Calibration Approaches for Measurement of Aerosol Multielemental Concentration using Spark Emission Spectroscopy

Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning

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.

The performance of an inexpensive spark-induced breakdown spectroscopy instrument for near real-time analysis of toxic metal particles

To meet the demand for identifying and controlling toxic air contaminants in environmental justice communities, we have recently developed a cost-effective spark-induced breakdown spectroscopy (SIBS) instrument for detecting and quantifying toxic metal air pollutants. We characterized the detection limit and linearity of this SIBS instrument by analyzing nebulized elemental standard solutions. The experimental parameters affecting SIBS performance were optimized, including the time delay to observation, the distance between electrodes, and the ablation voltage. The instrument successfully detected Cr, Cu, Mn, Fe, Zn, Co, and Ni, with limits of detection ranged from 0.05 μg m-3 to 0.81 μg m-3 at a flow rate of 15 l min-1 and a 30 min sampling duration. Similar to other investigations using ion breakdown spectroscopy, we did not observe strong emissions lines for As, Sb, Se, Hg, Pb, and Cd, which were likely due to spectral overlap, matrix effects, and the limited detection range of the optical components. Overall, SIBS is a promising technique for field measurements of toxic metals for environmental justice, industrial and urban applications.

Complexity of Respirable Dust Found in Mining Operations as Characterized by X-ray Diffraction and FTIR Analysis

The mineralogical complexity of mine dust complicates exposure monitoring methods for occupational, respirable hazards. Improved understanding of the variability in respirable dust characteristics, e.g., mineral phase occurrence and composition, is required to advance on-site monitoring techniques that can be applied across diverse mining sectors. Principal components analysis (PCA) models were applied separately to XRD and FTIR datasets collected on 130 respirable dust samples from seven mining commodities to explore similarities and differences among the samples. Findings from both PCA models classified limestone, iron, and granite mine samples via their analytical responses. However, the results also cautioned that respirable samples from these commodities may not always fit patterns observed within the model. For example, one unique sample collected in a limestone mine contained no carbonate minerals. Future predictive quantification models should account for unique samples. Differences between gold and copper mine dust samples were difficult to observe. Further investigation suggested that the key to their differentiation by FTIR may lie in the characterization of clays. The results presented in this study provide foundational information for guiding the development of quantification models for respirable mineral hazards in the mining industry.

Quantification of toxic metals using machine learning techniques and spark emission spectroscopy

The United States Environmental Protection Agency (US EPA) list of hazardous air pollutants (HAPs) includes toxic metal suspected or associated with development of cancer. Traditional techniques for detecting and quantifying toxic metals in the atmosphere are either not real time, hindering identification of sources, or limited by instrument costs. Spark emission spectroscopy is a promising and cost-effective technique that can be used for analyzing toxic metals in real time. Here, we have developed a cost-effective spark emission spectroscopy system to quantify the concentration of toxic metals targeted by the US EPA. Specifically, Cr, Cu, Ni, and Pb solutions were diluted and deposited on the ground electrode of the spark emission system. The least absolute shrinkage and selection operator (LASSO) was optimized and employed to detect useful features from the spark-generated plasma emissions. The optimized model was able to detect atomic emission lines along with other features to build a regression model that predicts the concentration of toxic metals from the observed spectra. The limits of detections (LODs) were estimated using the detected features and compared to the traditional single-feature approach. LASSO is capable of detecting highly sensitive features in the input spectrum; however, for some toxic metals the single-feature LOD marginally outperforms LASSO LOD. The combination of low-cost instruments with advanced machine learning techniques for data analysis could pave the path forward for data-driven solutions to costly measurements.

Real-Time Measurement of Airborne Carbon Nanotubes in Workplace Atmospheres

With growing applications of carbon nanomaterials, there is a concern over health risks presented by inhalation of carbon nanotube (CNT) aerosol in workplace atmospheres. Current methods used for CNT aerosol measurement lack selectivity to specific form of carbonaceous component or allotrope of interest. Moreover, the detection limits of these methods are also inadequate for short-term monitoring. Here, we describe, for the first time, a near real-time, field-portable instrument for selective quantification of airborne CNT concentration. The approach uses an automated cyclical scheme involving collect-analyze-ablate steps to obtain continuous near real-time measurement using Raman spectroscopy. The method achieves significantly lower detection limits by employing corona-assisted particle microconcentration for efficient coupling with laser Raman spectroscopy. A combination of techniques involving (i) use of characteristic Raman peaks, (ii) distinct ratio of disordered and graphitic peaks, and (iii) principal component classification and regression is employed to identify and quantify the specific form of the aerosolized carbonaceous nanomaterial. We show that the approach is capable of selectively quantifying trace single-walled CNT in the presence of interfering agents such as diesel particulate matter. The detection limit of the method for the single-walled CNT studied in this work was 60 ng m^-3, corresponding to a 10 min aerosol collection period, which is significantly lower than that for the NIOSH Method 5040 (∼0.15 μg m^-3 for an 8-h collection on a 25 mm filter at 4 L min^-1), a commonly used method for elemental carbon. We demonstrate the automated real-time capability of this field-portable method by continuously measuring a transient single-walled CNT aerosol.