Wastewater surveillance of SARS-CoV-2 and chemical markers in campus dormitories in an evolving COVID - 19 pandemic

Usefulness of aircraft and airport wastewater for monitoring multiple pathogens including SARS-CoV-2 variants

BACKGROUND

As global travel resumed in coronavirus disease 2019 (COVID-19) endemicity, the potential of aircraft wastewater monitoring to provide early warning of disease trends for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and other infectious diseases, particularly at international air travel hubs, was recognized. We therefore assessed and compared the feasibility of testing wastewater from inbound aircraft and airport terminals for 18 pathogens including SARS-CoV-2 in Singapore, a popular travel hub in Asia.

METHODS

Wastewater samples collected from inbound medium- and long-haul flights and airport terminals were tested for SARS-CoV-2. Next Generation Sequencing was carried out on positive samples to identify SARS-CoV-2 variants. Airport and aircraft samples were further tested for 17 other pathogens through quantitative reverse transcription polymerase chain reaction.

RESULTS

The proportion of SARS-CoV-2-positive samples and the average virus load was higher for wastewater samples from aircraft as compared with airport terminals. Cross-correlation analyses indicated that viral load trends from airport wastewater led local COVID-19 case trends by 2-5 days. A total of 10 variants (44 sub-lineages) were successfully identified from aircraft wastewater and airport terminals, and four variants of interest and one variant under monitoring were detected in aircraft and airport wastewater 18-31 days prior to detection in local clinical cases. The detection of five respiratory and four enteric viruses in aircraft wastewater samples further underscores the potential to expand aircraft wastewater to monitoring pathogens beyond SARS-CoV-2.

CONCLUSION

Our findings demonstrate the feasibility of aircraft wastewater testing for monitoring infectious diseases threats, potentially detecting signals before clinical cases are reported. The triangulation of similar datapoints from aircraft wastewater of international travel nodes could therefore serve as a useful early warning system for global health threats.

Impact of urban pollution on freshwater biofilms: Oxidative stress, photosynthesis and lipid responses

Urban ecosystems are subjected to multiple anthropogenic stresses, which impact aquatic communities. Artificial light at night (ALAN) for instance can significantly alter the composition of algal communities as well as the photosynthetic cycles of autotrophic organisms, possibly leading to cellular oxidative stress. The combined effects of ALAN and chemical contamination could increase oxidative impacts in aquatic primary producers, although such combined effects remain insufficiently explored. To address this knowledge gap, a one-month experimental approach was implemented under controlled conditions to elucidate effects of ALAN and dodecylbenzyldimethylammonium chloride (DDBAC) on aquatic biofilms. DDBAC is a biocide commonly used in virucidal products, and is found in urban aquatic ecosystems. The bioaccumulation of DDBAC in biofilms exposed or not to ALAN was analyzed. The responses of taxonomic composition, photosynthetic activity, and fatty acid composition of biofilms were examined. The results indicate that ALAN negatively affects photosynthetic yield and chlorophyll production of biofilms. Additionally, exposure to DDBAC at environmental concentrations induces lipid peroxidation, with an increase of oxylipins. This experimental study provides first insights on the consequences of ALAN and DDBAC for aquatic ecosystems. It also opens avenues for the identification of new biomarkers that could be used to monitor urban pollution impacts in natural environments.

Urban Wastewater-Based Surveillance of SARS-CoV-2 Virus: A Two-Year Study Conducted in City of Patras, Greece

