Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques

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.

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review

As the pursuit of "carbon neutrality" gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.

Nitrous oxide emissions from two full-scale membrane-aerated biofilm reactors

The upcoming change of legislation in some European countries where wastewater treatment facilities will start to be taxed based on direct greenhouse gas (GHG) emissions will force water utilities to take a closer look at nitrous oxide (N2O) production. In this study, we report for the first time N2O emissions from two full-scale size membrane aerated biofilm reactors (MABR) (R1, R2) from two different manufacturers treating municipal wastewater. N2O was monitored continuously for 12 months in both the MABR exhaust gas and liquid phase. Multivariate analysis was used to assess process performance. Results show that emission factors (EFN2O) for both R1 and R2 (0.88 ± 1.28 and 0.82 ± 0.86 %) were very similar to each other and below the standard value from the Intergovernmental Panel on Climate Change (IPCC) 2019 (1.6 %). More specifically, N2O was predominantly emitted in the MABR exhaust gas (NTRexh) and was strongly correlated to the ammonia/um load (NHx,load). Nevertheless, the implemented Oxidation Reduction Potential (ORP) control strategy increased the bulk contribution (NTRbulk), impacting the overall EFN2O. A thorough analysis of dynamic data reveals that the changes in the external aeration (EA)/loading rate patterns suggested by ORP control substantially impacted N2O mass transfer and biological production processes. It also suggests that NTRexh is mainly caused by ammonia-oxidizing organisms (AOO) activity, while ordinary heterotrophic organisms (OHO) are responsible for NTRbulk. Different methods for calculating EFN2O were compared, and results showed EFN2O would range from 0.6 to 5.5 depending on the assumptions made. Based on existing literature, a strong correlation between EFN2O and nitrogen loading rate (R2 = 0.73) was found for different technologies. Overall, an average EFN2O of 0.86 % N2O-N per N load was found with a nitrogen loading rate >200 g N m-3 d-1, which supports the hypothesis that MABR technology can achieve intensified biological nutrient removal without increasing N2O emissions.

Machine learning for modeling N2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability

Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating N2O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N2O emissions hold substantial potential. However, so far, a comprehensive approach is still overlooked, impeding the advancement of full-scale application. Therefore, this study develops a comprehensive approach for using machine learning to perform online process modeling of N2O emissions. The approach is tested on a long-term N2O emission dataset from a full-scale WWTP. Uniquely, the proposed approach emphasizes not just model accuracy, but it also considers model complexity, computational speed, and interpretability, equipping operators with the insights needed for informed corrective actions. Algorithms with varying levels of complexity and interpretability including k-Nearest Neighbors (kNN), decision trees, ensemble learning models, and deep neural networks (DNN) were considered. Furthermore, a parametric multivariate outlier removal method was adjusted to account for data statistical distributions, significantly reducing data loss. By employing an effective feature selection methodology, a trade-off between data acquisition, model performance, and complexity was found, reducing the number of features by 40 % and decreasing data collection cost, model complexity and computational burden without significant effect on modeling accuracy. The best performing models are kNN (R2 = 0.88), AdaBoost (R2 = 0.94), and DNN (R2 = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N2O mitigation decisions.

Contribution of nitrous oxide to the carbon footprint of full-scale wastewater treatment plants and mitigation strategies- a critical review

Nitrous oxide (N₂O), a potent greenhouse gas, significantly contributes to the carbon footprint of wastewater treatment plants (WWTPs) and contributes significantly to global climate change and to the deterioration of the natural environment. Our understanding of N₂O generation mechanisms has significantly improved in the last decade, but the development of effective N₂O emission mitigation strategies has lagged owing to the complexity of parameter regulation, substandard monitoring activities, and inadequate policy criteria. Based on critically screened published studies on N₂O control in full-scale WWTPs, this review elucidates N₂O generation pathway identifications and emission mechanisms and summarizes the impact of N₂O on the total carbon footprint of WWTPs. In particular, a linear relationship was established between N₂O emission factors and total nitrogen removal efficiencies in WWTPs located in China. Promising N₂O mitigation options were proposed, which focus on optimizing operating conditions and implementation of innovative treatment processes. Furthermore, the sustainable operation of WWTPs has been anticipated to convert WWTPs into absolute greenhouse gas reducers as a result of the refinement and improvement of on-site monitoring activities, mitigation mechanisms, regulation of operational parameters, modeling, and policies.

