Sanitary Sewer Overflows Research Articles

Sanitary sewer overflows, household sewage backups, and antibiotic-resistant bacteria: the new frontier of environmental health risks and disasters

Abstract Untreated sewage discharges leading to environmental contamination are increasingly common in communities across the globe. The cause of these discharges range from sewer lines in disrepair, blockages, and in the era of more extreme wet weather events, the infiltration of stormwater into the system during heavy downpours. Regardless of the driver of these events, the aftermath results in raw sewage spilling into local waterways, city streets, and commercial and residential structures. Historical research in public health has thoroughly documented the connection between exposure to untreated sewage and waterborne disease. Recent research has detected antibiotic-resistant bacteria at wastewater treatment facilities at a time when deaths by antibiotic-resistant infections are on the rise. However, no research has explored the exposure pathways of antibiotic resistant bacteria during sanitary sewer overflows and household-level sewage backups. In this commentary, we aim to introduce this new frontier of environmental health risks and disasters. To do this we describe the history of modern sanitation and sewer infrastructure with a particular focus on wastewater infrastructure in the U.S. We also explore emerging risks and current methods for identifying antibiotic-resistant bacteria in the environment. We end with future directions for interdisciplinary scholarship at the nexus of urban planning, engineering, and public health by introducing the Water Emergency Team (WET) Project. WET is a community-based multi-method effort to identify environmental risks in the aftermath of household backups through 1) residential surveys, 2) indoor visual inspections, 3) environmental sampling, and 4) laboratory processing and reporting. Our hope is that by introducing this comprehensive approach to environmental risks analysis, other scholars will join us in this effort and ultimately towards addressing this grand challenge of our time.

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.

Ecotoxicological assessment of sanitary sewer overflows and rainfall dynamics offers insights into conditions for potential adverse ecological outcomes

Sewer overflows are an environmental concern due to their potential to introduce contaminants that can adversely affect downstream aquatic ecosystems. As these overflows can occur during rainfall events, the influence of rainwater ingress from inflow and infiltration on raw untreated wastewater (influent) within the sewer is a critical factor influencing the dilution and toxicity of the contaminants. The Vineyard sewer carrier in the greater city of Sydney, Australia, was selected for an ecotoxicological investigation of a sanitary (separate from stormwater) sewerage system and a wet-weather overflow (WWO). Three influent samples were collected representing dry-weather (DW), intermediate wet-weather (IWW) and wet-weather (WW). In addition, a receiving water sample was also collected downstream in Vineyard Creek (WW-DS) coinciding with a WWO.We employed direct toxicity assessment (DTA) and toxicity identification evaluation (TIE) approaches to gain comprehensive insights into the nature and magnitude of the impact on influent from rainwater ingress into the sewer. Three standard ecotoxicological model species, a microalga, Chlorella vulgaris, the water flea, Ceriodaphnia dubia and the midge larva, Chironomus tepperi were used for both acute and chronic tests. The study revealed variable toxicity responses, with the sample of influent collected in wet-weather displaying lower toxicity compared to the dry-weather sample of influent. Ammonia, and metals, were identified in dry weather as contributors to the observed toxicity, however, this risk was alleviated through rainwater ingress in wet-weather with further dilution within the receiving water. Based on toxicity data, dilutions of influent to minimise effects on C. vulgaris and C. dubia ranged from 1 in 12 in DW to 1 in 2.8 in WW, and further diminished in the receiving water to 1 in 1.8. The successful application of ecotoxicological approaches enabled the assessment of cumulative effects of contaminants in influent, offering valuable insights into the sanitary sewer system under rainwater ingress.

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.

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.

Real-Time sanitary sewer blockage detection system using IoT

Sewer blockages and overflows have significant economic and environmental repercussions on communities. Thus, it is crucial to detect and remove sewer blockages prior to the occurrence of overflows. With the improvement in mobile networks and the development of high-quality and low-power sensors and loggers, wastewater network operators can now adopt monitoring devices enabled by the “Internet of Things” (IoT) technology for real-time monitoring. To this end, this paper studies the current state-of-the-art in sewer blockage management and introduces a novel methodology to monitor sanitary sewer blockages to prevent sanitary sewer overflows (SSO) with the least human interaction. The proposed system incorporates low-power level sensors and 4G telemetry for real-time monitoring of the manhole's sewage level to detect sewer blockages. The blockage detection methodology encompasses a set of decision rules and time series analysis to identify blockage events. The final blockage detection model offers a versatile capability to be implemented on sanitary sewer networks of different types with minimum computational costs.

