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

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.

Using Machine Learning to Identify Environmental Factors that Collectively Determine Microbial Community Structure of Activated Sludge

Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet Multinomial Mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The Extremely Randomized Trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.

Predicting the governing factors for the release of colloidal phosphorus using machine learning

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers. The study utilizes the structural equation model (SEM), multiple linear regression (MLR), and three machine learning (ML) models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)) to predict the release of CP from different soils by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, total organic carbon (TOC) and precipitation as independent variables. Results show that colloidal-cations (Fe, Al, Ca) and colloidal-TOC strongly influence CP release, while bioclimatic variables (precipitation) and pH have weaker effects. XGBoost outperforms the other models with an R2 of 0.94 and RMSE of 0.09. SHapley Additive Explanations described the outcomes since XGBoost is accurate. The relative relevance ranking indicated that colloidal TOC had the highest ranking in predicting CP. This was supported by the analysis of partial dependence plots, which showed that an increase in colloidal TOC increased soil CP release. According to our research, the SHAP XGBoost model provides significant information that can help determine the variables that considerably influence CP contents as compared to RF, SVM, and MLR.

Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review

Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

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.

Drivers of ammonia volatilization in Mediterranean climate cropping systems

Ammonia (NH3) volatilization is the major source of nitrogen (N) loss resulting from the application of synthetic and organic N fertilizers to croplands. It is well known that in Mediterranean cropping systems, there is a relationship between the intrinsic characteristics of the climate and nitrous oxide (N2O) emissions, but whether the same relation exists for NH3 emissions remains uncertain. Here, we estimated the impact of edaphoclimatic conditions (including meteorological conditions after N fertilization), crop management factors, and the measurement technique on both the cumulative emissions and the NH3 emission factor (EF) in Mediterranean climate zones, drawing on a database of 234 field treatments. We used a machine learning method, random forest (RF), to predict volatilization and ranked variables based on their importance in the prediction. Random forest had a good predictive power for the NH3 EF and cumulative emissions, with an R2 of 0.69 and 0.76, respectively. Nitrogen fertilization rate (N rate) was the top-ranked predictor variable, increasing NH3 emissions substantially when N rate was higher than 170 kg N ha-1. Soil pH was the most important edaphoclimatic variable, showing greater emissions (36.7 kg NH3 ha-1, EF = 19.3%) when pH was above 8.2. Crop type, fertilizer type, and N application method also affected NH3 emission patterns, while water management, mean precipitation, and soil texture were ranked low by the model. Our results show that intrinsic Mediterranean characteristics had only an indirect effect on NH3 emissions. For instance, relatively low N fertilization rates result in small NH3 emissions in rainfed areas, which occupy a very significant surface of Mediterranean agricultural land. Overall, N fertilization management is a key driver in reducing NH3 emissions, but additional field factors should be studied in future research to establish more robust abatement strategies.

A comprehensive framework for eco-environmental impact evaluation of wastewater treatment plants: Integrating carbon footprint, energy footprint, toxicity, and economic assessments

The need for clear and straightforward guidelines for carbon footprint (CFP) and energy footprint (EFP) evaluations is critical due to the non-transparent and misleading results that have been reported. This study aims to address this gap by integrating CFP, EFP, toxicity, and economic assessments to evaluate the eco-environmental impacts of wastewater treatment plants (WWTPs). The results indicate that the total CFP was below 0.6 kg CO2/kg COD removed, which is attributed to CO2 offset and biogas recovery. However, site-specific EFP varied considerably from 482.7 to 2294 kgCO2/kWh due to design differences of WWTPs and their aeration and mixing energy demand (46.96-66.1%). The use of crude oil and natural gas for electricity generation significantly increased EFP, CFP, and carcinogenic human toxicity. In contrast, a combined heat and power (CHP) installation enabled energy recovery ranging from 12.09% to 65.65%. Construction costs dominated the highest share of total costs (85.43%), with indirect construction costs (42.9%) and operation labor costs (61.4%) being the primary elements in the total net costs. It is worth noting that site-specific CO2 emission factors were used in the calculations to decrease model uncertainty. However, to improve modeling reliability, we recommend modifying the regional CO2 emission factor and focusing on emerging technologies to recover energy and biogas.

