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

Sorption of PFOS in 114 Well-Characterized Tropical and Temperate Soils: Application of Multivariate and Artificial Neural Network Analyses

(2021) Anthony C. Umeh et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

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

(2021) Soonho Hwangbo et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

(2021) Leihong Wu et al.   CHEMICAL RESEARCH IN TOXICOLOGY

A deep learning approach for prediction of air quality index in a metropolitan city

(2021) R. Janarthanan et al.   Sustainable Cities and Society

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models

(2021) Sakshi Jain et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Data Analytics for Environmental Science and Engineering Research

(2021) Suraj Gupta et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens

(2021) Liguo Wang et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

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

(2021) Shifa Zhong et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Use of a large dataset to develop new models for estimating the sorption of active pharmaceutical ingredients in soils and sediments

(2021) Jun Li et al.   JOURNAL OF HAZARDOUS MATERIALS

ADMET Predictability at Boehringer Ingelheim: State‐of‐the‐Art, and Do Bigger Datasets or Algorithms Make a Difference?

(2021) Stevan Aleksić et al.   Molecular Informatics

Chinese industrial water pollution and the prevention trends: An assessment based on environmental complaint reporting system (ECRS)

(2021) Yubao Wang et al.   Alexandria Engineering Journal

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

A Random Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and Ozone at Fine Spatial Resolution in Sweden

(2020) Massimo Stafoggia et al.   Atmosphere

Evolving Today to Best Serve Tomorrow

(2020) Julie Beth Zimmerman et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

(2020) Kai Zhang et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey

(2020) Aslı Bozdağ et al.   ENVIRONMENTAL POLLUTION

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city

(2020) Gevorg Tepanosyan et al.   ENVIRONMENTAL POLLUTION

Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features

(2020) Xiangfei Sun et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

(2020) Heather L. Ciallella et al.   LABORATORY INVESTIGATION

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction

(2020) Kimberley M. Zorn et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Computational planning of the synthesis of complex natural products

(2020) Barbara Mikulak-Klucznik et al.   NATURE

Multi-site household waste generation forecasting using a deep learning approach

(2020) Maximiliano Cubillos   WASTE MANAGEMENT

Deep learning chemistry ab initio

(2020) Gabriella Graziano   Nature Reviews Chemistry

Artificial intelligence is improving the detection of lung cancer

(2020) Elizabeth Svoboda   NATURE

Spatiotemporal prediction of air quality based on LSTM neural network

(2020) Dewen Seng et al.   Alexandria Engineering Journal

Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces

(2019) Jules Kerckhoffs et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Data-driven performance analyses of wastewater treatment plants: A review

(2019) Kathryn B. Newhart et al.   WATER RESEARCH

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model

(2019) Massimo Stafoggia et al.   ENVIRONMENT INTERNATIONAL

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

(2019) Heather L. Ciallella et al.   CHEMICAL RESEARCH IN TOXICOLOGY

Chemical Isotope Labeling Exposome (CIL-EXPOSOME): One High-Throughput Platform for Human Urinary Global Exposome Characterization

(2019) Shenglan Jia et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender

(2019) Songjing Chen et al.   INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH

Big data and machine learning algorithms for health-care delivery

(2019) Kee Yuan Ngiam et al.   LANCET ONCOLOGY

Deep learning for cellular image analysis

(2019) Erick Moen et al.   NATURE METHODS

A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China

(2019) Xiaolin Jia et al.   ENVIRONMENTAL POLLUTION

Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017

(2019) Baolei Lyu et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Distinguishing the sources of silica nanoparticles by dual isotopic fingerprinting and machine learning

(2019) Xuezhi Yang et al.   Nature Communications

Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater

(2019) Alireza Motevalli et al.   JOURNAL OF CLEANER PRODUCTION

Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes

(2019) Filip Miljković et al.   JOURNAL OF MEDICINAL CHEMISTRY

Deep Learning Prediction of Polycyclic Aromatic Hydrocarbons in the High Arctic

(2019) Yuan Zhao et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model

(2018) Cole Brokamp et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model

(2018) Yu Zhan et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Points of significance: Machine learning: supervised methods

(2018) Danilo Bzdok et al.   NATURE METHODS

Predicting daily PM 2.5 concentrations in Texas using high-resolution satellite aerosol optical depth

(2018) Xueying Zhang et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Relating N 2 O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques

(2018) V. Vasilaki et al.   WATER RESEARCH

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data

(2018) Qingyang Xiao et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

(2017) Xuefei Hu et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis

(2017) Milad Ebrahimi et al.   JOURNAL OF ENVIRONMENTAL MANAGEMENT

Points of Significance: Principal component analysis

(2017) Jake Lever et al.   NATURE METHODS

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

(2017) Alexios Koutsoukas et al.   Journal of Cheminformatics

Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States

(2016) Hyung Joo Lee et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Low-Concentration PM2.5 and Mortality: Estimating Acute and Chronic Effects in a Population-Based Study

(2015) Liuhua Shi et al.   ENVIRONMENTAL HEALTH PERSPECTIVES

High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America

(2015) Aaron van Donkelaar et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

(2014) Zongwei Ma et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

The Problem of Biased Data and Potential Solutions for Health and Environmental Assessments

(2014) Glenn W. Suter et al.   HUMAN AND ECOLOGICAL RISK ASSESSMENT

Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods

(2013) Qingda Zang et al.   Journal of Chemical Information and Modeling

Wastewater quality monitoring system using sensor fusion and machine learning techniques

(2011) Xusong Qin et al.   WATER RESEARCH

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression

(2010) Daniel Westreich et al.   JOURNAL OF CLINICAL EPIDEMIOLOGY

Use of principal components analysis (PCA) on estuarine sediment datasets: The effect of data pre-treatment

(2009) M.K. Reid et al.   ENVIRONMENTAL POLLUTION

A review of land-use regression models to assess spatial variation of outdoor air pollution

(2008) Gerard Hoek et al.   ATMOSPHERIC ENVIRONMENT

How do your data grow?

(2008) Clifford Lynch   NATURE