|Abstract:||Water, a limited resource even on our hydrous planet, has always been inextricably tied to the rise and fall of cities and human infrastructure. Clean, plentiful water drives our food and energy production, provides transport, and keeps humans and the environment healthy. Integrated urban water modelling and improved geospatial databases are allowing water management researchers to analyze the effects of process decisions in the water management sector on a broader scale and with higher spatiotemporal resolution than ever before. Research is driven by the desire to optimize limited resources, respond to changing user patterns, characterize the robustness of the system to climate change pressures, and define the downstream effects of new technologies. Water management decisions today not only require hydraulic and hydrologic knowledge, but also an understanding of energy production systems, environmental biochemistry, economics, and regulatory policy. Although integrated urban water models started by expanding on simple physical urban drainage models, they are now incorporating mechanisms for environmental change, social agents, and economic feedback.
Although originally built to protect public health and the local aquatic environment, wastewater treatment utilities have in recent years taken on additional objectives including greenhouse gas mitigation, reducing chemical use, and reducing long-term environmental impacts due to effluent nutrients and disinfection byproducts. National policies on water quality (EU Water Framework Directive, US Clean Water Act) and electricity demand (GHG emissions targets) both cover utilities, with the goal of improving their environmental sustainability. These multiple objectives may call for conflicting operational decisions, which presents a direct tradeoff to utility decision makers—increase electricity use for treatment, or allow worse effluent quality to flow into the local environment.
This thesis seeks to characterize the scale of impacts stemming from energy-water tradeoffs and identify sources of uncertainty in making this decision, by placing the operational tradeoff in a larger water-energy-environmental system context. The case study in Eindhoven, the Netherlands is selected for several reasons. The local water management authority has created a well-researched integrated urban water model, comprising the urban water system from raindrop through domestic use, sewer collection, wastewater treatment, and to the receiving river. The national water and energy policies are providing stricter standards for utilities, presenting this tradeoff decision previously mentioned. Finally, the local and national datasets for LCA inventory, meteorology, energy generation, and ecological response are well documented, allowing us to analyze the system from a holistic perspective.
The analysis of the energy-water quality tradeoff is completed by different modeling methods employed by water managers and regulators, to see if the different methods yield improved or conflicting results. First, we use traditional LCA inventory accounting which is the current standard for new capital investments in wastewater treatment. The LCA considers the impacts of kilowatt hours of electricity and ammonia released to the environment in wastewater effluent for four different standards of effluent quality. This analysis demonstrated a clear tradeoff between eutrophication and global warming (energy production emissions) impacts. Second, the spatiotemporal variation of these eutrophication and air emissions impacts is explored using biophysical models. The models include the calibrated integrated urban water system model developed for Eindhoven and the Dommel in conjunction with a generalized atmospheric dispersion model for emission byproducts of electricity generation. We study the downstream transport of ammonium in the river and particulate matter from the power plant emissions. The air emissions modeling found that even a single day of electricity demand associated with wastewater treatment could affect particulate matter concentrations hundreds of kilometers away, crossing international borders. The water quality modelling found that marginal improvements in the effluent quality (of 1 mg/L ammonium) could improve the worst-case ammonia concentrations downstream by up to 20%. Third, the biophysical model results are evaluated using literature-based characterization factors for human health exposure and ecosystem tolerances to the aforementioned ammonium and particulate matter emissions. These calculations framed our physical models in the context of local systems. On the air emissions side, the electricity generated for wastewater treatment was found to contribute less than 0.1% of the background particulate matter concentration in the region modelled. On the water quality side, the wastewater treatment plant significantly reduced the number of ecological exceedances compared to a no-treatment control scenario, on the order of about 50%. However, this control scenario does not account for the influence of other sources of ammonium in the river, such as other wastewater treatment plants or agricultural runoff.
The outcomes of this work show that energy investment in wastewater treatment creates a significant tension in environmental impacts. Our multi-tiered evaluation sought to explore the dimensions of these impacts on higher resolution spatial scales, to better understand how they fit into environmental systems. Ultimately, the physical modeling showed that energy impacts could cross international borders which might have some implication for international policymaking. However, through systems analysis these impacts were shown to be negligible in comparison to the water quality consequences for local ecosystems.