Sewage Monitoring Research Articles

Use of passive samplers as sewage surveillance tool to monitor a hepatitis A outbreak at a school in Amsterdam, the Netherlands, Oct 2022 – March 2023

BackgroundEnteric hepatitis A virus (HAV) infections during childhood are often asymptomatic but may cause severe illness in adults. To improve public health surveillance we assessed the applicability of sewage monitoring during an HAV outbreak at a primary school.MethodsBetween October 19 and December 27, 2022, five symptomatic HAV cases were notified to the Public Health Service Amsterdam; all attended the same primary school. Passive samplers, small absorbent tools, were deployed in sewage near the school from November 14, 2022, to March 22, 2023. The absorbents were subjected to RNA extraction, HAV PCR testing, and, if positive, sequencing. PCR and sequencing were also performed on plasma and feces samples of HAV cases.ResultsIn 22 out of 88 (25%) of sewage samples, HAV RNA was detected. All HAV-RNA-positive sewage samples until 8 February 2023 were subgenotype IB, matching the strain detected in all cases. Another strain of HAV (subgenotype IA) was detected in sewage from 15 February 2023 onwards, without associated cases.ConclusionsPassive sampler-based sewage monitoring is an effective method to rapidly detect HAV shedding linked to diagnosed cases. It detects unnoticed viral infections and allows monitoring of outbreaks. This suggests that passive sampler-based monitoring is a promising tool supporting the public health response during HAV and other outbreaks.

Outbreak-associated Salmonella Baildon found in wastewater demonstrates how sewage monitoring can supplement traditional disease surveillance.

Non-typhoidal Salmonella is a common cause of gastroenteritis worldwide, but current non-typhoidal Salmonella surveillance is suboptimal. Here, we evaluated the utility of wastewater monitoring to enhance traditional surveillance for this foodborne pathogen. In June 2022, we tested raw sewage collected twice a week from two treatment plants in central Pennsylvania for non-typhoidal Salmonella and characterized isolates using whole-genome sequencing. We recovered 43 Salmonella isolates from wastewater samples, differentiated by genomic analysis into seven serovars: 16 Panama (37.2%), 9 Senftenberg (20.9%), 8 Baildon (18.6%), and 3 or fewer of four other serovars. We assessed genetic relatedness and epidemiologic links between these wastewater isolates with those from patients with salmonellosis. All S. Baildon isolates from wastewater were genetically similar to those associated with a known contemporaneous salmonellosis outbreak. S. Baildon from wastewater and 42 outbreak-related isolates in the national outbreak detection database had the same core genome multilocus sequence typing, and outbreak code differed by zero or one single polynucleotide polymorphism. One of the 42 outbreak-related isolates was obtained from a patient residing in the wastewater sample collection catchment area, which serves approximately 17000 people. S. Baildon is a rare serovar (reported in <1% cases nationally, over five years). Our study underscores the value of monitoring sewage from a defined population to supplement traditional surveillance methods for the evidence of Salmonella infections and to determine the extent of outbreaks.IMPORTANCEDuring the COVID-19 pandemic, monitoring for SARS-CoV-2 in wastewater was highly effective in identifying the variants of concern earlier than clinical surveillance methods. Here, we show that monitoring domestic sewage can also augment traditional reporting of foodborne illnesses to public health authorities. Our study detected multiple Salmonella enterica serovars in samples from two wastewater treatment plants in central Pennsylvania. Using whole-genome sequencing, we demonstrated that the isolates of variant S. Baildon clustered with those from a foodborne salmonellosis outbreak that occurred in a similar time frame. Cases were primarily from Pennsylvania, and one individual lived within the wastewater treatment catchment area. This study highlights the effectiveness of domestic sewage testing as a proactive public health strategy to track and respond to infectious disease outbreaks.

An Epidemiological Assessment of SARS-CoV-2 in the Sewage System of a Higher Education Institution.

