Wet Weather Flow Research Articles

Wastewater flooding risk assessment for coastal communities: Compound impacts of climate change and population growth

The study introduces a wastewater modeling framework that evaluates the compound impacts of intense rainfall, groundwater infiltration, sewer aging based roughness and population growth on wastewater systems. It integrates property and city-level flooding risk assessment and wastewater treatment plant (WWTP) capacity analysis into a single methodological approach. The framework applied to the coastal city of Charlottetown, whose population increased from 30,887 in 1981 to 42,440 in 2023, but the wastewater system did not expand accordingly, resulting in frequent sewer backups, street and basement flooding, as witnessed during the extreme wet weather event of September 2, 2021. Using PCSWMM (Personal Computer Storm Water Management Model), the study assessed that the city-wide wastewater flooding risk in Charlottetown, based on 2023 population data and historical IDF curves, affects 13.31% of the network during a 2-year storm and 18.38% during a 100-year storm. These risks increase to 14.5% and 22.6% under future IDF scenarios, reaching 17.89% and 26.4% by 2060 with projected population growth. The WWTP capacity is exceeded by 27.8% during peak wet weather flows from a 2-year storm and by 86.3% during a 100-year storm, based on 2023 population and historic IDFs. Under future IDF scenarios for 2060 population, exceedances rise to 103.6% and 169.1% respectively, for a 2-year and 100-year storm. Basement flooding risk affects 13.35% of basements during a 2-year storm and 18.31% during a 100-year storm, for 2023 population and historic IDFs. Future IDF scenarios indicate risk increasing to 17.77% and 25.80% by 2060 for a 2-year and 100-year storm respectively. The hydraulic modeling results indicate that GWI is not currently impacting the study area, nor is it expected to in near future, because the groundwater table is over 10 m deep, while wastewater pipes are no deeper than 6 m. The framework and study have significant social implications and benefits, including protecting public health, enhancing the resilience of urban infrastructure, and safeguarding the environment, ultimately improving the quality of life for residents in coastal communities like Charlottetown.

Assessing human-source microbial contamination of stormwater in the U.S

Stormwater capture and use (SCU) projects have the potential to provide a significant portion of municipal water demand. However, uncertainty about the degree of microbial contamination in stormwater and the required treatment is a barrier for implementation of SCU projects. Stormwater runoff could become contaminated with human fecal matter in areas with deteriorating infrastructure where raw wastewater exfiltrates from sewer networks to stormwater collection networks, homeless encampments exist, or sanitary sewer overflows (SSOs) occur. Estimation of human fecal contamination can inform selection of stormwater treatment targets. This study investigates stormwater microbial contamination originating from human fecal matter using observed detections and concentrations of human microbial source tracking (MST) markers and potentially human-infectious pathogens (PHIPs). First, a systematic review complied measurements of human MST markers in wet and dry weather stormwater flows and influent wastewater. In addition, measurements of viral pathogens (e.g., adenoviruses, norovirus GI+GII, and enteroviruses) and protozoan pathogens (e.g., Giardia lamblia and Cryptosporidium parvum) in wet weather flows and influent wastewater were assessed. Human MST marker and PHIP data were statistically analyzed and applied to estimate a human fecal contamination analog (HFCA) which is an estimate of the amount of human fecal matter based on relative concentrations of microbial contaminants in stormwater compared to municipal wastewater. Human MST-based HFCAs in wet and dry weather flows ranged from <10−7.0 to 10−1.5 (median = 10−4.5) and 10−12 to 10−2.6 (median = 10−7.0), respectively. PHIP-based HFCAs in wet weather flows ranged from ∼10−8 to 10−0.14. Estimates of human MST-based HFCAs are more reliable than PHIP-based HFCAs because the current PHIP datasets are generally limited by the number of data points, percent detection, variability observed within the statistical distributions, and the geographical span of sampling locations. The use of human MST-based HFCAs is recommended to guide the selection of stormwater treatment process trains that are protective of public health based on the intended end use. Application of HFCA 10−1 (i.e., sewage dilution 10−1) remains a reasonable conservative estimate of human fecal contamination in stormwater to inform selection of pathogen log reduction targets based on the data presently available.

