Sewer Condition Research Articles

Evaluation of long-term aging of low-carbon cementitious materials under severe H2S impact in sewerage systems

The durability of cementitious materials in harsh environments, like marine or sulfate-rich soils, is influenced by various factors, with bacteria playing a significant role alongside environmental chemistry. To address sustainability concerns, research focuses on developing cement mixes that reduce emissions and withstand aggressive conditions. New standards require testing these mixes. This study developed and tested low-carbon cement mixes (CEM III, CEM V, CAC, and CSS) under real sewer conditions enriched with H2S and bacteria for four years. The specimens underwent SEM-EDS, XRD, TGA-DTA, and µRaman analysis to enhance understanding of degradation processes by conducting detailed and localized characterization of both initial and newly formed phases. Through this analysis, the work offers a precise description of the effects of bio-alteration on various cement matrices, elucidating their reactions under such conditions. The analysis of mortar specimens has revealed the presence of expansive products such as gypsum besides various polymorphs of calcium carbonate.

Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques

ABSTRACT The increasing availability of condition assessment data highlights the challenge of managing data imbalance in the asset management of aging infrastructure. Aging sewer pipes pose significant threats to health and the environment, underscoring the importance of proactive management practices to enhance asset maintenance and mitigate associated risks. While machine learning (ML) models are widely employed to model the complex deterioration process of sewer pipes, they face performance limitations when trained on imbalanced condition grade data. This paper addresses this issue by proposing a novel approach using conditional generative adversarial network (cGAN) for data augmentation. By generating synthetic data for minority classes, the skewed distribution of the sewer dataset is balanced, facilitating more robust and accurate predictive models. The utility of the proposed method is evaluated by training different ML classifiers, including neural network (NN), decision tree, quadratic discriminant analysis, Naïve Bayes, support vector machine (SVM), and K-nearest neighbor. Quadratic discriminant, Naïve Bayes, NN, and SVM classifiers demonstrated improvement. The cGAN-based data augmentation method also outperformed two other data imbalance handling techniques, random under-sampling, and cost-sensitive NN. Consequently, data generated by cGAN can effectively aid asset management by developing proactive classifiers that accurately predict pipes at a high risk of failure.

Physicochemical characteristics of grease-trap wastewater with different potential mechanisms of FOG solid formation, separation, and accumulation inside grease traps

This work investigates the physicochemical characteristics of grease-trap wastewater discharged from a large community market. It proposes potential mechanisms of fat, oil, and grease (FOG) solid formation, separation, and accumulation inside grease traps. Sixty-four samples, i.e., the floated scum, suspended solid-liquid wastewater, and settled sludge, were collected from the grease-trap inlet and outlet chambers. A lower pH of 5-6 at 25-29 °C inside the grease trap than those reported under the sewer conditions (pH 6-7) was revealed. A significant difference in solid and dissolved constituents was also discovered between the inlet and outlet chambers, indicating that the baffle wall could affect the separation mechanism. The sludge samples had 1.5 times higher total solids (TS) than the scum samples, i.e., 0.225 vs. 0.149 g g−1 TS, revealing that the sludge amount impacted more significantly the grease trap capacity and operation and maintenance. In contrast, the scum samples had 1.4 times higher volatile solids (VS) than the sludge samples, i.e., 0.134 vs. 0.096 g g−1 VS, matching with the 64.2 vs. 29.7% of carbon content from CHN analysis. About 2/3 of the free fatty acids (FFAs) with palmitic acids were the primary saturated FFAs, while the remaining 1/3 of unsaturated FFAs were found in the solid and liquid samples. Although up to 0.511 g g−1 FOG can be extracted from the scum samples, none from the sludge samples. More diverse minerals/metals other than Na, Cl, and Ca were found in the sludge samples than in the scum samples. Grease-trap FOG solids and open drain samples exhibited similar physicochemical properties to those reported in the literature. Four potential mechanisms (crystallization, emulsification, saponification, and baffling) were presented. This work offers insights into the physicochemical properties of grease-trap wastewater that can help explore its FOG solid formation, separation, and accumulation mechanisms inside a grease trap.

