Multi-element analysis of size-segregated fine and ultrafine particulate via Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry

No more waste at the elemental analysis of airborne particulate matter on quartz fibre filters

Particulate matter (PM) is the major environmental pollutant. Its elemental composition is routinely monitored. Inductively coupled plasma mass spectroscopy (ICPMS) is commonly applied after a PM sample has been digested by an acid during a microwave treatment. In this case, sample preparation procedure is laborious, sometimes incomplete and produces toxic waste. In this paper we show that direct sample introduction to ICPMS by laser ablation (LA-ICPMS) is of huge advantage. Minimal quantity of a sample is required for the analysis (<1 cm²) and no chemical waste is produced. The study focused on the most universal and widely used quartz fibre filter samples and we show that LA-ICPMS can be successfully applied for the determination of the elemental composition of such samples. Some effort is, however, still needed to develop an autosampler for the LA-ICPMS system and to provide commercial matrix-matched standards for this application to be implemented in environment laboratories worldwide.

Application of micro-dried droplets for quantitative analysis of particulate inorganic samples with LA-ICP-MS demonstrated on surface-modified nanoparticle TiO2 catalyst materials

A quick, flexible and reliable method was developed, based on laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), for accurate assessment of nanomaterial composition with sample amounts in the picogram to nanogram range. We demonstrate its capabilities for the analysis of surface-modified TiO₂ nanoparticulate (NP) catalyst materials. For sampling, suspensions of NP were deposited on a substrate material, ablated with a pulsed laser and then analysed using quadrupole ICP-MS. The calibration and quantification approach is based on the use of so-called micro-dried droplets (μDD) as the standard material. To overcome some of the major drawbacks of conventional dried droplet approaches, self-aliquoting wells were used in this work. By mimicking the ablation conditions for the sample and standard, it was possible to create a pseudo-matrix-matched calibration, not only for this specific NP composition but also for a larger variety of samples. A commercially available reference material (AUROlite™, Strem Chemicals) was used to compare the method against established methods such as slurry analysis and microwave-assisted digestion in combination with subsequent liquid sample measurement. The results obtained with the proposed procedure (0.74%wt ± 0.13%wt) are in good agreement to a certified value (0.8%wt) and added an additional layer of information. Due to the significantly reduced sampling size in comparison with the investigated liquid measurement approaches, it was possible to obtain information about the homogeneity of the catalyst material. The results indicate that the AUROlite™ reference material has a heterogeneous loading which requires more than 300 pg of material to be used to cancel out. This was not observed for the custom materials discussed in this work.

Toxic trace metals in size-segregated fine particulate matter: Mass concentration, respiratory deposition, and risk assessment

To characterise the mass concentration, size-distribution, and respiratory deposition of selected trace metals (Cr, Mn, Fe, Ni, Cu, Zn, Ba, and Pb) in size-segregated PM_2.5, a long-term monitoring campaign was undertaken at an urban background site in Como (Northern Italy). 96-h aerosol samples were collected weekly, from May 2015 to March 2016, using a 13-stage low pressure impactor and analysed via laser ablation-inductively coupled plasma-mass spectrometry. Significantly higher levels of trace metals were generally found during the heating season (two to more than four times) compared to the non-heating period at all size ranges, especially for concentrations in PM_0.1-1. Distinct distribution profiles characterised the different elements, even though the corresponding heating and non-heating shapes always exhibited similar features, with negligible seasonal shifts in the average mass median aerodynamic diameters. Fe, Ba, and Cu had >70% of their mass in PM_1-2.5, whereas Pb, Zn, and Ni showed higher contributions in the accumulation mode (>60%). Finally, broad size-distributions were found for Cr and Mn. The multiple-path particle dosimetry model estimated the overall deposition fractions in human airways varying between 27% (Pb) and 48% (Ba). The greatest deposition variability was always registered in the head region of the respiratory system, with the highest contributions for those metals predominantly accumulated in the PM_2.5 coarse modes. In contrast, the deposition in the deepest respiratory tract maintained nearly constant proportions over time, becoming notably important for Pb, Ni, and Zn (∼13%) with respect to their total deposition. The comparison with national limits established for Pb and Ni suggested the absence of significant risks for the local population, as expected, with average concentrations two orders of magnitude lower than the corresponding annual limit and objective value. Similar findings were reported for all the other metals, for which the estimated hazard quotients were always well <1.

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.