QSPR Molecular Approach for Estimating Henry’s Law Constants of Pure Compounds in Water at Ambient Conditions

Prediction of critical properties of sulfur-containing compounds: New QSPR models

In this study, new models have been proposed for the prediction of different critical properties (critical temperature (T_C), critical pressure (P_C), critical volume (V_C), and acentric factor (ω)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R²) were 0.936, 0.976, 0.990, and 0.917 for T_C, P_C, V_C, and ω, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded.

Determination of the Henry's law constants of low-volatility compounds via the measured air-phase transfer coefficients

Accurate Henry's law constants (H) are unavailable for the majority of organic pollutants, especially those having a low volatility. A novel kinetics-based experimental method is introduced to determine H for a wide range of low-H compounds. The method consists of measuring independently the water-to-air transfer coefficient (K_L) and the associated air-phase transfer coefficient (k_G) of a low-H chemical (solute) in water when K_L ≅ k_GH prevails according to the two-film theory. The k_G for a solute is obtained via a developed gas-dynamic equation that relates k_G to the solute molecular weight and the solute-vapor escaping efficiency (β) through a boundary air layer. The value of β is only a function of the in situ air turbulence level, independent of the chemical species. Thus, the required β for solutes can be estimated from the evaporative rates of pure volatile liquids under the same ambient setting. By relating the estimated k_G with the measured K_L of a low-H solute, the solute H is established. The H values of 45 low-H chemicals, including many complex pesticides, in the range of ∼10^-7 to ∼10^-3 have thus been determined. The accountability of the method is underscored by the consistency of the measured and credible literature H values for a number of the low-H compounds studied.

Prediction of Henry's Law Constants via group-specific quantitative structure property relationships

Henry's Law Constants (HLCs) for several hundred organic compounds in water at 25 °C were predicted by Quantitative Structure Property Relationship (QSPR) models, with the division of organic compounds into specific classes to yield more accurate models than generalised ones. Both multiple linear regression (MLR) and artificial neural network (ANN) versions of models were produced for three general cases, encompassing the entire data set; one used the six best descriptors, as determined by maximising the correlation coefficient; another used the twelve best descriptors in a similar manner, whilst the third used the same twelve descriptors as English and Carroll (2001). These achieved, respectively, root-mean square errors (RMSEs) of 0.719, 0.52 and 0.607 log(Hcc) units for the MLR version and 0.601, 0.394 and 0.431 for the test set of the ANN models, where Hcc is the ratio of the compound's concentration in the vapour phase to that in the liquid phase. These were compared with models for six specific chemical classes: (i) alkanes, (ii) cyclic alkanes, (iii) alkenes, (iv) halogenated compounds, (v) aldehydes, ketones and esters grouped together, and (vi) monoaromatics. These group-specific models had RMSEs of 0.153, 0.141. 0.097, 0.168, 0.122 and 0.104 respectively for the MLR versions and 0.684, 0.719, 0.856, 0.784, 0.875 and 0.861 for the test set of the ANN models. It was found that the class-specific models achieved lower RMSEs than the general models, when using MLR models. The use of ANN was found to improve the predictive accuracy of the general models but failed to improve that for the class-specific models vis-à-vis MLR.