Inhalation Of Particles Research Articles

Elements of dissemination in cryptococcosis.

As healthcare improves and our ability to support patients with compromised immune systems increases, such patients become more vulnerable to microbes in the environment. These include fungal pathogens such as Cryptococcus neoformans, the primary cause of fungal meningitis and a top priority pathogen on the World Health Organization fungal pathogen list. Like many other environmental pathogens, C. neoformans must adapt to and thrive in diverse environments in order to cause disease: (i) the environmental niche, (ii) the lungs following inhalation of infectious particles, (iii) the bloodstream and/or lymphatic system during dissemination, and (iv) the central nervous system (CNS), where it causes a deadly cryptococcal meningoencephalitis. Because CNS infection is the driver of mortality and the presenting illness, understanding the dissemination process from both host and fungal perspectives is important for treating these infections. In this review, we discuss the different stages of dissemination, how fungal cells interact with host cells during disease, and the ability to adapt to different environments within hosts.

Prediction of Transport and Deposition of Porous Particles in the Respiratory System Using Eulerian-Lagrangian Approach.

Deep lung delivery is crucial for respiratory disease treatment. Although nano and submicron particles exhibited a good deposition efficiency in deep regions of the lung, powder nonuniformity and particle agglomeration reduce their efficiency. Inhalation of porous particles (PPs) can overcome the mentioned challenges due to their larger size and low-density. The present study numerically investigates the deposition and penetration efficiency of orally inhaled PPs. A revised drag coefficient was implemented for PP transport. A realistic mouth-throat to the fifth generation of the lung was reconstructed from CT-scan images. A dilute suspension of uniformly distributed particles was considered at three inhalation flow rates (15, 30, and 45 L/min). Governing equations of the flow field and particle transport are solved using an Eulerian-Lagrangian approach. The results demonstrate that inhaling PPs significantly reduces the total and regional deposition of particles. There was also a critical porosity value under moderate and high inhalation flow rates for large PPs. Below this critical value, PP deposition efficiency substantially decreases. Additionally, it was also found that under low inhalation flow rates, the impact of porosity value is negligible. Almost 95% of the PPs penetrate the lower branches. These findings provide particle engineers and pharmaceutics with profound insight into developing novel inhalation techniques and drug delivery methods for deep lung delivery.

Multi-scale modeling of aerosol transport in a mouth-to-truncated bronchial tree system

Computational fluid particle dynamics (CFPD) is widely employed to predict aerosol transport in a truncated bronchial tree model on account of its capacity to reveal details of flow field and particle movement. However, setting a physiologically consistent boundary condition in the CFPD for the idealized or image-based truncated bronchial tree model is still a challenge. This paper proposes a multi-scale modeling method, which contains an Extend-Bronchial tree-Network (EBN) boundary condition for a mouth-to-truncated bronchi system. The comparison between EBN boundary condition and a commonly used uniform pressure (UP) boundary condition is conducted. Subsequently, EBN method is used to study the nano-micron (100nm-10μm) particles transport in the mouth-to-truncated bronchi model at different inhalation volume rates (15, 60, 90L/min). Results show that EBN method is more physiologically rational and two methods differ in flow distribution in lobes, vortex structure, and particle transport. The maximum difference in flow rate distribution in lobes between two methods is about 20%, while the maximum relative disparity of particle penetration fraction from lobes and deposition fraction in the TLB is about 93% and 30%, respectively. Meanwhile, this paper reveals the variation of deposition fraction and penetration fraction with the changes in particle diameter and inhalation volume. Deposition efficiency, deposition hotspots and deposition mechanism are also analyzed with inlet Stokes number (Stk) and Reynolds number (Re). This research establishes a foundation for the simulation of aerosol transport in a whole respiratory tract and provides references for inhalation drug delivery and air pollutant management.

Association between air pollutants, thyroid disorders, and thyroid hormone levels: a scoping review of epidemiological evidence.

