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.

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

We report on the precision and accuracy of measuring PM10-2.5 and its components with particles collected by passive aerosol samplers and analyzed by computer-controlled scanning electron microscopy with energy dispersive X-ray spectroscopy. Passive samplers were deployed for week-long intervals in triplicate and colocated with a federal reference method sampler at three sites and for 5 weeks in summer 2009 and 5 weeks in winter 2010 in Cleveland, OH. The limit of detection of the passive method for PM10-2.5 determined from blank analysis was 2.8 µg m(-3). Overall precision expressed as root-mean-square coefficient of variation (CVRMS) improved with increasing concentrations (37% for all samples, n = 30; 19% for PM10-2.5 > 10 µg m(-3), n = 9; and 10% for PM10-2.5 > 15 µg m(-3), n = 4). The linear regression of PM10-2.5 measured passively on that measured with the reference sampler exhibited an intercept not statistically different than zero (p = 0.46) and a slope not statistically different from unity (p = 0.92). Triplicates with high CVs (CV > 40%, n = 5) were attributed to low particle counts (and mass concentrations), spurious counts attributed to salt particles, and Al-rich particles. This work provides important quantitative observations that can help guide future development and use of passive samplers for measuring atmospheric particulate matter.

Elevated Concentrations of Lead in Particulate Matter on the Neighborhood-Scale in Delhi, India As Determined by Single Particle Analysis.

High mass concentrations of atmospheric lead particles are frequently observed in the Delhi, India metropolitan area, although the sources of lead particles are poorly understood. In this study, particles sampled across Delhi (August - December 2008) were analyzed by computer-controlled scanning electron microscopy with energy dispersive X-ray spectroscopy (CCSEM-EDX) to improve our understanding of the spatial and physicochemical variability of lead-rich particles (>90% lead). The mean mass concentration of lead-rich particles smaller than 10 µm (PM10) was 0.7 µg/m(3) (1.5 µg/m(3) std. dev.) with high variability (range: 0-6.2 µg/m(3)). Four samples (16% of 25 samples) with PM10 lead-rich particle concentrations >1.4 µg/m(3) were defined as lead events and studied further. The temporal characteristics, heterogeneous spatial distribution, and wind patterns of events, excluded regional monsoon conditions or common anthropogenic sources from being the major causes of the lead events. Individual particle composition, size, and morphology analysis indicate informal recycling operations of used lead-acid batteries as the likely source of the lead events. This source is not typically included in emission inventories, and the observed isolated hotspots with high lead concentrations could represent an elevated exposure risk in certain neighborhoods of Delhi.

Computer-controlled scanning electron microscope: Methodologies and application scenarios in atmospheric particle research.

Understanding the physicochemical properties of atmospheric particles and the refined source apportionment become a vital foundation for targeted control of air pollution. The rapid development of the computer-controlled scanning electron microscope (CCSEM) provides a new era for atmospheric particle research by improving the efficiency of individual particle analysis. This study summarized the methodologies for CCSEM-based individual particle analysis and introduced the principle, characteristics, and development of CCSEM. The application scenarios of CCSEM in the field of air quality assessment, health assessment, and climate effects of atmospheric particles were reviewed. CCSEM has a great application prospect in the refined particle source apportionment, health effect assessment, and particle source spectrum database establishment. Much attention should be paid to the establishment of a well-developed methodology system for CCSEM, including particle identification, classification method and standardization, quantitative source appointment method establishment, and analysis timeliness enhancement.

Source identification and apportionment of ambient particulate matter in Beijing using an advanced computer-controlled scanning electron microscopy (CCSEM) system.

The use of electron microscopy to analyze the morphology, composition, and sources of atmospheric particles has been studied extensively worldwide. However, in China, there are few studies on single-particle source analysis based on computer-controlled scanning electron microscopy (CCSEM) technology for a large number of particles, and the related technical methods need to be established and improved. In this study, ambient particulate matter (PM) was collected simultaneously from urban, suburban, and background areas of Beijing in spring 2018 and subsequently characterized using the IntelliSEM-EPAS™ technology (an advanced CCSEM software). The deposition velocity model was used to deduce the size distribution and calculate the concentration of ambient PM. Based on the k-means algorithm and empirical rules, all particles investigated were quantitatively apportioned to nine major sources, including soil/road dust, carbonates-silicates, carbonates, irregular carbonaceous particles, irregular iron oxides, combustion/industry, calcium sulfate, secondary particles, and salt-related particles. The size-resolved contributions (mass and number) of different sources were calculated. For example, soil/road dust (65.1 %), carbonate-silicate (16.1 %), and carbonate (7.1 %) were the top three sources contributing to PM10 mass. This study was the first localized application of IntelliSEM-EPAS technology in China, demonstrating its great promise in PM source apportionment. For further accurate and refined source apportionment, it is essential to build localized individual particle source profiles.

