Machine Learning in Environmental Chemistry: Application to Surface Complexation Modeling

Abstract

Environmental chemistry – or biogeochemistry – is the scientific discipline typically invoked when examining and quantifying groundwater or surface water contamination, and nutrient cycling in the environment. Over the last three decades, there have been significant advances in mechanistic model development to describe and predict these complex biogeochemical processes. In particular, surface complexation models (SCMs) have been developed to describe the rock/soil surface reactions of metals and radionuclides, and their partitioning between various mobile species in the aqueous phase or immobile species sorbed on solid surfaces. Often represented by a simplified linear isotherm constant – Kd – in reactive transport models, these reactions play a critical role in many environmental science applications; particularly in contamination risk assessments and nuclear waste disposal performance assessments. In the past several decades, efforts by various institutions across the world have focused on developing SCMs based on datasets from laboratory measurements, including the identification of key parameters such as equilibrium constants.