Magnetic Properties as a Proxy for Predicting Fine-Particle-Bound Heavy Metals in a Support Vector Machine Approach.

Abstract

The development of a reasonable statistical method of predicting the concentrations of fine-particle-bound heavy metals remains challenging. In this study, daily PM2.5 samples were collected within four different seasons from a Chinese mega-city. The annual average PM2.5 concentrations determined in industrial, city center, and suburban areas were 90, 81, and 85 μg m-3, respectively. Environmental magnetic measurements, including magnetic susceptibility, anhysteretic remanent magnetization, isothermal remanent magnetization, hysteresis loops, and thermomagnetism, indicated that the main magnetic mineral of PM2.5 is low-coercivity pseudosingle domain (PSD) magnetite. Using a support vector machine (SVM), both the volume- and mass-related concentrations of heavy metals were predicted by the PM2.5 mass concentrations and meteorological factors, with or without magnetic properties as input variables. The inclusion of magnetic variables significantly improved the prediction results for most heavy metals. Predictions based on models that included the magnetic properties of the metals Al, Fe, Mn, Ni, and Ti were promising, with R values of >0.8 in both the training and the test stages as well as relatively low errors. Our results demonstrate that the inclusion of environmental magnetism in a SVM approach aids in the effective monitoring and assessment of airborne heavy-metal contamination in cities.