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Copyright of Sustainable Earth Review is the property of Shahid Beheshti University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Antibiotics, widely used pharmaceuticals, enter wastewater treatment systems and ultimately the aquatic environment through the discharge of wastewater from residential areas, hospitals, breeding farms, and pharmaceutical factories, posing potential ecological and health risks. Due to the misuse and discharge of antibiotics, the spread of antibiotic resistance genes (ARGs) in water bodies and significant changes in microbial community structure have direct toxic effects on aquatic ecosystems and human health. This paper summarizes the occurrence of antibiotics in wastewater treatment systems and their ecological and health risks, focusing on the impact of antibiotics on aquatic microorganisms, aquatic plants and animals, and human health. It points out that existing wastewater treatment processes have poor removal capabilities for antibiotics and even become an important pathway for the spread of some antibiotics. In terms of detection technology, the article discusses the application of immunoassays, instrumental analysis, and emerging sensor technologies in detecting antibiotics in sewage, each with its advantages and limitations. Future efforts should combine multiple technologies to improve detection accuracy. Regarding the removal methods of antibiotics, the paper categorizes physical, chemical, and biodegradation methods, introducing various advanced technologies including membrane separation, adsorption, electrochemical oxidation, photocatalytic oxidation, and membrane bioreactors. Although these methods have shown good removal effects in the laboratory, there are still many limitations in large-scale practical applications. This paper innovatively takes urban wastewater treatment systems as the entry point, systematically integrating the sources of antibiotics, environmental risks, detection technologies, and treatment methods, providing targeted and practical theoretical support and technical guidance, especially in the removal of antibiotics in wastewater treatment, on a scientific basis. Future efforts should strengthen the control of antibiotic sources, improve the efficiency of wastewater treatment, optimize detection technologies, and promote the formulation and implementation of relevant laws and standards to more effectively manage and control antibiotic pollution in the aquatic environment. [ABSTRACT FROM AUTHOR]

In this study, the status and efficiency of the wastewater treatment system of a semi-recirculated aquaculture system (RAS) in Firoozkooh County were investigated. Information regarding aquaculture systems in the region was obtained with the guidance of the experts at the Agriculture Organization in Firoozkooh County. During visits to the recommended aquaculture systems, Namrud Aquaculture was selected. To evaluate the effect of water pollutants on fish, National Standards 8726 and 7961 were employed to determine water quality parameters and to guide sampling and sample protection. Water quality parameters were assessed at four stations to determine the extent of pollution. The results indicated that ammonia (NH3), nitrite (NO2), and nitrate (NO3) were the main issues in this aquaculture system. Rainbow trout (Oncorhynchus mykiss) exposed to ammonia levels exceeding the limits set by National Standard 8726 displayed behavioral changes, including altered swimming speed, total distance traveled, average direction change, and increased average distance from a central point. The second aeration tower was found to be ineffective in releasing dissolved gases due to its inappropriate design, including the spacing between floors and lack of proper atmospheric connection. It is recommended to redesign the second aeration tower, elevate the second entrance pond, and adjust the return flow to the grit chamber to reduce suspended particles and improve the water quality parameters of the effluent.