Background: The World Health Organization declared the end of the COVID-19 pandemic in May 2023, three years after the adoption of global emergency measures. Monitoring of SARS-CoV-2 in sewage underscores its importance due to its effectiveness and cost-effectiveness, highlighting the need to prioritize research on water resources and sanitation. Objectives: The aim of this study was to conduct an epidemiological assessment of SARS-CoV-2 in the sewage system of a higher education institution located in Vitória Espírito Santo State, Maruípe campus. Methods: Over a period of 66 days, from February 6 to April 12, 2023, 15 samples were collected. Each sample consisted of 1 L, collected in 1 hour, with 250 mL collected every 15 minutes. The samples were characterized by assessing their appearance, and pH was measured using a Horiba U-50 multiparameter probe. The extracted RNA was subjected to RT-qPCR using the Allplex™ 2019-nCovAssay Seegene kit. Results: The samples exhibited a cloudy appearance with impurities, and the pH ranged from 6.35 to 8.17. Among the evaluated samples, SARS-CoV-2 RNA was detected in two, and, by comparing this with the epidemiological bulletin issued by the State Health Department, an increase in cases in the state was observed during the collection period of these samples. Conclusions: Sewage monitoring proved to be an important tool in this post-pandemic period, serving as an alert and prevention mechanism for the population in relation to new outbreaks. Furthermore, it represents a low-cost mapping strategy and extensive testing of a population, aligning with the studies presented at the beginning of the pandemic. We recommend specific adjustments considering distinct populations.

Sewage surveillance of SARS-CoV-2 in Tokyo (May 2020-June 2022): method using high-throughput, fully-automated platform.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is shed in saliva, sputum, and feces of the novel coronavirus disease 2019 (COVID-19) patients, and SARS-CoV-2 RNA has been detected in sewage. Thus, many regions worldwide have implemented the measurement of SARS-CoV-2 RNA in sewage as a monitoring tool for COVID-19 prevalence. Here, we report the monitoring of two wastewater treatment plants in Tokyo from May 2020 to June 2022 using a fully automated genetic testing method for the solid fraction of sewage. We found that SARS-CoV-2 sewage monitoring using the Panther System with the Aptima SARS-CoV-2 Assay Kit is an effective method of qualitative monitoring. Furthermore, spike gene mutations (N501Y, L452R, E484A) by real-time PCR using test residues were detected in some samples, which reflected the epidemic status. Nevertheless, our findings demonstrate that the use of a fully automated testing platform is effective for SARS-CoV-2 RNA detection in the solid fraction of sewage.

Wastewater-based epidemiology to monitor 68 NPS/conventional drug use in Taipei metropolitan area in Taiwan during and after COVID-19 pandemic

Amidst far-reaching COVID-19 effects and social constraints, this study leveraged wastewater-based epidemiology to track 38 conventional drugs and 30 new psychoactive substances (NPS) in northern Taiwan. Analyzing daily samples from four Taipei wastewater plants between September 2021 and January 2024—encompassing club reopenings, holidays, Lunar New Year, an outbreak, and regular periods—thirty-one drugs were detected, including 5 NPS. Tramadol, zolpidem tartrate, CMA, and MDPV were newly detected in Taiwanese sewage with frequency of 1.4 %− 89.0 %. Conventional drug use typically increased post-pandemic, aside from benzodiazepines and methadone. Methamphetamine showed 100 % frequency, indicating ongoing daily consumption despite COVID-19 measures. Methamphetamine and morphine's consumption dipped then rose around club reopening, hinting at limited access. The consumption trend of methadone appeared to compensate for the use of morphine. Ketamine and NPS demonstrated similar patterns throughout the entire period. NPS as party drugs seemed influenced by an unstable supply chain and complexities in implementation. Benzodiazepines, commonly abused alongside synthetic cathinones in Taiwan exhibited an opposing trend to NPS while aligned with acetaminophen, suggesting elevated stress and anxiety levels during the pandemic. No significant differences were observed in drug consumption between weekdays and weekends, potentially indicating that COVID-19 measures blurred the traditional distinctions between these timeframes. Environmental ImplicationNew psychoactive substances refer to chemically modified variants of controlled drugs designed to mimic the effects of the original drugs while evading modern detection methods, categorizing them as hazardous materials. The study presents a sewage monitoring project conducted from 2021 to 2024, collecting samples from four WWTPs to analyze NPS and conventional drug trends during and after the COVID-19 pandemic. The findings uncovered connections between drug consumption patterns and pandemic-related policies. In light of the persistent drug abuse and their environmental presence, the results bear critical importance for both environmental and public health. We provide a thorough assessment of these relationships and prioritize areas for future research.

