Response to comment on “Primary sources and toxicity of PAHs in Milwaukee-area streambed sediments”—The authors' reply
No abstract available.
Environmental Toxicology and Chemistry↗
Geology topics
Christopher Magruder
Publications and source records attributed to Christopher Magruder.
Primary sources and toxicity of PAHs in Milwaukee-area streambed sediment
High concentrations of polycyclic aromatic hydrocarbons (PAHs) in streams can be a significant stressor to aquatic organisms. To understand the likely sources and toxicity of PAHs in Milwaukee-area streams, streambed sediment samples from 40 sites and parking lot dust samples from 6 sites were analyzed for 38 parent PAHs and 25 alkylated PAHs. Diagnostic ratios, profile correlations, principal components analysis, source-receptor modeling, and mass fractions analysis were used to identify potential PAH sources to streambed sediment samples, and land-use analysis was used to relate streambed sediment PAH concentrations to different urban-related land uses. On the basis of this multiple lines-of-evidence approach, coal-tar pavement sealant was indicated as the primary source of PAHs in a majority of streambed sediment samples, contributing an estimated 77% of total PAHs to samples, on average. Comparison to the Probable Effect Concentrations and (or) the Equilibrium Partitioning Sediment Benchmark indicates that 78% of stream sediment samples are likely to cause adverse effects to benthic organisms. Laboratory toxicity tests on a 16-sample subset of the streambed sites using the amphipod Hyalella azteca (28-day) and the midge Chironomus dilutus (10-day) measured significant reductions in one or more biological endpoints, including survival, in 75% of samples, with H. azteca more responsive than C. dilutus .
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Organic waste compounds as contaminants in Milwaukee-area streams
Organic waste compounds (OWCs) are ingredients and by-products of common agricultural, industrial, and household substances that can contaminate our streams through sources like urban runoff, sewage overflows, and leaking septic systems. To better understand how OWCs are affecting Milwaukee-area streams, the U.S. Geological Survey, in cooperation with the Milwaukee Metropolitan Sewerage District, conducted a three-year study to investigate the presence and potential toxicity of 69 OWCs in base flow, stormflow, pore water, and sediment at 14 stream sites and 3 Milwaukee harbor locations. This fact sheet summarizes the major findings of this study, including detection frequencies and concentrations, potential toxicity, the prevalence of polycyclic aromatic hydrocarbons (PAHs), and the influence of urbanization.
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Organic waste compounds in streams: Occurrence and aquatic toxicity in different stream compartments, flow regimes, and land uses in southeast Wisconsin, 2006–9
An assessment of organic chemicals and aquatic toxicity in streams located near Milwaukee, Wisconsin, indicated high potential for adverse impacts on aquatic organisms that could be related to organic waste compounds (OWCs). OWCs used in agriculture, industry, and households make their way into surface waters through runoff, leaking septic-conveyance systems, regulated and unregulated discharges, and combined sewage overflows, among other sources. Many of these compounds are toxic at elevated concentrations and (or) known to have endocrine-disrupting potential, and often they occur as complex mixtures. There is still much to be learned about the chronic exposure effects of these compounds on aquatic populations. During 2006–9, the U.S. Geological Survey, in cooperation with the Milwaukee Metropolitan Sewerage District (MMSD), conducted a study to determine the occurrence and potential toxicity of OWCs in different stream compartments and flow regimes for streams in the Milwaukee area. Samples were collected at 17 sites and analyzed for a suite of 69 OWCs. Three types of stream compartments were represented: water column, streambed pore water, and streambed sediment. Water-column samples were subdivided by flow regime into stormflow and base-flow samples. One or more compounds were detected in all 196 samples collected, and 64 of the 69 compounds were detected at least once. Base-flow samples had the lowest detection rates, with a median of 12 compounds detected per sample. Median detection rates for stormflow, pore-water, and sediment samples were more than double that of base-flow samples. Compounds with the highest detection rates include polycyclic aromatic hydrocarbons (PAHs), insecticides, herbicides, and dyes/pigments. Elevated occurrence and concentrations of some compounds were detected in