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The Small Volume Ethyl Acetate based Liquid-Liquid Extraction (SVEA-LLE) method, using Atmospheric Pressure Gas Chromatography-Mass Spectrometry (APGC-MS/MS) with Dual Head Robotic Tool Change (DH-RTC) Prep and Load (PAL) autosampler, was evaluated for the analysis of 17 PAHs in water and wastewater. The extraction method was first evaluated for extraction efficiency with respect to time using a one-way Analysis of Variance (ANOVA) and the effect of acidification. Method validation was performed as per SANTE/11312/2021 v2 2024 for analysis of PAHs in wastewater. Linearity was determined over a range of 5 to 80 ng/ml, meeting the acceptable coefficient of determination (r 2 ) and the values of % deviation from back-calculated concentration. The method was observed to be sensitive enough, as the estimated Limits of Detection (LOD) and Quantification (LOQ) were observed to be lower than or close to the Limits specified in EPA 625.1/2016 method, without the need for evaporative concentration. During the method validation trials, it was observed that 82.71 % of the observations were below 10 % RSD, 14.19 % were in the range of 10 % to 15 % RSD, and 3.08 % were between 15 % and 20 % RSD (n = 6). Similarly, in the case of mean % recovery, 92.59 % of the observations were between 80 to 120 % recovery, 3.70 % of the observations were between 70 to 80 % recovery, 3.08 % of the observations were 120 to 130 % recovery (n = 6). It was observed that mean % recovery of robustness study (n = 18) ranged from 90.21 to 117.82 (observed for phenanthrene and benzo (a) pyrene respectively). Further, the % RSD from robustness study ranged between 5.11 to 18.98 (observed for acenaphthylene and pyrene respectively). Measurement Uncertainty of all the analytes ranged from 3.73 to 8.92 % (observed for fluorene and pyrene, respectively). The method described has been observed to be fit for purpose, as it meets the method validation requirements with acceptable results in PT participation. The greenness assessment of the method using tools like the Analytical Eco-Scale (AES) and the Green Analytical Procedure Index (GAPI) indicated that method ranks excellent in greenness evaluation and is eco-friendly. The optimized sample preparation method is short, safe, and easy with an extract suitable for direct injection onto GC column, eliminating the concentration and reconstitution steps for high-throughput analysis of PAHs in water and wastewater., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Tracking metagenomic abundance in wastewater is undoubtedly a powerful tool to detect emerging variants and improve community health. However, there are a few factors that limit environmental water-based genomic monitoring: sampling variability, incomplete coverage, genetic fragmentation, degradation, data analysis and interpretation. The decreasing costs of high-throughput sequencing and high-end supercomputers have increased the use and accuracy of genomic data for microbial detection and monitoring in wastewater samples within any given region. To better understand the microbial dynamics and to determine the target sequencing throughput required to establish taxa that may pose as bio-indicators of an epidemiological outbreak, wastewater samples were collected from distinct locations within the Emirate of Abu Dhabi, United Arab Emirates using appropriate sampling methods. A reference database of ∼27,000 known species was developed and used for further analysis. The results showed that 15 % of data in each sample matched any of ∼27,000 known bacterial, viral, fungal, or protozoan species. Despite the high fraction of unclassified data (85 %), more than 2000 species from >800 genera across >30 phyla were detected in each sample. Both 5 Gb and 10 Gb of sequenced data detected the top ∼2000 species with highest abundance. Doubling the target sequencing throughput (i.e., 10 Gb vs 5 Gb) detected ∼500 additional low-abundance species per sample however it did not affect the overall sample composition or translate into higher per-sample species diversity captured. There was a marginal increase in the number of species detected in each sample beyond 0.20 Gb of classified data. Overall, the results indicate that sequencing to a 3 Gb throughput detects nearly 95 % of all species in the samples., Competing Interests: Declaration of competing interest The author(s) declare no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

During the recent COVID-19 pandemic, wastewater-based epidemiological (WBE) surveillance played a crucial role in evaluating infection rates, analyzing variants, and identifying hot spots in a community. This expanded the possibilities for using wastewater to monitor the prevalence of infectious diseases. The full potential of WBE remains hindered by several factors, such as a lack of information on the survival of pathogens in sewage, heterogenicity of wastewater matrices, inconsistent sampling practices, lack of standard test methods, and variable sensitivity of analytical techniques. In this study, we review the aforementioned challenges, cost implications, process automation, and prospects of WBE for full-fledged wastewater-based community health screening. A comprehensive literature survey was conducted using relevant keywords, and peer reviewed articles pertinent to our research focus were selected for this review with the aim of serving as a reference for research related to wastewater monitoring for early epidemic detection.

Introduction: The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings., Methods: This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process., Results: Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2., Discussion: The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized., Conclusion: Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Singh, Ahmed, Almansoori, Alameri, Adlan, Odivilas, Chattaway, Salem, Brudecki and Elamin.)