Sudipti Arora

Antibiotic‐resistant microorganisms in wastewater pose significant threats to public health and the environment due to the limited effectiveness of conventional treatment systems. This study investigates the potential of vermifiltration technology, utilising Eisenia fetida earthworms, to reduce antibiotic‐resistant bacteria (ARB) and pollutant load in institutional wastewater by comparing a vermifilter (VF) against a biofilter (BF) lacking earthworms at pilot scale. Treatment performance was evaluated based on physicochemical parameters, microbial community shifts and antimicrobial resistance (AMR) reduction assessed using the VITEK 2 system. VF achieved substantial reductions in biochemical oxygen demand (85%), chemical oxygen demand (82%), total suspended solids (64%), ammoniacal nitrogen and pathogen load. Microbial profiling revealed a shift toward non‐pathogenic Gram‐positive bacteria, with Bacillus (25%) becoming dominant and E. coli being reduced to 10%. PCA revealed reduced fluoroquinolone, beta‐lactam and aminoglycoside resistance in vermifiltration due to earthworm‐induced aeration and diversity, suggesting its sustainable potential for wastewater treatment and resistance control.

Wastewater-based epidemiology (WBE) is increasingly recognized as a valuable tool for monitoring disease cycles, including pandemics like COVID-19. Unlike pandemics, epidemics exhibit distinct dynamics, spread patterns, multiple origin points, and varying levels of population immunity. This study evaluates the applicability of WBE for epidemic monitoring and emergency preparedness by analyzing SARS-CoV-2 presence in Jaipur’s wastewater over one year post-second pandemic wave, covering a minor surge (third) and a mild resurgence (fourth) of COVID-19. A total of 1050 samples from different city localities were analyzed using a combination of two concentration methods (the direct method and the Polyethylene Glycol (PEG) method) and two detection kits (qualitative and quantitative). WBE effectively detected both minor and major epidemic outbreak cycles of SARS-CoV-2. A total of 62.91% samples out of all untreated samples tested, were found to be positive with viral genome; however, the positivity rate of any particular day did not exceed 25% even during the peaks. Notably, short-distance transportation under ambient conditions had no significant impact (p > 0.05) on detection, and the combination of the direct method with quantitative kits provided the highest sensitivity. Based on these findings, a cost-effective bi-phase surveillance model is proposed for year-round epidemic monitoring. This model suggests routine use of the faster, cheaper direct method, switching to the PEG concentration method during rising viral loads for enhanced resolution. Such an approach ensures sustainable, resource-efficient surveillance, particularly benefiting low- and middle-income countries facing financial constraints.

Wastewater-based epidemiology (WBE) is increasingly recognized as a valuable tool for monitoring disease cycles, including pandemics like COVID-19. Unlike pandemics, epidemics exhibit distinct dynamics, spread patterns, multiple origin points, and varying levels of population immunity. This study evaluates the applicability of WBE for epidemic monitoring and emergency preparedness by analyzing SARS-CoV-2 presence in Jaipur’s wastewater over one year. A total of 1050 samples from different city localities were analyzed using a combination of two concentration methods (Direct and PEG) and two detection kits (qualitative and quantitative). The study found that WBE effectively detects both minor fluctuations and major waves of SARS-CoV-2. Notably, short-distance transportation under ambient conditions had no significant impact on detection, and the combination of the direct method with quantitative kits provided the highest sensitivity. Based on these findings, a cost-effective bi-phase surveillance model is proposed for year-round epidemic monitoring. This model suggests routine use of the faster, cheaper direct method, switching to the PEG concentration method during rising viral loads for enhanced resolution. Such an approach ensures sustainable, resource-efficient surveillance, particularly benefiting low- and middle-income countries facing financial constraints.

This research addresses the critical issue of industrial wastewater pollution, focusing on the textile and paper and pulp industries. Conventional treatment methods often suffer from inefficiencies, making bioremediation a sustainable alternative for mitigating hazardous compounds in industrial effluents. The study characterizes novel indigenous bacterial isolates from both industries, highlighting their potential for azo dye decolorization and Cr (VI) bioreduction. Physico-chemical analysis of the influents helped design effective bioremediation strategies. Microbial diversity analysis revealed key genera, including Lactobacillus, Streptococcus, and Corynebacterium species in textile influents, and Bacillus spp. in paper and pulp influents. Notably, Streptococcus strains achieved over 90% azo dye decolorization, confirmed by Fourier transform infrared (FTIR) spectroscopy, while Bacillus tequilensis maximized Cr (VI) biotransformation to less toxic Cr (III). The strain associated with National Center for Biotechnology Information (NCBI) accession number MK811197.1 was identified via 16S rRNA sequencing. This study emphasizes the importance of biological processes, optimizing pH and temperature to enhance microbial performance, offering a sustainable solution for industrial pollution and contributing to environmental management and industrial sustainability.