Effects of inorganic ions on autotrophic denitrification by Thiobacillus denitrificans and on heterotrophic denitrification by an enrichment culture

Salinity of nitrate-laden wastewaters, such as those produced by metal industries, tanneries, and wet flue gas cleaning systems may affect their treatment by denitrification. Salt inhibition of denitrification has been reported, while impacts of individual ions remain poorly understood whilst being relevant for wastewaters where often the concentration of a single ion rather than the salts varies. The aim of this study was to determine the inhibition by inorganic ions (Na+, Cl-, SO42- and K+) commonly present in saline wastewaters on denitrification and reveal its potential for the treatment of such waste streams, like those produced by NOx-SOx removal scrubbers. The inhibitory effects were investigated for both heterotrophic (enrichment culture) and autotrophic (T. denitrificans) denitrification in batch assays, by using NaCl, Na2SO4, KCl and K2SO4 salts at increasing concentrations. The half inhibition concentrations (IC50) of Na+ (as NaCl), Na+ (as Na2SO4) and Cl- (as KCl) were: 4.3 ± 0.3, 7.9 ± 0.5 and 5.2 ± 0.3 g/L for heterotrophic, and 1-2.5, 2.5-5 and 4.1 ± 0.3 g/L for autotrophic denitrification, respectively. Heterotrophic denitrification was completely inhibited at 20 g/L Na+ (as NaCl), 30 g/L Na+ (as Na2SO4) and 30 g/L Cl- (as KCl), while autotrophic at 8 g/L Na+ (as NaCl), 10 g/L Na+ (as Na2SO4) and 15 g/L Cl- (as KCl). In both cases, Cl- addition had the most important role in decreasing denitrification rate, while Na+ at 1 g/L stimulated autotrophic denitrification but rapidly inhibited the rate at higher concentrations. Nitrite reduction was less inhibited by the ions than nitrate reduction and both the osmotic pressure and the toxicity of the single ions played key roles in the overall inhibition of denitrification. Eventually, both autotrophic and heterotrophic denitrification showed potential for the treatment of a saline wastewater from a NOx-SO2 removal scrubber from a pulp mill.

Electrochemical mechanism of synchronous ammonia and nitrate removal based on multi-objective optimization by coupling random forest with genetic algorithm

In this work, an electrochemical system was constructed for the simultaneous elimination of ammonia and nitrate using the prepared Ti foam/SnO2-Sb anode and a Cu foam cathode. The hybrid RF-GA method is proposed as a tool for the analysis and optimization of the simultaneous removal of ammonia and nitrate. The influence of independent variables including NaCl concentration, time, and current densities was studied. Results showed that the random forest (RF) model could successfully predict the behavior of electrochemical systems (R2 = 0.9751, RMSE = 0.4567 for the ammonia prediction model; R2 = 0.9772, RMSE = 0.0436 for the nitrate prediction model). The variable importance measures (VIM) analysis reveals that time has the maximum influence on the degradation rate of ammonia and nitrate. The RF model is used as an objective function for the genetic algorithm (GA) to determine the optimum conditions in combination with the calculated specific energy consumption. Based on the optimization results, the removal rates of ammonia and nitrate reach 94.4 % and 74.7 %, respectively, with a minimum specific energy consumption of 0.181 kwh·g-1. The electrochemical reaction mechanism of the composite pollutants in the Ti foam/SnO2-Sb and Cu foam electrode system is further elucidated. The results indicate that nitrate is reduced to nitrite, ammonia, or nitrogen gas at the cathode, accompanied by the mutual transformation of Cu(0), Cu(I), and Cu(II) on the Cu electrode. Ammonia is oxidized to nitrogen gas or nitrate at the anode. Ultimately, the nitrogen-containing composite pollutant is decomposed and discharged as nitrogen gas by cyclic redox reactions.

