenomenon is linked to the fact that points used for interpolation were typically not high-flow days. However, concentrations for atrazine, fecal coliform, and TP are positively skewed (Figure 4), and their largest values often occur on high-flow days and are orders of magnitude greater than those of low-flow conditions. A typical sampling protocol that misses these days when disproportionate amounts of pollutants are transported will underestimate the true load. In the rare instances where several high-flow days are sampled, loads can be overestimated. The effect is essentially reversed for nitrate. Nitrate concentrations are diluted during high streamflow, resulting in negatively skewed timeseries. Sampling schedules that miss high-flow days containing low nitrate concentrations will consequently overestimate nitrate loads. Therefore, Table 3 provides rule-of-thumb guidelines that reflect the effects of increasing sampling frequency on lowering load estimation errors. However, any individual load calculation is subject to hydrologic randomness that can cause values to deviate from these guidelines. Still, the prescriptive nature of these guidelines exemplifies the typical frequency one would need to quantify loads adequately. The desired error bounds will depend upon the goals of a specific study. It may be the case that quantifying some pollutants, such as fecal coliform, in smaller watersheds will be infeasible due to the sampling requirements. Conversely, grab sampling may prove adequate in larger rivers and relinquish the need for more cost-prohibitive methods. Conclusion This study explored a unique dataset that measured 18 different waterborne analytes for 90 consecutive days (May 4 – August 1, 2000) in three rivers in southeastern Iowa. These analytes included ten pesticides, four nutrient forms, and fecal coliform. Apart from atrazine, pesticide datasets contained significant numbers of samples below the detection limit. Non-detects were also common for ammonia and OP but rare for nitrate, TP, and fecal coliform. Among the four main pollutants with continuous detections, the highest degree of autocorrelation was observed for nitrate. Different pollutants had a unique relationship with local streamflow. Pesticide concentrations were greatest during high flows in May and early June, but by July, they were depleted from the landscape and largely absent within the rivers. Fecal coliform and TP levels were also driven by runoff but were never exhausted from the landscape. Nitrate was initially diluted during high flows, but concentrations began to rise in May and June as it was mobilized and transported to the streams while flows were falling. Loads for atrazine, fecal coliform, nitrate, and TP were calculated based on simulated intermittent sampling and compared to the true measured loads. Estimation errors were highest for fecal coliform, followed by TP and atrazine. Intermittent grab sampling was found to underpredict these loads due to several high-flow days with elevated pollutant levels. Conversely, errors for nitrate were the lowest, and intermittent sampling tended to overpredict loads due to nitrate dilution during high-flow days. These load estimates highlight the uncertainties and challenges associated with periodic grab sampling and argue for the use of discharge-based models and in-situ monitoring when possible. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors wish to thank the Iowa Department of Natural Resources for their efforts in collecting the water quality data used in this study. Funding Declaration No funding was provided for this study. Ethics Approval and Consent to Participate Declaration Not applicable. Author Contribution Statement Elliot Anderson conducted all data retrieval and statistical analyses, prepared the figures and tables, and wrote the initial version of the manuscript text. Keith Schilling provided guidance on the methods used to analyze data and quantify loads, helped formulate the study directives, and edited and revised the manuscript. Data Availability Declaration All water quality data used in this study can be retrieved through the EPA STORET database (https://www.epa.gov/waterdata/water-quality-data) or the IDNR AQuIA database (https://programs.iowadnr.gov/aquia/). All streamflow data used in this study can be retrieved through the USGS National Water Information System (https://waterdata.usgs.gov/nwis). References Ahn, S. R., & Kim, S. J. (2017). Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology. Hydrology and Earth System Sciences, 21 (11), 5583-5602. Ai, L., Shi, Z. H., Yin, W., & Huang, X. (2015). Spatial and seasonal patterns in stream water contamination across mountainous watersheds: Linkage with landscape characteristics. Journal of Hydrology, 523 , 398-408. doi:https://doi.org/10.1016/j.jhydrol.2015.01.082 Alilou, H., Nia, A. M., Keshtkar, H., Han, D., & Bray, M. (2018). 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Site IDNR ID USGS Name USGS ID Area (km 2 ) Lat Long Old Mans Creek 10520001 Old Mans Creek near Iowa City, IA 05455100 521 41.60640518 -91.61572419 English River 10920001 English River at Kalona, IA 05455500 1,487 41.4697387 -91.7146129 Iowa River 10480001 Iowa River at Marengo, IA 05453100 7,236 41.81272566 -92.064792 Table 2. Analytes included in sampling program and their percentage of non-detects in each month. Short Name Analyte Unit Detection Limit % Non-Detects May June July 2-Chloro-4 2-Chloro-4-isopropylamino-6-amino-s-triazine ug/l 0.1 29.8% 0.0% 3.1% 2-Chloro-6 2-Chloro-6-ethylamino-4-amino-s-triazine ug/l 0.1 91.7% 80.0% 100.0% Acetochlor Acetochlor ug/l 0.1 29.8% 64.4% 100.0% Alachlor Alachlor ug/l 0.1 92.9% 91.1% 96.9% Ammonia Ammonia-nitrogen (as N) mg/l 0.1 67.9% 48.9% 79.2% Atrazine Atrazine ug/l 0.1 0.0% 0.0% 0.0% Butylate Butylate ug/l 0.1 100.0% 100.0% 100.0% Cyanazine Cyanazine ug/l 0.1 83.3% 90.0% 99.0% DO Dissolved oxygen mg/l 0.1 0.0% 0.0% 0.0% Fecal Coliform Fecal Coliform MPN/100ml 10 0.0% 0.0% 0.0% Metolachlor Metolachlor ug/l 0.1 10.7% 11.1% 65.6% Metribuzin Metribuzin ug/l 0.1 96.4% 96.7% 100.0% Nitrate Inorganic nitrogen (nitrate and nitrite) (as N) mg/l 0.1 0.0% 0.0% 0.0% OP Orthophosphate (as P) mg/l 0.1 60.6% 27.6% 17.7% TP Phosphate-phosphorus (as P) mg/l 0.1 3.6% 2.2% 3.2% Temp Temperature, water deg C - 0.0% 0.0% 0.0% Trifluralin Trifluralin ug/l 0.1 100.0% 100.0% 100.0% Turbidity Turbidity NTU 1 0.0% 0.0% 0.0% Table 3. The percentage of days where samples need to be collected to ensure estimated loads are within an error threshold. Site Error Threshold % of Days that Require Sampling Atrazine Fecal Coliform Nitrate TP Old Mans Creek < 40% 13.3% 37.8% 4.4% 20.0% < 20% 24.4% 57.8% 20.0% 40.0% < 10% 37.8% 75.6% 43.3% 62.2% < 5% 57.8% 85.6% 65.6% 84.4% English River < 40% 13.3% 26.7% 3.3% 13.3% < 20% 26.7% 68.9% 6.7% 26.7% < 10% 45.6% 84.4% 17.8% 57.8% < 5% 86.7% 86.7% 55.6% 72.2% Iowa River < 40% 4.4% 13.3% 3.3% 3.3% < 20% 5.6% 22.2% 3.3% 8.9% < 10% 22.2% 40.0% 5.6% 15.6% < 5% 38.9% 76.7% 13.3% 43.3% Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3919178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281865234,"identity":"60a5d76e-1386-4932-8ce6-cf6f1585d398","order_by":0,"name":"Elliot Anderson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3PMUvDQBTA8XcctMvZri209itcONCltl/lwkGyKK4ZUwpxKc4Wv8StbpEH7RJwjditkMmhxSWiUFODqOAD3RzuPxz34P04DsDl+o81eZzWN/1+HgKvRxZThLOK6E+ifkXgK/E/NknS5mySbksYHDfNYltGq9Au+aJTwrBv059Jd8ri2ysN3s2sMNciK84sNoLuDAJFEYksRqF3zOanirMEKyKOcgHoU2RcExjb/PyJPScYyorcv8KOJJLXxK9e4XCQoN6TBwEpSTq4/0sAxmaF4iJDb46N8KUnjZoTpH0xxc1mCCd2adasjHDQukP0HqNR/5IgVPJv6y6Xy+X63hupTmO78jsypwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Iowa","correspondingAuthor":true,"prefix":"","firstName":"Elliot","middleName":"","lastName":"Anderson","suffix":""},{"id":281865235,"identity":"bb1bed8d-55d7-422b-938e-02a13be3a6c6","order_by":1,"name":"Keith Schilling","email":"","orcid":"","institution":"University of Iowa","correspondingAuthor":false,"prefix":"","firstName":"Keith","middleName":"","lastName":"Schilling","suffix":""}],"badges":[],"createdAt":"2024-02-02 00:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3919178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3919178/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13266-x","type":"published","date":"2024-10-30T16:20:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53243461,"identity":"9b3a6373-4af9-460e-a6b7-7b7c9db1e51c","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":474369,"visible":true,"origin":"","legend":"\u003cp\u003eSampling locations and their corresponding watersheds; the rainfall gauge located at the Iowa City Municipal Airport.