Current applications and future impact of machine learning in emerging contaminants: A review

[1]  Zhiwei Wang,et al.  Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review. , 2022, Water research.

[2]  Xiangang Hu,et al.  Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects. , 2022, Journal of hazardous materials.

[3]  G. Jiang,et al.  Data-Driven Machine Learning in Environmental Pollution: Gains and Problems. , 2022, Environmental science & technology.

[4]  A. Pyle,et al.  Author Correction: Visualizing group II intron dynamics between the first and second steps of splicing , 2022, Nature communications.

[5]  C. Laforsch,et al.  Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning , 2021, Environmental science & technology letters.

[6]  J. F. Beltrán,et al.  Functions predict horizontal gene transfer and the emergence of antibiotic resistance , 2021, Science advances.

[7]  G. Kalčíková,et al.  Seeking for a perfect (non-spherical) microplastic particle - The most comprehensive review on microplastic laboratory research. , 2021, Journal of hazardous materials.

[8]  J. Abdi,et al.  Estimation of tetracycline antibiotic photodegradation from wastewater by heterogeneous metal-organic frameworks photocatalysts. , 2021, Chemosphere.

[9]  S. Zendehboudi,et al.  A Critical Review of Biomass Kinetics and Membrane Filtration Models for Membrane Bioreactor Systems , 2021, Journal of Environmental Chemical Engineering.

[10]  Daniel C W Tsang,et al.  Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning. , 2021, Journal of hazardous materials.

[11]  Qiannan Duan,et al.  A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning , 2021, Frontiers of Environmental Science & Engineering.

[12]  Andrew A. May,et al.  Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model. , 2021, Journal of hazardous materials.

[13]  V. T. Nguyen,et al.  A comprehensive modelling approach to understanding the fate, transport and potential risks of emerging contaminants in a tropical reservoir. , 2021, Water research.

[14]  Xingmao Ma,et al.  Prediction of Plant Uptake and Translocation of Engineered Metallic Nanoparticles by Machine Learning. , 2021, Environmental science & technology.

[15]  Xiangang Hu,et al.  Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles , 2021, SciAdv.

[16]  Z. Yaseen An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. , 2021, Chemosphere.

[17]  Fang Cheng,et al.  Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning. , 2021, Chemosphere.

[18]  Ting Zhang,et al.  Meta-analysis of cellular toxicity for graphene via data-mining the literature and machine learning. , 2021, The Science of the total environment.

[19]  Daniel C W Tsang,et al.  Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption , 2021 .

[20]  Daniel C W Tsang,et al.  Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures , 2020 .

[21]  J. Amigo,et al.  Classification and quantification of microplastic (< 100 µm) using FPA-FTIR imaging system and machine learning. , 2020, Analytical chemistry.

[22]  Sean Ekins,et al.  Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. , 2020, Environmental science & technology.

[23]  C. Noutsopoulos,et al.  Evaluating the Fate of Emerging Contaminants in Wastewater Treatment Plants through Plant-Wide Mathematical Modelling , 2020, Environmental Processes.

[24]  Jing Xu,et al.  Predicting nanotoxicity by an integrated machine learning and metabolomics approach. , 2020, Environmental pollution.

[25]  Irini Furxhi,et al.  Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning , 2020, International journal of molecular sciences.

[26]  Charles T. Marx,et al.  Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers. , 2020, Environmental science & technology.

[27]  Jae-Hoon Hwang,et al.  Recent Developments of PFAS-Detecting Sensors and Future Direction: A Review , 2020, Micromachines.

[28]  J. Duan,et al.  Differentially charged nanoplastics demonstrate distinct accumulation in Arabidopsis thaliana , 2020, Nature Nanotechnology.

[29]  J. Kong,et al.  Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials , 2020, Advanced materials.

[30]  Seid Saeid Ghiasi,et al.  Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. , 2020, The Science of the total environment.

[31]  Xiangang Hu,et al.  Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles , 2020, Proceedings of the National Academy of Sciences.

