Performance of Passive Samplers Analyzed by Computer-Controlled Scanning Electron Microscopy to Measure PM10-2.5

Spatiotemporal Variations and Sources of Airborne Particulate Matter in Urban Metro Platforms and Carriages in China

Since 2013, China has significantly improved its control over atmospheric air pollution. However, air pollution in indoor environments, particularly in urban metro systems, has been largely neglected. In this study, we comprehensively investigated the spatiotemporal variations in particulate matter (PM) pollution inside metro stations and carriages across seven Chinese cities. Our results revealed consistently high levels of PM2.5 (16-621 μg/m3) within the urban metro platforms. Elevated concentrations of metro PM2.5 can occur even under low outdoor PM2.5 conditions. Furthermore, we found that metro PM2.5 levels exhibited significant spatiotemporal variations across metro environments; specifically, a higher fraction of PM2.5 was present in metro carriages than on platforms. Notably, black carbon (BC), a critical toxic constituent of PM2.5, exhibited significantly higher concentrations in carriages than those on platforms. Real-time monitoring and single-particle analysis revealed distinct sources of metro PM compared to outdoor air PM; particularly, Fe/Ni/Cr/Mn particles mainly originated from the abrasion of rails and brakes, while Cu-bearing particles and BC likely result from the degradation of conductive carbon brushes and wires. Our analysis demonstrated that irregular iron-containing particles derived from rail abrasion constituted approximately 90% of platform PM. These findings highlight the pressing need for air quality control in metro systems and provide valuable insights for public health assessments related to exposure to metro air.

Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning

To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS technology (scanning electron microscopy and energy-dispersive X-ray spectroscopy) can provide high-resolution imaging and detailed compositional analysis for particles with relatively stable physical and chemical properties. This study introduces an advanced source apportionment pipeline (RX model) that uniquely combines computer-controlled scanning electron microscopy with computer vision and machine learning to trace particle sources by integrating single-particle morphology, size, and chemical information. In the evaluation using a virtual data set with known source contributions, the RX model demonstrated high accuracy, with average errors of 0.60% for particle number and 1.97% for mass contribution. Compared to the chemical mass balance model, the RX model's accuracy and stability improved by 75.6 and 73.4%, respectively, and proved effective in tracing Fe-containing particles in the atmosphere of a steel city in China. This study indicates that particle morphology can serve as an effective feature for determining its source. The findings highlight the potential of electron microscopy technology coupled with computer vision and machine learning techniques to enhance our understanding of atmospheric pollution sources, offering valuable insights for PM health risk assessment and evidence-based policy-making.

Source variations of atmospheric particles in response to control measures in northern China during 2022 winter olympics and paralympics: Evidence from microscopic analysis

The physicochemical properties of atmospheric particles including morphologies and composition are directly related to their sources and formation mechanisms. However, most previous studies have been limited to a few sites and small numbers of particles. In this study, we employed computer-controlled scanning electron microscopy (CCSEM) to enhance measurement efficiency and enable long-term observations across multiple sites. To investigate properties and source variations of atmospheric particles in response to control measures before, during, and after the 2022 Winter Olympic Games (WOG) and Winter Paralympic Games (WPG), ambient particles were passively collected in 15 northern Chinese cities and automatically analyzed by an advanced CCSEM (IntelliSEM EPAS). Variations in particle numbers (PN), size distribution, elemental composition, and sources were comprehensively analyzed. Over one million particles were classified into mineral, carbonaceous, fly ash, sulfur-related, metal-containing, salt, and biological particles using the user-defined classification rules. The results showed that improved air quality was witnessed during the WOG with reduced PN and anthropogenic particles, but deteriorated during the WPG due to increased coarse-mode PN, mineral, and sulfur-related particles. Beijing and Zhangjiakou exhibited lower AQI, PN, and anthropogenic particles, while non-competition cities experienced higher levels of anthropogenic and sulfur-related particles. Notably, Taiyuan and Shijiazhuang showed distinct emission reductions during the WOG, while Baoding and Tangshan demonstrated less effective control with high levels of sulfur-related and anthropogenic Fe-rich particles. This study shows the capability of CCSEM to provide microscopic evidence of particle sources and behaviors, offering valuable insights into the efficacy of control measures during major events.

