PM2.5 constituents associated with mortality and kidney failure in childhood-onset lupus nephritis: A 19-year cohort study.

BACKGROUND: Childhood-onset lupus nephritis (cLN) is a severe form of systemic lupus erythematosus (SLE) with high morbidity and mortality. The impact of long-term exposure to fine particulate matter (PM2.5) on adverse outcomes in cLN remains unclear. METHODS: We combined a 19-years cLN cohort from seven provinces in China with high-resolution PM2.5 dataset from 2001 to 2020, investigating the association between long-term exposure to PM2.5 and its constituents (sulfate, nitrate, organic matter, black carbon, ammonium) with the risk of death and kidney failure, analyzed with multiple variables Cox models. We also evaluated the association between 3-year average PM2.5 exposure before study entry and baseline SLE disease activity index (SLEDAI) scores using linear regression models. RESULTS: Each 10 µg/m3 increase in annual average PM2.5 exposure was associated with an increased risk of death and kidney failure (HR = 1.58, 95 % CI: 1.24-2.02). Black carbon showed the strongest association (HR = 2.14, 95 % CI: 1.47-3.12). Higher 3-year average exposures to PM2.5 and its constituents were significantly associated with higher baseline SLEDAI scores. CONCLUSIONS: These findings highlight the significant role of environmental pollutants in cLN progression and emphasize the need for strategies to mitigate exposure to harmful PM2.5 constituents, particularly in vulnerable pediatric populations.

Spatial and temporal characteristic of PM2.5 and influence factors in the Yellow River Basin.

The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

Elimination of positive and negative sampling artifacts in particulate organic carbon and PAHs using multi-sorbent coated and uncoated denuders.

Within the scope of this study, two equivalent PM2.5 samplers were designed and developed to eliminate sampling artifacts in the results of atmospheric particulate organic carbon (OC) and particulate polycyclic aromatic hydrocarbons (PAH) caused by volatile organic compounds (VOCs) and gas phase PAH compounds, respectively. A mass loss of less than 10% due to the denuders was observed. Study results showed that if an impregnated denuder is not used, the results of atmospheric particle OC concentrations will be reported with higher values due to positive errors of 53.2 ± 7.23% (median: 52.00%) on average. It was observed that the total error (net error) was still positive, but decreased to an average of 35.1 ± 16.8% (median: 31.0%) after including the negative errors quantified from the backup filter into the calculation. In cases where denuders were not used in the sampling, it was observed that the results with positive errors of 41.0 ± 14.6% (median: 33.8%) on average would be obtained for the total PAHs. Ozone-induced negative interference was the highest in Acenapthylene (28%), followed by Fluoranthane (20%), Phenanthrene (18%), and 15% for Np and Benzo[g,h,i]perylene compounds, relative to their medians. Negative errors of 10% or less were found in all other individual PAH compounds.

Elevating Hourly PM2.5 Forecasting in Istanbul, Türkiye: Leveraging ERA5 Reanalysis and Genetic Algorithms in a Comparative Machine Learning Model Analysis.

Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models-Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020-2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF>LGB>XGB>MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 µg/m³), and low MAE (2.94 µg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China.

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

PM2.5 potentiates oxygen glucose deprivation-induced neurovascular unit damage via inhibition of the Akt/ß-catenin pathway and autophagy dysregulation.

Air pollution has recently emerged as a significant risk factor for ischemic stroke. Although there is a robust association between higher concentrations of ambient particulate matter (PM2.5) and increased incidence and mortality rates of ischemic stroke, the precise mechanisms underlying PM2.5-induced ischemic stroke remain to be fully elucidated. The purpose of this study was to examine the synergistic effect of PM2.5 and hypoxic stress using in vivo and in vitro ischemic stroke models. Intravenously administered PM2.5 exacerbated the ischemic brain damage induced by middle cerebral artery occlusion (MCAo) in Sprague Dawley rats. Alterations in autophagy flux and decreased levels of tight junction proteins were observed in the brain of PM2.5-administered rats after MCAo. The underlying mechanism of PM2.5-induced potentiation of ischemic brain damage was investigated in neurons, perivascular macrophages, and brain endothelial cells, which are the major components of the integrated neurovascular unit. Co-treatment with PM2.5 and oxygen-glucose deprivation (OGD) amplified the effects of OGD on the reduction of viability in primary neurons, immortalized murine hippocampal neuron (HT-22), and brain endothelial cells (bEND.3). After co-treatment with PM2.5 and OGD, the Akt/ß-catenin and autophagy flux were significantly inhibited in HT-22 cells. Notably, the protein levels of metalloproteinase-9 and cystatin C were elevated in the conditioned media of murine macrophages (RAW264.7) exposed to PM2.5, and tight junction protein expression was significantly decreased after OGD exposure in bEND.3 cells pretreated with the conditioned media. Our findings suggest that perivascular macrophages may mediate PM2.5-induced brain endothelial dysfunction following ischemia and that PM2.5 can exacerbate ischemia-induced neurovascular damage. © 2017 Elsevier Inc. All rights reserved.