A new method for identifying potential hazardous areas of heavy metal pollution in sediments

The combined effect of pollution source discharge and sediment adsorption leads to the rapid enrichment of heavy metals and other pollutants in lake sediments, which poses a serious threat to the lake ecosystem. Accurately identifying the risk areas of heavy metals in sediments is the key to lake sediment pollution control. Taking Taihu Lake as the study area, combined with the ecological risk status of heavy metals in sediments, the spatial clustering characteristics of pollution sources and the clustering information of sediment attributes, a potential toxic risk area identification method based on sediment source aggregation class (SLISA-SCA) was established. Through the source analysis of heavy metals in sediments, heavy metals such as Cr, Mn, Cu and Zn in Taihu Lake sediments were identified to have originated from natural sources and were subsequently disturbed by human activities to a certain extent. Cd was found to be strongly affected by human activities, and almost all Taihu Lake sediments were affected to varying degrees. In addition, the anthropogenic sources of heavy metals show high concentration clustering characteristics in the lake bay. By K-means cluster analysis of sediment attributes, three significant differences were obtained, which were determined as potential high pollution risk areas, potential medium risk areas and potential low risk areas, and the proportions were 5.6%, 27.6% and 66.8%, respectively. The SLISA-SCA model established in this study, from the perspective of source sinks, comprehensively considers the risks caused by pollution sources and sediment attributes to sediments and divides Taihu Lake into five different risk control areas (high-risk control area, potential high-risk control area, potential risk control area, potential low-risk control area and low-risk control area). This study identified areas with different levels of heavy metal pollution in Taihu Lake sediments, proposes corresponding treatment measures, and provides a scientific and systematic method and technology for the pollution management of other river and lake sediments in the world.

Removal of micropollutants using a membrane bioreactor coupled with powdered activated carbon - A statistical analysis approach

The occurrence of micropollutants in wastewater is largely documented as well as the environmental risk posed by their residues in the aquatic environment. Many investigations have been carried out and plan to study and improve their removal efficiency in existing wastewater treatment plants. At the same time, efforts are being made to develop new technologies or upgrade existing ones to increase the removal of a selection of micropollutants. Due to the great variability in their chemical and physical properties, it would be advisable to find representative compounds or identify the factors which most influence the removal mechanisms under specific conditions. This study analyses the removal efficiencies of a great number of micropollutants in wastewater treated in a membrane bioreactor coupled with powdered activated carbon (PAC), which was the subject of a review article we have recently published. The main operational parameters (i.e. PAC dosage, PAC retention time and sludge retention time) and compound physico-chemical properties (i.e. octanol-water distribution coefficient, charge and molecular weight) were first selected on the basis of a dedicated screening step and then an attempt was carried out to clarify their influence on the removal of micropollutants from wastewater during its treatment. To this end, a statistical analysis, mainly based on exploratory methods (cluster analysis and principal component analysis) and regression analysis, was carried out to compare and discuss the different results published in the scientific literature included in the cited review article. It emerged, that, based on the collected dataset, micropollutant charge and LogD_ow seem to play the most important role in the removal mechanisms occurring in MBR coupled with PAC.

The trade-off between N2O emission and energy saving through aeration control based on dynamic simulation of full-scale WWTP

This study investigated the trade-off between energy saving and N₂O emission reduction of WWTP under the precise control of dissolved oxygen (DO) concentration through model simulation. A long-term dynamic model for full-scale WWTP GHG emissions was established and calibrated with monitored year-round hourly water quality data to quantify the annual GHG emissions from WWTP. Results showed that N₂O dominated the direct emission, up to 76.1%, and the variability of N₂O generation could better be revealed by dynamic simulation. Furthermore, GHG emissions of the WWTP were mainly contributed by electric energy, among which the blower consumes the most electricity. To reduce the electricity consumption of blowers, improve mechanical efficiency and reduce DO concentration should be considered. DO setting played a significant role in the N₂O and CH₄ emission, electricity consumption and effluent quality, which was challenging to balance. The ultralow-oxygen (0-1/0.2-1 mg/L) and low oxygen (1-2 mg/L) control strategies were proposed, and their effects on total GHG emissions and effluent water quality were discussed. If the anaerobic environment (DO<0.2 mg/L)could be avoided, the control frequency (high and low) of the DO set-point did not have a significant effect on the emissions of N₂O and CH₄ and the effluent quality. The ultralow-oxygen strategy (0.2-1 mg/L) with a high-frequency control strategy achieved the lowest GHG emissions under the current energy mix. However, by 2050, as the energy supply gets cleaner, the total GHG emissions of WWTPs with ultralow-oxygen aeration (0.2-1 mg/L) will exceed low-oxygen aeration by 3.6%-4.2%, as N₂O dominates 61.6%. Therefore, considering the trade-off between N₂O emission and energy saving in WWTP, ultralow-oxygen aeration is a transition scheme to cleaner energy.

