Size Distribution Measurements Research Articles

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges

Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution.

Process of SCR Catalyst Deactivation by Dimethylsiloxanes Present in Marine Fuels

In recent years, there have been reports of the deactivation of selective catalytic reduction (SCR) exhaust gas catalysts after a short period of operation in marine applications. The trigger and the processes of this catalyst damage have now been investigated experimentally using a fuel burner test bench and various analysis techniques. It was found that organic silicon compounds (OSCs), which are present in marine fuels after use in mineral oil extraction and processing, cause the catalyst deactivation. In practice, these are predominantly dimethylsiloxanes. By the burner experiments, it was found that the OSCs were completely converted into combustion products, in particular SiO2. Measurements of particle size distributions and particle number concentrations up- and downstream of the catalyst showed that SiO2 particles < 20 nm in particular were retained in the catalyst. This filtration effect decreased until no further reduction was observed for particles > 50 nm. Other inorganic, particulate, SiO2-containing fuel impurities (catalyst fines) had no poisoning effect here, as these particles are mostly > 50 nm. The small particles formed a closed SiO2 layer with a thickness of approx. 5 µm on the catalyst surface which grew from the catalyst inlet to the outlet and blocked the catalyst as a diffusion barrier. As the SiO2 layer increased, the NO conversion rate decreased. The poisoning effects were observed experimentally at OSC concentrations of 5 to 60 mg Si kg−1 fuel. A field-aged catalyst that had been operated on board of a merchant ship revealed the same findings.

Investigations of airborne tire and brake wear particles using a novel vehicle design

Non-exhaust emissions have become an increasingly important issue as their levels continue to rise and the health effects of particulate matter (PM) are more widely discussed. To address this issue, a vehicle demonstrator with integrated emission reduction of tires and brakes was developed as part of the Zero Emission Drive Unit Generation-1 (ZEDU-1) project. This novel concept includes the removal of tire road wear particles (TRWP) with a strong ventilation/filtering system and an enclosed multi-disk brake, making it a suitable tool for the investigation of non-exhaust emissions. Particle number (PN) and particle size distribution (PSD) measurements down to 2.5 nm were performed on a chassis dynamometer and on a test track. Due to the low background concentrations on the chassis dynamometer, it is possible to distinguish between tire and brake wear and to characterize even a small number of particle emissions. It could be shown that about 30 % less particles are emitted by the vehicle, when using the novel multi-disk brake instead of the conventional brake. The highest TRWP emissions were collected during acceleration and harsh braking. Characterization of the collected particles using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) revealed diverse particle shapes and differences between particles generated on the dynamometer and on a test track.Graphical

Impact of vehicles at the roadside of expressway in urban area: Simultaneous measurement of particle size distribution and positive matrix factorization

This study conducted real-time monitoring of size-resolved particle concentrations ranging from 9 nm to 10 μm simultaneously at four sites on the park ground and the roof of a five-story apartment buildings in the upwind and downwind areas of the Olympic Expressway next to apartment complex areas of Seoul, Korea. Using a positive matrix factorization model for source apportionment, eight factors were resolved at each monitoring site: four exhaust emissions of vehicles, one non-exhaust emission of vehicle, two regional sources, and one unknown source. After categorizing monitoring data into three cases by wind conditions, impact and contribution of each vehicle-related source on the local road to the roadside pollution was quantified and characterized by subtracting the urban background concentrations. Throughout the measurement period, the contribution of vehicle-related sources to the particle number concentration at each monitoring site ranged from 61 % to 69 %, while that to the particle mass concentration ranged from 39 % to 87 %. During periods of steady traffic flow and wind blowing from the road to three downwind sites at speeds exceeding >0.5 m/s during working hours, the particle number concentrations at the downwind sites were 2.2–2.5 times higher than the average levels. Among vehicle-related sources, gasoline vehicles with multiple injections or high-emitting diesel vehicles showed the highest contribution to particle number concentrations at all sites. As wind speed increased, the number concentrations of particles from vehicle exhaust and non-exhaust emissions decreased and increased, respectively, probably due to enhanced dilution and transport, respectively. In addition, particle number concentrations showed a parabolic curve-like trend with traffic volumes increasing to approximately 10,000 vehicles/h, and then decreasing for both vehicle exhaust and non-exhaust emissions. These results can be utilized in numerical modeling studies and in establishing traffic-related environmental policies to reduce seasonal and temporal particle exposure near the roadsides.

