Sewer Overflows Research Articles

Can blue–green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment

Abstract Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue–green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities’ contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77–0.91 for COD and 0.47–0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic discharges, and parameter uncertainty contributed 15, 66, and 19% to COD, and 58, 35, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6, 69, and 25% for COD and 41, 42, and 17% for TP. According to the attention weights of the coupled model, the primary drivers of lateral anthropogenic pollution sources, in descending order of importance, were temperature, date, and precipitation, reflecting seasonal pollution discharge, industrial effluent, and first flush effect and combined sewer overflows, respectively. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC–MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC–MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Climate change induced precipitation changes can impact wastewater influent volumes and, in turn, the design and operation of wastewater treatment plants (WWTPs). As influent volumes approach or exceeds the design capacity of the WWTP, the likelihood of poor treatment or untreated discharges that result in environmental damage, increases. To date, there has been a lack of research analysing the impact changes in precipitation may have on influent volumes of WWTPs with combined sewerage systems. This study leverages data driven models of observed precipitation and influent volumes for 14 Irish WWTPs with combined sewerage systems, to project monthly wastewater influent volumes in 2041 – 2060 using Ireland’s most up-to-date high resolution multi-model RCM projections under RCPs 4.5 and 8.5. With changing monthly average daily precipitation, influent volumes for all the WWTPs demonstrated maximum decreases during summer months for both RCP 4.5 and RCP 8.5. Winter months showed increasing trends in influent volumes, particularly under RCP 8.5. The results indicate that with projected increase in high and very high precipitation days in Ireland (under CMIP5), the return periods for influent volumes that exceed the hydraulic capacity of 5 of the WWTPs will reduce. This work also presents a framework by which newer high-resolution downscaled climate models can be used to developed updated impact analysis of changing rainfall patterns on wastewater. It likely that wastewater treatment infrastructure will need to adapt to more intense precipitation to minimise the occurrence of combined sewer overflows.

Superhydrophobic cotton for addressing fatbergs through oily wastewater treatment

Fats, oils and grease (FOGs) deposits in sewers have recently become a significant problem, causing financial strain on water companies, damaging sewer lines, and exposing the environment to dirty water through sanitary sewer overflows. Despite the proactive use of grease traps/interceptors for physical oil-water separation, the issue of FOG deposits persists. This study proposes the use of adsorption-based oil-water separation, employing superhydrophobic cotton, as a new alternative method for removing FOGs. Durable superhydrophobic cotton was successfully prepared using a simple two-step sol-gel method, with octadecyltrimethoxysilane (ODTMS) as a modifying silane. The resulting cotton samples demonstrated remarkable superhydrophobicity, evidenced by water contact angle (WCA) above 154°. Additionally, it exhibited exceptional durability and stability when exposed to hot water, harsh acidic and alkaline solutions, as well as during a laundry test. Moreover, the cotton displayed excellent oil-water separation efficiency (> 98 %) and maintained consistent performance throughout 20 reuse cycles, highlighting its high reusability. This approach holds the potential to address the prevailing FOG deposit issues and contribute to more efficient and sustainable wastewater management practices.

Quantifying microbial risk from drinking water production process under changing climate and socio-economic conditions