· CLEAR OBJECTIVES: The paper doesn’t clearly state its goals. · INTRODUCTION: The introduction doesn’t explain why the study is important. · SIMPLER LANGUAGE: The writing could be clearer, especially when talking about how certain mutations aff...

Wastewater-based epidemiology (WBE) has emerged as an effective environmental surveillance tool for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease outbreaks in high-income countries (HICs) with centralized sewage infrastructure. However, few studies have applied WBE alongside epidemic disease modelling to estimate the prevalence of SARS-CoV-2 in low-resource settings. This study aimed to explore the feasibility of collecting untreated wastewater samples from rural and urban catchment areas of Nagpur district, to detect and quantify SARS-CoV-2 using real-time qPCR, to compare geographic differences in viral loads, and to integrate the wastewater data into a modified Susceptible-Exposed-Infectious-Confirmed Positives-Recovered (SEIPR) model. Of the 983 wastewater samples analyzed for SARS-CoV-2 RNA, we detected significantly higher sample positivity rates, 43.7% (95% confidence interval (CI) 40.1, 47.4) and 30.4% (95% CI 24.66, 36.66), and higher viral loads for the urban compared with rural samples, respectively. The Basic reproductive number, R₀, positively correlated with population density and negatively correlated with humidity, a proxy for rainfall and dilution of waste in the sewers. The SEIPR model estimated the rate of unreported coronavirus disease 2019 (COVID-19) cases at the start of the wave as 13.97 [95% CI (10.17, 17.0)] times that of confirmed cases, representing a material difference in cases and healthcare resource burden. Wastewater surveillance might prove to be a more reliable way to prepare for surges in COVID-19 cases during future waves for authorities.

The COVID-19 pandemic has renewed research needs for the detection and monitoring of various pathogens in urban wastewater systems including sewerage systems and wastewater treatment or recycling plants

The continuous rise in consumer demand for dairy products has led to significant advancements in food and dairy technology, resulting in a substantial increase in dairy production. However, the expansion of the dairy industry has placed a strain on the environment due to the generation of waste products containing elevated levels of constituents such as casein, lactose, fat, inorganic salts, as well as detergents and sanitizers. These contribute to elevated Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD) values. While dairy facilities have implemented their own treatment plants to address these environmental challenges, there is an ongoing need to assess the efficiency of these wastewater treatment systems.In this study, a two-stage model was developed, comprising a primary tank involving microbial treatment and a secondary tank with a layer of activated charcoal powder at the bottom and sawdust above it. The treated dairy effluent from these filtration units underwent analysis for various physicochemical parameters and was compared with samples obtained directly from the dairy industry. A total of eight indigenous bacterial isolates were derived from the processing of dairy wastewater through the model. Isolates R2 and R3 were obtained from raw wastewater, S1, S2, S3 isolates from secondary treated effluent, and T1, T2, T3 isolates from tertiary treated effluent. These isolates were further characterized biochemically as Micrococcus spp., Staphylococcus aureus, Bacillus spp., Staphylococcus spp., etc. Among all isolates, S3 demonstrated the highest reduction efficiency in water parameters, namely Electrical Conductivity, Chemical Oxygen Demand, and Biochemical Oxygen Demand, with reductions of 59.26%, 70.19%, and 74%, respectively

A few of the reasons behind the introduction of the concept of biomaterials, such as biopolymers to address problems like plastic pollution, include a lower reliance on fossil resources, reduced carbon emissions, a focus on non-renewable resource allocation, and, most importantly, promoting a circular economy. The high production cost is a hindrance in scaling up the production and market for biobased and biodegradable plastics, but microbial production of such monomers or polymers using easily available and inexpensive substrates, like waste streams, can be seen as a potential strategy to overcome this challenge. These polymers, with properties similar to conventional plastics, can be applied as alternatives in different sectors. According to the Intergovernmental Panel on Climate Change (IPCC), climate change now requires immediate mitigation. Looking at this need, the construction industry has started using biobased materials to focus on reducing CO₂ emissions. Similarly, the improved barrier, mechanical, antimicrobial, and antioxidant properties; biocompatibility; and biodegradability of biomaterials like PLA, PHA, etc., make them suitable for various other sectors. The present paper will focus on highlighting the multi-functionality of such biobased materials that will further open many opportunities for significant innovation.