Combining High-Resolution Mass Spectrometry and Chemiluminescence Analysis to Characterize the Composition and Fate of Total N-Nitrosamines in Wastewater Treatment Plants.

Monitoring the prevalence and persistence of N-nitrosamines and their precursors in wastewater treatment plants (WWTPs) and effluent-receiving aquatic compartments is a priority for utilities practicing wastewater recycling or exploiting wastewater-impacted source waters. In this work, we developed an analytical framework that combines liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with acidic triiodide-chemiluminescence analysis to characterize the composition and fate of total N-nitrosamines (TONO) and their precursors along the treatment trains of eight WWTPs in New York. Through the parallel application of LC-HRMS and chemiluminescence methods, the TONO scores for 41 N-nitrosamines containing structurally diverse substituents on their amine nitrogen were derived based on their solid-phase extraction recoveries and conversion efficiencies to nitric oxide. Correcting the compositional analysis of TONO using the TONO scores of target N-nitrosamines refined the assessment of the reduction or accumulation of TONO and their precursors across treatment steps in WWTPs. Nontargeted analysis prioritized seven additional N-nitrosamines for confirmation by reference standards, including three previously uncharacterized species: N-nitroso-tert-butylphenylamine, N-nitroso-2-pyrrolidinmethanol, and N-nitrosodesloratadine, although they only served as minor components of TONO. Overall, our study establishes an adaptable methodological framework for advancing the quantitative and qualitative analysis of specific and unknown components of TONO across water treatment and reuse scenarios.

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors.

Efficient management of wastewater treatment plants (WWTPs) necessitates accurate forecasting of influent water quality parameters (WQPs) and flow rate (Q) to reduce energy consumption and mitigate carbon emissions. The time series of WQPs and Q are highly non-linear and influenced by various factors such as temperature (T) and precipitation (Precip). Conventional models often struggle to account for long-term temporal patterns and overlook the complex interactions of parameters within the data, leading to inaccuracies in detecting WQPs and Q. This work introduced the Pre-training enhanced Spatio-Temporal Graph Neural Network (PT-STGNN), a novel methodology for accurately forecasting of influent COD, ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH and Q in WWTPs. PT-STGNN utilizes influent data of the WWTP, air quality data and meteorological data from the service area as inputs to enhance prediction accuracy. The model employs unsupervised Transformer blocks for pre-training, with efficient masking strategies to effectively capture long-term historical patterns and contextual information, thereby significantly boosting forecasting accuracy. Furthermore, PT-STGNN integrates a unique graph structure learning mechanism to identify dependencies between parameters, further improving the model's forecasting accuracy and interpretability. Compared with the state-of-the-art models, PT-STGNN demonstrated superior predictive performance, particularly for a longer-term prediction (i.e., 12 h), with MAE, RMSE and MAPE at 12-h prediction horizon of 2.737 ± 0.040, 4.209 ± 0.060 and 13.648 ± 0.151 %, respectively, for the algebraic mean of each parameter. From the results of graph structure learning, it is observed that there are strong dependencies between NH3-N and TN, TP and Q, as well as Precip, etc. This study innovatively applies STGNN, not only offering a novel approach for predicting influent WQPs and Q in WWTPs, but also advances our understanding of the interrelationships among various parameters, significantly enhancing the model's interpretability.

Tracking SARS-CoV-2 and its variants in wastewater in Tunisia.

Wastewater-based genomic surveillance can improve community prevalence estimates and identify emerging variants of pathogens. Wastewater influents and treated effluents from six wastewater treatment plants (WWTPs) in Tunisia were analyzed between December 2021 and July 2022. Wastewater samples were analyzed with reverse transcription solid digital PCR (RT-sdPCR) and whole-genome sequencing to determine the amount of SARS-CoV-2 RNA and assign SARS-CoV-2 lineages. The virus variants detected in wastewater samples were compared with COVID-19 prevalence data. The quantitative results in wastewater influents revealed that viral RNA concentrations at the treatment plants corroborate with locally reported clinical cases and show an increase before the increment of clinically diagnosed new COVID-19 cases between April and July 2022. Delta and Omicron variants were identified in the Tunisian wastewater. Interestingly, the presence of variant BA.5 was detected in samples prior to its inclusion as a variant of concern (VOC) by the Tunisian National Health Authorities. SARS-CoV-2 was detected in wastewater effluents, indicating that the wastewater treatment techniques used in the majority of Tunisian WWTPs are inefficient in removing the virus traces. This study reports the first identification of SARS-CoV-2 VOCs in Tunisian wastewater samples.

Research advances of biodegradable microplastics in wastewater treatment plant: Current knowledge and future directions.