Consumption of drinking water containing pathogenic microorganisms may pose serious health risks from waterborne diseases. Quantifying such risks is essential for guiding interventions and policy decisions. Quantitative microbial risk assessment (QMRA) is a very useful method to estimate the public's risk of infection from disease-causing microorganisms in water sources. QMRA of drinking water production process is limited worldwide and so far no such QMRA study has been conducted in Bangladesh. Moreover, climate and socio-economic changes may impact waterborne pathogens and associated health risks, but to what extent remains unclear, because comprehensive QMRA by taking into account combined impact of climatic and socio-economic factors has never been done worldwide so far. In this study, the Swedish QMRA tool was applied to evaluate public health risk from drinking water production process in Dhaka, Bangladesh as a case study. At first, current risk was quantified, and then the potential future risk was projected by taking into account climate and socio-economic factors. The results revealed that the annual infection risks at the current (2020s) baseline condition were below the acceptable risk threshold 10–4 infections per person per year (as proposed by several USEPA scientists) for all three pathogens Salmonella, norovirus and Giardia. However, after extreme events with sewer overflow and agricultural runoff, norovirus violates the acceptable risk thresholds, and the risks for Salmonella and Giardia are in borderline. The selected sustainable future scenario showed some improvement in terms of annual infection risks, while the uncontrolled scenario resulted in substantially higher infection risks both in the near and far future compared to the current scenarios. installment of a UV treatment step as an additional treatment barrier resulted in significant infection risk reduction. According to the sensitivity analysis results, socio-economic factors such as human population, livestock, and pathogen removal in wastewater were found to have greater influence on the infection risks, compared to climate change. The study can help policy makers and water managers to identify interventions to reduce the burden of disease on the population. The tool can be used to assess the health risk associated with drinking water production process in other areas of the world with similar characteristics.

Long-term impact of basin-wide wastewater management on faecal pollution levels along the entire Danube River

The Danube River is, at 2857 km, the second longest river in Europe and the most international river in the world with 19 countries in its catchment. Along the entire river, faecal pollution levels are mainly influenced by point-source emissions from treated and untreated sewage of municipal origin under base-flow conditions. In the past 2 decades, large investments in wastewater collection and treatment infrastructure were made in the European Union (EU) Member States located in the Danube River Basin (DRB). Overall, the share of population equivalents with appropriately biologically treated wastewater (without disinfection) has increased from 69% to more than 85%. The proportion of tertiary treatment has risen from 46 to 73%. In contrast, no comparable improvements of wastewater infrastructure took place in non-EU Member States in the middle and lower DRB, where a substantial amount of untreated wastewater is still directly discharged into the Danube River. Faecal pollution levels along the whole Danube River and the confluence sites of the most important tributaries were monitored during four Danube River expeditions, the Joint Danube Surveys (JDS). During all four surveys, the longitudinal patterns of faecal pollution were highly consistent, with generally lower levels in the upper section and elevated levels and major hotspots in the middle and lower sections of the Danube River. From 2001 to 2019, a significant decrease in faecal pollution levels could be observed in all three sections with average reduction rates between 72 and 86%. Despite this general improvement in microbiological water quality, no such decreases were observed for the highly polluted stretch in Central Serbia. Further improvements in microbiological water quality can be expected for the next decades on the basis of further investments in wastewater infrastructure in the EU Member States, in the middle and lower DRB. In the upper DRB, and due to the high compliance level as regards collection and treatment, improvements can further be achieved by upgrading sewage treatment plants with quaternary treatment steps as well as by preventing combined sewer overflows. The accession of the Western Balkan countries to the EU would also significantly boost investments in wastewater infrastructure and water quality improvements in the middle section of the Danube. Continuing whole-river expeditions such as the Joint Danube Surveys is highly recommended to monitor the developments in water quality in the future.

The effect of sewage source on HF183 risk-based threshold estimation for recreational water quality management

Host-associated fecal indicator measurements can be coupled with quantitative microbial risk assessment to develop risk-based thresholds for recreational use of potential sewage-contaminated waters. These assessments require information on the relative concentrations of indicators and pathogens in discharged sewage, typically based on data collected from wastewater treatment plant influent samples. However, most untreated sewage releases occur from within the collection system itself (i.e. compromised sewer laterals, compromised gravity and force mains, sanitary sewer overflows), where these relationships may differ. This study therefore analyzed the concentrations of a selected reference pathogen (norovirus) and fecal indicator (HF183) in sewage samples from upper and lower segments of gravity sewage collection systems, wastewater pumpstations, and the influent and effluent of treatment plants, to characterize variability in their relative concentrations. Norovirus detection rates were lower and more variable in upper collection system samples due to the smaller population represented; whereas, HF183 was routinely detected at all sites with higher concentrations in the collection system compared to treatment plant influent, resulting in variable comparative relationships across sample locations (types). Mean HF183:NoV ratios ranged from 1.0 × 105 for sewer lateral samples to 7 × 10° for force main samples. Results were used to develop risk-based thresholds for HF183 based on estimated recreational exposure to norovirus following a release from each potential sewage source, with higher thresholds for treatment facility influent compared to forced mains, or effluent. Consequently, this approach can allow for the rapid application of potential risk-based thresholds for recreational water quality applications based on different types of sewage discharge events.