Over the past two decades, the incidence of thyroid disorders has been steadily increasing. There is evidence to suggest that air pollution may be one of the etiological factors of thyroid diseases. This comprehensive review aimed to examine the evidence related to air pollutants and thyroid disorders and thyroid hormones levels from an epidemiological perspective. The scoping review adopted a systematic approach to search for, identify, and include peer-reviewed articles published in English. We performed a comprehensive search of three databases-PubMed, Embase, and Web of Science to identify relevant literature on the relationship between air pollution [particulate matter, nitrogen oxide, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2)] exposure and thyroid disorders, including hypothyroidism, congenital hypothyroidism (CH), thyroid nodules, thyroid cancer, autoimmune thyroid diseases, as well as thyroid hormone levels, such as thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4). Articles published until August 1, 2023, were included. A total of 3,373 studies were retrieved, and among them, 25 studies covering eight different air pollutants were relevant. The most frequently studied air pollutants in this review included fine particulate matter (with fine particulate matter (PM2.5), n=21; inhalable particles (PM10), n=10; PM10-2.5, n=1) and nitrogen oxides (with NO2, n=13; NOx, n=3). The thyroid disorders and thyroid hormone levels most commonly associated with evidence of air pollution exposure were hypothyroidism (n=7) and TSH (n=12). Despite variations in study designs and exposure assessments, the findings consistently highlight the substantial health risks that air pollution, particularly PM2.5, poses to thyroid health, especially among vulnerable populations. Given that our study was limited to epidemiological investigations and the increasing prevalence of toxic substances in the environment, there is an urgent need for further research to elucidate the mechanisms by which these pollutants disrupt thyroid function and contribute to the development of thyroid diseases.

Resuspension of inhalable particles from clothing: A manikin-based chamber study

Clothing plays a significant role in facilitating inhalation exposure to particulate matter (PM). Nevertheless, there is a lack of studies investigating PM resuspension from clothing. This study aims to quantify size-resolved PM resuspension rate (RR) from clothing while exploring the effects of factors influencing this process. In a controlled chamber, we deposited Arizona Test Dust on a seated and clothed thermal manikin. The seated manikin was then placed in a resuspension chamber to perform arm movements using a consistent test mechanism. We evaluated the size-resolved resuspension rates in the diameter range 0.3–10 μm as a function of long-sleeve clothing type (cotton shirts vs. polyacrylic sweaters), relative humidity (RH) (35 vs. 70 %), dust loading (204 vs. 321 mg/m2), and movement intensity (0.25, 0.5, and 0.75 Hz). The short-term PM10 concentration in the breathing zone was 1.25 times higher than in the bulk air during resuspension. The size-dependent RR varied between 0.01 h−1 and 0.06 h−1. Higher RR was associated with higher movement intensity and dust loading on clothing, while a small dependence was found on clothing type and RH. The results offer a valuable dataset for enhancing current inhalation exposure models related to indoor aerosols and for developing targeted interventions to reduce exposure to particles associated with clothing.

Seasonal variation of particle number concentration in a busy urban street with exposure assessment and deposition in human respiratory tract

Ultrafine particles (UFP) associated with air quality and health impacts are a major concern in growing urban regions. Concentrations of UFP (particles of size between 10 and 100 nm) and accumulation mode (Nacc) (particles of size >100 and up to 1000 nm), are analyzed over a highly polluted megacity, Delhi, in conjunction with vehicular flow density, during peak (morning, and evening) and non-peak hours. UFP contributes ≥60% to total particle concentration during autumn and monsoon. UFP concentrations are about 50,000 particles per cm3 in winter which reduces to about 25,000 particles during monsoon. Nacc are about 20,000 (winter) and 10,000 (monsoon) particles per cm3. UFP concentration and Nacc during peak hours are at least twice higher than those obtained in non-peak hours, confirming the dominant influence of emissions from vehicular exhaust in the study region. Seasonal analysis of UFP size distribution reveals that direct emissions dominate the particle concentrations during winter and autumn, whereas new particle formation mechanism contributes the highest in spring and summer. Assessment of inhalable particle number concentration and particle deposition in the human respiratory tract using Multiple Path Particle Dosimetry (MPPD) model, performed for the first time, shows that the order in which these particles deposit in the human respiratory tract is alveoli > bronchiole > bronchus. The deposition ranges between 10 and 18 million nanoparticles during different hours of the day, whereas the estimated inhalable particle concentration (IPN) varies between 0.5 and 1 billion. Results on the IPN during activities classified from light (walking), medium, heavy, very heavy to severe (long-distance running) provide insights into health effects on vulnerable populations. These quantitative results obtained over a megacity on hourly and seasonal variations of nanoparticles along with IPN and deposition rates for different activities are important, and are invaluable inputs for developing mitigation policies aimed to improve air quality and public health, both of which are major concerns in South Asia.

