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Inappropriate planting patterns can increase pollutant concentrations and threaten human health. This study examined three greening patterns (trees, trees + hedges, and hedges) using the ENVI-met model to evaluate the different effects of various planting patterns on PM2.5 dispersion within an idealized 3D street canyon under three typical wind directions. Results showed that street greenbelts alter the PM2.5 concentration field within canyons, and the horizontal and vertical distribution characteristics of PM2.5 under different wind directions were significantly different. The arbor-hedge vegetation structure showed the highest total vegetation deposition amount due to larger canopy volumes while hedges have better deposition amounts per unit volume due to their proximity to emission sources. Additionally, this research selected the averaged relative difference in PM2.5 concentration (ARDC) indicator to assess the influence of different green scenarios on the dispersion of PM2.5 concentrations. Wind direction and planting patterns jointly affect the dispersion of PM2.5 in canyons, and the ARDC varied from -4.39 % to 105.36 %. Unilateral-trees on the windward side or two rows of hedges may be the optimal vegetation layout by trade-off with other services. ARDC was significantly correlated (p < 0.01) with most of the 3D green indicators. These results could provide effective suggestions for optimizing the layout of greenbelts in street canyons to improve air quality.

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There is mounting evidence implicating the potential neurotoxic effects of PM2.5 during brain development, as it has been observed to traverse both the placental barrier and the fetal blood-brain barrier. However, the current utilization of 2D cell culture and animal models falls short in providing an accurate representation of human brain development. Consequently, the precise mechanisms underlying PM2.5-induced developmental neurotoxicity in humans remain obscure. To address this research gap, we constructed three-dimensional (3D) cortical organoids that faithfully recapitulate the initial stages of human cerebral cortex development. Our goal is to investigate the mechanisms of PM2.5-induced neurotoxicity using 3D brain organoids that express cortical layer proteins. Our findings demonstrate that exposure to PM2.5 concentrations of 5 µg/mL and 50 µg/mL induces neuronal apoptosis and disrupts normal neural differentiation, thereby suggesting a detrimental impact on neurodevelopment. Furthermore, transcriptomic analysis revealed PM2.5 exposure induced aberrations in mitochondrial complex I functionality, which is reminiscent of Parkinson's syndrome, potentially mediated by misguided axon guidance and compromised synaptic maintenance. This study is a pioneering assessment of the neurotoxicity of PM2.5 pollution on human brain tissues based on 3D cortical organoids, and the results are of great significance in guiding the formulation of the next air pollution prevention and control policies in China to achieve the sustainable improvement of air quality and to formulate pollution abatement strategies that can maximize the benefits to public health.

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Fine particulate matter (PM2.5) has been identified as a significant contributing factor to the exacerbation of chronic obstructive pulmonary disease (COPD). It has been observed that PM2.5 may induce lung fibrosis in COPD, although the precise molecular mechanism behind this remains unclear. In a previous study, we demonstrated that PM2.5 upregulates oxidative stress induced growth inhibitor 1 (OSGIN1), which in turn leads to injury in airway epithelial cells, thereby, suggesting a potential link between PM2.5 exposure and COPD. Based on this, we hypothesized that OSGIN1 plays a role in PM2.5-induced fibrosis in COPD. Human bronchial epithelial cells (HBEs) were treated with cigarette smoke extract (CSE) to construct an in vitro model of COPD. Our findings revealed that PM2.5 increased fibrosis indicators and upregulated OSGIN1 in CSE-stimulated HBEs (CSE-HBEs), and knockdown of OSGIN1 reduced the expression of fibrosis indicators. Through the use of microRNA target prediction software and the Gene Expression Omnibus database, we predicted miRNAs that targeted OSGIN1 in COPD. Subsequently, real-time polymerase chain reaction and western blot analysis confirmed that PM2.5 modulated miR-654-5p to regulate OSGIN1 in CSE-HBEs. Western blot demonstrated that OSGIN1 induced autophagy, thereby exacerbating fibrosis in CSE-HBEs. In summary, our results suggest that PM2.5 upregulates OSGIN1 through inhibiting miR-654-5p, leading to increased autophagy and fibrosis in CSE-HBEs.

