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.

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.

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.

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.

Microplastics and Endocrine Disruptors in Typical Wastewater Treatment Plants in Megacity Shanghai.

The fast development of China's urbanization has led to a notable release of emerging pollutants, including microplastics (MPs) and endocrine disruptors (EDCs). Generally, these pollutants enter the coastal environment through the discharge of wastewater treatment plants (WWTPs) and finally threaten the organisms in the receiving waterbody. The study investigated the environmental behavior of MPs and EDCs in two typical WWTPs in one of the megacities in China, Shanghai. The abundance of MPs in the influent ranged from 321 to 976 items/L. Four shapes (films, fragments, fibers, and microbead) were found, while fibers and films dominated. Transparent (31-63%) and white (20-47%) MPs were more frequently observed, while polyethylene terephthalate, cellulose, and cellophane were the main polymetric materials. The size of the MPs fell between 15.8 µm and 2220 µm, and the smaller one (<500 µm) dominated. The removal efficiencies of the two WWTPs for MPs ranged from 64% to 92%, and both WWTPs performed better for large pieces of MPs (>500 µm). For EDCs, total concentrations in the influent were detected, ranging from 113 to 2780 ng/L. Two groups, including phenolic estrogens (PEs) and steroid estrogens (SEs), were detected, and PEs, especially bisphenol A (BPA), were the predominant individuals among the studied EDCs. Specifically, PEs ranged from 82.8 to 2637 ng/L, while SEs ranged from 27.3 to 143 ng/L. The removal efficiencies of the WWTPs for EDCs varied (82.8-100%) as well, possibly due to the different treatment compartments and contamination load in the influent. Seasonal variations for both MPs and EDCs were observed. Specifically, concentrations of MPs and EDCs in WWTPs influent were higher in the wet season, as well as the removal efficiency. Furthermore, there was a correlation observed between the concentrations of MPs and EDCs, suggesting that MPs and EDCs may originate from the same source and that EDCs released by MPs cannot be ignored during treatment. Finally, the study evaluated the environmental risk of the effluents. MPs led to a minor risk (Level I), while EDCs might lead to an adverse impact on algae (RQs = 0.0014-0.024) and fish (RQs = 3.4-30.2). In summary, WWTPs received considerable amounts of MPs and EDCs. Although the WWTPs removed the contaminants efficiently, the environmental risk of the effluent needs to be noted.

Enhanced Detection of Estrogen-like Compounds by Genetically Engineered Yeast Sensor Strains.

The release of endocrine-disrupting compounds (EDCs) to the environment poses a health hazard to both humans and wildlife. EDCs can activate or inhibit endogenous endocrine functions by binding hormone receptors, leading to potentially adverse effects. Conventional analytical methods can detect EDCs at a high sensitivity and precision, but are blind to the biological activity of the detected compounds. To overcome this limitation, yeast-based bioassays have previously been developed as a pre-screening method, providing an effect-based overview of hormonal-disruptive activity within the sample prior to the application of analytical methods. These yeast biosensors express human endocrine-specific receptors, co-transfected with the relevant response element fused to the specific fluorescent protein reporter gene. We describe several molecular manipulations of the sensor/reporter circuit in a Saccharomyces cerevisiae bioreporter strain that have yielded an enhanced detection of estrogenic-like compounds. Improved responses were displayed both in liquid culture (96-well plate format) as well as in conjunction with sample separation using high-performance thin-layer chromatography (HPTLC). The latter approach allows for an assessment of the biological effect of individual sample components without the need for their chemical identification at the screening stage.

Machine learning reveals the selection pressure exerted by nonantibiotic pharmaceuticals at environmentally relevant concentrations on antibiotic resistance genotypes.