Detection of Leptospira sp. Bacteria and Factors Related to the Incidence of Leptospirosis in Semarang City

Objective: The Case Fatality Rate (CFR) of Leptospirosis in Semarang City has shown a tendency to increase from 2019 to 2022. In 2022, there were 30 cases reported, with 8 deaths (CFR 27%). The purpose of this study was to detect Leptospira sp. bacteria in rats and analyze the risk factors for leptospirosis in Semarang City.Methods: This study employed an observational approach with a Case-Control Study design. The research was conducted in Semarang City from November to December 2023. Non-random sampling techniques were used, specifically the total sampling method, resulting in a sample size of 45 case groups and 45 control groups. Bivariate analysis was performed using the Chi-square test, with a significance level set at <0.05.Results: Based on the Chi-Square test results, the following variables were found to be associated with the incidence of leptospirosis in Semarang City: the presence of rats (p<0.0001), the presence of Leptospira sp. bacteria (p<0.0001), house conditions (p=0.260), sewer condition (p<0.0001), presence of pets (p=0.001), condition of landfills (p=0.001), presence of vegetation (p=0.005), temperature (p=0.299), humidity (p=0.495), lighting (p=0.023), history of wounds (p=0.001), and knowledge level (p=0.025).Conclusion: The risk factors for leptospirosis in Semarang City include the presence of rats, the presence of Leptospira sp. bacteria, sewer conditions, the presence of pets, conditions of landfills, the presence of vegetation, lighting, history of injury, and level of knowledge.

Utilization of Augmented Reality Technique for Sewer Condition Visualization

Wastewater pipelines are largely buried underground, and techniques for assessing and visualizing their condition are critical for planning and rehabilitation. This paper introduces a framework for integrating Geographic Information System (GIS), 3D-creation platform, augmented reality (AR) techniques, and machine learning algorithms for the dynamic visualization of the condition of sewer networks. A sewer network in Ålesund City, Norway, was used as a case study, and the developed framework was implemented on an Android OS and Microsoft HoloLens. The results show the potential applications of the integrated framework of GIS, AR, and 3D models for sewer condition visualization. The positioning accuracy of the application for 2D objects is equivalent to that of well-designed GPS receivers (approximately 1–3 m), depending on the handheld device used. Loading and locating 3D objects will be limited by the performance of the devices used.

Inflow and infiltration assessment of a prototype sanitary sewer network in a coastal city in China

Abstract A 16-month monitoring program was conducted on a prototype sanitary system in a coastal city in China. The groundwater infiltration (GWI) on dry weather days and the rain-derived inflow and infiltration (RDII) on wet weather days were quantified and analyzed. The proportion of monthly averaged GWI to total flow can be as high as 70% during the observation period mainly due to the high groundwater level. The results also show that the ratio of RDII volume to total rainfall volume (defined as R-value) reaches a limited value of approximately 10% for the studied system when the total rainfall depth increases. A reference indicator Rlim for the limited R-value was proposed for assessing the conditions of sewer systems in terms of RDII. The Rlim value depends on local sewer conditions and in general, a lower Rlim value represents a better performance on RDII and vice versa. This study enriches the case studies on the performance of a specific sanitary sewer system on inflow and infiltration in a typical coastal city with exceptionally high groundwater levels, excess rainfall events in the monitoring season and possible typhoon events, which addresses the unique locational and hydrological properties of a representative coastal city.

Comparison of Four Sewer Condition Assessment Protocols Based on Field Data

In order to better plan new or update sewer pipe condition assessment protocols, this paper presents systematic comparisons of four of the most widely used sewer condition assessment protocols, including the fourth edition of the Sewer Rehabilitation Manual (SRM-4) in the UK, the Pipeline Assessment and Certification Program (PACP) in America, the Sewer Physical Condition Grading Protocols (SPCCM) in Canada, and the Technical Specification for Inspection and Evaluation of Urban Sewer (TSIEUR) in China. In the qualitative comparison, the defects categories, deduct values, and assessment methods of the four protocols are analyzed. A new concept of defect weight is firstly introduced to make comparisons between protocols applicable and easy; in the quantitative comparison, the protocols are used to evaluate the same 182 sewer pipe segments based on field data and the assessment results are compared. It is found that the main reasons for the different evaluation results are due to the different defect weights and evaluation methods used. Finally, PACP shows obvious advantages and is recommended for asset managers when making new or updating protocols in the future.

