Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect

Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms governing the vertical migration of MPs in soil, particularly aged MPs, remain unclear. In this study, we investigate the transport of MPs under varying MPs properties, soil texture and hydrology conditions. Under nearly all controlled conditions, aged MPs demonstrated a higher vertical mobility compared to virgin MPs. By employing interpretable machine learning models (IML), we not only identified the dominant role of individual parameters in the vertical migration of MPs but also discovered that the increased contribution of carbonyl index and O/C ratio in aged MPs, along with the enhanced interaction with other feature parameters, collectively promotes the elevated vertical mobility of aged MPs. The varying contributions of different feature parameters under individual control variables revealed the mechanisms of MPs vertical migration under different gradients and highlighted the dual constraints of physical obstruction and chemical retention between MPs and soil particles. The established machine learning model was also utilized to predict the differences in vertical mobilities of MPs with varying degrees of aging. The nonlinear increasing relationship between MPs vertical mobility and simulated aging time suggests that MPs can migrate to deeper soil layers shortly after entering the soil environment. The integration of laboratory experiment with IML elucidates the key drivers of vertical MP migration. It also provides a theoretical basis for the timely removal of MPs from soil and the assessment of their potential risks.

Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms

Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I- and IO3- (designated as I-/IO3- in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters. Shapley additive explanation (SHAP) analysis, partial dependence plots (PDPs), and individual conditional expectation (ICE) analysis were then employed to explain the models' workings and feature interactions, aiding in identification and quantification of underlying mechanisms. A type of DOM component (namely DC_b) was found as the greatest contributor and identified as reduced quinones associated with broken-down lignin within higher plant-derived fulvic substance, serving as precursors and electron shuttles for I-DBP formation. Based on the interactional effects acquired from explanation results, the ejection of e-aq from excited DOM and pre-existing I- in the I-/IO3- system were identified responsible for the enhanced generation of I-DBPs compared to that in the I- or IO3- alone systems; extra DOM scavenged reactive iodine species (RIS), contributing to a limited enhancement. These findings and the methodology developed here together enhance our understanding of the mechanisms how DOM limitedly promotes I-DBP formation during UV-chloramine sequential disinfection of I-/IO3--containing water and facilitate effective online monitoring in the future.

Impact and mechanism of polyethylene terephthalate microplastics with different particle sizes on sludge anaerobic digestion

Municipal wastewater treatment plants (WWTPs) are important sinks for microplastics, and the vast majority of microplastics entering WWTPs are trapped in residual sludge. In order to investigate the effect of microplastics on anaerobic digestion of sludge, polyethylene terephthalate (PET) microplastics with common particle size and physical aging were selected to conduct a comparative study. Regardless of aging, the addition of 300 and 500 μm PET microplastics inhibited methane production, with their cumulative methane production reduced by 11.3-24.9% compared to the control group. In contrast, when 100 μm microplastics were added, the raw PET promoted methane production, yielding 337 L CH4/kg VS, while the aged experimental group showed similar yields to the control group. For the 800 μm microplastics treatment group, aged microplastics facilitated methane production while raw microplastics inhibited it, with methane production of 91.0% and 111% of the control group, respectively. The effects were also investigated by model fitting, stage discussion, and microbial community structure analysis. The results discovered that the main rate-limiting steps of adding microplastics with smaller or larger particle sizes (100, 800 μm) to methane production were solubilization and hydrolysis, while the main rate-limiting step of microplastics with medium particle sizes (300, 500 μm) was methanogenesis. Physically aged PET microplastics with smaller or larger sizes showed a more significant effect on methane production. Furthermore, PET microplastics altered the microbial community structure, shifting methanogens from acetotrophic pathways to hydrotrophic pathways. This study offers new insights into the performance analysis of sludge anaerobic digestion in practical WWTPs.

Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, Stacking, and Categorical Boosting were applied as artificial intelligence methods to predict chemical oxygen demand (COD) removal efficiency, biomass productivity, biomass yield, and energy yield. Among these, the Stacking model demonstrated superior predictive performance across all targets. Interpretable machine learning methods were employed to identify key features and establish their workable ranges, which included dissolved oxygen (0.3-2.8 mg L⁻1), illuminance (2995.3-6000.0 lux), and light energy (20.0-40.0 kWh) for COD removal efficiency; organic loading rate (OLR, 5.7-7.5 g COD L⁻1 d⁻1), hydraulic retention time (HRT, 0.2-3.2 d), and COD concentration (5.3-10.1 g L⁻1) for biomass productivity; COD/N ratio (609.0-800.0), OLR (0.1-2.4 g COD L⁻1 d⁻1), and illuminance (2661-6000 lux) for biomass yield; and pH (6.5-7.9) and HRT (1.2-2.6 d) for energy yield. The two-dimensional partial dependence plots revealed that optimal interactions between two key input features resulted in COD removal efficiency >72%, biomass productivity >28 g L⁻1 d⁻1, biomass yield> 0.96 g CODbiomass g CODremoved⁻1, energy yield> 0.49 g kWh⁻1. This work advances the understanding of PSB optimization in wastewater treatment through a combination of advanced machine learning and interpretability analysis, offering potential for more efficient resource recovery and process optimization.

Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning

Accurate modeling of methane (CH4) and sulfide (H2S) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML) framework (Micro-ML), which integrates microbial process representations from mechanistic models (microbial information) with ML models. Results indicate that Micro-ML model enhanced predictions of CH4 and H2S production, where microbial information provides more information for model optimization. The feature importance of microbial information performed comparable weightings for 58.12 % and 55.16 %, respectively, but their relative significance in influencing Micro-ML model performance varies considerably. The application of Micro-ML performed great potential in reducing CH4 and H2S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH4 and H2S predictions but also offers a valuable tool for effective management strategies for sewer systems.

Exploring interactive effects of environmental and microbial factors on food waste anaerobic digestion performance: Interpretable machine learning models

Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal relationship between target variables and AD performance, this study developed four machine learning models to predict biogas yield and consider the interaction among various factors. Results indicated that the highest prediction accuracy of AD performance was achieved by adding bacterial genera dataset with environmental factors. Random forest model exhibited the highest accuracy, with the testing coefficient of determination equal to 0.9879. Among two types of input features, the bacterial genera accounted for 89.9 % of the impact on biogas yield, followed by environmental factors. The results revealed Keratinibaculum and Acetomicrobium as critical bacteria. The volatile fatty acid controlled below 2000 mg/L and the improved stirring system in AD process were recommended to achieve maximum biogas yield.

Effects of microplastics on microbial community and greenhouse gas emission in soil: A critical review

Microplastics (MPs) are ubiquitous in soil ecosystems and significantly impact soil microorganisms and greenhouse gas (GHG) emissions. Although some reviews have summarized their impact on greenhouse gas emissions, no systematic analysis has been conducted on how soil physicochemical and microbial properties affect these emissions. Firstly, this review details that MPs alter microbial abundance, structure, activity and gene expression, directly stimulating CO2 and N2O emissions, though their impact on CH4 remains inconclusive. Additionally, MPs change rhizosphere microbial growth, cause soil nutrient loss, and induce plant toxicity, indirectly affecting GHG emissions. Finally, this article suggests strengthening research on rhizosphere and MPs surface microbial communities, exploring interactions with clay and minerals, and investigating GHG emission mechanisms to understand the ecological effects of MPs.

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics

Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of MPs on algal growth and to evaluate the relative importance of different features (MP properties, algal characteristics, and experimental conditions) through model interpretability analysis. Based on literature search, 408 samples were collected as inputs for the models. Three integrated machine learning algorithms, Random Forest (RF), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM), were used to construct classification prediction models for algal growth. Our results show that the LightGBM model yields the best performance, with a total accuracy rate of 0.8305 and a Kappa value of 0.7165. The model interpretability analysis indicates that "Exposure time", "MP concentrations", and "MP size" are the most influential features affecting algal growth. For "Exposure time", which belongs to experimental conditions, 72-216 h of MP exposure was found to exert the greatest effects on algal growth. The impact of MPs on algal growth increases with increasing MP concentrations over the range of 0 to 300 mg/L. Smaller MPs exert more effects on algal growth, and MPs are more likely to inhibit algal growth when the ratio of algal cell size to MP size is higher. Our study successfully established prediction models for evaluating the effects of various MP properties on algal growth. This study also provides insights into the prediction of MP toxicity in organisms.

Dissecting the effects of co-exposure to microplastics and sulfamethoxazole on anaerobic digestion

Microplastics (MPs) and antibiotics are frequently and simultaneously detected in sewage and sludge, raising global concerns in recent years. However, their combined effects on anaerobic digestion (AD) remain unclear. Herein, we evaluated the effects of the combinations of different MPs (i.e., polyethylene, polystyrene, polyvinyl chloride and polyethylene terephthalate) with sulfamethoxazole (SMX) on AD performance and microbial communities. The combined stress slightly decreased the chemical oxygen demand removal rate and total gas/methane production. Furthermore, co-exposure to MPs and SMX visibly changed the anaerobic sludge morphology during AD, reduced the methanogen activity, and increased the residual propionic acid concentration versus a control. The decreased relative abundances of Euryarchaeota ranged from 1.88% to 4.63% in the experimental groups compared with CK, suggesting that the microbial communities were inevitably affected by exposure to SMX alone or combined MPs/SMX. Interestingly, among the top 50 genera, only two were negatively related to a few antibiotic resistance genes, implying that sludge exhibited widespread multiple resistances. The correlation analysis between the MPs and microbial communities suggested that the MP properties, such as the aperture-desorption of MPs, may impact the microbial variations. This study will contribute to a deeper understanding of the impact of coexisting MPs/SMX on AD.

