Analysis of Crystalline Silica Aerosol Using Portable Raman Spectrometry: Feasibility of Near Real-Time Measurement

A Raman spectroscopic method for measuring the crystalline silica content in coal dust

The applicability of Raman spectroscopy for quantitative analysis of crystalline silica content within coal dust was investigated. We prepared the formulated coal dust samples with known crystalline silica content and ashed them using a muffle furnace, followed by redeposition onto aluminum substrates to form dry sample deposits. These samples were then analyzed using Raman spectroscopy. Both univariate and multivariate calibration models were constructed for relating the Raman spectra from these dry sample deposits to the crystalline silica contents. The R2 value of the unary linear regression (ULR) model is 0.900, with a detection limit of 0.96 %. Meanwhile, the R2 value of the partial least squares regression (PLSR) model can reach 0.988, and the detection limit can be reduced to 0.18 %. A PLSR model for field coal dust samples collected from a broader range of geological conditions was established and used for predicting the crystalline silica content in unknown coal dust samples. The measurement results agree well with those obtained from the standard infrared (IR) spectrometric method, with a root mean square error of 2.35 %. This study demonstrates the potential of Raman spectroscopy for accurately measuring crystalline silica content in coal dust.

Compact, high-flow, water-based, turbulent-mixing, condensation aerosol concentrator for collection of spot samples

A new high-flow, compact aerosol concentrator, using rapid, turbulent mixing to grow aerosol particles into droplets for dry spot sample collection, has been designed and tested. The "TCAC (Turbulent-mixing, Condensation Aerosol Concentrator)" is composed of a saturator for generating hot vapor, a mixing section where the hot vapor mixes with the cold aerosol flow, a growth tube where condensational droplet growth primarily occurs, and a converging nozzle that focuses the droplets into a beam. The prototype concentrator utilizes an aerosol sample flow rate of 4 L min-1. The TCAC was optimized by varying the operating conditions, such as relative humidity of the aerosol flow, mixing flow ratio, vapor temperature, and impaction characteristics. The results showed that particles with a diameter ≥ 25 nm can be grown to a droplet diameter > 1400 nm with near 100% efficiency. Complete activation and growth were observed at relative humidity ≥ 25% of the aerosol sample flow. A consistent spot sample with a diameter ofD 90 = 1.4 mm (the diameter of a circle containing 90% of the deposited particles) was obtained regardless of the aerosol particle diameter (d p = 20 - 1900 nm ). For fiber counting applications using phase contrast microscopy, the TCAC can reduce the sampling time, or counting uncertainty, by two to three orders of magnitude, compared to the 25-mm-filter collection. The study shows that the proposed mixing-flow scheme enables a compact spot sample collector suitable for handheld or portable applications, while still allowing for high flow rates.

Crystalline silica particle functionalized by PEG for its collision-enhanced detection at ultramicroelectrode

Detecting individual particulate matter is highly significant in many areas, such as mine safety, environment, and human health. The analytical method based on single entity electrochemistry (SEE) has shown great potential in detecting, counting, and measuring individual particles, especially conductive metals or carbon particles, based on their unique charge transfer reactions at an ultramicroelectrode (UME). In this study, we report an innovative SEE method for improving the sensitivity of the detection of electrochemical inert crystalline silica particles by functionalizing silica particles with polyethylene glycol (PEG) molecules. The PEG surface functionalization of the silica was characterized by Fourier-transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) techniques. The morphology of silica particles was characterized by a scanning electron microscope (SEM), and a transmission electron microscope (TEM) was employed to calibrate size distribution and determine the elemental composition of silica particles. The surface charges of silica particles were measured by dynamic light scattering techniques. The collision behaviors of crystalline silica particles with UME were investigated by cyclic voltammetric experiments, which are rarely reported in the literature. The crystalline silica particles were detected based on electrochemically blocking the flux of the redox mediator at the surface of UME, which showed significant signal amplification in the proposed method. Our method was demonstrated for detecting crystalline silica functionalized with or without PEG, acquiring the limit of quantification (LOQ) values of 0.391 μM (23.45 μg/L) and 0.824 μM (49.45 μg/L), respectively, which confirmed that a more than two times improvement in LOQ could be achieved over the PEG functionalized silica particles. We further presented a theoretical model using finite element simulations with COMSOL Multiphysics. We deduced a quantitative relation between the distribution of the current step size and the size distribution of silica particles. Therefore, the reported method here provides a paradigm for SEE-based detection of electrochemically inert crystalline silica particles, which extends the previous report substantially concerning particle detection.