Wastewater biotreatment systems harbor a rich diversity of microorganisms, and the effectiveness of biotreatment systems largely depends on the activity of these microorganisms. Specifically, viruses play a crucial role in altering microbial behavior and metabolic processes throughout their infection phases, an aspect that has recently attracted considerable interest. Two metagenomic approaches, viral‐like particle‐concentrated (VPC, representing free viral‐like particles) and non‐concentrated (NC, representing the cellular fraction), were employed to assess their efficacy in revealing virome characteristics, including taxonomy, diversity, host interactions, lifestyle, dynamics, and functional genes across processing units of three wastewater treatment plants (WWTPs). Our findings indicate that each approach offers unique insights into the viral community and functional composition. Their combined use proved effective in elucidating WWTP viromes. We identified nearly 50,000 viral contigs, with Cressdnaviricota and Uroviricota being the predominant phyla in the VPC and NC fractions, respectively. Notably, two pathogenic viral families, Asfarviridae and Adenoviridae, were commonly found in these WWTPs. We also observed significant differences in the viromes of WWTPs processing different types of wastewater. Additionally, various phage‐derived auxiliary metabolic genes (AMGs) were active at the RNA level, contributing to the metabolism of the microbial community, particularly in carbon, sulfur, and phosphorus cycling. Moreover, we identified 29 virus‐carried antibiotic resistance genes (ARGs) with potential for host transfer, highlighting the role of viruses in spreading ARGs in the environment. Overall, this study provides a detailed and integrated view of the virosphere in three WWTPs through the application of VPC and NC metagenomic approaches. Our findings enhance the understanding of viral communities, offering valuable insights for optimizing the operation and regulation of wastewater treatment systems. Highlights: This study comprehensively compared viral‐like particle‐concentrated (VPC) and non‐concentrated (NC) metagenomic approaches in virome investigation, highlighting their respective efficacies in detecting different viral communities and functional elements in wastewater treatment plants (WWTPs).Eukaryotic viruses belonging to Cressdnaviricota were the most prevalent in VPC metagenomes, while bacterial viruses belonging to Uroviricota were the most abundant in NC metagenomes.Diverse phage‐born auxiliary metabolic genes (AMGs) were discovered in the virome of WWTPs, and a large array of AMGs involved in biogeochemical cycles like carbon, sulfur, and phosphorus cycling showed transcriptional activation in wastewater treatment systems.A tiny proportion (less than 0.08% of viral operational taxonomic units) of viruses was discovered to harbor antibiotic resistance genes (ARGs) (29 phage‐born ARGs) only in the cellular fraction, and phage‐born ARG types primarily included macrolide–lincosamide–streptogramin, tetracycline, and aminoglycoside resistance. [ABSTRACT FROM AUTHOR]

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH3 levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH3, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models. [ABSTRACT FROM AUTHOR]

The occurrence of pharmaceutically active compounds (PhACs), nutrients, and an artificial sweetener acesulfame in wastewater, and subsequent removal in an engineered system comprising aerobic wetland, anaerobic wetland, and steel slag cells, were investigated. The PhACs evaluated in this study covered a range of octanol–water partition coefficients (log Kow = 0.07–2.45) and acid dissociation constants (pKa = 1.7–13.9) and included carbamazepine, caffeine, sulfamethoxazole, ibuprofen, and naproxen. The mean flow rate in the system was 0.89 m3 day−1 (0.02 to 4.27 m3 day−1), representing a hydraulic retention time of 5 days. The removal efficiencies of PO4-P, NH3-N, and cBOD5 in the treatment system were >99, 82, and 98%. The removal efficiencies for the PhACs and acesulfame were classified into four groups, including those that were (a) efficiently removed (caffeine by >75%); (b) moderately removed (ibuprofen by 50–75%); (c) poorly removed (sulfamethoxazole and naproxen by 25–50%); and (d) recalcitrant (carbamazepine and acesulfame by <25%). Variability in concentrations and treatment efficiencies was observed in different sampling events, which may be due to variations in input concentrations or changes in the flow rate. The addition of a steel slag cell increased the overall removal efficiency of the studied compounds, except for carbamazepine. [ABSTRACT FROM AUTHOR]

Anammox bacteria can remove ammonium directly, which is different from what was previously believed. This is an important process for the global nitrogen cycle. Anammox bacteria were first identified in sewage treatment systems and were later proven to exist widely in natural ecosystems. To better understand the relationship between the anammox reaction and different systems, and to maintain the stability of the nitrogen cycle, anammox functional microorganisms found in different natural environments were summarized. In addition, anammox nitrogen production rate and the contribution of anammox to nitrogen were discussed under different ecological environments. A literature analysis showed that the contribution rate of nitrogen removal of anammox was the highest in the Terrestrial ecosystem, up to 87.5%. The Terrestrial ecosystem is more likely to form an anoxic or even anaerobic environment conducive to anaerobic ammoxidation. Therefore, the control of DO is an important factor in the activity of anaerobic ammoxidation. Other environmental factors affecting the contribution of anammox to nitrogen removal include temperature, pH, organic matter content, inorganic nitrogen concentration, and salinity. However, the dominant influencing factors of anammox reactions in different ecosystems are evidently different. Therefore, the mechanism of the impact of different environmental factors on the anaerobic ammonia oxidation process is necessary to discuss. This provides a scientific basis for the global nitrogen cycle and is of great significance to improve nitrogen's biogeochemical cycle in the ecosystem. [ABSTRACT FROM AUTHOR]