Applying machine learning and genetic algorithms accelerated for optimizing ethanol production

Corn straws can produce bioethanol via simultaneous saccharification and co-fermentation (SSCF). However, identifying optimal combinations of operating parameters from numerous possibilities through a cost-effective strategy to improve SSCF efficiency and yield remains challenging. The eXtreme Gradient Boost (XGB) and deep neural network (DNN) models were constructed to accurately predict ethanol yield from only five input variables, achieving >83 % accuracy. Subsequently, the XGB and the DNN models were merged with the genetic algorithm (GA) as the new optimization strategies. Experimental validation showed that the new strategy optimize the efficiency and yield of the SSCF ethanol production system quickly and accurately. Moreover, the potential optimization mechanism was investigated through the comprehensive interpretability analysis for XGB and the microbial ecology analysis. Enzyme Solution Volume (61.7 %) dominated, followed by time (12.9 %), substrate concentration (10.4 %), temperature (7.7 %), and inoculum volume (7.3 %). This efficient and accurate algorithm design strategy can significantly reduce the time required to optimize biochemical systems.

Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean

The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this study, traditional machine learning (ML) and H2O automated machine learning (H2O AutoML) were utilized to offer a powerful framework for detecting PE-MPs (0.1 %, 1 %, and 2 % by dry soil weight) and the co-contamination of PE-MPs and fomesafen (a common herbicide) in soil. The development of the framework was based on the results of the metabolic reprogramming of soybean plants. Our study stated that traditional ML exhibits lower accuracy due to the challenges associated with optimizing complex parameters. H2O AutoML can accurately distinguish between clean soil and contaminated soil. Notably, H2O AutoML can detect PE-MPs as low as 0.1 % (with 100 % accuracy) and co-contamination of PE-MPs and fomesafen (with 90 % accuracy) in soil. The VIP and SHAP analyses of the H2O AutoML showed that PE-MPs and the co-contamination of PE-MPs and fomesafen significantly interfered with the antioxidant system and energy regulation of soybean. We hope this study can provide a reliable scientific basis for sustainable development of the environment.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Machine learning-aided unveiling the relationship between chemical pretreatment and methane production of lignocellulosic waste

Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challenge. Here, based on 254 LW samples, a machine learning (ML) model to predict the methane production performance of pretreated feedstock was constructed using two automated ML platforms (tree-based pipeline optimization tool and neural network intelligence). Furthermore, the interactive effects of pretreatment conditions, feedstock properties, and digestion conditions on methane production of pretreated LW were studied through model interpretability analysis. The optimal ML model performed well on the validation set, and the digestion time, pretreatment agent, and lignin content (LC) were found to be key factors affecting the methane production of pretreated LW. If the LC in the raw LW was lower than 15%, the maximum CMY might be achieved using the NaOH, KOH, and alkaline hydrogen peroxide (AHP) with concentrations of 3.8%, 4.4%, and 4.5%, respectively. On the other hand, if LC was higher than 15%, only high concentrations of AHP exceeding 4% could significantly increase methane production. This study provides valuable guidance for optimizing pretreatment process, comparing different chemical pretreatment approaches, and regulating the operation of large-scale biogas plants.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things

Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning

Biochar is commonly used to enhance the anaerobic digestion of organic waste solids and wastewater, due to its electrochemical properties, which intensify the electron transfer of microorganisms attached to its large surface area. However, it is difficult to create biochar with both high conductivity and high capacitance, which makes selecting the right biochar for engineering applications challenging. To address this issue, two Auto algorithms (TPOT and H2O) were applied to model the effects of different biochar properties on anaerobic digestion processes. The results showed that the gradient boosting machine had the highest predictive accuracy (R2 = 0.96). Feature importance analysis showed that feedstock concentration, digestion time, capacitance, and conductivity of biochar were the main factors affecting methane yield. According to the two-dimensional (2D) partial dependence plots, high-capacitance biochar (0.27-0.29 V·mA) is favorable for substrates with low-solid content (< 19.6 TS%), while the high-conductivity biochar (80.82-170.58 mS/cm) is suitable for high-solids substrates (> 20.1 TS%). The software, based on the optimal model, can be used to obtain the ideal range of biochar for AD trials, aiding researchers in practical applications prior to implementation.