Development of an Optimal RTK Calculation Program Using Genetic Algorithm

Rising levels of underground water and inappropriate pipe alignment during rainy weather have led to a rise in rainfall-derived infiltration and inflow (RDII), increasing incidences of separate sewer overflows (SSOs) and reducing the efficiency of sewage treatment systems. RTK analysis based on measured RDII data of the target area through computer modeling is essential when maintaining sewer pipes in order to address this RDII problem. In the Sanitary Sewer Overflow Analysis and Planning (SSOAP) program used for RTK analysis, calculating RTK using a trial and error method is a bit challenging. Accordingly, this study introduces a program to calculate the optimal RTK at the measurement point using a genetic algorithm. In ROP, accuracy was added to the RDII prediction mechanism by incorporating error rates based on time intervals into the evaluation factors of SSOAP, namely rainfall inflow rate and peak flow error rate. The error rate for the SSOAP program and ROP was validated as 2.66%.

The sanitary sewer unit hydrograph model: A comprehensive tool for wastewater flow modeling and inflow-infiltration simulations

Sanitary sewer systems are critical urban water infrastructure that protect both human and environmental health. Their design, operation, and monitoring require novel modeling techniques that capture dominant processes while allowing for computationally efficient simulations. Open water flow in sewers and rivers are intrinsically similar processes. With this in mind, we formulated a new parsimonious model inspired by the Width Function Instantaneous Unit Hydrograph (WFIUH) approach, widely used to predict rainfall-runoff relationships in watersheds, to a sanitary sewer system consisting of nearly 10,000 sewer conduits and 120,000 residential and commercial sewage connections in Northern Virginia, U.S.A. Model predictions for the three primary components of sanitary flow, including Base Wastewater Flow (BWF), Groundwater Infiltration (GWI), and Runoff Derived Infiltration and Inflow (RDII), compare favorably with the more computationally demanding industry-standard Storm Water Management Model (SWMM). This novel application of the WFIUH modeling framework should support a number of critical water quality endpoints, including (i) sewer hydrograph separation through the quantification of BWF, GWI, and RDII outflows, (ii) evaluation of the impact of new urban developments on sewage flow dynamics, (iii) monitoring and mitigation of sanitary sewer overflows, and (iv) design and interpretation of wastewater surveillance studies.

Using multilinear regressions developed from excitation-emission matrices to estimate the wastewater content in urban streams impacted by sanitary sewer leaks and overflows

Failing sewer infrastructure introduces unknown quantities of raw wastewater into urban streams, raising human and ecological health concerns. To address this problem, we developed multilinear regressions that relate fluorescent dissolved organic matter to wastewater content. The models were constructed with the area-normalized regional volumes of excitation-emission matrices measured for mixtures of deionized water, surface water from a wastewater-impacted stream, wastewater from a sanitary sewer adjacent to the stream, and Suwannee River natural organic matter. The best performing multilinear regression had a standard error of 0.55 % wastewater. A matrix-matched calibration was used to internally validate the approach and confirm the wastewater content of select samples. The multilinear model was externally validated through (i) comparison to concentrations of contaminants of emerging concern in surface water and wastewater and (ii) extension to samples from previous campaigns that employed alternative wastewater indicators. Using the validated model, we estimated an average wastewater content of 2.4 ± 4.0 % in 165 samples collected from 14 locations in the Gwynns Falls watershed (USA) between April 2019 and April 2023. The maximum wastewater content was 35 % at a site where sanitary sewer leaks and overflows have been previously documented. The reported approach represents a cost-effective and scalable technique to estimate wastewater content in urban streams through analysis of fluorescent dissolved organic matter.

