NNSS Drinking Water Systems and Wastewater Systems Monitoring
Presentation of NNSS Drinking Water and Wastewater 2021 monitoring
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Presentation of NNSS Drinking Water and Wastewater 2021 monitoring
This quick-reference sheet provides a list of funding programs for the solid waste and wastewater sectors available nationally and for select states (those with participating communities in the Waste-to-Energy Technical Assistance Program for Local Governments during FY21 and FY22).
Abstract Fenton‐like reaction has been widely used for organics degradation. However, most Fenton‐like reaction works at low pH range (pH < 4) with uncontrollable selectivity of hydroxyl radicals from H 2 O 2 activation, and unsatisfied catalyst stability, which is compromised advanced oxidation performance for water/wastewater treatments. In this work, to solve the drawbacks, novel copper catalysts were fabricated via hydrogen reduction/calcination of Cu 2+ ‐supported Al/MCM‐41 with precisely controllable copper valence state. Compared with catalysts with monovalence copper (i.e., CuO, Cu, and Cu 2+ ), the obtained catalysts with multivalence copper present higher selectivity, excellent stability towards •OH radical pathways, and outperformance in p CBA degradation efficiency at neutral state. In addition, the fabricated catalysts also exhibited excellent phenol removal efficiency (75.5%) and H 2 O 2 utilization efficiency (47.9%) within neutral environment. Moreover, the degradation efficiency of phenol approaches to 100% within only 2 h. The catalyst also shows good stability for organic pollutants removal, which shows good potential in catalytic oxidation for phenolic compounds‐containing wastewater in Fenton‐like reaction, especially under neutral pH conditions. Practitioner Points Multivalence copper presents great potentials for organic compounds removal at neutral condition. Multivalence copper shows higher selectivity toward •OH and good stability at neutral condition. Multivalence copper exhibiters outperformed phenol removal efficiency at neutral condition.
Converting organic wastes into hythane, a blend of hydrogen (5% to 25%) and methane (75% to 95%), will not only reduce waste discharge but also maximize energy recovery. Herein, a membrane electrochemical cell was investigated to produce hythane from biogas generated in anaerobic digestion of brewery wastewater (BW). The key parameters including current densities, electrolyte concentrations, and biogas flow rates were examined in batch tests. Under an optimal condition (210 mA, 100 mM electrolyte, 1 mL min −1 of biogas flow), the system achieved the production of hythane containing 70.6% ± 1.1% CH 4 , 27.3% ± 0.5% H 2 , and 2.1% ± 1.6% CO 2 (corresponding to 91.1% ± 6.4% CO 2 removal). Meanwhile, the H 2 S concentration was decreased from 513 to 2 ppm, 99.9% ± 0.2% removal. Energy efficiency of this system was estimated 61.8% ± 10.7%, and energy output increased by 54.4% ± 10.6% with biogas upgrading to hythane. Furthermore, these results encourage further exploration of electrochemical approach for simultaneous biogas upgrading and hythane production.
Rotating algal biofilm reactors (RABRs) are innovative systems designed to cultivate microalgae biofilms efficiently. In this paper, we have developed a novel mathematical model to accurately capture the growth dynamics of algae biofilms within RABR. By considering the spatial heterogeneity of the RABR, we introduce a PDE-based model that addresses the spatial variations across the substratum, enabling a more accurate simulation of biofilm growth in RABRs. The photosynthesis process is modeled through reactive kinetics, driving the growth of the algae biofilm. To analyze the system's behavior, we employ finite difference numerical methods to solve the complex PDE model. We then conduct extensive numerical simulations to understand algae biofilm growth in the RABR environment under various operational factors and environmental conditions. One primary focus in these simulations is to investigate the impact of various harvesting strategies, harvesting frequencies, light intensity, and light exposure on the overall biomass productivity of the algae biofilm. The numerical results provide valuable insights into optimizing algae biofilm growth and designing harvesting techniques in RABR systems. Our proposed novel mathematical model provides an effective platform for the theoretical investigation and design of RABRs for wastewater treatment.
The filtration membranes utilized in advanced treatment of municipal wastewater are prone to biological fouling. Permeability loss and water recovery limitations due to fouling necessitate additional pretreatment and membrane cleaning, which raise the cost of water reuse. Better fouling mitigation calls for a deeper understanding of biofilm behavior on water reuse membranes. This study uses confocal microscopy to monitor biofouling under realistic conditions. To simulate water reuse, a miniature flow cell with microfiltration and ultrafiltration membranes was operated under typical filtration conditions using secondary effluent from California's East Bay Municipal Utility District. The growing biofilms were stained for extracellular polymeric substances and live and dead cells, and they were monitored in situ under filtration conditions with a confocal laser scanning microscope to produce 2-D and 3-D images. In situ image series revealed biofilm growth and removal processes including internal and external fouling, extracellular polymeric substance production, wrinkling, delamination, and film re-deposition. Additionally, changes in biofilm morphology between in situ and ex situ images highlighted limitations of ex situ imaging. Here, these results provide insight into the physical and biological mechanisms of biofouling in hopes of informing the development of improved techniques for biofouling mitigation.
