Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning

Use of machine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air

Volatilization from soil to air is a key process driving the distribution and fate of semi-volatile organic contaminants. However, quantifying this process and the key environmental governing factors remains difficult. To address this issue, the volatilization fluxes of polybrominated diphenyl ethers (PBDEs) and organophosphate esters (OPEs) from soil were determined in 16 batch experiments orthogonally with six variables (chemical property, soil concentration, air velocity, ambient temperature, soil porosity, and soil moisture) and analyzed with machine learning methods. The results showed that gradient-boosting regression tree models satisfactorily predicted the volatilization fluxes of PBDEs (r2 = 0.82 ± 0.07) and OPEs (r2 = 0.62 ± 0.13). Permutation importance analysis showed that partitioning potential of chemicals between soil and air was the most important factor regulating the volatilization of the target compounds from soil. Temperature and soil porosity played a secondary role in controlling the migration of PBDEs and OPEs, respectively, due to higher volatilization enthalpies of PBDEs than those of OPEs and dominant adsorption of OPEs on mineral surface. The effect of soil moisture was negative and positive for the volatilization fluxes of PBDEs and OPEs, respectively. These results suggested different responses in the soil-air diffusive transport of PBDEs and OPEs to high temperature and rainstorm induced by climate change.