Effects of Temperature and Water Types on the Decay of Coronavirus: A Review

The analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) gene copy numbers in wastewater samples can provide quantitative information on Coronavirus Disease-19 (COVID-19) cases within a sewer catchment. However, many wastewater-based epidemiology (WBE) studies have neglected virus decay during the wastewater transportation process in sewers while back-calculating COVID-19 prevalence. Among various sewer condition parameters, wastewater temperature and dilution by fresh/saltwater infiltration may result in a significant change to the virus decay, in terms of both infectivity and Ribonucleic Acid (RNA). This paper reviewed the literature to identify and discuss the effects of temperature and water types (i.e., wastewater, freshwater, and seawater) on coronavirus decay based on the decay rate constants that were collected from published papers. To evaluate the importance of virus decay, a sensitivity analysis was then conducted with decay rates of SARS-CoV-2 RNA based on a WBE back-calculation equation. Finally, the decay rates of coronavirus in wastewater were also compared with those of other viruses to further understand the difference among virus species. The decay of SARS-CoV-2 RNA was found to be less impacted by temperature variation than viable coronaviruses. Nevertheless, WBE back-calculation was still sensitive to the RNA decay rates increased by warm wastewater (i.e., over 26 °C), which could lead to a two-times higher relative variance in estimated COVID-19 prevalence, considering the wastewater temperature variation between 4 and 37 °C in a sewer catchment with a 12-h hydraulic retention time. Comparatively, the sensitivity of the WBE estimation to the enveloped SARS-CoV-2 was greater than nonenveloped enteric viruses, which were less easily degradable in wastewater. In addition, wastewater dilution by stormwater inflow and accompanied cold weather might alleviate the decay of coronavirus infectivity, thus increasing the potential risk of COVID-19 transmission through wastewater. Overall, this paper aims to better understand the impact of in-sewer processes on coronavirus decay and its potential implications for WBE. The outcome could quantitatively inform WBE and improve awareness of the increased risk of COVID-19 infection via wastewater during heavy rainfall events. Given the identified scarcity of data available for coronavirus decay in salt water or with chemical additions, future research on the fate of SARS-CoV-2 subjected to chemical dosing for sewer or wastewater treatment plant operations is recommended.

Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which can affect the robustness of the model in the constraint environment. As a result, this paper proposes a sewer condition monitoring framework based on deep learning, which can effectively detect and evaluate defects in sewer pipelines with high accuracy. We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline. This study modified the original RegNet model by modifying the squeeze excitation (SE) block and adding the dropout layer and Leaky Rectified Linear Units (LeakyReLU) activation function in the Block structure of RegNet model. This study explored different deep learning methods such as RegNet, ResNet50, very deep convolutional networks (VGG), and GoogleNet to train on the sewer defect dataset. The experimental results indicate that the proposed system framework based on the modified-RegNet (RegNet+) model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models. The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Predicting the condition of sewer pipes plays a vital role in the formulation of predictive maintenance strategies to ensure the efficient renewal of sewer pipes. This study explores the potential application of ten machine learning (ML) algorithms to predict sewer pipe conditions in Ålesund, Norway. Ten physical factors (age, diameter, depth, slope, length, pipe type, material, network type, pipe form, and connection type) and ten environmental factors (rainfall, geology, landslide area, population, land use, building area, groundwater, traffic volume, distance to road, and soil type) were used to develop the ML models. The filter, wrapper, and embedded methods were used to assess the significance of the input factors. A dataset consisting of 1159 inspected sewer pipes was used to construct the sewer condition models, and 290 remaining inspections were used to verify the models. The results showed that sewer material and age are the most significant factors, otherwise the network type is the least contributor affecting the sewer conditions in the study area. Among the considered ML models, the Extra Trees Regression (R2 = 0.90, MAE = 11.37, and RMSE = 40.75) outperformed the other ML models and it is recommended for predicting sewer conditions for the study area. The results of this study can support utilities and relevant agencies in planning predictive maintenance strategies for their sewer networks.