NanoSpot™ collector for aerosol sample collection for direct microscopy and spectroscopy analysis

We describe design and characterization of an aerosol NanoSpot^™ collector, designed for collection of airborne particles on a microscopy substrate for direct electron and optical microscopy, and laser spectroscopy analysis. The collector implements a water-based, laminar-flow, condensation growth technique, followed by impaction onto an optical/electron microscopy substrate or a transmission electron microscopy grid for direct analysis. The compact design employs three parallel growth tubes allowing a sampling flow rate of 1.2 L min^-1. Each growth tube consists of three-temperature regions, for controlling the vapor saturation profile and exit dew point. Following the droplet growth, the three streams merge into one flow and a converging nozzle enhances focusing of grown droplets into a tight beam, prior to their final impaction on the warm surface of the collection substrate. Experiments were conducted for the acquisition of the size-dependent collection efficiency and the aerosol concentration effect on the NanoSpot^™ collector. Particles as small as 7 nm were activated and collected on the electron microscopy stub. The collected particle samples were analyzed using electron microscopy and Raman spectroscopy for the acquisition of the particle spatial distribution, the spot sample uniformity, and the analyte concentration. A spot deposit of approximately 0.7-mm diameter is formed for particles over a broad particle diameter range, for effective coupling with microscopic and spectroscopic analysis. Finally, the NanoSpot^™ collector's analytical measurement sensitivity for laser Raman analysis and counting statistics for fiber count measurement using optical microscopy were calculated and were compared with those of the conventional aerosol sampling methods.

Benefits and limitations of field-based monitoring approaches for respirable dust and crystalline silica applied in a sandstone quarry

With the advent of new sensing technologies and robust field-deployable analyzers, monitoring approaches can now generate valuable hazard information directly in the workplace. This is the case for monitoring respirable dust and respirable crystalline silica concentration levels. Estimating the quartz amount of a respirable dust sample by nondestructive analysis can be carried out using portable Fourier transform infrared spectroscopy (FTIR) units. Real-time respirable dust monitors, combined with small video cameras, allow advanced assessments using the Helmet-CAM methodology. These two field-based monitoring approaches, developed by the National Institute for Occupational Safety and Health (NIOSH), have been trialed in a sandstone quarry. Twenty-six Helmet-CAM sessions were conducted, and forty-one dust samples were collected around the quarry and analyzed on-site during two events. The generated data generated were used to characterize concentration levels for the monitored areas and workers, to identify good practices, and to illustrate activities that could be improved with additional engineered control technologies. Laboratory analysis of the collected samples complemented the field finding and provided an assessment of the performance of the field-based techniques. Only a fraction of the real-time respirable dust monitoring sessions data could be corrected with laboratory analysis. The average correction factor ratio was 5.0. Nevertheless, Helmet-CAM results provided valuable information for each session. The field-based quartz monitoring approach overestimated the concentration by a factor of 1.8, but it successfully assessed the quartz concentration trends in the quarry. The data collected could be used for the determination of a quarry calibration factor for future events. The quartz content in the dust was found to vary from 14% to 100%, and this indicates the need for multiple techniques in the characterization of respirable dust and quartz concentration and exposure. Overall, this study reports the importance of the adoption of field-based monitoring techniques when combined with a proper understanding and knowledge of the capabilities and limitations of each technique.

Review of Filters for Air Sampling and Chemical Analysis in Mining Workplaces

This review considers the use of filters to sample air in mining workplace environments for dust concentration measurement and subsequent analysis of hazardous contaminants, especially respirable crystalline silica (RCS) on filters compatible with wearable personal dust monitors (PDM). The review summarizes filter vendors, sizes, costs, chemical and physical properties, and information available on filter modeling, laboratory testing, and field performance. Filter media testing and selection should consider the characteristics required for mass by gravimetry in addition to RCS quantification by Fourier-transform infrared (FTIR) or Raman spectroscopic analysis. For mass determination, the filters need to have high filtration efficiency (≥99% for the most penetrable particle sizes) and a reasonable pressure drop (up to 16.7 kPa) to accommodate high dust loading. Additional requirements include: negligible uptake of water vapor and gaseous volatile compounds; adequate particle adhesion as a function of particle loading; sufficient particle loading capacity to form a stable particle deposit layer during sampling in wet and dusty environments; mechanical strength to withstand vibrations and pressure drops across the filter; and appropriate filter mass compatible with the tapered element oscillating microbalance. FTIR and Raman measurements require filters to be free of spectral interference. Furthermore, because the irradiated area does not completely cover the sample deposit, particles should be uniformly deposited on the filter.