Tracking contaminants of concern in wet-weather sanitary sewer overflows

Four representative sites in the greater city of Sydney, Australia, were selected for a study of the wet-weather overflow of sanitary (separate to stormwater) sewerage systems. Water samples were collected by autosamplers from up to eight wet weather overflow events over 16 months and from companion receiving water sites. The objective was to identify the risks posed by sewage contaminants to aquatic biota in the receiving waters, to aid in prioritising management actions. Twelve organic contaminants were identified in influents across the four sites under rainfall ingress diluted conditions, with measurements showing that the highest concentrations were restricted to the anti-inflammatory acetaminophen and the diabetes medication metformin. Lesser contaminants included theobromine, ibuprofen, sucralose, and three benzotriazoles (mainly 1-H benzotriazole). An assessment of the toxicity of the identified organic chemicals indicated that none appeared to pose concerns for ecosystem health before wet-weather ingress dilution, and this was even less likely after dilution in the receiving waters. Metal concentrations were low; however, ammonia concentrations in the influent did pose a risk to ecosystem health, although receiving water dilution diminished this risk at four of the five receiving water locations studied.

Impacts of Extreme Rainfalls on Sewer Overflows and WSUD-Based Mitigation Strategies: A Review

Extreme rainfall events cause an increase in the flow into aging sewer networks, which can lead to Sanitary Sewer Overflows (SSOs). This literature review presents a complete assessment of the application of Water Sensitive Urban Design (WSUD) approaches as mitigation strategies for reducing rainfall-induced SSOs. The review highlights the various WSUD techniques identified in past studies for reducing sewer overflows. In these studies, it was identified that permeable pavements, green roofs, raingardens/bio-retention cells and rainwater tanks were the most popular WSUD strategies that have been extensively used in the past for the mitigation of sewer overflows. WSUD or “green” approaches also have enormous environmental, social and economic benefits when compared to the conventional “gray” approaches for sewer overflow mitigation. However, there have been limited studies conducted in the past that highlight and quantify the benefits of WSUD approaches for sewer overflow mitigation, particularly when such strategies are applied at a large scale (e.g., city scale). This review has identified the modelling software, SWMM, to be the most widely applied tool that has been used in the literature for WSUD modelling. It was also identified that with climate change-induced extreme rainfall events on the increase, WSUD-based “green” strategies alone may not be enough for the mitigation of sewer overflows. A suitable sewer overflow mitigation strategy could be green or a hybrid green-gray strategy, which would need to be identified based on a detailed context specific analysis.

Spatiotemporal analysis of fluorescent dissolved organic matter to identify the impacts of failing sewer infrastructure in urban streams

Failing sewer infrastructure introduces raw wastewater into streams. We used fluorescence excitation-emission matrix (EEM) spectroscopy and parallel factor analysis (PARAFAC) to track hotspots of raw wastewater in low- and medium-order urban streams that do not receive wastewater effluent but are impacted by sanitary sewer overflows, septic systems, and sewer exfiltration. After analyzing 296 surface water samples from 27 sites in two watersheds over a one-year period, we proposed that the (i) area-normalized ratio of soluble microbial product-like to humic acid-like fluorescence (R4/R5 ≥ 0.85) and (ii) ratio of EEM-PARAFAC components with tryptophan-like and fulvic acid-like fluorescence (C4/C3 ≥ 1.45) could distinguish when and where untreated wastewater is introduced to urban streams. The proposed ratios were validated by co-detection of contaminants of emerging concern, such as sucralose, antibiotics, and UV filters, at concentrations as high as 1354, 108, and 212 ng L−1, respectively. Based on the aggregate data, we identified three sites in rural/suburban areas that were impacted by septic systems and ten sites in urban sections affected by sanitary sewer overflows and/or sewer exfiltration. Moreover, the ratiometric C4/C3 and R4/R5 parameters were immune to dilution effects caused by rain events. Impacts on upstream sites were mostly identified in spring and early summer, but urban hotspots occurred in almost every month. These findings confirmed the potential for EEM-PARAFAC-based wastewater indicators as a quick, easy, cost-effective, and scalable technique to screen for failing sewer infrastructure in low-order streams.

