Towards understanding respiratory particle transport and deposition in the human respiratory system: Effects of physiological conditions and particle properties

Composition characteristics, source analysis and risk assessment of PAHs in surface waters of Lipu

Polycyclic aromatic hydrocarbons (PAHs) are a group of aromatic hydrocarbons with serious toxic effects on ecosystems and human health. In this study, Lipu River Basin in a typical karst area was selected as a case to study the influence of anthropogenic activities on the distribution and fate of PAHs in surface water. The results showed that 16 priority controlled PAHs were detected in all samples. PAHs discharges from industrial activities were the primary pollution sources, and the non-point sources such as traffic emission, biomass combustion and surface runoff with agricultural origin also contributed to the high PAHs concentrations. The coexistence of multiple PAHs posed a high risk to the aquatic organisms and human health. The quality of surface water was continuously improved, but water pollution problems remain. The results of this study provide valuable theoretical support for environmental protection and policy making.

Patient-specific simulation of particle dynamics in the respiratory airways from CT-scan-reconstructed images using a continuous phase modelling framework

Understanding particle transport and deposition in human airway bifurcations is vital for respiratory health and inhaled therapies. CT scan images of respiratory pathways provide detailed anatomical models up to 6-12 airway generations, which are useful for airflow modelling in larger airways. However, imperfections in CT imaging, particularly at branching points, complicate the simulation of airflow and particle dynamics in smaller airways. To address these challenges, we present a computationally efficient albeit patient-specific simulation framework for the transport of micron-sized particles in respiratory pathways. This framework employs a Eulerian modelling approach that incorporates CT-scan derived patient-specific geometry along with the underlying vascular structures. To address the challenges due to limited resolution and minimize entrance effects in airflow simulations, flow extensions are added at the inlet regions, preventing distortion of airflow patterns in the lung. Particles are modelled as a continuous phase by solving equations for their concentration distribution, which are coupled with the fluid flow equations to simulate particle dynamics efficiently. The results show that particle size, flow rate, and airway structure significantly influence deposition patterns: small particles (1 μm) penetrate deeply with minimal deposition, medium-sized particles (10 μm) exhibit a balance between inertial impaction and gravitational settling, and large particles (30 μm) predominantly deposit in the upper airways. Compared to the traditional particle-tracking framework, our approach reduces computational costs while effectively capturing the key effects of the flow field on particle transport in realistic airway bifurcations. This advancement enables faster and more scalable personalized simulations for respiratory health assessments, offering a more efficient alternative to resource-intensive methods, and is crucial for applications like improving aerosol drug delivery, assessing exposure to airborne pollutants, and designing preventive health strategies.

Quantitative microbial risk assessment for on-site employees in a wastewater treatment plant and implicated surrounding residents exposed to S. aureus bioaerosols

Wastewater treatment plants (WWTPs) have increased dramatically in number due to rapid urbanization. However, these facilities release significant amounts of potentially hazardous airborne microorganisms, including Staphylococcus aureus bioaerosol, which poses health risks to employees and nearby residents. Therefore, this study estimated the direct exposure risks of bioaerosols in WWTPs using a quantitative microbial risk assessment (QMRA) framework evaluated through sensitivity analysis methods. The results showed that the sludge yard had the highest mean bioaerosol concentration but the lowest aerosolization ratio. The disease health risk burden values followed a descending order: residential area > office > sludge yard > inverted umbrella aeration tank > microporous aeration tank > control room. Meanwhile, the risk values were shrunken by 14.1-17.3 times when personal protective equipment (PPE) was used. Sensitivity analysis for individual and multifarious contributions showed that the removal fraction achieved by using PPE was consistently the most influential parameter, followed by aerosol ingestion rate or exposure concentration. This suggests that isolation strips, such as green belts, between the WWTP and residential area is alternative effective measures for residents and wearing masks is essential measure for on-site employees. Furthermore, the multifarious contribution analysis showed that stepwise risk mitigation approaches were equally effective as one-step solutions, as indicated by their identical sensitivity coefficient rankings. This indicates that, when resources for mitigating risk are limited, taking a stepwise approach to risk reduction can be equally effective as allocating all resources at once. This study can advance the understanding of the characteristics and health risks of WWTP bioaerosol emissions and supplement the multifarious contributions of the sensitivity analysis implemented in the QMRA model, which contributes to public wellness development. The findings of this study will help in the optimization of control strategies for local wastewater utilities and implicated surrounding residents.