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There is growing global concern regarding the detrimental health impacts of PM2.5 emissions from traditional stoves that utilize polluting fuels. Conventional methods for estimating daily personal PM2.5 exposure involve personal air samplers and measuring devices placed in a waist pouch, but these instruments are cumbersome and inconvenient. To address this issue, we developed a novel neck-mounted PM2.5 monitoring device (Pocket PM2.5 Logger) that is compact, lightweight, and can operate continuously for 1 week without recharging. Twelve participants who utilized charcoal, firewood, or propane gas for cooking in rural regions of Rwanda wore the Pocket PM2.5 Logger continuously for 1 week, and time-series variations in personal PM2.5 exposure were recorded at 5-min intervals. Individual daily exposure concentrations during cooking differed significantly among users of the different fuel types, and PM2.5 exposure was at least 2.6 and 3.4 times higher for charcoal and firewood users, respectively, than for propane gas users. Therefore, switching from biomass fuels to propane gas would reduce daily individual exposure by at least one-third. An analysis of cooking times showed that the median cooking time per meal was 30 min; however, half the participants cooked for 1.5 h per meal, and one-third cooked for over 4.5 h per meal. Reducing these extremely long cooking times would reduce exposure with all fuel types. The Pocket PM2.5 Logger facilitates the comprehensive assessment of personal PM2.5 exposure dynamics and is beneficial for the development of intervention strategies targeting household air pollution.

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In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low-carbon transition.

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Background: With the increasing prevalence of wildfire smoke in the Pacific Northwest, it is important to quantify health impacts to plan for adequate health services. The Rogue Valley region has historically faced some of the greatest wildfire threats in the state. Health impacts from smoke have been estimated in several recent studies that include Oregon's Rogue Valley, but the results between studies are conflicting. Objective: The objective is to critically examine impacts of wildfire smoke on health in the Rogue Valley area and translate the results to support hospital staffing decisions. Study design: The study adopts a case-crossover approach. Methods: Apply a conditional Poisson regression to analyze time stratified counts while controlling for mean temperature. Results: Every 10 µ/m3 increase in PM2.5 is associated with a 2% increase in same-day hospital or emergency room admission rates for respiratory conditions during fire season after adjusting for temperature and time (OR = 1.020; 95% CI: 1.004-1.034); a 10 µ/m3 increase in PM2.5 lasting nine days is associated with a 4% increase in admission rates (OR = 1.041; 95% CI: 1.018-1.065). In other words, for each 10 µ/m3 single day increase in pollution from smoke, an additional 0.26 respiratory patients would be expected in the area hospitals. With a single day increase from 10 µ/m3 to 150 µ/m3, hospitals could expect an additional four patients. Conclusions: There are small but significant health impacts in the Rogue Valley. These impacts are smaller than some statewide estimates. We need further research to understand these differences.

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BACKGROUND: Expiratory airflow limitation (EAL) is closely associated with respiratory health in youth and adulthood. Owing to limited evidence, we aim to estimate the association between air pollutants, both individually and in combination, along with their chemical compositions, and the risk of EAL in youth based on data obtained from Northeast China Biobank. METHODS: Pulmonary function was evaluated using a medical-grade pulmonary function analyzer, with EAL defined as a forced expiratory flow in 1 s/ forced vital capacity ratio of < 0.8. Land use regression models were used to predict exposure to six air pollutants. Air pollution score (APS) for each participant was constructed as combined exposure. The chemical composition of particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5) was determined using a validated machine-learning algorithm. Logistic regression models were employed to estimate effect sizes, and odds ratio (OR) and 95 % confidence intervals (CI) were calculated. RESULTS: In total, 905 EAL cases were identified among the 4301 participants, with a prevalence of 21.04 %. Each inter-quartile range increase in APS was associated with a 25 % higher risk of EAL (OR = 1.25, 95 % CI: 1.12, 1.39). Among the pollutants analyzed, PM2.5 exposure had the strongest association with the risk of EAL (OR = 1.33, 95 % CI: 1.18, 1.52). Out of the five chemical components, sulfate (SO2-4) (OR = 1.39, 95 % CI: 1.24, 1.57) and ammonium (NH+4) (OR = 1.39, 95 % CI: 1.23, 1.57) exhibited the strongest associations with the risk of EAL. CONCLUSIONS: Overall, combined effects of air pollution increased the risk of EAL in youth, with SO2-4 and NH+4 emerging as the predominant contributing chemical components in Northeast China.