Effects of watershed-scale green infrastructure retrofits on urban stormwater quality: A paired watershed study to quantify nutrient and sediment removal

Urban stormwater represents a substantial source of nutrients and sediment to aquatic ecosystems. Green infrastructure (GI), including bioretention and permeable pavement, is an increasingly utilized method to treat stormwater pollutants. Using soil and plants as natural filters, these systems are effective at the site scale, but little evidence exists regarding their performance at the watershed-scale. Blueprint Columbus is an effort by the City of Columbus, Ohio, USA to retrofit GI to eliminate sanitary sewer overflows and remove 20% of total suspended solids (TSS) from existing developed areas. Changes in water quality resulting from the combined effects of many GI practices installed in 11.5 and 47.8 ha treatment watersheds were quantified using a paired-watershed approach. Based on water quality data collected by automated samplers over a 3.5-year period, significant reductions in particulate and dissolved nutrients as well as sediment were observed following the installation of GI in both treatment watersheds compared to the control. Total nitrogen (TN), phosphorus (TP), and TSS concentrations decreased by 13.7–24.1%, 20.9–47.4%, and 61.6–67.7%, respectively. Runoff attenuation by GI contributed to pollutant load reductions of 24.0–25.4% (TN), 27.8–32.6% (TP), and 59.5–78.3% (TSS). Orthophosphate concentrations and loads increased in the watershed with bioretention only but significantly decreased in the treatment watershed with bioretention and permeable pavement. Reductions in TSS concentration were similar (within a margin of 5%) to the percent of the watershed imperviousness treated by GI. Results demonstrate that GI was effective in reducing runoff event mean concentrations and loads at the watershed-scale.

Investigation of used cooking oil on the formation of FOG deposits in sewer line clogging.

Fat, oil, and grease (FOG) deposits, resulting from saponification reaction, have been identified as the primary source of blockage of sewer pipes. This mainly emanates from the adhesion of these deposits on pipe walls, culminating in the sanitary sewer overflows (SSOs). This undesired phenomenon poses several challenges for municipalities, including environmental issues, health-related hazards, and an increase in incurred costs. Unlike the previous literature, the present study, for the first time, attempts to characterize the effect of used cooking oils (a mixture of different oils) as a perceived crux, triggering the genesis of deposits. The experimental results revealed that there exists a host of physical and chemical disparities between fresh oil calcium soaps (FOCSs) and used oil calcium soaps (UOCSs). Notably, when mixed with water, FOCSs produced non-miscible layers, whereas a homogenous, sticky, and viscous solution observed for UOCSs. Fourier transform infrared (FTIR) analysis casts light on the fact that the heating process would greatly influence the oil chemical structure and its resultant calcium soaps. In comparison with calcium chloride, as time elapsed, the optical microscope images illustrated that the calcium sulfate clots formation proceeded at an accelerated rate, delivering particles with larger sizes. Viscosity and adhesion are two prominent distinctions between soaps. In sharp contrast to soap produced from oil with a higher palmitic acid content, it was discerned that the oil containing less palmitic acid generates UOCSs with higher viscosity and adhesion than FOCSs. It can therefore be inferred that the distinct chemical structures driven by high temperature during the cooking process produce soaps with different characteristics as compared with fresh oil. This phenomenon would have a profound impact on the formation of the deposits in sewer lines.

Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate.

Sanitary sewer overflows caused by excessive rainfall derived infiltration and inflow is the major challenge currently faced by municipal administrations, and therefore, the ability to correctly predict the wastewater state of the sanitary sewage system in advance is especially significant. In this paper, we present the design of the Sparse Autoencoder-based Bidirectional long short-term memory (SAE-BLSTM) network model, a model built on Sparse Autoencoder (SAE) and Bidirectional long short-term memory (BLSTM) networks to predict the wastewater flow rate in a sanitary sewer system. This network model consists of a data preprocessing segment, the SAE network segment, and the BLSTM network segment. The SAE is capable of performing data dimensionality reduction on high-dimensional original input feature data from which it can extract sparse potential features from the aforementioned high-dimensional original input feature data. The potential features extracted by the SAE hidden layer are concatenated with the smooth historical wastewater flow rate features to create an augmented previous feature vector that more accurately predicts the wastewater flow rate. These augmented previous features are applied to the BLSTM network to predict the future wastewater flow rate. Thus, this network model combines two kinds of abilities, SAE's low-dimensional nonlinear representation for original input feature data and BLSTM's time series prediction for wastewater flow rate. Then, we conducted extensive experiments on the SAE-BLSTM network model utilizing the real-world hydrological time series datasets and employing advanced SVM, FCN, GRU, LSTM, and BLSTM models as comparison algorithms. The experimental results show that our proposed SAE-BLSTM model consistently outperforms the advanced comparison models. Specifically, we selected a 3 months period training dataset in our dataset to train and test the SAE-BLSTM network model. The SAE-BLSTM network model yielded the lowest RMSE, MAE, and highest R2, which are 242.55, 179.05, and 0.99626, respectively.

