One of the pressing issues currently faced by the water industry is incorporating sustainability considerations into design practice and reducing the carbon emissions of energy-intensive processes. Water treatment, an indispensable step for safeguarding public health, is an energy-intensive process. The purpose of this study was to analyze the energy consumption of an existing drinking water treatment plant (DWTP), then conduct a modeling study for using photovoltaics (PVs) to offset that energy consumption, and thus reduce emissions. The selected plant, located in southwestern United States, treats 0.425 m³ of groundwater per second by utilizing the processes of coagulation, filtration, and disinfection. Based on the energy consumption individually determined for each unit process (validated using the DWTP’s data), the DWTP was sized for PVs (as a modeling study). The results showed that the dependency of a DWTP on the traditional electric grid could be greatly reduced by the use of PVs. The largest consumption of energy was associated with the pumping operations, corresponding to 150.6 Wh m⁻³ for the booster pumps to covey water to the storage tanks, while the energy intensity of the water treatment units was found to be 3.1 Wh m⁻³. A PV system with a 1.5 MW capacity with battery storage (30 MWh) was found to have a positive net present value and a levelized cost of electricity of 3.1 cents kWh⁻¹. A net reduction in the carbon emissions was found as 950 and 570 metric tons of CO₂-eq year⁻¹ due to the PV-based design, with and without battery storage, respectively.

Drinking water treatment, wastewater treatment, and water distribution are energy-intensive processes. The goal of this study was to design the unit processes of an existing drinking water treatment plant (DWTP), evaluate the associated energy consumption, and then offset it using solar photovoltaics (PVs) to reduce carbon emissions. The selected DWTP, situated in the southwestern United States, utilizes coagulation, flocculation, sedimentation, filtration, and chlorination to treat 3.94 m³ of local river water per second. Based on the energy consumption determined for each unit process (validated using the plant’s data) and the plant’s available landholding, the DWTP was sized for solar PV (as a modeling study) using the system advisor model. Total operational energy consumption was estimated to be 56.3 MWh day⁻¹ for the DWTP including water distribution pumps, whereas energy consumption for the DWTP excluding water distribution pumps was 2661 kWh day⁻¹. The results showed that the largest consumers of energy—after the water distribution pumps (158.1 Wh m⁻³)—were the processes of coagulation (1.95 Wh m⁻³) and flocculation (1.93 Wh m⁻³). A 500 kW PV system was found to be sufficient to offset the energy consumption of the water treatment only operations, for a net present value of $0.24 million. The net reduction in carbon emissions due to the PV-based design was found to be 450 and 240 metric tons CO₂-eq year⁻¹ with and without battery storage, respectively. This methodology can be applied to other existing DWTPs for design and assessment of energy consumption and use of renewables.

Wastewater treatment is a very energy-intensive process. The growing population, increased demands for energy and water, and rising pollution levels caused by fossil-fuel-based energy generation, warrants the transition from fossil fuels to renewable energy. This research explored the energy consumption offset of a satellite water reuse plant (WRP) by using solar photovoltaics (PVs) and anaerobic digestion. The analysis was performed for two types of WRPs: conventional (conventional activated sludge system (CAS) bioreactor with secondary clarifiers and dual media filtration) and advanced (bioreactor with membrane filtration (MBR)) treatment satellite WRPs. The associated greenhouse gas (GHG) emissions were also evaluated. For conventional treatment, it was found that 28% and 31.1% of the WRP’s total energy consumption and for advanced treatment, 14.7% and 5.9% of the WRP’s total energy consumption could be generated by anaerobic digestion and solar PVs, respectively. When both energy-generating units are incorporated in the satellite WRPs, MBR WRPs were on average 1.86 times more energy intensive than CAS WRPs, translating to a cost savings in electricity of $7.4/1000 m³ and $13.3/1000 m³ treated, at MBR and CAS facilities, respectively. Further, it was found that solar PVs require on average 30% longer to pay back compared to anaerobic digestion. For GHG emissions, MBR WRPs without incorporating energy generating units were found to be 1.9 times more intensive than CAS WRPs and 2.9 times more intensive with energy generating units. This study successfully showed that the addition of renewable energy generating units reduced the energy consumption and carbon emissions of the WRP.

The Sacramento River Basin (SRB) and the San Joaquin River Basin (JRB) have a history of recurring droughts. Both are important for California, being the crucial source of water supply. The available instrumental records may not depict the long-term hydrologic variability encompassing the duration and frequency of the historic low flow events. Thus, streamflow reconstruction becomes important in the current scenario of climatic alteration, escalating population and growing water needs. Studies have shown that Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), and Pacific Ocean sea surface temperature (SST) influence the precipitation and streamflow volumes of southwestern United States, particularly California. The focus of this study is to enhance the traditional tree-ring chronology (TRC)-based streamflow reconstruction approach by incorporating the predictors of SST, PDO, and SOI together with TRC, in a stepwise linear regression (SLR) model. The methodology was successfully applied to selected gauges located in the SRB and the JRB using five SLR models (SLR 1–5), and reconstructions were developed from 1801 to 1980 with an overlap period of 1933–1980. An improved reconstruction skill was demonstrated by using SST in combination with TRC (SLR-3 and SLR-5) (calibration r² = 0.6–0.91 and cross-validation r² = 0.44–0.74) compared with using TRC only (SLR-1), or TRC along with SOI and PDO (SLR-2; calibration r² = 0.51–0.78 and cross-validation r² = 0.41–0.68).