Exploring the microbial influence on seasonal nitrous oxide concentration in a full-scale wastewater treatment plant using metagenome assembled genomes

Nitrous oxide is a highly potent greenhouse gas and one of the main contributors to the greenhouse gas footprint of wastewater treatment plants (WWTP). Although nitrous oxide can be produced by abiotic reactions in these systems, biological N₂O production resulting from the imbalance of nitrous oxide production and reduction by microbial populations is the dominant cause. The microbial populations responsible for the imbalance have not been clearly identified, yet they are likely responsible for strong seasonal nitrous oxide patterns. Here, we examined the seasonal nitrous oxide concentration pattern in Avedøre WWTP alongside abiotic parameters, the microbial community composition based on 16S rRNA gene sequencing and already available metagenome-assembled genomes (MAGs). We found that the WWTP parameters could not explain the observed pattern. While no distinct community changes between periods of high and low dissolved nitrous oxide concentrations were determined, we found 26 and 28 species with positive and negative correlations to the seasonal N₂O concentrations, respectively. MAGs were identified for 124 species (approximately 31% mean relative abundance of the community), and analysis of their genomic nitrogen transformation potential could explain this correlation for four of the negatively correlated species. Other abundant species were also analysed for their nitrogen transformation potential. Interestingly, only one full-denitrifier (Candidatus Dechloromonas phosphorivorans) was identified. 59 species had a nosZ gene predicted, with the majority identified as a clade II nosZ gene, mainly from the phylum Bacteroidota. A correlation of MAG-derived functional guilds with the N₂O concentration pattern showed that there was a small but significant negative correlation with nitrite oxidizing bacteria and species with a nosZ gene (N₂O reducers (DEN)). More research is required, specifically long-term activity measurements in relation to the N₂O concentration to increase the resolution of these findings.

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.

Estimation of countrywide N2O emissions from wastewater treatment in Switzerland using long-term monitoring data

Nitrous oxides (N₂O) emissions contribute to climate change and stratospheric ozone depletion. Wastewater treatment is an important, yet likely underestimated, source of N₂O emissions, as recent, long-term monitoring campaigns have demonstrated. However, the available data are insufficient to representatively estimate countrywide emission due to the brevity of most monitoring campaigns. This study showed that the emission estimates can be significantly improved using an advanced approach based on multiple continuous, long-term monitoring campaigns. In monitoring studies on 14 full-scale wastewater treatment plants (WWTPs), we found a strong variability in the yearly emission factors (EFs) (0.1 to 8% of the incoming nitrogen load) which exhibited a good correlation with effluent nitrite. But countrywide data on nitrite effluent concentrations is very limited and unavailable for emission estimation in many countries. Hence, we propose a countrywide emission factor calculated from the weighted EFs of three WWTP categories (carbon removal, EF: 0.1-8%, nitrification only: 1.8%, and full nitrogen removal: 0.9%). However, EF of carbon removal WWTPs are still highly uncertain given the expected variability in performance. The newly developed approach allows representative, country-specific estimations of the N₂O emissions from WWTP. Applied to Switzerland, the estimations result in an average EF of 0.9 to 3.6% and total emissions of 410 to 1690 tN₂O-N/year, which corresponds to 0.3-1.4% of the total greenhouse gas emissions in Switzerland. Our results demonstrate that better data availability and an improved understanding of long-term monitoring campaigns is crucial to improve current emission estimations. Finally, our results confirm several measures to mitigate N₂O emissions from wastewater treatment; year-round denitrification, limiting nitrite accumulation, and stringent control of sludge age in carbon removal plants.