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/e60da6b3e3968bdcd385ce37.png"},{"id":53243462,"identity":"5ff2cdd2-d369-4c2e-8cc9-c05353cf94e5","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75025,"visible":true,"origin":"","legend":"\u003cp\u003eMean daily streamflow at the three sampling sites and the daily rainfall reported at the Iowa City Municipal Airport.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/9717057c964c188f05104965.png"},{"id":53243465,"identity":"608f8b3e-3473-40d7-9879-fb7c7087466e","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126106,"visible":true,"origin":"","legend":"\u003cp\u003eDaily timeseries at the English River. Plots contain analyte concentrations (Atrazine, Acetochlor, Nitrate, Ammonia, TP, and Fecal Coliform) on the primary axis and streamflow on the secondary axis.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/8accc6dbefa89dd42cdd9543.png"},{"id":53243463,"identity":"962f1352-1100-4f14-8268-213807ffea8b","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60909,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of monthly analyte concentrations by site. Note the log scale for Fecal Coliform.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/af60767e9d1571c726403d7c.png"},{"id":53243464,"identity":"2e8bfe12-5c16-439e-80a3-3a0e77831c4f","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110569,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelograms for daily analyte concentrations. The shaded area represents the 95% confidence interval; values outside this interval are considered statistically significant (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/65a079060026bb062ff8d852.png"},{"id":53243466,"identity":"0e08e5f2-6919-4aa5-8576-9999b4f2ad85","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78473,"visible":true,"origin":"","legend":"\u003cp\u003eExample illustrating the interpolation method for nitrate at the English River. The black line displays the measured nitrate concentrations. The points represent subsets of measurements used for interpolation, while the colored lines represent the interpolated concentrations.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/84cd5deb11554afb9195add0.png"},{"id":53243467,"identity":"de5715c0-560e-4b02-b1f3-a02adf9fba4c","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":155588,"visible":true,"origin":"","legend":"\u003cp\u003ePercent Error of the interpolated loads.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/52bc2cabb4521a61d41834b6.png"},{"id":68207294,"identity":"f6e79575-f9ca-40a2-b65a-13ee7a18bb36","added_by":"auto","created_at":"2024-11-04 16:36:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1649845,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/e8376f45-c8a6-49a7-90d4-9ac207af8b51.pdf"},{"id":53243469,"identity":"60261420-1330-49e7-874c-1e79a6f8825e","added_by":"auto","created_at":"2024-03-22 10:42:24","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53986978,"visible":true,"origin":"","legend":"","description":"","filename":"supplemental.zip","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/c762005576bd29b9cea08040.zip"},{"id":53243460,"identity":"2df5329d-b3e6-48ca-8d04-8c700167ba01","added_by":"auto","created_at":"2024-03-22 10:42:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25974,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3919178/v1/8a96832b22bdeda8311843c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intensive short-term sampling with long-term consequences: characterizing pollutant transport with implications for developing monitoring","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRoutine grab sampling of rivers and streams is commonly used to provide valuable information about a watershed\u0026rsquo;s environmental health and water quality. Many agencies organize sampling programs that collect environmental data to help inform decision-makers on watershed management issues. Sometimes, these programs are constructed to assess the local aquatic health\u0026nbsp;(Ahn \u0026amp; Kim, 2017)\u0026nbsp;or the potential presence of pollutants\u0026nbsp;(Geissen et al., 2015). Other times, they aim to determine riverine loadings of various constituents\u0026nbsp;(Johnes, 2007; Littlewood, 1995). Monitoring programs often strive to provide new insights into the behavior of waterborne constituents in the environment\u0026nbsp;(Domagalski \u0026amp; Johnson, 2011; Wang et al., 2021)\u0026mdash;hoping to learn about the contaminants\u0026rsquo; persistence or transport mechanisms\u0026nbsp;(Barber et al., 2013; Kronvang \u0026amp; Bruhn, 1996).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor any grab sampling protocol, the frequency of sample collection is a key consideration. A balance must be struck between the costs associated with sampling and the necessary resolution to provide the desired information. However, developing guidelines on sampling frequency has remained a work in progress\u0026nbsp;(Schleppi et al., 2006; Strobl \u0026amp; Robillard, 2008), as a wide variety of factors can influence the dynamics of waterborne constituents\u0026nbsp;(Brauer et al., 2009). Study objectives, watershed hydrology, and constituent behavior should all inform riverine sampling considerations\u0026nbsp;(A. S. Jones et al., 2012). Universal recommendations are difficult because many waterborne pollutants exhibit vastly different behavior and are transported to streams under various flow conditions through distinct environmental pathways\u0026nbsp;(Tong et al., 2022; Zhang et al., 2022). Some constituents are characterized by abrupt changes in concentration, whereas others are more gradual\u0026nbsp;(Godsey et al., 2009). Some constituents are ubiquitous, while others are largely absent from waterbodies and only detected occasionally under specific circumstances\u0026nbsp;(Kolpin et al., 2021). Many demonstrate strong seasonality\u0026nbsp;(C. S. Jones, Schilling, et al., 2018), where concentrations are reliably higher during particular parts of the year\u0026nbsp;(Lin et al., 2019). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsequently, monitoring programs comprised of periodic sampling at fixed intervals (e.g., monthly, weekly) are not well formulated with the aforementioned considerations in mind\u0026nbsp;(Brauer et al., 2009; Robertson \u0026amp; Roerish, 1999). Resource limitations are almost always the primary concern in these instances. Higher-resolution sampling is often infeasible due to the logistics and analytical procedures required to produce quality datasets\u0026nbsp;(Kronvang \u0026amp; Bruhn, 1996). Insufficient sampling frequency introduces errors in water quality assessments\u0026nbsp;(Neilson \u0026amp; Chapra, 2003)\u0026nbsp;and load estimates\u0026nbsp;(Coynel et al., 2004), and increasing the sampling frequency has been found to consistently improve these studies\u0026nbsp;(Johnes, 2007; Rozemeijer et al., 2010).