Wastewater Loading Research Articles

Antibiotic Sensitivity Pattern of Isolates from Restaurant Wastewater

Background: This study was carried out to investigate the presence of antibiotic resistance in restaurant wastewater collected from three restaurants within the University environs. Methodology: The samples were examined microbiologically and tested for antibiotic sensitivity by conventional methods. The microbial load in the restaurant wastewater ranged from 1.18 x 105 – 9.9 x 105 and 2.9 x 105 -7.9 x 105 after tyndallization. The microbial isolates were identified to be Corynebacterium sp., Pseudomonas sp., Klebsiella sp., Staphylococcus sp., Bacillus sp., and the fungal isolates were Hafnia, and Providencia. The highest percentage of occurrence was observed for Corynebacterium (38%), and the lowest occurrence was observed for Staphylococcus, Bacillus, and Providencia (6.9%). Antibiotic susceptibility of the microns was assayed according to the Kirby- Bauer disc diffusion method. Results: The result of this study indicated that out of all the strains of microorganisms isolated, the highest multi-resistant pattern was found with Corynebacterium, it was resistant to nine isolates out of the ten isolates it was tested for, was susceptible to only Chloramphenicol (30µg). Pseudomonas was the least susceptible, being resistant to only three antibiotics namely Ciproflox (10µg), Amoxicillin (30µg), and Streptomycin (30µg). The results showed 50% resistance to Norobiocin (30µg), Ciproflox (10µg), Gentamicin (10µg), Amplicox (30µg), Streptomycin (30µg); 40% resistance to Levomycin (30µg), Rifampicin (10µg), 30% resistance to Amoxicillin (30µg), and 20% resistance to Chloramphenicol (30µg) and Erythromycin (10µg). Conclusion: With the current emphasis on environmental health and water pollution issues, there is an increasing awareness of the need to dispose of wastewater safely and beneficially by ensuring they are treated properly before reuse or final disposal into water bodies.

Relationship between antihistamine residues in wastewater and airborne pollen concentrations: Insights into population-scale pollinosis response.

Pollinosis is the most prevalent allergic disorder. Assessing the impact of real-world pollen exposure on symptoms remains challenging due to extensive patient-level efforts required. This study explores the potential of wastewater-based epidemiology (WBE) to investigate the relationship between airborne pollen concentrations and antihistamine residues in wastewater as an indicator of pollinosis symptom treatment at the population-scale. In Zurich (Switzerland), 279 wastewater samples were collected during 2021-2023. Each sample represents a 24-h period with excreta from approximately 471,000 individuals. Eleven antihistamine markers were analyzed in the samples using liquid chromatography high-resolution mass spectrometry. The relationship between antihistamine loads in wastewater and airborne pollen concentrations (47 taxa and miscellaneous pollen) was investigated by determining seasonal components of antihistamine loads and exploring pollen-specific contributions using Non-Negative Least Squares (NNLS) optimization. Four antihistamines were detected in quantifiable amounts in wastewater. The first-generation antihistamine, diphenhydramine, demonstrated rather constant levels. In contrast, the three second-generation antihistamines - bilastine, cetirizine, and fexofenadine - showed pronounced day-to-day variation with strong correlations among each other. For fexofenadine, which was investigated in detail for its correlation with airborne pollen concentrations, approximately 50% of the annual wastewater loads were associated with acute pollen exposure. Another 20% related to baseline consumption during the pollen season, while the remaining 30% seems unrelated to pollen. Birch, grasses, hazel, hornbeam, plane, and plantain explained most of the variance in wastewater loads (R2=0.82), with grass pollen alone accounting for a quarter of the annual loads. Increased fexofenadine loads during periods without elevated concentrations of common allergenic pollen suggests the presence of additional triggers for allergy symptoms, potentially yew pollen. Our study demonstrates that WBE can effectively reveal substantial day-to-day variation in antihistamine use related to pollen exposure. Thus, WBE presents an objective and questionnaire-independent method for investigating pollinosis symptom treatment at a population-scale.