Organic chloramines formation from algal organic matters: Insights from Fourier transform-ion cyclotron resonance mass spectrometry

Release of algal organic matter (AOM) from algae poses great threats to drinking water safety. As organic nitrogen in AOM is relatively higher compared to natural organic matter (NOM), the organic chloramine formation during chlorination cause overestimation of effective chlorine, which may lead to a biological risk. This study compared the organic chloramine formation from AOM and NOM, and confirmed that AOM tend to form more organic chloramines during chlorination. Furthermore, it was found that hydrophilic fraction and high molecular weight (>100 kDa) fraction of AOM generated major organic chloramines due to a high content of protein. Based on the results of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), Spearman's rank correlation was used to analyze the relationship between molecular composition of AOM and organic chloramine formation. Notably, molecules with high correlation to organic chloramine formation located in a triangle region of van Krevelen diagram, which is a typical area of peptides. Therefore, it indicates that the precursors of organic chloramine in AOM are mainly proteins/peptides, and appropriate treatment processes (e.g., biological treatment or membrane filtration) should be addressed to effectively remove the precursors before chlorination.

Long-term operation assessment of a full-scale membrane-aerated biofilm reactor under Nordic conditions

Membrane-aerated biofilm reactor (MABR) technology is an exciting alternative to conventional activated sludge, with promising results in bench and pilot-scale systems. Nevertheless, there is still a lack of long-term and full-scale data under different operational conditions. This study aims to report the performance of a full-scale hybrid MABR located in the North of Europe. Influent, effluent, and exhaust data were collected for 1 year (September 2019 to September 2020) using online sensors/gas-analyzers and off-line laboratory analysis. Next, oxygen transfer rate (OTR), oxygen transfer efficiency (OTE), and nitrification rates (NR) were quantified as process indicators. Finally, multivariate methods were used to find patterns among monitored variables. Observations revealed that lower airflows achieved higher OTE at the same values of OTR and OTR was strongly correlated to ammonia/um concentration in the MABR tank (NH_x,eff). The dynamics between oxygen concentration in the exhaust (O_2,exh) and NH_x,eff indicated that a nitrifying biofilm was established within 3 weeks. Average NR were calculated using four different methods and ranged between 1 and 2 g N m^-2d^-1. Principal component analysis (PCA) explained 81.4% of the sample variance with the first three components and cluster analysis (CA) divided the yearly data into five distinctive periods. Hence, it was possible to identify typical Nordic episodes with high frequency of heavy rain, low temperature, and high variations in pollution load. The study concludes that nitrification capacity obtained with MABR is robust during cold weather conditions, and its volumetric value is comparable to other well-established biofilm-based technologies. Moreover, the aeration efficiency (AE) obtained in this study, 5.8 kg O₂ kW h^-1, would suppose an average reduction in energy consumption of 55% compared to fine pore diffused aeration and 74% to the existing surface aeration at the facility.

Prioritize effluent quality, operational costs or global warming? - Using predictive control of wastewater aeration for flexible management of objectives in WRRFs

This study presents a general model predictive control (MPC) algorithm for optimizing wastewater aeration in Water Resource Recovery Facilities (WRRF) under different management objectives. The flexibility of the MPC is demonstrated by controlling a WRRF under four management objectives, aiming at minimizing: (A) effluent concentrations, (B) electricity consumption, (C) total operations costs (sum electricity costs and discharge effluent tax) or (D) global warming potential (direct and indirect nitrous oxide emissions, and indirect from electricity production) . The MPC is tested with data from the alternating WRRF in Nørre Snede (Denmark) and from the Danish electricity grid. Results showed how the four control objectives resulted in important differences in aeration patterns and in the concentration dynamics over a day. Controls B and C showed similarities when looking at total costs, while similarities in global warming potential for controls A and D suggest that improving effluent quality also reduced greenhouse gasses emissions. The MPC flexibility in handling different objectives is shown by using a combined objective function, optimizing both cost and greenhouse emissions. This shows the trade-off between the two objectives, enabling the calculation of marginal costs and thus allowing WRRF operators to carefully evaluate prioritization of management objectives. The long-term MPC performance is evaluated over 51 days covering seasonal and inter-weekly variations. On a daily basis, control A was 9-30% cheaper on average compared to controls A, D and to the current rule-based control. Similarly, control D resulted on average in 35-43% lower greenhouse gasses daily emission compared to the other controls. Difference between control performance increased for days with greater inter-diurnal variations in electricity price or greenhouse emissions from electricity production, i.e. when MPC has greater possibilities for exploiting input variations. The flexibility of the proposed MPC can easily accommodate for additional control objectives, allowing WRRF operators to quickly adapt the plant operation to new management objectives and to face new performance requirements.