[32]  Denis M. O'Carroll,et al.  Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples. , 2020, Chemosphere.

[33]  R. Gambari,et al.  The role of reactive oxygen species in the biological activity of antimicrobial agents: An updated mini review. , 2020, Chemico-biological interactions.

[34]  Vittorio Bianco,et al.  Microplastic Identification via Holographic Imaging and Machine Learning , 2020, Adv. Intell. Syst..

[35]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[36]  Jianshe Zhang,et al.  Effects of microplastics on growth, phenanthrene stress, and lipid accumulation in a diatom, Phaeodactylum tricornutum. , 2019, Environmental pollution.

[37]  B. Xie,et al.  Occurrence of microplastics in landfill systems and their fate with landfill age. , 2019, Water research.

[38]  Mikaël Kedzierski,et al.  A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea. , 2019, Chemosphere.

[39]  Carla A. Ng,et al.  Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List. , 2019, Environmental science & technology.

[40]  I. Chubarenko,et al.  Transport of marine microplastic particles: why is it so difficult to predict? , 2019, Anthropocene Coasts.

[41]  Bryan M. Wong,et al.  A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal , 2019, Environmental Science & Technology Letters.

[42]  G. Zeng,et al.  Recent advances in toxicological research of nanoplastics in the environment: A review. , 2019, Environmental pollution.

[43]  G. Dreyfuss,et al.  U1 snRNP regulates cancer cell migration and invasion , 2019, bioRxiv.

[44]  ChangKyoo Yoo,et al.  Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health. , 2019, Environmental pollution.

[45]  Fouzi Harrou,et al.  Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring , 2019, Sustainable Cities and Society.

[46]  Joel G. Burken,et al.  Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants. , 2019, Environmental pollution.

[47]  Shuhua Wang,et al.  The effect of bioelectrochemical systems on antibiotics removal and antibiotic resistance genes: A review , 2019, Chemical Engineering Journal.

[48]  B. Ni,et al.  Polyvinyl Chloride Microplastics Affect Methane Production from the Anaerobic Digestion of Waste Activated Sludge through Leaching Toxic Bisphenol-A. , 2019, Environmental science & technology.

[49]  José R. Vázquez-Canteli,et al.  Reinforcement learning for demand response: A review of algorithms and modeling techniques , 2019, Applied Energy.

[50]  Meral Yurtsever,et al.  Use of a convolutional neural network for the classification of microbeads in urban wastewater. , 2019, Chemosphere.

[51]  E. Zeng,et al.  Interaction of toxic chemicals with microplastics: A critical review. , 2018, Water research.

[52]  Ping-Huan Kuo,et al.  A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities , 2018, Sensors.

[53]  Lior Rokach,et al.  Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..

[54]  Tong Zhang,et al.  Tracking antibiotic resistance gene pollution from different sources using machine-learning classification , 2018, Microbiome.

[55]  E. Garner,et al.  DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data , 2018, Microbiome.

[56]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[57]  S. Richardson,et al.  Emerging environmental contaminants: Challenges facing our next generation and potential engineering solutions , 2017 .

[58]  R. Geyer,et al.  Production, use, and fate of all plastics ever made , 2017, Science Advances.

[59]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[60]  Y. Ying,et al.  Recent advances in nanomaterial-based biosensors for antibiotics detection. , 2017, Biosensors & bioelectronics.

[61]  D. Spandidos,et al.  Human exposure to endocrine disrupting chemicals: effects on the male and female reproductive systems. , 2017, Environmental toxicology and pharmacology.

[62]  Zhanyun Wang,et al.  A Never-Ending Story of Per- and Polyfluoroalkyl Substances (PFASs)? , 2017, Environmental science & technology.

[63]  H. Chhipa Nanofertilizers and nanopesticides for agriculture , 2016, Environmental Chemistry Letters.

[64]  S. Komatsu,et al.  Toxicity of heavy metals and metal-containing nanoparticles on plants. , 2016, Biochimica et biophysica acta.