Connecting blue-green infrastructure elements to reduce combined sewer overflows

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors—BGI types, implementation area, and runoff routing— and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.

Efficient disinfection of combined sewer overflows by ultraviolet/peracetic acid through intracellular oxidation with preserving cell integrity

Combined sewer overflows (CSOs) introduce microbial contaminants into the receiving water bodies, thereby posing risks to public health. This study systematically investigated the disinfection performance and mechanisms of the combined process of ultraviolet and peracetic acid (UV/PAA) in CSOs with selecting Escherichia coli (E. coli) as a target microbial contaminant. The UV/PAA process exhibited superior performance in inactivating E. coli in simulated CSOs compared with UV, PAA, and UV/H2O2 processes. Increasing the PAA dosage greatly enhanced the disinfection efficiency, while turbidity and organic matter hindered the inactivation performance. Singlet oxygen (1O2), hydroxyl (•OH) and organic radicals (RO•) contributed to the inactivation of E. coli, with •OH and RO• playing the prominent role. Variations of intracellular reactive oxygen species, malondialdehyde, enzymes activities, DNA contents and biochemical compositions of E. coli cells suggested that UV/PAA primarily caused oxidative damage to intracellular molecules rather than the damage to the lipids of the cell membrane, therefore effectively limited the regrowth of E. coli. Additionally, the UV/PAA process displayed an outstanding performance in disinfecting actual raw CSOs, achieving a 2.90-log inactivation of total bacteria after reaction for 4 min. These results highlighted the practical applicability and effectiveness of the UV/PAA process in the disinfection of CSOs.

Nitrate sources and transformations in a river-reservoir system: Response to extreme flooding and various land use

Nitrate (NO3−) pollution poses a global aquatic threat, promoting eutrophication and endangering human health. Large floods can swiftly drive significant nitrogen load into rivers and downstream water bodies, nevertheless, precisely quantifying NO3− sources in watersheds with complex land use patterns remains challenging. In June 2020, heavy and persistent rainfall triggered severe floods across southeastern China, causing Lake Qiandao, the largest reservoir in this region, to reach its highest recorded level. This study investigated NO3− sources and transformations within the Xin'an River-Lake Qiandao system, particularly focusing on the impact of the catastrophic summer flooding event. Through extensive sampling, we identified a distinct nitrogen concentration gradient: from the pristine Xin'an River source, characterized by 95 % forest cover and consistently low nitrogen levels for the initial 125 km, to a gradual rise as the river traversed human-impacted regions, culminating in peak concentrations within residential and agricultural areas. Isotopic analysis identified a general upward trend in nitrate stable isotope (δ15N–NO3–) associated with the expansion of agricultural and urban lands. Mean values of δ15N–NO3– gradually increased from headwaters and forest-dominant catchments (+2.88 ‰) to agricultural regions (+6.64 ‰) and residential areas (+7.10 ‰). A Bayesian modeling identified that during the flood season, soil erosion and chemical fertilizers were the primary contributors, collectively accounting for 74 % of NO3− sources in the Xin'an River. However, accumulated sewage in tributaries and the overflow of sewage from treatment plants, exacerbated by the flood, significantly impacted Lake Qiandao, particularly in densely populated urban areas, contributing 53 % of the total NO3− input. Additionally, NO3− levels and isotopic values in Lake Qiandao were influenced by a mixture of sources and nitrogen cycling processes, including nitrification and algae assimilation following the flood event. In contrast, during baseflow conditions, the contribution of domestic sewage and livestock wastewaters increased to 41 % in the upper river, while Lake Qiandao remained affected by non-point source pollution, with soil erosion and chemical fertilizers contributing 58 % to the total nitrogen pollution. This study sheds light on the complex dynamics of NO3− dynamics within river–reservoir systems, particularly in the context of extreme flooding, emphasizing the critical need for comprehensive water quality management strategies along the entire watershed.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Selective microbial attachment to LDPE plastic beads during passage through the wastewater network