Linking seasonal N2O emissions and nitrification failures to microbial dynamics in a SBR wastewater treatment plant

Nitrous oxide (N2O) is a strong greenhouse gas and causal for stratospheric ozone depletion. During biological nitrogen removal in wastewater treatment plants (WWTP), high N2O fluxes to the atmosphere can occur, typically exhibiting a seasonal emission pattern. Attempts to explain the peak emission phases in winter and spring using physico-chemical process data from WWTP were so far unsuccessful and new approaches are required. The complex and diverse microbial community of activated sludge used in biological treatment systems also exhibit substantial seasonal patterns. However, a potentially causal link between the seasonal patterns of microbial diversity and N2O emissions has not yet been investigated. Here we show that in a full-scale WWTP nitrification failure and N2O peak emissions, bad settleability of the activated sludge and a turbid effluent strongly correlate with a significant reduction in the microbial community diversity and shifts in community composition. During episodes of impaired performance, we observed a significant reduction in abundance for filamentous and nitrite oxidizing bacteria in all affected reactors. In some reactors that did not exhibit nitrification and settling failures, we observed a stable microbial community and no drastic loss of species. Standard engineering approaches to stabilize nitrification, such as increasing the aerobic sludge age and oxygen availability failed to improve the plant performance on this particular WWTP and replacing the activated sludge was the only measure applied by the operators to recover treatment performance in affected reactors. Our results demonstrate that disturbances of the sludge microbiome affect key structural and functional microbial groups, which lead to seasonal N2O emission patterns. To reduce N2O emissions from WWTP, it is therefore crucial to understand the drivers that lead to the microbial population dynamics in the activated sludge.

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.

Integrated Model for Understanding N2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach

This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N₂O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R² > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N₂O emission characteristics. For N₂O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R² > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N₂O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N₂O emissions from WWTPs.

Combined influences of process parameters on microorganism-derived dissolved organic nitrogen (mDON) formation at low temperatures: Multivariable statistical and systematic analysis

Regulation of process parameters is a cost-effective approach to control microorganism-derived dissolved organic nitrogen (mDON) formation in low-temperature biological wastewater conditions. However, the integrated influence of multiple parameters in this process is poorly defined. In this study, mathematical methodology was used to evaluate the combined effects of hydraulic retention time (HRT), solids retention time (SRT), and mixed liquor suspended solids (MLSS) on mDON formation at 8 °C. This study also systematically explored how multiple combinations of those three parameters affected mDON chemodiversity (fluorescent properties and molecular compositions), microbial compositions, and specific relationships between mDON molecules and microbial species in activated sludge systems. Results showed that combined effects significantly controlled the mDON formation at 8 °C (P < .05). The systematic analysis suggested that the multi-parameter effects modulated the distribution of different mDON compositions and shaped the microbial communities. Most bacterial phyla as the generalist and a few as the specialist were displayed in 2487 pairs of strong microbe-mDON connections (|r| ≥ 0.6, P < .05). Moreover, network analysis on microbe-mDON relationships identified the network centers as crucial media in terms of combined effects of process parameters on mDON formation. Our results provide comprehensive insight on the roles of multi-parameter covariation influences in regulating the high complexity of mDON traits and microbe-mDON linkages, thereby highlighting the necessity to focus on the combined effects of process parameters for effective and correct controlling strategies on mDON concentrations.

Effect of magnetic powder on nitrous oxide emissions from a sequencing batch reactor for treating domestic wastewater at low temperatures

Low temperatures can lead to an increase of N₂O generation and emission from the nitrogen removal process in wastewater treatment plants. This study investigated the effect of the addition of magnetic powder on N₂O generation and emission from a sequencing batch reactor treating domestic sewage at low temperatures. The results showed that the magnetic powder simultaneously inhibited N₂O generation and emission and improved the removal of NH₄⁺, total nitrogen (TN), and chemical oxygen demand at low temperatures. Furthermore, the conversion rate of N₂O (N₂O generation to TN removal) was reduced. The efficacy of the magnetic powder depended on its concentration, which could be ordered as 1 mg/L > 2 mg/L > 4 mg/L. With the addition of magnetic powder, especially at the 1 mg/L level, the activities of nitrification and denitrification enzymes in activated sludge were significantly improved and the growth of ammonium and nitrite oxidizing bacteria was also promoted.