Influence of L-leucine content on the aerosolization stability of spray-dried protein dry powder inhalation (DPI)

Inhalable formulations of medicines intended to act locally in the lung are therapeutically effective at lower doses with targeted delivery, compared to parenteral or oral administration. Meanwhile, different APIs, including biologics, have proven to be challenging regarding formulation and final bioavailability. This study focuses on the production, improved stability performance, and delivery of spray-dried, inhalable protein powders to the lungs. By spray-drying 11 aqueous formulations of proteinX with varying L-leucine content and by employing a Design of Experiment (DoE), two formulations have been selected for stability studies based on the highest fine particle fraction (FPF), highest monomer content, and lowest particle size. We found that 5 %w/w L-leucine (based on protein content) resulted in similar or higher FPF at 2–8 °C and 25 °C/60 %RH (67.12 % and 48.50 %) stored for six months than 10 %w/w L-leucine (68.49 % and 35.04 %). This indicates that less leucine may be sufficient to produce stable, spray-dried inhalable particles with an improved FPF, and by doubling the leucine content, the aerosolization stability can deteriorate. We have discussed the postulated hypothesis underlying the observed stability behavior based on solid-state and morphological analysis. Our results suggest that spray-dried proteinX-leu-powders can be delivered to the lung at a lower dose than for intravenous administration.

Model for atomization droplet size and energy distribution ratio at the distal end of an electrostatic nozzle

Electrostatic atomization minimum quantity lubrication (EMQL) employs the synergistic effect of multiple physical fields to atomize minute quantities of lubricant. This innovative methodology is distinguished by its capacity to ameliorate the atomization attributes of the lubricant substantially, which subsequently augments the migratory and infiltration proficiency of the droplets within the complex and demanding milieu of the cutting zone. Compared with the traditional minimum quantity lubrication (MQL), the EMQL process is further complicated by the multiphysical field influences. The presence of multiple physical fields not only increases the complexity of the forces acting on the liquid film but also induces changes in the physical properties of the lubricant itself, thus making the analysis of atomization characteristics and energy distribution particularly challenging. To address this objective reality, the current study has conducted a meticulous measurement of the volume average diameter, size distribution span, and the percentage concentration of inhalable particles of the charged droplets at various intercept positions of the EMQL nozzle. A predictive model for the volume-averaged droplet size at the far end of the EMQL nozzle was established with the observed statistical value F of 825.2125, which indicates a high regression accuracy of the model. Furthermore, based on the changes in the potential energy of surface tension, the loss of kinetic energy of gas, and the electric field work at different nozzle orifice positions in the EMQL system, an energy distribution ratio model for EMQL was developed. The energy distribution ratio coefficients under operating conditions of 0.1 MPa air pressure and 0 to 40 kV voltage on the 20 mm cross-section ranged from 3.094‰ to 3.458‰, while all other operating conditions and cross-sections had energy distribution ratios below 2.06‰. This research is expected to act as a catalyst for the progression of EMQL by stimulating innovation in the sphere of precision manufacturing, providing theoretical foundations, and offering practical guidance for the further development of EMQL technology.

Radiological characterization of materials used in the Italian heritage buildings

Building materials (BMs) can cause significant gamma dose indoors due to their natural radionuclide content. In 2013, the European Directive 59/2013/EURATOM introduced, in annex XIII, a list of types of BMs of interest for the content of gamma-emitting radionuclides among which materials or additives of natural igneous origin, such as granitoids, tuff and lava, are included. These materials have been largely employed for decorative and structural purposes in the course of centuries in the Italian heritage buildings. Aim of this study is to assess the contribute of BMs in terms of alpha exposure. Six samples of different materials, characterized in a previous study by the authors in terms of radiological content by means of a gamma spectrometry, have been analyzed in order to determine the specific exhalation rate of 222Rn, the radon emanation coefficient and excess in indoor radon concentration. Results confirm that BMs of natural igneous origin can play an important role in terms of alpha radiation dose and internal exposure due to the inhalation of the alpha particles emitted.

