Determining optimal sampling conditions in the TSI Nanometer Aerosol Sampler 3089

Résumé fourni par la source

Abstract. Physicochemical characterisation of airborne particles requires appropriate sampling and deposition methods. The TSI Nanometer Aerosol Sampler 3089 (TSI NAS) has predominantly been used as an electrostatic precipitator for depositing airborne particles, enabling subsequent offline characterisation through techniques such as electron microscopy. However, the optimal sampling time of TSI NAS for varying input concentrations has not been thoroughly investigated. This is extremely important as the concentrations of particles in different environments differ significantly. This study aimed to establish the appropriate sampling durations of TSI NAS for various input concentrations or, conversely, to determine suitable input concentrations for a fixed sampling duration. We developed an experimental setup and a linear regression model to predict conditions conducive to efficiently collecting loaded samples, particularly at low concentrations, such as exhaled particles from the human respiratory tract or marine aerosol particles. Experiments were conducted using the TSI NAS 3089 at a flow rate of 1 L min−1 and a voltage of −9 kV. Three particle types, nebulised from different solutions at low, medium, and high concentrations, were sampled over durations of 1, 3, and 6 h. Deposition densities were subsequently analysed using ImageJ software. The findings revealed a linear relationship between deposition density and the product of particle concentration and sampling time, with a recommended density range of 0.015 to 0.1 particles µm−2 for particles with a count median diameter of approximately 100 nm and average circularity of 0.56 ± 0.25. Despite potential factors affecting the accuracy of the model, such as the number of samples, random collisions, and potential overload in high-concentration experiments, it provides a valuable predictive tool for determining optimal sampling times. The suggested linear regression model is applicable across various research areas, enhancing the efficiency and accuracy of airborne particle characterisation.

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