STEM-in-SEM versus SE-InLens-type-SEM as a Reliable Analytical Pair-Tool for Measurement of Nanoparticle Size and Shape Distribution

STEM-in-SEM <i>versus</i> SE-InLens-type-SEM as a Reliable Analytical Pair-Tool for Measurement of Nanoparticle Size and Shape Distribution

Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface

In this work, we used the electrified liquid-liquid interface (eLLI) to study the fusion of DCE-in-water organic droplets (soft particles) upon coming into contact with the soft interface. Our focus was to define the effect of the neutral (1-tetradodecanol) and ionic (cetyltrimethylammonium bromide and sodium dodecylsulfonate) surfactants that stabilize the colloidal solution on the electroanalytical output of the impact events. The electrochemically and interfacially active salt used in our work (tetramethylammonium tetrakis(4-chlorophenyl)borate) was always initially dissolved in the organic phase droplets. When the potential drop at the interface was set below the formal Galvani potential of the tetramethylammonium cation ion transfer, we observed ionic current events upon the fusion of the droplets at the eLLI, as studied by chronoamperometry. This phenomenon was found to be severely affected by the surfactants. Our system was investigated with several methods, including cyclic voltammetry, chronoamperometry, interfacial tension measurements, and finally zeta potential and droplet size distribution measurements based on dynamic light scattering.

An NMR Approach for Investigating Membrane Protein-Lipid Interactions Using Native Reverse Micelles.

Peripheral membrane proteins (PMPs) are a subgroup of membrane-associated proteins that are water-soluble and bind to membranes, often reversibly, to perform their function. These proteins have been extensively studied in the aqueous state, but there is often a lack of high-resolution structural and functional studies of these proteins in the membrane-bound state. Currently, nuclear magnetic resonance (NMR) is among the best-equipped methods to study these relatively small proteins and domains, but current models have some disadvantages that prevent a full understanding of PMP interactions with membranes and lipids. Micelles, bicelles, and nanodiscs are all available for NMR observation but are based on synthetic lipids that may destabilize proteins or are too large to accommodate straightforward structural analysis. This protocol introduces a method for forming reverse micelles using lipids from natural sources, here called native reverse micelles. This technique allows the PMPs to embed within a shell of naturally derived lipids surrounding a small water core solubilized in an alkane solvent. PMP embedment in the lipid shell mimics binding to a cellular membrane. Here, naturally derived lipids from soy, bovine heart, and porcine brain are used in conjunction with n-dodecylphosphocholine (DPC) to encapsulate a PMP from either concentrated or dried protein, resulting in reverse micelles that may be confirmed via dynamic light scattering and NMR. This protocol allows for high-quality NMR data of PMPs interacting with membrane lipids within a biologically accurate environment. Key features • This protocol describes using natural lipids to construct reverse micelles for high-resolution NMR studies of proteins. • Initial optimization of encapsulation conditions proceeds through visual assessment, with dynamic light scattering (DLS) to measure size distribution, and NMR to observe protein behavior. • Membrane-interacting proteins are encapsulated in their membrane-bound state. Proteins that do not interact with membranes are housed in their water-solubilized state. • Structural, functional, and inhibitory studies may be performed on native reverse micelle-encapsulated proteins.