Physiochemistry and sources of individual particles in response to intensified controls during the 2022 Winter Olympics in Beijing

To investigate the particle sources before, during, and after the 2022 Beijing Winter Olympic and Paralympic (WOP) in Beijing, ambient particles were passively collected from January to March 2022. The physicochemical properties including morphology, size, shape parameters, and elemental compositions were analyzed by the IntelliSEM EPAS (an advanced computer-controlled scanning electron microscopy [CCSEM] system). Using the user-defined classification rules, 37,174 individual particles were automatically classified into 27 major groups and further attributed to seven major sources based on the source-associated characteristics, including mineral dust, secondary aerosol, combustion/industry, carbonaceous particles, salt-related particles, biological particles, and fiber particles. Our results showed that mineral dust (66.5%), combustion/industry (12.6%), and secondary aerosol (6.3%) were the three major sources in a wide size range of 0.2-42.8 μm. During the Winter Olympic Games period, low emission of anthropogenic particles and favorable meteorological conditions contributed to significantly improved air quality. During the Winter Paralympic Games period, more particles sourced from the dust storm, secondary formed particles, and the adverse meteorological conditions resulted in relatively worse air quality. The secondary aerosol all decreased during the competition period, while increased during the non-competition period. Sulfate-related particles had explosive growth and further aggravate the pollution degree during the non-competition period, especially under adverse meteorological conditions. These results provide microscopic evidence revealing variations of physicochemical properties and sources in response to the control measures and meteorological conditions.

Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009-2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO₂) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO₂ estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

Morphological and elemental characterization of leaf-deposited particulate matter from different source types: a microscopic investigation

Particulate matter (PM) deposition on urban green enables the collection of particulate pollution from a diversity of contexts, and insight into the physico-chemical profiles of PM is key for identifying main polluting sources. This study reports on the morphological and elemental characterization of PM_2-10 deposited on ivy leaves from five different environments (forest, rural, roadside, train, industry) in the region of Antwerp, Belgium. Ca. 40,000 leaf-deposited particles were thoroughly investigated by particle-based analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX) and their physico-chemical characteristics were explored for PM source apportionment purposes. The size distribution of all deposited particles was biased towards small-sized PM, with 32% of the particles smaller than 2.5 μm (PM_2.5) and median diameters of 2.80-3.09 μm. The source type influenced both the particles' size and morphology (aspect ratio and shape), with roadside particles being overall the smallest in size and the most spherical. While forest and rural elemental profiles were associated with natural PM, the industry particles revealed the highest anthropogenic metal input. PM_2-10 profiles for roadside and train sites were rather comparable and only distinguishable when evaluating the fine (2-2.5 μm) and coarse (2.5-10 μm) PM fractions separately, which enabled the identification of a larger contribution of combustion-derived particles (small, circular, Fe-enriched) at the roadside compared to the train. Random forest prediction model classified the source type correctly for 61-85% of the leaf-deposited PM. The still modest classification accuracy denotes the influence of regional background PM and demands for additional fingerprinting techniques to facilitate source apportionment. Nonetheless, the obtained results demonstrate the utility of leaf particle-based analysis to fingerprint and pinpoint source-specific PM, particularly when considering both the composition and size of leaf-deposited particles.

Using low cost open-face passive samplers to sample PM concentration and elemental composition in childcare facilities

Effective, low noise and low-cost samplers for airborne particulate matter (PM) in indoor environments are needed. In 15 northern California childcare facilities, we deployed open-faced polycarbonate substrate passive aerosol samplers and analyzed them using computer-controlled scanning electron microscopy to measure indoor PM particle size, number, and elemental concentration. Particle concentrations were compared to PM concentrations measured by filter-based active samplers. The PM mass concentrations measured after open-faced passive sampling were in good agreement with the PM concentrations obtained using co-located active samplers. The measured PM2.5 concentrations inside of the childcare facilities were in the range of 6-31 μg m-3, while the PM10 concentrations were in the range of 17-96 μg m-3. For PM10, 93.3% of the passive sampler results were within ±30% of those determined with the active sampler, and 67% of the PM2.5 results fell within ±30% of the active sampler measurements. The Pearson's correlation coefficient between PM mass concentrations estimated from the open-faced passive aerosol sampler (OPS) versus gravimetric (active sampler) methods was 0.77 for PM2.5 and 0.95 for PM10, respectively, indicating a strong correlation between the passive and active sampler results. PM and elemental composition varied by facility location and interior characteristics of the childcare facilities such as floor type, presence of combustion sources, occupant density, and the level of activity. The rich carbon content in the polycarbonate substrate produced positive bias in the carbon concentration analysis. Based on the carbon concentration, determined using a copper substrate, a correction factor was needed to determine the total carbon concentrations in the sampled PM for the polycarbonate substrate passive sampler. Overall, the results demonstrated that the OPS collected enough aerosol particles in one business day (6-8 hours) to assess PM levels.

