We have recently developed a low-cost spark-induced breakdown spectroscopy (SIBS) instrument for in-situ analysis of toxic metal aerosol particles that we call TARTA (toxic-metal aerosol real time analyzer). In this work, we applied machine learning methods to improve the quantitative analysis of elemental mass concentrations measured by this instrument. Specifically, we applied least absolute shrinkage and selection operator (LASSO), partial least squares (PLS) regression, principal component regression (PCR), and support vector regression (SVR) to develop multivariate calibration models for 13 metals (e.g., Cr, Cu, Mn, Fe, Zn, Co, Al, K, Be, Hg, Cd, Pb, and Ni), some of which are included on the US EPA hazardous air pollutants (HAPS) list. The calibration performance, adjusted coefficient of determination (R²) and normalized root mean square error (RMSE), and limit of detection (LOD) of the proposed models were compared to those of univariate calibration models for each analyte. Our results suggest that machine learning models tend to have better prediction accuracy and lower LODs than conventional univariate calibration, of which the LASSO approach performs the best with R² > 0.8 and LODs of 40–170 ng m⁻³ at a sampling time of 30 min and a flow rate of 15 l min⁻¹. We then assessed the applicability of the LASSO model for quantifying elemental concentrations in mixtures of these metals, serving as independent validation datasets. Ultimately, the LASSO model developed in this work is a very promising machine learning approach for quantifying mass concentration of metals in aerosol particles using TARTA.

我们最近开发了一种低成本的火花诱导击穿光谱 (SIBS) 仪器，用于有毒金属气溶胶颗粒的原位分析，我们称之为 TARTA（有毒金属气溶胶实时分析仪）。在这项工作中，我们应用机器学习方法来改进该仪器测量的元素质量浓度的定量分析。具体来说，我们应用最小绝对收缩和选择算子 (LASSO)、偏最小二乘 (PLS) 回归、主成分回归 (PCR) 和支持向量回归 (SVR) 来开发 13 种金属（例如 Cr、Cu）的多元校准模型。 、锰、铁、锌、钴、铝、钾、铍、汞、镉、铅和镍），其中一些已列入美国 EPA 有害空气污染物 (HAPS) 清单。将所提出模型的校准性能、调整确定系数 (R ² ) 和归一化均方根误差 (RMSE) 以及检测限 (LOD) 与每种分析物的单变量校准模型进行比较。我们的结果表明，机器学习模型往往比传统的单变量校准具有更好的预测精度和更低的 LOD，其中 LASSO 方法在 R ² > 0.8 和 LOD 为 40–170 ng m ⁻³的采样时间下表现最佳。 30分钟且流速为15 l min ^-1 。然后，我们评估了 LASSO 模型在量化这些金属混合物中元素浓度的适用性，作为独立的验证数据集。最终，这项工作中开发的 LASSO 模型是一种非常有前途的机器学习方法，用于使用 TARTA 量化气溶胶颗粒中金属的质量浓度。