Assessment of sampling strategies for estimation of site mean concentrations of stormwater pollutants

Highlights

• 17 stormwater sampling methods were assessed against high resolution monitoring data.

• Random sampling yielded better results than systematic sampling.

• Limited advantage in taking more than one random sample per event.

• Number of samples required was predicted well by the pollutant's variability (coefficient of variation).

Abstract

The estimation of stormwater pollutant concentrations is a primary requirement of integrated urban water management. In order to determine effective sampling strategies for estimating pollutant concentrations, data from extensive field measurements at seven different catchments was used. At all sites, 1-min resolution continuous flow measurements, as well as flow-weighted samples, were taken and analysed for total suspend solids (TSS), total nitrogen (TN) and Escherichia coli (E. coli). For each of these parameters, the data was used to calculate the Event Mean Concentrations (EMCs) for each event. The measured Site Mean Concentrations (SMCs) were taken as the volume-weighted average of these EMCs for each parameter, at each site. 17 different sampling strategies, including random and fixed strategies were tested to estimate SMCs, which were compared with the measured SMCs. The ratios of estimated/measured SMCs were further analysed to determine the most effective sampling strategies. Results indicate that the random sampling strategies were the most promising method in reproducing SMCs for TSS and TN, while some fixed sampling strategies were better for estimating the SMC of E. coli. The differences in taking one, two or three random samples were small (up to 20% for TSS, and 10% for TN and E. coli), indicating that there is little benefit in investing in collection of more than one sample per event if attempting to estimate the SMC through monitoring of multiple events. It was estimated that an average of 27 events across the studied catchments are needed for characterising SMCs of TSS with a 90% confidence interval (CI) width of 1.0, followed by E.coli (average 12 events) and TN (average 11 events). The coefficient of variation of pollutant concentrations was linearly and significantly correlated to the 90% confidence interval ratio of the estimated/measured SMCs (R² = 0.49; P < 0.01) as well as the number of events required to achieve certain accuracy, and hence could be a promising surrogate for determining the sampling frequency needed to accurately estimate SMCs of pollutants.

Introduction

Since the 1970s, many studies have demonstrated the environmental impacts caused by polluted stormwater runoff, such as urban water quality degradation and large lake or costal eutrophication (e.g. Jeng et al., 2005, Lewitus et al., 2008). The estimation of pollutant concentrations/loads derived from urban stormwater systems is an important requirement for sound urban water management. For example, it is required for adequate design of urban stormwater treatment systems, assessment of pollution impact on receiving water bodies, or stormwater model development and assessment (Overton and Meadows, 2013, Daly et al., 2014). Understanding of stormwater runoff pollutant levels is indeed very valuable for setting sound targets for environmental management in urban areas.

The Event Mean Concentration (EMC) and Site Mean Concentration (SMC) are two common approaches to reporting the concentration of pollutants in urban stormwater runoff. EMC is the total mass of the pollutant in a particular runoff event divided by the total volume of runoff during that event. The SMC is the total load of a pollutant divided by the total runoff volume for a given period of time (e.g. one or more years) and is calculated using EMCs and event volumes recorded over a number of runoff events (WEF and ASCE, 1998). If the SMC is known for a pollutant, the long term discharge load of the pollutant is simply estimated by multiplying it by the total runoff volume (that can be often modelled or measured). This approach is widely used in practice (Gromaire et al., 2002), although it is still not clear how to accurately determine the SMC (Mourad et al., 2005), as discussed below.

To assess the EMC, two approaches are often used: (i) random grab sampling during events (often limited to one or only a few per event) and (2) flow-weighted or time-weighted sampling resulting in creating a composite sample from sub-samples (Schiff, 1996, Law et al., 2008). Random grab sampling is the easiest method (USEPA, 2002), yet it can introduce high variability and bias; e.g. samples taken at the start of an event or during very intense periods of the event are usually more polluted than samples taken towards the end of, or during low intensity periods of, the same event. Grab sampling requires a crew to be sent out for collecting samples, and is feasible only if the crew is located near the monitoring area. Continuous auto-sampling and sampling using flow-weightings is often considered the most robust (Lee et al., 2007), but comes with additional costs and can sometimes be problematic due to the complex installations that are inevitably associated with automatic sampling equipment. Automatic sampling is also considered unsuitable for some pollutants, such as pH, oil and grease, due to the need to analyse samples within a short timeframe after collection (Khan et al., 2006, Gulliver and Anderson, 2007). In general, automatic sampling is not widely practiced in industry and municipalities, although it is very likely the best method available and the most frequently used by researchers. Nonetheless, the extensive stormwater runoff monitoring campaigns carried out since the 1980s across the world (USEPA, 1983, Duncan, 1999, Zgheib et al., 2012) have all used different methods and protocols, offering very little consistency and comparability (Schiff, 1996, Leecaster et al., 2002). This is mainly due to the lack of agreed protocols on how to monitor stormwater.

There are a limited number of studies on stormwater monitoring strategies (Kronvang and Bruhn, 1996, Leecaster et al., 2002, Lee et al., 2007, Harmel et al., 2010, Madarang and Kang, 2013). Leecaster et al. (2002) recommended that sampling seven storms annually was the most efficient method for obtaining small confidence intervals (amount of datable trend is 10%) for annual average Total Suspended Solids (TSS) concentrations. The same study also found that taking 12 flow-weighted samples can most effectively characterize single storms while the simple random sampling of all storms or medium and large stormwater events resulted in the least bias in estimating annual TSS mass emissions and concentrations (Leecaster et al., 2002). Madarang and Kang (2013) found that 12 storms were required to estimate annual TSS loads using random methods, and monitoring with equal numbers of storms from the wet and dry seasons best estimated annual loads, with median relative error being 0.22–1.42%. Both studies, however, used data sets collected at one or two catchments over relatively short periods (<2 years) and for TSS only. Lee et al. (2007) evaluated several stormwater monitoring programs to identify high discharges and total maximum daily loads (TMDLs) of pollutants using monitoring data sets of six different catchments, for some of which over ∼10 years period existed. Results show that the required number of samples is related to the coefficient of variation of sampled pollutant concentrations. This study, however did not assess specific sampling strategies, such as the number of storms to sample, or the number of samples to take per event. Also, very few studies have ever evaluated the influence of pollutant type on sampling strategies; the question should be asked whether recommendations made for TSS also hold for nutrients and microorganisms. The lack of relevant studies may be due to the fact that large data-sets of good quality are required for a number of different pollutants and catchment types to draw reliable conclusions, since stormwater quality is highly variable (both temporary and spatially).

This study aims to fill this gap and develop sound sampling strategies for estimation of Site Mean Concentrations a range of common and important stormwater pollutants which represent three different pollutant behaviour-types; TSS, which measures the presence of particles (and often is used as a surrogate for attached pollutants such as some heavy metals (Herngren et al., 2005)), total nitrogen (TN), which is highly soluble (and behaves differently to TSS (Taylor et al., 2005)) and Escherichia coli (E. coli) which is a microorganism and inherently behaves differently than the more conservative traditional pollutants. The study uses large data sets collected at seven different catchments in Melbourne over six years. The specific objectives were to:

• Assess a total of 17 different strategies, including random sampling and fixed sampling strategies for assessing SMCs of TSS, TN and E. coli;

• Estimate the number of samples and the number of events needed to reasonably represent SMC for different pollutants and catchments; and,

• Understand the correlation between the variability of the pollutants concentrations and the number of events required to adequately estimate pollutant concentrations.