Full-scale Wastewater Treatment Plant Research Articles

Monitoring of organic micropollutant behavior and fate in a full-scale conventional biological urban wastewater treatment plant

Monitoring of organic micropollutant behavior and fate in a full-scale conventional biological urban wastewater treatment plant

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4–6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.

Assessing the effects of salinity on microbial communities and pollutant removal in urban wastewater treatment plants

This study assessed the impact of electrical conductivity (EC) on the microeukaryotic community and pollutant removal efficiency in full-scale wastewater treatment plants (WWTPs) in Catalonia, Spain. Monthly samples from seven WWTPs (2010−2021) were collected, with microbial communities identified by microscopy and effluent quality parameters measured. Linear mixed-effects models (LMER), indicator species analysis (IndVal), and structural equation modeling (SEM) were used to evaluate the effects of EC on WWTP operation and microbial communities. EC levels varied widely among WWTPs (800 to 15,000 μS cm−1), with mean removal rates generally low, not exceeding 26 %. EC removal was inversely correlated with organic matter removal (p < 0.05). IndVal analysis revealed distinct microbial communities associated with each WWTPs along the EC gradient (e.g., Beggiatoa spp. and Epistylis sp. were associated with high and low EC, respectively). High EC and peaks negatively affected the abundance of certain microorganisms (e.g., Acineria uncinata) (p < 0.05). Ciliate genera were most affected by EC peaks, with different species showing different salinity tolerance ranges (e.g. Holophyra discolor was affected by EC > 3000 μS cm-1). Seasonal variations did not significantly alter community sensitivity to salinity (p > 0.05). LMER and SEM analyses revealed strong adverse effects of high EC and EC peaks on microbial diversity, richness, and the efficiency of organic matter and TKN removal (p < 0.05). This highlights the importance of monitoring and controlling EC levels in WWTP influent to maintain optimal treatment performance and the need for effective technologies or biological processes to mitigate saline discharges into rivers.

Occurrence, distribution, and fate evaluation of endocrine disrupting compounds in three wastewater treatment plants with different treatment technologies in Türkiye

Nowadays, two of the endocrine disrupting compounds (EDCs) in the group of alkylphenols (APs), nonylphenol (4-NP) and octylphenol (4-t-OP), have attracted great scientific and regulatory attention mainly due to concerns about their aquatic toxicity and endocrine disrupting activity. This paper investigated the occurrence, distribution behavior, fate, and removal of 4-NP and 4-t-OP in liquid and solid phases of three full-scale wastewater treatment plants (WWTPs) with different treatment technologies comparatively. In this context, (i) advanced biological WWTP, (ii) wastewater stabilization pond (WSP), and (iii) constructed wetland (CW) were utilized. In all three investigated WWTPs, the concentrations of 4-NP (219.9–19,354.4 ng/L) in raw wastewater were higher than those of 4-t-OP (13.9–2822.4 ng/L). Within the scope of annual average removal efficiencies, 4-NP was treated highly in advanced biological WWTP (93.5 %), while it was almost not treated in WSP (3.1 %) and treated with negative removal (<0 %) in CW. While 4-t-OP was treated at a similar removal rate (93.5 %) to 4-NP in advanced biological WWTP, it was treated moderately in WSP (52.5 %) and very poorly in CW (12.4 %). It has been determined that the most important removal mechanism of both 4-NP and 4-t-OP in WWTPs is biodegradation, followed by sorption onto sewage sludge. According to the mass balance performed in advanced biological WWTP, the biodegradation rates for 4-NP and 4-t-OP were found to be 70.4 % and 86.6 %, respectively, while the sorption onto sewage sludge were determined to be 23.3 % and 6.8 %. One of the critical findings obtained within the scope of the study is that while the concentrations of both metabolites, especially 4-NP, in wastewater and sewage sludge, decreased considerably under aerobic conditions, on the contrary, their concentrations increased significantly under anaerobic conditions. Both compounds were detected at higher concentrations in primary sludge compared to secondary sludge in advanced biological WWTP, while in WSP, they were determined at higher concentrations in anaerobic stabilization pond sludge compared to facultative stabilization pond sludge. Besides, it was also determined that the sorption behavior of these alkylphenols is much more dominant than desorption.