Effect of rainfall-derived inflow and infiltration on dissolved organic matter in urban sanitary sewers using chemometric and machine learning approaches

This study investigated changes in dissolved organic matter (DOM) properties within urban sanitary sewers influenced by groundwater infiltration and excessive rainfall-derived inflow and infiltration (RDII). It employed optical indices and fluorescence excitation-emission matrix-parallel factor analysis (PARAFAC) coupled with self-organizing map (SOM) to compare DOM characteristics during wet weather flows (WWFs) and dry-weather flows (DWFs). Sampling sites impacted by RDII were identified based on flowrate. Optical indices and PARAFAC components (C1–C4) were used to differentiate DOM properties between DWFs and WWFs. In WWFs, E&lt;sub&gt;2&lt;/sub&gt;/E&lt;sub&gt;3&lt;/sub&gt; and S&lt;sub&gt;350-400&lt;/sub&gt; increased, while spectral ratio (S&lt;sub&gt;R&lt;/sub&gt;) decreased, indicating a shift towards smaller organic matters. Reduced fluorescence and humidification indices suggested the input of fresher/terrestrial organic matters. C3 and C4 exhibited significant distinctions, showing increased C3 and decreased C4 levels. The PARAFAC-SOM modeling further illustrated that water samples in the urban sewer system could be categorized based on the dominance of DOM properties. Principal component analysis revealed separation between DWF and WWF samples in principal component 1 (PC1), associated with molecular size. PC2 was linked to microbial activity in WWFs. Notably, DWF samples from the NY-11 site shifted to the positive side of the PC1 axis, while their corresponding WWF samples moved to the negative side.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects.

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Reparametrizing the Antecedent Moisture Model

Developing simple and accurate hydrologic models for wet-weather sanitary sewer flow has long been enigmatic for the engineering community. Hydrologic models suitable for surface water hydrology often perform poorly when simulating sanitary sewer hydrology when they do not account for antecedent moisture conditions and seasonality. The result is underpredicting peak flow and volume in wet conditions and overpredicting the same in dry conditions. The Antecedent Moisture Model is an empirically-calibrated method which models a rainfall capture fraction that varies by antecedent moisture and season, and transforms the captured rainfall into a flow hydrograph. The Antecedent Moisture Model has been particularly successful in modeling sanitary sewer infiltration, for which other methods perform poorly. This paper suggests a reparameterization of the original model which provides virtually identical results while improving interpretability and ease of use. The reparametrized model is designed to be time step independent and more physically relatable, intuitive, and scaleable than the original parameterization while remaining functionally equivalent to the original parameterization. The paper also presents two computational examples which demonstrate the application of the Antecedent Moisture Model formulas and math.

Impact of rainwater intrusion on wastewater characterization and performance of nutrient removal activated sludge process

AbstractBACKGROUNDThe paper investigated the negative impact of rainwater intrusion and mixing with sewage, on the operation of an existing nutrient removal activated sludge plant. It evaluated the magnitude of observed changes, both in terms of flow increase and modification of major parameters, together with the resulting impact on system performance. The study was carried out at a selected plant. The influent characteristics of the plant were reviewed for 2021 and 2022, both for the dry and rainy periods. Collected data were evaluated together with the experimental support derived from the selected plant.RESULTSStatistical assessment of the collected data revealed significant changes likely to affect the performance of the existing plant: Sewage flow was increased from 580 000 to 670 000 m3 day−1. Although chemical oxygen demand and total nitrogen were slightly reduced, a higher level of inorganic solids was observed. Assessment of nitrification–denitrification processes yielded an aerobic sludge age, θXA of 9 days and a total sludge age, θX of 15 days, with a VD:VT ratio of 0.4. Solids mass balance indicated a biomass level of 6100 mg L−1 with 47% of fixed solids, which was increased to 7300 mg L−1 with 52% fixed solids with the wet weather flow. These values denoted reactor volume limitation for sustainable/safe operation.CONCLUSIONAside from the impact of substantial treatment cost increase resulting from the incremental flow of 13.7 million m3 year−1 during wet weather periods, control and decrease of inorganic solids emerged as the key issue to safeguard system operation. The study also indicated clues for diverting runoff mixing as a tangible remedial action. © 2023 Society of Chemical Industry.

