Christopher T. Nietch

Basin-Wide Analysis of the Dynamics of Fecal Contamination and Fecal Source Identification in Tillamook Bay, Oregon

ABSTRACT The objectives of this study were to elucidate spatial and temporal dynamics in source-specific Bacteroidales 16S rRNA genetic marker data across a watershed; to compare these dynamics to fecal indicator counts, general measurements of water quality, and climatic forces; and to identify geographic areas of intense exposure to specific sources of contamination. Samples were collected during a 2-year period in the Tillamook basin in Oregon at 30 sites along five river tributaries and in Tillamook Bay. We performed Bacteroidales PCR assays with general, ruminant-source-specific, and human-source-specific primers to identify fecal sources. We determined the Escherichia coli most probable number, temperature, turbidity, and 5-day precipitation. Climate and water quality data collectively supported a rainfall runoff pattern for microbial source input that mirrored the annual precipitation cycle. Fecal sources were statistically linked more closely to ruminants than to humans; there was a 40% greater probability of detecting a ruminant source marker than a human source marker across the basin. On a sample site basis, the addition of fecal source tracking data provided new information linking elevated fecal indicator bacterial loads to specific point and nonpoint sources of fecal pollution in the basin. Inconsistencies in E. coli and host-specific marker trends suggested that the factors that control the quantity of fecal indicators in the water column are different than the factors that influence the presence of Bacteroidales markers at specific times of the year. This may be important if fecal indicator counts are used as a criterion for source loading potential in receiving waters.

Controls on nitrous oxide production and consumption in reservoirs of the Ohio River Basin

Aquatic ecosystems are a globally significant source of nitrous oxide (N2O), a potent greenhouse gas, but estimates are largely based on studies conducted in streams and rivers with relatively less known about N2O dynamics in reservoirs. Due to long water residence times and high nitrogen (N) loading rates, reservoirs support substantial N processing and therefore may be particularly important sites of N2O production. Predicting N2O emissions from reservoirs is difficult due to complex interactions between microbial N processing in the oxygen-poor hypolimnion and oxygen-rich epilimnion. Here we present the results of a survey of N2O depth profiles in 20 reservoirs draining a broad range of land use conditions in four states in the U.S. Nitrous oxide was supersaturated in the epilimnion of 80% of the reservoirs and was undersaturated in only one, indicating that reservoirs in this region are generally a source of N2O to the atmosphere. Nitrous oxide was undersaturated in the hypolimnion of 10 reservoirs, supersaturated in 9, and transitioned from supersaturation to undersaturation in 1 reservoir that was monitored periodically from midsummer to fall. All reservoirs with a mean hypolimnion nitrate concentration less than 50 µg N L−1 showed evidence of net N2O consumption in the hypolimnion. All reservoirs sampled during lake turnover supported N2O production throughout the water column. These results indicate that N2O dynamics in reservoirs differ widely both among systems and through time but can be predicted based on N and oxygen availability and degree of thermal stratification.

A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.

Improving Predictive Models of In-Stream Phosphorus Concentration Based on Nationally-Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

Model stream channel testing of a UV-transparent polymer-based passive sampler for ultra-low-cost water screening applications

Passive samplers are increasingly being considered for analyses of waters for screening applications, to monitor for the presence of unwanted chemical compounds. Passive samplers typically work by accumulating and concentrating chemicals from the surrounding water over time, allowing analyses to identify temporally short concentration surges that might be missed by water grab samples, and potentially reducing analysis and sample handling costs, allowing a greater number of sites to be monitored. The work described here tests a recently-developed passive sampling device which was designed to provide an ultra-low-cost screening method for organic chemicals in waters. The device was originally designed for detection of endocrine disrupting chemicals, but has the advantage that it is capable of simultaneously detecting a wide range of other aqueous organic contaminants as well. The device is based on a UV-transparent polymer which is used both to concentrate dissolved chemicals, and as an optical cell for absorbance detection and full-spectrum deconvolution to identify compounds. This paper describes the results of a test of the device conducted at the US EPA Experimental Stream Facility in Milford, Ohio. The test examined detection of triclosan and 4-nonylphenol in model stream channels using two different deployment methods. Results indicate that deployment method can significantly impact measured results due to differences in mass transfer. Passive samplers deployed in vials with permeable membrane septa showed no detection of either compound, likely due to lack of water motion in the vials. In contrast, passive samplers deployed directly in the flow were able to track concentrations of both compounds, and respond to temporal changes in concentration. The results of the work highlight the importance of using internal spiking standards (performance reference compounds) to avoid false non-detection results in passive sampler applications.