Study on the Effects of Fe Doping on C‐ZFe Catalyst for Catalytic Removal of NO from the Low‐Temperature Flue Gas

AbstractThe denitrification efficiency of carbon‐based zero valent iron catalyst (C‐ZFe) for low‐temperature selective catalytic reduction (SCR) (&lt;140°C) highly depends on the structural species of iron oxide (FexOy) and the physicochemical properties of the catalyst. In the study, we investigated the main factors affecting the low‐temperature denitrification efficiency of C‐ZFe such as temperature, iron content and O2. The results showed that the average NOX conversion was 72.6 % at 135°C when the space velocity was 459 h−1 and the mass ratio of carbon to iron was 15 % to 85 %. The surface morphology and structure of C‐ZFe were characterized by the surface area and pore size distribution measurements, X‐ray diffraction (XRD), fourier transform infrared spectroscopy (FTIR) and X‐ray photoelectron spectroscopy (XPS). The results indicated that the iron on the catalyst surface was oxidized by O2 to Fe2O3, which could retain NO and then formed intermediate products. NO entered the mesoporous structure along C‐ZFe surface and proceeded a catalytic reduction reaction with the adjacent carbon.

Analysis of rolling contact and tooth root bending fatigue in a new high-strength steel: Experiments and micromechanical modelling

To find materials that meet the future requirements of increasing load density in wind turbine gear structures, the commonly used reference material, 18CrNiMo7-6, and another high-strength steel were analysed. Their fatigue properties were studied through rolling contact fatigue (RCF) and gear tooth root bending fatigue (TRBF) tests. The reference steel exhibited better fatigue performance under RCF conditions compared to the new steel; however, it was outperformed by the new steel under TRBF conditions. This study aims to understand gear contact fatigue at the microscopic level, moving beyond the macroscopic focus that dominates current research literature. To establish the causal link between the varied fatigue performance observed in these materials, we proposed a multiscale modelling workflow based on crystal plasticity. This crystal plasticity framework is combined with the fatigue indicator parameter (FIP) to explore the fatigue resistance of materials subjected to RCF and TRBF conditions. Emphasis has been placed on the role of retained austenite (RA) in fatigue performance. Utilizing representative elementary volumes (REVs) generated based on statistically representative crystallographic features and measured size distribution of RA, we examined the effects of various RA levels on fatigue resistance. Our findings reveals that the fatigue damage accumulation is significantly influenced by the level of RA. Different fatigue damage accumulation behaviours were observed under RCF and TRBF conditions. These insights offer new perspectives on RA’s role in fatigue resistance and highlight its complex influence under varied loading conditions.

Correlation of rheology and oral tribology with sensory perception of commercial hazelnut and cocoa-based spreads.

This study examined the effects of spread formulation and the structural/lubricant properties of six different commercial hazelnut and cocoa spreads on sensory perception. Rheology, tribology, and quantitative descriptive analysis (QDA) was assessed by also evaluating the correlation coefficients between the quality descriptor and the rheological and textural parameters. The viscosity was evaluated at different temperatures to better simulate conditions before and after ingestion. Tribological analysis was executed at 37°C to mimic the human oral cavity. The effect of saliva presence and the number of runs on tribological behaviors was investigated. Moreover, textural, calorimetric, and particle size distribution measurements were performed to reinforce the correlation between structural/thermal parameters (e.g., firmness, stickiness, sugar melting point) and sensory aspects. "Visual viscosity," defined as a sensory attribute evaluated prior to consumption, negatively correlated with apparent viscosity measured at 20°C and 10 s-1, whereas "body," defined during oral processing and related to creaminess, positively correlated with apparent viscosity measured at 37°C and 50 s-1. These attributes were mainly influenced by particulate microstructure and solid volume fraction within the formulation. Textural stickiness positively correlated with sensory "adhesiveness" and was related to fat composition and milk powder addition, while "sweetness" was related to sucrose content and sugar melting enthalpy. Tribological data provided meaningful information related to particle-derived attributes, as well as after-coating perception (fattiness/oiliness), thus better predicting food evolution during oral consumption.