Treatment of Acid Mine Water from the Breiner-Băiuț Area, Romania, Using Iron Scrap

Acid mine drainage (AMD) forms in mining areas during or after mining operations cease. This is a primary cause of environmental pollution and poses risks to human health and the environment. The hydrographic system from the Maramureș mining industry (especially the Baia Mare area) was heavily contaminated with heavy metals for many years due to mining activity, and after the closing of mining activity, it continues to be polluted due to water leaks from the abandoned galleries, the pipes, and the tailing ponds. The mineralization in the Băiuț area, predominantly represented by pyrite and marcasite associated with other sulfides, such as chalcopyrite, covelline, galena, and sphalerite, together with mine waters contribute to the formation of acid mine drainage. The Breiner-Băiuț mining gallery (copper mine) permanently discharges acidic water into the rivers. The efficiency of iron scrap (low-cost absorbent) for the treatment of mine water from this gallery was investigated. The treatment of mine water with iron shavings aimed to reduce the concentration of toxic metals and pH. Mine water from the Breiner-Baiut mine, Romania, is characterized by high acidity, pH = 2.75, and by the association of many heavy metals, whose concentration exceeds the limit values for the pollutant loading of wastewater discharged into natural receptors: Cu—71.1 mg/L; Zn—42.5 mg/L; and Fe—122.5 mg/L. Iron scrap with different weights (200 g, 400 g, and 600 g) was put in contact with 1.5 L of acid mine water. After 30 days, all three treatment variants showed a reduction in the concentrations of toxic metals. A reduction in Cu concentration was achieved below the permissible limit. In all three samples, the Cu concentrations were 0.005 for Sample 1, 0.001 for Sample 2, and <LOQ for Sample 3. The Zn concentration decreased significantly compared to the original mine water concentration from 42.5 mg/L to 1.221 mg/L, 1.091 mg/L, and 0.932 mg/L. These values are still above the permissible limit (0.5 mg/L). The Fe concentration increased compared to the original untreated water sample due to the dissolution of iron scrap. This research focuses on methods to reduce the toxic metal concentration in mine water, immobilizing (separating) certain toxic metals in sludge, and immobilizing various compounds on the surface of iron shavings in the form of insoluble crystals.

Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China

IntroductionCOVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.MethodViral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.ResultsThe results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model’s MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.ConclusionThis study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.

Viral concentration method biases in the detection of viral profiles in wastewater.

Viral detection methodologies used for wastewater-based epidemiology (WBE) studies have a broad range of efficacies. The complex matrix and low viral particle load in wastewater emphasize the importance of the concentration method. This study focused on comparing three commonly used virus concentration methods: polyethylene glycol precipitation (PEG), immuno-magnetic nanoparticles (IMNP), and electronegative membrane filtration (EMF). Influent and effluent wastewater samples were processed by the methods and analyzed by DNA/RNA quantification and sequencing for the detection of human viruses. SARS-COV-2, Astrovirus, and Hepatitis C virus were detected by all the methods in both sample types. PEG precipitation resulted in the detection of 20 types of viruses in influent and 16 types in effluent samples. The corresponding number of virus types detected was 21 and 11 for IMNP, and 16 and 8 for EMF. Certain viruses were unique to only one concentration method. For example, PEG detected three types of viruses in influent and six types in effluent compared to IMNP, which detected seven types in influent and one type in effluent samples. However, the EMF method appeared to be the least effective, detecting three types in influent and none in effluent samples. Rotavirus was detected in influent sample using IMNP method, whereas EMF and PEG methods failed to yield a similar outcome. Consequently, the potential false negative results pose a risk to the credibility of WBE applications. Therefore, implementation of a proper concentration technique is critical to minimize method biases and ensure accurate viral profiling in WBE studies.IMPORTANCEIn recent years, significant research efforts have been focused on the development of viral detection methodology for wastewater-based epidemiology studies, showing a range of variability in detection efficacies. A proper methodology is essential for an appropriate evaluation of disease prevalence and community health in such studies and necessitates designing a concentration method based on the target pathogenic virus. There remains a need for comparative performance evaluations of methods in the context of detection efficiencies. This study highlights the significant impact of sample matrix, viral structure, and nucleic acid composition on the efficacy of viral concentration methods. Assessing WBE techniques to ensure accurate detection and understanding of viral presence within wastewater samples is critical for revealing viral profiles in municipality wastewater samples.

Effects Of Physicochemical Parameters On River Jakara Water Quality In Kano Metropolis, Nigeria