Urban wastewater treatment plants (WWTP) represent key point-source discharges of microplastics (MP) into the environment, however, little is known about the microbial carrying capacity of plastics travelling through them. The purpose of this study was to quantify the number of cells that become associated with MP at different locations within a WWTP, and to assess differences in microbiome communities. We conducted a field experiment incubating low density polyethylene (LDPE) MP beads in WWTP influent and effluent, as well as tracking free floating beads during passage in wastewater from a large municipal hospital to an urban WWTP, where they were subsequently recovered. Using two cell counting methods - automated flow cytometric true absolute cell counts and indirect cell quantification via protein content based on a model E. coli cell - we quantified cell attachment to LDPE beads. LDPE associated counts ranged from 350 × 103 cells cm−2 after incubation in wastewater effluent, and 990 × 103 cells cm−2 after incubation in wastewater influent. 16S rRNA gene amplicon sequencing was used to determine the bacterial community structure of the plastic-associated microbiomes. Our results showed that distinct bacterial communities developed on the LDPE MP following exposure to each wastewater type. Influent (untreated) wastewater LDPE-associated microbiomes were dominated by Bacillota whereas the microbes that attached in wastewater effluent (tertiary treated) were dominated by Pseudomonadota. In conclusion, this study provides clear evidence that microplastics migrating through the sewer network and WWTP rapidly accumulate microbiomes with unique microbial community structures varying from sewage influent to effluent. These findings demonstrate the differential microbiological risk from MP associated with routine wastewater discharges to those released from intermittent combined sewer overflows (CSOs) during storm events.

Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and dificult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository:https://github.com/TeodorChiaburu/RIWWERTimeSeries.

Control of overflows in interception wells based on water quantity and quality in rainwater pipelines for reducing pollution to rivers

ABSTRACT The separate sewer system has led to severe river pollution because of domestic sewage infiltrations and initial rainfall pollution. Therefore, interception wells (IWs) are installed to divert sewage to the wastewater treatment plant but similarly contribute to overflow-induced river pollution on rainy days. The aim of the present study is to propose a control strategy for the regulation of IWs’ overflow based on the online monitoring data, control measures, and software simulation. The results indicate that during light rain, overflow from the IWs is minimal, requiring no control measures. However during heavy rainfall, the overflow of sewage leads to an increase in the river’s concentration of NH3-N from 0.25 to 3.04 mg/L. Finally, Simulations demonstrate a reduction in river NH3-N levels from 2.27 to 1.33 mg/L after implementation. The results indicated the substantial effectiveness of measures in mitigating the pollution originating from IWs’ overflows during rainfall events.

Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed

The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 to pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements, and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.

The Impact of Combined Sewer Overflows on Pharmaceutical and Illicit Drug Levels in New York/New Jersey Waterways.

Pharmaceuticals and drugs of abuse are organic micropollutants of emerging concern in both surface and groundwater worldwide. These compounds are considered to be pseudo-persistent because of their continuous release into water systems. The presence of these compounds in the environment at any concentration poses a potential risk to nontarget organisms. The main sources of these contaminants are wastewater treatment plants (WWTPs) and combined sewer overflows (CSOs). The primary goal of our study was to identify and quantify a panel of 28 commonly prescribed pharmaceuticals (mood-altering drugs, cardiovascular drugs, antacids, antibiotics) and high-prevalence drugs of abuse (cocaine, amphetamines, opioids, cannabis) in river water samples collected from 19 locations in the Hudson and East rivers in New York City. The second goal was to investigate the possible source (WWTP or CSOs) of these micropollutants. Samples were collected weekly from May to August 2021 (n = 224) and May to August 2022 (n = 232), and placed at -20 °C until analysis by liquid chromatography-tandem mass spectrometry. The most frequently detected analytes in 2021 were metoprolol (n = 206, 92%), benzoylecgonine (n = 151, 67%), atenolol (n = 142, 63%), and methamphetamine (n = 118, 53%), and in 2022 the most frequently detected were methamphetamine (n = 194, 84%), atenolol (n = 177, 76%), metoprolol (n = 177, 76%), and 2-ethylene-1,5-dimethyl-3,3-diphenylpyrrolidine (n = 159, 69%). Measured concentrations ranged from the limit of detection (0.50-5.00 ng/L) to 103 ng/L. More drugs and higher concentrations were detected in water contaminated by Enterococci (>60 most probably number) and after rainfall, indicating the influence of CSOs. The presence of drugs in samples with little to no Enterococci and after dry weather events indicates that WWTPs contribute to the presence of these substances in the river, probably due to a low removal rate. Environ Toxicol Chem 2024;43:1592-1603. © 2024 SETAC.