Wastewater-based epidemiology, during the COVID-19 pandemic years, has been applied as a complementary approach, worldwide, for tracking SARS-CoV-2 virus into the community and used as an early warning of the prevalence of COVID-19 infection. The present study presents the results of the 2-year surveillance project, in the city of Patras, Greece. The purpose of the study was to monitor SARS-CoV-2 and implement WBE as an early warning method of monitoring Public Health impact. The presence of SARS-CoV-2 was determined and quantified in 310 samples using RT-qPCR assays. For the years 2022 and 2023, 93.5% and 78.7% of samples were found positive, respectively. Comparison of detection methods have been conducted to select the method with the highest recovery of the viral load. A seasonal variation of the virus was recorded, showing a recession in summer months confirming the country's epidemiological data as indicated by positive correlation of wastewater viral load with registered cases of COVID-19 infections during these years (p < 0.05) and moreover sealed with a significant negative correlation observed with Daily Average (p < 0.01) and Daily Maximum Temperature (p < 0.01). More research was carried out to elucidate a possible association of physicochemical characteristics of wastewater with viral load showing positive correlation with Chlorides (p < 0.01) advocating possible increased use of chlorine-based disinfectants and Electrical Conductivity (p < 0.01) indicates that wastewater during periods of increased infections is more heavily loaded with ions from chemical and biological pollutants. No correlation found with rainfall and physicochemical indicators, such as COD, BOD5, Total Phosphorus, Total Nitrogen, and Total Suspended Solids. According to the findings, WBE represents a useful tool in the management of epidemics based on an environmental approach and it can also shed light on the interacting parameters that capture Public Health since any infections that may lead to epidemics lead to a parallel change in the use of pharmaceuticals, antimicrobials, disinfectants, and microbial load in urban wastewater.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

This study presents the results of systematic wastewater monitoring of SARS-CoV-2 RNA and basic wastewater parameters from four different wastewater treatment plants (WWTPs) in the Czech Republic over the 2020-2022 epidemic. Two-step reverse-transcription quantitative PCR targeting genes encoding the N and Nsp12 proteins was employed to detect SARS-CoV-2 RNA loading in 420 wastewater samples. The results obtained were used to evaluate the potential of wastewater analysis for describing the epidemiological situation in cities of different sizes and determining temporal differences based on the prevailing SARS-CoV-2 variant. Strong correlations between the number of active and hospitalised COVID-19 cases in each WWTP catchment area and the concentration of SARS-CoV-2 RNA detected in the wastewater clearly demonstrated the suitability of this wastewater-based epidemiological approach for WWTPs of different sizes and characteristics, despite differences in SARS-CoV-2 variant waves, with some WWTPs showing high predictive potential. This study demonstrated on the data from the Czech Republic that targeted systematic monitoring of wastewater provides sufficiently robust data for surveillance of viral loads in sample populations, and thus contributes to preventing the spread of infection and subsequent introduction of appropriate measures.

Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Photochemical fate of quaternary ammonium compounds (QACs) and degradation pathways predication through computational analysis

Quaternary ammonium compounds (QACs) are commonly used in many products, such as disinfectants, detergents and personal care products. However, their widespread use has led to their ubiquitous presence in the environment, posing a potential risk to human and environmental health. Several methods, including direct and indirect photodegradation, have been explored to remove QACs such as benzylalkyldimethyl ammonium compounds (BACs) and alkyltrimethyl ammonium compounds (ATMACs) from the environment. Hence, in this research, a systematic review of the literature was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to understand the fate of these QACs during direct and indirect photodegradation in UV/H2O2, UV/PS, UV/PS/Cu2+, UV/chlorine, VUV/UV/chlorine, O3/UV and UV/O3/TiO2 systems which produce highly reactive radicals that rapidly react with the QACs, leading to their degradation. As a result of photodegradation, several transformation products (TPs) of QACs are formed, which can pose a greater risk to the environment and human health than the parent QACs. Only limited research in this area has been conducted with fewer QACs. Hence, quantum mechanical calculations such as density functional theory (DFT)-based computational calculations using Gaussian09 software package were used here to explain better the photo-resistant nature of a specific type of QACs, such as BACs C12-18 and ATMACs C12, C14, C18, and their transformation pathways, providing insights into active sites participating in the phototransformation. Recognizing that different advanced oxidation processes (AOPs) come with pros and cons in the elimination of QACs, this review also highlighted the importance of implementing each AOP concerning the formation of toxic transformation products and electrical energy per order (EEO), especially when QACs coexist with other emerging contaminants (ECs).

