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

Abstract

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

Life Cycle Assessment under Uncertainty: A Scoping Review

“…Alyaseri and Zhou [66] showed the impact of uncertainty from the life cycle inventory assessment method on LCA outcomes by using MCS and a case-study-based approach on three wastewater sludge treatment processes, multiple hearth incineration, and two proposed alternative processes: fluid bed incineration and anaerobic digestion. Based on hydrologic analysis and LCA, Tavakol-Davani [67] identified critical uncertainty sources in designing an ecologically sustainable urban drainage system. The objective of the uncertainty analysis was to describe and analyze the relative contributions of unreliability, incompleteness, technological difference, geographical and temporal variation in LCIA data, as well as natural variability in hydrologic data.…”

“…The most significant sources of uncertainties reported in the studies were uncertainty in model and process parameters, data variability, and uncertainty due to using different methodologies and databases. Uniform [34,41,57,65,70,95,110,113] Beta [57,96,116] Gamma [41,67] BETA-PERT […”

“…[73] Unreliability, incompleteness, technological difference, and spatial and temporal variation in life cycle impact assessment (LCIA) data, as well as the natural variability in hydrologic data. [67] Model parameters. [96] Variation in input data of the LCA model.…”

Impact of sewer overflow on public health: A comprehensive scientometric analysis and systematic review

“…Examples include assess risk of WWTP effluent exceeding regulatory requirements and potential savings in comprehensive plant optimization (Benedetti et al, 2006;Rousseau et al, 2001). Advantages of Monte Carlo include relatively easy to understand, assessment of the uncertainty in model output via sensitivity analysis and identification of major input factor responsible for most of the model output variability (Korving et al, 2002;Sriwastava et al, 2018;Tavakol-Davani et al, 2019;Torres-Matallana et al, 2020). Disadvantages of Monte Carlo include output accuracy depends on utilization of reasonable/fair assumptions, tendency to underestimate risk events, computational requirements, time-consuming and susceptible to overfitting (Dilks et al, 1992;Han et al, 2007;Thorndahl et al, 2008).…”

Assessing the Lifecycle Environmental Resilience of Urban Green Infrastructures Coping with Acute Disturbances and Chronic Stresses

“…infrastructure [40], multi-objective optimal design of rainwater management systems [41], LCA analysis of climate change and the city [38], LCA analysis of rainwater harvesting measures at different scales, etc. [42]. However, there are fewer results of a comprehensive evaluation of the resilience benefits of actual green infrastructure in acute disturbances and chronic stresses.…”