SPATIAL ANALYSIS AND RISK FACTORS OF LEPTOSPIROSIS IN INDONESIA. A SYSTEMATIC REVIEW

Leptospirosis is a disease that often occurs in tropical climates and has spread widely in advance of the earth including Indonesia. The purpose of the study is to provide some information on review studies on spatial analysis and risk factors for leptospirosis events that have been studied in Indonesia. This study used the review study method by identifying, recording and analyzing from previously studied research from 14 scientific articles related to the incidence of leptospirosis. The results of the study based on the review study that has been carried out obtained that the mapping is carried out by buffering and the pattern of spreading disease events in Indonesia is spread unevenly and the most influential risk factors in the distribution of leptospirosis cases in Indonesia are a significant relationship with variables related to the p value = 0.001 and having sewer conditions, the presence of rats , the presence of rice fields and the presence of rivers from the case house, age, gender, occupation, landfill, standing water, pets against leptospirosis incidence or greater or value affected by Leptospirosis event . Risk factors for leptospirosis events include age, gender, occupation, the presence of rats, landfills, puddles, pets, sewer conditions for the incidence of leptospirosis

SPATIAL ANALYSIS AND RISK FACTORS OF LEPTOSPIROSIS IN INDONESIA. A SYSTEMATIC REVIEW

Leptospirosis is a disease that often occurs in tropical climates and has spread widely in advance of the earth including Indonesia. The purpose of the study is to provide some information on review studies on spatial analysis and risk factors for leptospirosis events that have been studied in Indonesia. This study used the review study method by identifying, recording and analyzing from previously studied research from 14 scientific articles related to the incidence of leptospirosis. The results of the study based on the review study that has been carried out obtained that the mapping is carried out by buffering and the pattern of spreading disease events in Indonesia is spread unevenly and the most influential risk factors in the distribution of leptospirosis cases in Indonesia are a significant relationship with variables related to the p value = 0.001 and having sewer conditions, the presence of rats , the presence of rice fields and the presence of rivers from the case house, age, gender, occupation, landfill, standing water, pets against leptospirosis incidence or greater or value affected by Leptospirosis event . Risk factors for leptospirosis events include age, gender, occupation, the presence of rats, landfills, puddles, pets, sewer conditions for the incidence of leptospirosis

Applicability Analysis of Trunk Drainage Sewer System for Reduction of Inundation in Urban Dense Areas

Urban development naturally aggravates flood damage, causing severe damage yearly. Preparation for flood damage is a part of urban planning, but it is not easy to establish clear mitigation measures in densely populated urban areas. This study analyzed the applicability of trunk drainage sewers as an alternative to installing abatement facilities, a typical structural measure for reducing flood damage in dense urban areas. The study areas included three areas in South Korea where flood damage had previously occurred, and the input parameters of the flood analysis model were calibrated based on the measured runoff, followed by testing with inundation traces. The results of three watersheds were qualitatively evaluated using the Lee Sallee Shape Index (LSSI) method. The applicability of the trunk drainage sewer system in the Gunja and Dowon watersheds were “Excellent” and “Good” in the Dorim watershed. The analysis results for each trunk drainage sewer condition indicated that the peak flow reduction was the greatest at 40% and 60% dimensionless upstream area ratio (DUAR) for 1000–5000 m3 and 10,000 m3, respectively. High hydrological applicability under the same rainfall conditions was demonstrated consequent to analyzing the applicability of the installation of a typical reduction facility and trunk drainage sewer.

The effect of anode potential on electrogenesis, methanogenesis and sulfidogenesis in a simulated sewer condition

Methane emissions from the sewer system are considered to be a non-negligible source of aggravating the greenhouse effect. Meanwhile, the sewer system has long been plagued by sulfide-induced corrosion problems. This study explored the possibility of using a bioelectrochemical system to intensify the competition between electroactive bacteria, methanogens and sulfate-reducing bacteria, thereby reducing the production of methane and sulfide. Dual-chamber bioelectrochemical reactors were constructed and operated in fed-batch mode with the coexistence of Electroactive bacteria, Methanogenic archaea and Sulfate-reducing bacteria. Acetate was supplied as the sole carbon source. The results indicated that electrogenesis induced by the anode potentials of -0.42 V and -0.2 V (vs. Ag/AgCl) had advantages over methanogenesis and sulfidogenesis in consuming acetate. The stimulated electrogenesis by anode potentials resulted in a decrease in pH. Methane production was suppressed in the reactors with anode potentials of -0.42 and -0.2 V compared to open circuit controls. In contrast to methane, the capacity for sulfide production was facilitated in the reactors with the anode potentials of -0.42 V and -0.2 V compared to open circuit controls. 16s rRNA gene analysis showed that Geobacter was the most abundant genus on the anode biofilm in the anode potential-controlled reactor, while acetoclastic methanogens dominated in open circuit controls. Methanosaeta and Methanosarcina were the most abundant methanogens in open circuit controls. Collectively, our study demonstrates that the use of electrodes with anode potential control can help to control methane emissions, but could not yet prevent sulfide production, which requires further research.