Optimization of biological nitrogen removal in full-scale municipal WWTPs using activated sludge model simulation

The study evaluated the most efficient biological nitrogen removal (BNR) process in four full-scale municipal wastewater treatment plants (WWTPs) by using BioWin, a simulation software based on the activated sludge model (ASM). A series of experiments were conducted to determine the kinetic and stoichiometric parameters for the ASM. Results indicated that autotrophic maximum specific growth rates in the studied WWTPs were generally higher compared to previous findings, likely due to their low COD/N ratios, emphasizing the importance of local parameterization. Continuous water quality monitoring in each plant was employed to validate the model. Dynamic simulation results indicated that the error remained within an acceptable range, with a mean relative error of less than 20%, confirming the reliability of ASM parameters derived from batch experiments. Subsequently, various operational scenarios were analyzed to determine the optimal BNR process for each plant, considering influent flow rate, internal recycling, and methanol addition. Simulation outcomes suggested that O/A (oxic/anoxic) operation is preferable for plants with low organic content (COD = 60–110 mg/L), considering both operational costs and total nitrogen removal rate. Conversely, A/O (anoxic/oxic) operation might be advantageous for plants with higher influent organic matter (COD = 200 mg/L).

Exploring transformation of dissolved organic matters and dissolved organic nitrogen in full-scale anammox wastewater treatment: Temperature and microbial roles

Variation of dissolved organic matters (DOM) in mainstream anammox process has received limited attention. This study systematically characterized DOM and dissolved organic nitrogen (DON) in a full-scale mainstream anammox wastewater treatment plant (WWTP) using spectroscopy and Fourier transform-ion cyclotron resonance mass spectrometry. Roles of bacterial community structures related with temperatures on DOM and DON transformations were analyzed. Results indicated that the WWTP removed highly bioavailable, S-containing DOM while producing more unsaturated, aromatic, and N-containing DOM. Higher relative abundances of Proteobacteria and Chloroflexi at low temperature resulted in greater removal rates of proteins, SMP-like and humic acid-like substances. At high temperature, higher relative abundance of Actinobacteriota increased lignin production. Principal component analysis revealed that temperature significantly impacted DOM characteristics compared to DON. These findings are crucial for understanding DOM and DON transformation during mainstream anammox WWTP.

Sequential Anaerobic-Aerobic Treatment Enhances Sulfamethoxazole Removal: From Batch Cultures to Observations in a Large-Scale Wastewater Treatment Plant.

Sulfamethoxazole (SMX) passes through conventional wastewater treatment plants (WWTPs) mainly unaltered. Under anoxic conditions sulfate-reducing bacteria can transform SMX but the fate of the transformation products (TPs) and their prevalence in WWTPs remain unknown. Here, we report the anaerobic formation and aerobic degradation of SMX TPs. SMX biotransformation was observed in nitrate- and sulfate-reducing enrichment cultures. We identified 10 SMX TPs predominantly showing alterations in the heterocyclic and N4-arylamine moieties. Abiotic oxic incubation of sulfate-reducing culture filtrates led to further degradation of the major anaerobic SMX TPs. Upon reinoculation under oxic conditions, all anaerobically formed TPs, including the secondary TPs, were degraded. In samples collected at different stages of a full-scale municipal WWTP, anaerobically formed SMX TPs were detected at high concentrations in the primary clarifier and digested sludge units, where anoxic conditions were prevalent. Contrarily, their concentrations were lower in oxic zones like the biological treatment and final effluent. Our results suggest that anaerobically formed TPs were eliminated in the aerobic treatment stages, consistent with our observations in batch biotransformation experiments. More generally, our findings highlight the significance of varying redox states determining the fate of SMX and its TPs in engineered environments.

