Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ manner is still a challenging task. In this work, we explore the laser trapping-LIBS-machine learning to analyze four elements (Zn, Ni, Cu, and Cr) absorbed in a single micro-carbon black particle in air. By employing a hollow laser beam for trapping, the particle can be restricted in a range as small as ∼1.72 μm, which is much smaller than the focal diameter of the flat-topped LIBS exciting laser (∼20 μm). Therefore, the particle can be entirely and homogeneously radiated, and the LIBS spectrum with a high signal-to-noise ratio (SNR) is correspondingly achieved. Then, two types of calibration models, i.e., the univariate method (calibration curve) and the multivariate calibration method (random forests (RF) regression), are employed for data processing. The results indicate that the RF calibration model shows a better prediction performance. The mean relative error (MRE), relative standard deviation (RSD), and root-mean-squared error (RMSE) are reduced from 0.1854, 363.7, and 434.7 to 0.0866, 179.8, and 216.2 ppm, respectively. Finally, simultaneous and quantitative determination of the four metal contents with high accuracy is realized based on the RF model. The method proposed in this work has the potential for online single aerosol particle analysis and further provides a theoretical basis and technical support for the precise prevention and control of composite air pollution.

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The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laserinduced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hardto-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

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