A semantic segmentation-based algorithm for fast flotation bubble size distribution measurement

The visual characteristics of froths on flotation cell surfaces are crucial for the optimal control of the flotation process. However, previous research has primarily focused on optimizing markers to prevent the over-segmentation of froth images, often overlooking computational efficiency. This work proposes a deep learning-based froth segmentation algorithm that prioritizes both segmentation speed and quality. We propose a dataset construction strategy based on pseudo-labeling, which utilizes traditional methods to extract marker maps from froth images to build the dataset for deep learning. Subsequently, a semantic segmentation model is trained to derive marker maps from raw froth images. These marker maps are then employed in a marker-based watershed algorithm, enabling end-to-end segmentation of froth images. The proposed method is validated using froth images from a gold and nickel flotation plant, demonstrating the ability to accurately and efficiently identify different bubbles in real froth images without requiring complex pre-processing or post-processing steps. Industrial experiments show a tenfold increase in segmentation efficiency with virtually no loss of accuracy.

Design of inductive electrostatic boom spray system based on embedded closed electrode structure and droplet distribution test in soybean field.

The large water demand, insufficient deposition on the back of the leaf and the uneven distribution of droplets are the problems of traditional agricultural ground plant protection machinery, which leads to low agricultural control efficiency. Combined with the advantages of electrostatic spray technology and the characteristics of high working efficiency and low probability of droplets drift of ground sprayer, an inductive electrostatic boom spray system based on embedded electrode structure is designed and mounted on a large self-propelled boom sprayer for field testing. Based on the working characteristics of the fan nozzle and the analysis of the theory of charge, the inductive electrostatic spray device is designed. The performance of the device is tested and the rationality of the system design is verified by COMSOL numerical simulations, charge-to-mass ratio, and particle size distribution measurements. The spray deposition scanning software and the Box-Behnken experimental design method are used to analyze the spray droplet deposition rate and coverage density of the sprayer on the front and back of the target leaves. The results show that the embedded closed electrode structure designed in this paper can avoid the problem of electrode wetting, and the electric field generated by it is mainly concentrated in the spray liquid film area, and the intensity reaches 6~7 V/m. At the conventional application height (500 mm), the maximum charge-to-mass ratio is 2.91 mC/kg, and the average particle size is 168.22 μm, which is 12.87% lower than that of ordinary spray, when the spray pressure is 0.3 MPa and the electrostatic voltage is 12 kV. The results of field experiments show that the optimum combination of the working parameters with the spray speed is 8.40 m/s, the spray pressure is 0.35 MPa, the charging voltage is 11.50 kV, the amount of droplet deposition in the lower dorsal area of the blade is 1.44 µL·cm-2. This study can provide a certain basis for the application of electrostatic spray technology in ground sprayers.

A novel framework for deriving water retention behavior of multimodal unsaturated soils based on pore size distribution data

The soil water retention curve (SWRC) strongly influences the hydro-mechanical properties of unsaturated soils. It plays a decisive role in geotechnical and geo-environmental applications in the vadose zone. This paper advances a novel framework to derive the water retention behavior of multimodal deformable soils based on the pore size distribution (PSD) measurements. The multiple effects of suction on the soil pore structure and total volume during SWRC tests are considered. The complete picture of soil microstructure is quantitatively described by the void ratio (for the pore volume) and a newly defined microstructural state parameter (for pore size distribution) from a probabilistic multimodal PSD model. Assuming a reversible microstructure evolution, a unique PSD surface for wetting and drying links the SWRC and PSD curves in the pore radius-suction-probability space. A closed-form water retention expression is obtained, facilitating the model's implementation in particle applications. The model is validated using the water retention data of four different soil types, showing a strong consistency between the measurement and the reproduced curve. The proposed method provides new insights into the pore structure evolution, the water retention behavior and the relationship between them for multimodal deformable soils.