Twinning quality sensors in wastewater treatment process via optimized echo state network-based soft sensors

The presence of a large amount of quality-related but hard-to-measure variables usually makes effective monitoring of industrial processes difficult, and even impossible. Soft computing techniques and digital twins can revolutionize standard approaches to solve this issue, even though industrial data are frustrated by nonlinearity, disturbances, and dynamic behaviors. In this light, this paper proposes a data-driven soft sensor with the help of the Fourier amplitude sensitivity test (FAST) and improved probabilistic regularized echo state network (IPRESN), then to twin the quality hardware sensors. Within this framework, the initial step is to rely on the FAST method to measure the collaborated importance of multiple process variables with respect to each quality variable. This quantification facilitates the selection of auxiliary variables for the training of IPRESN. Furthermore, a probabilistic regularization method is learned to improve the robust performance of the echo state network (ESN) by re-designing a new objective function to minimize the mean and variance of the modeling error. Additionally, the whale optimization algorithm (WOA) is improved and then used to optimize the critical hyper-parameters of ESN, thus mitigating the risks associated with suboptimal hyperparameter selection. The effectiveness of the proposed method is verified through a Benchmark Simulation Model 2 (BSM2) and a full-scale wastewater treatment plant data set. The findings show the potential of the proposed method in facilitating digital twin implementations for hardware sensors.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD – chemical oxygen demand, NH4-N – ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

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.

Intelligent dosing of sodium hypochlorite in municipal wastewater treatment plants: Experimental and modeling studies

Sodium hypochlorite (NaClO), a disinfectant extensively employed in municipal wastewater treatment plants (WWTPs), is frequently overdosed to ensure qualified effluent quality consistently. In light of the escalated disinfectant expenses and detrimental environmental impacts, it is critical to have a precise control model in place to enable intelligent dosing of NaClO. The lack of real-time disinfection evaluation indicators presents difficulties in precisely and swiftly regulating the dosage. Herein, the CT value (the product of residual chlorine concentration and contact time) was initially determined experimentally as a metric for assessing disinfection in municipal WWTPs. Fecal coliform (FC) concentrations become less than 1.0 × 103 MPN/L when the effluent CT value is regulated at 0.20 mg·min/L (calculated as Cl2). In addition, a model employing long short-term memory (LSTM) was developed to predict the dosage, utilizing simplified and suitable parameters such as COD, NH3-N concentration, flow rate, and historical dosage of NaClO as input factors, which demonstrated satisfactory performance (R2 = 0.948). Notably, dosage adjustments were made concurrently in response to CT value feedback. The intelligent dosing model under simultaneous feedforward and feedback control was applied at a full-scale WWTP for nearly two months, considerably reducing disinfectant consumption and meeting the discharge standard of FC.

Dynamics of antimicrobial resistance and susceptibility profile in full-scale hospital wastewater treatment plants.

Drug resistance has become a matter of great concern, with many bacteria now resist multiple antibiotics. This study depicts the occurrence of antibiotic-resistant bacteria (ARB) and resistance patterns in five full-scale hospital wastewater treatment plants (WWTPs). Samples of raw influent wastewater, as well as pre- and post-disinfected effluents, were monitored for targeted ARB and resistance genes in September 2022 and February 2023. Shifts in resistance profiles of Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii antimicrobial-resistant indicators in the treated effluent compared to that in the raw wastewater were also worked out. Ceftazidime (6.78 × 105 CFU/mL) and cefotaxime (6.14 × 105 CFU/mL) resistant species showed the highest concentrations followed by ciprofloxacin (6.29 × 104 CFU/mL), and gentamicin (4.88 × 104 CFU/mL), in raw influent respectively. WWTP-D employing a combination of biological treatment and coagulation/clarification for wastewater decontamination showed promising results for reducing ARB emissions from wastewater. Relationships between treated effluent quality parameters and ARB loadings showed that high BOD5 and nitrate levels were possibly contributing to the persistence and/or selection of ARBs in WWTPs. Furthermore, antimicrobial susceptibility tests of targeted species revealed dynamic shifts in resistance profiles through treatment processes, highlighting the potential for ARB and ARGs in hospital wastewater to persist or amplify during treatment.

