Predicting chloroform production from organic precursors.

Area of Science:

• Environmental Chemistry
• Water Treatment Science

Background:

• Quantitative methods linking molecular structure to disinfection byproduct formation are limited.
• Chloroform (trichloromethane) is a common disinfection byproduct.
• Understanding precursor influence is crucial for water safety.

Purpose of the Study:

• To develop a predictive mathematical model for chloroform yields from organic precursors.
• To identify key molecular descriptors influencing chloroform formation.
• To provide insights into the impact of specific functional groups on byproduct generation.

Main Methods:

• Collated experimental chloroform yield data from 211 precursors across 22 literature studies.
• Employed multiple linear regression with 19 molecular descriptors.
• Utilized five-way leave-many-out cross-validation for model calibration.

Main Results:

• Developed a predictive model using three descriptors, achieving an R² of 0.91.
• Identified two novel empirical descriptors quantifying substituent effects on chlorine substitution sites.
• Experimental validation with 10 new precursors showed a mean discrepancy of 5.3% mol/mol.

Conclusions:

• The model accurately predicts chloroform yields, offering insights into functional group influences.
• Novel descriptors highlight the significance of adjacent substituents in chloroform formation.
• The approach is adaptable for future inclusion of more precursors and byproduct groups.