\u003c/p\u003e\n\u003cp\u003eHigh-resolution measurements can be obtained for some analytes using in-situ sensors. Streamflow, for instance, is repeatedly measured throughout the United States using stream gauges owned and operated by the United States Geological Survey (USGS). The 15-minute frequency of these observations provides real-time flow estimates that are much more thorough than any obtained by periodic measurements\u0026nbsp;(Hirsch \u0026amp; Costa, 2004). Certain constituents, such as nitrate\u0026nbsp;(C. S. Jones, Schilling, et al., 2018)\u0026nbsp;or dissolved oxygen\u0026nbsp;(Kim et al., 2021), can also be measured on-site using sensor technology, and these high-resolution datasets can supplement periodic samples. The concentrations of other pollutants, such as salinity or total phosphorus (TP), can be approximated using surrogacy models\u0026nbsp;(McCleskey et al., 2023; Schilling et al., 2017; Viviano et al., 2014). Although these models are imperfect and introduce some errors during implementation, they are typically preferable to infrequent grab sampling\u0026nbsp;(Leigh et al., 2019; Villa et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, these in-situ sensors and surrogate relationships do not exist for other problematic pollutants. Many pathogens, nutrient forms, synthetic chemicals, and emerging contaminants cannot be monitored using field-deployed sensors. For these constituents, water samples must be collected on-site, transported to a laboratory, and then quantified via analytical chemistry procedures\u0026nbsp;(Richardson \u0026amp; Kimura, 2019). Producing high-resolution datasets for these constituents thus requires significant labor efforts, and daily timeseries for such pollutants remain rare, especially those containing several co-occurring constituents\u0026nbsp;(Alilou et al., 2018; Gulati et al., 2014; Skeffington et al., 2015; Torres et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe State of Iowa is plagued by water quality challenges associated with nutrients (C. S. Jones et al., 2019; C. S. Jones, Nielsen, et al., 2018; Schilling et al., 2020), pathogens (Brendel \u0026amp; Soupir, 2017; Givens et al., 2016), sediment (C. S. Jones \u0026amp; Schilling, 2011; Streeter et al., 2021), and pesticides (Evelsizer \u0026amp; Skopec, 2018; Kolpin et al., 2010). Because of these water quality risks, the Iowa Department of Natural Resources (IDNR) oversees a comprehensive river sampling program to monitor these pollutants, consisting of monthly grab samples in 60 locations (Schilling et al., 2017). However, the extent to which monthly grab sampling can adequately characterize agricultural pollutant concentrations in Iowa rivers is not well understood. In this study, we used a unique, high-temporal resolution water quality dataset from three Iowa rivers to: 1) assess short-term patterns of transport for 18 different analytes in a typical 90-day hydrologic season and 2) develop insights into how best to characterize pollutant concentrations and understand load estimation errors over longer timeframes when periodic grab sampling is conducted at variable frequency. Contaminant transport in Iowa is complex (Kalkhoff et al., 2000; Liang et al., 2021), and it is imperative that watershed managers know how well their sampling programs are capturing pollutant concentrations and loading patterns before nonpoint source reduction strategies are implemented.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eSite and Data Description\u003c/h2\u003e\n\u003cp\u003eThe IDNR collected grab samples from three Iowa rivers for 90 consecutive days from May 4 to August 1, 2000. The three rivers (Old Mans Creek, English River, and Iowa River) are all located in eastern Iowa (Figure 1). Grab samples were collected at one fixed location along each river that aligned with a USGS stream gauge (Table 1). Samples were typically gathered each day between 7:00 am \u0026ndash; 1:00 pm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe three rivers share similar hydrologic, geologic, and land use characteristics\u0026nbsp;(Prior, 1991; Schilling et al., 2015). The smallest basin is Old Mans Creek (521 km\u003csup\u003e2\u003c/sup\u003e), which is equivalent to one HUC10, followed by the English River (1,487 km\u003csup\u003e2\u003c/sup\u003e), which encompasses four HUC10s. These two watersheds lie entirely within the Southern Iowa Drift Plain\u0026mdash;a landform region characterized by rolling hills, well-developed surface drained, and loess soils over glacial till. The Iowa River basin is the largest (7,236 km\u003csup\u003e2\u003c/sup\u003e) and contains two HUC08s. While this watershed\u0026rsquo;s northern area extends into landscapes made up of poorly drained plains, the basin\u0026rsquo;s southern portion resides in the Southern Iowa Drift Plain. All three watersheds are dominated by row crop agriculture and contain significant subsurface tile drainage networks. Additionally, the three rivers are largely free-flowing and contain no major impoundments upstream of the sampling sites. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEighteen analytes were measured in each grab sample (Table 2), thereby producing daily timeseries for each analyte in all three rivers. Ten of these analytes were conventional pesticides associated with weed and pest suppression on row crop fields\u0026nbsp;(Curwin et al., 2002; Kolpin et al., 1998; Schnoebelen, 2003). Four were nutrients: inorganic nitrogen (nitrate), ammonia, TP, and orthophosphate (OP). Inorganic nitrogen is often simply referred to as nitrate because other inorganic nitrogen forms are highly unstable in Iowa surface waters and only present in small quantities\u0026nbsp;(Hatfield et al., 2009). Fecal coliform, a standard indicator pathogen, was also measured. Nutrients and pathogens have long plagued Iowa\u0026rsquo;s water resources\u0026nbsp;(C. S. Jones, Schilling, et al., 2018; Pandey \u0026amp; Soupir, 2014)\u0026nbsp;and are considered widespread pollutants that impair waters throughout the world\u0026nbsp;(Cabral, 2010; de Souza et al., 2020; Smith \u0026amp; Schindler, 2009). Three final analytes included water temperature, dissolved oxygen (DO), and turbidity, all common field measurements for surface waters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDaily concentrations for the 15 chemical pollutants were quantified, and all water quality data are publicly available in the EPA STORET database. Table 2 summarizes the units and detection limits associated with the IDNR\u0026rsquo;s analytical methods, which are detailed in the Supplemental Materials. Non-detection limits reflect standard protocols for surface water sampling in the early 2000s. Many of these limits have decreased in recent years with improvements in analytical chemistry. Non-detects have historically been common (\u0026gt;50% of samples) for some pollutants, such as ammonia or certain pesticides, in Iowa surface waters. For others, such as fecal coliform, nitrate, and TP, non-detects are rare (\u0026lt;5% of samples). Infrequently, issues with collection logistics resulted in missing data points, but only eight of the 54 timeseries had any missing values, with no timeseries containing more than seven.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA USGS gauge adjacent to each sampling site continuously monitored streamflow over the project\u0026rsquo;s 90 days. Mean daily values were aggregated to create streamflow records coincident with the water quality timeseries. Figure 2 displays these streamflow records alongside daily precipitation observed at a nearby rain gauge (Iowa City Municipal Airport). A total of 337.3 mm of rainfall occurred between May 4 \u0026ndash; August 1, 2000, which was typical precipitation (~55\u003csup\u003eth\u003c/sup\u003e percentile) for the time of year. Eight days saw significant rainfall (\u0026gt;10 mm), leading to several high-flow events at each site. Due to its larger watershed area, streamflow in the Iowa River was typically higher and less flashy than the other locations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eStreamflow data were plotted alongside the IDNR data to visualize the interplay between contaminant levels and river hydrology (Figure 3, Supplemental Materials). Specifically, we were interested in how concentrations behaved during high flows, as many pollutants are mobilized following wet weather\u0026nbsp;(Hooda et al., 2000; Ryden et al., 1974)\u0026nbsp;at certain times of the year\u0026nbsp;(Ai et al., 2015; Lin et al., 2019). We calculated and summarized the descriptive statistics for every analyte (Supplemental Materials), including the number of non-detects. We also calculated temporal autocorrelation values for each daily timeseries and created correlograms denoting whether these values were statistically significant (Figure 5, Supplemental Materials). Statistically significant values can reveal the length of time over which it is possible to infer the concentration of an analyte based on a previous measurement\u0026nbsp;(Feng et al., 2013). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate seasonal behavior, we categorized data points by their collection month (May, June, or July) and then created boxplots of their concentrations (Figure 4). The sampling schedule resulted in approximately 30 observations per month. Non-detects were set to half their detection limit for parts of the analysis that required non-censored values, such as calculating the mean or autocorrelation. Likewise, the few missing data points (\u0026lt;1% of samples) were estimated using linear interpolation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eError Quantification of Periodic Sampling\u003c/h2\u003e\n\u003cp\u003eFour of the pollutants analyzed contained minimal non-detects (\u0026lt;5%): atrazine, fecal coliform, nitrate, and TP. Non-censored data allowed for a more robust analysis of these four pollutants\u0026nbsp;(Kayhanian et al., 2002; Olsen et al., 2012)\u0026nbsp;and enabled us to accurately quantify loads for these parameters in all three rivers\u0026nbsp;(Robertson \u0026amp; Roerish, 1999). Daily loads for the 90-day period were calculated for atrazine, fecal coliform, nitrate, and TP by multiplying daily concentrations by daily streamflow measured at the coincident USGS gauge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe high-resolution monitoring record was later subsampled to evaluate how intermittent grab sampling would impact load estimations. Evenly spaced subsets of samples were selected from the full 90-day datasets to represent regular intervals of grab sampling. Timeseries were divided into consistent increments beginning with the first and last data points, and subsets of samples (ranging from two to 90) were selected based on these increments. Linear interpolation was then used to estimate concentrations not included within these subsets, and loads were calculated based on these interpolated concentrations (Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor example, when two data points were used to estimate loads, we simply interpolated daily concentrations based on the sampling start and end dates. When four data points were used, the four points were evenly spaced throughout the timeseries\u0026mdash;always including the start and end dates\u0026mdash;and then interpolated. Beginning with two samples, this process was repeated until all 90 measurements were included in the load calculation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis method simulates several intervals used in typical grab sampling protocols\u0026nbsp;(Madrid \u0026amp; Zayas, 2007; Schleppi et al., 2006). A subset of size four corresponds to monthly sampling, i.e., analytes are collected and measured every 30 days. A subset of size 13 corresponds to weekly sampling (~every seven days). A subset of size 90 uses every day and is equivalent to the original timeseries. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe utilized the %Error metric to quantify errors from the load estimates constructed using sample subsets. %Error is a unitless metric that enables comparison of errors among the four pollutants, even when they are present at various scales in aquatic environments. %Error was found for each load estimate, and it was formally defined as \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1711103576.png\"\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDaily timeseries were plotted for all 18 analytes, and various statistics were calculated. The Supplemental Materials contain a complete collection of plots and statistics produced in this study. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePollutants\u0026rsquo; Relationships to Streamflow\u003c/h2\u003e\n\u003cp\u003eFor many pollutants, there was a strong relationship of concentration to streamflow (Figure 3, Supplemental Materials). For pesticides, high concentrations coincided with streamflow events in May and June. Although the highest concentrations did not necessarily co-occur with the highest streamflow values, a runoff component was always present during pesticide spikes. During low flows, pesticide levels were minimal, and non-detects dominated the datasets. By late June and July, pesticide levels were consistently low, regardless of streamflow.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFecal coliform and TP also correlated with streamflow, with their highest values coinciding with peak flows. However, unlike pesticides, their presence did not diminish in June or July, and wet weather events routinely triggered high levels of fecal coliform, TP, and turbidity in all three months. Fecal coliform proved very dynamic, with concentrations often increasing by several orders of magnitude during high flows.\u003c/p\u003e\n\u003cp\u003eOn the other hand, nitrate behavior was markedly different, and concentrations were typically diluted during high-flow events (Figure 3). After initial dilution, concentrations typically rebounded and increased while flows receded. Following several events in May and June, nitrate concentrations increased well above their pre-event levels (e.g., June 1 event in the English River, Figure 3). The rising nitrate levels coincided with the falling limbs of the hydrograph rather than hydrograph peaks. After June 15, nitrate concentrations generally declined. High flows continued to dilute nitrate concentrations, but nitrate levels largely returned to their pre-event concentrations during this period of decline rather than exceeding them as they did in May and early June.\u003c/p\u003e\n\u003cp\u003ePatterns of ammonia, OP, and streamflow were more difficult to ascertain. Ammonia and OP levels were typically low but had occasional concentration spikes lasting one or two days. Sometimes, these high-nutrient days coincided with peak flows, but other times, they arose with no discernable change in hydrology. Spikes in ammonia and OP were equally present across May, June, and July. The remaining analytes (DO and temperature) were less related to streamflow. Some dilution of DO may have occurred during peak flows at the Old Mans Creek and the English River sites, but the numerous fluctuations in DO made it challenging to draw definitive conclusions about the relation to streamflow. Water temperature and river hydrology appeared completely independent.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Properties of Pollutants\u003c/h2\u003e\n\u003cp\u003eThe percentage of measurements below the detection limit varied tremendously among the 18 analytes (Table 2), although the rates of non-detections were consistent across the three sampling locations. Two pesticides (butylate and trifluralin) were not detected at all, whereas others contained over 90% non-detects (2-Chloro-6, alachlor, cyanazine, metribuzin). Atrazine was unique among pesticides in that it contained no non-detects. The abundance of samples below the detection limit was more mixed for the remaining pesticides: 2-Chloro-4 (7%), acetochlor (66%), and metolachlor (30%). Pesticides generally had a lower percentage of non-detects in May, followed by greater frequency of non-detects in June and July.\u003c/p\u003e\n\u003cp\u003eSimilarly, ammonia was frequently non-detected in the river water samples (66%), with June containing the lowest percentage (49%). Fewer OP samples were non-detects (33%), but DO, fecal coliform, nitrate, temperature, and turbidity datasets contained zero non-detects, and values below the detection limit were also rare for TP (3% of samples).\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were calculated for all 18 analytes (Supplemental Materials), but specific focus was given to the four pollutants with minimal non-detects (atrazine, fecal coliform, nitrate, and TP). Figure 4 contains boxplots displaying the range of values for these four pollutants organized by month and site. Concentrations were similar across the three river sites but varied considerably across the three months. Atrazine values ranged from 0.12 to 37\u0026nbsp;\u0026mu;g/l, but the maximum values always occurred within May and June. By July, the range of values was much smaller (0.12 \u0026ndash; 1.2\u0026nbsp;\u0026mu;g/l). Each atrazine dataset was positively skewed\u0026mdash;concentrations on a few days were far greater than median values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFecal coliform values were very positively skewed and spanned several orders of magnitude, ranging between 10 and 450,000 MPN/100ml. TP also exhibited positive skew, albeit to a lesser degree than fecal coliform. TP samples ranged from values below the detection limit (\u0026lt;0.1) to 4.7 mg/l. Nitrate concentrations ranged from 0.1 to 14 mg/l, but patterns were much more symmetric. Seasonal patterns were evident for fecal coliform, nitrate, and TP, but patterns for atrazine were different (Figure 4). Concentrations of fecal coliform, nitrate, and TP typically increased from May to June, but there was not a consistent pattern for atrazine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAutocorrelation within the timeseries of the four pollutants was explored (Figure 5). No autocorrelation was present among the fecal coliform samples (values within the 95% confidence interval), and it was minimal for TP. Atrazine displayed significant autocorrelation at lags of one and two days in the English and Iowa Rivers. Autocorrelation was particularly high at the one-day lag in the English River (0.8). Nitrate exhibited the highest degree of autocorrelation at all sites, and it increased along with watershed size. Autocorrelation values remained statistically significant at the Iowa River for lags up to seven days.