Journal of Aerosol Science最新文献

Low-cost PM_2.5 sensors often suffer from environmental cross-sensitivities, requiring regular calibration across a wide range of concentrations. This is typically achieved by co-locating LCS with regulatory stations and using statistical models. However, this approach becomes challenging in regions with limited regulatory monitoring stations or access. To address this challenge, we explored building separate calibration models for the pseudo-regional component of the total PM_2.5 concentration, which represents background concentration, and the hyper-local component of the total concentration. This is based on the premise that the regional concentration is consistent across a given region and therefore direct co-location is less necessary, and the idea that the local concentration is not influenced by geographic properties and therefore can be calibrated based on co-location elsewhere. In this work, we used publicly-available PurpleAir data for 2022 from five different cities in South Asia and North America, and built city-specific calibration models for the regional concentrations using multiple linear regression. We tested the model performance in the city the model was built in (intra-city models; trained and cross-validated in the same city) and in other cities (inter-city models; trained and cross-validated in different cities). The regional calibration model reduced the normalized root mean square error (nRMSE) of both intra-city models, from 51% to 26%, and inter-city models, from 55% to 25% compared to PurpleAir reported concentrations. Overall, the results of this work demonstrate the potential for improved transferability of calibration models and provides evidence that calibration models built for regional concentration and local concentration separately may be a viable solution when deploying in places with limited regulatory monitoring or access to monitoring stations.

A flexible way to generate bimetallic nanoparticles with high control of their composition is to use spark ablation of alloyed electrodes. It has been generally accepted and stated that particles produced using spark ablation of alloyed electrodes obtain the same chemical composition as the electrodes. However, we identify a lack of studies fully supporting the connection between electrode and particle composition, presented in a small literature survey. The aim of the study is, hence, to explore the validity of the statement by analysing the relation between alloyed electrodes and their resulting particle composition using three sets of AgAu electrodes containing Au and 25, 50, and 75 atomic % Ag, respectively. The resulting composition is thoroughly investigated using both single particle (scanning- and transmission electron microscopy) and ensemble particle techniques (inductive coupled plasma-mass spectroscopy, x-ray photoelectron spectroscopy, x-ray fluorescence, and optical measurements of surface plasmon resonance. We also investigate how sample size (e.g., the number of particles analysed) affects the reliability of the resulting sample mean. For single-particle measurements of a sample with a compositional standard deviation of a few atomic percentage points, a sample size of 20 particles is a good benchmark for obtaining reliable results of the sample mean. Furthermore, this article aims to challenge the practice in which the composition of nanoparticles is measured, presented, and interpreted, to improve and facilitate future research related to this topic. From the results of this study, it could be concluded that for the investigated Ag–Au material system, the particles obtained a composition very similar to the alloyed AgAu electrodes.

To ensure the safe and effective decommissioning of the Fukushima Daiichi Nuclear Power Plant (NPP), it is crucial to investigate the behavior of radioactive aerosols generated during the laser decontamination of radiation hot spots and laser cutting of fuel debris. Understanding the generation and dispersion patterns of these aerosols is of utmost importance for the proper planning and execution of decommissioning activities. This study focuses on the performance evaluation of high-power continuous wave (CW) fiber lasers for cleaning surfaces composed of carbon steel, stainless steel, and concrete. The effective confinement and scavenging of radioactive aerosols are critical in minimizing the risk of radiation exposure during decommissioning processes. Test samples, made of carbon steel (CS), stainless steel (SS), and concrete, coated with ZrO₂, CeO₂, and CsI, were subjected to laser decontamination using a class-4 fiber laser within the UTARTS (University of Tokyo Aerosol Removal Test with Sprays) facility. The aerosol particles generated during the laser decontamination process were confined and captured utilizing a spray and mist system. The study proposes the utilization of a combined spray and mist technique, which has demonstrated high efficiency in scavenging aerosols generated through laser irradiation. This research contributes to the broader goal of promoting best practices and innovative solutions in the field of nuclear decommissioning, thereby safeguarding the environment and human health.