Characterization of aerosolized particles from nanoclay-enabled composites during manipulation processes

Manufacturing, processing, use, and disposal of nanoclay-enabled composites potentially lead to the release of nanoclay particles from the polymer matrix in which they are embedded; however, exposures to airborne particles are poorly understood. The present study was conducted to characterize airborne particles released during sanding of nanoclay-enabled thermoplastic composites. Two types of nanoclay, Cloisite® 25A and Cloisite® 93A, were dispersed in polypropylene at 0%, 1%, and 4% loading by weight. Zirconium aluminum oxide (P100/P180 grits) and silicon carbide (P120/P320 grits) sandpapers were used to abrade composites in controlled experiments followed by real-time and offline particle analyses. Overall, sanding the virgin polypropylene with zirconium aluminum oxide sandpaper released more particles compared to silicon carbide sandpaper, with the later exhibiting similar or lower concentrations than that of polypropylene. Thus, a further investigation was performed for the samples collected using the zirconium aluminum oxide sandpaper. The 1% 25A, 1% 93A, and 4% 93A composites generated substantially higher particle number concentrations (1.3-2.6 times) and respirable mass concentrations (1.2-2.3 times) relative to the virgin polypropylene, while the 4% 25A composite produced comparable results, regardless of sandpaper type. It was observed that the majority of the inhalable particles were originated from composite materials with a significant number of protrusions of nanoclay (18-59%). These findings indicate that the percent loading and dispersion of nanoclay in the polypropylene modified the mechanical properties and thus, along with sandpaper type, affected the number of particles released during sanding, implicating the cause of potential adverse health effects.

Passive personal air sampling of dust in a working environment-A pilot study

The aim of this study was to make a preliminary evaluation of the University of North Carolina passive aerosol sampler (UNC sampler) for personal air sampling of particles. Nine personal air samplings of respirable fraction were conducted in an open-pit mine, with pairwise UNC samplers and a respirable cyclone mounted on the chest of workers. UNC samples were analyzed with scanning electron microscopy (SEM) and to some extent energy dispersive X-ray spectroscopy (EDS). Respirable cyclone filter samples were weighed. Correlations and particle elemental compositions were described. Microscopic imaging of the collection surface showed that the particles were heterogeneously deposited across the surface of the UNC sampler. Collected particles were shaped as gravel particles and the resulting particle size distribution in air showed a peak at ca. 3 µm aerodynamic diameter, similarly to what has previously been reported from the same mine. The elemental composition indicated mineral origin. All correlations between the airborne mass concentrations from UNC samplers and respirable cyclones (Pearson = 0.54 and Spearman = 0.43) and between pairs of parallel UNC samplers (Pearson = 0.55 and Spearman = 0.67) were weak. The UNC sampler mass concentrations were approximately 30 times higher than those measured with the respirable cyclone. In conclusion, the UNC sampler, when used for personal sampling in a mine, provides a reasonable particle size distribution and the deposited particles appeared to be of mineral origin and not from textile or skin but the approximately 30-fold overestimation of mass concentrations when comparing with respirable cyclone sampling indicates that further improvements are necessary. Positioning of the sampler may be critical and moving the UNC sampler from the chest to e.g. the top of a helmet might be an improvement. Grounding of the sampler in order to avoid static electricity might also be useful. The UNC sampler should continue to be researched for personal sampling, as passive sampling might become a useful alternative to more laborious sampling techniques.

Choosing the number of images and image position when analyzing the UNC Passive Aerosol Sampler for occupational exposure assessment