River Jakara is a major source of water for Kano city, and its pollution is of great concern due to the potential health and environmental consequences. The study therefore examined the effects of the physicochemical parameters on it. Eight sampling points that conveys wastewater to River Jakara were purposefully established and a total of 96 water samples were collected at different locations from June 2021 – April 2022 during wet and dry season. Using the Principal Components Analysis (PCA), 9 physicochemical parameters and 2 microbial colonies were chosen and subjected to laboratory analysis. The results showed that the wastewater contributed significantly to the river pollution for concentrations of all the pollutants exceeded the Nigeria discharge standards such as National Environmental Standard Regulatory Enforcement Agency (NESREA, 2011). Laboratory analysis recorded high concentrations of parameters: temperature 38°C, pH 12, turbidity (TUR) 38NTU, electric conductivity (EC) 3861mg/L, total hardness (TH) 574mg/L, total dissolved solid (TDS) 3915mg/L, dissolved oxygen (DO) 7.5mg/L, biological oxygen demand (BOD) 185mg/L, nitrate (NO) 283.5mg/L, total coliform 395cfu/100ml and escherichia coli (E.coli) 452cfu/100ml in wet season. The T-test analysis indicated a strong significant variation between wastewater loads and the water quality parameters as well wet and dry season at P = .05. The results suggested that discharge of wastewater into the river had adversely affected its water quality and could pose a great risk to aquatic life and public health. Therefore, effective management strategies are needed to reduce wastewater loads and improve the water quality of River Jakara.

Water efficiency analysis and optimization of chemical usage on productivity of demineralizer plant PT. Pupuk Iskandar Muda

Abstract A demineralization process is carried out in industrial water treatment, and this process requires large amounts of chemicals that can increase the pollutant load of wastewater and harm ecosystems, biodiversity, and climate. Therefore, this study aims to analyze water efficiency and chemicals used in the water treatment process at the Demineralizer Plant. The analysis was carried out through a fishbone diagram and Nominal Group Technique (NGT) method to identify the problem so that the corrective solution is obtained, namely by backwashing the cation and anion resins periodically at 100,000 m3, maintaining the flow service setting for the demineralized plant at 90 - 100 m3/hour, and adjusting the specific gravity at the time of regeneration of Cation in the first Chemical. The results showed that PT made improvements. Pupuk Iskandar Muda has succeeded in carrying out a water efficiency of 484.87 m3. That impacts production costs, which decrease along with reduced chemical use. The company saved IDR 4.251.782 in 2021 after the repairs were carried out. Water efficiency and optimization of chemical use can save the cost of purchasing chemicals, minimize environmental impacts, and provide the best and most practical solutions at PT. Pupuk Iskandar Muda.

Effect of Gold Nanoparticles in Microbial Fuel Cells Based on Polypyrrole-Modified Saccharomyces cerevisiae.

Microbial fuel cells (MFCs) are a candidate for green energy sources due to microbes' ability to generate charge in their metabolic processes. The main problem in MFCs is slow charge transfer between microorganisms and electrodes. Several methods to improve charge transfer have been used until now: modification of microorganisms by conductive polymers, use of lipophilic mediators, and conductive nanomaterials. We created an MFC with a graphite anode, covering it with 9,10-phenatrenequinone and polypyrrole-modified Saccharomyces cerevisiae with and without 10 nm sphere gold nanoparticles. The MFC was evaluated using cyclic voltammetry and power density measurements. The peak current from cyclic voltammetry measurements increased from 3.76 mA/cm2 to 5.01 mA/cm2 with bare and polypyrrole-modified yeast, respectively. The MFC with polypyrrole- and nanoparticle-modified yeast reached a maximum power density of 150 mW/m2 in PBS with 20 mM Fe(III) and 20 mM glucose, using a load of 10 kΩ. The same MFC with the same load in wastewater reached 179.2 mW/m2. These results suggest that this MFC configuration can be used to improve charge transfer.

Detection of SARS-CoV-2 in wastewater as an earlier predictor of COVID-19 epidemic peaks in Venezuela

Wastewater-based epidemiological surveillance has proven to be a useful and cost-effective tool for detecting COVID-19 outbreaks. Here, our objective was to evaluate its potential as an early warning system in Venezuela by detecting SARS-CoV-2 RNA in wastewater and its correlation with reported cases of COVID-19. Viral RNA was concentrated from wastewater collected at various sites in Caracas (northern Venezuela), from September 2021 to July 2023, using the polyethylene glycol (PEG) precipitation method. Viral quantification was performed by RT-qPCR targeting the N1 and ORF1ab genes. A significant association (p < 0.05) was found between viral load in wastewater and reported cases of COVID-19 up to six days after sampling. During the whole study, two populated areas of the city were persistent hotspots of viral infection. The L452R mutation, suggestive of the presence of the Delta variant, was identified in the only sample where a complete genomic sequence could be obtained. Significant differences (p < 0.05) between the physicochemical conditions of the wastewater samples positive and negative for the virus were found. Our results support proof of concept that wastewater surveillance can serve as an early warning system for SARS-CoV-2 outbreaks, complementing public health surveillance in those regions where COVID-19 is currently underreported.

