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• A computational framework to quantify the economic impact of harmful algae blooms • Economic loss quantification ($74.50/kg) of excess P runoff from livestock waste. • Coordinated market study to prove incentives for nutrient management programs. • Framework application to support policies to mitigate nutrient pollution impacts. Nutrient pollution from livestock waste impacts both fresh and marine coastal waters. Harmful algae blooms (HABs) are a common ecosystem-level response to such pollution that is detrimental to both aquatic life and human health and that generates economic losses (e.g., property values and lost tourism). Waste treatment and management technologies are not well established practices due, in part, to the difficulty to attribute economic value to associated social and environmental impacts of nutrient pollution. In this work, we propose a computational framework to quantify the economic impacts of HABs. We demonstrate the advantage of quantifying these impacts through a case study on livestock waste management in the Upper Yahara watershed region (in the state of Wisconsin, USA). Our analysis reveals that every excess kilogram of phosphorus runoff from livestock waste results in total economic losses of 74.5 USD. Furthermore, we use a coordinated market analysis to demonstrate that this economic impact provides a strong enough incentive to activate a nutrient management and valorization market that can help balance phosphorus within the study area. The proposed framework can help state, tribes, and federal regulatory agencies develop regulatory and non-regulatory policies to mitigate the impacts of nutrient pollution. [ABSTRACT FROM AUTHOR]

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework – which we call HydroGraphs – for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices. • We present a general framework for representing hydrological systems as graphs. • The graph-based framework can help identify pollutant transport and fate across waterbodies, rivers, and watersheds. • Representing these systems as graphs enables advanced metrics and algorithms to aid data analytics and decision making. • We apply our framework to case studies in Wisconsin, USA to highlight applications. [ABSTRACT FROM AUTHOR]

• Presents a graph-structured dynamical market coordination framework. • Captures spatiotemporal behavior of product prices through market clearing. • Draws insights from primal, dual, and Lagrangian formulations. • Presents case study based on Wisconsin dairy farming and MISO electricity prices. In this work we present a generalization of market coordination to multi-product supply chains and use this framework to analyze their spatio-temporal economic properties. We interpret a supply chain as a market consisting of independent stakeholders (suppliers, consumers, transportation, and technology providers) who bid into a coordination system that is managed and cleared by an independent entity to obtain product allocations and prices. The proposed model provides a general graph representation of spatio-temporal product transport that helps capture geographical transport, time delays, and storage (temporal transport) in a unified and compact manner. This representation allows us to establish fundamental economic properties for the supply chain (revenue adequacy, cost recovery, and competitiveness) and to establish bounds for space-time prices. To illustrate these concepts, we consider a case study in which organic waste is used for producing biogas and electricity. Our market model shows that incentives for waste storage emerge from electricity demand dynamics and illustrates how space-time price dynamics for waste and derived products emerge from geographical transport and storage. [ABSTRACT FROM AUTHOR]