P2O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants

Wastewater treatment plants (WWTPs) are critical infrastructures responsible for processing wastewater before discharging effluent to rivers and other potential uses. WWTPs use large, connected deep tunnels for storing sanitary and wet-weather flows for treatment. However, wastewater in those systems cannot exceed safe tunnel levels in order to prevent overflows of untreated wastewater into the environment. Further, WWTPs are among the 16 national lifeline infrastructure sectors in which the utilization of sensor technology has increased, making the sectors vulnerable to all forms of cyber threats. Considering these challenges, the work presented in this manuscript uncovers the role of AI at WWTPs by focusing on two problems: tunnel water-level prediction and detection of security threats. This is done by proposing an AI framework: P2O (prediction, protection, and optimization). The prediction module forecasts the tunnel water level using deep-learning models based on the current wastewater flow in the tunnel and other inputs from the sensors and gauges. The protection module focuses on classifying the intentionality of an anomaly, i.e., whether an attack is adversarial in nature or merely an outlier, using recurrent neural network models. Last, the optimization module aims to provide actionable recommendations to pump operators using a genetic algorithm. The experimental results of P2O indicate that the prediction module can predict the tunnel water level with 85% accuracy, and the protection module can detect about 97% of intentional attacks on WWTPs. AI models within P2O are evaluated; the experimental results are presented and discussed.Practical ApplicationsThis manuscript presents P2O, which is a novel AI framework that can predict about 85% of wastewater overflow incidences and about 95% of intentional cyberattacks on a WWTP, as indicated in the experiments. The deployment of P2O at a WWTP is essential, especially considering the adverse effects of overflowing wastewater on the environment (i.e., rivers and other water bodies). Moreover, cyberattacks on WWTPs can be subtle, making them challenging to detect; on average, most of them are noticed within one week to one month after the attack. This makes national infrastructure vulnerable to external and internal threats, influencing the well-being of water bodies and overall national security. P2O provides a real-time monitoring interface and can recommend optimal actions in different scenarios (i.e., outliers) for pump operators and process engineers at WWTPs.

Transformation of organic carbon through medium pressure (polychromatic) UV disinfection of wastewater effluent during wet weather events

An observed decrease in total organic carbon (TOC) and dissolved organic carbon (DOC) concentrations following wastewater disinfection with medium pressure (MP, polychromatic) ultraviolet (UV) irradiation during wet weather flows is investigated. When antecedent rainfall in the previous 7-days was >2 in (5 cm), TOC and DOC concentrations decreased dramatically following MP-UV disinfection. Organic carbon surrogate measurements of biological oxygen demand (BOD), TOC, DOC, turbidity, UVA – 254 nm, SUVA (specific UVA), scanning UV–Visible spectra (200–600 nm), fluorescence excitation-emission matrix (EEM) spectra, and light scattering data are presented for wastewater resource recovery facility (WRRF) influent, secondary effluent (pre-UV-disinfection), and MP-UV-disinfected (final effluent) samples. TOC and DOC in wastewater influent and secondary effluent (i.e., pre-UV disinfection) correlated with antecedent rainfall conditions. The percent TOC and DOC removal through secondary treatment (i.e., from influent to effluent pre-UV) and the percent TOC and DOC removal through MP-UV disinfection (i.e., from effluent pre-UV to effluent post-UV) were compared and the latter approached 90 % through MP-UV disinfection during high antecedent rainfall conditions. Spectroscopy (UV, visible, or fluorescence) was performed on samples after filtration through 0.45 μm filters, i.e., the operationally defined DOC fraction of aquatic carbon. Scanning UV–visible spectra indicated transformation of an unidentified wastewater component into light-scattering entities regardless of antecedent rainfall conditions. The types of organic carbon (diagenetic, biogenic, or anthropogenic) and the significance of wet weather are discussed. An organic carbon contribution via infiltration and inflow was attributed as a source-of-interest in this research.