The University of North Carolina passive aerosol sampler (UNC sampler) could be an alternative when measuring occupational dust exposure, but the time required for microscopic imaging of the sampler needs to be reduced to make it more attractive. The aims of this study were to (1) characterize the effect on precision when reducing imaging, in order to shorten analysis time and (2) assess if the position of the images makes a difference. Eighty-eight samplers were deployed in different locations of an open pit mine. Sixty images were captured for each UNC sampler, covering 51% of its collection surface, using scanning electron microscopy. Bootstrapped samples were generated with different image combinations, to assess the within-sampler coefficient of variation (CV_ws) for different numbers of images. In addition, the particle concentration relative to the distance from the center of the sampler was studied. Reducing the number of images collected from the UNC sampler led to up to 8.3% CV_ws for 10 images when calculating respirable fraction. As the overall CV has previously been assessed to 36%, the additional contribution becomes minimal, increasing the overall CV to 37%. The mean concentrations of the images were modestly related to distance from the center of the sampler. The CV_ws changed from 8.26% to 8.13% for 10 images when applying rules for the image collection based on distance. Thus, the benefit of these rules on the precision is small and the images can therefore be chosen at random. In conclusion, reducing the number of images analyzed from 60 to 10, corresponding to a reduction of the imaged sampling area from 51% to 8.5%, results in a negligible loss in precision for respirable fraction dust measurements in occupational environments.

Improving the UNC Passive Aerosol Sampler Model Based on Comparison with Commonly Used Aerosol Sampling Methods

Objectives

In an occupational environment, passive sampling could be an alternative to active sampling with pumps for sampling of dust. One passive sampler is the University of North Carolina passive aerosol sampler (UNC sampler). It is often analysed by microscopic imaging. Promising results have been shown for particles above 2.5 µm, but indicate large underestimations for PM_2.5. The aim of this study was to evaluate, and possibly improve, the UNC sampler for stationary sampling in a working environment.

Methods

Sampling was carried out at 8-h intervals during 24 h in four locations in an open pit mine with UNC samplers, respirable cyclones, PM₁₀ and PM_2.5 impactors, and an aerodynamic particle sizer (APS). The wind was minimal. For quantification, two modifications of the UNC sampler analysis model, UNC sampler with hybrid model and UNC sampler with area factor, were compared with the original one, UNC sampler with mesh factor derived from wind tunnel experiments. The effect of increased resolution for the microscopic imaging was examined.

Results

Use of the area factor and a higher resolution eliminated the underestimation for PM₁₀ and PM_2.5. The model with area factor had the overall lowest deviation versus the impactor and the cyclone. The intraclass correlation (ICC) showed that the UNC sampler had a higher precision and better ability to distinguish between different exposure levels compared to the cyclone (ICC: 0.51 versus 0.24), but lower precision compared to the impactor (PM₁₀: 0.79 versus 0.99; PM_2.5: 0.30 versus 0.45). The particle size distributions as calculated from the different UNC sampler analysis models were visually compared with the distributions determined by APS. The distributions were obviously different when the UNC sampler with mesh factor was used but came to a reasonable agreement when the area factor was used.

Conclusions

High resolution combined with a factor based on area only, results in no underestimation of small particles compared to impactors and cyclones and a better agreement with the APS’s particle size distributions. The UNC sampler had lower precision than the impactors, but higher than the respirable cyclone. The UNC sampler with area factor could be used for PM_2.5, PM₁₀ and respirable fraction measurements in this working environment without wind.

Application of Scanning Electron Microscopy With Energy-Dispersive X-Ray Spectroscopy for Analyzing Ocular Surface Particles on Schirmer Strips

PURPOSE

To demonstrate the application of scanning electron microscopy with energy-dispersive x-ray spectroscopy (SEM/EDS) for analyzing Schirmer strips for particle concentration, size, morphology, and type distribution.

METHODS

A cross-sectional design was used. Patients were prospectively recruited from the Miami Veterans Affairs (VA) Healthcare System eye clinic, and they underwent a complete ocular surface examination. The size, type, and chemical composition of particulate matter on Schirmer strips (from the left eye) were analyzed using SEM/EDS.

RESULTS

Schirmer strips from all 6 patients showed particle loading, ranging from 1 to 33 particles, whereas the blank Schirmer strip that served as a control showed no particle loading. Most particles were coarse, with an average size of 19.7 μm (95% confidence interval 15-24.4 μm). All samples contained organic particles (eg, pollen and mold), and 5 of the 6 samples contained nonorganic particles. The nonorganic particles were composed of silicon, minerals, and metals, including gold and titanium. The size of aluminum and iron particles was ≥62 μm, whereas the size of 2 other metals, zinc and gold, was smaller, that is, <20 μm. Most metal particles were elongated compared with the organic particles, which were round.

CONCLUSIONS

Although SEM/EDS has been extensively used in biomedical research, its novel application to assess the size, morphology, and chemical composition of the ocular surface particles offers an unprecedented opportunity to tease out the role of particulate matter exposure in ocular surface disease and disorders.