The emergence and increasing prevalence of antibiotic resistance pose a global public risk for human health, and nonantimicrobial pharmaceuticals play an important role in this process. Herein, five nonantimicrobial pharmaceuticals, including acetaminophen (ACT), clofibric acid (CA), carbamazepine (CBZ), caffeine (CF) and nicotine (NCT), tetracycline-resistant strains, five ARGs (sul1, sul2, tetG, tetM and tetW) and one integrase gene (intI1), were detected in 101 wastewater samples during two typical sewage treatment processes including anaerobic-oxic (A/O) and biological aerated filter (BAF) in Harbin, China. The impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on both the resistance genotypes and resistance phenotypes were explored. The results showed that a significant impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on tetracycline resistance genes encoding ribosomal protection proteins (RPPs) was found, while no changes in antibiotic phenotypes, such as minimal inhibitory concentrations (MICs), were observed. Machine learning was applied to further sort out the contribution of nonantibiotic pharmaceuticals at environmentally relevant concentrations to different ARG subtypes. The highest contribution and correlation were found at concentrations of 1400-1800 ng/L for NCT, 900-1500 ng/L for ACT and 7000-10,000 ng/L for CF for tetracycline resistance genes encoding RPPs, while no significant correlation was found between the target compounds and ARGs when their concentrations were lower than 500 ng/L for NCT, 100 ng/L for ACT and 1000 ng/L for CF, which were higher than the concentrations detected in effluent samples. Therefore, the removal of nonantibiotic pharmaceuticals in WWTPs can reduce their selection pressure for resistance genes in wastewater.

Monitoring Pharmaceuticals and Personal Care Products in Healthcare Effluent Wastewater Samples and the Effectiveness of Drug Removal in Wastewater Treatment Plants Using the UHPLC-MS/MS Method.

A multi-residue UHPLC-MS/MS analytical method, previously developed for monitoring 52 pharmaceuticals in drinking water, was used to analyse these pharmaceuticals in wastewater originating from healthcare facilities in the Czech Republic. Furthermore, the methodology was expanded to include the evaluation of the effectiveness of drug removal in Czech wastewater treatment plants (WWTPs). Of the 18 wastewater samples analysed by the validated UHPLC-MS/MS, each sample contained at least one quantifiable analyte. This study reveals the prevalence of several different drugs; mean concentrations of 702 µg L-1 of iomeprol, 48.8 µg L-1 of iopromide, 29.9 µg L-1 of gabapentin, 42.0 µg L-1 of caffeine and 82.5 µg L-1 of paracetamol were present. An analysis of 20 samples from ten WWTPs revealed different removal efficiencies for different analytes. Paracetamol was present in the inflow samples of all ten WWTPs and its removal efficiency was 100%. Analytes such as caffeine, ketoprofen, naproxen or atenolol showed high removal efficiencies exceeding 80%. On the other hand, pharmaceuticals like furosemide, metoprolol, iomeprol, zolpidem and tramadol showed lower removal efficiencies. Four pharmaceuticals exhibited higher concentrations in WWTP effluents than in the influents, resulting in negative removal efficiencies: warfarin at -9.5%, indomethacin at -53%, trimethoprim at -54% and metronidazole at -110%. These comprehensive findings contribute valuable insights to the pharmaceutical landscape of wastewater from healthcare facilities and the varied removal efficiencies of Czech WWTPs, which together with the already published literature, gives a more complete picture of the burden on the aquatic environment.

Regulatory of salinity on assembly of activated sludge microbial communities and nitrogen transformation potential in industrial plants of the lower Yangtze River basin.

This study aims to thoroughly investigate the impact mode of salinity carried by industrial wastewater on the anaerobic-anoxic-oxic (A2O) sludge in wastewater treatment plants (WWTPs). Through comprehensive investigation of the A2O stage activated sludge (AS) from 19 industrial WWTPs in the downstream area of the Yangtze River, China, A total of 38 samples of anaerobic sludge and oxic sludge were collected and analyzed. We found that salinity stress significantly inhibits the growth of the AS community, particularly evident in the anaerobic sludge community. Furthermore, the high-saline environment induces changes in the structure and functional patterns of the AS community, leading to intensive interactions and resource exchanges among microorganisms. Halophilic microorganisms may play a crucial role in this process, significantly impacting the overall community structure, especially in the oxic sludge community. Additionally, salinity stress not only suppresses the nitrogen transformation potential of the AS but also leads to the accumulation of nitrite, thereby increasing the emission potential of both NO and N2O, exacerbating the greenhouse effect of the A2O process in industrial WWTPs. The findings of this study provide necessary theoretical support for maintaining the long-term stable operation of the A2O sludge system in industrial WWTPs, reducing carbon footprint, and improving nitrogen removal efficiency.