Applying Raman imaging to capture and identify microplastics and nanoplastics in the garden

The characterisation of microplastics is still a challenge, and the challenge is even greater for nanoplastics, of which we only have a limited knowledge so far. Herewith we employ Raman imaging to directly visualise microplastics and nanoplastics which are released from the trimmer lines during lawn mowing. The signal-noise ratio of Raman imaging is significantly increased by generating an image from hundreds or thousands of Raman spectra, rather than from a single spectrum, and is further increased by combining with the logic-based and PCA-based algorithms. The increased signal-noise ratio enables us to capture and identify microplastics and particularly nanoplastics, including plastic fragments or shreds (with diameters / widths of 80 nm - 3 µm) and nanoparticles (with diameters of < 1000 nm) that are released during the mimicked mowing process. Using Raman imaging, we estimate that thousands of microplastics (0.1-5 mm), and billions of nanoplastics (< 1000 nm), are released per minute when a line trimmer is used to mow lawn. Overall, Raman imaging provides effective characterisation of the microplastics and is particularly suitable for nanoplastics.

Plasmonic alloy nanochains assembled via dielectrophoresis for ultrasensitive SERS

It is great challenge and interesting for researchers to fabricate substrates for enhanced Raman and sensor, and assemble some easy-to-synthesize metallic nanomaterials into controllable nanostructures with special morphologies and arrangements, via alternating current (AC) electric field. The Au-Ag alloy nanoparticles (Au-Ag alloy NPs) colloidal suspension with excellent dispersibility synthesized by wet chemical method, and the morphology of the assembly can be well controlled by regulating the frequency of the AC electric field. Au-Ag alloy nanochains array (Au-Ag ANCs) with dense plasmonic "hot spots" is formed when the AC electric field of 4V_pp-30kHz is applied, which is supported by the result of finite element method (FEM) numerical simulation. Experimental results demonstrate that Au-Ag ANCs show excellent SERS activity: Au-Ag ANCs can detect both Rhodamine 6G (Rh6G) and crystal violet (CV) in the magnitude order of 10^-10 M, and the Raman peaks intensity and analyte concentration has a strong linear correlation (R² is 0.99339 and 0.95916, respectively). Besides, the introduction of Au-Ag ANCs makes the Raman spectra intensity of thiram (a pesticide) with a concentration of 30 ppm on the surface of the blank ITO glass significantly enhanced, and it can detect thiram with a concentration as low as 0.03 ppm. In addition, Au-Ag ANCs substrate exhibits great uniformity and stability, so they have considerable application potential in the field of quantitative detection of trace substances.

Aerosol Analysis Using Handheld Raman Spectrometer: On-site Quantification of Trace Crystalline Silica in Workplace Atmospheres

A method for aerosol chemical analysis using handheld Raman spectrometer has been developed and its application to measurement of crystalline silica concentration in workplace atmosphere is described. The approach involves collecting aerosol as a spot sample using a wearable optical aerosol monitor, followed by direct-on-filter quantitative analysis of the spot sample for crystalline silica using handheld Raman spectrometer. The filter cassette of a commercially available optical aerosol monitor (designed to collect aerosol for post-shift analysis) was modified to collect 1.5-mm-diameter spot sample, which provided adequate detection limits for short-term measurements over a few tens of minutes or hours. The method was calibrated using aerosolized α-quartz standard reference material in the laboratory. Two Raman spectrometers were evaluated, one a handheld unit (weighing less than 410 g) and the other a larger probe-based field-portable unit (weighing about 5 kg). The lowest limit of quantification for α-quartz of 16.6 μg m-3 was obtained using the handheld Raman unit at a sample collection time of 1 h at 0.4 l min-1. Short-term measurement capability and sensitivity of the Raman method were demonstrated using a transient simulated workplace aerosol. Workplace air and personal breathing zone concentrations of crystalline silica of workers at a hydraulic fracturing worksite were measured using the Raman method. The measurements showed good agreement with the co-located samples analyzed using the standard X-ray powder diffraction (XRD) method, agreeing within 0.15-23.2% of each other. This magnitude of difference was comparable to the inter- and intra-laboratory analytical precision of established XRD and infrared methods. The pilot study shows that for silica-containing materials studied in this work it is possible to obtain quantitative measurements with good analytical figures of merit using handheld or portable Raman spectrometers. Further studies will be needed to assess matrix interferences and measurement uncertainty for several other types of particle matrices to assess the broader applicability of the method.

