Input data used by the MSM and HSPF for QMRA application to the Manitowoc Basin. This dataset is associated with the following publication: Whelan, G., K. Kim, R. Parmar, G. Laniak, K. Wolfe, M. Galvin, M. Molina, Y. Pachepsky, P. Duda, R. Zepp, L. Prieto, J. Kinzelman, G.T. Kleinheinz, and M. Bouchard. Capturing microbial sources distributed in a mixed-use watershed within an integrated environmental modeling workflow. ENVIRONMENTAL MODELLING AND SOFTWARE. Elsevier Science Ltd, New York, NY, USA, 99: 126-146, (2018).

The quantitative microbial risk assessment (QMRA) approach applied to the evaluations presented in this publication was described previously in a peer-reviewed published paper. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

This data set includes water quality data and microbial community abundance tables for periphyton samples from this project. The data set also includes extensive R markdown code used to process the data and generate the results included in the report. This dataset is associated with the following publication: Hagy, J., R. Devereux, K. Houghton, D. Beddick, T. Pierce, and S. Friedman. Developing Microbial Community Indicators of Nutrient Exposure in Southeast Coastal Plain Streams using a Molecular Approach. US EPA Office of Research and Development, Washington, DC, USA, 2018.

The Maumee River and associated tributaries are an example of a system influenced by a mosaic of contaminant inputs from point and nonpoint sources along a gradient of land uses. To assess the potential effects of contaminants on aquatic biota in a system this complex requires a combination of targeted and nontargeted analytical and biological monitoring techniques to provide data that can be assembled and interpreted in an integrated manner. The aim of the current paper was to provide a practical demonstration of this type of approach using a variety of state-of-the-science pathway-based tools. Studies conducted in 2012 and 2106 showed that contaminants in the upper part of the Maumee River reflect agricultural practices, while downstream, the suite of chemicals present includes those from agriculture in conjunction with contaminants more indicative of a general urban setting, influenced in some areas by WWTP inputs. Biological responses using in vitro assays with surface water samples, and measures of biological responses in caged fish deployed a various sites in the Maumee River were used to assess the potential for perturbation of specific biological pathways. Overall there was little evidence for contaminant effects on endocrine pathways involved is reproduction or development. However, multiple lines of evidence suggested the presence of contaminants that could inhibit or induce cytochrome P450-based enzymes thereby influencing biological pathways/processes associated with these ubiquitous proteins. This dataset is associated with the following publication: Ankley, G., J. Berninger, B. Blackwell, J. Cavallin, T. Collette, D. Ekman, K. Fay, D. Feifarek, K. Jensen, M. Kahl, J. Mosley, S. Poole, E. Randolph, D. Rearick, A. Schroeder, J. Swintek, and D. Villeneuve. Pathway-based approaches for assessing biological hazards of complex mixtures of contaminants: A case study in the Maumee River. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 40(4): 1098–1122, (2021).

This dataset contains simulated pathogen concentrations in locally-collected greywater and wastewater. Each .zip file includes 21 .csv files, each containing 10,000 years of simulated daily pathogen concentrations in the water type designated by the title (Combined Greywater, Laundry Greywater, Shower Greywater, Sink Greywater, or Onsite Wastewater). Each .csv file corresponds to one pathogen (Ad Adenovirus, Ca Campylobacter, Cr Cryptosporidium, Gi Giardia, No Norovirus, Ro Rotavirus, or Sa Salmonella) for one population size (5-, 100-, or 1000-person). For example, ConcAdCombGW100.csv contains concentrations (Conc) of Adenovirus (Ad) in combined greywater (CombGW) for a 100-person population size (100). Data is structured as 365 rows (days) by 10,000 columns (years), with the first row and column containing year and day indices, respectively. Units are # pathogens/L water. This dataset is associated with the following publication: Jahne, M., M. Schoen, J. Garland, and N. Ashbolt. Simulation of enteric pathogen concentrations in locally-collected greywater and wastewater for microbial risk assessments. Microbial Risk Analysis. Elsevier B.V., Amsterdam, NETHERLANDS, 5: 44-52, (2017).

The dataset contains the raw data for the graphs in the paper. This dataset is associated with the following publication: Rauglas, E., S. Martin, K. Bailey, C. Starr, M. Magnuson, R. Phillips, and W. Harper. The Effect of Malathion on the Activity, Performance, and Microbial Ecology of Activated Sludge- journal. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 220-228, (2016).

HF183/BacR287 qPCR data from standard curves and coastal water samples used to seed simulations in study. This dataset is associated with the following publication: Cao, Y., M. Sivaganesan, C. Kelty, D. Wang, A. Boehm, J. Griffith, S. Weisberg, and O. Shanks. A Human Fecal Contamination Score for Ranking Recreational Sites using the HF183/BacR287 Quantitative Real-Time PCR Method. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 128: 148-156, (2018).

• Figure 1. Ratio of cumulative released cells to cells initially present in the manure at Week 0 as they vary by time, manure type and age, microbe, and Event (i.e., season). The 95% confidence intervals of the observed median number of cells in microbial runoff are shown as the shaded area. • Figure 2. Typical observed and simulated cumulative microbial runoff for Plots A403 and C209 with individual plot calibration. • Figure 3. Observed versus simulated microbial runoff associated with the Approach 1, adjusted for cumulative results by manure type and Event. Results accounted for counts associated with field monitoring time intervals described in Section 2.1 Field method. NS=Nash-Sutcliffe modeling efficiency, EC=E. coli, En=enterococci, FC= fecal coliforms. • Figure 4. Ratio of cumulative released cells/mass to cells/mass initially present in the aged manure by time and component (e.g., microbe) for solid manure (a) and (b), and amended, dry litter, and slurry manure (c). Solid lines (Equation (11) correspond to values in Table 3 for solid manure, and dry litter and slurry manure, respectively: (a) uses individual b values, and (b) and (c) use the combined values for b. Bounds of first and third quartiles associated with the present study’s results for cattle. Bounds of first and third quartiles associated with the present study’s results for poultry and swine. The full color versions of all figures are available in the online version of this paper, at http://dx.doi.org/10.2166/wh.2016.239. • Figure 5. Ranges in α and β values for bovine (dairy calf, cattle, or cow) manure, as published or computed from the literature: (1) Bradford & Schijven (2002), (2) Schijven et al. (2004), (3) Guber et al. (2013), (4) Equation (9), (5) Blaustein et al. (2015), and (6) estimated in the present study (PEST and bootstrap). This dataset is associated with the following publication: Kim, K., G. Whelan , M. Molina , T. Purucker , Y. Pachepsky, A. Guber, M. Cyterski , D. Franklin, and R. Blaustein. Rainfall-induced release of microbes from manure: model development, parameter estimation, and uncertainty evaluation on small plots. JOURNAL OF WATER AND HEALTH. IWA Publishing, London, UK, 14(2): wh2016239, (2016).

This is the dataset for the research article "Enteric pathogen treatment requirements for non-potable water reuse despite limited exposure data". It contains log reduction results for various exposure conditions as described in the text. This dataset is associated with the following publication: Schoen, M., M. Jahne, and J. Garland. A risk-based evaluation of onsite, non-potable reuse systems developed in compliance with conventional water quality measures. JOURNAL OF WATER AND HEALTH. IWA Publishing, London, UK, 18(3): 331-344, (2020).