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eErrors of Estimated Loads\u003c/h2\u003e\n\u003cp\u003eThe %Error associated with periodic grab sampling was investigated for atrazine, fecal coliform, nitrate, and TP at the three sites (Figure 7). The expected gradual decrease in %Error was evident, and errors converged to 0 by the time 80 interpolation points were included. However, there was a high degree of variation in the %Error as the number of interpolation points increased (Figure 7). For example, the TP %Error in the English River increased from -47.3% to 4.01% based on an increase from 13 to 14 interpolation points. A rolling mean (of size 5) was included to smooth the %Error values and better assess the overall decrease in error (Supplemental Materials).\u003c/p\u003e\n\u003cp\u003eDespite occasional large fluctuations in %Error, broad patterns in the load estimates were apparent. When interpolating loads for atrazine, fecal coliform, and TP with fewer samples collected, loads tended to be underestimated (negative %Error values). In contrast, interpolated nitrate loads largely overestimated the true values. Nitrate errors were the lowest, but interpolation errors associated with atrazine and TP were comparable. For a given contaminant, estimation errors typically decreased in larger watersheds. Errors were higher in the smallest basin (Old Mans Creek) and lower in the largest (Iowa River). On the other hand, fecal coliform had the greatest errors in load quantification using interpolated values (Figure 7).\u003c/p\u003e\n\u003cp\u003eTo provide prescriptive sampling guidelines, we identified the number of interpolation points needed to bring the %Error values within certain thresholds using the rolling means. This number was then divided by the total amount of samples (90) to generalize the results for an arbitrary timeframe. The result was the percentage of days that required sampling to bring estimated loads within a given error threshold (Table 3). Using atrazine in Old Mans Creek as an example, once 12 samples were utilized in calculating the load, %Error values were consistently \u0026lt;40%. Dividing 12 by 90 yields 13.3%\u0026mdash;this value is the percentage of samples needed to restrict error within the 40% threshold. Table 3 quantifies the general behavior reflected in our results, but the observed fluctuations in %Error mean that this guidance is not absolute. Any periodic sampling schedule may miss key dates that introduce significant errors to estimates\u0026nbsp;(Henjum et al., 2010).\u003c/p\u003e\n\u003cp\u003eNitrate required the fewest number of samples to meet the 40% threshold. Monthly sampling (~4% of samples) would bring errors beneath the 40% threshold at each site, and weekly sampling (~14% of samples) would achieve the 5% threshold at the Iowa River. Atrazine and TP required weekly sampling to meet the 40% threshold at Old Mans Creek and the English River and the 20% threshold at the Iowa River. Fecal coliform required the most samples at all sites; weekly sampling would only achieve the 40% error limit in the Iowa River. Subweekly monitoring was needed to hit the 10% threshold for all parameters (except nitrate at the Iowa River). Ensuring errors are within 5% in the smaller watersheds was exceptionally burdensome (\u0026gt;50% of days required).\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003ePollutant Transport Pathways\u003c/h2\u003e\n\u003cp\u003ePairing streamflow alongside daily pollutant concentrations confirms many of the mechanisms noted by studies of contaminant hydrology. Pesticide concentration patterns were emblematic of the \u0026ldquo;spring flush\u0026rdquo; phenomenon\u0026nbsp;(Phillips \u0026amp; Bode, 2004; Spalding et al., 1994; Thurman et al., 1991), which has been previously documented in Iowa streams\u0026nbsp;(Kolpin et al., 2010). Pesticides are mobilized by wet weather during the spring, where they run off agricultural fields into nearby waterbodies. After the first few rainfall events, the mass of pesticides is mostly extinguished on the landscape, and thus, concentrations are not typically found in streams by July. This behavior aligns with the standard agricultural practice, where most pesticides are applied in the spring\u0026nbsp;(Curwin et al., 2002).\u003c/p\u003e\n\u003cp\u003eIn this study, pesticides largely demonstrated the \u0026ldquo;spring flush\u0026rdquo; phenomenon, but atrazine was unique in that it never diminished below detection limits. Atrazine\u0026rsquo;s prolific usage and lack of degradation have made it a ubiquitous pollutant in Iowa groundwater\u0026nbsp;(Kolpin et al., 1995). The atrazine timeseries shown herein suggest it is constantly delivered to Iowa streams under low flow conditions but is exacerbated during the \u0026ldquo;spring flush\u0026rdquo; in runoff.\u003c/p\u003e\n\u003cp\u003eFecal coliform and TP were also runoff-driven, but other factors complicate their relationships with streamflow. Wet weather events trigger erosion from agricultural landscapes that carry sediments and pathogens\u0026nbsp;(Harmel et al., 2010)\u0026nbsp;and TP\u0026nbsp;(Mallarino et al., 2002). Both pollutants spike during high streamflow, but the magnitude of these spikes is not always proportional to flow, i.e., the highest flow events do not necessarily produce the highest fecal coliform or TP concentrations. Instead, local and event-specific factors likely confound a simple relationship among streamflow, TP, and fecal coliform. Unlike the \u0026ldquo;spring flush,\u0026rdquo; fecal coliform and TP do not decline in the summer. Their ubiquity on Iowa\u0026rsquo;s landscape has been well documented\u0026nbsp;(Brendel \u0026amp; Soupir, 2017; Schilling et al., 2020), so any storm event has the potential to elevate their concentrations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNitrate concentration patterns are consistent with subsurface delivery of nitrate to streams with groundwater flow\u0026nbsp;(Schilling et al., 2007)\u0026nbsp;and tile drainage\u0026nbsp;(Schilling et al., 2012). Wet weather events initially dilute nitrate levels due to an influx of runoff with lower nitrate levels\u0026nbsp;(Poor \u0026amp; McDonnell, 2007), but as rainfall permeates the soils, it mobilizes nitrate and transports it through subsurface drainage networks\u0026nbsp;(Sebilo et al., 2013). This subsurface discharge often enters the river network as peak flows recede, resulting in increasing nitrate concentrations\u0026nbsp;(Poor \u0026amp; McDonnell, 2007). This complicated relationship highlights the influence of hysteresis on nitrate transport, which has been observed in many watersheds with the advent of continuous nitrate sensor data\u0026nbsp;(Baker \u0026amp; Showers, 2019; Vaughan et al., 2017).\u003c/p\u003e\n\u003cp\u003eAmmonia contamination appears to be more binary than other nutrient species (e.g., TP and nitrate). It is usually absent from eastern Iowa waters\u0026nbsp;(Garrett, 2012), but rivers occasionally suffer from abrupt concentration spikes. These spikes sometimes coincide with high streamflow, indicating a runoff-driven process observed in similar agricultural conditions where ammonia is applied in manure or fertilizer\u0026nbsp;(Moore Jr et al., 2000). Other times, ammonia spikes during low flow conditions. Elevated ammonia during low flows is often the result of industrial or municipal discharges\u0026nbsp;(Van Damme et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eImplications for Sampling Programs\u003c/h2\u003e\n\u003cp\u003eMost water quality sampling programs have one of two goals: 1) determine the mechanisms that dictate how pollutants are delivered to streams or 2) assess the immediate or long-term presence of contaminants in a waterbody. This study has implications for both goals. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe daily timeseries evaluated in this study confirmed the disparate nature of pollutant transport. High-resolution sampling proved valuable in deciphering agricultural pollutants\u0026rsquo; transport pathways, which would have been much harder to discern when evaluating periodic sampling data\u0026nbsp;(Godsey et al., 2019). In regions where uncertainty surrounds contaminant sources and transport mechanisms, conducting a short-term, high-resolution sampling study may be used to inform long-term monitoring plans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnowledge of transport pathways is also crucial for understanding limitations in periodic sampling programs. Monthly sampling routines can be effective at gauging the overall presence of some pollutants that are slow-moving and normally distributed (Telci et al., 2009), but large uncertainties arise for pollutants that are dynamic and positively skewed (Johnes, 2007). High autocorrelation for nitrate suggests that this pollutant can be reasonably monitored through periodic samples. As watershed size increases, the autocorrelation of nitrate also increases (Benson et al., 2006). Since other hydrologic processes are less dynamic in larger watersheds (Singh, 1997), periodic sampling is often more effective in larger rivers (Madrid \u0026amp; Zayas, 2007) compared to small rivers, such as Old Mans Creek.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, atrazine, fecal coliform, and TP are subject to sudden shifts in concentration\u0026mdash;evidenced by their daily timeseries and minimal autocorrelation. Other studies have noted similar behaviors in Iowa watersheds (Liang et al., 2021). Evaluating their presence cannot realistically be captured through periodic sampling alone. When attempting to comprehensively assess parameters, such as atrazine or TP, it is essential to deploy methods that fill in the gaps between samples. Potential techniques include water quality surrogates (Castrillo \u0026amp; Garc\u0026iacute;a, 2020; Schilling et al., 2017; Viviano et al., 2014) or flow-based models, such as LOADEST (Runkel et al., 2004) or WRTDS (Hirsch et al., 2010). These models utilized other in-situ measurements to estimate pollutant concentrations on days without sampled data and have proved successful in many studies (Hirsch et al., 2010; A. S. Jones et al., 2011; Rowland et al., 2021). However, constructing these models requires substantial historical data (often 10+ years and 150+ observations; (Hirsch et al., 2010)). They can also suffer from significant errors in short timeframes and are most appropriate when evaluating concentrations over the course of decades rather than specific seasons or events (Lee et al., 2019). \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eImplications for Short-Term Load Estimates\u003c/h2\u003e\n\u003cp\u003eFor some studies, including calculating wastewater discharge limits or total maximum daily loads (TMDLs), accurate quantification of short-term loads (\u0026lt;5 years) is required. Our analysis of errors associated with interpolated loads provides general prescriptive guidelines on the frequency of sampling needed to cap errors below specific thresholds (Table 3). These guidelines depend upon the pollutant of interest and watershed size. For example, if a project required quantification of nitrate loads in a HUC08 watershed, Table 3\u0026rsquo;s values for the Iowa River are most relevant, as it is a watershed of a similar size. The 5.6% value for nitrate prescribes the number of days needed to calculate loads within 10% error. If the project timeframe lasts a year, this would involve collecting samples every 20 days (365 days * 5.6% \u0026asymp; 20 days).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur interpolation method was bracketed around a project\u0026rsquo;s start and end date. It required that measurements take place on each of these days, which is a widespread practice of short-term sampling projects\u0026nbsp;(Halliday et al., 2012). Our method of dividing up the 90-day timeframe into regular intervals simulated various fixed-interval sampling schedules. Fixed-interval sampling is a widespread approach among water quality monitoring programs\u0026nbsp;(Behmel et al., 2016). Although some programs include samples collected during high-flow conditions\u0026nbsp;(Lewis, 1996), this approach is more expensive and onerous, as the logistics of event-specific sampling make it difficult to apply at large spatial scales and timeframes\u0026nbsp;(Lopez et al., 2000). Interpolation based on high-flow samples can also introduce errors by overrepresenting rare hydrologic conditions\u0026nbsp;(Cooper \u0026amp; Watts, 2002). Therefore, the analysis included in our study is representative of sampling protocols that are commonly deployed in monitoring programs.\u003c/p\u003e\n\u003cp\u003eAs noted earlier, less error is generally associated with load estimates of larger rivers, i.e., fewer samples are needed as watershed size increases\u0026nbsp;(de Almeida et al., 2023; Telci et al., 2009). Dynamic pollutants like fecal coliform are much more difficult to quantify than slow-moving ones like nitrate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, there is substantial uncertainty endemic to periodic sampling. The errors of load estimation can vary considerably by slightly shifting the sampling frequency (Figure 7). This is mainly due to randomness associated with capturing days with high streamflow. The large water volume on these particular days makes them high-leverage events that greatly impact overall load calculations. A schedule that either hits or misses disproportionate amounts of high-flow days will be subject to large errors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe direction of the errors shown in Figure 7 is also noteworthy and related to analyte skewness. In most cases, errors were positive for nitrate and negative for atrazine, fecal coliform, and TP. This generalized phenomenon is linked to the fact that points used for interpolation were typically not high-flow days. However, concentrations for atrazine, fecal coliform, and TP are positively skewed (Figure 4), and their largest values often occur on high-flow days and are orders of magnitude greater than those of low-flow conditions. A typical sampling protocol that misses these days when disproportionate amounts of pollutants are transported will underestimate the true load. In the rare instances where several high-flow days are sampled, loads can be overestimated. The effect is essentially reversed for nitrate. Nitrate concentrations are diluted during high streamflow, resulting in negatively skewed timeseries. Sampling schedules that miss high-flow days containing low nitrate concentrations will consequently overestimate nitrate loads.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, Table 3 provides rule-of-thumb guidelines that reflect the effects of increasing sampling frequency on lowering load estimation errors. However, any individual load calculation is subject to hydrologic randomness that can cause values to deviate from these guidelines. Still, the prescriptive nature of these guidelines exemplifies the typical frequency one would need to quantify loads adequately. The desired error bounds will depend upon the goals of a specific study. It may be the case that quantifying some pollutants, such as fecal coliform, in smaller watersheds will be infeasible due to the sampling requirements. Conversely, grab sampling may prove adequate in larger rivers and relinquish the need for more cost-prohibitive methods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored a unique dataset that measured 18 different waterborne analytes for 90 consecutive days (May 4 \u0026ndash; August 1, 2000) in three rivers in southeastern Iowa. These analytes included ten pesticides, four nutrient forms, and fecal coliform. Apart from atrazine, pesticide datasets contained significant numbers of samples below the detection limit. Non-detects were also common for ammonia and OP but rare for nitrate, TP, and fecal coliform. Among the four main pollutants with continuous detections, the highest degree of autocorrelation was observed for nitrate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferent pollutants had a unique relationship with local streamflow. Pesticide concentrations were greatest during high flows in May and early June, but by July, they were depleted from the landscape and largely absent within the rivers. Fecal coliform and TP levels were also driven by runoff but were never exhausted from the landscape. Nitrate was initially diluted during high flows, but concentrations began to rise in May and June as it was mobilized and transported to the streams while flows were falling. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLoads for atrazine, fecal coliform, nitrate, and TP were calculated based on simulated intermittent sampling and compared to the true measured loads. Estimation errors were highest for fecal coliform, followed by TP and atrazine. Intermittent grab sampling was found to underpredict these loads due to several high-flow days with elevated pollutant levels. Conversely, errors for nitrate were the lowest, and intermittent sampling tended to overpredict loads due to nitrate dilution during high-flow days. These load estimates highlight the uncertainties and challenges associated with periodic grab sampling and argue for the use of discharge-based models and in-situ monitoring when possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the Iowa Department of Natural Resources for their efforts in collecting the water quality data used in this study. \u003c/p\u003e\n\u003cp\u003eFunding Declaration\u003c/p\u003e\n\u003cp\u003eNo funding was provided for this study. \u003c/p\u003e\n\u003cp\u003eEthics Approval and Consent to Participate Declaration\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution Statement\u003c/p\u003e\n\u003cp\u003eElliot Anderson conducted all data retrieval and statistical analyses, prepared the figures and tables, and wrote the initial version of the manuscript text.\u003c/p\u003e\n\u003cp\u003eKeith Schilling provided guidance on the methods used to analyze data and quantify loads, helped formulate the study directives, and edited and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability Declaration\u003c/p\u003e\n\u003cp\u003eAll water quality data used in this study can be retrieved through the EPA STORET database (https://www.epa.gov/waterdata/water-quality-data) or the IDNR AQuIA database (https://programs.iowadnr.gov/aquia/). All streamflow data used in this study can be retrieved through the USGS National Water Information System (https://waterdata.usgs.gov/nwis).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhn, S. R., \u0026amp; Kim, S. J. (2017). Assessment of integrated watershed health based on the natural environment, hydrology, water quality, and aquatic ecology. \u003cem\u003eHydrology and Earth System Sciences, 21\u003c/em\u003e(11), 5583-5602.\u003c/li\u003e\n\u003cli\u003eAi, L., Shi, Z. H., Yin, W., \u0026amp; Huang, X. (2015). Spatial and seasonal patterns in stream water contamination across mountainous watersheds: Linkage with landscape characteristics. \u003cem\u003eJournal of Hydrology, 523\u003c/em\u003e, 398-408. doi:https://doi.org/10.1016/j.jhydrol.2015.01.082\u003c/li\u003e\n\u003cli\u003eAlilou, H., Nia, A. M., Keshtkar, H., Han, D., \u0026amp; Bray, M. (2018). 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High‐frequency dissolved organic carbon and nitrate measurements reveal differences in storm hysteresis and loading in relation to land cover and seasonality. \u003cem\u003eWater Resources Research, 53\u003c/em\u003e(7), 5345-5363.\u003c/li\u003e\n\u003cli\u003eVilla, A., F\u0026ouml;lster, J., \u0026amp; Kyllmar, K. (2019). Determining suspended solids and total phosphorus from turbidity: comparison of high-frequency sampling with conventional monitoring methods. \u003cem\u003eEnvironmental monitoring and assessment, 191\u003c/em\u003e, 1-16.\u003c/li\u003e\n\u003cli\u003eViviano, G., Salerno, F., Manfredi, E. C., Polesello, S., Valsecchi, S., \u0026amp; Tartari, G. (2014). Surrogate measures for providing high frequency estimates of total phosphorus concentrations in urban watersheds. \u003cem\u003eWater Research, 64\u003c/em\u003e, 265-277.\u003c/li\u003e\n\u003cli\u003eWang, T., Zhong, M., Lu, M., Xu, D., Xue, Y., Huang, J., . . . Yu, G. (2021). Occurrence, spatiotemporal distribution, and risk assessment of current-use pesticides in surface water: A case study near Taihu Lake, China. \u003cem\u003eScience of the Total Environment, 782\u003c/em\u003e, 146826.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Rashid, A., Guo, S., Jing, Y., Zeng, Q., Li, Y., . . . Yu, C.-P. (2022). Spatial autocorrelation and temporal variation of contaminants of emerging concern in a typical urbanizing river. \u003cem\u003eWater Research, 212\u003c/em\u003e, 118120.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Sites included in sampling program.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.98076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.455128205128204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDNR ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSGS Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSGS ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.98076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eOld Mans Creek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.455128205128204%\" valign=\"top\"\u003e\n \u003cp\u003e10520001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003eOld Mans Creek near Iowa City, IA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e05455100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e41.60640518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e-91.61572419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.98076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eEnglish River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.455128205128204%\" valign=\"top\"\u003e\n \u003cp\u003e10920001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003eEnglish River at Kalona, IA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e05455500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e1,487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e41.4697387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e-91.7146129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.98076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eIowa River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.455128205128204%\" valign=\"top\"\u003e\n \u003cp\u003e10480001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003eIowa River at Marengo, IA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e05453100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e7,236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e41.81272566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.224358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e-92.064792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 2. Analytes included in sampling program and their percentage of non-detects in each month.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection Limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.52166934189406%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Non-Detects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJune\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJuly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003e2-Chloro-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003e2-Chloro-4-isopropylamino-6-amino-s-triazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e29.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003e2-Chloro-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003e2-Chloro-6-ethylamino-4-amino-s-triazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e91.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e80.