Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials.

Gabriel Sigmund,Thilo Hofmann,Mehdi Gharasoo,Thorsten Hüffer

doi:10.1021/acs.est.9b06287

Abstract

Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log KF and n (R2 > 0.98 for log KF, and R2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.

Highlights

Persistent organic contaminants (POPs) are hydrophobic organic compounds that include the original 12 compounds regulated in the Stockholm convention
A state-of-the-art approach to predict the sorption of neutral hydrophobic organic contaminants to a given material are poly-parameter linear free-energy relationships.[3−6] The ppLFER concept for neutral compounds is based on the Abraham parameters E, S, A (H-bond acidity), B (H-bond basicity), V (McGowan molar volume, cm[3] mol−1/100), and L
The sorption of organic compounds to carbonaceous sorbents is concentration-dependent, a factor that was recently introduced into ppLFER for predicting the sorption of neutral organic compounds to activated carbon[4] and to soot.[5]

Summary

INTRODUCTION

Persistent organic contaminants (POPs) are hydrophobic organic compounds that include the original 12 compounds regulated in the Stockholm convention (the “dirty dozen”). The sorption of organic compounds to carbonaceous sorbents is concentration-dependent (non-linear), a factor that was recently introduced into ppLFER for predicting the sorption of neutral organic compounds to activated carbon[4] and to soot.[5] Carbonaceous sorbent materials, such as activated carbon, soot, biochar, and carbon nanotubes (CNTs), have a wide range of applications, including drinking water filtration systems, wastewater treatment plants, and soil and sediment remediation. The results showed that this newly developed method is able to predict the sorption of anionic, cationic, and zwitterionic ionizable organic compounds to carbonaceous sorbents and is widely applicable It is based on parameters generally available for carbonaceous sorbents and additional compound descriptors that are freely available from online databases. A freely accessible graphical user interface is provided by the authors

METHODS

RESULTS AND DISCUSSION

■ ACKNOWLEDGMENTS

■ REFERENCES