Plastic and microplastic pollution in the environment has become a significant global concern. Biodegradable plastics (BPs), as environmentally friendly alternatives to conventional plastics, have also emerged as a crucial topic of global discussion. The successful application of BPs appears to offer a solution to the potential ecological risks posed by conventional plastics. However, BPs have negative impacts on the ecological environment and human health. BPs can gradually degrade into biodegradable microplastics (BMPs) in the environment. Wastewater treatment plants (WWTPs) have become an undeniable source and sink of microplastics. With the production and application of BPs, BMPs will inevitably enter WWTPs. This paper reviews the pollution status, degradation behavior of BMPs, and their potential impact on wastewater treatment performance. The focus is on the environmental behavior of BMPs in wastewater treatment systems. The influences of BMPs on microbial communities, sludge treatment, and disposal are thoroughly discussed. The results indicate that BMPs are more easily decomposed into micro/nanoplastics and release additives compared to conventional microplastics. The effects of BMPs on microbial communities and wastewater treatment depend on their characteristics. The numerous oxygen-containing functional groups on the surface of BMPs enable them to serve a dual purpose as transport media and potential sources of environmental pollutants. Finally, in light of existing knowledge gaps, suggestions and prospects for future research on BMPs are proposed.

Analytical Methods for the Determination of Pharmaceuticals and Personal Care Products in Solid and Liquid Environmental Matrices: A Review.

Among the various compounds regarded as emerging contaminants (ECs), pharmaceuticals and personal care products (PPCPs) are of particular concern. Their continuous release into the environment has a negative global impact on human life. This review summarizes the sources, occurrence, persistence, consequences of exposure, and toxicity of PPCPs, and evaluates the various analytical methods used in the identification and quantification of PPCPs in a variety of solid and liquid environmental matrices. The current techniques of choice for the analysis of PPCPs are state-of-the-art liquid chromatography coupled to mass spectrometry (LC-MS) or tandem mass spectrometry (LC-MS2). However, the complexity of the environmental matrices and the trace levels of micropollutants necessitate the use of advanced sample treatments before these instrumental analyses. Solid-phase extraction (SPE) with different sorbents is now the predominant method used for the extraction of PPCPs from environmental samples. This review also addresses the ongoing analytical method challenges, including sample clean-up and matrix effects, focusing on the occurrence, sample preparation, and analytical methods presently available for the determination of environmental residues of PPCPs. Continuous development of innovative analytical methods is essential for overcoming existing limitations and ensuring the consistency and diversity of analytical methods used in investigations of environmental multi-class compounds.

Temperature-Driven Activated Sludge Bacterial Community Assembly and Carbon Transformation Potential: A Case Study of Industrial Plants in the Yangtze River Delta.

Temperature plays a critical role in the efficiency and stability of industrial wastewater treatment plants (WWTPs). This study focuses on the effects of temperature on activated sludge (AS) communities within the A2O process of 19 industrial WWTPs in the Yangtze River Delta, a key industrial region in China. The investigation aims to understand how temperature influences AS community composition, functional assembly, and carbon transformation processes, including CO2 emission potential. Our findings reveal that increased operating temperatures lead to a decrease in alpha diversity, simplifying community structure and increasing modularity. Dominant species become more prevalent, with significant decreases in the relative abundance of Chloroflexi and Actinobacteria, and increases in Bacteroidetes and Firmicutes. Moreover, higher temperatures enhance the overall carbon conversion potential of AS, particularly boosting CO2 absorption in anaerobic conditions as the potential for CO2 emission during glycolysis and TCA cycles grows and diminishes, respectively. The study highlights that temperature is a major factor affecting microbial community characteristics and CO2 fluxes, with more pronounced effects observed in anaerobic sludge. This study provides valuable insights for maintaining stable A2O system operations, understanding carbon footprints, and improving COD removal efficiency in industrial WWTPs.

Multimedia distribution and release characteristics of emerging PFAS in wastewater treatment plants in Tianjin, China.

Legacy and emerging PFAS in the air, wastewater, and sludge from two wastewater treatment plants (WWTPs) in Tianjin were investigated in this study. The semi-quantified nontarget PFAS accounted for up to 99 % of Æ©PFAS in the gas phase, and aqueous film-forming foam (AFFF)-related PFAS were predominant in wastewater (up to 2250 ng/L, 79 % of Æ©PFAS) and sludge (up to 4690 ng/g, 95 % of Æ©PFAS). Furthermore, field-derived air particle-gas, air-wastewater, and wastewater particle-wastewater distribution coefficients of emerging PFAS are characterized, which have rarely been reported. The emerging substitute p-perfluorous nonenoxybenzenesulfonate (OBS) and AFFF-related cationic and zwitterionic PFAS show a stronger tendency to partition into particle phase in air and wastewater than perfluorooctane sulfonic acid (PFOS). The estimated total PFAS emissions from the effluent and sludge of WWTP A were 202 kg/y and 351 kg/y, respectively. While the target PFAS only accounted for 20-33 % of the total emissions, suggesting a significant underestimation of environmental releases of the nontarget PFAS and unknown perfluoroalkyl acid precursors through the wastewater and sludge disposal. Overall, this study highlights the importance of comprehensive monitoring and understanding the behavior of legacy and emerging PFAS in wastewater systems, and fills a critical gap in our understanding of PFAS exposure.

A comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge for nutrient removal in full-scale wastewater treatment plants.

Understanding the microbial community structure of sludge is crucial for improving the design, operation and optimisation of full-scale wastewater treatment plants (WWTPs). This study aimed to have a comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge from two full-scale sequential batch reactors-based WWTPs with nutrient removal for the first time. To better understand key functional bacteria such as polyphosphate accumulating bacteria (PAOs), competitive bacteria such as glycogen accumulating bacteria (GAOs) and nitrifying bacteria for both nitrogen and phosphorus removal, another two full-scale WWTPs with only carbon (C) removal and C and nitrogen (N) removal were compared too. It was found that the richness and diversity of the microbial population in sludge increased with pollutant removal from only C, C and N, to C,N, P removal. For C, N P removal, granule structure led to a more diverse and rich microbial community structure than flocculent structure. Although more abundant nitrifying bacteria were enriched in granular sludge than flocculent sludge, the abundance of total putative PAOs was equivalent. However, the most typical putative PAOs such as Tetrasphaera and Candidatus Accumulibacter seemed to be more correlated with biological phosphorus removal performance, which might be more proper to be used as an indication for P removal potential. The higher abundance of GAOs in flocculent sludge with better phosphorus removal performance might suggest that further investigation is needed to understand the functions of GAOs. In addition, the equivalent abundances of PAOs in the WWTPs with only C removal and with C, N, and P removal, respectively, indicate that many newly reported putative PAOs might not contribute to P removal. This study provides insight into the microbial communities and functional bacteria in aerobic granular sludge and flocculent sludge in full-scale SBRs, which can provide microbes-informed optimisation of reactor operation for better nutrient removal.

Design and Validation of Primer Sets for the Detection and Quantification of Antibiotic Resistance Genes in Environmental Samples by Quantitative PCR.

The high prevalence of antibiotic resistant bacteria (ARB) in several environments is a great concern threatening human health. Particularly, wastewater treatment plants (WWTP) become important contributors to the dissemination of ARB to receiving water bodies, due to the inefficient management or treatment of highly antibiotic-concentrated wastewaters. Hence, it is vital to develop molecular tools that allow proper monitoring of the genes encoding resistances to these important therapeutic compounds (antibiotic resistant genes, ARGs). For an accurate quantification of ARGs, there is a need for sensitive and robust qPCR assays supported by a good design of primers and validated protocols. In this study, eleven relevant ARGs were selected as targets, including aadA and aadB (conferring resistance to aminoglycosides); ampC, blaTEM, blaSHV, and mecA (resistance to beta-lactams); dfrA1 (resistance to trimethoprim); ermB (resistance to macrolides); fosA (resistance to fosfomycin); qnrS (resistance to quinolones); and tetA(A) (resistance to tetracyclines). The in silico design of the new primer sets was performed based on the alignment of all the sequences of the target ARGs (orthology grade > 70%) deposited in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, allowing higher coverages of the ARGs' biodiversity than those of several primers described to date. The adequate design and performance of the new molecular tools were validated in six samples, retrieved from both natural and engineered environments related to wastewater treatment. The hallmarks of the optimized qPCR assays were high amplification efficiency (> 90%), good linearity of the standard curve (R2 > 0.980), repeatability and reproducibility across experiments, and a wide linear dynamic range. The new primer sets and methodology described here are valuable tools to upgrade the monitorization of the abundance and emergence of the targeted ARGs by qPCR in WWTPs and related environments.

Emerging Contaminants in the Effluent of Wastewater Should Be Regulated: Which and to What Extent?

Effluent discharged from urban wastewater treatment plants (WWTPs) is a major source of emerging contaminants (ECs) requiring effective regulation. To this end, we collected discharge datasets of pharmaceuticals (PHACs) and endocrine-disrupting chemicals (EDCs), representing two primary categories of ECs, from Chinese WWTP effluent from 2012 to 2022 to establish an exposure database. Moreover, high-risk ECs' long-term water quality criteria (LWQC) were derived using the species sensitivity distribution (SSD) method. A total of 140 ECs (124 PHACs and 16 EDCs) were identified, with concentrations ranging from N.D. (not detected) to 706 µg/L. Most data were concentrated in coastal regions and Gansu, with high ecological risk observed in Gansu, Hebei, Shandong, Guangdong, and Hong Kong. Using the assessment factor (AF) method, 18 high-risk ECs requiring regulation were identified. However, only three of them, namely carbamazepine, ibuprofen, and bisphenol-A, met the derivation requirements of the SSD method. The LWQC for these three ECs were determined as 96.4, 1010, and 288 ng/L, respectively. Exposure data for carbamazepine and bisphenol-A surpassed their derived LWQC, indicating a need for heightened attention to these contaminants. This study elucidates the occurrence and risks of ECs in Chinese WWTPs and provides theoretical and data foundations for EC management in urban sewage facilities.