In hospitals, antibiotics are utilized to treat human infectious illnesses, where extra antibiotics are frequently discharged and left in hospital wastewater. However, investigating antibiotic residues using conventional activated sludge wastewater treatment plants in hospitals has still been very limited, especially in Thailand. This research studied the contamination of antibiotics in the conventional activated sludge wastewater treatment plants from three hospitals: a first-level hospital (H1), a middle-level hospital (H2), and an advanced-level hospital (H3). The antibiotics were focused on 3 groups: bata-lactams, quinolones, and tetracyclines, which included 7 types of antibiotics (amoxicillin, ampicillin, ciprofloxacin, norfloxacin, levofloxacin, ofloxacin, and doxycycline). Antibiotics were extracted by solid-phase extraction and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). The results showed that amoxicillin had the highest concentrations of H2, H1, and H3 in the influent (18,434 ± 99, 11,541 ± 42 and 10,994 ± 56 ng/L, respectively). Furthermore, with 7,990 ± 53, 5,879 ± 52 and 4,892 ± 63 ng/L of H2, H3, and H1, respectively, amoxicillin appeared to have the highest concentrations in the effluent. The removal efficiency was the highest for norfloxacin (94.78%) in H1, followed by doxycycline (93.99%) in H2. However, ampicillin had the lowest removal efficiency (19.02%) in H2. This finding from the research on antibiotic contamination in hospital wastewater reveal that the conventional activated sludge treatment system could not completely remove all antibiotics in the wastewater. Therefore, the advanced treatment technology should be investigated to increase the antibiotics removal efficiency. [ABSTRACT FROM AUTHOR]

As zoonotic pathogens are threatening public health globally, the virulence factor genes (VFGs) they carry underlie latent risk in the environment. However, profiling VFGs in the environment is still in its infancy due to lack of efficient and reliable quantification tools. Here, we developed a novel high-throughput qPCR (HT-qPCR) chip, termed as VFG-Chip, to comprehensively quantify the abundances of targeted VFGs in the environment. A total of 96 VFGs from four bacterial pathogens including Klebsiella pneumoniae, Acinetobacter baumannii, Escherichia coli, and Salmonella enterica were targeted by 120 primer pairs, which were involved in encoding five types of virulence factors (VFs) like toxin, adherence, secretion system, immune evasion/invasion, and iron uptake. The specificity of VFG-Chip was both verified computationally and experimentally, with high identity of amplicon sequencing and melting curves analysis proving its robust capability. The VFG-Chip also displayed high sensitivity (by plasmid serial dilution test) and amplification efficiency averaging 97.7%. We successfully applied the VFG-Chip to profile the distribution of VFGs along a wastewater treatment system with 69 VFGs detected in total. Overall, the VFG-Chip provides a robust tool for comprehensively quantifying VFGs in the environment, and thus provides novel information in assessing the health risks of zoonotic pathogens in the environment.

Automotive manufacturing is one of the potential sources of air pollution particularly involving volatile organic compounds (VOCs). This study intensively evaluated VOC emissions and their dispersion from the industry. The measured VOCs were speciated for further evaluation of their odor threats according to the characteristics of each compound. Mathematical emission and air dispersion models were applied to assist in elaborating the source–receptor relationship allowing the determining of existing business-as-usual conditions with proposed mitigation measures to manage the pollution of the factory studied in this paper. Seven VOC species potentially caused odor problems to the surrounding community, including 1-butanol, ethyl benzene, toluene, m,p xylene, o xylene, methyl ethyl ketone, and methyl isobutyl ketone. The results from the AERMOD dispersion model revealed that the smell from these chemicals could reach up to about 800 m from the source. Analysis of mitigation measures indicated that two interesting scenarios should be considered according to their effectiveness. The concentrations of VOCs can decrease by up to 4.7, 14.0 and 24.9% from increasing the physical stack height by +1, +3 and +5 m from its existing height, respectively. Modification of the aeration tank of the wastewater treatment unit to a closed system also helped to reduce about 27.8% of emissions resulting in about a 27.6% decreased ambient air concentration. This study provided useful information on the characteristics of VOCs emitted by the automobile manufacturing industry. It also demonstrated the relevant procedures and highlights the necessity to comprehensively analyze the source–receptor relationship to evaluate the most appropriate measures in managing industrial air pollution. [ABSTRACT FROM AUTHOR]

