A process for comparing and interpreting differences in two benthic indices in New York Harbor.

Often when various estuarine benthic indices disagree in their assessments of benthic condition, they are reflecting different aspects of benthic condition. We describe a process to screen indices for associations and, after identifying candidate metrics, evaluate metrics individually against the indices. We utilize radar plots as a multi-metric visualization tool, and conditional probability plots and receiver operating characteristic curves to evaluate associations seen in the plots. We investigated differences in two indices, the US EPA Environmental Monitoring and Assessment Program's benthic index for the Virginian Province and the New York Harbor benthic index of biotic integrity using data collected in New York Harbor and evaluated overall agreement of the indices and associations between each index and measures of habitat and sediment contamination. The indices agreed in approximately 78% of the cases. The New York Harbor benthic index of biotic integrity showed stronger associations with sediment metal contamination and grain size.

Beyond Old Pipes and Ailing Budgets: Systems Thinking on Twenty-First Century Water Infrastructure in Chicago.

Cities are increasingly burdened by aging water infrastructure. Deferred maintenance and upgrades are compounded by emerging concerns over contaminants, extreme weather events, demographic shifts, equity, and affordability of water services. These and other evolving twenty-first century conditions prompt changes to urban water infrastructure and related systems that have wide ranging outcomes. This work demonstrates two complementary techniques for analyzing these complex systems, through the example case of Chicago. Chicago has some of the oldest urban water infrastructure in the US and supplies drinking water to more than 5 million people. Recent efforts to improve the physical and financial components of Chicago's water system have run into a gamut of social and environmental issues. Here, a socio-environmental systems (SES) context for Chicago's water infrastructure is structured using a rigorous systems thinking method and visual grammar to map the SES in terms of distinctions, systems, relationships and perspectives (DSRP). DSRP maps structure information about how water flows through city and how money flows through the public utilities responsible for drinking water delivery, wastewater treatment and stormwater management. Flows are evaluated, using open data and methods, over a 23-year period (1995-2017). Overall declines in water use and wastewater production are accompanied by an increase in the costs of water services, costs that support not only water infrastructure operations, maintenance and capital improvements, but also other municipal functions. Trends in the integrated data are interpreted through iterative refinement of DSRP maps to include additional components and to consider the SES from different points of view. Findings suggest that systems thinking is important for designing urban water system upgrades that are responsive to diverse socio-environmental concerns. As changes are made, transparent, reproducible methods for tracking outcomes can support analysis of differential impacts on users. The methods applied here at the city scale may be used to better understand localized, complex issues surrounding water infrastructure upgrades in Chicago and other cities.

CProb: a computational tool for conducting conditional probability analysis.

Conditional probability is the probability of observing one event given that another event has occurred. In an environmental context, conditional probability helps to assess the association between an environmental contaminant (i.e., the stressor) and the ecological condition of a resource (i.e., the response). These analyses, when combined with controlled experiments and other methodologies, show great promise in evaluating ecological conditions from observational data and in defining water quality and other environmental criteria. Current applications of conditional probability analysis (CPA) are largely done via scripts or cumbersome spreadsheet routines, which may prove daunting to end-users and do not provide access to the underlying scripts. Combining spreadsheets with scripts eases computation through a familiar interface (i.e., Microsoft Excel) and creates a transparent process through full accessibility to the scripts. With this in mind, we developed a software application, CProb, as an Add-in for Microsoft Excel with R, R(D)com Server, and Visual Basic for Applications. CProb calculates and plots scatterplots, empirical cumulative distribution functions, and conditional probability. In this short communication, we describe CPA, our motivation for developing a CPA tool, and our implementation of CPA as a Microsoft Excel Add-in. Further, we illustrate the use of our software with two examples: a water quality example and a landscape example. CProb is freely available for download at http://www.epa.gov/emap/nca/html/regions/cprob.

Predicting estuarine sediment metal concentrations and inferred ecological conditions: an information theoretic approach.

Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.

An R Package for Open, Reproducible Analysis of Urban Water Systems, With Application to Chicago.

Urban water systems consist of natural and engineered flows of water interacting in complex ways. System complexity can be understood via mass conservative models that account for the interrelationships among all major flows and storages. We have developed a generic urban water system model in the R package CityWaterBalance. CityWaterBalance provides a reproducible workflow for studying urban water systems by facilitating automated retrievals of open data and post-processing with open source R functions. It allows the user to 1) rapidly assemble a quantitative, comprehensive assessment of flows thorough an urban area, and 2) easily change the spatial and temporal boundaries of analysis. We use CityWaterBalance to evaluate the water system in the Chicago metropolitan area on a monthly basis for water years 2001-2010. Results are used to consider 1) impacts of management decisions aimed at reducing stormwater and combined sewer overflows and 2) the significance of future changes in precipitation.

Estimating summer nutrient concentrations in Northeastern lakes from SPARROW load predictions and modeled lake depth and volume.

Global nutrient cycles have been altered by the use of fossil fuels and fertilizers resulting in increases in nutrient loads to aquatic systems. In the United States, excess nutrients have been repeatedly reported as the primary cause of lake water quality impairments. Setting nutrient criteria that are protective of a lakes ecological condition is one common solution; however, the data required to do this are not always easily available. A useful solution for this is to combine available field data (i.e., The United States Environmental Protection Agency (USEPA) National Lake Assessment (NLA)) with average annual nutrient load models (i.e., USGS SPARROW model) to estimate summer concentrations across a large number of lakes. In this paper we use this combined approach and compare the observed total nitrogen (TN) and total phosphorus (TN) concentrations in Northeastern lakes from the 2007 National Lake Assessment to those predicted by the Northeast SPARROW model. We successfully adjusted the SPARROW predictions to the NLA observations with the use of Vollenweider equations, simple input-output models that predict nutrient concentrations in lakes based on nutrient loads and hydraulic residence time. This allows us to better predict summer concentrations of TN and TP in Northeastern lakes and ponds. On average we improved our predicted concentrations of TN and TP with Vollenweider models by 18.7% for nitrogen and 19.0% for phosphorus. These improved predictions are being used in other studies to model ecosystem services (e.g., aesthetics) and dis-services (e.g. cyanobacterial blooms) for ~18,000 lakes in the Northeastern United States.