Development of a Water Quality Management Model for Dry Rivers in Arid Regions: Application on Wadi Rumah, Saudi Arabia

In dry rivers, urban wastewater discharge forms a pond in the dry period, and surface runoff shapes a longer pond in the wet period. A water quality management model developed for dry rivers is applied on Wadi Rumah in Saudi Arabia. The sub-basin was delineated from 30-m × 30-m spatial resolution digital elevation model. Hydrologic loss, runoff rates, and time of concentration were determined using the soil conservation service curve number, soil conservation service unit hydrograph methods, and Kirpich formula. Hydrologic Engineering Center Hydrologic Modeling System estimated the wet weather flows for 10 low-to-medium rainfall events. A steady-state hydrodynamic model including river flows, effluent discharge, and evaporation and infiltration losses estimated the hydraulic retention time for the explicit case of ponding conditions of dry rivers. Mass balance and first-order kinetics modeled the physical (total dissolved solid and total suspended solid), chemical (biochemical oxygen demand, dissolved oxygen, chemical oxygen demand, NH3, PO4, and Cl2), and microbiological (total coliform and fecal coliform) parameters. The hydrodynamic model and biokinetic rate coefficients were calibrated with satellite images, reported literature, and field measurements. A water quality index compared the aggregated water quality against the standards for 4 intended uses. The study yields “very good” water quality for restricted and unrestricted irrigation for the dry period, “fair” for livestock drinking, and “marginal” for fishery and recreation for the dry period, while water quality remained “marginal” for all the uses in the wet period. Water quality management implies regular water quality monitoring, continuous improvement of treatment facilities, and flood plain management initiatives for the long-term sustainability of dry rivers in arid regions.

A decade of monitoring micropollutants in urban wet-weather flows: What did we learn?

Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.

Examining How a Smart Rainwater Harvesting System Connected to a Green Roof Can Improve Urban Stormwater Management

This study quantified how a smart rainwater harvesting cistern that collected stormwater runoff from a green roof reduced stormwater flow into a combined sewer system (CSS) during wet-weather flow. The studied smart rainwater harvesting cistern collected runoff from a green roof located in Bronx, New York City; it used the Continuous Monitoring and Adaptive Control (CMAC) smart sensor provided by OptiRTC, Inc., to regulate the water flow from the cistern. The cistern collected stormwater runoff from the roof, usually draining completely after 24 h of dry weather. However, the smart sensor used weather forecasting data, and if additional rainfall was predicted immediately following another storm, the cistern only drained a specific amount, calibrated to mitigate the CSO. Five years of data from the cistern system were used to understand the role of the cistern’s smart sensor in reducing stormwater flow into the CSS during storms. The study results demonstrate that connecting the smart cistern system to the green-roof maximized stormwater collection (compared with the green roof alone) for storm sizes between 2 mm and 25 mm and for antecedent dry-weather periods greater than 2 days. The total of 65.2% of rainfall retained over the monitoring period by the green roof alone increased to 75.6% when considering the total stormwater retained and detained together by the green roof and cistern, thus yielding a 10% improvement. The study results also demonstrate that the smart sensor’s use of weather forecasting data failed to improve system performance.

Modeling the impacts of climate change and flooding on sanitary sewage system using SWMM simulation: A case study

Climate change has several impacts on rainfall patterns, increasing surface runoff, leading to sewer flooding, and receiving water contamination. This study analyzed the effects of climate change and flooding on the Al-Shuhada Quarter sewer system in Samawah City, Iraq, using the Storm Water Management Model (SWMM). The sanitary system's performance was evaluated before and after the leaking of stormwater at different return periods of two, five, ten, and twenty-five years. The SWMM model was manually calibrated by changing the influential sub-catchment characteristic using modeled and measured discharges. NMSE (0.123) and R (0.86) values were within the limits and proved that the model is valid. Evaluation results indicated that the system operated very well at dry weather flow but flooded at some locations during the leaking of stormwater at wet weather flow. As a response to climate change, changing return period from 2 to 25 years showed an increase in sanitary sewage system flooding volume and percentage of flooding manholes from 2504 m3 to 10% to 8868 m3 and 24%, respectively. By contrast, proposing the addition of extra two sewer lines has highly reduced the flooding events. To conclude, SWMM was a very powerful tool in predicting flooding volumes and locations at different rainfall intensities, and it assessed the capability of the proposed solutions to minimize flooding.