Three-dimensional characterization of particle size, shape, and internal porosity for Apollo 11 and Apollo 14 lunar regolith and JSC-1A lunar regolith soil simulant

Samples of soils collected by the Apollo 11 mission (10084,2036) and the Apollo 14 mission (14163,940) were obtained from the NASA Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) program. The particle size, shape, and internal porosity were characterized in three dimensions (3D) using a combination of X-ray computed tomography (XCT) and mathematical analysis, with various size and shape parameters measured and calculated for each particle. High-resolution scanning electron microscopy (SEM) was used to image the particles that were too small for the two XCT instruments used. Similar characterization was carried out on samples of JSC-1A lunar soil simulant, updating a previous analysis. Approximately 14,000 lunar regolith particles and 128,000 JSC1-A particles, covering a wide range of size and shape, were characterized for this paper and the results stored in a publicly accessible database. This large number of particles enabled, for the first time, statistically valid particle shape distributions to be generated. The 3D shape distributions of the two regoliths and JSC-1A were quantitatively compared and it was found that the way particle shape and porosity depends on particle size was different between regolith and simulant. The measured size distribution of particles in the lunar soils was applied to estimate the relative contributions of different sizes to the ensemble average particle single scattering albedo and phase function. By linking our particle counts to published sieve weight fractions for the lunar samples, we find that ∼80% of the total cross-section area is contributed by particles <20 μm diameter and ∼ 50% by particles <8 μm diameter. The orientation-averaged two-dimensional projected areas of the actual regolith particles were computed so that this estimate was also based on real particle shapes. Such small sizes dominating the total cross-section area suggest that calculations of the elements of the scattering matrix for individual particles may be possible with modest computing capabilities leading to the development of improved models for the quantitative interpretation of remote sensing spectrophotometry and polarimetry. This 3D characterization and database will enable other computational work to be done with real lunar regolith particle shapes, including discrete element method mechanical modeling, packing simulations, further light scattering calculations, dust contamination modeling, and modeling of lunar rover interactions with collected and packed regolith particles.

Development of Starch Nanoparticles as a Green Consolidant for Paper-based Objects: Synthesis and Design

ABSTRACT Starch is one of the oldest adhesives, and has been utilised for a variety of applications, including as a consolidant for artworks and archives, after cooking. Although widely available, cost-effective, and versatile it has limitations such as large particle size, which makes it challenging for the adhesive to penetrate deeply into objects. Attempts to increase the concentration of starch adhesives for enhanced strength often lead to increased viscosity, hindering penetration and causing an undesirable film on the surface. Achieving a workable consistency often leads to reduced adhesive power. An approach to using nanosized starch as a binder for paper and cellulose-based objects is presented. Starch nanoparticles (SNPs) from wheat, rice, maize and waxy maize were successfully synthesised using a ‘top-down’ approach that is facile as well as energy efficient. Size distribution measurements indicate most particles to be less than 50 nm, smaller than the pore size of most historic papers. SNPs could make a better sizing adhesive than traditionally-made starch paste, and they are also a green alternative to a variety of chemically formulated, organic solvent-based adhesives. The compatibility, non-toxicity, and biodegradability of starch remained uncompromised during the modification.

The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations

Airborne in situ observations of aerosols and clouds provide relevant data sets crucial to improving the understanding of cloud microphysical processes and reducing uncertainties connected to future climate predictions. However, the selection and classification of cloud sequences in such data sets can be very time-consuming if done manually, and criteria used by the community are numerous and prone to misclassification, especially when coarse aerosol particles (> 1 μm diameter) are present.In this study, we present the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The Cloud Indicator was developed using data from three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016–2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017), and FIREX-AQ (Fire Influence on Regional to Global Environments Experiment and Air Quality; 2019). The algorithm utilizes size distribution measurements, combined with measurements of relative humidity and temperature, to automatically detect flight sequences in clouds and classify the cloud type. As an additional criterion for the Cloud Indicator, we established the cloud-aerosol volume factor fCA to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning thereby reducing misclassifications. The Cloud Indicator algorithm was developed with data from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) which was calibrated with a novel calibration procedure – introduced in this study - that results in a refractive index independent calibration and works also in the field. However, the Cloud Indicator algorithm is not restricted specifically to the CAPS and thus allows its application to a variety of in situ instruments that measure coarse aerosol and cloud particle size.Case studies from ATom and A-LIFE demonstrate the ability of the Cloud Indicator to precisely screen (airborne) in situ data sets for clouds. The algorithm separates the data set into cloud-free periods, Aerosol-Cloud Transition Regime (ACTR), liquid clouds, clouds in the Mixed-Phase Temperature Regime (MPTR), and cirrus clouds. The unique ability of the Cloud Indicator to successfully differentiate between layers of enhanced coarse-mode aerosol concentrations and clouds is demonstrated by measurements in a complex mixture of clouds embedded into a mineral dust layer during the A-LIFE. The empirically derived parameter thresholds of the Cloud Indicator are in good agreement with values in the literature.

