UC Berkeley

Climate change and increasing patterns of drought throughout the world are challenging the effectiveness of conventional water systems. A growing population in conjunction with more extreme weather events, threatens water supply infrastructure and increases uncertainty about how utilities will meet demand without sacrificing water quality. This issue has recently manifested in California, prompting utilities to invest in alternative water sources as a means of ensuring that water infrastructure is resilient to climate change scenarios.

Decentralized wastewater treatment is a promising option for increasing the sustainability of water infrastructure as it spatially merges supply and demand, minimizing large conveyance requirements. Decentralization can also promote nutrient management and recovery as it enables the source separation of the different wastewater sources. Specifically closing the nitrogen loop, by capturing it and reusing it to generate valuable high-end products can potentially improve the efficiency of the system and create revenue streams.

However, smaller decentralized water treatment units are potentially more energy intensive and costly than their centralized alternatives per unit of water treated. Due to these efficiency tradeoffs, planning tools and frameworks for holistically assessing decentralized water treatment systems need to be developed to optimally manage the new urban water supply paradigm. Better data management and data-driven decision support tools can provide valuable insight on the benefits and impacts of implementing future water systems.

This research assesses the technical performance of emerging decentralized technologies and implementation scenarios for residential uses, by assessing the feasibility of integrating decentralized facilities in cities with existing wastewater infrastructure. This work aims to create algorithmic models that integrate the spatial design of a wastewater treatment and distribution network with a life-cycle assessment to determine the associated environmental impacts. This dissertation utilizes spatial modeling to contextually evaluate the implementation and distribution potential and uses a life-cycle assessment approach to provide an extensive analysis of all the life-cycle impacts. By incorporating environmental indicators and metrics, a planning support framework can be created to help guide the water industry towards smart investments for a less energy-intensive future. Specifically, this work will 1) investigate how spatial terrain variability, demographics and distribution affect the performance of decentralized water treatment systems, 2) analyze the major parameters that affect the energy intensity, cost and greenhouse gas emissions of these systems, 3) quantify the unit processes that mostly impact the prementioned metrics and 4) identify the optimal system scale for decentralized infrastructure implementation under various spatial and demographic conditions.

The insights from this dissertation can help wastewater researchers and practitioners understand the complex relationships between the system scale and system performance. By evaluating the potential benefits and tradeoffs, this work can lead to management tools that will help transition away from traditional water management and create a water supply that (1) is resilient to changes in climate, (2) uses local water sources, and (3) leaves more water in natural ecosystems. This dissertation further adds to the growing body of literature on decentralized wastewater treatment assessing optimal scales, reuse potential, resource recovery and sewerage connections to investigate key factors affecting future implementation.