Chemical and Microbial Markers for Discriminating Sanitary Sewer Contamination in Coastal, Urban Streams

Water quality benchmarks for fecal indicator bacteria (FIB) are often exceeded in many urban streams in southern California. Possible sources of elevated stream FIB concentrations within urban areas include sanitary sewer exfiltration, sanitary sewer overflows (SSOs), illegal discharges, and human or animal fecal material on the ground surface. Teasing apart the different sources remains a challenge, especially when untreated wastewater and runoff from open defecation sites both contain human fecal material. To distinguish the various sources of microbial contamination in an urban stream, temporal trends in biological and chemical markers of anthropogenic contamination were evaluated in the San Diego River and its tributaries during storm events in two consecutive hydrologic years. Temporal trends in FIB, HF183, pepper mild mottle virus (PMMoV), caffeine, sucralose, chloride, bromide, specific ultraviolet absorbance, and fluorescence index indicated that untreated wastewater flushed from the vadose zone was the main source of microbial pollution to the San Diego River, while open defecation near homeless encampments in the river margins was not a major source. We demonstrated that the combined use of caffeine/sucralose ratios and HF183 and PMMoV holds promise for identifying sewage inputs to surface waters. These findings highlight the need for maintenance and repair of aging sewer infrastructure.

The role of restaurant wastewater for producing bioenergy towards a circular bioeconomy: A review on composition, environmental impacts, and sustainable integrated management

Population inflation has led to the unprecedented increase in urbanization, thus causing negative impacts on environmental sustainability. Recently, there is an upsurge in the number of restaurants due to the changing lifestyles of the people round the globe. For instance, there were 167,490 food and beverage establishments in 2015, representing an annual growth rate of 5.1% since 2010 in Malaysia. The rapid growth of restaurants has implicated a negative impact due to the generation of highly polluted restaurant wastewater (RWW). RWW is mainly generated during the cooking, washing, and cleaning operations. RWW typically contain fat, oil, and grease (FOG) resulting from residues of meat, deep-fried food, baked items and butter, and has caused serious blockages of sewer due to clogging and eventually sewage backup. This has increased the required frequency of cleaning and sanitary sewer overflows (SSOs). Results from the previous studies have shown that FOG can be treated using physical, chemical, and biological processes. Different technologies have been applied for the treatment of FOG and other pollutants (COD, BOD, SS and NH4–N) present in RWW. Therefore, this review aims to provide an in-depth understanding of the characteristics of RWW, chemical and physical characteristics of FOG with the mechanism of its formation and utilization for biocomposites, biogas and biodiesel productions for circular bioeconomy. Besides, this review has discussed the potential treatment technologies comprehensively for RWW which is currently remain understudied. Integrated sustainable management of FOG with technoeconomic analysis of bioproducts, sustainable management with international initiatives and previous studies are also summarized. Hence, this review aims towards providing better alternatives in managing RWW at sources, including its treatment and potential of its biorefinery, therefore eventually contributing towards environmental sustainability.

