Abstract
Household decentralized water systems, including rainwater harvesting and greywater recycling, are often touted as a means to improve the sustainability and resiliency of centralized municipal systems. This research is focused on the spatial distributions of life cycle energy savings, consumer cost savings, and percent demand met of decentralized, household rainwater harvesting (RWH) and greywater recycling (GWR) system adoptions when they are being optimized for either life cycle cost or energy savings. The city of Boston was used as a testbed for applying the modeling framework in this study. A dynamic model was developed for both RWH and GWR systems to simulate daily water and energy usages and savings over a 30-year time frame using Python. The cost and energy optimal sizes of the RWH and GWR systems for each residential building in Boston were calculated using the Brent's method. Household characteristics such as distance from the centralized plants, number of tenants, and roof size were either directly obtained through Boston GIS data or approximated based upon existing data. Overall, GWR systems were found to perform generally better than RWH systems across the entire Boston. An average life cycle cost saving of $909–948/year and an average life cycle energy saving of 586–622 MJ/year can be achieved via installing cost- or energy-optimized GWR systems. The middle city area is generally good for both RWH and GWR system adoptions from both cost and energy perspectives. Incentives might be provided to foster decentralized system adoptions with infrastructure renovation efforts in this area. On the other hand, the southern sub-urban areas are generally the most suitable for installing RWH systems, while the downtown and its surrounding areas are generally the most suitable for promoting GWR systems. When taking stormwater management into consideration, the downtown area can also benefit from combined RWH and GWR systems as well as shared systems with local parks or other buildings with larger irrigation demands.
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