Profiling the bacterial microbiome diversity and assessing the potential to detect antimicrobial resistance bacteria in wastewater in Kimberley, South Africa

Wastewater treatment plants (WWTPs) are hotspots for pathogens, and can facilitate horizontal gene transfer, potentially releasing harmful genetic material and antimicrobial resistance genes into the environment. Little information exists on the composition and behavior of microbes in WWTPs, especially in developing countries. This study used environmental DNA (eDNA) techniques to examine the microbiome load of wastewater from WWTPs. The DNA was isolated from wastewater samples collected from the treatment trains of three WWTPs in Kimberley, South Africa, and the microbial diversity and composition was compared through 16 S rRNA gene sequencing. The microbes detected were of the Kingdom Bacteria, and of these, 48.27% were successfully identified to genus level. The majority of reads from the combined bacterial data fall within the class Gammaproteobacteria, which is known to adversely impact ecological and human health. Arcobacteraceae constituted 19% of the bacterial reads, which is expected as this family is widespread in aquatic environments. Interestingly, the most abundant bacterial group was Bacteroides, which contain a variety of antibiotic-resistant members. Overall, various antibiotic-resistant taxa were detected in the wastewater, indicating a concerning level of antibiotic resistance within the bacterial community. Therefore, eDNA analysis can be a valuable tool in monitoring and assessing the bacterial microbiome in wastewater, thus providing important information for the optimization and improvement of wastewater treatment systems and mitigate public health risks.

Evaluation of sequencing batch reactor performance in handling organic shock loads of ammonia wastewater

Evaluation of sequencing batch reactor performance in handling organic shock loads of ammonia wastewater

Mitigating pesticide pollution: A comprehensive study on the biological treatment of clodinafop-propargyl and organic load in agricultural wastewater using a moving bed biofilm reactor (MBBR)

The widespread use of pesticides in modern agriculture poses significant threats to public health and the environment. Addressing the growing environmental and economic concerns requires effective strategies for removing and mitigating agricultural toxins. This study evaluates the efficiency of a Moving Bed Biofilm Reactor (MBBR) in degrading clodinafop-propargyl (CF) and reducing organic matter in a controlled laboratory setting. The research focuses on initial CF concentration and hydraulic retention time (HRT) as key factors influencing removal efficiency. The biodegradation of CF was systematically carried out using the MBBR bioreactor. Results demonstrated maximum removal efficiencies of 82.51 % for CF, 88.78 % for Chemical Oxygen Demand (COD), 89.58 % for Total Phosphorus (TP), and 86.31 % for Total Nitrogen (TN). These findings highlight the MBBR's strong capability in eliminating CF and reducing nitrogen in wastewater, positioning it as a cost-effective and environmentally sustainable solution for pesticide pollution. The novelty of this study lies in its detailed examination of the MBBR's performance under varying operational conditions, providing critical insights into its efficiency and optimization. Overall, this research offers valuable contributions to environmental engineering, presenting a promising method for treating pesticide-laden agricultural wastewater and supporting sustainable farming practices.

Wastewater-Based Surveillance Reveals the Effectiveness of the First COVID-19 Vaccination Campaigns in Assisted Living Facilities.

The COVID-19 pandemic has disproportionately affected vulnerable populations, including residents of assisted living facilities (ALFs). This study investigates the impact of non-pharmaceutical interventions (NPIs) and mass vaccination campaigns on SARS-CoV-2 transmission dynamics within four ALFs in Maricopa County, Arizona, United States from January to April 2021. Initial observations reveal a significant SARS-CoV-2 prevalence in Maricopa County, with 7452 new COVID-19 cases reported on 4 January 2021. Wastewater surveillance indicates elevated viral loads within ALFs with peak concentrations reaching 1.35 × 107 genome copies/L at Facility 1 and 4.68 × 105 copies/L at Facility 2. The implementation of NPIs, including isolation protocols, resulted in a rapid decline in viral loads in wastewater. Following mass vaccination campaigns, viral loads reduced across all facilities, except Facility 4. Facility 1 demonstrated a mean viral load decrease from 1.65 × 106 copies/L to 1.04 × 103 copies/L post-vaccination, with a statistically significant U-statistic of 28.0 (p-value = 0.0027). Similar trends are observed in Facilities 2 and 3, albeit with varying degrees of statistical significance. In conclusion, this study provides evidence supporting the role of NPIs and vaccination campaigns in controlling SARS-CoV-2 transmission within ALFs.

Simulation and Analysis of Water Quality Improvement Measures for Plain River Networks Based on Infoworks ICM Model: Case Study of Baoying County, China

The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects.