Advanced Characterization of Industrial Smoke: Particle Composition and Size Analysis with Single Particle Aerosol Mass Spectrometry and Optimized Machine Learning

With the acceleration of industrialization, industrial smoke particles containing complex chemical compositions and varying particle sizes pose a serious threat to the environment and human health. As a powerful tool for aerosol measurement, mass spectrometry can effectively analyze particulate matter. However, due to the high dimensionality and complexity of mass spectrometry data, research on the relationship between particle size and composition remains very limited. To address this gap, this study innovatively combines single particle aerosol mass spectrometry (SPAMS) with optimized machine learning, achieving for the first time the precise prediction of smoke particle size based on mass spectrometry data. Nonlinear dimensionality reduction of mass spectrometry data was performed using kernel principal component analysis (KPCA) to extract key features. Combined with random forest (RF) for prediction, the R2 of the test set reached 0.843 after optimization. Additionally, to address the issue of imbalanced sample distribution, a systematic stratified random sampling algorithm (SSRSA) was developed, significantly enhancing the model's generalization ability and stability during training and testing. This study also simulated a soldering scenario to analyze lead (Pb) isotope abundances and particle size distributions in smoke at different soldering temperatures. Results indicate a significant correlation between the abundance of lead isotopes and the soldering temperature. Additionally, as the soldering temperature increases, the proportion of smaller sized particles increases noticeably. This research provides an innovative approach for precise analysis of industrial smoke particle composition and size, offering critical scientific insights for health risk assessment and the development of pollution control strategies.

Review of in vitro studies evaluating respiratory toxicity of aerosols: impact of cell types, chemical composition, and atmospheric processing

In recent decades, several cell-based and acellular methods have been developed to evaluate ambient particulate matter (PM) toxicity. Although cell-based methods provide a more comprehensive assessment of PM toxicity, their results are difficult to comprehend due to the diversity in cellular endpoints, cell types, and assays and the interference of PM chemical components with some of the assays' techniques. In this review, we attempt to clarify some of these issues. We first discuss the morphological and immunological differences among various macrophage and epithelial cells, belonging to the respiratory systems of human and murine species, used in the in vitro studies evaluating PM toxicity. Then, we review the current state of knowledge on the role of different PM chemical components and the relevance of atmospheric processing and aging of aerosols in the respiratory toxicity of PM. Our review demonstrates the need to adopt more physiologically relevant cellular models such as epithelial (or endothelial) cells instead of macrophages for oxidative stress measurement. We suggest limiting macrophages for investigating other cellular responses (e.g., phagocytosis, inflammation, and DNA damage). Unlike monocultures (of macrophages and epithelial cells), which are generally used to study the direct effects of PM on a given cell type, the use of co-culture systems should be encouraged to investigate a more comprehensive effect of PM in the presence of other cells. Our review has identified two major groups of toxic PM chemical species from the existing literature, i.e., metals (Fe, Cu, Mn, Cr, Ni, and Zn) and organic compounds (PAHs, ketones, aliphatic and chlorinated hydrocarbons, and quinones). However, the relative toxicities of these species are still a matter of debate. Finally, the results of the existing studies investigating the effect of aging on PM toxicity are ambiguous, with varying results due to different cell types, different aging conditions, and the presence/absence of specific oxidants. More systematic studies are necessary to understand the role of different SOA precursors, interactions between different PM components, and aging conditions in the overall toxicity of PM. We anticipate that our review will guide future investigations by helping researchers choose appropriate cell models, resulting in a more meaningful interpretation of cell-based assays and thus ultimately leading to a better understanding of the health effects of PM exposure.