Diesel exhaust particle inhalation in conjunction with high-fat diet consumption alters the expression of pulmonary SARS-COV-2 infection pathways, which is mitigated by probiotic treatment in C57BL/6 male mice

BackgroundBoth exposure to air pollutants and obesity are associated with increased incidence and severity of COVID-19 infection; however, the mechanistic pathways involved are not well-characterized. After being primed by the transmembrane protease serine 2 (TMPRSS2) or furin protease, SARS-CoV-2 uses the angiotensin-converting enzyme (ACE)-2 receptor to enter respiratory epithelial cells. The androgen receptor (AR) is known to regulate both TMPRSS2 and ACE2 expression, and neuropilin-1 (NRP1) is a proposed coreceptor for SARS-CoV-2; thus, altered expression of these factors may promote susceptibility to infection. As such, this study investigated the hypothesis that inhalational exposure to traffic-generated particulate matter (diesel exhaust particulate; DEP) increases the expression of those pathways that mediate SARS-CoV-2 infection and susceptibility, which is exacerbated by the consumption of a high-fat (HF) diet.MethodsFour- to six-week-old male C57BL/6 mice fed either regular chow or a HF diet (HF, 45% kcal from fat) were randomly assigned to be exposed via oropharyngeal aspiration to 35 µg DEP suspended in 35 µl 0.9% sterile saline or sterile saline only (control) twice a week for 30 days. Furthermore, as previous studies have shown that probiotic treatment can protect against exposure-related inflammatory outcomes in the lungs, a subset of study animals fed a HF diet were concurrently treated with 0.3 g/day Winclove Ecologic® Barrier probiotics in their drinking water throughout the study.ResultsOur results revealed that the expression of ACE2 protein increased with DEP exposure and that TMPRSS2, AR, NRP1, and furin protein expression increased with DEP exposure in conjunction with a HF diet. These DEP ± HF diet-mediated increases in expression were mitigated with probiotic treatment.ConclusionThese findings suggest that inhalational exposure to air pollutants in conjunction with the consumption of a HF diet contributes to a more susceptible lung environment to SARS-CoV-2 infection and that probiotic treatment could be beneficial as a preventative measure.

Eucalyptus Wood Smoke Extract Elicits a Dose-Dependent Effect in Brain Endothelial Cells

The frequency, duration, and size of wildfires have been increasing, and the inhalation of wildfire smoke particles poses a significant risk to human health. Epidemiological studies have shown that wildfire smoke exposure is positively associated with cognitive and neurological dysfunctions. However, there is a significant gap in knowledge on how wildfire smoke exposure can affect the blood–brain barrier and cause molecular and cellular changes in the brain. Our study aims to determine the acute effect of smoldering eucalyptus wood smoke extract (WSE) on brain endothelial cells for potential neurotoxicity in vitro. Primary human brain microvascular endothelial cells (HBMEC) and immortalized human brain endothelial cell line (hCMEC/D3) were treated with different doses of WSE for 24 h. WSE treatment resulted in a dose-dependent increase in IL-8 in both HBMEC and hCMEC/D3. RNA-seq analyses showed a dose-dependent upregulation of genes involved in aryl hydrocarbon receptor (AhR) and nuclear factor erythroid 2-related factor 2 (NRF2) pathways and a decrease in tight junction markers in both HBMEC and hCMEC/D3. When comparing untreated controls, RNA-seq analyses showed that HBMEC have a higher expression of tight junction markers compared to hCMEC/D3. In summary, our study found that 24 h WSE treatment increases IL-8 production dose-dependently and decreases tight junction markers in both HBMEC and hCMEC/D3 that may be mediated through the AhR and NRF2 pathways, and HBMEC could be a better in vitro model for studying the effect of wood smoke extract or particles on brain endothelial cells.