Discrete element modelling of the elastic-plastic and viscoelastic properties of a lithium-ion battery electrode layer

Mechanical degradation mechanisms are one of the leading causes of charge capacity loss in lithium-ion batteries. This study further develops a discrete element method (DEM) simulation framework, which investigates how the local contact behaviour affect the global mechanical properties of the active layer. The local microstructure consists of active particles held together by a binder domain, making up a granular medium. This study investigates the impact of the layer's global properties from the type of particle contact model. Experiments were also performed to measure size distribution and the material properties of the active material. The time dependency of the active layer, stemming from the viscoelastic binder domain, was studied in relaxation simulations, which were based on experimental measurements.

Size and Shape Selective Classification of Nanoparticles

As nanoparticle syntheses on a large scale usually yield products with broad size and shape distributions, the properties of nanoparticle-based products need to be tuned after synthesis by narrowing the size and shape distributions or via the removal of undesired fractions. The development of property-selective classification processes requires a universal framework for the quantitative evaluation of multi-dimensional particle fractionation processes. This framework must be applicable to any property and any particle classification process. We extended the well-known one-dimensional methodology commonly used for describing particle size distributions and fractionation processes to the multi-dimensional case to account for the higher complexity of the property distribution and separation functions. In particular, multi-dimensional lognormal distributions are introduced and applied to diameter and length distributions of gold nanorods. The fractionation of nanorods via centrifugation and by orthogonal centrifugal and electric forces is modeled. Moreover, we demonstrate that analytical ultracentrifugation with a multi-wavelength detector (MWL-AUC) is a fast and very accurate method for the measurement of two-dimensional particle size distributions in suspension. The MWL-AUC method is widely applicable to any class of nanoparticles with size-, shape- or composition-dependent optical properties. In addition, we obtained distributions of the lateral diameter and the number of layers of molybdenum disulfide nanosheets via stepwise centrifugation and spectroscopic evaluation of the size fractions.

Chemical composition and trajectories of atmospheric particles at the Machu Picchu Peruvian Antarctic scientific station (62.09º S, 58.47º W)

Antarctica is a remote and relatively pristine region, but the regional transport of aerosols may be a source of pollution, especially in the Antarctic Peninsula. Few studies have characterized atmospheric aerosols and evaluated the contribution of their emission sources. The Peruvian Antarctic research station Machu Pichu (ECAMP, by its Spanish acronym) is located on King George Island in the Antarctic Peninsula. During February 2020, atmospheric particulate mass (PM10 and PM2.5) was sampled and analyzed to characterize its elemental composition and was supplemented by measurements of equivalent black carbon and aerosol size distributions. Chemical elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), multivariate techniques, and enrichment factors. The most abundant elements in PM10 and PM2.5 were Na, Fe, Mg, and Si, with the most important local sources being marine (Na, Mg, Mn, Ca) and crustal (Fe, Al, P). Sources of weathering (Ba and Si) from glacial thawing and sources of combustion linked to the use of oil (V) and emission of black carbon were recorded. Air mass back-trajectory analysis using the HYSPLIT model helped identify external sources of particulate matter in the air masses reaching the ECAMP site. Overall, this study supports the growing evidence of the anthropogenic impact of distant and local sources on the white continent.