Determination of submicron aerosols effective density using two-stage low-pressure impactor and aerosol electrometer based on pre-measured particle size distribution

A novel methodology was presented for determining the representative effective density of aerosols of a given size distribution, using a lab-made two-stage low-pressure impactor and an aerosol electrometer. Electrical currents upstream (Imeasured, up) and downstream (Imeasured, down) of the 2nd stage of the impactor were measured using a corona charger and the aerosol electrometer. In addition, the electrical currents upstream (Icalculated, up) and downstream (Icalculated, down) of the 2nd stage of the impactor were calculated using the aerosol charging theory. Then, the difference between the ratio of Imeasured,down to Imeasured,up and the ratio of Icalculated,down to Icalculated,up was iterated with varying the presumed effective density until the difference was smaller than 0.001. The methodology was validated using poly-disperse sodium chloride (NaCl) particles. The effective densities of ambient aerosols were then obtained from indoor and outdoor environments and compared with those calculated from a relation between mobility (scanning mobility particle sizer (SMPS) measurement) and aerodynamic (electrical low-pressure impactor (ELPI) measurement) diameters. Compared to the effective densities obtained with SMPS and ELPI measurements, the effective densities obtained using the methodology introduced in this paper differed within 10 % deviation, depending on measurement location. After an averaged effective density for given size distribution is obtained at a measurement location, the number-based size distribution can be easily converted to mass-based size distribution using the representative effective density.

Composition analysis and health risk assessment of the hazardous compounds in cooking fumes emitted from heated soybean oils with different refining levels

The cooking fumes generated from thermal cooking oils contains various of hazardous components and shows deleterious health effects. The edible oil refining is designed to improve the oil quality and safety. While, there remains unknown about the connections between the characteristics and health risks of the cooking fumes and oils with different refining levels. In this study, the hazardous compounds, including aldehydes, ketones, polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM) in the fumes emitted from heated soybean oils with different refining levels were characterized, and their health risks were assessed. Results demonstrated that the concentration range of aldehydes and ketones (from 328.06 ± 24.64 to 796.52 ± 29.67 μg/m3), PAHs (from 4.39 ± 0.19 to 7.86 ± 0.51 μg/m3), and PM (from 0.36 ± 0.14 to 5.08 ± 0.15 mg/m3) varied among soybean oil with different refining levels, respectively. The neutralized oil showed the highest concentration of aldehydes and ketones, whereas the refined oil showed the lowest. The highest concentration levels of PAHs and PM were observed in fumes emitted from crude oil. A highly significant (p < 0.001) positive correlation between the acid value of cooking oil and the concentrations of PM was found, suggesting that removing free fatty acids is critical for mitigating PM concentration in cooking fumes. Additionally, the incremental lifetime cancer risk (ILCR) values of PAHs and aldehydes were 5.60 × 10-4 to 8.66 × 10-5 and 5.60 × 10-4 to 8.66 × 10-5, respectively, which were substantially higher than the acceptable levels (1.0 × 10-6) established by US EPA. The present study quantifies the impact of edible oil refining on hazardous compound emissions and provides a theoretical basis for controlling the health risks of cooking fumes via precise edible oil processing.

Chitosan-Based Nanocarriers for Pulmonary and Intranasal Drug Delivery Systems: A Comprehensive Overview of their Applications

The optimization of respiratory health is important, and one avenue for achieving this is through the application of both Pulmonary Drug Delivery System (PDDS) and Intranasal Delivery (IND). PDDS offers immediate delivery of medication to the respiratory system, providing advantages, such as sustained regional drug concentration, tunable drug release, extended duration of action, and enhanced patient compliance. IND, renowned for its non-invasive nature and swift onset of action, presents a promising path for advancement. Modern PDDS and IND utilize various polymers, among which chitosan (CS) stands out. CS is a biocompatible and biodegradable polysaccharide with unique physicochemical properties, making it well-suited for medical and pharmaceutical applications. The multiple positively charged amino groups present in CS facilitate its interaction with negatively charged mucous membranes, allowing CS to adsorb easily onto the mucosal surface. In addition, CS-based nanocarriers have been an important topic of research. Polymeric Nanoparticles (NPs), liposomes, dendrimers, microspheres, nanoemulsions, Solid Lipid Nanoparticles (SLNs), carbon nanotubes, and modified effective targeting systems compete as important ways of increasing pulmonary drug delivery with chitosan. This review covers the latest findings on CS-based nanocarriers and their applications.