Seafood plays a critical role in promoting a nutritious and climate-friendly food system but faces challenges from marine contamination that pose food safety risks. Here, to address these challenges, we developed a Marine Pollution-induced Food System Risk Assessment and Response (MP-FSRAR) model to assess the impact of consumer perceptions of marine pollution risk events on nutrient supply and greenhouse gas (GHG) emissions. Applying this model to the proposed Fukushima Nuclear Wastewater Discharge (FNWD) into the Pacific Ocean, we identified the potential variation in nutrient supply and GHG emissions resulting from changes in Chinese consumers' willingness to consume seafood under 12 scenarios. Our analysis revealed nutrient supply gaps in daily protein, minerals, vitamins, and fatty acids, ranging from 1.5 to 2.7 g/person, 2.5–4.5 mg/person, 5.6–10.3 mcg/person, and 0.08–0.14 g/person, respectively. Closing the protein gap in China would require an additional 3.2–15.8 billion kg yr -1 protein-rich foods, including aquatic, livestock, dairy, and pulses. Additional food supply would increase extra GHG emissions by 1–79 billion kg CO 2 e yr -1 , resulting in potential GHG emissions being transmitted to other countries through the trade chain. Furthermore, we found that the livestock-based food substitution pathway had 40 times higher GHG emissions than the plant-based one. Our MP-FSRAR model provides global stakeholders with new perspectives on the potential consequences of consumer perceptions of marine pollution risks and offers valuable insights for developing countermeasures to build a more resilient and low-carbon food system.
Wastewater treatment plants (WWTPs) consume significant amount of energy to sustain their operation. From this point, the current study aims to enhance the capacity of these facilities to meet their energy needs by integrating renewable energy sources. The study focused on the investigation of two primary solar energy systems in As Samra WWTP in Jordan. The first system combines parabolic trough collectors (PTCs) with thermal energy storage (TES). This system primarily serves to fulfill the thermal energy demands of the plant by reducing the demands from boiler units, which allows more biogas for electricity generation. The second system is a photovoltaic (PV) system with Lithium-Ion batteries, which directly produces electricity that will be used to cover part of the electrical energy demands of plant. To assess the optimal configuration, two distinct scenarios have been formulated and compared to the current case scenario (SC#1). The first scenario focuses on maximizing the net present value (NPV) and minimizing the levelized cost of electricity (LCOE). The second scenario is centred on minimizing the levelized cost of heat (LCOH). The findings indicate that both scenarios succeeded in reducing the reliance on the grid to a value that reach 1 %. Moreover, they both reduced biogas percentage in energy production from 88 % to approximately 65 % through the integration of the PV system. In terms of thermal demand, SC#2 reduced the reliance on biogas boiler units from 100 % to 25 %, while SC#3 achieved an even more impressive reduction to just 8 %. The best LCOE value was attained in SC#2, at 0.0895 USD/kWh, with an NPV of 10.54 million USD. Conversely, SC# 3 yielded an LCOH value of 0.0432 USD/kWh th compared to 0.0534 USD/kWh th USD for SC#2. In conclusion, despite their relatively high capital and operating costs, SC#2 and SC#3 managed to substantially decrease the annual electricity expenditure from approximately 2 million USD to 86,000 USD and 0 USD, respectively.
Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (D INVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L -1 ) and steady biomass concentration (TSS monthly avg . = 400–700 mg·L −1 ). Operational variables including pH, alkalinity, and influent ammonium (NH 4 + ), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO 2 − accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Altogether, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).
On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. Here, we develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.
On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.
The removal of selenite (SeO32-) from water is challenging due to the risk of secondary pollutants. To address this, we developed RuO2-based nanocatalysts on the titanium plate (RuO2/TP) for direct electrochemical reduction of Se(IV) to elemental selenium [Se(0)]. Optimizing Sn doping in RuO2 nanoparticles to induce charge redistribution enabled the Ru0.9Sn0.1Ox/TP catalyst to achieve ∼90% Se(IV) removal across concentrations of 0.1, 1, and 10 mM at -2 mA cm-2 over 8 h, outperforming undoped RuO2/TP. Furthermore, Ru0.9Sn0.1Ox/TP also maintained ∼90% removal efficiency in 1 mM of Se(IV) solutions containing competitive anions (0.5 M Cl-, 0.1 M SO42-, 0.01 M NO3-, and their mixtures), demonstrating suitability for complex wastewater treatment. Importantly, the catalysts were recyclable, with no observable contamination introduced into the solution. Density functional theory (DFT) calculations suggest that Sn doping effectively reduces the energy barrier for the reduction of Se(IV) to Se(0).