Stream restoration reduces elevated phosphorus levels in an urban watershed

Lake Erie is located in North America with its southern shoreline bordering Ohio, as well as parts of Michigan, Pennsylvania and New York. A one-year study investigated nutrient pollution in a Central Basin Lake Erie headwater tributary watershed near Cleveland Ohio. Results suggest that the 2019 annual phosphorus load entering Lake Erie from the Euclid Creek watershed was approximately 22,600 pounds, over four times the watershed’s target of 5545 pounds. Multiple upstream sites were the major nonpoint sources of nutrient pollution. Four sampling sites averaged phosphorus levels 12–15 times the watershed target of 0.05 mg/L. Leaking sanitary sewers in residential areas were believed to contribute more pollution than the three golf courses and the regional airport located within the watershed. The presence of the Cleveland Metroparks along the riparian corridor in the Main Branch significantly reduced (p < 0.05) nutrients during dry weather. In particular, stream restoration within the Acacia Reservation lowered phosphorus levels by an average 0.31 mg/L. Spring storms contributed the most to the annual phosphorus load (∼47 %). To mitigate nutrient pollution in urban watersheds, best management practices (BMPs) would require a two-fold strategy. During dry weather, BMPs should incorporate stream restoration and forested riparian buffers where feasible. Green infrastructure should be designed to mitigate the impact of wet-weather storm-induced flows.

Enhanced primary treatment during wet weather flow using ferrate as a coagulant, coagulant aid and disinfectant

This study evaluated the dual-function of ferrate as a coagulant and disinfectant for chemically-enhanced primary treatment during wet weather flow (WWF). For the first time, ferrate was thoroughly examined as a coagulant aid with aluminum sulfate (alum) to minimize the organic and inorganic contents along with microbial level during WWF. Ferrate as a coagulant was evaluated based on a two-level factorial design. At an optimized condition, a ferrate dose of 0.5 mg/L Fe with a cationic polymer (1.25 mg/L) removed 83% of turbidity, 87% of total suspended solids (TSS), 70% of chemical oxygen demand (COD), and 23% of ortho-phosphate (OP). Linear models were developed and used to adequately predict the removals. Ferrate as a coagulant aid added with alum showed better removal of TSS while no improvement was observed in the removals of turbidity and COD. The disinfection capacity of ferrate was evaluated at different dosing points when it was used as a coagulant, coagulant aid and as post dosed as a disinfectant. In particular, ferrate dose of 8 mg/L Fe removed only 2 logs of E. coli when it was used as a coagulant compared to more than 3-log removal of E. coli when ferrate was used as a coagulant aid and as a disinfectant. At optimal ferrate dose of 10 mg/L Fe as a coagulant aid with 6 mg/L Al achieved the target levels of turbidity (<8 NTU), TSS (<25 mg/L), and ferrate-induced iron particles (<0.6 mg/L) along with 5-log removal of E. coli within 31 min. This study suggested that using ferrate as a coagulant aid/disinfectant might be considered an effective approach for treating the wastewater during WWF.

Modelling dry-weather temperature profiles in urban stormwater management ponds

One of the unintended consequences of urban stormwater quality management wet ponds is the thermal enrichment of the shallow pond water during the summer months. The outflow from thermally enriched ponds can degrade the cold and cool water aquatic habitat in urban streams. An accurate model of the pond temperature profile is needed to assess the pond effluent thermal load. However, most models developed for this purpose are process-based and not simple to use, requiring a lengthy monitoring dataset to calibrate. This paper introduces a new design/assessment tool to predict the hourly water temperatures in these ponds at different depths during the summer season dry-weather warming periods and the diurnal cycles. We compiled monitoring data from five ponds in the Greater Toronto Area from 2013 − 2016 to evaluate the ponds' thermal impact on the receiving cold water streams. Using these datasets, we developed a new equation that allows for separating the active upper portion and stable lower portion of the pond. This ensures the focus is on the active upper part, where the majority of the heat transfer within the pond occurs and exhibits diurnal temperature fluctuations. We also used machine-learning tools to develop accurate equations for the bottom temperature and surface temperature, as well as, key equation parameters that characterize the thermal profiles. The developed thermal profile equation allows for the simple determination of other key thermal metrics such as average temperature, energy stored, thermocline location, which may be used as inputs to model wet weather flow conditions and pond outflow temperature. The results show that the proposed machine-learning model is acceptably accurate and capable, according to the low mean absolute percentage error (MAPE) and coefficient of determination (R2) values of 5% and 0.965, respectively, for temperature prediction for all depths. This work provides a building block for the overall objective to develop a new, easy to use, wet weather explicit equation for predicting the outlet temperature from stormwater management wet ponds since it establishes the initial thermal condition before the storm hits.