samples from urban sites, as compared with more rural sites, especially during stormflow conditions. These include the PAHs and the domestic waste-water-indicator compounds, among others. Urban runoff and storm-related leaks of sanitary sewers and (or) septic systems may be important sources of these and other compounds to the streams. The Kinnickinnic River, a highly urbanized site, had the highest detection rates and concentrations of compounds of all the sampled sites. The Milwaukee River near Cedarburg—one of the least urban sites—and the Outer Milwaukee Harbor site had the lowest detection rates and concentrations. Aquatic-toxicity benchmarks were exceeded for 12 of the 25 compounds with known benchmarks. The compounds with the greatest benchmark exceedances were the PAHs, both in terms of exceedance frequency (up to 93 percent for some compounds in sediment samples) and magnitude (concentrations up to 1,024 times greater than the benchmark value). Other compounds with toxicity-benchmark exceedances include Bis(2-ethylhexyl) phthalate (a plasticizer), 2-Methylnapthalene (a component of fuel and oil), phenol (an antimicrobial disinfectant with diverse uses), and 4-Nonylphenol (sum of all isomers; a detergent metabolite, among other uses). Analyzed as a mixture, the suite of PAH compounds were found to be potentially toxic for most non-base-flow samples. Bioassay tests were conducted on samples from 14 streams: Ceriodaphnia dubia in base-flow samples, Ceriodaphnia dubia and Hyallela azteca in pore-water samples, and Hyallela azteca and Chironomus tentans in sediment samples. The greatest adverse effect was observed in tests with Chironomus tentans from sediment samples. The weight of Chironomus tentans after exposure to sediments decreased with increased OWC concentrations. This was most evident in the relation between PAH results and Chironomus tentans bioassay results for the majority of samples; however, solvents and flame retardants appeared to be important for one site each. These results for PAHs were consistent with assessment of PAH potency factors for sediment, indicating that PAHs were likely to have adverse effects on aquatic organisms in many of the streams studied.
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Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11
In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.
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Use of real-time monitoring to predict concentrations of select constituents in the Menomonee River drainage basin, Southeast Wisconsin, 2008-9
The Menomonee River drainage basin in southeast Wisconsin is undergoing changes that may affect water quality. Several rehabilitation and flood-management projects are underway, including removal of concrete channels and the construction of floodwater retention basins. The city of Waukesha may begin discharging treated wastewater into Underwood Creek, thus approximately doubling the current base-flow discharge. In addition, the headwater basins, historically dominated by agriculture and natural areas, are becoming increasingly urbanized. In an effort to monitor these and future changes to the basin, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study in 2008 to develop regression models to estimate real-time concentrations and loads of selected water-quality constituents. Water-quality sensors and automated samplers were installed at five sites in the Menomonee River drainage basin. The sensors continuously measured four explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Regression models were developed to continuously estimate the response variables on the basis of the explanatory variables. The models to estimate chloride concentrations all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity as explanatory variables. Adjusted R 2 values for the chloride models ranged from 0.74 to 0.97. Models to estimate total suspended solids and total phosphorus used turbidity as the only explanatory variable. Adjusted R 2 values ranged from 0.77 to 0.94 for the total suspended solids models and from 0.55 to 0.75 for the total phosphorus models. Models to estimate indicator bacteria used water temperature and turbidity as the explanatory variables, with adjusted R 2 values from 0.54 to 0.69 for Escherichia coli bacteria models and from 0.54 to 0.74 for fecal coliform bacteria models. Dissolved oxygen was not used in any of the final models. These models may help managers measure the effects of land-use changes and improvement projects, establish total maximum daily loads, estimate important water-quality indicators such as bacteria concentrations, and enable informed decision making in the future.
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