首页|Jinan University Reports Findings in Machine Learning (Use of ma- chine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air)
Jinan University Reports Findings in Machine Learning (Use of ma- chine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 FEB 27 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news report- ing from Guangzhou, People’s Republic of China, by NewsRx journalists, research stated, “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.” The news correspondents obtained a quote from the research from Jinan University, “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 prop- erty, 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 satis- factorily predicted the volatilization fluxes of PBDEs (r = 0.82 ± 0.07) and OPEs (r = 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.”
GuangzhouPeople’s Republic of ChinaAsiaChemicalsCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
