Stormwater Monitoring Research Articles

The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring

The presented work studies the influence of the sampling strategy on the quality of locally calibrated UV-VIS probe measurements in combined sewer overflows (CSO) and the receiving river. Results indicate that UV-VIS spectrometers are not able to provide reliable measurements of water quality in urban stormwater without being calibrated to local conditions with laboratory analyses of water samples. The use of the global calibration (supplied by the manufacturer) led to errors of at least 30 and 45% for CSO load and river concentration of chemical oxygen demand (COD), respectively. Even with reliable local calibration, COD loads contained significant uncertainties close to 20%. Uncertainties in COD load and concentration decrease below 30% if more than 15–20 samples (i.e. 3 and 4 stormwater events) are sampled for local calibration. The effort and associated sampling costs to gain more than 15–20 samples are much less effective, since load and concentration uncertainties remain relatively stable with an increasing number of samples used for the calibration. The presented analysis aims at supporting practitioners in the planning, operation and calibration of UV-VIS spectrometer probes.

Accuracy of a Stormwater Monitoring Program for Urban Landuses

This study examined the accuracy of an urban stormwater monitoring program in estimating the annual discharge load (L(T)) and the annual reduction rate by a stormwater treatment device (R(T)) for total suspended solids. A calibrated stormwater management model was used to generate the entire stormwater runoff events in one year and was used to estimate L(T) and R(T) under different monitoring strategies having limited numbers of runoff events, including random, wet season, antecedent dry days (ADD)-based, monthly, and seasonally weighted. For random monitoring, 12 storms were required to estimate the values of L(T) and R(T) with mean relative errors of 13.98 and 0.24%, respectively. Monthly monitoring had slightly greater mean relative errors compared to random monitoring. Wet season and ADD-based monitoring under- or overestimated both L(T) and R(T). Monitoring with equal numbers of storms from the wet and dry seasons best estimated L(T) and R(T).

Prioritizing Subwatersheds for Stormwater Pollution to Wachusett Reservoir

The Wachusett Reservoir is a primary drinking water resource for the greater Boston, Massachusetts, area. With a drainage area of 280 km2, the watershed has been gradually urbanized with increased residential, commercial, industrial, and transportation land uses. Increased impervious surface area as a result of urbanization results in increased runoff volume and pollutant loads to the reservoir. This study estimated annual stormwater pollutant mass loads in the watershed to prioritize sub-basins and to identify areas susceptible to stormwater pollution. Catchment Prioritization Index (CPI) was calculated using annual stormwater pollutant mass loads, which were further used to identify clustered hotspots through application of the Getis-Ord Gi* statistic. Validation with observed data showed higher levels of fecal coliform bacteria loading from identified hotspots. This approach will be useful to prioritize sub-basins for future (1) development of stormwater monitoring strategies and (2) best management practices (BMPs) in the watershed.

Accuracy of a stormwater monitoring program for urban land uses

Accuracy of a stormwater monitoring program for urban land uses

빗물 자동모니터링장치와 SWMM 모델을 이용한 강우시 도시지역 오염부하량 예측에 관한 연구

An automatic flow and water quality monitoring system was applied to estimate pollutant loads to an urban stream during storm events in DTV (Daeduk Techno Valley), Daejeon, Korea. The monitoring system consists of rainfall gage, ultrasonic water level meter, water quality sensors for DO, temperature, pH, conductivity, turbidity and automatic water sampler for further laboratory analysis. All data are transmitted through on-line system and the monitoring system is designed to be controlled manually in the field and remotely from laboratory computer. Flow rates were verified with field measurements during storm events and showed good agreements. Automatic sampler was used to collect real time samples and analyzed for BOD, COD, TN, TP, SS and other pollutant concentrations in the laboratory. SWMM (Storm Water Management Model) urban watershed model was applied and calibrated using the observed flow and water quality data for the study area. While flow modeling results showed good agreement for all events, water quality modeling results showed variable levels of agreement. These results indicate that current options in the SWMM model to predict pollutant build up and wash-off effects are not sufficient to satisfy modeling of all the rainfall events under study and thus need further modification. This study showed the automatic monitoring system can be used to provide data to assist further refinement of modeling accuracy. This automatic stormwater monitoring and modeling system can be used to develop basin scale water quality management strategies of urban streams in storm events.

