Ice-nucleating particles (INPs) are aerosol particles that influence mixed-phase clouds and in doing so impact weather and climate worldwide. To improve our understanding of ice production in mixed-phase clouds, we need techniques capable of accurately measuring atmospheric INP concentration spectra. However, there are sometimes discrepancies between the techniques commonly used to measure ambient INP concentrations, particularly when used in environments with abundant biogenic ice-nucleating material. Proteins and macromolecules adsorb to surfaces, such as filters, but the impact of this interaction on INP measurements is unknown. Here, we compare a widely used technique that involves washing collected aerosol particles off polycarbonate filters into aqueous suspension for subsequent INP droplet freezing assay (wash-off), with a technique where droplets are placed directly atop PTFE filters on a cold stage (drop-on) and also an online technique using an expansion chamber. Our results show that the wash-off technique underestimates the INP activity when free ice-nucleating proteins are present due to the poor recovery of the proteins from the filter into the wash-off suspension. However, there is much better agreement between techniques for INPs associated with coarse-mode mineral dust particles or cell fragments and for polysaccharide INPs from pollen. These findings indicate that some field-based INP measurements that use a wash-off technique may produce atmospheric INP concentrations that are biased low, particularly in regions with abundant proteinaceous INPs.
The transport of tyre wear particles (TWPs) remains poorly understood despite its recognized contribution to airborne microplastic pollution. We address this knowledge gap by investigating how idealized tyre wear particles detach from an idealized reference surface under controlled wind tunnel conditions. Our study aimed at simplifying the system and isolating the fundamental mechanisms controlling how particle size and shape influence this important initial transport mode. The experiments were conducted in a boundary-layer wind tunnel, where a near-monolayer of particles was seeded onto glass substrates. Time-resolved visual imaging at 0.1 Hz was combined with automatic image analysis using an open-source, deep learning segmentation model, which allows for detecting individual particles, quantifying their detachment, and tracking their size and shape with high model accuracy. For the detachment experiments, pristine tyre wear particles generated on a laboratory test stand with car test tyres supplied by Continental Reifen Deutschland GmbH, providing a well-characterized and idealized particle source. Among the seeding methods tested, we identified a low-cost pressurized seeding approach to produce the most uniform and reproducible particle distribution for subsequent detachment analysis. Across the analysed size range (80 to 300 µm), larger and more irregularly shaped particles exhibited significantly higher threshold friction velocities for detachment than smaller and more rounded particles. Ensemble fits yield a bulk threshold friction velocity of approximately 0.26 m s−1, with size- and shape-resolved detachment threshold velocity values varying by a factor of approximately 1.3 between the most easily detached and most resistant particles. The application of the Shao and Lu semi-empirical fluid threshold model reproduced the size-dependent threshold friction velocity of smooth polyethylene microspheres investigated in a preceding study using the identical wind tunnel, but it underestimates that of tyre wear particles unless the effective cohesion and aerodynamic scaling parameter are increased beyond values typically used for dust and sand. This behaviour is consistent with tyre wear particles experiencing stronger, more effective adhesion than smooth, rounded grains of similar size due to their irregular morphology and multiple contact points with the substrate. The density differences between the tyre wear particles (∼1300 kg m−3) and microspheres (∼1025 kg m−3) showed negligible influence within the studied size range (106 to 125 µm). We conclude that particle morphology, specified by both size and shape, plays a dominant role in controlling the aerodynamic detachment from the idealized glass
This study investigates the size distribution and particle morphology of analytes dried through an evaporative light scattering detector (ELSD), a widely used detector based on aerosol light scattering in pharmaceutical and materials analysis. We employed multiple particle sizing techniques, including a phase Doppler particle analyser (PDPA), aerodynamic aerosol classifier (AAC), and scanning mobility particle sizer (SMPS), to characterise droplet and particle distributions at various stages within the ELSD. Initial droplet size distributions were reconstructed using dioctyl sebacate (DOS) as a non-evaporating surrogate and correlated to water droplets. Downstream particle measurements were conducted for caffeine, dextran, and citric acid under different concentrations and operating conditions. Scanning electron microscopy (SEM) was used to examine dried-particle morphology. Results show that analyte properties significantly influence final particle size and morphology, with implications for ELSD signal and detection. This is the first comprehensive characterisation of the particle drying and scattering process within an ELSD and provides both physical insights into its operation and data for the validation of a model.