Biological treatment of N-methylpyrrolidone, cyclopentanone, and diethylene glycol monobutyl ether distilled residues and their effects on nitrogen removal in a full-scale wastewater treatment plant

Manufacturing processes in semiconductor and photonics industries involve the use of a significant amount of organic solvents. Recycle and reuse of these solvents produce distillate residues and require treatment before being discharged. This study aimed to evaluate the performance of the biological treatment system in a full-scale wastewater treatment plant that treats wastewater containing distillate residues from the recycling of electronic chemicals. Batch experiments were conducted to investigate the optimal operational conditions for the full-scale wastewater treatment plant. To achieve good nitrogen removal efficiency with effluent ammonia and nitrate concentrations below 20 mg N/L and 50 mg N/L, respectively, it was suggested to control the ammonia concentration and pH of the influent below 500 mg N/L and 8.0, respectively. In addition, the biodegradability of N-methylpyrrolidone, diethylene glycol monobutyl ether, and cyclopentanone distillate residues from the electronic chemicals manufacturing process were evaluated under aerobic, anoxic, and anaerobic conditions. N-methylpyrrolidone and cyclopentanone distillate residues were suggested to be treated under anoxic condition. However, substrate inhibition occurred when using cyclopentanone distillate residue as a carbon source with chemical oxygen demand (COD) levels higher than 866 mg/L and nitrate levels higher than 415 mg N/L. Under aerobic condition, the COD from both N-methylpyrrolidone and cyclopentanone distillate residues could be easily degraded. Nevertheless, a negative effect on nitrification was observed, with a prolonged lag time for ammonia oxidation as the initial COD concentration increased. The specific ammonia oxidation rate and nitrate production rate decreased under high COD concentration contributed by N-methylpyrrolidone and cyclopentanone distillate residues. Furthermore, the biodegradability of diethylene glycol monobutyl ether distillate residue was found to be low under aerobic, anoxic, and anaerobic conditions. With respect to the abundance of nitrogen removal microorganisms in the wastewater treatment plant, results showed that Comammox may have an advantage over ammonia oxidizing bacteria under high pH conditions. In addition, Comammox may have higher resistance to environmental changes. Dominance of Comammox over ammonia oxidizing bacteria under high ammonia condition was first reported in this study.

HPB-Chip: An accurate high-throughput qPCR-based tool for rapidly profiling waterborne human pathogenic bacteria in the environment

Waterborne pathogens are threatening public health globally, but profiling multiple human pathogenic bacteria (HPBs) in various polluted environments is still a challenge due to the absence of rapid, high-throughput and accurate quantification tools. This work developed a novel chip, termed the HPB-Chip, based on high-throughput quantitative polymerase chain reactions (HT-qPCR). The HPB-Chip with 33-nL reaction volume could simultaneously complete 10,752 amplification reactions, quantifying 27 HPBs in up to 192 samples with two technical replicates (including those for generating standard curves). Specific positive bands of target genes across different species and single peak melting curves demonstrated high specificity of the HPB-Chip. The mixed plasmid serial dilution test validated its high sensitivity with the limit of quantification (LoD) of averaged 82 copies per reaction for 25 target genes. PCR amplification efficiencies and R2 coefficients of standard curves of the HPB-Chip averaged 101 % and 0.996, respectively. Moreover, a strong positive correlation (Pearson’ r: 0.961–0.994, P < 0.001) of HPB concentrations (log10 copies/L) between HPB-Chip and conventional qPCR demonstrated high accuracy of the HPB-Chip. Subsequently, the HPB-Chip has been successfully applied to absolutely quantify 27 HPBs in municipal and hospital wastewater treatment plants (WWTPs) after PMA treatment. A total of 17 HPBs were detected in the 6 full-scale WWTPs, with an additional 19 in the hospital WWTP. Remarkably, Acinetobacter baumannii, Legionella pneumophila, and Arcobacter butzler were present in the final effluent of each municipal WWTP. Overall, the HPB-Chip is an efficient and accurate high-throughput quantification tool to comprehensively and rapidly quantify 27 HPBs in the environment.