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OBJECTIVE: Studies have shown that fine particulate matter (PM2.5) has adverse effects on the liver function, but epidemiological evidence is limited, especially regarding pregnant women. This study aims to investigate the association between PM2.5 exposure in early pregnancy and maternal liver function during pregnancy. METHODS: This retrospective cohort study included 13,342 pregnant participants. PM2.5 and Ozone (O3) exposure level, mean temperature, and relative humidity for each participant were assessed according to their residential address. The levels of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (TBIL) were measured during the second and third trimesters. Data on PM2.5 and O3 exposure level were sourced from Tracking Air Pollution in China (TAP), while the mean temperature and relative humidity were obtained from the ERA5 dataset. The Generalized Additive Model (GAM) was used to analyze the associations between PM2.5 exposure and maternal liver function during pregnancy, adjusting for potential confounding factors. RESULTS: According to the results, each 10 µg/m3 increase in PM2.5 was associated with an increase of 3.57% (95% CI: 0.29%, 6.96%) in ALT and 4.25% (95% CI: 2.33%, 6.21%) in TBIL during the second trimester and 4.51% (95% CI: 2.59%, 6.47%) in TBIL during the third trimester., respectively. After adjusting for O3, these associations remained significant, and the effect of PM2.5 on ALT during the second trimester was further strengthened. No significant association observed between PM2.5 and AST. CONCLUSIONS: PM2.5 exposure in early pregnancy is associated with increasement of maternal ALT and TBIL, suggesting that PM2.5 exposure may have an adverse effect on maternal liver function. Although this finding indicates an association between PM2.5 exposure and maternal liver function, more research is needed to confirm our findings and explore the underlying biological mechanisms.

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Air pollution, particularly fine particulate matter (PM2.5) with <2.5 µm in diameter, is a major public health concern. Studies have consistently linked PM2.5 exposure to a heightened risk of cardiovascular diseases (CVDs) such as ischemic heart disease (IHD), heart failure (HF), and cardiac arrhythmias. Notably, individuals with pre-existing age-related cardiometabolic conditions appear more susceptible. However, the specific impact of PM2.5 on CVDs susceptibility in older adults remains unclear. Therefore, this review addresses this gap by discussing the factors that make the elderly more vulnerable to PM2.5-induced CVDs. Accordingly, we focused on physiological aging, increased susceptibility, cardiometabolic risk factors, CVDs, and biological mechanisms. This review concludes by examining potential interventions to reduce exposure and the adverse health effects of PM2.5 in the elderly population. The latter includes dietary modifications, medications, and exploration of the potential benefits of supplements. By comprehensively analyzing these factors, this review aims to provide a deeper understanding of the detrimental effects of PM2.5 on cardiovascular health in older adults. This knowledge can inform future research and guide strategies to protect vulnerable populations from the adverse effects of air pollution.