This study presents a comprehensive model for simulating aerosol dynamics and signal response in the Evaporative Light Scattering Detector (ELSD), a widely used analytical technique in liquid and supercritical fluid chromatography. The model integrates a zero-dimensional model including droplet atomization, convection, impingement, evaporation and finally light scattering of the droplet cloud. The physically based model includes chemical species properties, operational settings and environmental conditions. The model accounts for complex phenomena such as multi-component evaporation, particle impingement and size-dependent light scattering. The use of computational fluid dynamic (CFD) simulations provides detailed insights into flow characteristics within the ELSD geometry and allows estimation of the droplet losses by impingement. Model predictions are compared against experimental data for various analytes and solvents across a range of concentrations and temperatures. The model accurately captures experimentally measured trends for volatile and semi-volatile species, but discrepancies are observed for non-volatile analytes at higher temperatures. The present simulations are the very first framework for modelling ELSD operation, and the developed model provides the first tool for optimizing detector performance and interpreting results in chromatographic applications.
Mixed-phase clouds, which are dominant in mid- and high-latitude regions, strongly influence Earth's radiative balance and precipitation processes. Their formation depends critically on the presence of ice-nucleating particles (INPs), which are rare relative to cloud condensation nuclei. The HyICE-2018 measurement campaign took place at the SMEAR II station in the high-latitude boreal forest of Hyytiälä, Finland, between February and June 2018. Two continuous-flow diffusion chambers (CFDCs), PINC and PINCii (Portable Ice Nucleation Chambers I and II), were deployed with high-frequency sampling to measure INP concentrations. We applied machine-learning techniques to explore predictors of INP concentrations using more than 500 high-resolution atmospheric, aerosol, and ecosystem variables measured continuously at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, of which 84 were retained after quality screening for the analysis. We identify distinct differences between winter and spring–summer measurements. The winter measurements conducted with PINC appear to be nearly independent of any monitored variable. In contrast, the spring–summer measurements conducted with PINCii appear to be more closely linked to and responsive to ambient aerosol properties. Furthermore, we find that classical parameterisations based on aerosol particle concentration overestimate observed INP concentrations in the boreal environment. However, similar empirical fits based on local proxies, such as a marker of biogenic aerosol or nitrate, yield improved agreement during spring and summer, while no improvement occurs during winter. A core cautionary finding of this study is that, even with more than 500 co-located, high-resolution variables at one of the world's most heavily instrumented atmospheric stations, strong, deterministic links between INP concentrations and monitored parameters remain elusive. These results underscore the need for site-specific parameterisations to capture INP variability in the complex boreal environments; local biogenic and chemical proxies, such as fluorescent particle concentrations and nitrate aerosol mass, emerge as the most promising predictors for the spring and summer period.
A multiscale modeling framework, integrating MD (molecular dynamics)-derived soot nucleation and surface growth rates into a coupled computational fluid dynamics (CFD)–monodisperse particle dynamics (PD) model, is implemented and benchmarked for a premixed ethylene burner-stabilized stagnation (BSS) flame. The proposed coupled model is validated by comparing the soot number density, volume fraction, and particle size with measurements across the BSS flame, as well as with the results obtained by CFD-PD using a semi-empirical nucleation rate by Moss–Brookes and the hydrogen abstraction-carbon addition (HACA) surface growth rate. Incorporation of the MD-derived nucleation rate is in excellent agreement with both experimental data and a detailed sectional model from the literature, especially in the post-flame region. The proposed MD-informed CFD-PD model is computationally efficient compared to detailed population balance equation models as it does not rely on reaction kinetic modeling. To the best of our knowledge, this work is the first systematic assessment of the potential of MD-derived nucleation and surface growth rate constants for soot modeling. In addition, the proposed CFD-coupled monodisperse PD framework can serve as a predictive tool for soot modeling and design-oriented simulations of practical combustion and aerosol systems.