Heterogeneous condensation of water vapor on particles is widely used in the fields of atmospheric cloud physics and industrial particulate abatement. Particles surface wettability plays a fundamental role in water vapor condensation, but a microscopic insight of the impact of particles surface wettability on vapor condensation is lacked. Therefore, the Environmental Scanning Electron Microscope (ESEM) is adopted to directly visualize vapor condensation on particles with different surface wettability at a microscopic scale. Firstly heterogeneous condensation of water vapor on the hydrophilic and hydrophobic particles is obtained by the ESEM. The results show that when water vapor condenses on the hydrophilic particles, a spherical cap-shaped embryo first appears and subsequently develops into a spherical droplet to finally wrap the single particle. While on the hydrophobic particles, a spherical cap-shaped embryo will first appear and continue to grow into a spherical droplet to finally detach from the particle. Then the wetting coefficient is introduced to characterize the wetting degree of the droplet on the particle. The wetting coefficient will increase with the decrease of the contact angle, so the wettability of water vapor on the hydrophilic particles is better than that on the hydrophobic particles. Meanwhile based on classical nucleation theory, the geometrical factor and the critical supersaturation will decrease when the contact angle decreases, making it easier for water vapor to condense on the hydrophilic particles. Finally the line tension is investigated to explain the condensation mechanism. On the hydrophilic particles, when the droplet has crossed the equatorial line of the particle, the positive line tension promotes the spreading movement of the droplet over the particle to form a wrapped spherical droplet. Therefore, there is a transition from a spherical cap-shaped embryo before crossing the equatorial line of the particle to a wrapped spherical droplet after crossing the equatorial line. While on the hydrophobic particles, the negative line tension suppresses the spreading movement of the droplet over the particle to form a detached spherical droplet. So there is a transition from a spherical cap-shaped embryo before crossing the equatorial line of the particle to a detached spherical droplet after crossing the equatorial line.

Aggregated aerosol particles released from high temperature processes and combustion processes are often described as quasi-fractal aggregates, where the shape of these particles is represented by the scaling law. A detailed understanding of the morphology is quite important as various properties are strongly dependent on the particle shape. Electron microcopy based image analysis is the most commonly used technique to visualize and study the morphological features. In this study, we propose a machine learning (ML)-assisted retrieval method where ML techniques are combined with optimization algorithms to predict the morphological features and the corresponding 3-dimensional structures from microscopic images. The proposed algorithm is comprehensively tested with “synthetic” images as well as Transmission Electron Microcopy images. Various ML models, including Linear regression, Artificial Neural Network, K-nearest neighbours, Random Forest regression, and XGBoost are used for preliminary prediction of the morphological features (Number of monomers (N), fractal prefactor (k_f) and fractal Dimension (D_f)). These are used to narrow down the search space in the optimization algorithms. Random Forest and XGBoost methods achieved approximately 0.96 R2 score for N, 0.85 for D_f and 0.73 for k_f. Multiple optimization methods, including PSO, JAYA, and JAYA-SA, were tested in the study. The method was tested across a wide range of parameters, including N (up to 500), D_f (1.1–2.7), and k_f (0.6–2.1), and the results are quite promising while comparing various 3-dimensional properties of the retrieved structures. The retrieved fractal parameters, N and D_f, exhibited errors under 10%, and the predicted k_f values were found within approximately 15% using the proposed method. Results also show that the 3-dimensional properties of the predicted structure are quite close to the structures used for testing the algorithm. The algorithm was also parallelized to improve the computational time. The results show that the predicted fractal parameters and the retrieved 3-dimensional structures are quite similar to the structures used for testing across a wide range of particle morphologies. The incorporation of ML models has significantly improved the accuracy and computational speed, compared to the existing retrieval techniques.