An assessment and characterization of pharmaceuticals and personal care products (PPCPs) within the Great Lakes Basin: Mussel Watch Program (2013-2018).

Defining the environmental occurrence and distribution of chemicals of emerging concern (CECs), including pharmaceuticals and personal care products (PPCPs) in coastal aquatic systems, is often difficult and complex. In this study, 70 compounds representing several classes of pharmaceuticals, including antibiotics, anti-inflammatories, insect repellant, antibacterial, antidepressants, chemotherapy drugs, and X-ray contrast media compounds, were found in dreissenid mussel (zebra/quagga; Dreissena spp.) tissue samples. Overall concentration and detection frequencies varied significantly among sampling locations, site land-use categories, and sites sampled proximate and downstream of point source discharge. Verapamil, triclocarban, etoposide, citalopram, diphenhydramine, sertraline, amitriptyline, and DEET (N,N-diethyl-meta-toluamide) comprised the most ubiquitous PPCPs (> 50%) detected in dreissenid mussels. Among those compounds quantified in mussel tissue, sertraline, metformin, methylprednisolone, hydrocortisone, 1,7-dimethylxanthine, theophylline, zidovudine, prednisone, clonidine, 2-hydroxy-ibuprofen, iopamidol, and melphalan were detected at concentrations up to 475 ng/g (wet weight). Antihypertensives, antibiotics, and antidepressants accounted for the majority of the compounds quantified in mussel tissue. The results showed that PPCPs quantified in dreissenid mussels are occurring as complex mixtures, with 4 to 28 compounds detected at one or more sampling locations. The magnitude and composition of PPCPs detected were highest for sites not influenced by either WWTP or CSO discharge (i.e., non-WWTPs), strongly supporting non-point sources as important drivers and pathways for PPCPs detected in this study. As these compounds are detected at inshore and offshore locations, the findings of this study indicate that their persistence and potential risks are largely unknown, thus warranting further assessment and prioritization of these emerging contaminants in the Great Lakes Basin.

Soluble carbon source recovery using preconditioning coagulants for applicable short-term fermentation of waste activated sludge in WWTPs.

A huge production of waste activated sludge (WAS) has been a burden for wastewater treatment plants (WWTPs) with high disposal cost and little benefit back to wastewater purification. The short-chain fatty acids (SCFAs) produced by a short-term acidogenic fermentation of WAS before methane production have been proven to be a high-quality carbon source available for microbial denitrification process. The dual purpose of full recovery of fermentation liquid products and facilitating disposal of residual solid waste necessitate an efficient solid-liquid separation process of short-term fermentation liquid. The transformation and loss of various soluble carbon sources between solid and liquid are very important issues for carbon recovery efficiency when combining short-term fermentation and sludge dewatering in WWTPs. Here we testified the three conventional preconditioning coagulants, Polyferric Sulfate (PFS), Poly Aluminum Chloride (PAC) and Polyacrylamide (PAM), to improve the efficiency of subsequent solid-liquid separation. The results show that conversion yield of SCFAs in the liquid phase of sludge after short-term fermentation was 195 mg COD/g VSS, when using the coagulants PFS, PAC, and PAM for recovery, the recovery ratio was 79.5%, 82.0%, and 85.9%, respectively, while the dewaterability could be improved after preconditioning short-term fermentation sludge. The complexation of Al3+/Fe3+ in metal coagulants with carboxyl groups of SCFA demonstrated by Density Functional Theory calculation led to small part of soluble carbons co-migration to the solid phase, mainly a loss of high molecular weight organic compounds (carbohydrate, proteins, humic acids), while the application of PAM had little impact on carbon recovery. Economic calculations further showed PAM preconditioning short-term fermentation liquid of WAS could achieve higher recovery benefits.

Case study: Bioaugmenting the comammox dominated biomass from B-stage to enhance nitrification in A-stage at Blue Plains AWWTP.