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ETHNOPHARMACOLOGICAL RELEVANCE: Platycodonis radix (PR), the root of Platycodon grandiflorus (Jacq.) A. DC., is a traditional Chinese medicine recognized for its dual role as both a medicinal and dietary substance, exhibiting significant anti-inflammatory properties. It is frequently utilized in the treatment of lung diseases. However, the molecular mechanisms by which PR exerts its effects in the treatment of acute lung injury (ALI) remain unclear. AIM OF THE STUDY: This study presents a novel strategy that integrates network pharmacology, molecular docking, untargeted metabolomics analysis and experimental validation to investigate the molecular mechanisms through which PR treats ALI. MATERIALS AND METHOD: Initially, the bioactive components of PR, along with its targets and pathways in the treatment of ALI, were identified using network pharmacology. Following this, preliminary validation was conducted through molecular docking. The active ingredients in the aqueous extract of PR were characterized using HPLC-MS. Finally, in vivo and in vitro experiments were performed to further validate the findings from the network pharmacology. RESULTS: A total of 14 bioactive components and 156 effective targets were identified using the TCMSP, DisGeNET, Genecard, OMIM databases and Venny 2.1.0. Protein-protein interaction (PPI) analysis revealed 22 core targets including TP53, AKT1, STAT3 and JUN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these targets primarily participate in the regulation of cellular apoptosis, lung cancer and inflammatory pathways. Molecular docking demonstrated that four bioactive components exhibited strong affinities with their respective docking targets. LC-MS analysis confirmed that the aqueous extract of PR contained 87 components, including two active ingredients identified through network pharmacology and molecular docking. Preliminary validation was conducted in mice with ALI induced by acute PM2.5 exposure, revealing that the aqueous extract of PR reduced inflammatory factor levels in bronchoalveolar lavage fluid, enhanced antioxidant capacity in lung tissue, and decreased lung cell apoptosis in PM2.5-exposed mice. Notably, PR alleviated PM2.5-induced ALI through the STAT3, JUN, and AKT1 signaling pathways. Similarly, the results of in vitro intervention experiments further confirmed that the aqueous extract of PR protected pulmonary epithelial cells against PM2.5 exposure through activating AKT1 sinalling pathway, and inhibiting STAT3 and JUN signalling pathways. CONCLUSION: This study identifies the active components of PR and elucidates the molecular mechanisms by which PR alleviates ALI, specifically by inhibiting the phosphorylation levels of STAT3 and c-JUN, or by activating the phosphorylation level of AKT1. These results provide a foundational basis for the application of PR in the treatment or prevention of lung injuries induced by particulate matter.

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Although total carbon (TC) is an important component of fine particulate matter (PM2.5: particulate matter with aerodynamic diameter of < 2.5 µm); its sources remain partially unidentified, especially in coastal urban areas. With ongoing development of the global economy and maritime activities, ship-generated TC emissions in port areas cannot be neglected. In this study, from 11 September 2017 to 31 August 2018, we collected 355 PM2.5 samples in Qingdao, China, to determine the water-soluble ion concentrations, TC concentrations, and stable carbon isotopes (δ13CTC). During the open fishing season (OFS; 11 September 2017 to 30 April 2018) and the closed fishing season (CFS; 1 May 2018 to 31 August 2018), the TC concentrations were 9.30 ± 5.38 µg/m3 and 3.36 ± 2.10 µg/m3 respectively, and the corresponding δ13CTC values were -24.53‰ ± 1.17‰ and -27.03‰ ± 0.91‰, respectively, indicating significant differences (p < 0.05) between the two periods. The differences in TC concentrations and the δ13CTC values between the OFS and CFS reflect changes in the source of contamination. Bayesian model was used to quantify the contributions of different TC sources, revealing that ship emissions accounted for approximately 35.3% of the total, which was close to the contribution from the largest source, i.e., motor vehicles (39%). Using the ship emission inventory, Qingdao's ship emissions were further quantified at 455 metric tons, representing 35%-40% of the total TC emissions around Qingdao. Notably, fishing ships contributed approximately 40% of the total ship emissions. These findings underscore the considerable impact of ship emissions, particularly those from fishing ships, on TC concentrations in coastal urban areas.

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This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.

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Type 2 diabetes causes early mortality worldwide. Air pollution's relationship with T2DM has been studied. The association between them is unclear because of inconsistent outcomes. Studies on this topic have been published since 2019, but not thoroughly evaluated. We conducted a systematic review and meta-analysis using relevant data. The study protocol was registered in PROSPIRO and conducted according to MOOSE guidelines. In total, 4510 manuscripts were found. After screening, 46 studies were assessed using the OHAT tool. This meta-analysis evaluated fine particles with T2DM using OR and HR effect estimates. Evaluation of publication bias was conducted by Egger's test, Begg's test, and funnel plot analysis. A sensitivity analysis was conducted to evaluate the influence of several studies on the total estimations. Results show a significant association between PM2.5 and PM10 exposure and T2DM. Long-term exposure to fine air particles may increase the prevalence and incidence of T2DM. Fine air pollution increases the chance of developing T2DM mainly via systemic inflammation, oxidative stress, and endoplasmic reticulum stress.