Determination of Heavy Metal Concentrations at Ewu-Elepe, Ikorodu Dumpsite, Lagos, Nigeria

Heavy metal contamination of soil at dumpsites poses risks and hazards to humans and the ecosystems through inhalation of dust particles or dermal contact with the contaminated soil. Dumpsites are usually located at the outskirts of residential areas, but due to population increase in the urban centers as result of rural-urban movement which has become living habitats without considering the health implications. Thus, Ewu-Elepe dumpsite, located on the outskirts of Ikorodu, Lagos may pose a serious threat to residents of this area due to the improper disposal and ineffective management of waste at the dumpsite also, due to the metalic health issue recorded in this area. Therefore, this study was designed to determine the Heavy Metal Concentrations (HMC) and identify the type of Key Environmental Indicators (KEIs) responsible for the heavy metal contamination at the dumpsite. The Principal Component Analysis (PCA), Nemerow Integrated Pollution Index (NIPI), and Pollution Index (PI) procedures were adopted. The Akaike Information Criteria (AIC) was employed to determine the best KEI responsible for the presence of a particular heavy metal on the dumpsite. The Heavy Metals (HMs) found on the dumpsite were Zinc, Copper, Lead, Cadmium, Calcium, Manganese, and Iron. The identified KEIs on the dumpsite were: potential Hydrogen (pH), Electrical Conductivity (EC), Total Organic Carbon (TOC), Total Nitrogen (TN), Phosphorous (P), and Carbon Exchange Capacity (CEC). The AIC at 5% showed that the most significant KEI responsible for Zn was EC with the least value (16.21), pH for Pb (26.70), P for Ca (20.71), TOC for Cu (24.61) and Mn (81.09), TN for Cd (44.97), and CEC for Fe (41.04). The PCA and NIPI estimates for the heavy metals across the 20 sample points were (1760.57, 3.00); (1825.85, 2.30); (1330.80, 2.60); (1644.68, 2.40); (1602.57, 9.70); (1469.93, 2.40); (1379.85, 3.20); (1872.82, 2.40); (1859.30, 8.40); (1397.56, 2.30); (1995.32, 4.70); (1518.62, 3.10); (1565.33, 1.80); (1332.29, 5.10); (1748.59, 2.50); (1664.70, 3.90); (1792.24, 4.10); (1801.79, 2.30); (1801.18, 2.30); (1743.27, 2.00), respectively, implying that the dumpsite was highly concentrated in HMs. Copper, lead, cadmium, calcium, manganese, and iron highly polluted Ewu-Elepe dumpsite with potential hydrogen, electrical conductivity, total organic carbon, total nitrogen, phosphorous, and carbon exchange capacity as the key environmental indicators for the heavy metals.

Coupled Discrete Phase and Eulerian Wall Film Models for Drug Deposition Efficacy Analysis in Human Respiratory Airways.

The current study explores the effectiveness of drug particle deposition into human respiratory airways to cure various pulmonary-bound ailments. It has been assumed that drug solutions are inhaled in the form of tiny droplets or mist, which after striking create a thin layer along the inner surface of airways where the virus initially resides to infect the human body. A coupled Eulerian wall film (EWF) and discrete phase model (DPM) based simulation approach is used to capture these dynamics. Here, the Lagrangian DPM technique tracks the dynamics of tiny droplets, while the liquid layer formation after striking is captured using the Eulerian thin film approximations or the EWF model. Previous studies in this field primarily employed only the DPM method, which is inadequate to predict the poststriking dynamics of drug layer deposition and their spread to neutralize the respiratory virus. The drug delivery effectiveness is characterized by three different particle sizes, 1, 5, and 10 μm at the inhalation rates of 15, 30, and 60 L per minute (LPM). It has been found that the size of the drug particles significantly influences drug delivery effectiveness. The film thickness increases monotonically with particle sizes and inhalation rates. However, this increase in averaged film thickness is prominent in the range 5 to 10 μm (≈60%) compared to 1 to 5 μm (≈10%) droplet sizes at generation level 4 (G4). The other deposition parameters, e.g., deposition fraction, deposition density, and area coverage) roughly show similar behavior with the increase in droplet sizes. Therefore, it is recommended to vary the droplet sizes between 5 and 10 μm for better deposition effectiveness. The sizes of more than 10 μm mostly stuck into the oral cavity and cannot reach the targeted generations. In contrast, less than 5 μm may reach much deeper generations than the targeted one.