Insight into the mechanism of nutrients removal and response regulation of denitrifying phosphorus removal system under calcium ion stress

The influent quality is an important factor affecting the nutrients removal and operational stability of denitrifying phosphorus removal (DPR) system. This study investigated the effects of calcium ion (Ca2+) on the nutrients removal, nitrogen oxide (N2O) release, microbial community, and quorum sensing in DPR system. Results showed that high accumulation of Ca2+ had a significant impact on the carbon footprint of DPR system. Specifically, N2O release reached 2.11 mg/L under Ca2+ of 150 mg/L, which represented 214.93% increase compared to 0 mg/L of Ca2+. The DPR system demonstrated its adaptability to elevated Ca2+ concentrations by modifying key enzyme activities involved in nitrogen and phosphorus removal, altering the microbial community structure, and adjusting the type and content of signal molecules. These findings hold significant implications for understanding the stress mechanism of Ca2+ on DPR system, ultimately aiding in the maintenance and enhancement of stable operational performance in biological wastewater treatment process.

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.

The key role of denitrification and dissimilatory nitrate reduction in nitrogen pollution along vertical landfill profiles from metagenomic perspective

Landfill are persistent sources of nitrogen (N) pollution even in the decades after closure. However, the biological pathways of N-pollution, particularly N₂O and NH₄⁺, at different landfill depths have received little attention. In this study, metagenomic analysis was conducted on landfill refuse from vertical reservoir profiles in two closed landfills named XT and MT. NH₄⁺ concentrations were found to be higher in deeper layers of MT, while greater potential for N₂O emissions occurred in XT and the shallow layers of MT. Furthermore, the community structure and function of N-metabolizing microbes were more strongly defined by landfill depth than landfill type. Denitrification, involving abundant nirK and norB genes, was identified as the major pathway for N₂O production in both XT and MT-shallow, while dissimilatory nitrate reduction with abundant nirBD genes was identified as the major pathway for NH₄⁺ accumulation. Microbes of norB-type and nirBD-type were positively affected by NO₃^- in XT, whereas negatively affected by contents of organic material and moisture in MT-shallow. The mechanism by which nitrogen fixation, with abundant nifH genes, contributes to NH₄⁺ accumulation in MT-deep should be further elucidated. These findings can provide a theoretical basis for governing scientific N-pollution control strategies throughout the entire landfill process.

Biotrickling Filtration for the Reduction of N2O Emitted during Wastewater Treatment: Results from a Long-Term In Situ Pilot-Scale Testing

Wastewater treatment plants (WWTPs) are a major source of N₂O, a potent greenhouse gas with 300 times higher global warming potential than CO₂. Several approaches have been proposed for mitigation of N₂O emissions from WWTPs and have shown promising yet only site-specific results. Here, self-sustaining biotrickling filtration, an end-of-the-pipe treatment technology, was tested in situ at a full-scale WWTP under realistic operational conditions. Temporally varying untreated wastewater was used as trickling medium, and no temperature control was applied. The off-gas from the covered WWTP aerated section was conveyed through the pilot-scale reactor, and an average removal efficiency of 57.9 ± 29.1% was achieved during 165 days of operation despite the generally low and largely fluctuating influent N₂O concentrations (ranging between 4.8 and 96.4 ppmv). For the following 60-day period, the continuously operated reactor system removed 43.0 ± 21.2% of the periodically augmented N₂O, exhibiting elimination capacities as high as 5.25 g N₂O m^-3·h^-1. Additionally, the bench-scale experiments performed abreast corroborated the resilience of the system to short-term N₂O starvations. Our results corroborate the feasibility of biotrickling filtration for mitigating N₂O emitted from WWTPs and demonstrate its robustness toward suboptimal field operating conditions and N₂O starvation, as also supported by analyses of the microbial compositions and nosZ gene profiles.