Digester-equipped municipal wastewater facilities generate recycle streams with high nutrient loads that increase energy consumption and can cause environmental pollution. The reduction of these loads through electrochemical nutrient recovery (ENR) could enhance the food–energy–water nexus by producing fertilizer (struvite). This study investigated the recovery process through a 1 year sampling of recycle streams and the implementation of nutrient recovery. Time series analyses showed that P (as orthophosphate) concentration was time-variant in digester effluent streams, while N (as ammonia) concentration was time-variant in only the aerobic system. Furthermore, these two nutrient concentrations did not correlate in any of the recycle streams. Subsequent multivariate screening analyses identified anode type, NH 4 + concentration, cathodic potential, P concentration, and temperature as most significant for ENR. Finally, the optimum conditions of cathodic potential, anode area-to-volume ratio, and temperature applied to a real recycle stream resulted in 95% P recovery with 0.03 kWh/kg P. This energy consumption is significantly lower than process energy for conventional P fertilizers (1.1 kWh/kg P) and chemical recovery processes at scale (1.7–12.9 kWh/kg P). Overall, this study recommended specific process controls for nutrient recovery, expanded the variables evaluated for ENR, and demonstrated the ability to significantly impact energy demand associated with P-based fertilizers.
As one of the largest biotechnological applications, activated sludge (AS) systems in wastewater treatment plants (WWTPs) harbor enormous viruses, with 10-1,000-fold higher concentrations than in natural environments. However, the compositional variation and host-connections of AS viruses remain poorly explored. Here, we report a catalogue of ~50,000 prokaryotic viruses from six WWTPs, increasing the number of described viral species of AS by 23-fold, and showing the very high viral diversity which is largely unknown (98.4-99.6% of total viral contigs). Most viral genera are represented in more than one AS system with 53 identified across all. Viral infection widely spans 8 archaeal and 58 bacterial phyla, linking viruses with aerobic/anaerobic heterotrophs, and other functional microorganisms controlling nitrogen/phosphorous removal. Notably, Mycobacterium, notorious for causing AS foaming, is associated with 402 viral genera. Our findings expand the current AS virus catalogue and provide reference for the phage treatment to control undesired microorganisms in WWTPs.
Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus , as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway.
Abstract Flowing electrode capacitive deionization systems (FE-CDI) have recently garnered attention because of their ability to prevent cross contamination and operate in uninterrupted cycles ad infinitum. Typically, FE-CDI electrodes suffer from low conductivity, reducing deionization performance. Utilization of higher mass loadings to combat this leads to poor rheological properties. Herein, Ti 3 C 2 T x MXene was introduced as 1 mg mL −1 slurry electrodes in an FE-CDI system for the removal and recovery of ammonia from simulated agricultural wastewater. The electrode performance was evaluated by operating the FE-CDI system with a feed solution of 500 mg L −1 NH 4 Cl running in batch mode at a constant voltage of 1.20 and −1.20 V in charging and discharging modes, respectively. Despite the low loading, Ti 3 C 2 T x flowing electrodes showed markedly improved performance, achieving 60% ion removal efficiency in a saturation time of 115 min with an adsorption capacity of 460 mg g −1 . To understand the high adsorption performance of the electrodes, physiochemical and structural analysis was done via a variety of characterization techniques such as SEM, TEM, XRD, DLS, and Raman spectroscopy. Cyclic voltammetry and galvanostatic charge/discharge profiles were obtained to evaluate the electrochemical properties of the electrodes. The system proved to be an energy-saving technology by exhibiting a charge efficiency of 58–70% while operating at an energy consumption of 0.45 kWh kg −1 . A 92% regeneration efficiency showed that the electrodes were stable and suitable for long term and scalable usage. The results demonstrate that MXenes have the potential to improve the FE-CDI process for energy-efficient removal and recovery of ammonia.
Abstract Wastewater treatment plants (WWTPs) are large electricity and natural gas consumers with untapped potential to recover carbon-neutral biogas and provide energy services for the grid. Techno-economic analysis of emerging energy recovery and management technologies is critical to understanding their commercial viability, but quantifying their energy cost savings potential is stymied by a lack of well curated, nationally representative electricity and natural gas tariff data. We present a dataset of electricity tariffs for the 100 largest WWTPs in the Clean Watershed Needs Survey (CWNS) and natural gas tariffs for the 54 of 100 WWTPs with on-site cogeneration. We manually collected tariffs from each utility’s website and implemented data checks to ensure their validity. The dataset includes facility metadata, electricity tariffs, and natural gas tariffs (where cogeneration is present). Tariffs are current as of November 2021. We provide code for technical validation along with a sample simulation.
Drinking water infrastructure in urban settings is increasingly affected by population growth and disruptions like extreme weather events. This study explores how the integration of direct wastewater reuse can help to maintain drinking water service when the system is compromised.