The water environment in Chinese rural areas has seriously deteriorated to the extent that the development of rural areas is now under threat. To deal with this issue, the Chinese government has been promoting wastewater treatment systems (WTS) in rural areas since 2005. However, some of these wastewater treatment plants have shut down after just a few years. Thus, even though the number of newly built plants in rural areas has been rapidly increasing, the failure of these plants can impede the development of an efficient wastewater treatment in Chinese rural areas. This paper investigates the challenges faced by the management of the constructed wastewater treatment plants in Chinese rural areas using the case study method. Two cases, of Zhejiang and Hainan provinces, were analysed separately; the operation was successful in the former province and a failure in the latter one. The study demonstrates that the WTS in rural areas are faced with diverse implementation challenges, ranging from unsustainable financial sources for operation and maintenance cost to inappropriate governance structure, potential risks due to the local leadership rotation system of the government, limited participation by farmers in the management and technical complexity. HIGHLIGHTS Wastewater treatment systems are promoted in Chinese rural areas while many built systems have gradually fallen into ruin.; Financial sources for the O&M of wastewater treatment systems are always unsustainable.; The inappropriate governance structure, potential political risk, limited public participation and technical difficulties are all challenges in the sustainability of wastewater treatment systems.;

IntroductionIn order to accurately model the IC reactor of the wastewater treatment system and efficiently control and adjust the water treatment process, this paper proposes a method to predict the operation effect of the IC reactor using an artificial neural network model. This paper takes the IC reactor section of a papermaking wastewater treatment plant as the research object, and predicts the COD value of its effluent through the neural network model established. The experimental results show that the simulation prediction value of BP neural network is basically consistent with the change trend of the actual value, and has a certain prediction ability. Among the 20 groups of sample data for simulation prediction, the prediction relative error value of 9 sample data pairs is less than 5%, that is, the prediction error of 45% sample data pairs is within 5%; The relative error value of 15 sample data pairs is less than 10%, that is, 75% of sample data pairs have a prediction error of less than 10%; The maximum relative error is 18.6%. Through the regression analysis of the real value and the predicted value, the correlation coefficient is 0.7431.ConclusionThe BP neural network can capture the non-linear mapping relationship between the selected input factors and the output, and can predict the COD value of the effluent of IC reactor in advance.

Water and wastewater treatment plants are essential for the supply of drinking water for consumption and the treatment of effluents produced by human/industrial activities. However, few studies deal with the investments and sustainability of these services, which consider both the contribution of nature and society. This study uses the emergy approach to evaluate a wastewater treatment plant located in the northeastern part of Romania, in Iași city. An assessment of the environmental costs of natural fluxes required for the treatment processes was performed, considering that the treated effluent is, still, loaded with contaminants that have to be absorbed by the receiving water natural system. The work done by nature to assimilate this load, generally considered free, is esteemed as a further cost in the total emergy budget of the wastewater treatment processes. The sustainability perspective was approached by calculating and analyzing the emergy yield ratio (EYR), environmental load rate (ELR), and emergy sustainability development index (ESI). The use of local renewable natural resources in Iași municipal wastewater treatment plants is negligible (1.71% of the total plant emergy budget), as compared to that of the purchased resources (98.29% of the total plant emergy budget) mainly processed with the support of fossil fuels' generated energy. The unit emegy value was, also, calculated and compared to other studies relevant for wastewater treatment plants. The analysis suggests that the large amount of emergy that wastewater contains is proportional to the number of resources employed for wastewater treatment and the extensive effects on surrounding ecosystems, where wastewater is discharged. [ABSTRACT FROM AUTHOR]