Contrasts in concentrations and loads of conventional and alternative indicators of fecal contamination in coastal stormwater

Fecal contamination in stormwater is often complex. Because conventional fecal indicator bacteria (FIB) cannot be used to ascertain source of fecal contamination, alternative indicators are being explored to partition these sources. As they are assessed for future use, it is critical to compare alternative indicators to conventional FIB under a range of stormwater delivery conditions. In this study, conventional FIB and fecal Bacteroides spp. were monitored throughout the duration of five storm events from coastal stormwater outfalls in Dare County, North Carolina, USA to characterize relationships among FIB concentrations, alternative fecal markers, and loading of contaminants. Water samples were collected multiple times during each storm and analyzed for Enterococcus sp. and Escherichia coli using enzymatic tests and fecal Bacteroides spp. by QPCR. Both conventional FIB and fecal Bacteroides spp. concentrations in stormwater were generally high and extremely variable over the course of the storm events. Over the very short distances between sites, we observed statistically significant spatial and temporal variability, indicating that stormwater monitoring based on single grab-samples is inappropriate. Loading of FIB and fecal Bacteroides spp. appeared to be affected differently by various hydrologic factors. Specifically, Spearman correlations between fecal Bacteroides spp. and drainage area and antecedent rainfall were lower than those between conventional FIB and these hydrologic factors. Furthermore, the patterns of fecal Bacteroides spp. concentrations generally increased over the duration of the storms, whereas E. coli and Enterococcus sp. concentrations generally followed the patterns of the hydrograph, peaking early and tailing off. Given the greater source-specificity and limited persistence of fecal Bacteroides spp. in oxygenated environments, differences in these patterns suggest multiple delivery modes of fecal contamination (i.e. landscape scouring versus groundwater discharge).

Advancing assessment and design of stormwater monitoring programs using a self-organizing map: Characterization of trace metal concentration profiles in stormwater runoff

Stormwater runoff poses a great challenge to the scientific assessment of the effects of diffuse pollution sources on receiving waters. In this study, a self-organizing map (SOM), a research tool for analyzing specific patterns in a large array of data, was applied to the monitoring data obtained from a stormwater monitoring survey to acquire new insights into stream water quality profiles under different rainfall conditions. The components of the input data vectors used by the SOM included concentrations of 10 metal elements, river discharge, and rainfall amount which were collected at the inlet and endpoint of an urban segment of the Yeongsan River, Korea. From the study, it was found that the SOM displayed significant variability in trace metal concentrations for different monitoring sites and rainfall events, with a greater impact of stormwater runoff on stream water quality at the upstream site than at the downstream site, except under low rainfall conditions (≤4 mm). In addition, the SOM clearly determined the water quality characteristics for “non-storm” and “storm” data, where the parameters nickel and arsenic and the parameters chromium, cadmium, and lead played an important role in reflecting the spatial and temporal water quality, respectively. When the SOM was used to examine the efficacy of stormwater quality monitoring programs, between 34 and 64% of the sample size in the current data set was shown to be sufficient for estimating the stormwater pollutant loads. The observed errors were small, generally being below 10, 6, and 20% for load estimation, map resolution, and clustering accuracy, respectively. Thus, the method recommended may be used to minimize monitoring costs if both the efficiency and accuracy are further determined by examining a large existing data set.

Stormwater Monitoring and Resident Behavior in a Semi-Arid Region

Stormwater from a small municipality in Utah was monitored for flow and phosphorus. A survey was also administered to ascertain potential behavioral impacts on stormwater. The majority (53.7%) of stormwater measured was non-storm related. It is suspected that summer irrigation was adding to baseflow. Concentrations of phosphorus were below national means, but were high enough to lead to excessive algal growth. Survey results showed that most residents (92.3%) fertilize their lawn, although very few (1.7%) use a soil test to determine if fertilization is necessary. The study highlights the need for monitoring, surveying and collaboration to address current stormwater issues.

Prioritizing Subwatersheds for Stormwater Pollution to Wachusett Reservoir

The Wachusett Reservoir is a primary drinking water resource for the greater Boston, Massachusetts, area. With a drainage area of 280 km 2 , the watershed has been gradually urbanized with increased residential, commercial, industrial, and transportation land uses. Increased impervious surface area as a result of urbanization results in increased runoff volume and pollutant loads to the reservoir. This study estimated annual stormwater pollutant mass loads in the watershed to prioritize sub-basins and to identify areas susceptible to stormwater pollution. Catchment Prioritization Index (CPI) was calculated using annual stormwater pollutant mass loads, which were further used to identify clustered hotspots through application of the Getis-Ord Gi * statistic. Validation with observed data showed higher levels of fecal coliform bacteria loading from identified hotspots. This approach will be useful to prioritize sub-basins for future (1) development of stormwater monitoring strategies and (2) best management practices (BMPs) in the watershed. 1.