Towards resilient municipal wastewater sewerage systems in khartoum: A surcharged flow measurement device

Sewerage and sanitation-related disasters are becoming more frequent, especially in developing countries. The aftermath of natural disasters is no less devastating than the immediate destruction they cause. In this way, one of the most pressing needs in rapidly urbanizing cities, especially African capitals, is the efficient sanitary facilities in which the flow measurement is an essential element to evaluate the sewerage systems' performance. The traditional devices of measurements are not suitable due to the enormous discharge of sewage network and many solid particles in wastewater might cause a blockage in sewers. Consequently, the accuracy of measurement declines rapidly. This research aims to develop a flow-measuring device for full pipe flow, also known as sanitary sewer overflow. This particular case is an existing phenomenon that faces sewerage authorities worldwide and impacts the whole society. Flow measurement in such a situation is a formidable challenge and needs cutting-edge research to find fitting flowmeters. The device is a modified fiberglass-built venturi that was manufactured locally without the diverging part (cutthroat venturi). The manufacturing was both simple and achieved at a relatively low cost. Two sensors were used to measure the surcharged flow, one in the inlet section and the other in the throat. The modified venturi connects to an Arduino board that is programmed to output the differential pressure. To calibrate the device, a closed pipe submerged into a hydraulic flume channel is adopted to simulate the overflow sewers. To calculate the actual flow, a stopwatch was used together with the known volume of the channel to compute the actual flow, which was compared with the discharge measured by the device. The results showed an acceptable discharge coefficient “Cd” of about 0.9, which meets the standard discharge coefficient of the venturi meter.

Analysis of the Inflow Characteristics of Separate Sewer Systems for Rainfall Using an XP-SWMM Model and the SSOAP Toolbox

Even after the sewer system rehabilitation project, sewer system-related problems caused by inflow still persist. However, analysis of the characteristics of the inflow has been limited to specific rainfall events and monitoring points. This study analyzed inflow characteristics according to rainfall events using an XP-SWMM model and the Sanitary Sewer Overflow Analysis and Planning (SSOAP) Toolbox. In this study, the XP-SWMM model was built for sewers and collecting pipes in urban areas where classification projects were completed. The R, T, and K parameters were calculated using the SSOAP tool based on the sewer volume data of the study area. The calculated parameters were inputted into the XP-SWMM model and used to analyze R, T, and K of unmeasured sampling intervals. The amount of rainfall-derived inflow and infiltration (RDII) increased according to the amount of rainfall, and the correlation coefficient between the amount of rainfall and the amount of RDII was 0.9352, indicating a high correlation. The results of this study can support efficient facility planning that reflects the rainfall characteristics of specific areas, including areas where actual survey of sewage data is not possible.

Detection of SARS-CoV-2 in urban stormwater: An environmental reservoir and potential interface between human and animal sources

While wastewater has been found to harbor SARS-CoV-2, the persistence of SARSCoV-2 in stormwater and potential transmission is poorly understood. It is plausible that the virus is detectable in stormwater samples where human-originated fecal contamination may have occurred from sources like sanitary sewer overflows, leaky wastewater pipes, and non-human animal waste. Because of these potential contamination pathways, it is possible that stormwater could serve as an environmental reservoir and transmission pathway for SARS-CoV-2. The objectives of this study are: 1) determine whether the presence of SARS-CoV-2 could be detected in stormwater via RT-ddPCR (reverse transcription-digital droplet PCR); 2) quantify human-specific fecal contamination using microbial source tracking; and 3) examine whether rainfall characteristics influence virus concentrations. To accomplish these objectives, we investigated whether SARS-CoV-2 could be detected from 10 storm sewer outfalls each draining a single, dominant land use in Columbus, Xenia, and Springboro, Ohio. Of the 25 samples collected in 2020, at minimum one SARS-CoV-2 target gene (N2 [US-CDC and CN-CDC], and E) was detected in 22 samples (88%). A single significant correlation (p = 0.001), between antecedent dry period and the USCDC N2 gene, was found between target gene concentrations and rainfall characteristics. Grouped by city, two significant relationships emerged showing cities had different levels of the SARS-CoV-2 E gene. Given the differences in scale, the county-level COVID-19 confirmed cases COVID-19 rates were not significantly correlated with stormwater outfall-scale SARS-CoV-2 gene concentrations. Countywide COVID-19 data did not accurately portray neighborhood-scale confirmed COVID-19 case rates. Potential hazards may arise when human fecal contamination is present in stormwater and facilitates future investigation on the threat of viral outbreaks via surfaces waters where fecal contamination may have occurred. Future studies should investigate whether humans are able to contract SARS-CoV-2 from surface waters and the factors that may affect viral longevity and transmission.