Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

摘要: The goal of this research is identifying major sources uncertainty an environmentally-sustainable urban drainage infrastructure design, based on hydrologic analysis and life cycle assessment (LCA). intends to characterize compare relative roles unreliability, incompleteness, technological difference, spatial temporal variation in impact (LCIA) data, as well natural variability data. Uncertainties are analyzed using a robust Monte Carlo simulation approach, performed by High Throughput Computing (HTC) interpreted Morse-Scale regression models. platform applied watershed-scale LCA rainwater harvesting systems (RWH) control combined sewer overflows (CSOs). To consider the implications, RWH simulated serve for both water supply CSO watershed Toledo, Ohio, USA. Results suggest that, among studied parameters, rainfall depth (as parameter) caused more than 86% uncertainty, while only 7% was LCIA parameters. Such emphasis necessity data associated analyses increase reliability LCA-based design. In addition, results that such topology-inspired model capable rendering optimal system capacity function annual depth. Specifically, if could capture 1/40th each event from rooftops, would be and, thus, lead minimized impacts terms global warming potential (GWP) aquatic eco-toxicity (ETW). This around 2.1 cm Toledo (given 85 cm/year 200 m2 typical roof area), which achieved through with 4.25 m3 per household, assuming uniform plan entire watershed. Capacities smaller suggested value likely result loss potable treatment savings benefits, capacities larger incur excessive wastewater burden construction phase systems.