In recent years, emotional neural networks (ENNs) have been extensively used in the field of time series prediction. As a variant of ENN, the radial basis emotional neural network (RBENN) is chosen as the prediction model of time series in this paper, because it has a special type of structure that can preprocess the interference in the data. However, it is difficult for many existing methods to determine network structure automatically while adjusting network parameters. To solve this problem, an RBENN based on adaptive inertia weight comprehensive learning particle swarm optimization algorithm (ADw-CLPSO-RBENN) is designed. Firstly, an adaptive inertia weight adjustment strategy based on the CLPSO algorithm (ADw-CLPSO) is exploited to balance the global and local search ability of particles. Secondly, a particle-variable dimensional learning mechanism (PVDLM) is developed based on the ADw-CLPSO algorithm, which enables particles to find the appropriate network structure while searching for the optimal parameter solution. Finally, the proposed method is evaluated in two time series and a real wastewater treatment system. The simulation results demonstrate that the proposed ADw-CLPSO-RBENN can automatically adjust to a suitable network structure, and the prediction accuracy is also better than other methods. Therefore, the proposed method has higher superiority in time series prediction. [ABSTRACT FROM AUTHOR]

As the most important production space for the development of chemical industry in China, chemical industrial parks (CIPs) are facing a new challenge of water pollution prevention and control, in addition to those posed by traditional and emerging pollutants. It is thus important to systematically reveal the compositewater‐environmental impact of CIPs for contributing to the sustainable development of the chemical industry. CIPs have the issue of intensive wastewater generation characterized by complex pollutant components and high pollution loads. Thus, establishing an environmentally friendly wastewater treatment system has always been a major challenge for CIPs. China has developed many CIPs and the two‐stage wastewater treatment system is widely employed in practice, that is, in‐plant wastewater treatment plants (WWTPs) in individual enterprises followed by centralized WWTPs in parks. Since the CIP wastewater emission limits are continuously improved, a comprehensive evaluation of the environmental impacts of the in‐use wastewater treatment system will be insightful for future policy making. This study employed life cycle assessment (LCA) to reveal the life cycle environmental impact of the two‐stage wastewater treatment system in a typical CIP. We found that the life cycle environmental impacts of the whole system mainly stem from the in‐plant WWTPs of chemical manufacturers because of chemicals and electricity consumed in the multi‐stage advanced oxidation technologies, while the increase of human toxicity potential and marine aquatic ecotoxicity potential are primarily due to sludge incineration. Three scenarios targeting the reduction of environmental impacts were proposed based on the LCA results. We evaluated the pollution mitigation potential, which can reach at most 8% of the original measured impacts. Finally, from the perspective of systematic thinking, four policy implications for improving the water management system of CIPs were proposed. [ABSTRACT FROM AUTHOR]

Treating high salt and high organic matter wastewater (HHW) generated during rapid socio-economic development is a significant challenge. This study aims to optimize a closed-cycle low-temperature evaporation (CCLE) system using mathematical modelling to be adapted to industrial applications. By using mathematical modelling and computational fluid dynamics (CFD), this study investigated the operating mechanism of the system under different operating conditions. Parametric analysis shows that increasing the compressor evaporation temperature and decreasing the condensation temperature is conducive to improving the performance of the heat pump unit, thereby increasing the wastewater treatment efficiency of the system and that a smaller heat transfer coil windward area is conducive to heat and mass transfer within the humidifier. The unique characteristics of the CCLE system are identified, and the wastewater treatment process under various operating conditions is explained. These findings may provide supporting information for the treatment of HHW by the CCLE system. • Optimization models for new wastewater treatment systems. • Proper heat pump unit parameters enhance system performance. • Tube spacing affects the state of evaporated water film. • Provides theoretical support for the industrial application of the system. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the changes of microbial communities in different treatment units of a full-scale wastewater treatment plant (WWTP) equipped with a serially combined rotating biological contactor (RBC) and anaerobic-anoxic-oxic-oxic (A²/O²) processes. Highthroughput sequencing results showed that functional communities including Methylotenera, Nitrospiraceae, Terrimonas, Zoogloea, Lysobacter, and Rhodobacter were the most dominant communities in the treatment system. The sequencing results demonstrated that the microbial communities along the different treatment units showed distinct community profiles. Additionally, dominant functional communities with Methylotenera and Methanolobus were significantly enriched on RBC films compared to those in the activated sludge of the RBC treatment unit, indicating that the RBC plays essential functions in the WWTP. In general, thoroughly screening the microbial communities in WWTP equipped with RBC and A²/O² and monitoring the changes over seasons contributed to maintain and adjust the wastewater treatment system in case of decayed treatment efficiencies. Besides, our work could guide us to rapidly set up a new similar WWTP. [ABSTRACT FROM AUTHOR]