Profile-wise enrichment of metallic pollutants (Pb, Cr and Cd) in various sewage and non-sewage irrigated soils of Prayagraj

The study examined the profile-wise enrichment of metallic pollutants (Pb, Cr and Cd) at different sites, i.e., Naini, Draupadi Ghat, Phaphamau and Beligaon in Prayagraj (U.P.), India. The soil samples were collected from different soil profile depths: 0-15, 15-30, 30-45 and 45-60 cm. It was observed that maximum enrichment of Pb, Cr and Cd was found at the (0-15 cm) depth of sewage and non-sewage irrigated soil (5.12±0.42, 4.08±038 and 3.58±031 mg kg-1 ) and (1.58±0.09, 1.08±0.09 and 1.02±0.08 mg kg-1 ) and minimum enrichment was found at the (45-60 cm) depth (2.18, 0.56 and 0.48 mg kg-1 ) and (0.69±0.04, 0.56±0.04 and 0.47±0.03 mg kg-1 ) in Naini, respectively. Maximum enrichment of Pb, Cr and Cd was found at the (0-15 cm) depth of sewage and non-sewage irrigated soil (2.44±0.22, 2.19±0.14 and 2.08±0.16 mg kg-1 ) and (1.09±0.07, 0.97±0.06 and 0.84±0.07 mg kg-1 ) and minimum enrichment was found at the (45-60 cm) depth (1.01±0.06, 0.98±0.05 and 0.71±0.06 mg kg-1 ) and (0.19±0.02, 0.15±0.01 and 0.11±0.01 mg kg-1 ) in Beligaon, respectively. To find the maximum enrichment of heavy metals in Naini and the minimum enrichment in Beligaon. The enrichment of metallic pollution decreased with increasing soil profile depth in both sewage and non-sewage irrigated soils. Metallic contamination in soil can persist for a long time and create serious problems; therefore, proper monitoring of sewage irrigated soils is required to reduce metallic pollution

Evaluation of a constructed wetlands hybrid system with and without recirculation

The objective of this study was to evaluate the organic matter behavior under the effect of the recirculation of treated domestic sewage in a hybrid system of constructed wetlands. This study was carried out at the sewage treatment plant in the Instituto Federal Goiano campus, in Rio Verde city, Goias, Brazil. The treatment system consists of a septic tank followed by a hybrid system of constructed wetlands (CW). Sewage monitoring was performed at 3 points: PTS (previously treated sewage), TS1 (VS-CW output) and TS2 (HS-CW output). Water quality analysis were carried out weekly, between May and December 2021, in two types of operation: no recirculation (10 weeks) and 100% recirculation (17 weeks). In this research, three kinetics models were evaluated (1st Order, Monod and Monod Multi) combined with two types of idealized dynamic flow: PFR and CSTR. Results show that the water behavior of the recirculation system increasing by 335% and 59.25% in inlet and outlet flows, respectively, from the VS-CW. The overall efficiency in BOD removing was 89.26% in linear system and 83.95% in the recirculating system. In the case of COD, an overall removal system was 69.7% in the linear and 25.7% in the recirculating system. The Monod kinetic model was the most representative to describe the BOD removal in the linear system (R² = 0.80) and in the recirculation (R² = 0.61). None of the models were included in the description of the linear COD removal processes in the system. In the recirculating system, the Monod kinetic model (R² = 0.81) was the most representative in terms of concentration. Sewage recirculation brought new treatment dynamics and was efficient in BOD reducing (concentration). It is noteworthy that the BOD and COD removal efficiency was lower than in the recirculation by sewage dilution in the suction well.

Wastewater-Based Epidemiology for Viral Surveillance from an Endemic Perspective: Evidence and Challenges.

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decisions.

Detection of biological loads in sewage using the automated robot-driven photoelectrochemical biosensing platform.

Real-time polymerase chain reaction (RT-PCR) remains the most prevalent molecular detection technology for sewage analysis but is plagued with numerous disadvantages, such as time consumption, high manpower requirements, and susceptibility to false negatives. In this study, an automated robot-driven photoelectrochemical (PEC) biosensing platform is constructed, that utilizes the CRISPR/Cas12a system to achieve fast, ultrasensitive, high specificity detection of biological loads in sewage. The Shennong-1 robot integrates several functional modules, involving sewage sampling and pretreatment to streamline the sewage monitoring. A screen-printed electrode is employed with a vertical graphene-based working electrode and enhanced with surface-deposited Au nanoparticles (NPs). CdTe/ZnS quantum dots (QDs) are further fabricated through the double-stranded DNA (dsDNA) anchored on Au NPs. Using the cDNA template of Omicron BA.5 spike gene as a model, the PEC biosensor demonstrates excellent analytical performance, with a lower detection limit of 2.93×102 zm and an outstanding selectivity at the level of single-base mutation recognition. Furthermore, the rapid, accurate detection of BA.5 in sewage demonstrates the feasibility of the PEC platform for sewage monitoring. In conclusion, this platform allows early detection and tracking of infectious disease outbreaks, providing timely data support for public health institutions to take appropriate prevention and control measures.

Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors—the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T—were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0–5.0 m range, with a standard deviation of 0.44–1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22–0.23 m within the 2.0–5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

Metagenomic evaluation of the performance of passive Moore swabs for sewage monitoring relative to composite sampling over time resolved deployments

Moore swabs have re-emerged as a versatile tool in the field of wastewater-based epidemiology during the COVID-19 pandemic and offer unique advantages for monitoring pathogens in sewer systems, especially at the neighborhood-level. However, whether Moore swabs provide comparable results to more commonly used composite samples remains to be rigorously tested including the optimal duration of Moore swab deployment. This study provides new insights into these issues by comparing the results from Moore swab samples to those of paired composite samples collected from the same sewer lines continuously over six to seventy-two hours post-deployment, during low COVID-19 prevalence periods. Our results show that Moore swabs accumulated approximately 10-fold higher PMMoV concentrations (on a basis of mL of Moore swab squeezed filtrate to mL of composite sewage) and showed comparable trends in terms of bacterial species abundance when compared to composite samples. Moore swabs also generally captured higher SARS-CoV-2 N1/N2 RNA concentrations than composite samples. Moore swabs showed comparable trends in terms of abundance dynamics of the sewage microbiome to composite samples and variable signs of saturation over time that were site and/or microbial population-specific. Based on our dual ddRT-PCR and shotgun metagenomic approach, we find that Moore swabs at our sites were optimally deployed for 6 h at a time at two sites.

The cases of tracing the source of patients infected with Omicron variant of SARS-CoV-2 based on wastewater-based epidemiology in Shenzhen

Wastewater-based epidemiology (WBE) is an emerging discipline, which has been applied to drug abuse tracking and infectious disease pathogen surveillance. During the COVID-19 epidemic, WBE has been applied to monitor the epidemic trend and SARS-CoV-2 variants etc. In order to detect hidden COVID-19 cases and prevent transmission in the community, wastewater surveillance system for monitoring SARS-CoV-2 RNA was developed in Shenzhen. The sewage sampling sites were set up in key places such as the port areas, urban villages and residential communities of Futian, Nanshan, Luohu and Yantian districts. From July 26 to November 30, 2022, a total of 369 sewage sampling sites were set up, covering 1.93 million people. Continuous sampling was carried out for 3 hours in the peak period of water use every day. Sewage virus enrichment and SARS-CoV-2 nucleic acid detection were carried out by polyethylene glycol precipitation method and RT-qPCR, and a positive water sample disposal process was molded. This article aims to introduce the case of source tracing of COVID-19 infected patients based on urban sewage in Shenzhen. The sewage monitoring of Honghu water treatment plant in Luohu District played an early warning role, and the source of infection was traced. In the disposal of positive water samples in Futian South Road, Futian District, the important experience of monitoring point layout was obtained. In the sewage monitoring of Nanshan village, Nanshan District, the existence of occult infection was revealed. Sharing the experience of tracing the source of COVID-19 patients to avoid the spread of COVID-19 in the community based on wastewater surveillance of SARS-CoV-2 RNA in Shenzhen, and summarizing the advantages and application prospects of sewage surveillance can provide new ideas for monitoring emerging or re-emerging pathogens that are known to exhibit gastrointestinal excretion in the future.