Identification of primary effecters of N2O emissions from full-scale biological nitrogen removal systems using random forest approach

Wastewater treatment plants (WWTPs) have long been recognized as point sources of N₂O, a potent greenhouse gas and ozone-depleting agent. Multiple mechanisms, both biotic and abiotic, have been suggested to be responsible for N₂O production from WWTPs, with basis on extrapolation from laboratory results and statistical analyses of metadata collected from operational full-scale plants. In this study, random forest (RF) analysis, a machine-learning approach for feature selection from highly multivariate datasets, was adopted to investigate N₂O production mechanism in activated sludge tanks of WWTPs from a novel perspective. Standardized measurements of N₂O effluxes coupled with exhaustive metadata collection were performed at activated sludge tanks of three biological nitrogen removal WWTPs at different times of the year. The multivariate datasets were used as inputs for RF analyses. Computation of the permutation variable importance measures returned biomass-normalized dissolved inorganic carbon concentration (DIC·VSS^-1) and specific ammonia oxidation activity (sOUR_AOB) as the most influential parameters determining N₂O emissions from the aerated zones (or phases) of activated sludge bioreactors. For the anoxic tanks, dissolved-organic-carbon-to-NO₂^-/NO₃^- ratio (DOC·(NO₂^--N + NO₃^--N)^-1) was singled out as the most influential. These data analysis results clearly indicate disparate mechanisms for N₂O generation in the oxic and anoxic activated sludge bioreactors, and provide evidences against significant contributions of N₂O carryover across different zones or phases or niche-specific microbial reactions, with aerobic NH₃/NH₄⁺ oxidation to NO₂^- and anoxic denitrification predominantly responsible from aerated and anoxic zones or phases of activated sludge bioreactors, respectively.

Recent advances in nitrous oxide production and mitigation in wastewater treatment

Nitrous oxide (N₂O) emitted from wastewater treatment plants has caused widespread concern. Over the past decade, people have made tremendous efforts to discover the microorganisms responsible for N₂O production, elucidate metabolic pathways, establish production models and formulate mitigation strategies. The ultimate goal of all these efforts is to shed new light on how N₂O is produced and how to reduce it, and one of the best ways is to find key opportunities by integrating the information obtained. This review article critically evaluates the knowledge gained in the field within a decade, especially in N₂O production microbiology, biochemistry, models and mitigation strategies, with a focus on denitrification. Previous research has greatly deepened the understanding of the N₂O generation mechanism, but further efforts are still needed due to the lack of standardized methodology for establishing N₂O mitigation strategies in full-scale systems. One of the challenges seems to be to convert the denitrification process from a net N₂O source into an effective sink, which is recommended as a key opportunity to reduce N₂O production in this review.

A knowledge discovery framework to predict the N2O emissions in the wastewater sector

Data Analytics is being deployed to predict the dissolved nitrous oxide (N₂O) concentration in a full-scale sidestream sequence batch reactor (SBR) treating the anaerobic supernatant. On average, the N₂O emissions are equal to 7.6% of the NH₄-N load and can contribute up to 97% to the operational carbon footprint of the studied nitritation-denitritation and via-nitrite enhanced biological phosphorus removal process (SCENA). The analysis showed that average aerobic dissolved N₂O concentration could significantly vary under similar influent loads, dissolved oxygen (DO), pH and removal efficiencies. A combination of density-based clustering, support vector machine (SVM), and support vector regression (SVR) models were deployed to estimate the dissolved N₂O concentration and behaviour in the different phases of the SBR system. The results of the study reveal that the aerobic dissolved N₂O concentration is correlated with the drop of average aerobic conductivity rate (spearman correlation coefficient equal to 0.7), the DO (spearman correlation coefficient equal to -0.7) and the changes of conductivity between sequential cycles. Additionally, operational conditions resulting in low aerobic N₂O accumulation (<0.6 mg/L) were identified; step-feeding, control of initial NH₄⁺ concentrations and aeration duration can mitigate the N₂O peaks observed in the system. The N₂O emissions during aeration shows correlation with the stripping of accumulated N₂O from the previous anoxic cycle. The analysis shows that N₂O is always consumed after the depletion of NO₂^- during denitritation (after the "nitrite knee"). Based on these findings SVM classifiers were constructed to predict whether dissolved N₂O will be consumed during the anoxic and anaerobic phases and SVR models were trained to predict the N₂O concentration at the end of the anaerobic phase and the average dissolved N₂O concentration during aeration. The proposed approach accurately predicts the N₂O emissions as a latent parameter from other low-cost sensors that are traditionally deployed in biological batch processes.