Ammonia-oxidizing bacteria (AOB) and archaea (AOA) are two microbial groups mediating nitrification, yet little is presently known about their abundances and community structures at the transcriptional level in wastewater treatment systems (WWTSs). This is a significant issue, as the numerical abundance of AOA or AOB at the gene level may not necessarily represent their functional role in ammonia oxidation. Using amoA genes as molecular markers, this study investigated the transcriptional abundance and community structure of active AOA and AOB in 14 WWTSs. Quantitative PCR results indicated that the transcriptional abundances of AOB amoA (averaged: 1.6 × 108 copies g−1 dry sludge) were higher than those of AOA (averaged: 3.4 × 107 copies g−1 dry sludge) in all WWTSs despite several higher abundances of AOA amoA at the gene level. Moreover, phylogenetic analysis demonstrated that Nitrosomonas europaea and unknown clusters accounted for 37.66% and 49.96% of the total AOB amoA transcripts, respectively, suggesting their dominant role in driving ammonia oxidation. Meanwhile, AOA amoA transcripts were only successfully retrieved from 3 samples, and the Nitrosospaera sister cluster dominated, accounting for 83.46%. Finally, the substrate utilization kinetics of different AOA and AOB species might play a fundamental role in shaping their niche differentiation, community composition, and functional activity. This study provides a basis for evaluating the relative contributions of ammonia-oxidizing microorganisms (AOMs) to nitrogen conversions in WWTSs. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this research was to study the population dynamics of Mud Crab (Scylla spp.) using length-frequency data collected during May 2012 to April 2013 at a mangrove forest that receives effluent from the Phetchaburi Municipal Wastewater Treatment System at Laem Phak Bia, The King's Royally Initiated Laem Phak Bia Environmental Research and Development Project (The LERD Project), Phetchaburi Province, Thailand. Monthly length frequency data of Mud Crab were analyzed by FiSAT_II. Asymptotic length (L∞) and growth co-efficient (K) were 16.54 cm and 0.29 year-1, respectively. The growth performance index (j/) was 1.90. Total mortality (Z) by length-converted catch curve was 2.81 year-1, of which fishing mortality (F) was 0.94 year-1and natural mortality (M) was 1.87 year-1. The exploitation level (E) of Mud Crab was 0.33. The recruitment pattern was continuous with one major peak in the months of February to June. [ABSTRACT FROM AUTHOR]

Recent studies have confirmed the existence of aerobic denitrifying bacteria, particularly exploring their diversity and heterotrophic nitrification-aerobic denitrification capability in a wastewater treatment plant (WWTP). In this study, the sewage treatment technology, which combined a rotating biological contactor (RBC) with an anaerobic-anoxic-oxic-oxic (A2 /O2 ) process, demonstrated strong removal efficiency for nitrate pollutants. Five types of activated sludge in a WWTP were used to isolate 226 strains of aerobic denitrifying bacteria, which were classified into 12 genera based on the 16S rDNA. Pseudomonas stutzeri, Pseudomonas monteilii, and Gordonia cholesteroliborans were the most abundant aerobic denitrifying bacteria in the five types of activated sludge. P. stutzeri, Pseudomonas pseudoalcaligenes, Pseudomonas chengduensis, Beijerinckia fluminensis, and Arthrobacter protophormiae demonstrated strong heterotrophic nitrification-aerobic denitrification capacity. G. cholesteroliborans, Pseudomonas pseudoalcaligenes, P. chengduensis, and B. fluminensis also showed aerobic denitrifying capacity, which was not reported previously. The Venn diagram and the relative abundance diagram revealed the different structures of aerobic denitrifiers among the five types of activated sludge. This study may provide a reference for screening and application of aerobic denitrifying bacteria in WWTPs. [ABSTRACT FROM AUTHOR]

