Microbial diversity characterizations, associated pathogenesis and antimicrobial resistance profiling of Najafgarh drain

Assessment of industrial effluent discharges contributing to Ganga water pollution through a multivariate statistical framework: investigating the context of Indian industries

The swift industrial expansion has posed significant environmental challenges, particularly in the context of water pollution. Industrial effluents consist of substantial amounts of harmful pollutants that enter the main rivers via various tapped and untapped drains/local water streams, causing alterations in their physical and chemical properties. This study investigated 153 grossly polluting industries (GPIs) that were identified to release their effluents into the main rivers through different drains within multiple sectors in the industrial zone of four northern states of India in 2023. Physicochemical analysis, multivariate statistical analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and spatial analysis were conducted to evaluate the impact of these discharges. The results show significant variations in mean concentrations, such as pH (6.55-8.42), biochemical oxygen demand (6-707.83 mg/l), chemical oxygen demand (20-1504.25 mg/l), total suspended solids (5-417 mg/l), total dissolved solids (560-9908 mg/l), and chloride (101-4360.7 mg/l) across all the sectors. PCA results indicated that two principal loadings significantly influence the wastewater chemistry. PC1 accounts for 49.85% of the variance, reflecting organic and nutrient pollution, while PC2 contributes 19.128% of the total variance, reflecting the dominance of chloride, dissolved solids, and chemical oxygen demand. HCA classified the GPIs into six clusters for their substantial roles in releasing highly polluted (C3, C4, C5, and C6), moderately polluted (C2), and less polluted (C1) wastewater. Overall findings reveal the alarming magnitude of industrial wastewater discharge into the rivers, emphasizing the urgent need for improved regulatory frameworks, stricter enforcement of environmental laws, and greater corporate responsibility.

Assessment of Bacterial Community Structure, Associated Functional Role, and Water Health in Full-Scale Municipal Wastewater Treatment Plants

The present study collected wastewater samples from fourteen (14) full-scale wastewater treatment plants (WWTPs) at different treatment stages, namely, primary, secondary, and tertiary, to understand the impact of WWTP processes on the bacterial community structure, their role, and their correlation with environmental variables (water quality parameters). The findings showed that the bacterial communities in the primary, secondary, and tertiary treatment stages are more or less similar. They are made up of 42 phyla, 84 classes, 154 orders, 212 families, and 268 genera. Proteobacteria, Bacteroidetes, Cloacimonetes, Firmicutes, Euryarchaeota, Verrucomicrobia, Cyanobacteria, Desulfomicrobium, Thauera, Zavarzinia, and Nitrospirae, among others, dominated the bacterial community structure in all treatment stages. The biochemical oxygen demand was 7-12 times, chemical oxygen demand (COD) was 6 times, and total suspended solids (TSS) was 3.5 times higher in the wastewater than what the Central Pollution Control Board (CPCB) in New Delhi, India, allows as standard discharge. The correlation analysis using the Pearson r matrix and canonical correspondence analysis (CCA) also confirmed the fact that these water quality parameters (especially BOD and COD) play a pivotal role in deciphering the community structure in WWTPs.

Distribution and driving mechanisms of antibiotic resistance genes in urbanized watersheds

Antibiotic resistance genes (ARGs) have emerged as a global concern, posing significant threats to human health and safety. Understanding the contamination levels and driving mechanisms behind ARG proliferation is urgently needed. Urban watersheds, influenced by human activities, serve as critical reservoirs for ARGs; however, the impact of urbanization on ARG spread of and the underlying driving mechanisms remain unclear. This study evaluates the diversity and abundance of ARGs in water and sediment samples from the Jialing River watershed in Chongqing City, China. The obtained results indicate that aminoglycoside and multidrug ARGs are the primary contributors to ARG presence in both sediments and water. Additionally, the diversity and abundance of ARGs are higher in water than in sediments. ARGs in watershed show a significant positive correlation with mobile genetic elements (MGEs). While environmental factors in urbanized watersheds affect ARG abundance and distribution to some extent, they are not the primary drivers. Urbanization itself emerges as a prominent factor influencing ARG diversity and abundance in river basins. Specifically, livestock, healthcare, and agriculture are identified as the main social factors influencing ARG proliferation in the highly urbanized areas of the Jialing River watershed. Further investigation into other contributing social factors, such as industrial development, is warranted. This study reveals the factors driving ARG distribution in urbanized watersheds, providing a foundation for future efforts to maintain ecological health in these environments.

Metabarcoding Analysis of Microorganisms Inside Household Washing Machines in Shanghai, China

Washing machines are one of the tools that bring great convenience to people's daily lives. However, washing machines that have been used for a long time often develop issues such as odor and mold, which can pose health hazards to consumers. There exists a conspicuous gap in our understanding of the microorganisms that inhabit the inner workings of washing machines. In this study, samples were collected from 22 washing machines in Shanghai, China, including both water eluted from different parts of washing machines and biofilms. Quantitative qualitative analysis was performed using fluorescence PCR quantification, and microbial communities were characterized by high-throughput sequencing (HTS). This showed that the microbial communities in all samples were predominantly composed of bacteria. HTS results showed that in the eluted water samples, the bacteria mainly included Pseudomonas, Enhydrobacter, Brevibacterium, and Acinetobacter. Conversely, in the biofilm samples, Enhydrobacter and Brevibacterium were the predominant bacterial microorganisms. Correlation analysis results revealed that microbial colonies in washing machines were significantly correlated with years of use and the type of detergent used to clean the washing machine. As numerous pathogenic microorganisms can be observed in the results, effective preventive measures and future research are essential to mitigate these health problems and ensure the continued safe use of these household appliances.