Nanostructure-Based Surface-Enhanced Raman Spectroscopy Techniques for Pesticide and Veterinary Drug Residues Screening

Pesticide and veterinary drug residues in food and environment pose a threat to human health, and a rapid, super-sensitive, accurate and cost-effective analysis technique is therefore highly required to overcome the disadvantages of conventional techniques based on mass spectrometry. Recently, the surface-enhanced Raman spectroscopy (SERS) technique emerges as a potential promising analytical tool for rapid, sensitive and selective detections of environmental pollutants, mostly owing to its possible simplified sample pretreatment, gigantic detectable signal amplification and quick target analyte identification via finger-printing SERS spectra. So theoretically the SERS detection technology has inherent advantages over other competitors especially in complex environmental matrices. The progress in nanostructure SERS substrates and portable Raman appliances will promote this novel detection technology to play an important role in future rapid on-site assay. This paper reviews the advances in nanostructure-based SERS substrates, sensors and relevant portable integrated systems for environmental analysis, highlights the potential applications in the detections of synthetic chemicals such as pesticide and veterinary drug residues, and also discusses the challenges of SERS detection technique for actual environmental monitoring in the future.

Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) Regression in the Quantitative Analysis of Respirable Crystalline Silica by Fourier-Transform Infrared Spectroscopy (FTIR)

Respirable crystalline silica (RCS) overexposure can lead to the development of silicosis which is a chronic, irreversible, potentially fatal respiratory disease. The most significant prerequisite for any silica exposure control plan is an accurate occupational exposure assessment. The results of crystalline silica analysis are often affected by other mineral interferences and are influenced by an analyst's knowledge of mineralogy to accurately interpret infrared spectra and correct matrix interferences. Partial least squares (PLS) and artificial neural networks (ANNs) are two multivariate calibration methods to overcome the problem of spectral interferences without the need for an analyst intervention. The performance of these two methods in quantitative analysis of quartz in the presence of mineral interferences was evaluated and compared in this study. Fifty mixtures with different crystalline silica content ratios were prepared by mixing quartz with four common mineral interferences including kaolinite, albite, muscovite, and amorphous silica. Fourier-transform infrared spectra of the mixtures were split into training and test datasets. The optimal architecture of the ANN model was achieved using a two-level full factorial design experiment and data were modeled using ANN and PLS regression analysis. Root mean squared error of prediction values of 1.69 and 6.12 µg quartz for ANN and PLS models, respectively, revealed the fact that the both models performed very well in quantitative analysis of quartz in the presence of mineral interferences, with a better relative performance of the ANN model which can be related to the inherent nonlinear predictive ability of ANNs. Given the excellent predictive ability of the ANN model which can deal with a completely overlapped peak without any need of user's intervention, it is recommended that the ANN model be optimized in future studies and utilized for reliable and rapid on-field assessment of RCS exposure.

Review of Respirable Coal Mine Dust Characterization for Mass Concentration, Size Distribution and Chemical Composition

Respirable coal mine dust (RCMD) exposure is associated with black lung and silicosis diseases in underground miners. Although only RCMD mass and silica concentrations are regulated, it is possible that particle size, surface area, and other chemical constituents also contribute to its adverse health effects. This review summarizes measurement technologies for RCMD mass concentrations, morphology, size distributions, and chemical compositions, with examples from published efforts where these methods have been applied. Some state-of-the-art technologies presented in this paper have not been certified as intrinsically safe, and caution should be exerted for their use in explosive environments. RCMD mass concentrations are most often obtained by filter sampling followed by gravimetric analysis, but recent requirements for real-time monitoring by continuous personal dust monitors (CPDM) enable quicker exposure risk assessments. Emerging low-cost photometers provide an opportunity for a wider deployment of real-time exposure assessment. Particle size distributions can be determined by microscopy, cascade impactors, aerodynamic spectrometers, optical particle counters, and electrical mobility analyzers, each with unique advantages and limitations. Different filter media are required to collect integrated samples over working shifts for comprehensive chemical analysis. Teflon membrane filters are used for mass by gravimetry, elements by energy dispersive X-ray fluorescence, rare-earth elements by inductively coupled plasma-mass spectrometry and mineralogy by X-ray diffraction. Quartz fiber filters are analyzed for organic, elemental, and brown carbon by thermal/optical methods and non-polar organics by thermal desorption-gas chromatography-mass spectrometry. Polycarbonate-membrane filters are analyzed for morphology and elements by scanning electron microscopy (SEM) with energy dispersive X-ray, and quartz content by Fourier-transform infrared spectroscopy and Raman spectroscopy.