Assessment of airborne transmitted infection risk in classrooms using computational fluid dynamics and machine learning-based surrogate modeling

In recent years, the assessment of airborne transmitted infection risk has been extensively performed using Computational Fluid Dynamics (CFD) simulations, especially in response to the COVID-19 pandemic. Nevertheless, the high computational demands and time-intensive nature of CFD simulations highlight the need for fast or real-time infection risk predictions. This capability is crucial for swift decision-making in dynamic environments where timely health interventions are critical. This paper presents a thorough analysis of airborne infection risks in classroom environments based on CFD simulations to understand key factors such as ventilation strategies, air change rates, occupant arrangements, source locations, and particle sizes. This study also employs data-driven supervised learning methods—specifically, Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN)—to generate surrogate models for predicting airborne infection risk. Key findings reveal that different ventilation strategies significantly affect airborne infection risk, reducing it by 49 %–77 %. Moreover, the conventional Wells-Riley model was identified as lacking in its ability to accurately predict local infection risks. The study further challenges the assumption that higher air change rates are universally beneficial, considering that occupants seated in the back rows of a classroom experienced up to a 166 % increased risk, despite elevating air change rates from 1.1 h−1 to 11 h−1. These results suggest that physical distancing alone may be insufficient and highlight the importance of considering other factors such as occupant arrangements. Regarding the model performance, the ANN-based surrogate model demonstrated varying prediction accuracy. For inhalable particle concentration predictions for susceptible occupants, R2 values ranged from 0.31 to 0.65 with CVRMSE values between 100 % and 180 %. In contrast, the model achieved an R2 of 0.79 and a CVRMSE of 34 % for infectors. The insights and methodologies from this study can inform HVAC system design and operation strategies to better mitigate infectious disease transmission in densely occupied indoor environments.

Per- and polyfluoroalkyl substances (PFAS) in Indian environment: Prevalence, impacts and solutions

Per- and polyfluoroalkyl substances (PFAS) pose a significant and growing environmental challenge worldwide due to their remarkable persistence, widespread presence, and potential to adversely affect human health. This review offers a holistic investigation of current knowledge regarding PFAS in India. Existing research demonstrates the presence of PFAS in various environmental compartments, including water bodies, soil/sediment, atmosphere, and even wildlife. Furthermore, the review explores the potential ecological and human exposure pathways. Some of these pathways include consumption of contaminated drinking water, PFAS-laden food, and inhalation of dust particles containing PFAS. The potential health impacts on humans and biota are then elaborated, highlighting developmental problems, endocrine disruption, and even an increased risk of certain cancers. The findings strongly suggest that industrial activity, urbanization, and consumer product usage are significant contributors to environmental PFAS contamination. A critical gap identified within this review is the absence of established regulations for PFAS usage and disposal within the Indian environment. The review summarizes existing remediation techniques, providing a comprehensive strategy for managing PFAS in the Indian context. The implementation of stringent regulations governing PFAS production, use, and environmental discharge, coupled with the exploration of non-fluorinated alternatives, is imperative. Additionally, the deployment of advanced water treatment technologies specifically designed for PFAS removal and the fostering of collaborative research and development efforts are crucial steps towards mitigating PFAS contamination. In conclusion, this review emphasizes the urgent need for a multifaceted approach to manage PFAS in India.

Relationships between water quality of a long-distance inter-basin water diversion project and air pollution emissions along the canal: Distributions, lag effects, and nonlinear responses

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators—permanganate index (CODMn), NH3-N, pH, turbidity, conductivity, and dissolved organic matter (DOM)—and six air pollution indicators—O3, CO, NO2, SO2, 2.5 μm particulate matter (PM2.5), and inhalable particles (PM10)—using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann–Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for CODMn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO2, SO2, PM2.5, and PM10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for CODMn, NH3-N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution.

Impact of Earthquakes on Lung Health.

