机器学习方法在预测废水中PFAS剖面和归宿中的应用

Applications of Machine Learning Approaches to Predict PFAS Profiles and Fate in Wastewater1Christopher I. Olivares chris.olivares@uci.eduDepartment of Civil and Environmental EngineeringUniversity of California, Irvine Figure adapted from AgriLife Today (TAMU) Field SamplingLow PFAS cross-contamination LC-MS/MSSkilled data interpretation2PFAS concerns•Sampling and measurement of PFAS in WWTP•Observed increase in quantifiable PFAS in effluents•Biosolids –heterogenous matrixData Availability3Machine Learning Approaches to predict PFAS profilesin WWTPsObjective⮚Statewide WWTP database (PFAS, wastewater characteristics, treatment)⮚Machine learning models to predict PFAS in effluent and biosolids ⮚Identify features that influence PFAS composition in effluents, biosolids4Data sources5California Water BoardsGeotracker-PFASWater Quality –WWTP Effluent Self-Monitoring Report DataWastewater User Charge Data -SewageCOVID-19 Wastewater Surveillance dataGeotracker-QuestionnaireDemographic