Pollution risk estimation of the Cu element in atmospheric sedimentation samples by laser induced breakdown...

Laser-induced breakdown spectroscopy (LIBS) combined with the random forest (RF) algorithm was proposed to predict three pollution indexes (geo-accumulation index, enrichment factor, and potential ecological risk index) of the Cu element in atmospheric sedimentation samples to evaluate the pollution risk. To begin with, the LIBS spectra of 15 atmospheric sedimentation samples from different locations were collected and the copper element was identified using the National Institute of Standards and Technology (NIST) database. Then, the influence of different spectral pretreatment methods (MSC, WT and D_1st) on the predictive performance of the RF was discussed according to the calibration set with the determination coefficient (R_c²) and mean relative error (MREC) as evaluation indexes. Next, in order to obtain a better RF calibration model, a variable importance (VI) measurement was applied to select input variables from LIBS spectral data based on the optimal spectral pretreatment method, and the optimal variable importance threshold was selected as the input variable to establish the RF calibration model. Finally, the predictive performance of the optimal RF calibration model was verified using the prediction set with the determination coefficient (R_p²) and the mean relative error (MREP). The results show that R_p² of the geo-accumulation index, enrichment factor and potential ecological risk index is up to 0.9971, 0.9919 and 0.9290, respectively, and MREP of the three indexes is 0.0234, 0.1173 and 0.0810, respectively; the average relative standard deviation (RSD) of the prediction set for the three indexes is 2.16%, 5.78% and 0.71%, respectively. Furthermore, it can be inferred that Cu was at levels corresponding to serious pollution primarily because of anthropogenic activities based on the predictive I_geo, E_r and RI values. Therefore, LIBS combined with the RF algorithm is a promising means which can achieve fast and simple estimation of the pollution risk degree of Cu in atmospheric sedimentation samples without complicated sample preparation to provide a basis for pollution prevention and control measures.