Complete long-term monitoring of greenhouse gas emissions from a full-scale industrial wastewater treatment plant with different cover configurations

The greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), consisting mainly of methane (CH4) and nitrous oxide (N2O), have been constantly increasing and become a non-negligible contributor towards carbon neutrality. The precise evaluation of plant-specific GHG emissions, however, remains challenging. The current assessment approach is based on the product of influent load and emission factor (EF), of which the latter is quite often a single value with huge uncertainty. In particular, the latest default Tier 1 value of N2O EF, 0.016 ± 0.012 kgN2O–N kgTN−1, is estimated based on the measurement of 30 municipal WWTPs only, without involving any industrial wastewater. Therefore, to resolve the pattern of GHG emissions from industrial WWTPs, this work conducted a 14-month monitoring campaign covering all the process units at a full-scale industrial WWTP in Shanghai, China. The total CH4 and N2O emissions from the whole plant were, on average, 447.7 ± 224.5 kgCO2-eq d−1 and 1605.3 ± 2491.0 kgCO2-eq d−1, respectively, exhibiting a 5.2- or 3.9-times more significant deviation than the influent loads of chemical oxygen demand (COD) or total nitrogen (TN). The resulting EFs, 0.00072 kgCH4 kgCOD−1 and 0.00211 kgN2O–N kgTN−1, were just 0.36% of the IPCC recommended value for CH4, and 13.2% for N2O. Besides, the parallel anoxic-oxic (A/O) lines of this industrial WWTP were covered in two configurations, allowing the comparison of GHG emissions from different odor control setup. Unit-specific analysis showed that the replacement of enclosed A/open O with enclosed A/O reduced the CH4 EF by three times, from 0.00159 to 0.00051 kgCH4 kgCOD−1, and dramatically decreased the N2O EF by an order of magnitude, from 0.00376 to 0.00032 kgN2O–N kgTN−1, which was among the lowest of all full-scale WWTPs.

Modelling competitive adsorption of organic micropollutants onto powdered activated carbon in continuous stirred tank reactors for advanced wastewater treatment

This work investigates the validation and application of a competitive model approach for full-scale wastewater treatment plants (WWTP) with external recirculation of partially loaded powdered activated carbon (PAC) for removal of organic micropollutants (OMP). It is based on the ideal adsorbed solution theory (IAST) for multisolute mixtures combined with calibration of fictive organic components and correction of single-solute model parameters for OMP by use of the tracer model (TRM). Adsorption kinetics are represented by a pseudo first order reaction (PFO) and compared to mass transfer calculated with the homogenous surface diffusion model (HSDM). Model validation with operational data from two different WWTPs showed a strong dependency of model results on the batch sample quality used for model calibration. In contrast, the kinetic approach is of less importance for predicting full-scale OMP removal with long PAC sludge retention times. Further model application demonstrated that external PAC recirculation significantly improves the OMP removal with regard to both adsorption capacity and compensation of competitive effects of Dissolved Organic Carbon (DOC).

Relating the carbon sources to denitrifying community in full-scale wastewater treatment plants

