Identifying feasible nonpoint source pollutant sampling intervals for watersheds with paddy field and urban land uses

Monitoring provides data and information necessary for water quality assessment, but often it is prohibitive, especially when frequent sampling is required. In this study, we explored feasible sampling intervals for improved efficiency of nonpoint source (NPS) pollution assessment. We compared NPS pollutant loads calculated with concentration samples collected at 1, 2, 3, 4, and 6-hour intervals for the first 24 hours of 13 storm events and investigated the effect of different sampling intervals on load estimation for three watersheds that have different land uses. When compared to load estimates made from concentrations sampled at the reference (1-hour) interval, differences in load estimates were less than 10% in the cases of the 2-hour and 3-hour intervals in the urbanized and agricultural watersheds, respectively, except in the case of suspended solids (SS). When it comes to the total load estimation, up to 3-hour interval sampling provided load estimates with acceptable accuracy, except for SS. Thus, the 3-hour sampling interval was considered feasible for long-term pollutant load assessment, while the 2-hour sampling interval was suggested for SS. Such findings are expected to facilitate NPS pollution assessment by providing information required to improve monitoring efficiency.


INTRODUCTION
Nonpoint source (NPS) pollution is closely associated with hydrological processes including the generation and accumulation of pollutants during a dry period and their transport and loading to downstream waterbodies during a rainfall event. Thus, there are various environmental factors such as land use, management practices and weather events that influence NPS pollution processes (Beman et al. ).
A frequent sampling can capture the details of highly variable pollutant generation and transport processes, and thus it is recommended as a method to quantify NPS pollution (Halliday et al. ; Frazar et al. ). Such a monitoring strategy is also required to establish control measures and identify the characteristics of NPS pollutants (Kirchner et al. ; Kal et al. ). In relatively small watersheds, the best method for determining the contribution of these sources is likely to be a high-frequency sampling strategy using an autosampler (Harmel et al. ; Harmel et al. b). Factors that affect uncertainty in data produced by automated samplers (sampling threshold, sampling interval, discrete or composite sample type) have been evaluated and discussed (e.g., Shih et al. A frequent sampling strategy is recommended for its accuracy, but it requires additional costs, which sometime makes monitoring impractical. Efficient water quality monitoring requires a balance between cost and accuracy. Thus, it is critical to find a threshold sampling interval that provides insignificant differences between actual loads and load estimates made from the limited amount of water quality sampling data (Han & Kim ; Harmel et al. a). The US Environmental Protection Agency (USEPA ) recommends obtaining at least 10 samples per rainfall event for water quality characterization. Thompson et al. ()  () estimated nutrient and sediment loads with concentration data from four low-frequency sampling strategies (single stage, random, peak, and rise fall) and reported that load estimates were often poor. Low-frequency sampling programs can still be appropriate in some larger watersheds when paired with statistical techniques to estimate constituent loads (Haggard et al. ). Bowes et al. () and Jordan et al. () found that more than 80% of the annual phosphorus load is generated by two to three large rainfall events, highlighting the importance of monitoring nutrient concentrations at short sampling intervals for high flow rates. The previous studies tried to identify efficient water quality sampling frequency, but they focused on a few specific pollutants and/or agricultural watersheds without paddy fields under a monsoonal climate.

Study areas
The three watersheds are nested and located within the  Table 1).
The WJ watershed is mostly covered by agricultural areas (62.2%, mostly rice paddy fields) with small urban land uses (6.2%), while the JS watershed is relatively highly urbanized (36.0%) including industrial areas, residential areas, offices and restaurants, and agricultural areas cover about 28.8% of the watershed. Overall, the PYJ watershed consists of agricultural land uses (46.9%) and urban area (25.7%) ( Table 1).
Weather data collected at one of the Korea Meteorological Administration's stations, Gwangju, showed that the study areas receive rainfall of 1,391 mm annually on average, and the average annual maximum and minimum temperatures are 29.3 C and 1.9 C, respectively. More than half of the rainfall (50-60%) concentrates in summer

Load estimations using different sampling intervals
The sampling intervals were manipulated to compare NPS pollutant load estimates made from different sampling frequencies ( Figure 2). For instance, the 1-hour and 3-hour interval sampling strategies gave 24 and 8 pollutant concentration measurements in the first 24 hours of a storm event, respectively. In this study, the sampling intervals were increased from 1 hour to 2, 3, 4, and 6 hours.
In addition, a feasible sampling frequency was identified when the differences in load estimates made using the concentrations of water samples taken at the finest (or

RESULTS AND DISCUSSION
Observed date, rainfall amount, duration and intensity, and antecedent dry days of each storm event are summarized in     Table 2).
Nitrogen compounds, such as nitrogen effluent from CSO, are considered to affect nitrogenous BOD (NBOD) concentrations by depleting oxygen content in water. Jang  (Table 3). The differences between the NPS load estimates tend to increase with increases in the sampling intervals; however, it was not always the case (Table 4).
The Wilcoxon test results showed there was no significant