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RESEARCH PAPER · Environmental Science & Technology · 2021

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

Shifa Zhong, Kai Zhang, Majid Bagheri, Joel G. Burken, April Z. Gu, Baikun Li, Xingmao Ma, Babetta L. Marrone, Zhiyong Jason Ren, Joshua Schrier, Wei Feng Shi, Haoyue Tan, Tianbao Wang, Xu Wang, Bryan Matthew Wong, Xusheng Xiao, Xiong Bill Yu, Jun‐Jie Zhu, Huichun Judy Zhang

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42 direct citing works

Machine Learning: New Ideas and Tools in Environmental Science and EngineeringOriginalPredicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities289Antimony, beryllium, cobalt, and vanadium in urban park soils in Beijing: Machine learning-based source identification and health risk-based soil environmental criteria60Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants58Sustainable and efficient technologies for removal and recovery of toxic and valuable metals from wastewater: Recent progress, challenges, and future perspectives192Machine Learning-Assisted QSAR Models on Contaminant Reactivity Toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer126Evaluating the capability of municipal solid waste separation in China based on AHP-EWM and BP neural network55Developments in diagnostic applications of saliva in human organ diseases93Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers55Data-Driven Machine Learning in Environmental Pollution: Gains and Problems548Potential Application of Machine-Learning-Based Quantum Chemical Methods in Environmental Chemistry59Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM2.5 during the COVID-19 outbreak in Hubei Province, China56Machine learning for the prediction of heavy metal removal by chitosan-based flocculants77The structure-activity relationship of aromatic compounds in advanced oxidation processes：a review75Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches149Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process61Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review231Data-driven prediction of neutralizer pH and valve position towards precise control of chemical dosage in a wastewater treatment plant87Emerging Challenges and Opportunities for Electrified Membranes to Enhance Water Treatment46Insight into the synthesis and adsorption mechanism of adsorbents for efficient phosphate removal: Exploration from synthesis to modification228Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic87Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al2O3 Nanocomposites Synthesized by In Situ Method69A comprehensive review on algae removal and control by coagulation-based processes: mechanism, material, and application117Iron-based advanced oxidation processes for enhancing sludge dewaterability: State of the art, challenges, and sludge reuse155Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach95Enhanced understanding of osmotic membrane bioreactors through machine learning modeling of water flux and salinity54Recent Trends in AI-Based Intelligent Sensing62Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study98A QSAR–ICE–SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity53Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives95Multiple Roles of Dissolved Organic Matter in Advanced Oxidation Processes459Application of persulfate-based oxidation processes to address diverse sustainability challenges: A critical review191Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water75Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass64RETRACTED: Advanced insights on removal of antibiotics by microalgae-bacteria consortia: A state-of-the-art review and emerging prospects54An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites71Contamination vertical distribution and key factors identification of metal(loid)s in site soil from an abandoned Pb/Zn smelter using machine learning48Graph Neural Networks in IoT: A Survey196Comparison of sulfate radical with other reactive species85Molecularly Imprinted Ratiometric Fluorescence Nanosensors67Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization77Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation63Ecological risk assessment based on soil adsorption capacity for heavy metals in Taihu basin, China62Exploring available input variables for machine learning models to predict biogas production in industrial-scale biogas plants treating food waste47Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination74A novel machine learning method for evaluating the impact of emission sources on ozone formation62Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth56Environmental behavior, human health effect, and pollution control of heavy metal(loid)s toward full life cycle processes124Bioplastic design using multitask deep neural networks62Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization56Algal community structure prediction by machine learning64Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China94Artificial Intelligence-Assisted Prediction of Effluent Phosphorus in a Full-Scale Wastewater Treatment Plant with Missing Phosphorus Input and Removal Data49Prediction heavy metals accumulation risk in rice using machine learning and mapping pollution risk126On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale optimizer73Spatial prediction of soil contamination based on machine learning: a review42Enhancing Biochar-Based Nonradical Persulfate Activation Using Data-Driven Techniques163Predicting THM Formation and Revealing Its Contributors in Drinking Water Treatment Using Machine Learning47A Review of Physics-Informed Machine Learning in Fluid Mechanics248Prediction of wear rates of Al-TiO2 nanocomposites using artificial neural network modified with particle swarm optimization algorithm55Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale44Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence64Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management71Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse44Prediction of Photochemical Properties of Dissolved Organic Matter Using Machine Learning92The application of machine learning to air pollution research: A bibliometric analysis60Application of machine learning to guide efficient metal leaching from spent lithium-ion batteries and comprehensively reveal the process parameter influences59Life cycle assessment of greenhouse gas emissions of typical sewage sludge incineration treatment route based on two case studies in China55Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model71Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review233Development, Evaluation, and Application of Machine Learning Models for Accurate Prediction of Root Uptake of Per- and Polyfluoroalkyl