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence of stressors and receptors using empirical data, open-source statistical software, and Geographic Information Systems tools and data. To illustrate the approach, we apply the framework to bioassessment data on stream fishes and nutrients collected from a watershed in southwestern Ohio. The results highlighted the joint model’s ability to parse and exploit statistical dependencies in order to provide empirical insight into the potential environmental and ecotoxicological interactions influencing co-occurrence. We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences. For practitioners, we believe that this data-driven approach to modeling and mapping co-occurrence can lead to more quantitatively transparent and robust assessments of ecological risk.

Exploring Nontraditional Participation as an Approach to Make Water Quality Trading Markets More Effective

Water quality trading (WQT) has potential to be a low-cost means for achieving water quality goals. WQT allows regulated wastewater treatment plants (WWTPs) facing discharge limits the flexibility to either reduce their own discharge or purchase pollution control from other WWTPs or nonpoint sources (NPSs) such as agricultural producers. Under this limited scope, programs with NPSs have been largely unsuccessful at meeting water quality goals. The decision to participate in trading depends on many factors including the pollution control costs, uncertainty in pollution control, and discharge limits. Current research that focuses on making WQT work tends to identify how to increase participation by traditional traders such as WWTPs and agricultural producers. As an alternative, but complementary approach, we consider whether augmenting WQT markets with non-traditional participants would help increase the number of trades. Determining the economic incentives for these potential participants requires the development of novel benefit functions requiring not only economic considerations, but also accounting for ecological and engineering processes. Existing literature on non-traditional participants in environmental markets tends to center on air quality and only increasing citizen participation as buyers. Here, we consider the issues for broadening participation (both buyers and sellers) in WQT and outline a multidisciplinary approach to begin evaluating feasibility.

Evaluating the portability of satellite derived chlorophyll- a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earths orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Evidence of Genetic Fecal Marker Interactions between Water Column and Periphyton in Artificial Streams

Periphyton is a complex mixture of algae, microbes, inorganic sediment, and organic matter that is attached to submerged surfaces in most flowing freshwater systems. This natural community is known to absorb pollutants from the water column, resulting in improved water quality. However, the role of periphyton in the fate and transport of genetic fecal markers suspended in the water column remains unclear. As application of genetic-based methodologies continues to increase in freshwater settings, it is important to identify any interactions that could potentially confound water quality interpretations. A 16 week indoor mesocosm study was conducted to simultaneously measure genetic fecal markers in the water column and in the associated periphyton when subject to wastewater source loading. Treated wastewater effluent was pumped directly from a treatment facility adjacent to the experimental stream facility. Inflow and outflow surface water grabs were paired with the collection of periphyton samples taken from the mesocosm substrates on a weekly basis. Samples were analyzed with three genetic fecal indicator quantitative real-time polymerase chain reaction assays targeting Escherichia coli (EC23S857), enterococci (Entero1), and Bacteroidales (GenBac3), as well as, two human host-associated fecal pollution markers (HF183 and HumM2). In addition, periphyton dry mass was measured. During wastewater effluent loading, genetic markers were detected in periphyton at frequencies up to 100% (EC23S857, Entero1, and GenBac3), 59.4% (HF183), and 21.9% (HumM2) confirming sequestration from the water column. Mean net-flux shifts in water column inflow and outflow genetic indicator concentrations further supported interactions between the periphyton and water column. In addition, positive correlations were observed between periphyton dry mass and genetic marker concentrations ranging from r = 0.693 (Entero1) to r = 0.911 (GenBac3). Overall, findings support the notion that genetic markers suspended in the water column can be trapped by periphyton, further suggesting that the benthic environment in flowing freshwater systems may be an important factor to consider for water quality management with molecular methods.