Biopolymers recovery: dynamics and characterization of alginate-like exopolymers in an aerobic granular sludge system treating municipal wastewater without sludge inoculum

Alginate-like exopolymers (ALE) are present in the extracellular polymeric substances (EPS) of biological sludge such as aerobic granular sludge (AGS). The recovery of ALE from excess sludge produced by wastewater treatment plants (WWTP) is a relevant approach for the recovery of valuable products of industrial interest. However, little is known about dynamics of ALE content in sludge and associated factors. Thus, this study aimed at assessing the dynamics of EPS and ALE in terms of content, some chemical properties and influencing environmental factors along granulation in a sequencing batch reactor treating municipal wastewater. Results indicated that the EPS content was not correlated with the development of AGS, while the ALE content was higher, more stable and steadily increased after granulation achievement. Overall, 236 ± 27 mg VS_ALE/g VS_sludge was recovered from AGS and 187 ± 94 mg VS_ALE/g VS_sludge from flocs. However, the lower ALE content in flocs may be compensated by the higher sludge production rate in activated sludge systems. Principal component analysis (PCA) revealed that ALE content positively correlates with the nutrient and organic substrate conversion, and with the fraction of large AGS. Microbial analyses indicated that a stable microbial community composition was associated with a higher and more stable ALE content. ALE recovered from both flocs and AGS was endowed with hydrogel property, and no clear difference in their elemental composition and functional groups was observed. Therefore, our study provides insights about quantitative and qualitative aspects of ALE which are helpful for the improvement of waste biological sludge valorization.

Study of the Percentage of Greenhouse Gas Emissions from Aviation in the EU-27 Countries by Applying Multiple-Criteria Statistical Methods

The transport sector, including air transport, represents an important source of air pollution. The present article deals with the current situation regarding greenhouse gas emissions in the air in 27 European Union (EU-27) member states. Every member state is characterized by selected parameters that determine the unique nature of a particular country (e.g., population, area, life expectancy, gross domestic product (GDP) per capita, etc.). In addition to these parameters, there were also other parameters which were monitored as they characterize the amount of greenhouse gas emissions and the impact of aviation on these emissions. The main purpose of the article is to compare the European Union member states on the basis of 15 examined parameters. The identification of similarities between the EU-27 member states with regard to the selected parameters was carried out by applying principal component analysis (PCA) and hierarchical cluster analysis. The average linkage method was applied to create a dendrogram representing the similarities between the examined member states. The value of the cophenetic correlation coefficient CC = 0.923 confirmed the correct application of the average linkage method. The cluster analysis outputs were five similarity-based homogeneous groups (clusters) into which the 27 member states were divided on the basis of the examined variables.

Assessment of Full-Scale N2O Emission Characteristics and Testing of Control Concepts in an Activated Sludge Wastewater Treatment Plant with Alternating Aerobic and Anoxic Phases

This work aims to obtain full-scale N₂O emission characteristics translatable into viable N₂O control strategies and conduct full-scale testing of the proposed N₂O control concepts. Data of a long-term monitoring campaign was first used to quantify full-scale N₂O emission and probe into the seasonal pattern. Then trends between N₂O production/emission and process variables/conditions during typical operating cycles were revealed to explore the dynamic N₂O emission behavior. A multivariate statistical analysis was performed to find the dependency of N₂O emission on relevant process variables. The results show for the first time that relatively low/high N₂O emission took place in seasons with a decreasing/increasing trend of water temperature, respectively. Aerobic phase contributed to N₂O production/emission probably mainly through the hydroxylamine pathway. Comparatively, heterotrophic bacteria had a dual role in the anoxic phase and could be responsible for both net N₂O production and consumption. Incomplete denitrification might contribute mainly to the N₂O production/emission in the anoxic phase and the accumulation of N₂O to be significantly emitted in the following cycle due to the competition between different denitrification steps for electron donors. Therefore, properly extending the length of anoxic phase could serve as a potential control means to regulate N₂O accumulation in the anoxic phase. The full-scale testing not only verified the efficacy of reduced dissolved oxygen set-point in reducing N₂O emission by 60%, but also confirmed the proposed concepts of control over the aerobic and anoxic phases collectively.