Data-driven interpretable analysis for polysaccharide yield prediction

Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.

Machine learning-aided inverse design for biogas upgrading through biological CO2 conversion

The biogas upgrading process through bioconversion of CO2 to CH4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH4% and H2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH4% and H2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning

A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.

Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks

With the rise in global plastic production and agricultural demand, the released microplastics (MPs) have increasingly influenced the elemental cycles of soils, leading to notable effects on greenhouse gas emissions. Despite initial research, there remains a gap in establishing a detailed modeling approach that comprehensively explores the impacts of MPs on GHG emissions. Herein, we utilized literature mining to assemble a comprehensive dataset examining the interplays between MPs and emissions of CO2, CH4, and N2O. Five automated machine learning frameworks were employed for predictive modeling. The GAMA framework was particularly effective in predicting CO2 (Q2 = 0.946) and CH4 (Q2 = 0.991) emissions. The Autogluon framework provided the most accurate prediction for N2O emission, though it exhibited signs of overfitting. Interpretability analysis indicated that the type of MPs significantly influenced CO2 emission. Degradable MPs (i.e., polyamide) inherently led to elevated CO2 emission, and the environmental aging further exacerbated this effect. Although both linear and nonlinear correlations between MPs and CH₄ emission were not identified, the incorporation of specific MPs that elevate soil pH, augment soil water retention, and cultivate anaerobic conditions may potentially elevate soil CH₄ emission. This research underscores the profound influence of MPs on soil GHG emissions, providing vital insights for shaping agricultural policies and soil management practices in the context of escalating plastic use.

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.

Quantifying source contributions to ambient NH3 using Geo-AI with time lag and parcel tracking functions

Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3's properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.

Machine learning-based prediction for settling velocity of microplastics with various shapes

Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = dn(g/v2)1/3 > 80, where dn is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.

Machine learning-based prediction of methane production from lignocellulosic wastes

The biochemical methane potential test is a standard method to determine the biodegradability of lignocellulosic wastes (LWs) during anaerobic digestion (AD) with disadvantages of long experiment duration and high operating expense. This paper developed a machine learning model to predict the cumulative methane yield (CMY) using the data of 157 LWs regarding physicochemical characteristics, digestion condition and methane yield, with the coefficient of determination equal to 0.869. Model interpretability analyses underscored lignin content, organic loading, and nitrogen content as pivotal attributes for CMY prediction. For the feedstocks with a cellulose content exceeding about 50%, the CMY in the early AD stage would be relatively lower than those with low cellulose content, but prolonging digestion time could promote methane production. Besides, lignin content in feedstock surpassing 15% would significantly inhibit methane production. This work contributes to valuable guidance for feedstock selection and operation optimization for AD plants.

Microbiome-functionality in anaerobic digesters: A critical review

Microbially driven anaerobic digestion (AD) processes are of immense interest due to their role in the biovalorization of biowastes into renewable energy resources. The function-versatile microbiome, interspecies syntrophic interactions, and trophic-level metabolic pathways are important microbial components of AD. However, the lack of a comprehensive understanding of the process hampers efforts to improve AD efficiency. This study presents a holistic review of research on the microbial and metabolic "black box" of AD processes. Recent research on microbiology, functional traits, and metabolic pathways in AD, as well as the responses of functional microbiota and metabolic capabilities to optimization strategies are reviewed. The diverse ecophysiological traits and cooperation/competition interactions of the functional guilds and the biomanipulation of microbial ecology to generate valuable products other than methane during AD are outlined. The results show that AD communities prioritize cooperation to improve functional redundancy, and the dominance of specific microbes can be explained by thermodynamics, resource allocation models, and metabolic division of labor during cross-feeding. In addition, the multi-omics approaches used to decipher the ecological principles of AD consortia are summarized in detail. Lastly, future microbial research and engineering applications of AD are proposed. This review presents an in-depth understanding of microbiome-functionality mechanisms of AD and provides critical guidance for the directional and efficient bioconversion of biowastes into methane and other valuable products.