Single-particle SEM-EDX analysis of iron-containing coarse particulate matter in an urban environment: sources and distribution of iron within Cleveland, Ohio.

The physicochemical properties of coarse-mode, iron-containing particles and their temporal and spatial distributions are poorly understood. Single-particle analysis combining X-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively collected particles was used to investigate the physicochemical properties of iron-containing particles in Cleveland, OH, in summer 2008 (Aug-Sept), summer 2009 (July-Aug), and winter 2010 (Feb-March). The most abundant classes of iron-containing particles were iron oxide fly ash, mineral dust, NaCl-containing agglomerates (likely from road salt), and Ca-S containing agglomerates (likely from slag, a byproduct of steel production, or gypsum in road salt). The mass concentrations of anthropogenic fly ash particles were highest in the Flats region (downtown) and decreased with distance away from this region. The concentrations of fly ash in the Flats region were consistent with interannual changes in steel production. These particles were observed to be highly spherical in the Flats region, but less so after transport away from downtown. This change in morphology may be attributed to atmospheric processing. Overall, this work demonstrates that the method of passive collection with single-particle analysis by electron microscopy is a powerful tool to study spatial and temporal gradients in components of coarse particles. These gradients may correlate with human health effects associated with exposure to coarse-mode particulate matter.

Measurements of OC and EC in coarse particulate matter in the southeastern United States.

The organic carbon (OC) and elemental carbon (EC) content of filter-based, 24-hr integrated particulate matter with aerodynamic diameters between 2.5 and 10 microm (PM10-2.5) was measured at two urban and two rural locations in the southeastern United States. On average, total carbon (OC + EC) comprised approximately 30% of PM10-2.5 mass at these four sites. Carbonate carbon was measured on a subset of samples from three sites and was found to be undetectable at a rural site in central Alabama, less than 2% of PM10-2.5 at an urban site in Georgia, and less than 10% of PM10-2.5 at an urban-industrial site in Alabama. Manual scanning electron microscopy (SEM) and computer-controlled SEM (CCSEM) along with energy dispersive X-ray spectroscopy (EDS) were used to identify individual carbonaceous particles in a selected subset of samples collected at one rural site and one urban-industrial site in Alabama. CCSEM results showed that biological material (e.g., fungal spores, pollen, and vegetative detritus) accounted for 60-70% of the carbonaceous mass in PM10-2.5 samples with concentrations in the range of 2-16 microg/m3. Samples with higher PM10-2.5 concentrations (25-42 microg/m3) at the urban-industrial site were found by manual SEM to have significant amounts of unidentified carbonaceous material, likely originating from local industrial activities. Both filter-based OC and EC concentrations and SEM-identified biological material tended to have higher concentrations during warmer months. Upper limits for organic mass (OM) to OC ratios (OM/OC) are estimated for PM10-2.5 samples at 2.1 for urban sites and 2.6-2.7 for rural sites.

Source Apportionment of Soil Samples by the Combination of Two Neural Networks Based on Computer-Controlled Scanning Electron Microscopy.

The apportionment of ambient aerosol mass to different sources of airborne soil is a difficult problem because of the similarity of the chemical composition of crustal sources. However, additional information can be obtained using individual particle analysis. A novel approach based on the combination of two neural networks, the adaptive resonance theory-based neural network (ART-2a) and the back-propagation (BP) neural network with electron microscopy data, has been developed to apportion the mass contributions of the crustal sources to ambient particle samples. The crustal source samples were analyzed using computer-controlled scanning electron microscopy (CCSEM). CCSEM provides elemental compositions and size parameters for individual particles as well as estimates of the shape and density from which the volume and mass of each particle can be estimated. The ART-2a neural network was first used to partition particles into homogeneous classes based on the elemental composition data. After the different particle type classes were produced by ART-2a, their mass fractions were calculated. In this way, the source profiles for the crustal dust sources can be obtained in terms of the mass fractions for different particle types. Then the BP neural network was applied to build the model between the mass fractions of different particle types and the mass contributions. Using the three physical source samples prepared for this study, artificial ambient samples were generated by randomly mixing particles from the three source samples. These samples were then used to examine the proposed method. Satisfactory predictions for the mass contributions of the three sources to the ambient samples have been obtained, indicating the proposed method is a promising tool for the source apportionment of chemically similar soil samples.