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.