Levels and patterns of fecal indicator bacteria in stormwater runoff from homogenous land use sites and urban watersheds

Routine stormwater monitoring programs focus on quantification of average fecal indicator bacteria (FIB) concentration at the terminal watershed discharge point. While important for permit compliance, such monitoring provides little insight into relative bacteria levels from different land use types or the mechanisms that influence FIB concentrations. The goal of this study was to quantify the relative levels and flux patterns of Escherichia coli, enterococci, and total coliforms from representative land use (LU) types. Bacteria concentrations were measured over the entire storm duration from 8 different LU types over 13 storm events in 5 southern California watersheds during the 2000-2005 storm seasons. In addition, runoff samples were collected from 8 bottom of the watershed mass emission (ME) sites. Intra-storm and intra-season patterns were investigated in order to identify mechanisms that influence patterns of FIB concentrations. Mean FIB event mean concentrations (EMCs) at LU sites ranged from 10(3) to 10(5) MPN/100 mi. Recreational (horse stables) LU sites contributed significantly higher storm EMCs than other LU types. Early season storms repeatedly produced higher EMCs than comparably sized late season storms. For most storms sampled, the highest bacterial concentrations occurred during the early phases of stormwater runoff with peak concentrations usually preceding peak flow.

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.

Impact of Sampling Techniques on Measured Stormwater Quality Data for Small Streams

Science-based sampling methodologies are needed to enhance water quality characterization for setting appropriate water quality standards, developing Total Maximum Daily Loads, and managing nonpoint source pollution. Storm event sampling, which is vital for adequate assessment of water quality in small (wadeable) streams, is typically conducted by manual grab or integrated sampling or with an automated sampler. Although it is typically assumed that samples from a single point adequately represent mean cross-sectional concentrations, especially for dissolved constituents, this assumption of well-mixed conditions has received limited evaluation. Similarly, the impact of temporal (within-storm) concentration variability is rarely considered. Therefore, this study evaluated differences in stormwater quality measured in small streams with several common sampling techniques, which in essence evaluated within-channel and within-storm concentration variability. Constituent concentrations from manual grab samples and from integrated samples were compared for 31 events, then concentrations were also compared for seven events with automated sample collection. Comparison of sampling techniques indicated varying degrees of concentration variability within channel cross sections for both dissolved and particulate constituents, which is contrary to common assumptions of substantial variability in particulate concentrations and of minimal variability in dissolved concentrations. Results also indicated the potential for substantial within-storm (temporal) concentration variability for both dissolved and particulate constituents. Thus, failing to account for potential cross-sectional and temporal concentration variability in stormwater monitoring projects can introduce additional uncertainty in measured water quality data.

Impacts of Impervious Cover and Other Factors on Storm-Water Quality in Austin, Tex.

Changes in impervious cover result in changes in both urban water quality and quantity. The changes in storm-water quantity have been studied for many years; however, the impacts of impervious cover on storm-water quality are not as well understood. This paper examines the impacts of impervious cover on both storm-water quality loads and concentrations using data collected from 38 storm-water monitoring stations in Austin, Tex., between 1985 and 2005. The writer determined whether there was a significant relationship between impervious cover and the mean concentrations of several pollutants, including nutrients, metals, solids, and bacteria. In cases where no significant relationship existed, the data were examined to determine whether a difference in storm-water runoff quality from developed and undeveloped areas existed. A final comparison was performed using impervious cover and land use as independent variables and mean watershed concentrations of several common pollutants as dependant variables. This...

Sampling Issues in Urban Runoff Monitoring Programs: Composite versus Grab

Storm-water monitoring generally uses flow-weighted automatic composite samplers to collect a representative sample of an entire storm event. Automatic samplers are convenient but unfortunately they can be expensive, especially for temporary sampling needs or for short-term research projects. An alternative method is to use a series of grab samples. This paper examines the accuracy of event mean concentrations (EMCs) and mass first flush ratios calculated from a finite number of grab samples, and compares them to results from flow-weighted automatic samples. Both sampling techniques were evaluated using data collected from a three-year investigation of three highway sites. A large number of grab samples is needed to approach the accuracy and precision of flow weighted composite samples, and 30 grab samples per storm event generally estimated the EMCs within 20% average error. To detect a first flush, it is necessary to take even more grab samples or to adjust the timing of the sample collection toward the beginning of the storm. The superiority of automatic sampling for estimating EMCs for constituents compatible with automatic sampling is demonstrated.

Peristaltic Pump Autosamplers for Solids Measurement in Stormwater Runoff

Regulatory agencies approve automatic samplers containing peristaltic pumps as a sample collection method for stormwater characterization and for treatment-device evaluation. Autosampler performance, as discussed in the limited available literature, can vary across the entire particle size range typically found in stormwater from different source areas and outfalls-reasonably consistent performance for particle sizes < 250 microm, but much less consistency for particles > 250 microm. Therefore, a series of experiments was undertaken to quantify the upper range of consistent particle capture that may occur with sampling stormwater suspended sediment and particulate-bound pollutants. These experiments, based on triplicate sampling at each experimental condition, found that peristaltic pump autosamplers commonly used in stormwater monitoring could not repeatedly and effectively capture particles > 250 microm from a simulated stormwater whose particles have a specific gravity of 2.65. It was expected that the effective size for autosamplers would be correspondingly larger for particles having smaller specific gravities. The height of the sampler had no influence on particle recovery up to a height of 2.5 m, with slightly decreasing recoveries of large particles occurring at greater heights, as a result of reduced sampler intake velocity. Therefore, to characterize the solids across the entire size range and specific gravities that may occur in stormwater runoff, autosamplers should be deployed in conjunction with bedload and floatables sampling.

