Managing the water-energy-nutrient nexus for the built environment requires, in part, a full system analysis of energy consumption, global warming and eutrophication potentials of municipal water services. As an example, we evaluated the life cycle energy use, greenhouse gas (GHG) emissions and aqueous nutrient releases of the whole anthropogenic municipal water cycle starting from raw water extraction to wastewater treatment and reuse/discharge for five municipal water and wastewater systems. The assessed options included conventional centralized services and four alternative options following the principles of source-separation and water fit-for-purpose. The comparative life cycle assessment identified that centralized drinking water supply coupled with blackwater energy recovery and on-site greywater treatment and reuse was the most energy- and carbon-efficient water service system evaluated, while the conventional (drinking water and sewerage) centralized system ranked as the most energy- and carbon-intensive system. The electricity generated from blackwater and food residuals co-digestion was estimated to offset at least 40% of life cycle energy consumption for water/waste services. The dry composting toilet option demonstrated the lowest life cycle eutrophication potential. The nutrients in wastewater effluent are the dominating contributors for the eutrophication potential for the assessed system configurations. Among the parameters for which variability and sensitivity were evaluated, the carbon intensity of the local electricity grid and the efficiency of electricity production by the co-digestion with the energy recovery process were the most important for determining the relative global warming potential results.

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.