Substances81Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment209Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification91Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions83Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater79Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation85Heavy metal pollution in Mongolian-Manchurian grassland soil and effect of long-range dust transport by wind61Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment86Fluorescent Dissolved Organic Matter Components as Surrogates for Disinfection Byproduct Formation in Drinking Water: A Critical Review56Machine Learning in Environmental Research: Common Pitfalls and Best Practices672Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning44Microplastics and nanoplastics analysis: Options, imaging, advancements and challenges119Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants’ Activities and Properties151Advances and applications of machine learning and deep learning in environmental ecology and health119Predicting aqueous sorption of organic pollutants on microplastics with machine learning90A Review of Computational Modeling in Wastewater Treatment Processes146Interpretation and Prediction of the CO2 Sequestration of Steel Slag by Machine Learning82Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances59Polystyrene microplastics induce size-dependent multi-organ damage in mice: Insights into gut microbiota and fecal metabolites184Using artificial intelligence-based algorithms to identify critical fouling factors and predict fouling behavior in anaerobic membrane bioreactors51Applications of artificial intelligence technologies in water environments: From basic techniques to novel tiny machine learning systems47Application of machine learning in river water quality management: a review50Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges48Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review99Recent progress and prospects in solid acid-catalyzed CO2 desorption from amine-rich liquid45Combining physical-based model and machine learning to forecast chlorophyll-a concentration in freshwater lakes48Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters52Machine learning prediction and interpretation of the impact of microplastics on soil properties51Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning62Guideline for modeling solid-liquid adsorption: Kinetics, isotherm, fixed bed, and thermodynamics148Does artificial intelligence technology enhance green transformation of enterprises: based on green innovation perspective86An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models43Photocatalytic NO x removal and recovery: progress, challenges and future perspectives69AI and machine learning in climate change research: A review of predictive models and environmental impact54A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models69Machine learning for membrane design in energy production, gas separation, and water treatment: a review111Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment64Research progress on the environmental risk assessment and remediation technologies of heavy metal pollution in agricultural soil217Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies70The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes44Deep learning for water quality364Emerging contaminants: A One Health perspective578Active Learning-Based Guided Synthesis of Engineered Biochar for CO 2 Capture60Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/H2O2 treatment of wastewater58Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP67Predicting maturity and identifying key factors in organic waste composting using machine learning models49The role of nanocatalysts in green hydrogen production and water splitting59Prediction and interpretation of concrete corrosion induced by carbon dioxide using machine learning48Real time electronic-waste classification algorithms using the computer vision based on Convolutional Neural Network (CNN): Enhanced environmental incentives48Stochastic processes shape microbial community assembly in biofilters: Hidden role of rare taxa60Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery47Estimation methods of wetland carbon sink and factors influencing wetland carbon cycle: a review89Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media69Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies121Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications61Machine Learning-Assisted Optimization of Mixed Carbon Source Compositions for High-Performance Denitrification56Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures47Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning56Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking44Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach78Improved mapping of heavy metals in agricultural soils using machine learning augmented with spatial regionalization indices60Building sustainability through a novel exploration of dynamic LCA uncertainty: Overview and state of the art65Artificial intelligence in wastewater treatment: Research trends and future perspectives through bibliometric analysis100Hybrid deep learning based prediction for water quality of plain watershed48Understanding the Selective Removal of Perfluoroalkyl and Polyfluoroalkyl Substances via Fluorine–Fluorine Interactions: A Critical Review73Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment44AI ‐driven adaptive learning for sustainable educational transformation561Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers60Environmental Machine Learning, Baseline Reporting, and Comprehensive Evaluation: The EMBRACE Checklist48Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships108Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water84How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant47Artificial intelligence in smart drug delivery systems: a step toward personalized medicine56Prediction of nitrate concentration and the impact of land use types on groundwater in the Nansi Lake Basin72Exploring the Intersection of Machine Learning and Big Data: A Survey53A global estimate of multiecosystem photosynthesis losses under microplastic pollution76What Does the “Trojan Horse” Carry? The Pollutants Associated with Microplastics/Nanoplastics in Water Environments46Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications66Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection96Artificial intelligence in environmental and Earth system sciences: explainability and trustworthiness47Predicting water quality index using stacked ensemble regression and SHAP based explainable artificial intelligence78

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  50. Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning

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