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Drought induces dry hazards, including wildfire, and increased air pollution from wildfire may be a mechanism by which drought increases health risks. We examined whether the drought-wildfire pathway increases the risk of childhood stunting. We analyzed all geocoded children under five across 44 low- and middle-income countries (LMICs). We first conducted mixed-effect regressions to examine the three pairwise associations between standardized precipitation evapotranspiration index (SPEI), fire-sourced PM2.5, and childhood stunting. We then employed a causal mediation analysis to determine whether compounding drought-wildfire (cascading or co-occurring) events significantly impact the drought-stunting pathway. We found that each 1-unit decrease in SPEI exposure was associated with a 2.16% [95% confidence interval (CI): 0.79, 3.49%] increase in stunting risk and 0.57 (95% CI 0.55, 0.59%) µg/m3 increase in fire-sourced PM2.5. Additionally, each 1 µg/m3 increase in 24 month average fire-sourced PM2.5 was associated with an increased risk of 2.46% (95% CI: 2.16, 2.76%) in stunting. Drought-mediated fires accounted for 26.7% (95% CI: 14.5, 36.6%) of the linkage between SPEI and stunting. Our study revealed fire-sourced PM2.5 is a mediator in the drought-stunting pathway in LMICs. To protect child health under increasing drought conditions, personal interventions against wildfire should be considered to enhance climate resilience.

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Air pollution has been a significant environmental and public health concern in Ulaanbaatar, the capital city of Mongolia, for many years. The city experiences severe air pollution, particularly during the winter months. This study investigated the annual trends of outdoor and indoor PM2.5 concentrations at two neighboring sites in Ulaanbaatar: an office and a household, using low-cost sensors. Both locations exhibited similar fluctuations in outdoor PM2.5 concentrations over time, with ambient PM2.5 levels beginning to rise in October and declining in April. During the mid-term of the heating season (November to February), hourly averaged PM2.5 concentrations were exceptionally high, with peak pollution events exceeding ~ 1000 µg/m3. Notably, PM2.5 concentrations were elevated during this heating season period. Time-activity patterns showed a decrease in PM2.5 concentrations during the periods of 06:00-08:00 and 14:00-18:00. Furthermore, the study found that the indoor environment could remain safe when windows were well-sealed, even under severe outdoor pollution conditions. Overall, this study provided accurate insights into the annual patterns of PM2.5 concentrations and demonstrated their fluctuations during the heating season when pollution levels were particularly high. The findings offer valuable recommendations for individuals to consider when going outside and for taking actions to improve indoor air quality in Ulaanbaatar.

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This paper analyzes the dynamic impact of economic, social, and governance factors on PM2.5 concentrations in 89 countries from 2006 to 2019. Using the GMM-PVAR approach and Impulse-Response Functions, we examine how shocks to specific variables affect PM2.5 concentrations over a 10-year period. Our findings reveal that the influence of these factors on PM2.5 levels varies over time. For example, a shock in urbanization has no effect on PM2.5 concentrations in the first year, but in the second year, pollution increases significantly. In the third period, PM2.5 levels decrease, but they rise again in the fourth period, albeit not significantly. By the fifth period, pollution decreases until a new equilibrium is reached in the sixth period. Additionally, a shock in financial development, government effectiveness, industrialization, trade openness, or GDP has no effect on PM2.5 concentrations in the initial period. However, during the second period, air pollution decreases, followed by an increase in the third period and a decrease again in the fourth period. These dynamic patterns highlight the need for environmental policies that consider the evaluation time horizon. Our analysis is supplemented by the Granger causality test, guiding specific policy recommendations based on our findings.