In-Sewer Stability Assessment of Anabolic Steroids and Selective Androgen Receptor Modulators.

Wastewater-based epidemiology is a potential complementary technique for monitoring the use of performance- and image-enhancing drugs (PIEDs), such as anabolic steroids and selective androgen receptor modulators (SARMs), within the general population. Assessing in-sewer transformation and degradation is critical for understanding uncertainties associated with wastewater analysis. An electrospray ionization liquid chromatography mass spectrometry method for the quantification of 59 anabolic agents in wastewater influent was developed. Limits of detection and limits of quantification ranged from 0.004 to 1.56 μg/L and 0.01 to 4.75 μg/L, respectively. Method performance was acceptable for linearity (R2 > 0.995, few exceptions), accuracy (68-119%), and precision (1-21%RSD), and applicability was successfully demonstrated. To assess the stability of the selected biomarkers in wastewater, we used laboratory-scale sewer reactors to subject the anabolic agents to simulated realistic sewer environments for 12 h. Anabolic agents, including parent compounds and metabolites, were spiked into freshly collected wastewater that was then fed into three sewer reactor types: control sewer (no biofilm), gravity sewer (aerobic conditions), and rising main sewer (anaerobic conditions). Our results revealed that while most glucuronide conjugates were completely transformed following 12 h in the sewer reactors, 50% of the investigated biomarkers had half-lives longer than 4 h (mean residence time) under gravity sewer conditions. Most anabolic agents were likely subject to biofilm sorption and desorption. These novel results lay the groundwork for any future wastewater-based epidemiology research involving anabolic steroids and SARMs.

Automated Vision Systems for Condition Assessment of Sewer and Water Pipelines

Sewer networks and water distribution systems are among the most valuable and critical urban assets for a community. These systems have their respective time span to provide continuous service to the residents. Hence, it is paramount to assess the condition of sewer and water pipelines to ensure sustainable, reliable, and cost-effective transportation of sewerage and water supply. The assessment is usually done by the inspection robots, which are equipped with machine vision systems and/or sensors. The inspection robots acquire the inspection data, and the operators then conduct the survey of the captured video and/or sensory data to interpret the results. Nowadays, automated solutions are being adopted by industry to achieve an accurate and efficient assessment. This article surveys the state of the art of the automated vision systems, which are employed for condition assessment of sewer and water pipelines, and identifies the challenges for future research. The following areas are highlighted in this survey: 1) the typical types of the faults and failures, which include the concept and definition of the sewer and water pipelines; 2) the inspection systems, e.g., robotic platforms for sewer and water pipeline inspection; 3) the machine vision systems for fault detection; 4) the computational frameworks for condition assessment; and 5) the challenges and suggestions for future research. This article summarizes the current state of automation in the machine vision technology for condition assessment (both hardware and software perspectives) of sewer and water pipelines and also provides a reference for researchers to further advance the technology in this field. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation behind this article was the current challenges and the open issues of the vision system for the condition assessment of sewer and water pipelines. This article presents detailed reviews of the state-of-the-art of the vision system (both hardware and software) and discusses the existing problems. This survey aims to help engineers and researchers to resolve and improve the problems and extend the field of the existing automated frameworks. This article is organized in the form of a survey so that researchers can benefit and get all the useful information at a glance. This article provides a rigorous overview of typical faults, inspection robots, visual techniques, and automated frameworks for the condition assessment and discusses the challenges and future research directions. The objective of this article is to create a baseline for the readers to acquaint themselves with the state of the art and advance the research in this field.