Robust PD/A biofilms sustain advanced real municipal wastewater treatment: Reinforcement of interspecific communication and electron transfer

In this study, a robust partial denitrification/anammox (PD/A) biofilm system was efficiently established within merely 83 days using solely conventional activated sludge as the inoculum, employing a multivariate regulation strategy involving the addition of biofillers, optimization of municipal wastewater feeding ratios, prolongation of hydraulic retention time, and introduction of hydrazine. The performance variability of PD/A system within a long-term, intricate mainstream substrate environment was further investigated by incrementally elevating municipal wastewater proportion (50 % → 75 % → 100 %). The results demonstrated that PD/A system ultimately achieved an impressive total nitrogen removal efficiency of 93.6 % and an effluent total nitrogen concentration of 8.6 mg/L, accompanied by anammox contributing over 85 % to nitrogen removal. The biofilm’s layered spatial structure nurtured Candidatus Brocadia, enabling it to reach an abundance of 2.5 % under high real municipal wastewater loading. Metabolic function analyses revealed that biofilm enhanced the abundance of genes associated with interspecies electron transfer and quorum sensing communication in PD/A consortia after prolonged exposure to municipal wastewater. These findings underscore the significances of interspecific interactions, bacterial communications, and synergistic metabolic traits within PD/A biofilm systems in the stabilization of real municipal wastewater treatment.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

Normalization of viral loads in Wastewater-Based Epidemiology using routine parameters: One year monitoring of SARS-CoV-2 in urban and tourist sewersheds

In wastewater-based epidemiology, normalization of experimental data is a crucial aspect, as emerged in the recent surveillance of COVID-19. Normalization facilitates the comparison between different areas or periods, and it helps in evaluating the differences due to the fluctuation of the population due to seasonal employment or tourism. Analysis of biomarkers in wastewater (i.e. drugs, beverage and food compounds, microorganisms such as PMMoV or crAssphage, etc.) is complex to perform, and it is not routinely monitored. This study compares the results of alternative normalization approaches applied to SARS-CoV-2 loads in wastewater using population size calculated with conventional hydraulic and/or chemical parameters (i.e. total suspended solids, chemical oxygen demand, nitrogen forms, etc.) commonly used in the routine monitoring of water quality. A total of 12 wastewater treatment plants were monitored, and 1068 samples of influent wastewater were collected in urban areas and in highly touristic areas (summer and/or winter). The results indicated that both census and population estimated with ammonium are effective and reliable parameters with which to normalize SARS-CoV-2 loads in wastewater from urban sewersheds with negligible fluctuating populations. However, this study reveals that, in the case of tourist locations, the population calculated using NH4-N loads can provide a better normalization of the specific viral load per inhabitant.

Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology.

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

Performance Evaluation of a Taro (Colocasia esculenta) Assisted Vermifilter for Swine Wastewater Treatment

Untreated swine wastewater is one of the main contributors to the problem of water pollution in areas where swine farming is prevalent. Lack of wastewater treatment facilities can cause nutrient buildup in bodies of water, that result in adverse environmental effects such as eutrophication and can cause the buildup of pathogens in bodies of water. This study evaluated the feasibility of a vermifilter using African Night Crawlers (Eudrilus eugeniae) and taro (Colocasia esculenta) in treating swine wastewater. The cylindrical vermifilters each had a diameter of 35cm, 50cm of freeboard, 15cm of soil substrate, and 35cm of gravel of mixed sizes. One vermifilter was planted with taro plants (TAVF) while the other was not (VF). Water samples were collected from the effluent of the respective setups, and were analyzed for electrical conductivity (EC), total dissolved solids (TDS), oxidation-reduction potential (ORP), dissolved oxygen (DO), and pH. Results from the Taro Assisted Vermifilter showed an average of 70.45% and 70.50% removal efficiencies for EC and TDS, respectively. The observed average increase in pH was 0.66, while the effluent ORP values for the TAVF exceeded 220mV. The TAVF showed no signs of clogging throughout the wastewater loading period, and a significant increase in the earthworm mass was observed. The plants used were also observed to have grown significantly throughout the experiment. The TAVF, however, did not perform better than the VF in terms of removal efficiency. Overall, the system demonstrated potential as a treatment facility for swine farms with significant wastewater effluent and showed efficiency with extended periods of acclimatization.

Estimating the COVID-19 prevalence from wastewater

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland–Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland–Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland–Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor (0.208±0.031\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.208 \\pm 0.031$$\\end{document}) and a delay (5.07±2.30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.07 \\pm 2.30$$\\end{document} days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.