Exploring patterns of antibiotics during and after COVID-19 pandemic in wastewaters of northern Greece: Potential adverse effects on aquatic environment

Antibiotics, recognized as Emerging Contaminants (ECs), have raised concerns due to their pervasive presence in wastewater treatment plants (WWTPs) and subsequent release into aquatic environments, posing potential ecological risks and contributing to the development of antibiotic-resistant genes. The COVID-19 pandemic prompted an unprecedented surge in antibiotic consumption, necessitating a comprehensive assessment of its impact on antibiotic levels in wastewater. In this light, a four-year monitoring study (2020-2023) was conducted in a WWTP located in the Northern Greece (Thessaloniki), employing High-Resolution Mass Spectrometry (HRMS) technology to monitor twenty antibiotics, during distinct phases pre-, during, and post-COVID-19. Our findings revealed that macrolides and fluoroquinolones were among the most often detected categories during the sampling period. Among the compounds detected, azithromycin and clarithromycin showed the most significant increases during the pandemic, doubling their average concentrations. This establishes a clear correlation between the rise in their concentrations and the incidence of COVID-19 cases. A general downward trend after 2021 was attributed to the new restrictions posed in Greece during this year, regarding the liberal prescription of antibiotics. Seasonal variation revealed a minute augmentation of antibiotics' use during the months that infections are increased. Additionally, the study highlights the ecological risks associated with elevated antibiotic presence and emphasizes the need for continued monitoring and regulatory measures to mitigate potential ecological repercussions. These findings contribute to our understanding of the complex interplay between antibiotic consumption, environmental presence, and the COVID-19 pandemic's impact on antibiotic pollution in WWTPs.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Estimating COVID-19 cases on a university campus based on Wastewater Surveillance using machine learning regression models

Wastewater Surveillance (WS) is a crucial tool in the management of COVID-19 pandemic. The surveillance is based on enumerating SARS-CoV-2 RNA concentrations in the community's sewage. In this study, we used WS data to develop a regression model for estimating the number of active COVID-19 cases on a university campus. Eight univariate and multivariate regression model types i.e. Linear Regression (LM), Polynomial Regression (PR), Generalised Additive Model (GAM), Locally Estimated Scatterplot Smoothing Regression (LOESS), K Nearest Neighbours Regression (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Random Forest (RF) were developed and compared. We found that the multivariate RF regression model, was the most appropriate for predicting the prevalence of COVID-19 infections at both a campus level and hostel-level. We also found that smoothing the normalised SARS-CoV-2 data and employing multivariate modelling, using student population as a second independent variable, significantly improved the performance of the models. The final RF campus level model showed good accuracy when tested using previously unseen data; correlation coefficient of 0.97 and a mean absolute error (MAE) of 20 %. In summary, our non-intrusive approach has the ability to complement projections based on clinical tests, facilitating timely follow-up and response.

Positive association of SARS-CoV-2 RNA concentrations in wastewater and reported COVID-19 cases in Singapore - A study across three populations

Wastewater testing of SARS-CoV-2 has been adopted globally and has shown to be a useful, non-intrusive surveillance method for monitoring COVID-19 trends. In Singapore, wastewater surveillance has been widely implemented across various sites and has facilitated timely COVID-19 management and response. From April 2020 to February 2022, SARS-CoV-2 RNA concentrations in wastewater monitored across three populations, nationally, in the community, and in High Density Living Environments (HDLEs) were aggregated into indices and compared with reported COVID-19 cases and hospitalisations. Temporal trends and associations of these indices were compared descriptively and quantitatively, using Poisson Generalised Linear Models and Generalised Additive Models. National vaccination rates and vaccine breakthrough infection rates were additionally considered as confounders to shedding. Fitted models quantified the temporal associations between the indices and cases and COVID-related hospitalisations. At the national level, the wastewater index was a leading indicator of COVID-19 cases (p-value <0.001) of one week, and a contemporaneous association with hospitalisations (p-value <0.001) was observed. At finer levels of surveillance, the community index was observed to be contemporaneously associated with COVID-19 cases (p-value <0.001) and had a lagging association of 1-week in HDLEs (p-value <0.001). These temporal differences were attributed to differences in testing routines for different sites during the study period and the timeline of COVID-19 progression in infected persons. Overall, this study demonstrates the utility of wastewater surveillance in understanding underlying COVID-19 transmission and shedding levels, particularly for areas with falling or low case ascertainment. In such settings, wastewater surveillance showed to be a lead indicator of COVID-19 cases. The findings also underscore the potential of wastewater surveillance for monitoring other infectious diseases threats.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

