Predicting a Wide Range of Fractal Dimensions of Salt-Induced Aggregates in Water Using a Random Forest Model

Salt-induced colloidal aggregates can significantly influence contaminant fate and transport in natural and engineered systems. These aggregates' fractal dimensions (<i>d</i>_<i>f</i>), ranging from 1.4 to 2.2, depend on various system variables. However, the quantitative relationship between these variables and <i>d</i>_<i>f</i> of aggregates has not been fully explored, especially in predicting a wide range of <i>d</i>_<i>f</i>. Here, we developed a random forest model capable of predicting the complete range of aggregate <i>d</i>_<i>f</i> using just four simple physical and chemical parameters of the aggregating system as inputs. The model accurately predicts the <i>d</i>_<i>f</i> of aggregates formed by colloids of different sizes, ranging from nano to micro sizes, after being trained and tested on appropriate data sets. Ionic strength (IS) has the most significant influence on the <i>d</i>_<i>f</i> of aggregates formed by microsized particles followed by the relative hydrodynamic radius of aggregates (<i>R</i>_<i>h</i>/<i>R</i>_<i>p</i>), particle concentration (<i>C</i>_<i>p</i>), and primary particle radius (<i>R</i>_<i>p</i>). For aggregates formed by both nano- and microsized particles, IS still has a strong influence on the <i>d</i>_<i>f</i>, with the significance of <i>R</i>_<i>p</i> increasing. All four inputs are negatively correlated with predicting the <i>d</i>_<i>f</i> of aggregates. The predictions align well with the physical interpretations.