Carbon source is a key factor determining the denitrifying effectiveness and efficiency in wastewater treatment plants (WWTPs). Whereas, the relationships between diverse and distinct denitrifying communities and their favorable carbon sources in full-scale WWTPs were not well-understood. This study performed a systematic analysis of the relationships between the denitrifying community and carbon sources by using 15 organic compounds from four categories and activated sludge from 8 full-scale WWTPs. Results showed that, diverse denitrifying bacteria were detected with distinct relative abundances in 8 WWTPs, such as Haliangium (1.98–4.08%), Dechloromonas (2.00–3.01%), Thauera (0.16–1.06%), Zoogloea (0.09–0.43%), and Rhodoferax (0.002–0.104%). Overall, acetate resulted in the highest denitrifying activities (1.21–4.62 mg/L/h/gMLSS), followed by other organic acids (propionate, butyrate and lactate, etc.). Detectable dissimilatory nitrate reduction to ammonium (DNRA) was observed for all 15 carbon sources. Methanol and glycerol resulted in the highest DRNA. Acetate, butyrate, and lactate resulted in the lowest DNRA. Redundancy analysis and 16S cDNA amplicon sequencing suggested that carbon sources within the same category tended to correlate to similar denitrifiers. Methanol and ethanol were primarily correlated to Haliangium. Glycerol and amino acids (glutamate and aspartate) were correlated to Inhella and Sphaerotilus. Acetate, propionate, and butyrate were positively correlated to a wide range of denitrifiers, explaining the high efficiency of these carbon sources. Additionally, even within the same genus, different amplicon sequence variants (ASVs) performed distinctly in terms of carbon source preference and denitrifying capabilities. These findings are expected to benefit carbon source formulation and selection in WWTPs.

Characteristics and key driving factors of nitrous oxide emissions from a full-scale landfill leachate treatment system

The characteristics of N2O emission from a full-scale landfill leachate treatment system were investigated by in-situ monitoring over 1.4 years and driving factors responsible for these emissions were identified by statistical analysis of multidimensional environmental variables. The results showed that the maximum N2O emission flux of 2.21 × 107 mg N·h−1 occurred in the nitrification tanks, where 98.5 % of the total N2O was released, with only 1.5 % of the total N2O emitted from the denitrification tanks. Limited oxygen in nitrification tank was responsible for N2O hotspot. The N2O emissions from the parallel lines A and B (both comprising the primary biochemical system) accounted for 52.6 % and 46.6 %, respectively, while the secondary biochemical system contributed only 0.8 % to the total emissions. Higher nitrite concentration in line A and lower nitrogen loading in the secondary biochemical system caused these discrepancies. We found that during the steady state of leachate treatment, intensive N2O emissions of 253.4–1270.5 kg N·d−1 were measured. The corresponding N2O emission factor (EF) ranged from 8.86 to 49.6 %, much higher than those of municipal wastewater treatment. But N2O EF was inconceivably as low as 0.42 % averagely after system maintenance. Influent with low salinity was the key reason, followed by the high MLSS and varying microbial community after maintenance. The dominant genus shifted from Lentimicrobium and Thauera to Norank-F-Anaerolineaceae and Unclassified-F-Rhodocyclaceae. This study underscores the significance of landfill leachate treatment in urban nitrogen management and provides valuable insights into the characteristics and driving factors of N2O emissions from such systems. The findings offer important references for greenhouse gas emission inventories and strategies for N2O control in full-scale wastewater treatment plants.

A Study of the System Performance and the Microbial Community Composition of Chemical Wastewater in an AO-MBBR Treatment Process

To improve the nitrogen removal and reduce the chemical oxygen demand (COD) of a full-scale wastewater treatment plant, two sequential batch reactor devices were used to treat chemical wastewater with biocarriers in low carbon-to-nitrogen (C/N) ratio conditions. The results showed that the addition of biocarriers to the anoxic tank reduced the average concentration of COD in the effluent from 98.1 mg/L to 80.7 mg/L and increased total nitrogen (TN) removal by 9.4%. Metagenomic sequencing was performed to study the composition and function of microbial community samples taken from anoxic sludge and anoxic-carrier biofilms in this wastewater treatment plant. The results showed that Proteobacteria and Actinobacteria were the dominant phyla in the two samples, ensuring their capability for organic matter removal. The anoxic-carrier biofilms were mainly enriched with denitrifying bacteria such as Thauera (10.7%) and Comammonas (2.2%) and the anammox bacteria Candidatus Kuenenia (0.03%). Meanwhile, the nitrogen metabolism pathway was elaborated and the abundance of the functional genes involved in the nitrogen metabolism pathway was quantified. In addition, results from qPCR showed increased copy numbers of denitrification and anammox genes in the anoxic-carrier biofilms compared to those in the anoxic sludge, further confirming the enrichment of functional bacteria.

EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.