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  1. Abstract Background

    The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.


    A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children’s physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver’s income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.


    Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores driven by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.


    This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children’s environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.

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  2. Economic models and watershed models provide useful results, but when seeking to integrate these systems, the temporal units typically utilized by these models must be reconciled. A hydrologic-economic modeling framework is built to couple the Hydrological Simulation Program-Fortran (HSPF), representing the watershed system, with the Rectangular Choice-of-Technology (RCOT) model, an extension of the basic input-output (I-O) model. This framework is implemented at different sub-annual timesteps to gain insight in selecting temporal units best suited for addressing questions of interest to both economists and hydrologists. Scenarios are designed to examine seasonal increases in nitrogen concentration that occur because of agricultural intensification in Cedar Run Watershed, located in Fauquier County, northern Virginia. These scenarios also evaluate the selection among surface water, groundwater, or a mix of (conjunctive use) practices for irrigation within the crop farming sector in response to these seasonal impacts. When agricultural intensification occurs in Cedar Run Watershed, implementing conjunctive use in irrigation reduces the seasonal increases in nitrogen concentration to specified limits. The most efficient of the conjunctive use strategies explicitly considered varies depending on which timestep is utilized in the scenario: a bi-annual timestep (wet and dry season) vs. a seasonal timestep. This modeling framework captures the interactions between watershed and economic systems at a temporal resolution that expands the range of questions one can address beyond those that can be analyzed using the individual models linked in this framework. 
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  3. Economic input-output and watershed models provide useful results, but these kinds of models do not use the same spatial units, which typically limits their integration. A modular hydrologic-economic modeling framework is designed to couple the Rectangular Choice-of-Technology (RCOT) model, a physically constrained, input-output (I-O) model, with the Hydrological Simulation Program-Fortran (HSPF). Integrating these two models can address questions relevant to both economists and hydrologists, beyond addressing only administrative or watershed concerns. This framework is utilized to evaluate alternative future development prospects within Fauquier County, northern Virginia, specifically residential build-up, and agricultural intensification in the upstream location of the local watershed. Scenarios are designed to evaluate the downstream impacts on watershed health caused by upstream development and changes made within the economic sectors in response to these impacts. In the first case, an alternative residential water technology is more efficient than the standard for ensuring adequate water supply downstream. For scenarios involving upstream agricultural intensification, a crop shift from grains to fruits and vegetables is the most efficient of the alternatives considered. This framework captures two-way feedback between watershed and economic systems that expands the types of questions one can address beyond those that can be analyzed using these models individually. 
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  4. null (Ed.)
    To capture the interactions between hydrologic and economic systems necessary for modeling water quality at a sufficient level of spatial detail, we have designed a modular framework that couples an economic model with a watershed model. To represent the economic system, the Rectangular Choice-of-Technology (RCOT) model was used because it represents both the physical and monetary aspects of economic activities and, unlike traditional input-output or general equilibrium models, it can optimize choices among operational technologies in addition to the amount and location of production. For the first implementation of this modeling framework, RCOT is coupled with a watershed model, Hydrological Simulation Program-Fortran (HSPF), which was calibrated to represent Cedar Run Watershed in northern Virginia. This framework was used to analyze eight scenarios related to the expansion of agricultural activity in Fauquier County. The database for RCOT used county-level input-output data representative of the region in 2012. Thus, when crop farming was expanded to fully utilize the farmland available in the watershed, the nitrogen concentration at the outflow of the watershed increased from 0.6 to 4.3 mg/L. However, when RCOT could select between a standard and a more nitrogen-efficient management practice, the outflow nitrogen concentration only increased to 2.6 mg/L because RCOT selected the more resource-efficient practice. Building on this modular framework, future work will involve designing more realistic scenarios that can test policy options and regional planning decisions in a wide range of watersheds. 
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  5. null (Ed.)
  6. null (Ed.)
    The pathways taken throughout any model-based process are undoubtedly influenced by the modeling team involved and the decision choices they make. For interconnected socioenvironmental systems (SES), such teams are increasingly interdisciplinary to enable a more expansive and holistic treatment that captures the purpose, the relevant disciplines and sectors, and other contextual settings. In practice, such interdisciplinarity increases the scope of what is considered, thereby increasing choices around model complexity and their effects on uncertainty. Nonetheless, the consideration of scale issues is one critical lens through which to view and question decision choices in the modeling cycle. But separation between team members, both geographically and by discipline, can make the scales involved more arduous to conceptualize, discuss, and treat. In this article, the practices, decisions, and workflow that influence the consideration of scale in SESs modeling are explored through reflexive accounts of two case studies. Through this process and an appreciation of past literature, we draw out several lessons under the following themes: (1) the fostering of collaborative learning and reflection, (2) documenting and justifying the rationale for modeling scale choices, some of which can be equally plausible (a perfect model is not possible), (3) acknowledging that causality is defined subjectively, (4) embracing change and reflection throughout the iterative modeling cycle, and (5) regularly testing the model integration to draw out issues that would otherwise be unnoticeable. 
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  7. null (Ed.)
    Increasing numbers of chemicals are on the market and present in consumer products. Emerging evidence on the relationship between environmental contributions and prevalent diseases suggests associations between early-life exposure to manufactured chemicals and a wide range of children’s health outcomes. Using current assessment methodologies, public health and chemical management decisionmakers face challenges in evaluating and anticipating the potential impacts of exposure to chemicals on children’s health in the broader context of their physical (built and natural) and social environments. Here, we consider a systems approach to address the complexity of children’s environmental health and the role of exposure to chemicals during early life, in the context of nonchemical stressors, on health outcomes. By advancing the tools for integrating this more complex information, the scope of considerations that support chemical management decisions can be extended to include holistic impacts on children’s health. 
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