A decade of nitrous oxide (N2O) monitoring in full-scale wastewater treatment processes: A critical review

Direct nitrous oxide (N₂O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N₂O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N₂O calculation. The full-scale N₂O monitoring campaigns help to expand our knowledge on the N₂O production pathways and the triggering operational conditions of processes. The accurate N₂O monitoring could help to find better process control solutions to mitigate N₂O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N₂O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N₂O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N₂O pathways and triggering operational conditions, techniques using operational data and N₂O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N₂O emissions and the development of emission factor (EF) databases; the N₂O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N₂O monitoring campaigns lasting less than one month, have reported N₂O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N₂O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N₂O fluxes behaviour. The acquisition of reliable full-scale N₂O monitoring data is significant for the calibration and validation of the mechanistic models -describing the N₂O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N₂O emission patterns. Finally, a gap between the identification of effective N₂O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N₂O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N₂O data, and iii) better understanding of the trade-offs among N₂O emissions, energy consumption and system performance to support the optimization of the WWTPs operation.

CO2 and N2O from water resource recovery facilities: Evaluation of emissions from biological treatment, settling, disinfection, and receiving water body

Water resource recovery facilities (WRRFs) contribute to climate change and air pollution, as they are anthropogenic potential sources of direct and indirect emission of greenhouse gases (GHGs). Studies concerning the monitoring and accounting for GHG emissions from WRRFs are of increasing interest. In this study, the floating hood technique for gas collection was coupled with the off-gas method to monitor and apportion nitrous oxide (N₂O) and carbon dioxide (CO₂) emissions from both aerated and non-aerated tanks in a municipal water resource recovery facility, in order to investigate its carbon footprint (CFP). To our knowledge, this is the first time that the chamber technique was applied to evaluate gas fluxes from the settler, where an emission factor (EF) of 4.71 ∗ 10^-5 kg_CO2,eq kg_bCOD^-1 was found. Interesting results were found in the disinfection unit, which was the major contributor to direct N₂O emissions (with a specific emission factor of 0.008 kg_CO2,eq kg_bCOD^-1), due to the chemical interaction between hydroxylamine and the disinfectant agent (hypochlorite). The specific emission factor of the biological aerated tank was 0.00112 kg_CO2,eq kg_bCOD^-1. The average direct CO₂ emission was equal to 0.068 kg_CO2 kg_bCOD^-1 from the activated sludge tank and to 0.00017 kg_CO2 kg_bCOD^-1 from the secondary clarifier. Therefore, taking into account the contribution of both direct N₂O and CO₂ emissions, values of 0.069 kg_CO2,eq kg_bCOD^-1, 0.008 kg_CO2,eq kg_bCOD^-1 and 0.00022 kg_CO2,eq kg_bCOD^-1_, were found for the net CFP of the aerated compartment, the disinfection unit and the clarifier, respectively. The plant energy Footprint (eFP) was also evaluated, confirming that the aeration system is the major contributor to energy consumption, as well as to indirect CO₂ emission, with a specific eFP of 1.49 kWh kg_bCOD^-1.

The future of WRRF modelling - outlook and challenges

The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.

Energy balance and life cycle assessment of a microalgae-based wastewater treatment plant: A focus on alternative biogas uses

The techno-environmental performance of a medium-scale wastewater treatment system using high-rate algal ponds was evaluated through mass and energy balances and life cycle assessment. The system involves wastewater primary treatment, microalgae-based secondary treatment, thermal hydrolysis with steam explosion of microalgae, anaerobic co-digestion of pre-treated microalgal biomass and primary sludge, and biogas cogeneration. Furthermore, two scenarios based on alternative biogas uses were considered: (i) biogas for heat and electricity, and (ii) biogas for heat, electricity, and biomethane. Pumping wastewater to the primary settler arose as the main source of electricity consumption. When compared to conventional activated sludge plants, a large decrease in the energy consumption was observed for the secondary treatment. Moreover, a favourable life-cycle performance was generally found for the microalgae-based systems when displacing conventional energy products. Finally, the preference for a specific scenario on biogas use was found to be highly conditioned by the techno-environmental aspects prioritised by decision-makers.