(51c) Supply Chain Management of Livestock Waste for Spatio-Temporal Control of Nutrient Pollution in Water Bodies

Harmful algal blooms (HABs) pose severe health threats due to the release of toxins and the appearance of hypoxia in water bodies. In addition, HABs lead to significant economic losses since they impact tourism, recreational and commercial activities, and property values [1]. Approximately 30% of the lakes from 36 U.S. states report persistent HABs problems [2]. The US EPA reported that the corresponding tourism losses and commercial fishing losses reached one billion and more than ten million dollars per year respectively. It is widely known that nutrient pollution (especially nitrogen and phosphorous) is a key factor in the development of HABs. Nutrient pollution is a complex spatio-temporal and multiscale process that originates from two types of nutrient sources: non-point source and point source. Non-point sources include nutrient release from agricultural lands, natural lands, and stormwater while point sources include nutrient releases from organic waste originated in permitted locations such as livestock facilities.

Nutrient pollution and HABs issues have been widely studied from the perspectives of human health, economic analysis, prediction and monitoring, treatment, and remediation [3,4]. Interestingly, a dimension of the nutrient pollution problem that has not received as much attention is how nutrients interact with the current agricultural supply chain management practices. This is important because the transport of nutrients to water bodies is a spatio-temporal phenomenon that involves multiple scales and that is tightly related to the spatial layout and geography of agricultural lands surrounding the water bodies, to the timing of fertilizer application, and to regional nutrient imbalances.

In this work, we combine multiple types of modeling tools to analyze the relationship between supply chain management strategies, nutrient transport, and HABs development. The supply chain component captures balances and transformation of waste, nutrients, and products at multiple locations as well as waste transportation [5]. This supply chain model can achieve coordination among different objectives, which include investment, transportation, and operational costs, economic losses, and environmental impacts caused by HABs. The second component of our framework is a nutrient transport model, which can track the nutrient releases from organic waste and their transport process from the soil to the aquatic systems [6]. The third component is an algal bloom prediction model that relates nutrient concentration and other natural factors (e.g., temperature and sunlight) to algal blooms [7,8]. Specifically, by designing an effective supply chain management that stores, mobilizes, and processes organic waste, it is possible to balance and recycle nutrients more effectively and with this, control the timing of toxic bloom occurrence and identify which locations are best suited to reduce nutrient loading to ambient water. We apply our framework to a series of real case studies in the Yahara Watershed in the State of Wisconsin to illustrate the model structure and practicability.

References:

[1] Heisler J, Glibert P M, Burkholder J A M, et al. Eutrophication and harmful algal blooms: a scientific consensus[J]. Harmful Algae, 2008, 8(1): 3-13.

[2] Graham J, Dortch Q. An Overview of Harmful Algal Blooms (HABs) and Their Impacts in Freshwater and Marine Ecosystems. EPA Presentation

[3] Backer L, Manassaram-Baptiste D, LePrell R, et al. Cyanobacteria and algae blooms: review of health and environmental data from the harmful algal bloom-related illness surveillance system (HABISS) 2007–2011[J]. Toxins, 2015, 7(4): 1048-1064.

[4] Adams C M, Larkin S L, Hoagland P, et al. Assessing the Economic Consequences of Harmful Algal Blooms: A Summary of Existing Literature, Research Methods, Data, and Information Gaps[J]. Harmful Algal Blooms: A Compendium Desk Reference, 2018: 337-354.

[5] Sampat A M, Martin E, Martin M, et al. Optimization formulations for multi-product supply chain networks[J]. Computers & Chemical Engineering, 2017, 104: 296-310.

[6] Mayorga E, Seitzinger S P, Harrison J A, et al. Global nutrient export from WaterSheds 2 (NEWS 2): model development and implementation[J]. Environmental Modelling & Software, 2010, 25(7): 837-853.

[7] Elliott J A. Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria[J]. Water Research, 2012, 46(5): 1364-1371.

[8] Reynolds C S, Irish A E, Elliott J A. The ecological basis for simulating phytoplankton responses to environmental change (PROTECH)[J]. Ecological Modeling, 2001, 140(3): 271-291.