Coagulation-flocculation followed by catalytic ozonation processes for enhanced primary treatment during wet weather conditions

Combined sewer overflows (CSO), generated during the wet weather flow from the combination of the inflow and stormwater runoff in sewer system, result in an overflow of untreated wastewater from sewer system, which might ultimately contain different micropollutants (MPs). In this study, a coagulation-flocculation-sedimentation (CFS) pretreated CSO spiked with MPs was treated by catalytic ozonation using carbon, iron, and peroxide-based catalysts. The catalysts were characterized and their activity on MPs removal was studied at two different ozone (O3) doses (5 and 10 mg L−1). The effect of the treatment on the spiked CSO effluent was also assessed from the acute toxicity of the effluent using Microtox®, Yeast, and Macrophage cell-line toxicity assay tests. All the carbon-based catalysts showed large surface area, which was strongly influenced by the activation technique in the preparation of the catalysts. The CFS treatment strongly reduced the turbidity (≥60%) but had marginal effect on the UV254, dissolved organic carbon (DOC), and pH. Sludge Based Carbon (SBC) showed strong adsorption capacity (≥60% removal efficiency) for all MPs studied compared to other carbon and iron-based catalysts. Ozonation alone was effective for the degradation of easily oxidizable MPs (sulfamethoxazole, mecoprop, and 2,4-dichlorophenoxyl acetic acid), achieving more than 80% degradation efficiency at 10 mg L−1 of ozone, but not effective for atrazine (≤60% degradation efficiency) at similar O3 dose. Catalytic ozonation (at 10 mg L−1 O3 dose) improved the degradation of the MPs at low catalyst dosage but higher dosage strongly inhibited their degradation. In all cases, the effluents showed negligible acute toxicity, indicating the suitability of the process for the treatment of CSO.

Current and future approaches to wet weather flow management: A review.

Sewers can become hydraulically overburdened during high-intensity precipitation resulting in untreated water entering receiving streams. Combined (CSOs) and sanitary sewer overflows (SSOs) cause adverse public health and environmental impacts as well as management challenges for many wastewater utilities. This novel review presents information regarding wet weather flow regulation, impacts, and current management methods, and offers ideas for future approaches in the United States. Currently, storage followed by conventional municipal water reclamation facility treatment after precipitation events is often employed. Stand-alone alternative technologies include high-rate solids removal, rapid disinfection, filtration, and green infrastructure. However, most current stand-alone approaches do not address soluble BOD5 or emerging contaminants in stormwater and wastewater. As the needs for wet weather flow management change, future approaches should include a goal of zero overflows and achieve effluent quality as good as or better than conventional treatment. To help achieve zero overflows and complete treatment, the "peaker facility" concept is proposed. The peaker facility often remains idle but treats excess flow when needed. Considering the challenges of remaining idle for long periods, starting up quickly, and handling high flows, chemical oxidation may be an applicable peaker facility component. However, more research and development are needed to determine best practices. PRACTITIONER POINTS: Combined (CSO) and sanitary sewer overflows (SSOs) pose both environmental and public health risks as untreated water is discharged into lakes and rivers during high-intensity rain events. Current stand-alone approaches for managing or treating CSOs focus on particulate BOD/COD and solids removal, and do not typically address soluble BOD or emerging contaminants in stormwater and wastewater (including pathogens). New wet weather policies and regulations encourage more holistic approaches by wastewater utilities, and future approaches should include a zero-overflow goal for all CSOs and SSOs. To help achieve zero overflows, the concept of the "peaker facility" is proposed. Chemical oxidation may be an applicable component of peaker facilities for its short detention time and ability to remove, oxidize, or inactive water impairment-causing contaminants.