A comprehensive case study was undertaken at the Blue Plains wastewater treatment plant (WWTP) to explore the bioaugmentation technique of introducing nitrifying sludge into the non-nitrifying stage over the course of two operational years. This innovative approach involved the return of waste activated sludge (WAS) from the biological nutrient removal (BNR) system to enhance the nitrification in the high carbon removal rate system. The complete ammonia oxidizer (comammox) Nitrospira Nitrosa was identified as the main nitrifier in the system. Bioaugmentation was shown to be successful as nitrifiers returned from BNR were able to increase the nitrifying activity of the high carbon removal rate system. There was a positive correlation between returned sludge from the BNR stage and the specific total kjeldahl nitrogen (TKN) removal rate in A stage. The bioaugmentation process resulted in a remarkable threefold increase in the specific TKN removal rate within the A stage. Result suggested that recycling of WAS is a simple technique to bio-augment a low SRT system with nitrifiers and add ammonia oxidation to a previously non-nitrifying stage. The results from this case study hold the potential for applicable implications for other WWTPs that have a similar operational scheme to Blue Plains, allowing them to reuse WAS from the B stage, previously considered waste, to enhance nitrification and thus improving overall nitrogen removal performance. PRACTITIONER POINTS: Comammox identifying as main nitrifier in the B stage. Comammox enriched sludge from B stage successfully bio-augmented the East side of A stage up to threefold. Bioaugmentation of comammox in the West side of A stage was potentially inhibited by the gravity thickened overflow. Sludge returned from B stage to A stage can improve nitrification with a very minor retrofits and short startup times.

Micro-aeration and low influent C/N are key environmental factors for achieving ANAMMOX in livestock farming wastewater treatment plants.

Anaerobic ammonium oxidation (ANAMMOX)-mediated system is a cost-effective green nitrogen removal process. However, there are few examples of successful application of this advanced wastewater denitrification process in wastewater treatment plants, and the understanding of how to implement anaerobic ammonia oxidation process in full-scale is still limited. In this study, it was found that the abundance of anaerobic ammonia-oxidizing bacteria (AnAOB) in the two livestock wastewater plants named J1 and J2, respectively, showed diametrically opposed trends of waxing and waning with time. The microbial communities of the activated sludge in the two plants at different time were sampled and analyzed by high-throughput sequencing of 16S rRNA genes. Structural equation models (SEMs) were used to reveal the key factors affecting the realization of the ANAMMOX. Changes in the concentration of dissolved oxygen and C/N had a significant effect on the relative abundance of anaerobic ammonia oxidation bacteria (AnAOB). The low concentration of DO (0.2∼0.5 mg/L) could inhibit the activity of nitrifying bacteria (NOB) to achieve partial oxidation of ammonia nitrogen and provide sufficient substrate for the growth of AnAOB, similar to the CANON (Completely Autotrophic Nitrogen removal Over Nitrite). Unlike CANON, heterotrophic denitrification is also a particularly critical part of the livestock wastewater treatment, and a suitable C/N of about 0.6 could reduce the competition risk of heterotrophic microorganisms to AnAOB and ensure a good ecological niche for AnAOB. Based on the results of 16S rRNA and microbial co-occurrence networks, it was discovered that microorganisms in the sludge not only had a richer network interaction, but also achieved a mutually beneficial symbiotic interaction network among denitrifying bacteria (Pseudomonas sp., Terrimonas sp., Dokdonella sp.), AnAOB (Candidatus Brocadia sp.) at DO of 0.2∼0.5 mg/L and C/N of 0.6. Among the top 20 in abundance of genus level, AnAOB had a high relative abundance of 27.66%, followed by denitrifying bacteria of 3.67%, AOB of 0.64% and NOB of 0.26%, which is an essential indicator for the emergence of an AnAOB-dominated nitrogen removal cycle. In conclusion, this study highlights the importance of dissolved oxygen and C/N regulation by analyzing the mechanism of ANAMMOX sludge extinction and growth in two plants under anthropogenic regulation of AnAOB in full-scale wastewater treatment systems.

Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods.

The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively.