Complex Aerosol Characterization by Scanning Electron Microscopy Coupled with Energy Dispersive X-ray Spectroscopy

A review on vulnerable atmospheric aerosol nanoparticles: Sources, impact on the health, ecosystem and management strategies

The Earth's atmosphere contains ultrafine particles known as aerosols, which can be either liquid or solid particles suspended in gas. These aerosols originate from both natural sources and human activities, termed primary and secondary sources respectively. They have significant impacts on the environment, particularly when they transform into ultrafine particles or aerosol nanoparticles, due to their extremely fine atomic structure. With this context in mind, this review aims to elucidate the fundamentals of atmospheric-derived aerosol nanoparticles, covering their various sources, impacts, and methods for control and management. Natural sources such as marine, volcanic, dust, and bioaerosols are discussed, along with anthropogenic sources like the combustion of fossil fuels, biomass, and industrial waste. Aerosol nanoparticles can have several detrimental effects on ecosystems, prompting the exploration and analysis of eco-friendly, sustainable technologies for their removal or mitigation.Despite the adverse effects highlighted in the review, attention is also given to the generation of aerosol-derived atmospheric nanoparticles from biomass sources. This finding provides valuable scientific evidence and background for researchers in fields such as epidemiology, aerobiology, and toxicology, particularly concerning atmospheric nanoparticles.

Polarization Characterization of Porous Particles Based on DDA Simulation and Multi-Angle Polarization Measurements

Porous suspended particles are hazardous to human health due to their strong absorption capacity for toxic substances. A fast, accurate, in situ and high-throughput method to characterize the microporous structure of porous particles has extensive application value. The polarization changes during the light scattering of aerosol particles are highly sensitive to their microstructural properties, such as pore size and porosity. In this study, we propose an overlapping sphere model based on the discrete dipole approximation (DDA) to calculate the polarization scattering characteristics of porous particles. By combining scattering calculations with multi-dimensional polarization indexes measured by a multi-angle polarized scattering vector detection system, we achieve the identification and classification of pore-type components in suspended particles. The maximum deviation based on multiple indexes is less than 0.16% for the proportion analysis of mixed particles. Simultaneously, we develop a quantitative inversion algorithm on pore size and porosity. The inversion results of the three porous polymer particles support the validity and feasibility of our method, where the inversion error of partial particles is less than 4% for pore size and less than 6% for porosity. The study demonstrates the potential of polarization measurements and index systems applied in characterizing the micropore structure of suspended particles.

Machine Learning for grouping nano-objects based on their morphological parameters obtained from SEM analysis

Nanoparticles have unique properties that make them useful in a variety of applications, but their potential toxicity raises concerns about their safety. Accurate characterization of nanoparticles is essential for understanding their behavior and potential risks. In this study, we employed machine learning algorithms to automatically identify nanoparticles based on their morphological parameters, achieving high classification accuracy. Our results demonstrate the effectiveness of machine learning for nanoparticle identification and highlight the need for more precise characterization methods to ensure their safe use in various applications.

Uncovering the cytotoxic effects of air pollution with multi-modal imaging of in vitro respiratory models

Annually, an estimated seven million deaths are linked to exposure to airborne pollutants. Despite extensive epidemiological evidence supporting clear associations between poor air quality and a range of short- and long-term health effects, there are considerable gaps in our understanding of the specific mechanisms by which pollutant exposure induces adverse biological responses at the cellular and tissue levels. The development of more complex, predictive, in vitro respiratory models, including two- and three-dimensional cell cultures, spheroids, organoids and tissue cultures, along with more realistic aerosol exposure systems, offers new opportunities to investigate the cytotoxic effects of airborne particulates under controlled laboratory conditions. Parallel advances in high-resolution microscopy have resulted in a range of in vitro imaging tools capable of visualizing and analysing biological systems across unprecedented scales of length, time and complexity. This article considers state-of-the-art in vitro respiratory models and aerosol exposure systems and how they can be interrogated using high-resolution microscopy techniques to investigate cell-pollutant interactions, from the uptake and trafficking of particles to structural and functional modification of subcellular organelles and cells. These data can provide a mechanistic basis from which to advance our understanding of the health effects of airborne particulate pollution and develop improved mitigation measures.

A Collection of Molecular Fingerprints of Single Aerosol Particles in Air for Potential Identification and Detection Using Optical Trapping-Raman Spectroscopy

Characterization, identification, and detection of aerosol particles in their native atmospheric states remain a challenge. Recently, optical trapping-Raman spectroscopy (OT-RS) has been developed and demonstrated for characterization of single, airborne particles. Such particles in different chemical groups have been characterized by OT-RS in recent years and many more are being studied. In this work, we collected single-particle Raman spectra measured using the OT-RS technique and began construction of a library of OT-RS fingerprints that may be used as a reference for potential detection and identification of aerosol particles in the atmosphere. We collected OT-RS fingerprints of aerosol particles from eight different categories including carbons, bioaerosols (pollens, fungi, vitamins, spores), dusts, biological warfare agent surrogates, etc. Among the eight categories, spectral fingerprints of six groups of aerosol particles have been published previously and two other groups are new. We also discussed challenges, limitations, and advantages of using single-particle optical trapping-Raman spectroscopy for aerosol-particle characterization, identification, and detection.

Use of SEM/EDX Methods for the Analysis of Ambient Particulate Matter Adhering to the Skin Surface

BACKGROUND

The skin is exposed to numerous particulate and gaseous air pollutants. The ones that need particular attention are the particles that adhere to the skin surface, which can later cause direct skin damage. This study aimed to characterize air pollution (AP) particles adhered to the human skin by using Scanning Electron Microscopy (SEM) combined with X-ray Dispersive Energy Spectrometry (EDX).

METHODS

Tape stripping was performed from six healthy volunteers exposed to urban AP to collect stratum corneum samples from the cheeks and forehead. The samples were analyzed using SEM equipped with EDX system with a silicon drift detector at an accelerating voltage of 20 keV. After the preliminary examination, the particles were located and counted using 1000 x magnification. Each particle was analyzed, increasing magnification up to 5000 x for precise dimension measurement and elemental composition analysis. At least 100 fields or a surface of approximately 1 mm² were examined.

RESULTS

Particles adhered to the skin were identified in all samples, with a particle load ranging from 729 to 4525. The average area and perimeter of all particles identified were 302 ± 260 μm² and 51 ± 23 μm subsequently, while the equivalent circular diameter was, on average, 14 ± 6 μm. The particles were classified into ten groups based on morphology and elemental composition. Chlorides were the most numerous particle group (21.9 %), followed by carbonaceous organic particles (20.3%), silicates (18 %), carbonates (16.4%), metal-rich particles (14%), and a minor number of bioaerosols, quartz-like, and fly ash particles.

CONCLUSION

The SEM-EDX analysis provides evidence of the contamination of exposed skin to various airborne PM of natural or anthropogenic origin. This method may provide new insights into the link between exposure to AP and AP-induced skin damage.

Spatial Image Resolution Assessment by Fourier Analysis (SIRAF)

Determining spatial resolution from images is crucial when optimizing focus, determining smallest resolvable object, and assessing size measurement uncertainties. However, no standard algorithm exists to measure resolution from electron microscopy (EM) images, though several have been proposed, where most require user decisions. We present the Spatial Image Resolution Assessment by Fourier analysis (SIRAF) algorithm that uses fast Fourier transform analysis to estimate resolution directly from a single image without user inputs. The method is derived from the underlying assumption that objects display intensity transitions, resembling a step function blurred by a Gaussian point spread function. This hypothesis is tested and verified on simulated EM images with known resolution. To identify potential pitfalls, the algorithm is also tested on simulated images with a variety of settings, and on real SEM images acquired at different magnification and defocus settings. Finally, the versatility of the method is investigated by assessing resolution in images from several microscopy techniques. It is concluded that the algorithm can assess resolution from a large selection of image types, thereby providing a measure of this fundamental image parameter. It may also improve autofocus methods and guide the optimization of magnification settings when balancing spatial resolution and field of view.

Characterization of aerosol particles during a high pollution episode over Mexico City

More than 7 thousand wildfires were recorded over Mexico in 2019, affecting almost 640 thousand hectares. Most of these fires occurred during the spring season generating dense smoke plumes, impacting urban areas in the central part of the Mexican plateau. From May 10 to 17, 2019, biomass burning (BB) plumes affected Mexico City (MC) and diffused across the basin, producing PM_2.5 levels ~ 2 times higher than the nation's air quality standards. Average PM_2.5 concentrations increased sharply from 29.4 ± 7.2 µg m⁻³ to 65.1 ± 13.6 µg m⁻³ when the dense smoke plumes were detected. The higher particle concentration impacted the aerosol optical depth (AOD) as values ~ 3 times greater than the annual mean (0.32 ± 0.12) were measured, which resulted in a 17% loss of global horizontal irradiation (GHI). Under these severe pollution conditions, the visibility (V_a) was reduced by ~ 80%. The high incidence of strong absorbent particles, such as soot and tarballs was revealed through electron microscopy and X-ray fluorescence (XRF) analysis. These techniques show chemical similarities between MC aerosols and those from the high-altitude (~ 4010 m. a. g. l.) Altzomoni Atmospheric Observatory, evidencing a strong influence of the BB emissions, suggesting a regional transport of these pollutants.

A new sample preparation protocol for SEM and TEM particulate matter analysis

A new methodology has been developed to prepare electron microscopy, both SEM and TEM, specimens starting from particulate matter collected using environmental sampling systems. The approach is based on the extraction of the particles to be analyzed from the harvesting substrates. The extracted particles can be directly observed in an SEM, possibly in low-vacuum mode to prevent electrical charging. In order to prepare electron transparent samples, TEM observations require a further step, consisting in embedding the particles in an electron transparent carbon film deposited before dissolving the acetate extracting substrate. The protocol has been tested by analyzing particles collected during bench tests on brake pads and discs, carried out on a dynamometer equipped with a particulate matter sampling apparatus. The main advantages of the approach are: the complete extraction of the particulate matter specimens from the original substrates, that in this way do not interfere with the analyses; the extracted samples retain the topological information of the collection in the specimens prepared for SEM; possibility to be applied to any kind of particulate matter harvesting substrates.

Real time and online aerosol identification based on deep learning of multi-angle synchronous polarization scattering indexes

In this study, we employ our developed instrument to obtain high-throughput multi-angle single-particle polarization scattering signals. Based on experimental results of a variety of samples with different chemical composition, particle size, morphology, and microstructure, we trained a deep convolutional network to identify the polarization signal characteristics during aerosol scattering processes, and then investigate the feasibility of multi-dimensional polarization characterization applied in the online and real-time fine and accurate aerosol recognition. Our model shows a high classification accuracy rate (>98%) and can achieve aerosol recognition at a very low proportion (<0.1%), and shows well generalization ability in the test set and the sample types not included in the training set. The above results indicate that that the time series pulses from multi-angle polarization scattering contain enough information related with microscopic characteristics of an individual particle, and the deep learning model shows its capability to extract features from these synchronous multi-dimensional polarization signals. Our investigations confirm a good prospect of aerosol attribute retrieval and identifying and classifying individual aerosols one by one by the combination of multi-dimensional polarization scattering indexes with deep learning method.

Complex Aerosol Characterization by Scanning Electron Microscopy Coupled with Energy Dispersive X-ray Spectroscopy

Particulate matter (PM) air pollution is a central concern for public health. Current legislation relies on a mass concentration basis, despite broad acceptance that mass alone is insufficient to capture the complexity and toxicity of airborne PM, calling for additional and more comprehensive measurement techniques. We study to what extent scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM/EDS) can be applied for physicochemical characterization of complex aerosols, and investigate its potential for separating particle properties on a single particle basis, even for nanosized particles. SEM/EDS analysis is performed on impactor samples of laboratory generated aerosols, consisting of either NaCl, Halloysite fibers, soot-like Printex90 agglomerates, or their combination. The analysis is automated and performed as EDS maps, covering a statistically relevant number of particles, with analysis times of approximately one hour/sample. Derived size distributions are compared to scanning mobility particle sizer (SMPS) and electric low-pressure impactor (ELPI) results. A method is presented to estimate airborne number concentrations and size distributions directly from SEM results, within a factor 10 of SMPS and ELPI outcomes. A classification scheme is developed based on elemental composition, providing class-specific information with individual particle statistics on shape, size, and mixing state. This can identify primary particles for source apportionment and enables easy distinction between fibrous and dense particle classes, e.g. for targeted risk assessments. Overall, the SEM/EDS analysis provides a more detailed physicochemical characterization of PM than online measurements, e.g. SMPS and ELPI. The method has the potential to improve assessments of PM exposure and risk, and facilitates source identification, even without prior knowledge at sampling.

Analysis of Electron Transparent Beam-Sensitive Samples Using Scanning Electron Microscopy Coupled With Energy-Dispersive X-ray Spectroscopy

Scanning electron microscopy, coupled with energy-dispersive X-ray spectroscopy (EDS), is a powerful tool used in many scientific fields. It can provide nanoscale images, allowing size and morphology measurements, as well as provide information on the spatial distribution of elements in a sample. This study compares the capabilities of a traditional EDS detector with a recently developed annular EDS detector when analyzing electron transparent and beam-sensitive NaCl particles on a TEM grid. The optimal settings for single particle analysis are identified in order to minimize beam damage and optimize sample throughput via the choice of acceleration voltage, EDS acquisition time, and quantification model. Here, a linear combination of two models is used to bridge results for particle sizes, which are neither bulk nor sufficiently thin to assume electron transparent. Additionally, we show that the increased count rate obtainable with the annular detector enables mapping as a viable analysis strategy compared with feature detection methods, which only scan segmented regions. Finally, we discuss advantages and disadvantages of the two analysis strategies.