Advancing the treatment of primary influent and effluent wastewater during wet weather flow by single versus powdered activated carbon-catalyzed ozonation for the removal of trace organic compounds

For the first time, single and PAC-catalyzed ozonation were explored for the wastewater treatment during wet weather flow in a prompt and efficient process. The effect of varying the ozone (O3) specific dose on the removal of micropollutants (MPs) was first investigated with a mixture of pharmaceuticals, herbicides and perfluorinated compounds in clean water. Most MPs showed higher affinity towards catalytic ozonation. Carbamazepine and Atrazine were found to be good surrogates for fast and slow reacting compounds, respectively. Applying single or PAC-catalyzed ozonation for 1 min only after coagulation was more efficient than applying them simultaneously. PAC-catalyzed ozonation was more efficient for the removal of organics and O3-resistant MPs. Both single and PAC-catalyzed ozonation achieved 4 log removal of E. coli, reduced the acute and genetic toxicity, and estrogenic activity of the wastewater. A detailed cost analysis revealed that applying single ozonation after coagulation costs between 0.06 and 0.32 $/m3 while applying PAC-catalyzed ozonation costs between 0.32 and 0.63 $/m3 for a flow rate between 100 and 600 MLD. Through a comprehensive performance assessment, PAC-catalyzed ozonation was deemed superior with one drawback related to the disposal of PAC.

Urban Stormwater and Sewerage Modelling: An Approach for Peak Runoff and Volume Assessment

In this study, an integrated approach for runoff estimation using the storm water management model (SWMM) was applied to undertake a stormwater and sewerage modelling in urban setting. The main objectives of the research and this manuscript include overload detection of sewer systems during extreme rainfall events with SWMM and to model and predict the relationship between precipitation parameters and overflooding of sewer collection system that includes emergency ponds to relieve flow from pump stations. The study takes into account monitored concurrent extreme rainfall event data and peak wet weather flows observed at outfall of collection system entering a wastewater treatment plant draining the urban centre. In the study SWMM was modified and adapted for the Tati and Ntshe confluence rivers draining the urban centre of Francistown in Northern Botswana. Landuse, soil, geological, drainage and sewerage network and imperviousness data sets were acquired and developed in GIS database. The runoff coefficient is found to range between 0.12 and 0.24 in the studied catchments. The calibrated model was able to predict the observed outputs with reasonable accuracy for calibration datasets of two peak flood events of 2016-Jan 12 and 2017-Feb 16 and verification flood events of 2016-Feb 05 and 2017-Feb 26. For six watersheds that drain the study area considered with a seventh entire collection system catchment area, we have evaluated the model performance using different criteria. We have found that correlation coefficients range from 0.539 to 0.813 and NSE ranges from 40.9% to 89.0%, and RSR ranges from 0.330 to 0.812 for the calibration datasets. Whereas, for the verification dataset, the correlation coefficients range from 0.539 to 0.813 and NSE values range from 40.9% to 89.0%, and RSR values range from 0.330 to 0.812. Using the criteria adopted, the SWMM-simulated runoff values are in acceptable agreement with the observed hydrographs.

Performance and operational strategy of simultaneous nitrification, denitrification, and phosphorus removal system under the condition of low organic loading rate in wet weather

A pilot-scale aerobic granular sequencing batch reactor (SBR) with domestic wastewater was operated to evaluate the effects of the low organic loading rate (OLR) due to wet weather flow conditions on simultaneous nitrification, denitrification, and phosphorus removal (SNDPR). As the OLR decreased from 0.85 to 0.43 kg COD m−3 d−1, the total nitrogen (TN) and total phosphorus (TP) removal efficiencies decreased from 84.0% and 94.1% to 51.3% and 73.8%, respectively, the sludge volume index (SVI) increased from 42.3 to 85.5 mL g−1, and the average granular size decreased from 1022 to 742 μm; however, no sludge disintegration and biomass loss were observed. The poor nutrient removal efficiencies and settling ability were due to the shrinking anoxic zone and substrate scarcity inside the granules, wherein the activity decay of ammonia-oxidizing bacteria and overgrowth of filamentous bacteria played an important role. Alternating the aeration intensity was effective in enhancing nitrogen removal and sludge settling by improving the anoxic activity in granules and inhibiting the proliferation of filamentous bacteria. Returning 20% of sludge from the end of one anaerobic stage to the beginning of the next anaerobic stage (midway sludge return) was beneficial for phosphorus removal as it improved phosphorus storage by phosphorus-accumulating bacteria. A smaller granular size with stronger stability and better nutrient removal performance was the new steady state of the SNDPR system under wet-weather flow conditions.