Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70037690

Impact of sampling techniques on measured stormwater quality data for small streams

R. D. Harmel·R.M. Slade·R.L. Haney
View original source ↗Export metadata ↓

Abstract

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. Copyright ?? 2010 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Explore related subjects

Journal of Environmental QualityArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R. D. Harmel, R.M. Slade, R.L. Haney. 2010. Impact of sampling techniques on measured stormwater quality data for small streams. https://doi.org/10.2134/jeq2009.0498

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Association of runoff risk with private well contamination in a fractured dolostone aquifer

In the United States, private well owners are responsible for monitoring and maintaining their drinking water but have little control over neighboring contamination sources and limited information about daily water quality. Potential for rapid contamination in karst settings exacerbates the challenge. The Runoff Risk Advisory Forecast (RRAF) is a real-time online tool available in five US states that helps nutrient applicators avoid times and locations of high runoff risk. We wanted to determine if RRAF could be repurposed in areas susceptible to groundwater contamination to inform well owners of conditions when contamination is likely. Private wells were repeatedly sampled on seven dates (32–36 wells per date) and analyzed for nitrate-nitrogen, total coliforms, and bovine manure markers. Three-day windows of RRAF risk prior to sampling were selected post hoc, ranging from 1 to 3 days prior for manure markers to 10–12 days prior for nitrate. Contaminants were associated with RRAF values of ≥75% within these windows based on observed weather. A hydrologic association between RRAF risk and groundwater depth was also apparent. Overall, results suggest the RRAF model anticipates conditions of elevated contamination risk for susceptible wells. For nitrate, between-well variation and delayed peak concentration suggested that well location and construction factors may be more informative than runoff events. Microbial contamination was rapid and distributed among wells, with one or more detections of coliforms in 31% of wells and bovine markers in 61% of wells. Thus, a warning system based on the RRAF model could help identify conditions of acute pathogen exposure.

Wisconsin↗
USGS2026

Quantitative microbial risk assessment with microbial source tracking for enteric pathogens in southwest Wisconsin private wells

Private wells supply drinking water for many households, and their contamination by fecal microbes presents a risk of acute gastrointestinal illness (AGI). Risk is thought to vary by contaminating fecal source, but specific associations with fecal source are unknown for most private wells in the United States. This study characterizes AGI risk in Grant, Iowa, and Lafayette counties in southwestern Wisconsin. AGI risk was assessed for 10 viral, bacterial, and protozoan pathogens detected in private wells using quantitative microbial risk assessment. Exposure assessment was based on sampling 138 private wells in the study area, and risk was associated with fecal source by microbial source tracking (MST). Median Monte Carlo estimates indicated 4450 AGI cases/year in the study area associated with drinking water from contaminated private wells (95% confidence interval: 90–37,990). Most annual cases were associated with detection of human MST markers, including median estimates of 2550 associated with only human markers and 880 with human and livestock MST markers detected together. Note that 50 AGI cases/year were associated with detection of only livestock markers, and nearly 1000 cases were estimated to occur in the absence of detectable MST markers. This study characterizes AGI risk for households served by private wells in southwestern Wisconsin. It illustrates that human fecal sources can predominate risk associated with drinking water from private wells, even in a rural region characterized by substantial agricultural activity. MST can characterize the relationship between risk and fecal source; contributions of fecal sources to risk cannot be assumed from land use.

Wisconsin↗