Hazardous radical-coupled transformation products of benzophenone-3 formed during manganese dioxide treatment

Radical-coupled transformation products (TPs) have been identified as the byproducts of various transformation processes, including both natural attenuation and artificial treatments, of phenolic micropollutants. Benzophenone-3 (BP-3), an organic UV filter of emerging concern, has been previously reported with ubiquitous occurrence in the natural environment and water bodies. Current research has demonstrated how TPs are formed from BP-3 when it is treated with manganese oxide (MnO2). The ecological and toxicological risks of these TPs have also been assessed. Polymerization of BP-3 through radical coupling was observed as the major pathway by which BP-3 is transformed when treated with MnO2. These radical-coupled TPs haven't shown further degradation after formation, suggesting their potential persistence once occurred in the environment. In silico experiments predict the radical-coupled TPs will increase in mobility, persistence and ecotoxicity. If true, they also represent an ever-increasing threat to the environment, ecosystems and, most immediately, aquatic living organisms. In addition, radical-coupled TPs produced by MnO2 transformation of BP-3 have shown escalated estrogenic activity compared to the parent compound. This suggests that radical coupling amplifies the toxicological impacts of parent compound. These results provide strong evidence that radical-coupled TPs with larger molecular sizes are having potential adverse impacts on the ecosystem and biota.

Knowledge, attitudes and practice regarding environmental friendly disinfectants for household use among residents of China in the post-pandemic period

Background

A sharp rise in household consumption of disinfectants triggered by COVID-19 pandemic has generated tremendous environmental burden and risks of disinfectant emissions in the post-pandemic period. To address this emerging challenge, replacing highly hazardous disinfectants with more environmental friendly alternatives has been accepted as an inherently effective solution to environment issues posed by disinfectant emerging contaminants. However, no study has yet been done to explore the potential customers' attitudes and the market prospect of environmental friendly disinfectants until now.

Methods

This cross-sectional questionnaire-based survey was conducted from January to March, 2022, among resident volunteers in China, to explore the practices, knowledge and attitudes of the public regarding environmental friendly disinfectants for household use.

Results

Among a total of 1,861 Chinese residents finally included in the analyses, 18% agreed or strongly agreed that they paid special attention to the environmental certification label on the product, and only bought the environmental certified disinfectant products; 16% and 10% were using environmental friendly disinfectants for hand sanitization and environmental disinfection, respectively. The mean self-assessed and actual knowledge scores were 2.42 ± 1.74 and 2.12 ± 1.97, respectively, out of a total of 5. Participants having good practices of consuming environmental friendly disinfectants achieved higher knowledge scores. Residents' overall attitudes toward the development, consumption and application of environmental friendly disinfectants were very positive. "Possible conflict between disinfection effectiveness and environmental factor of disinfectants in a context of severe COVID-19 pandemic" was considered as the most important barrier jeopardizing the participants' usage intention for environmental friendly disinfectants.

Conclusions

These data suggested most residents of China had a positive attitude, poor knowledge and practices toward environmental friendly disinfectants. More should be done to enhance the residents' environmental knowledge levels about disinfectants, and to further develop and promote disinfectant products with both excellent disinfection activity and environmentally friendly attributes.