A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules. We compare the efficacy of linear (Partial Least Squares) and non-linear (Support Vector Machine) machine learning methods to describe a broad chemical space and produce a user-friendly model. We employ cross-validation, y-scrambling, and external validation for quality control. The recommended Support Vector Machine model trained on 95 compounds having 23 descriptors offered a good balance between good performance statistics, low error, and low probability of over-fitting while describing a wide range of chemical features. The cross-validated model using a log-uptake (qe) response calculated at an aqueous equilibrium concentration (Ce) of 1 μM described the training dataset with an r(2) of 0.932, had a cross-validated r(2) of 0.833, and an average residual of 0.14 log units.

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To evaluate natural organic matter (NOM) processing impacts on preformed monochloramine (PM) reactivity and as a first step in creating concentrated disinfection byproduct (DBP) mixtures from PM, a rational methodology was developed to proportionally scale PM NOM-related demand in unconcentrated source waters to waters with concentrated NOM. Multiple NOM preparations were evaluated, including a liquid concentrate and reconstituted lyophilized solid material. Published kinetic models were evaluated and used to develop a focused reaction scheme (FRS) that was relatively simple to implement and focused on monochloramine loss, including considerations for inorganic chloramine stability (i.e., autodecomposition) and bromide and iodide impacts. The FRS included critical reaction pathways and accurately simulated (without modification) monochloramine experimental data with and without bromide and iodide present over a range of PM-dosed NOM-free waters. For NOM-containing waters, addition of two NOM reactions in the FRS allowed (i) apportioning monochloramine loss to either inorganic or NOMrelated reactions and (ii) selecting experiment conditions to provide an equivalent monochloramine NOM-related demand in unconcentrated and concentrated waters. The methodology provides a framework for future experimentation to evaluate DBP scaling and their speciation in concentrated water matrices when providing an equivalent NOM-related monochloramine demand in unconcentrated and concentrated matrices.