Earthquakes are catastrophic natural disasters that cause extensive damage to infrastructure and disrupt the lives of millions worldwide. Beyond the immediate physical and psychological damage caused by earthquakes, these events can significantly impact respiratory health. The inhalation of dust, smoke, particulates, toxic gases, and asbestos exposure can lead to various respiratory health pathologies. These include respiratory infections, exacerbations of pre-existing respiratory diseases, chest traumas, and pulmonary and venous thromboembolism. Longitudinal studies are necessary to assess the long-term respiratory health effects in affected populations. By addressing these knowledge gaps, future mitigation strategies and preparedness measures can be developed to minimize the respiratory health impacts of earthquakes and improve the well-being of affected communities. Robust building infrastructure and comprehensive earthquake preparedness are emerging as the most important determinants for not only mitigating building collapse but also significantly reducing the potential health impacts that follow. This comprehensive review aims to provide a systematic overview of the lung health impacts of earthquakes. It highlights the need for further research to identify specific pollutants, air contaminants, and environmental factors contributing to respiratory health issues following earthquakes.

Personalized air curtain optimized to reduce fine particle exposure in industrial environments with different indoor airflow conditions

Recently, personalized air curtains combined with safety helmets have shown great potential in reducing particle inhalation exposure of workers in industrial settings. In this study, the helmet of the basic air curtain (BA) was optimized. One optimization method was to add a mask to the helmet of the basic air curtain (BAF), and the other optimization method was to add a face shield and sealing cloth to the helmet of the basic air curtain (BAFS). Computational fluid dynamics (CFD) method was used to compare the protective effect (PE) of the air curtain helmets with BA, BAF, and BAFS configurations. The impacts of the air supply velocity (0.05, 0.1, 0.2, 0.3, 0.4, and 0.5 m/s) of the air curtain in a static wind environment, and the direction and speed of the indoor airflow (0.1, 0.3, 0.5, 0.7 and 1 m/s) on the PE of the helmets were also investigated. Results showed that the BAFS helmet had the best respiratory protection, followed by the BAF and the BA helmet, regardless of indoor airflow conditions. The directional diversion effect of the face shield and the bidirectional blocking effect of the sealing cloth strengthened the PE. In the static indoor airflow environment, air supply velocity and helmet configurations had a remarkable impact on the PE. With the increase of the air supply velocity, the PE of the BA and optimized helmets showed S-shape and C-shape trends. When the air supply velocity reached 0.5 m/s, the PE of the BA, BAF, and BAFS helmets were close to 0.844, 0.985, and 1, respectively. In the uniform indoor airflow environments, the PE of the BAFS helmet was close to 1 and was almost undisturbed by the indoor airflow. This work may help inspire the development of respiratory protective equipment to reduce particulate exposure in industrial settings.

Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis

Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis.

A novel approach for large-scale characterization of residential cooking-generated PM with computer vision and low-cost sensors

Cooking is one of the major sources of indoor particulate matter (PM), which poses significant health risks and is a severe health hazard. Current studies lack an economical and effective analytical framework for quantifying inhalable particles (PM10) and fine particulate matter (PM2.5) from residential cooking activities on a large scale under real-world scenarios. This study bridges this gap by employing computer vision (CV) technology and readily available sensors. We collected data over a month in real-world settings, including cooking videos and air quality data (indoor PM10, PM2.5, CO2, temperature, relative humidity, and outdoor PM10 and PM2.5 concentrations). To classify high-emission (pan-frying, stir-frying, deep-frying) and low-emission (stewing, steaming, boiling, non-cooking) activities, we developed and validated a robust CV model named “Cooking-I3D.” This model leverages a pre-trained Two-Stream Inflated 3D ConvNet (I3D) architecture. We then assessed the efficacy of the CV-predicted cooking method in PM characterization using a first-order multivariate autoregressive model, controlling for environmental factors. The Cooking-I3D model achieved exceptional performance, boasting an accuracy of 95 % and an Area Under the Curve (AUC) of 0.98. Our results indicate that a single 6-minute high-emission cooking event triggers a 21–25 % increase in indoor PM concentrations and a 23–24 % increase in the indoor/outdoor ratio, with relative errors in these estimates ranging from 10 to 21 %. This innovative method offers a powerful tool for long-term assessment of cooking-related indoor air pollution and facilitates precision exposure assessment in human health studies.