Prediction of nano, fine, and medium colloidal phosphorus in agricultural soils with machine learning

Soil colloids have been shown to play a critical role in soil phosphorus (P) mobility and transport. However, identifying the potential mechanisms behind colloidal P (P_coll) release and the key influencing factors remains a blind spot. Herein, a machine learning approach (random forest (RF) coupled with partial dependence plot analyses) was applied to determine the effects of different soil physicochemical parameters on P_coll content in three colloidal subfractions (i.e., nano- (NC): 1-20 nm, fine- (FC): 20-220 nm and medium-sized colloids (MC): 220-450 nm) based on a regional dataset of 12 farmlands in Zhejiang Province, China. RF successfully predicted P_coll content (R² = 0.98). Results showed that colloidal- organic carbon (OC_coll) and minerals were the major determinants of total P_coll content (1-450 nm); their critical values for increasing P_coll release were 87.0 mg L^-1 for OC_coll, 11.0 mg L^-1 for iron (Fe_coll) or aluminium (Al_coll), 2.6 mg L^-1 for calcium (Ca_coll), 9.0 mg L^-1 for magnesium (Mg_coll), 2.5 mg L^-1 for silicon (Si_coll), and 1.4 mg L^-1 for manganese (Mn_coll). Among three colloidal subfractions, the major factors determining P_coll were soil Olsen-P (P_Olsen; 125.0 mg kg^-1), Ca_coll (2.5 mg L^-1), and colloidal P saturation (21.0%) in NC; Mn_coll (1.5 mg L^-1), Mg_coll (6.8 mg L^-1), and P_Olsen (135.0 mg kg^-1) in FC; while Mn_coll (1.5 mg L^-1), Al_coll (2.5 mg L^-1), and Fe_coll (3.8 mg L^-1) in MC, respectively. OC_coll had a considerable effect in the three fractions, with critical values of 80.0 mg L^-1 in NC or FC, and 50.0 mg L^-1 in MC. Our study concluded that the information gleaned using the RF model can be used as crucial evidence to identify the key determinants of different size fractionated P_coll contents. However, we still need to discover one or more easy-to-measure parameters that can help us better predict P_coll.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

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 comprehensive analysis of evolution and underlying connections of water research themes in the 21st century

This work aimed to reflect the advancements in water-related science, technology, and policy and shed light on future research opportunities related to water through a systematic overview of Water Research articles published in the first 21.5 years of the 21st century. Specific bibliometric analyses were performed to i) reveal the temporal and spatial trends of water-related research themes and ii) identify the underlying connections between research topics. The results showed that while top topics including wastewater (treatment), drinking water, adsorption, model, biofilm, and bioremediation remained constantly researched, there were clear shifts in topics over the years, leading to the identification of trending-up and emerging research topics. Compared to the first decade of the 21st century, the second decade not only experienced significant uptrends of disinfection by-products, anaerobic digestion, membrane bioreactor, advanced oxidation processes, and pharmaceuticals but also witnessed the emerging popularity of PFAS, anammox, micropollutants, emerging contaminants, desalination, waste activated sludge, microbial community, forward osmosis, antibiotic resistance genes, resource recovery, and transformation products. On top of the temporal evolution, distinct spatial evolution existed in water-related research topics. Microplastics and Covid-19 causing global concerns were hot topics detected, while metagenomics and machine learning were two technical approaches emerging in recent years. These consistently popular, trending-up and emerging research topics would most likely attract continuous/increasing research input and therefore constitute a major part of the prospective water-related research publications.

Identification of key water parameters and microbiological compositions triggering intensive N2O emissions during landfill leachate treatment process

Landfill leachate treatment processes tend to emit more N₂O compared to domestic wastewater treatment. This discrepancy may be ascribed to leachate water characteristics such as high refractory COD, ammonium (NH₄⁺) content, and salinity. In this work, the leachate influent was varied to examine the N₂O emission scenarios. NH₄⁺-N, COD, and Cl^- concentrations ranged between 1000-2500, 1000-10,000, and 500-3000 mg L^-1, respectively. Simultaneously, we attempted to combine statistical analysis with high-throughput sequencing to understand the microbial mechanism with regards to N₂O emission. Results show that the strong N₂O emissions occur in the nitrifying tank due to the intensive aeration. The system receiving the lowest COD shows the maximum N₂O emission factor of 42.7% of the removed nitrogen. Both redundancy analysis and a structural equation model verify that insufficient degradable organics are the key water parameter triggering intensive N₂O emission within the designed influent limits. Furthermore, two essential but non-abundant functional bacteria, Flavobacterium (acting as a denitrifier) and Nitrosomonas (acting as a nitrifier), are identified as the core functional species that dramatically influence N₂O emissions. An increase in influent COD promotes the proliferation of Flavobacterium and inhibits Nitrosomonas, which in turn reduce N₂O release. Meanwhile, two keystone species of Castellaniella and Saprospiraceae unclassified are recognized. They may supply a suitable niche and integrity of the microbial community for N-cycle functional bacteria. These findings reveal the essential role of non-abundant species in microbial community, and expand the current understanding of microbial interactions underlying N₂O dynamics in leachate treatment systems.