Combining a statistical model with machine learning to predict groundwater flooding (or infiltration) into sewer networks

Groundwater flooding (or infiltration) in sewer systems leads to significant negative consequences such as discharge of untreated sewage, reduction of system capacity, structural deterioration, and dilution of the wastewater stream delivered to a treatment plant causing malfunction. Cities with aging networks along coastal areas, where aquifers are shallow, are particularly vulnerable. Rehabilitation is necessary to mitigate the negative impact of infiltration but costly. Therefore, a prioritization strategy of intervention is required. This paper presents a decision-support model to identify the probability of infiltration into aging sewer when observations of infiltration and sewer conditions are sparse and time-limited. The model is based on logistic regression, where the variables are: material, soil, water table, and pipe size and shape. As a proof-of-concept, the method was applied to the city of Hoboken, NJ. Machine learning was used to calibrate, validate, and test the model using infiltration measurements, provided by the water authority. Upon calibration, model predictions agree well with the measurements with an accuracy of 82%. Sensitivity analysis of the model was carried out and shows that the most important parameter is the water table of the shallow aquifer. Overall, the proposed approach can be a valuable tool for strategic intervention of sewer repair and flood mitigation in urban areas.

In-sewer stability of selected analgesics and their metabolites

Understanding the in-sewer stability of analgesic biomarkers is important for interpreting wastewater-based epidemiology (WBE) data to estimate community-wide analgesic drugs consumption. The in-sewer stability of a suite of 19 analgesics and their metabolites was assessed using lab-scale sewer reactors. Target biomarkers were spiked into wastewater circulating in simulated gravity, rising main and control (no biofilm) sewer reactors. In-sewer transformation was observed over a hydraulic retention time of 12 h. All investigated biomarkers were stable under control reactor conditions. In gravity sewer conditions, diclofenac, desmetramadol, ibuprofen carboxylic acid, ketoprofen, lidocaine and tapentadol were highly stable (0–20% transformation in 12 h). Valdecoxib, parecoxib, etoricoxib, indomethacin, naltrexone, naloxone, piroxicam, ketoprofen, lidocaine, tapentadol, oxymorphone, hydrocodone, meperidine, hydromorphone were considered as moderately stable biomarkers (20–50% transformation in 12 h). Celecoxib and sulindac were considered unstable biomarkers (>50% transformation in 12 h). Ketoprofen, lidocaine, tapentadol, meperidine, hydromorphone were transformed to 0–20% whereas diclofenac, desmetramadol, ibuprofen carboxylic acid, valdecoxib, parecoxib, etoricoxib, indomethacin, naltrexone, piroxicam were transformed up to 20–50% in 12 h in rising main reactor (RMR). These biomarkers were considered as highly stable and stable biomarkers in RMR, respectively. Sulindac, celecoxib, naloxone, oxymorphone and hydrocodone were transformed more than 50% in 12 h and considered as unstable biomarkers in RMR. This study provides the information for a better understanding of the in-sewer loss of the analgesics before using them in WBE biomarkers for estimating drug loads at the population level.

Rapid dynamic quantification of sulfide generation flux in spatially heterogeneous sediments of gravity sewers

Compared with anaerobic pressure sewers, gravity sewers have much more complex operational conditions, such as anaerobic/aerobic spatial variations along variable structures of the pipe network. This greatly complicates the prediction of sulfide generation from spatially heterogeneous sewer sediments. This study proposes a novel quantitative approach for rapidly estimating the sulfide generation flux by understanding the sulfidogenic conversion under complex sewer conditions. Significant anaerobic/aerobic spatial variations were the most critical factor affecting the sulfide production in residential gravity sewers. The dynamic aeration-related process stimulated the growth of sulfide-oxidizing bacteria (SOB) in the surface zone, while the sulfidogenic and methanogenic zone moved into deeper layers. A detailed mechanism model incorporating dynamic alternative anaerobic/aerobic transformation was developed to predict apparent sulfide production, as well as the microscale spatial profiles of chemicals and microbial communities in sediments. The model was evaluated to establish a rapid quantitative approach that only depended on a few key parameters (e.g., flow velocity, pipe diameter, slope, mean hydraulic depth and sulfate concentration), which can provide an important basis for estimating different sulfide generation fluxes under various sewer factors. The identification of sulfide generation hotspots will greatly help determine how to economically control sulfide generation by chemical dosing or pipe structural modification.

Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.