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Measurements of the water activity-dependent viscosity of aerosol particles from two techniques are compared, specifically from the coalescence of two droplets in holographic optical tweezers (HOT) and poke-and-flow experiments on particles deposited onto a glass substrate. These new data are also compared with the fitting of dimer coagulation, isolation, and coalescence (DCIC) measurements. The aerosol system considered in this work are ternary mixtures of sucrose-citric acid-water and sucrose-NaNO3-water, at varying solute mass ratios. Results from HOT and poke-and-flow are in excellent agreement over their overlapping range of applicability (∼103-107 Pa s); fitted curves from DCIC data show variable agreement with the other two techniques because of the sensitivity of the applied modeling framework to the representation of water content in the particles. Further, two modeling approaches for the predictions of the water activity-dependent viscosity of these ternary systems are evaluated. We show that it is possible to represent their viscosity with relatively simple mixing rules applied to the subcooled viscosity values of each component or to the viscosity of the corresponding binary mixtures.

The importance of organic aerosol particles in the environment has been long established, influencing cloud formation and lifetime, absorbing and scattering sunlight, affecting atmospheric composition and impacting on human health. Conventionally, ambient organic particles were considered to exist as liquids. Recent observations in field measurements and studies in the laboratory suggest that they may instead exist as highly viscous semi-solids or amorphous glassy solids under certain conditions, with important implications for atmospheric chemistry, climate and air quality. This review explores our understanding of aerosol particle phase, particularly as identified by measurements of the viscosity of organic particles, and the atmospheric implications of phase state.

Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method determining the cross sectional sand flux and estimating its uncertainty. This SDC (for Sand Discharge Computing) method combines suspended-sand concentrations from point samples with ADCP (Acoustic Doppler Current Profiler) high-resolution depth and velocity measurements. The MAP (for Multitransect Averaged Profile) method allows to obtain an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modelling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isεave;re and Amazon Rivers, with computed flux uncertainties ranging between 18 and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the ISO 4363 method ranges between -16 and +3 %. This method, which comes with a flexible, open-source code, is the first proposing an applicable uncertainty estimation, that could be adapted to other flux computation methods. [ABSTRACT FROM AUTHOR]

In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and confirming that the fundamental patterns within the data were captured reliably. An added dimension to the study was the integration of an autoencoder (AE). When coupled with K-means, the AE enhanced outlier detection, particularly in identifying compositional loadings of each cluster. Conversely, whilst the AE's application to all methods revealed a potential for noise reduction by removing infrequent, larger particles, in the case of HCA, this information distortion during the encoding process may have affected the clustering outcomes by reducing the number of observably distinct clusters. The findings from this study indicate that GenieClust, when applied both with and without an AE, was effective in delineating a notable number of distinct clusters. Furthermore, each cluster's compositional loadings exhibited greater internal variability, distinguishing up to 3× more particle types per cluster compared to traditional means, and thus underscoring the algorithms' ability to differentiate subtle data patterns. The work here postulates that the application of GenieClust both with and without an AE may provide important information through initial outlier detection and enriched speciation with an AE applied, evidenced by a greater number of distinct clusters within the main body of the data. [ABSTRACT FROM AUTHOR]

Biological particulate matter (BioPM) is a poorly constrained, ubiquitous, and diverse subset of atmospheric aerosols. They influence climate, air quality, and health via many mechanisms, spurring renewed interest in constraining their emissions to elucidate their impacts. In order to build the framework required to assess the role of BioPM in these multidisciplinary areas, it is necessary to develop robust, high time-resolution detection methodologies so that BioPM emissions can be understood and characterized. In this study, we present ambient results from intensive monitoring at UK peri-urban and coastal ground sites using high time-resolution real-time bioaerosol spectrometers. We demonstrate the utility of a new dimensional reduction-driven BioPM classification scheme, where laboratory sample training data collected at the ChAMBRe facility were used to generate broad taxonomic class time series data of key species of interest. We show the general trends of these representative classes, spanning spring, early summer, and autumn periods between 2019 and 2021. Diurnal behaviors and meteorological relationships were investigated and contextualized; a key result arising from this study was the demonstration of rainfall-induced enhancement of nighttime Penicillium-like aerosol, where rainfall crucially only acts to enhance the quantity emitted without significantly influencing the early morning timing of peak spore liberation. [ABSTRACT FROM AUTHOR]