Recent Advances in Occupational Exposure Assessment of Aerosols

Exposure science is underpinned by characterization (measurement) of exposures. In this article, six recent advances in exposure characterization by sampling and analysis are reviewed as tools in the occupational exposure assessment of aerosols. Three advances discussed in detail are (1) recognition and inclusion of sampler wall deposits; (2) development of a new sampling and analytical procedure for respirable crystalline silica that allows non-destructive field analysis at the end of the sampling period; and (3) development of a new sampler to collect the portion of sub-300 nm aerodynamic diameter particles that would deposit in human airways. Three additional developments are described briefly: (4) a size-selective aerosol sampler that allows the collection of multiple physiologically-relevant size fractions; (5) a miniaturized pump and versatile sampling head to meet multiple size-selective sampling criteria; and (6) a novel method of sampling bioaerosols including viruses while maintaining viability. These recent developments are placed in the context of the historical evolution in sampling and analytical developments from 1900 to the present day. While these are not the only advances in exposure characterization, or exposure assessment techniques, they provide an illustration of how technological advances are adding more tools to our toolkit. The review concludes with a number of recommended areas for future research, including expansion of real-time and end-of-shift on-site measurement, development of samplers that operate at higher flow-rates to ensure measurement at lowered limit values, and development of procedures that accurately distinguish aerosol and vapor phases of semi-volatile substances.

An indirect Raman spectroscopy method for the quantitative measurement of respirable crystalline silica collected on filters inside respiratory equipment

This article describes the development of an analytical method to measure respirable crystalline silica (RCS) collected on filters by a miniature sampler placed behind respirators worn by workers to evaluate their 'true' exposure. Test samples were prepared by aerosolising a calibration powder (Quin B) and by pipetting aliquots from suspensions of bulk material (NIST 1878a and Quin B) onto filters. Samples of aerosolised RCS collected onto polyvinyl chloride PVC filters were ashed and their residue was suspended in isopropanol and filtered into a 10 mm diameter area onto silver filters. Samples were also collected by the Health and Safety Executive's (HSE) miniature sampler from within the facepiece of a respirator on a breathing manikin during a simulated work activity. Results obtained using Raman spectroscopy were compared with X-ray diffraction (XRD) measurements, which was used as a reference method and a linear relationship was obtained. Raman has similar estimates of uncertainty when compared with the XRD methods over the measurement range from 5 to 50 μg and obtained the lowest limit of detection (LOD) of 0.26 μg when compared with XRD and Fourier Transform Infrared FTIR methods. A significant intercept and slope coefficient greatly influenced the higher LOD for indirect XRD method. The level of precision and low LOD for Raman spectroscopy will potentially enable workplace measurements at lower concentrations below the Workplace Exposure Limit (WEL) than are achieved using current analytical instrumentation. Different inward leakage ratio (ILR) measurement approaches were compared using six aerosolised sandstone dust tests. For the three highest inward leakage ratios the Portacount® obtained higher values than the RCS mass or the miniWRAS ratios, the latter of which reporting both particle number and quartz mass concentration. However, these limited ILR data were insufficient to establish statistical correlations between the measurement methods.

Cu/Ag Sphere Segment Void Array as Efficient Surface Enhanced Raman Spectroscopy Substrate for Detecting Individual Atmospheric Aerosol

Surface enhanced Raman spectroscopy (SERS) shows great promise in studying individual atmospheric aerosol. However, the lack of efficient, stable, uniform, large-array, and low-cost SERS substrates constitutes a major roadblock. Herein, a new SERS substrate is proposed for detecting individual atmospheric aerosol particles. It is based on the sphere segment void (SSV) structure of copper and silver (Cu/Ag) alloy. The SSV structure is prepared by an electrodeposition method and presents a uniform distribution, over large 2 cm² arrays and at low cost. The substrate offers a high SERS enhancement factor (due to Ag) combined with lasting stability (due to Cu). The SSV structure of the arrays generates a high density of SERS hotspots (1.3 × 10¹⁴/cm²), making it an excellent substrate for atmospheric aerosol detection. For stimulated sulfate aerosols, the Raman signal is greatly enhanced (>50 times), an order of magnitude more than previously reported substrates for the same purpose. For ambient particles, collected and studied on a heavy haze day, the enhanced Raman signal allows ready observation of morphology and identification of chemical components, such as nitrates and sulfates. This work provides an efficient strategy for developing SERS substrate for detecting individual atmospheric aerosol.