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eAcetochlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eAcetochlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e29.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e64.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eAlachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eAlachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e92.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e91.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e96.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eAmmonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eAmmonia-nitrogen (as N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e67.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e48.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e79.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eAtrazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eAtrazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eButylate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eButylate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eCyanazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eCyanazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e90.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e99.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eDissolved oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eFecal Coliform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eFecal Coliform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eMPN/100ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eMetolachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eMetolachlor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e11.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e65.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eMetribuzin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eMetribuzin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e96.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eInorganic nitrogen (nitrate and nitrite) (as N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eOrthophosphate (as P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e60.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e17.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate-phosphorus (as P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003emg/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature, water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003edeg C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eTrifluralin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eTrifluralin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eug/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.680577849117174%\" valign=\"top\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.20224719101124%\" valign=\"top\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396468699839486%\" valign=\"top\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.199036918138042%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.507223113964686%\" valign=\"top\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3. The percentage of days where samples need to be collected to ensure estimated loads are within an error threshold.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.638447971781305%\" rowspan=\"2\" valign=\"top\" style=\"width: 13.6232%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" rowspan=\"2\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eError Threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.54673721340389%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of Days that Require Sampling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAtrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFecal Coliform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNitrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.638447971781305%\" rowspan=\"4\" valign=\"top\" style=\"width: 13.6232%;\"\u003e\n \u003cp\u003eOld Mans Creek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.689594356261022%\" valign=\"top\"\u003e\n \u003cp\u003e37.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.355371900826448%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\" valign=\"top\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.43801652892562%\" valign=\"top\"\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.735537190082646%\" valign=\"top\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.84297520661157%\" valign=\"top\"\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.355371900826448%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\" valign=\"top\"\u003e\n \u003cp\u003e37.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.43801652892562%\" valign=\"top\"\u003e\n \u003cp\u003e75.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.735537190082646%\" valign=\"top\"\u003e\n \u003cp\u003e43.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.84297520661157%\" valign=\"top\"\u003e\n \u003cp\u003e62.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.355371900826448%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\" valign=\"top\"\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.43801652892562%\" valign=\"top\"\u003e\n \u003cp\u003e85.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.735537190082646%\" valign=\"top\"\u003e\n \u003cp\u003e65.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.84297520661157%\" valign=\"top\"\u003e\n \u003cp\u003e84.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.638447971781305%\" rowspan=\"4\" valign=\"top\" style=\"width: 13.6232%;\"\u003e\n \u003cp\u003eEnglish River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.814814814814815%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75485008818342%\" valign=\"top\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.689594356261022%\" valign=\"top\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.81657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.355371900826448%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.628099173553718%\" valign=\"top\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.43801652892562%\" valign=\"top\"\u003e\n \u003cp\u003e68.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.735537190082646%\" valign=\"top\"\u003e\n \u003cp\u003e6.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.84297520661157%\" valign=\"top\"\u003e\n \u003cp\u003e26.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.355371900826448%\" valign=\"top\" style=\"width: 16.3768%;\"\u003e\n \u003cp\u003e\u0026lt; 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However, gaps between sample collection introduce errors to these efforts, and guidance prescribing sampling frequency remains sparse. The magnitude of error often depends on the type of contaminant and watershed size, 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.\u003c/p\u003e\n\u003cp\u003ePesticide 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.\u003c/p\u003e\n\u003cp\u003eLoads 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 actual loads 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.\u003c/p\u003e","manuscriptTitle":"Intensive short-term sampling with long-term consequences: characterizing pollutant transport with implications for developing monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 10:42:16","doi":"10.21203/rs.3.rs-3919178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-24T00:35:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T14:12:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T14:12:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-02-02T00:54:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3f78e5e1-88d1-460a-bbac-be5c7b294f87","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:29:47+00:00","versionOfRecord":{"articleIdentity":"rs-3919178","link":"https://doi.org/10.1007/s10661-024-13266-x","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-10-30 16:20:39","publishedOnDateReadable":"October 30th, 2024"},"versionCreatedAt":"2024-03-22 10:42:16","video":"","vorDoi":"10.1007/s10661-024-13266-x","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13266-x","workflowStages":[]},"version":"v1","identity":"rs-3919178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3919178","identity":"rs-3919178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}