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.

Identification of nosZ-expressing microorganisms consuming trace N2O in microaerobic chemostat consortia dominated by an uncultured Burkholderiales

Microorganisms possessing N₂O reductases (NosZ) are the only known environmental sink of N₂O. While oxygen inhibition of NosZ activity is widely known, environments where N₂O reduction occurs are often not devoid of O₂. However, little is known regarding N₂O reduction in microoxic systems. Here, 1.6-L chemostat cultures inoculated with activated sludge samples were sustained for ca. 100 days with low concentration (<2 ppmv) and feed rate (<1.44 µmoles h⁻¹) of N₂O, and the resulting microbial consortia were analyzed via quantitative PCR (qPCR) and metagenomic/metatranscriptomic analyses. Unintended but quantified intrusion of O₂ sustained dissolved oxygen concentration above 4 µM; however, complete N₂O reduction of influent N₂O persisted throughout incubation. Metagenomic investigations indicated that the microbiomes were dominated by an uncultured taxon affiliated to Burkholderiales, and, along with the qPCR results, suggested coexistence of clade I and II N₂O reducers. Contrastingly, metatranscriptomic nosZ pools were dominated by the Dechloromonas-like nosZ subclade, suggesting the importance of the microorganisms possessing this nosZ subclade in reduction of trace N₂O. Further, co-expression of nosZ and ccoNO/cydAB genes found in the metagenome-assembled genomes representing these putative N₂O-reducers implies a survival strategy to maximize utilization of scarcely available electron acceptors in microoxic environmental niches.

Controlled release urea improves rice production and reduces environmental pollution: a research based on meta-analysis and machine learning

To reveal the comprehensive impacts of controlled release urea (CRU) on rice production, nitrogen (N) loss, and greenhouse gas (GHG) emissions, a research based on global meta-analysis and machine learning (ML) was conducted. The results revealed that the CRU application instead of conventional fertilizer can increase rice yield, N use efficiency (NUE), and net benefits by 5.24%, 20.18%, and 9.30%, respectively, under the same amount of N. Furthermore, the emission of N2O and CH4, global warming potential (GWP), the loss of N leaching, and NH3 volatilization were respectively reduced by 25.64%, 18.33%, 21.10%, 14.90%, and 35.88%. The enhancing effects of CRU on rice yield and NUE were greater when the nitrogen application rate was 150 kg N ha-1. Nevertheless, the reducing effects of CRU on GHG emission reduction, nitrogen leaching, and NH3 volatilization was greater at high nitrogen application rate (≥150 kg ha-1). Mitigating effects of CRU on N2O and CH4 emission were significant when soil pH ≥ 6, while CRU posed a measurable effect on reducing nitrogen leaching and NH3 volatilization in paddy fields with soil organic carbon lower than 15 g kg-1 and pH lower than 6. Based on the data collected from meta-analysis, the results of ML demonstrated that it was feasible to use soil data and N application rate to predict N losses in rice fields under CRU. The performance of random forest is better than multilayer perceptron regression in predicting N losses from paddy fields. Thus, it is necessary to promote the application of CRU in paddy fields, especially in coarse soil, in which scenario the environmental pollution would be decreased and the rice yields, NUE, and net benefits would be increased. Meanwhile, machine learning models can be used to predict N losses in CRU paddy fields.

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.