Effects of microplastics on the properties of different types of sewage sludge and strategies to overcome the inhibition: A review

Microplastics have been identified as an emerging pollutant. When microplastics enter wastewater treatment plants, the plant traps most of the microplastics in the sludge during sewage treatment. Therefore, the effects of microplastics on sludge removal performance, and on the physical and chemical properties and microbial communities in sludge, have attracted extensive attention. This review mainly describes the presence of microplastics in wastewater treatment plants, and the effects of microplastics on the decontamination efficiency and physicochemical properties of activated sludge, aerobic granular sludge, anaerobic granular sludge and anaerobic ammonium oxidation sludge. Further, the review summarizes the effects of microplastics on microbial activity and microbial community dynamics in various sludges in terms of type, concentration, and contact time. The mechanisms used to strengthen the reduction of microplastics, such as biochar and hydrochar, are also discussed. This review summarizes the mechanism by which microplastics influence the performance of different types of sludge, and proposes effective strategies to mitigate the inhibitive effect of microplastics on sludge and discusses removal technologies of microplastics in sewage. Biochar and hydrochar are one of the effective measures to overcome the inhibition of microplastics on sludge. Meanwhile, constructed wetland may be one of the important choice for the future removal of microplastics from sewage. The goal is to provide theoretical support and insights for ensuring the stable operation of wastewater treatment plants and reducing the impact of microplastics on the environment.

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.

Effect of microplastics on the degradation of tetracycline in a soil microbial electric field

The degradation of organic pollutants and the adsorption of organic pollutants onto microplastics (MPs) in the environment have recently been intensively studied, but the effects of biocurrents, which are widespread in various soil environments, on the environmental behavior of MPs and antibiotic pollutants have not been reported. In this study, it was found that polylactic acid (PLA) and polyvinyl chloride (PVC) MPs accelerated the mineralization of humic substances in microbial electrochemical systems (MESs). After tetracycline (TC) was introduced into the MESs, the internal resistance of the soil MESs decreased. Additionally, the presence of MPs enhanced the charge output of the soil MESs by 40% (PLA+TC) and 18% (PVC+TC) compared with a control group without MPs (424 C). The loss in MP mass decreased after TC was added, suggesting a promotion of TC degradation rather than MP degradation for charge output. MPs altered the distribution of the highest occupied molecular orbitals and lowest unoccupied molecular orbitals of TC molecules and reduced the energy barrier for the TC hydrolysis reaction. The microbial community of the plastisphere exhibited a greater ability to degrade xenobiotics than the soil microbial community, indicating that MPs were hotspots for TC degradation. This study provides the first glimpse into the influence of MPs on the degradation of TC in MESs, laying a theoretical and methodological foundation for the systematic evaluation of the potential risks of environmental pollutants in the future.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Automated machine learning-based models for predicting and evaluating antibiotic removal in constructed wetlands

Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94-13.68, coefficient of determination = 0.780-0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.

Differential effects of petroleum-based and bio-based microplastics on anaerobic digestion: A review

The number of plastics is increasing owing to the rapid development of the plastics industry. Microplastics (MPs) are formed during the use of both petroleum-based plastics and newly developed bio-based plastics. These MPs are inevitably released into the environment and are enriched in wastewater treatment plant sludge. Anaerobic digestion is a popular sludge stabilization method for wastewater treatment plants. Understanding the potential impacts of different MPs on anaerobic digestion is critical. This paper provides a comprehensive review of the mechanisms of petroleum-based MPs and bio-based MPs in anaerobic digestion methane production and compares their potential effects on biochemical pathways, key enzyme activities, and microbial communities. Finally, it identifies problems that must be solved in the future, proposes the focus of future research, and predicts the future development direction of the plastics industry.

A review on mechanistic understanding of microplastic pollution on the performance of anaerobic digestion

Anaerobic digestion (AD) has emerged as a promising technology for diverting the organic waste from the landfills along with the production of clean energy. AD is a microbial-driven biochemical process wherein the plethora of microbial communities participate in converting the putrescible organic matter into biogas. Nevertheless, the AD process is susceptible to the external environmental factors such as presence of physical (microplastics) and chemical (antibiotics, pesticides) pollutants. The microplastics (MPs) pollution has received recent attention due to the increasing plastic pollution in terrestrial ecosystems. This review was aimed for holistic assessment of impact of MPs pollution on AD process to develop efficient treatment technology. First, the possible pathways of MPs entry into the AD systems were critically evaluated. Further, the recent literature on the experimental studies pertaining to the impact of different types of MPs at different concentrations on the AD process was reviewed. In addition, several mechanisms such as direct exposure of MPs on the microbial cells, indirect impact of MPs through the leaching of toxic chemicals and reactive oxygen species (ROS) formation on AD process were elucidated. Besides, the risk possessed by the increase of antibiotic resistance genes (ARGs) after the AD process due to the MPs stress on microbial communities were discussed. Overall, this review deciphered the severity of MPs pollution on AD process at different levels.

Predicting bacterial transport through saturated porous media using an automated machine learning model

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.