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The findings on the relationship between prenatal exposure to particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) and its constituent and children's growth trajectories are inconsistent. This association's sensitive exposure time window and possible gender differences remain unclear. Our aim was to determine the association between prenatal exposure to PM2.5 and its component and children's growth trajectories by the age of two. From 2015 to 2021, 6407 mother-infant pairs were enrolled in the study. The PM2.5 include sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM), and black carbon (BC), from the ChinaHighAirPollutants (CHAP) datasets. Children were followed at birth, 1, 3, 6, 9, 12, 18, and 24 months. Population-based and individual-based methods were used to simulate child growth trajectories: slow growth, normal growth, and rapid growth. The distributed lags modeling was used to identify sensitive time windows for the effects of prenatal exposure to PM2.5 and its components on child growth. Sex-stratified analyses estimated sex differences. Median concentrations [interquartile ranges (IQRs)] were 57.46(17.3), 10.59(3.8), 14.26(4.4), 8.69(2.8), 13.05(3.4), and 2.53(0.7) µg/m3 for PM2.5, SO42-, NO3-, NH4+, OM, and BC, respectively. Compared with the normal growth trajectory group, exposure to PM2.5 was significantly associated with a higher risk of rapid growth trajectory in boys (ORs with 95% CI for the entire, first trimester, and second trimester of pregnancy, respectively: 1.016[1.006,1.025], 1.007[1.002,1.011], 1.007[1.002,1.011]). Exposure to PM2.5 was significantly associated with a higher risk of slow growth trajectory in girls (ORs with 95% CI for the entire, second trimester, and third trimester of pregnancy, respectively: 1.010 [1.001,1.018], 1.006 [1.001,1.011], 1.007 [1.002,1.012]). Prenatal PM2.5 and its composition exposure was positively associated with BMI peak in boys (ßs with 95% CI for PM2.5, SO42-, NO3-, NH4+, OM, BC: 0.004[0.000,0.007], 0.025[0.006,0.044], 0.012[0.002,0.023], 0.022[0.004,0.039], 0.016[0.001,0.031], 0.082[0.005,0.159]), and not statistically significant in girls. We observed a more pronounced BC effect in our cohort. Prenatal exposure to PM2.5 and its component, especially at 10-22 weeks of gestation, is associated with a higher risk of rapid growth in boys and a risk of slow growth in girls.

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The effect of fine particulate matter (PM2.5) on the development of uveitis remains unclear. Therefore, this study was designed to investigate the role of PM2.5 in experimental autoimmune uveitis (EAU) and its potential mechanism. Our results showed that PM2.5 could exacerbate the activity of EAU, as evidenced by severer clinical and pathological changes, correlated with elevated Th17 cells frequency and IL-17A expression. Proteomic analysis revealed ferroptosis was the most significant pathway. In vivo, the levels of Fe2+, ROS, lipid ROS, and malondialdehyde, as well as the expression of TFRC, HMOX1, FTH1, and FTL1 in CD4+ T cells were increased, while GSH/GSSG ratio and the expression of ACSL1 and GPX4 were decreased after PM2.5 exposure. In vitro, the expression of TFRC and HMOX1 were increased, while the expression FTH1, FTL1, ACSL1, and GPX4 were decreased after PM2.5 exposure. Ferrostatin-1 effectively alleviated PM2.5-induced intraocular inflammation and suppressed the frequency of Th17 cells. These results suggest that PM2.5 could aggravate intraocular inflammation and immune response in EAU mice through ferroptosis. Ferroptosis could be a potential marker for the prevention and treatment of uveitis.

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Emergency ambulance dispatches (EAD) have been proven to be associated with ambient particulate matter with diameter < 2.5 µm (PM2.5) concentration, but the associations of circulatory EAD remained inconclusive, especially in heavily polluted areas. In this time series conducted in Shenyang City, Northeastern China, we explored the associations between circulatory EAD and ambient PM2.5 and its constituents. Data including 113,508 circulatory EAD records, five types of PM2.5 constituents, and meteorological information spanning from 2014 to 2019 were retrieved. Using generalized additive models (GAMs), we explored the association between circulatory EAD and calculated excess risks induced by a 10 µg/m3 increase (ERR10) in PM2.5 mass and its constituents. ERR by percentage change (ERRpc) to compare among the different constituents were also calculated. Positive associations between circulatory EAD and PM2.5 mass, sulfates, organic matters, and black carbon, were found particularly at lag0 and lag0-5, with the ERR10 of 3.8% (3.2%-4.4%), 6.5% (2.2%-10.8%), 4.2% (1.7%-6.6%), and 30.2% (17.2%-43.4%) at lag0-5, respectively. Similar associations were observed for cardiovascular EAD, while cerebrovascular EAD suggested a positive association with O3 rather than PM2.5 or its constituents. Notably, PM2.5 mass exhibited the largest ERRpc for circulatory and cardiovascular EAD, followed by sulfates and black carbon. Moreover, the risks were enhanced for circulatory and cardiovascular EAD in males compared to females and during warmer seasons compared to colder seasons. Our findings contribute new evidence on PM2.5 exposure and circulatory EAD in relatively polluted areas.

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One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

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