A high-precision and wide-range pH monitoring system based on broadband cavity-enhanced absorption spectrum

A high-precision pH monitoring system over a wide pH range is introduced. The system comprises a cavity-enhancement module constructed by two high-reflectivity mirrors, a microfluidic pH sensing chip based on a binary-indicator membrane of Congo red and m-cresol purple, and a hyperspectral transmission module. This structure extends the effective absorption optical path of the sensing chip, significantly amplifying the spectral differences at various pH values. The spectrum of the transmitted light is recorded by a self-developed hyperspectral module and then converted to broadband cavity-enhanced absorption spectrum (BBCEAS) via the Beer-Lambert law. An artificial neural network (ANN) is employed to predict pH values of the solution. With such a design, this system exhibits a wide detecting range of 2 M [H+] − 2 M [OH–] (corresponding to pH −0.3–14.3) with a response time of about 120 s. The system can achieve a higher detection accuracy with root mean square error (RMSE) of 0.073, as compared to 0.137 without the cavity enhancement. The system also possesses good properties of repeatability, long-term stability, ion resistance, and organic corrosion resistance. These excellent properties make the proposed system a promising candidate technology for harsh environments, such as seawater acidification warning, chemical plant sewage monitoring, and biological sample detection.

Forecasting acute rainfall driven E. coli impacts in inland rivers based on sewer monitoring and field runoff

Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events.

Prototyping an integrated IoT-based real-time sewer monitoring system using low-power sensors

Improper monitoring of sewage networks may raise various issues such as overflows, pipe blockages, theft of manhole covers, leading to flooding and pollution, infrastructure damage, vehicles accidents, injury, and even death from falling into open manholes. The key objective of this research was to examine different elements and create a prototype architecture for a real-time sewer monitoring system. Implementation of the architecture involved constructing a data gathering station and experimenting with various wireless sensing devices to assess the precision of the sensors. In addition, the study sought to design a geographic information system that integrates algorithms capable of identifying sewer overflow, blocked pipes, and the presence of manhole covers. The performance of Sharp GP2Y0A41SK0F infrared, TF-Luna Benewake LiDar, TOF400 VL53L1X laser, JSN-SR04T ultrasonic distance sensors was tested in terms of their ability to monitor water level and manhole cover. Tests revealed the most favorable results in TOF400 VL53L1X at distances between 0.2 and 1.0 m (presumed distance to the manhole cover) with a standard deviation of 0.13–0.24, and in TF-Luna Benewake at distances between 1.0 and 5.0 m (presumed distance to the chamber bottom) with a standard deviation of 0.44–1.15. The deviation analysis has yielded equations that can be utilized to provide rough estimates of the accuracy levels of the aforementioned sensors, based on the measured distance. Additionally, the FC-28 analog and YL-63 infrared sensors were evaluated for detecting pipe blockages, with the YL-63 being more suitable. The outcomes of this study furnish valuable insights that can aid in achieving sustainable resolutions for issues related to sewer monitoring

Sensing System for Mixed Inorganic Salt Solution Based on Improved Double Label Coupling RFID

As a crucial part in sewage, high-salt wastewater contains abundant corrosive components and heavy metal elements, which makes it difficult for invasive monitoring. Therefore, based on coupling Radio Frequency Identification(RFID) tags, this paper proposes a non-invasive monitoring method for concentration of inorganic salt in sewage. In this method, Cole-Cole model and Slit Cylindrical Capacitance(SCC) model are combined to explore the relationship between capacitance of RFID and concentration of inorganic salt in sewage pipeline. A New Efficient Levenberg-Marquardt(NELM) algorithm is used for regression between relative permittivity and concentration of inorganic salt categorically. Then, measurement data is processed under modified relative permittivity mixing rule of electrolyte and solvent. Finally the concentration of each component in the multi-component inorganic salt wastewater are obtained. Experimental results show that the maximal relative error (MRE) and average relative error(ARE) of single inorganic salt solution are 1.1% and 0.5%, while mixed solution are 2.6% and 1.89%. Such results indicate significant improvement comparing with previous works. Great potential of RFID in working sewage monitoring is sufficiently confirmed.

IoT Based Monitoring and Alert System for Sewage Worker’s Safety

Sewage system maintenance is a cumbersome task that has to be carried out properly since it is inevitable. Fatality occurs every year due to improper and inadequate facilities available as well as dangerous gases emitted during sewage disposal. Sewage workers are unaware of the dangers posed by a sudden attack of poisonous gases since these gases are odourless and if exposed for a prolonged period of time might lead to respiratory issues. To deal with this issue, an effective sewage monitoring and alert system is required. The proposed project continuously monitors the poisonous gases and oxygen present in sewage as well as the sanitation workers' health. This effectively reduces the impact of the poisonous gases on the sewage worker.

