Evaluation of the potential ecological risk of metals in atmospherically deposited particulate matter via laser-induced breakdown spectroscopy combined with machine learning

With the acceleration of industrialization and urbanization, China faces increasingly serious urban air pollution, such as frequent haze. Heavy metals have attracted extensive attention because of their non-degradability, significant biotoxicity, and persistence. In this work, laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF) was utilized to directly evaluate the potential ecological risk of metals. The LIBS spectra of 17 atmospherically deposited particulate matter samples were collected, and three metal elements (V, Cr, and Zn) were identified from the National Institute of Standards and Technology database. The influence of predictive ability with different spectral preprocessing methods on the RF calibration model was then investigated, and the input variable was selected by variable importance measurement to further enhance the accuracy of predictive ability. Under the optimized spectral preprocessing method, VI threshold, and model parameters, the mean relative error of prediction set (MREP) for potential ecological risk index analysis (V, Cr, and Zn) was calculated as 0.0206, 0.0529, and 0.2218, respectively, and the determination coefficient of prediction set (RP2) was 0.9220, 0.9458, and 0.9359, respectively. These values inferred the overall lack of significant contamination at the time of the study. In summary, a novel approach based on LIBS combined with RF provides a new idea and method to directly evaluate the potential ecological risk of air pollution.