Adding plasmas to a flame has been shown to introduce high concentrations of charges, ions, and radicals to the said flame. This technique of adding plasma to a flame is called plasma-assisted combustion (PAC), and this addition has been shown to make a flame more stable and efficient. At the same time, PAC has also been shown to alter particle formation during combustion. Here, we investigate the effect of a high-frequency (∼21 kHz) alternating current (AC) corona discharge on particle formation and growth in a premixed flame, especially at the initial stages (with particle sizes below 10 nm). We first examined the mobility size distribution of ions generated from non-plasma combustion and corona-discharge-assisted combustion. The mobility size for positive ions does not change with the introduction of plasma. However, the negative ions change towards a larger size, likely due to different ion chemistry from plasma. We then introduced corona discharge with varying powers into the flame that contains titanium isopropoxide (TTIP) or tetraethyl orthosilicate (TEOS) and obtained the size distribution of the synthesized nanoparticles. For the precursor feed rates used in this study (TTIP: 9.6–48 mg h−1, TEOS: 60–100 mg h−1), we found that particle growth is suppressed by the corona discharge under relatively higher precursor feed rates (above ∼29 mg h−1∼80 mg h−1
An inter-laboratory comparison (ILC) involving optical particle size spectrometers (OPSSs) was organised at the French national level. The aim of this study was to make an inventory of the metrological capabilities of particle number size distribution (PNSD) measurements using OPSSs. This laboratory study was conducted over an 18-month period and involved 16 partners and 35 OPSSs. This large number of instruments provides strong statistical weight to the dataset, offering robust insight into the overall instrumental capabilities
The free fall of a drop through gas loaded with solid particles gives rise to multiple physical interactions, which remain poorly documented, especially when the drop is no longer spherical. In particular, no model predicts the particle collection efficiency for drops undergoing deformations or oscillations. This study aims to contribute to this effort by investigating numerically the dynamics of water drops freely falling in air laden with dispersed solid particles for drop Reynolds and Weber numbers such that the drops do (or do not) deform or oscillate (e.g., Re=30, 70, 500, and 876). A Eulerian–Lagrangian framework is adopted. The drop internal and external flows are simulated with direct numerical simulation (DNS), and the dynamics of the liquid–gas interface are tracked using a combination of the volume of fluid (VOF) and level set methods; this approach predicts the interface dynamics in line with experimental data. The trajectories of solid particles are simulated using Lagrangian tracking and taking into account drag, gravity, and Brownian motion. For spherical drops with Reynolds numbers below 200, our methodology replicates previous results. In the presence of oscillations and/or deformations, the flow parameters of the two continuous phases are correctly predicted. The particle collection efficiency also follows the experimental trend, but the values differ significantly from measurements found in the literature. We therefore propose certain areas of improvement with the goal of obtaining better fits to the available experimental data.
The contributions and evolution of fine elemental particulate matter (PM) sources were investigated in the Marseille–Fos basin (south of France) based on a 1-year-long (January–December 2023) study using online X-ray fluorescence (Xact) PM1
The contribution of agricultural emissions of fine particulate matter (PM2.5) poses significant health and environmental challenges, particularly in the UK where intensive farming activities contribute to elevated pollutant levels. This contribution includes direct emissions and PM2.53). The study aims to analyse the impact of a series of mitigation measures through emission scenarios (low, medium, high uptake) on the dairy, pig, and poultry sectors in 2030, mainly focusing on NH3332.52.53-rich atmosphere reducing the impact of these reductions in NH32.532.52.532.52.52.52.52.5, as well as to improve our understanding of the chemical atmospheric processes involved in secondary inorganic aerosols.