Sewage Monitoring and Maintenance Alert using IoT

bstract: We're planning to create a smart gadget that can detect when the drainage system becomes clogged, and if water overflows as a result of this clogging, we'll notify the local authority so they can take the appropriate measures. Additionally, the Android application allows for real-time monitoring of the drainage system's status. Four sensors will be used as input devices for the project: a pH sensor to measure the acidity of the sewage water as accurately as possible so that the water can be treated in a wastewater treatment plant and can be reused; an ultrasonic sensor to detect blockages; a water level/float sensor to detect overflows of water; and a gas sensor to measure the to detect all toxic gases released from the sewage. Finally, by inputting the picture samples collected during sewage monitoring, we may use image processing to find fractures in the pipes. The ESP8266, which serves as both a microcontroller and a wireless module, receives and processes the data from these sensors. Here, a push notice is given to the mobile device in case of Blockage or Overflow in addition to the real-time ultrasonic distance being supplied to the Blynk/Adafruit Application. Using our smartphones, we can control any IoT device using the Blynk/Adafruit application, which can operate on Android, iOS, and Windows platforms. To develop the graphical user interface for our IoT application, we may create our own GUI

Hepatitis A virus monitoring in wastewater: A complementary tool to clinical surveillance

Monitoring wastewater is an effective tool for tracking information on trends of enteric viral dissemination. This study aimed to perform molecular detection and genetic characterization of HAV in wastewater and to correlate the results with those obtained from clinical surveillance.Wastewater samples (n=811) of the second most populous city in Argentina were collected from the main wastewater treatment plant (BG-WWTP, n=261), and at 7 local neighborhood collector sewers (LNCS, n=550) during 2017–2022. Clinical samples of acute hepatitis A cases (HA, n=54) were also analyzed. HAV molecular detection was performed by real time RT-PCR, and genetic characterization by RT-Nested PCR, sequencing and phylogenetic analysis.RNA-HAV was detected in sewage samples throughout the entire period studied, and detection frequencies varied according to the location and year (2.9% - 56.5%). In BG-WWTP, 23% of the samples were RNA-HAV+. The highest detection rates were in 2017 (30.0%), 2018 (41.7%) and 2022 (56.5%), which coincides with the highest number of HA cases reported. Twenty-eight (28) sequences were obtained (from clinical and sewage samples), and all were genotype IA. Two monophyletic clusters were identified: one that grouped clinical and wastewater samples from 2017–2018, and another with specimens from 2022, evidencing that environmental surveillance might constitute a replica of viral circulation in the population.These findings evidence that WBE, in a centralized and decentralized sewage monitoring, might be an effective strategy to track HAV circulation trends over time, contributing to the knowledge of HAV in the new post-vaccination epidemiological scenarios in Argentina and in Latin America.

Detection of BK and JC polyomaviruses in sewage water of the urban areas of Lahore, Pakistan.

The surveillance of sewage water has become an extremely essential tool to trace the circulation of viruses in a population and to predict the outbreak of viral diseases. Sewage monitoring is important for those viruses which cause subclinical infections since it is difficult to determine their prevalence. Polyomaviruses are ubiquitously present, circular double-stranded DNA viruses that can infect humans as well. Among all human polyomaviruses, BK polyomavirus and JC polyomavirus associated with the development of aggressive diseases in immunocompromised individuals, are highly prevalent. This study aimed to investigate the presence and the quantitative prevalence of these two disease-associated human polyomaviruses in sewage water collected from six drains of Lahore, Pakistan. The viruses present in the environmental samples were concentrated by PEG method before isolating viral nucleic acids. Conventional PCR amplifications were performed for molecular detection of BK polyomavirus and JC polyomavirus targeting their large tumor antigen genetic region. The presence of BK polyomavirus and JC polyomavirus was confirmed in the DNA extracted from concentrated sewage samples of each drain by performing both qualitative and quantitative PCR. Our data shows that the viral load ranged from 1278 to 178368 copies per µg of environmental DNA for BK polyomavirus and 5173 to 79129 copies per µg of environmental DNA for JC polyomavirus. In conclusion, here we report first time the detection of BK polyomavirus and JC polyomavirus in sewage water collected from six main drains in urban areas of Lahore, Pakistan showing the high prevalence ofthese viruses in the Pakistani population. This assay could be used as a proxy to determine the prevalence of these viruses in the Pakistani population.