Due to the population growth and urbanization's trend, peri-urban areas that surround major cities are haphazardly increasing without proper infrastructure in the developing world. Even though the literature review points out that there are several suitable wastewater treatment system (WWTS) alternatives which could mitigate that issue, one of the main problems lies on the process of making decisions. Thus, this study applies the Structured Decision Making (SDM) instrument through the use of a particular tool for supporting the evaluation. In this view, the ValueCharts is a user-friendly tool that can easier narrow down comparable Overall multi-attributes values, mainly obtained from the performances and preferences given by participants of the decision. The outcomes from the cited tool's application have indicated the propensity to succeed WWTS alternatives that bear efficient processes in terms of environmental removals indicators. [ABSTRACT FROM AUTHOR]

Horizontal transfer of ARGs was generally considered to be mediated by three methods – transformation, conjugation and transduction through phages – during which the contribution of bacteriophages to gene transfer in the environment is unclear or even questioned. In this study, a multiple-antibiotic-resistant Escherichia coli strain and its phage (YZ1) were isolated from a municipal wastewater treatment system. The results of the morphological and genomic analyses of phage YZ1 showed that it is a member of the T7 viral genus in the subfamily Autographivirinae. Its genome is similar to that of the E. coli phage K1F in both organization and sequence and does not encode ARGs. However, 28 paired reads in the raw sequencing data aligned to ARGs, including those promoting β-lactam, aminoglycoside, and fluoroquinolone resistance, among others. Quantitative PCR showed that ARGs were present in bacteriophage DNA (approximately 10 3  copies/mL) and were also detected in the bacterial host DNA. The results suggested that while infrequent, some ARG-carrying transducing phages were presumably generated by erroneous packaging during infection of antibiotic-resistant bacteria, which may create the possibility of horizontal transfer of ARGs. [ABSTRACT FROM AUTHOR]

Nonlinear system modeling plays an important role in practical engineering, and deep learning-based deep belief network (DBN) is now popular in nonlinear system modeling and identification because of the strong learning ability. However, the existing weights optimization for DBN is based on gradient, which always leads to a local optimum and a poor training result. In this paper, a DBN with partial least square regression (PLSR-DBN) is proposed for nonlinear system modeling, which focuses on the problem of weights optimization for DBN using PLSR. Firstly, unsupervised contrastive divergence (CD) algorithm is used in weights initialization. Secondly, initial weights derived from CD algorithm are optimized through layer-by-layer PLSR modeling from top layer to bottom layer. Instead of gradient method, PLSR-DBN can determine the optimal weights using several PLSR models, so that a better performance of PLSR-DBN is achieved. Then, the analysis of convergence is theoretically given to guarantee the effectiveness of the proposed PLSR-DBN model. Finally, the proposed PLSR-DBN is tested on two benchmark nonlinear systems and an actual wastewater treatment system as well as a handwritten digit recognition (nonlinear mapping and modeling) with high-dimension input data. The experiment results show that the proposed PLSR-DBN has better performances of time and accuracy on nonlinear system modeling than that of other methods. [ABSTRACT FROM AUTHOR]

In this paper, a self-organizing deep belief network (SODBN) with growing and pruning algorithms is proposed for nonlinear system modeling. Although deep learning-based DBN has been widely used in recent years, actually more detailed researches about how to dynamically determine its structure are seldom observed in the existing literatures. The SODBN can automatically determine its structure using growing and pruning algorithms instead of artificial experience. Firstly, the structure of SODBN is constructed automatically by changing the number of both hidden layers and the hidden neurons during the training process. The self-organizing strategy is implemented by automatic growing and pruning algorithm (AGP), which is actually equivalent to adding and pruning the connecting weights between neurons. Secondly, the weights are dynamically adjusted during the process of structure self-organization. SODBN is able to adjust the weights in the dynamic process of self-organizing structure, and is helpful to improve the network performances, including running time and accuracy. Finally, the proposed SODBN has been tested on three benchmark problems, including nonlinear system modeling, water quality prediction in practical wastewater treatment system as well as air pollutants concentrations prediction. The corresponding experimental results show that SODBN has better performances than some existing neural networks. [ABSTRACT FROM AUTHOR]