Nutrient and Sediment Production, Watershed Characteristics, and Land Use in the Tahoe Basin, California‐Nevada1

Abstract: In efforts to control the degradation of water quality in Lake Tahoe, public agencies have monitored surface water discharge and concentrations of nitrogen, phosphorus, and suspended sediment in two separate sampling programs. The first program focuses on 20 watersheds varying in size from 162 to 14,000 ha, with continuous stream gaging and periodic sampling; the second focuses on small urbanized catchments, with automated sampling during runoff events. Using data from both programs, we addressed the questions (1) what are the fluxes and concentrations of nitrogen and phosphorus entering the lake from surface runoff; (2) how do the fluxes and concentrations vary in space and time; and (3) how are they related to land use and watershed characteristics? To answer these questions, we calculated discharge‐weighted average concentrations and annual fluxes and used multiple regression to relate those variable to a suite of GIS‐derived explanatory variables. The final selected regression models explain 47‐62% of the variance in constituent concentrations in the stormwater monitoring catchments, and 45‐72% of the variance in mean annual yields in the larger watersheds. The results emphasize the importance of impervious surface and residential density as factors in water quality degradation, and well‐developed soil as a factor in water quality maintenance.

Land Development Characteristics in Jefferson County, Alabama

For a stormwater monitoring study to be successful, a careful examination of the study watershed is required. An inventory of watershed development conditions …

Harmonic Analysis of Environmental Time Series with Missing Data or Irregular Sample Spacing

The Lomb periodogram and discrete Fourier transform are described and applied to harmonic analysis of two typical data sets, one air quality time series and one water quality time series. The air quality data is a 13 year series of 24 hour average particulate elemental carbon data from the IMPROVE station in Washington, D.C. The water quality data are from the stormwater monitoring network in Milwaukee, WI and cover almost 2 years of precipitation events. These data have irregular sampling periods and missing data that preclude the straightforward application of the fast Fourier transform (FFT). In both cases, an anthropogenic periodicity is identified; a 7-day weekday/ weekend effect in the Washington elemental carbon series and a 1 month cycle in several constituents of stormwater. Practical aspects of application of the Lomb periodogram are discussed, particularly quantifying the effects of random noise. The proper application of the FFT to data that are irregularly spaced with missing values is demonstrated on the air quality data. Recommendations are given when to use the Lomb periodogram and when to use the FFT.

Design of stormwater monitoring programs

Stormwater runoff is now the leading source of water pollution in the United States, and stormwater monitoring programs have only recently been developed. This paper evaluates several stormwater monitoring programs to identify ways of increasing the likelihood of identification of high-risk dischargers and increasing data reliability for assisting in the development of total maximum daily loads. No relationship was found between various types of industrial activity or landuse and water quality data. Stormwater data collected with grab samples has much greater pollutant concentration variability than in potable water or wastewater monitoring programs. Industrial land use is an important source of metals. For grab samples, sampling time during the storm event will affect results. Data from California, which has distinct dry periods, showed a seasonal first flush, whereas data from areas with more uniform rainfall throughout the year did not show a seasonal first flush. Selecting a subset of sites from each monitored category using a flow-weighted composite sampler is an alternative strategy, and may result in lower overall cost with improved accuracy and variability in mass emissions, but may not be less successful in identifying high-risk dischargers.

Utility of LANDSAT-Derived Land Use Data for Estimating Storm-Water Pollutant Loads in an Urbanizing Area

In many watersheds located in southern California, efforts are being focused on urban runoff because of its adverse impact on receiving water quality. The Sweetwater River watershed is a good example, where the drainage area is rapidly urbanizing and deteriorating reservoir water quality. Contaminated storm water is captured and diverted but as urbanization increases, additional runoff will be generated which will overload the existing infrastructure. To better manage the diversion systems and minimize future construction, storm-water volumes and pollutant loadings need to be estimated. Due to the lack of real-time storm-water runoff monitoring data, pollutant loadings must be estimated from land use information. We used satellite imagery to estimate selected storm-water pollutant loads and compared the results to predictions using land use information from public records. Satellite imagery was useful in estimating storm-water pollutant loads and identifying high loading areas. Satellite imagery with appropriate classification is a promising tool for watershed management and for prioritizing best management practices.