High dissolved oxygen regulation by side-stream supersaturated oxygenation impedes black-odor removal from rivers affected by combined sewer overflows

In rivers affected by combined sewer overflows (CSOs), achieving fine-grained management of dissolved oxygen (DO) regulation is critical for effective suppression of black odor after rainfall and reduction of river management costs. However, systematic investigations of the DO regulation mechanisms underlying the removal of key pollutants that cause black, odorous waters after CSOs are lacking. In this study, we investigated the effects of different DO regulation levels associated with short- and long-term side-stream supersaturated (SSS) oxygenation on the formation of carbon, nitrogen, sulfur, and other black odorous pollutants after CSOs. In addition, the effects of different DO levels regulated by SSS oxygenation were determined using the modified entropy weight method. The results showed that the number of CSOs, the rate of the black-odor phenomenon (12 h), and its extent increased. After different SSS oxygenation steps, the odorous degree of CSOs was reduced to varying extents. Additionally, for SSS oxygenation-mediated DO regulation to yield a DO ≥ 3.0 mg L−1, the oxygen-deficient reduction state of the interface between the overlying water and CSO-induced turbid water was effectively reversed. The removal rates of sulfide, Fe2+, and other black odorous substances varied; high levels of SSS oxygenation-mediated DO regulation were more conducive to the emergency improvement of redox conditions (182.67%) and removal of NH3–N (0.616–0.638 mg L−1·d−1). However, regulation of such high DO levels was not as effective for the long-term removal of black malodorous substances, leading to a rapid decline in pH (lowest decrease to 5.68) and denitrification efficiency in later stages. Additionally, evaluating the effectiveness of different SSS oxygenations provided further evidence that medium to high DO levels (4.5 mg L−1) achieved the highest comprehensive scores of 23.81 and had the optimal cost-effectiveness for long-term regulation. These results provide a scientific basis for improving the management of black odorous water in rivers after CSOs.

Categorization of precipitation for predicting combined sewer overflows. Application to the City of Montréal

Combined sewer systems are widespread in America and Europe. They often face limitations in transport or treatment capacity, especially during heavy rain events or thaw periods, resulting in combined sewer overflows (CSOs). Predictive modeling for CSOs is essential in a risk management context, and some studies have presented methods to categorize precipitations based on their potential to generate overflows. However, the precipitation classification is usually based on a few characteristics, and its predictive power is limited. The objective of this study is to present a simple yet powerful method to categorize precipitation for predicting CSO occurrences. A prediction model, based on an optimized classification tree, is proposed to predict CSO occurrences as a function of publicly accessible precipitation data. We fit the model on 9 overflow outlets in Montréal city from 2013 to 2019 and use this model to predict CSOs in 2020. The results showed a very good predictive power of overflows, with a prediction rate of 89%, a sensitivity rate of 83%, and a specificity rate of 91%. The method is also more accurate than the 5-category classification currently used by the City of Montréal. The proposed method could be easily applied to another region where CSO data are available, providing a simple and rigorous method for predicting CSOs across urban drainage networks containing many overflow outlets.

Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management

ABSTRACT The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management.