[65]  David A Winkler,et al.  Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. , 2016, Toxicology and applied pharmacology.

[66]  Athanasios S Stasinakis,et al.  Assessing the risk associated with the presence of emerging organic contaminants in sludge-amended soil: A country-level analysis. , 2016, The Science of the total environment.

[67]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[68]  V. Sharma,et al.  Pharmaceuticals and personal care products in waters: occurrence, toxicity, and risk , 2015, Environmental Chemistry Letters.

[69]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[70]  Supratik Kar,et al.  On a simple approach for determining applicability domain of QSAR models , 2015 .

[71]  S. Saponaro,et al.  Bisphenol A, nonylphenols, benzophenones, and benzotriazoles in soils, groundwater, surface water, sediments, and food: a review , 2014, Environmental Science and Pollution Research.

[72]  Teresa M. Coque,et al.  What is a resistance gene? Ranking risk in resistomes , 2014, Nature Reviews Microbiology.

[73]  Lu Sun,et al.  Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors. , 2014, Ecotoxicology and environmental safety.

[74]  M. Wong,et al.  Pharmaceuticals and personal care products (PPCPs): a review on environmental contamination in China. , 2013, Environment international.

[75]  T. Royer,et al.  Pharmaceuticals suppress algal growth and microbial respiration and alter bacterial communities in stream biofilms. , 2013, Ecological applications : a publication of the Ecological Society of America.

[76]  R. Aminov Horizontal Gene Exchange in Environmental Microbiota , 2011, Front. Microbio..

[77]  Giulio Rastelli,et al.  Structure-based design of potent aromatase inhibitors by high-throughput docking. , 2011, Journal of medicinal chemistry.

[78]  Jose R Peralta-Videa,et al.  Interaction of nanoparticles with edible plants and their possible implications in the food chain. , 2011, Journal of agricultural and food chemistry.

[79]  Jerzy Leszczynski,et al.  Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.

[80]  Wei Xu,et al.  Endocrine disrupting chemicals targeting estrogen receptor signaling: identification and mechanisms of action. , 2011, Chemical research in toxicology.

[81]  Y. Oytam,et al.  Small amounts of zinc from zinc oxide particles in sunscreens applied outdoors are absorbed through human skin. , 2010, Toxicological sciences : an official journal of the Society of Toxicology.

[82]  Mazdak Arabi,et al.  Tracking antibiotic resistance genes in the South Platte River basin using molecular signatures of urban, agricultural, and pristine sources. , 2010, Environmental science & technology.

[83]  Sang-June Choi,et al.  The methods of identification, analysis, and removal of endocrine disrupting compounds (EDCs) in water. , 2009, Journal of hazardous materials.

[84]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[85]  A. Demain,et al.  Microbial drug discovery: 80 years of progress , 2009, The Journal of Antibiotics.

[86]  Shane Weaver,et al.  The importance of the domain of applicability in QSAR modeling. , 2008, Journal of molecular graphics & modelling.

[87]  B. Nowack,et al.  Occurrence, behavior and effects of nanoparticles in the environment. , 2007, Environmental pollution.

[88]  Gianni Andreottola,et al.  Sub-critical fouling in a membrane bioreactor for municipal wastewater treatment: experimental investigation and mathematical modelling. , 2007, Water research.

[89]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[90]  L. Murr,et al.  Chemistry and nanoparticulate compositions of a 10,000 year-old ice core melt water. , 2004, Water research.

[91]  Aleem Ahmed Khan,et al.  Diclofenac residues as the cause of vulture population decline in Pakistan , 2004, Nature.

[92]  T. Ternes,et al.  Water analysis: emerging contaminants and current issues. , 2003, Analytical chemistry.

[93]  C. Jang,et al.  A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards. , 2017, Atmospheric environment.

[94]  G. Gilardi,et al.  Human aromatase: Perspectives in biochemistry and biotechnology , 2013, Biotechnology and applied biochemistry.