Combined transcriptome and microbiome analysis reveals the thyrotoxic effects of PM2.5 in female rats.

Pervasive environmental pollutants, specifically particulate matter (PM2.5), possess the potential to disrupt homeostasis of female thyroid hormone (TH). However, the precise mechanism underlying this effect remains unclear. In this study, we established a model of PM2.5-induced thyroid damage in female rats through intratracheal instillation and employed histopathological and molecular biological methods to observe the toxic effects of PM2.5 on the thyroid gland. Transcriptome gene analysis and 16S rRNA sequencing were utilized to investigate the impact of PM2.5 exposure on the female rat thyroid gland. Furthermore, based on the PM2.5-induced toxic model in female rats, we evaluated its effects on intestinal microbiota, TH levels, and indicators of thyroid function. The findings revealed that PM2.5 exposure induced histopathological damage to thyroid tissue by disrupting thyroid hormone levels (total T3 [TT3], (P < 0.05); total T4 [TT4], (P < 0.05); and thyrotropin hormone [TSH], (P < 0.05)) and functional indices (urine iodine [UI], P > 0.05), thus further inducing histopathological injuries. Transcriptome analysis identified differentially expressed genes (DEGs), primarily concentrated in interleukin 17 (IL-17), forkhead box O (FOXO), and other signaling pathways. Furthermore, exposure to PM2.5 altered the composition and abundance of intestinal microbes. Transcriptome and microbiome analyses demonstrated a correlation between the DEGs within these pathways and the flora present in the intestines. Moreover, 16 S rRNA gene sequencing analysis or DEGs combined with thyroid function analysis revealed that exposure to PM2.5 significantly induced thyroid hormone imbalance. We further identified key DEGs involved in thyroid function-relevant pathways, which were validated using molecular biology methods for clinical applications. In conclusion, the homeostasis of the "gut-thyroid" axis may serve as the underlying mechanism for PM2.5-induced thyrotoxicity in female rats.

Intensification mechanisms and moisture dynamics of super cyclonic storm 'Amphan' over the Bay of Bengal: Implications for aerosol re-distribution.

The present research investigates the dynamics and underlying causes contributing to the exceptional intensity of Super Cyclonic Storm (SuCS) Amphan (16th to 21st May 2020) over the Bay of Bengal (BoB), as well as its impact on aerosol redistribution along the four cities of eastern coast and north-eastern India. Notably, the SuCS was formed during the first phase of the COVID-19 lockdown in India, giving it a unique aspect of study and analysis. Our analysis based on 30 years of climatology data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis reveals 'positive' monthly anomalous winds (0.8 to 1.6 m/s) prevailed over the central BoB for May 2020. The present study further found the evolution of 'barrier layer thickness'(BLT) leading up to landfall, noting a thickening trend from 8 to 3 days before landfall, contributing to maintaining warmer sea surface temperatures near the coast. Additionally, utilizing European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis version-5 (ERA-5) data, a mean positive sea surface temperature (SST) anomaly of 0.8 to 1 °C was observed 'before' cyclone period (10-15 May 2020) near the cyclogenesis point. A detailed examination of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cross-section plots during the cyclone's intensification stage reveals the presence of high-altitude clouds composed primarily of ice crystals. Further, analysis also indicates that the cyclone transported Sea-salt PM2.5 aerosols from the ocean, dispersing them in the landfall region.The aerosol optical Depth (AOD) data obtained from the National Aeronautics and Space Administration's (NASA) 'Clouds and the Earth's Radiant Energy System (CERES)' mission and MERRA-2 were also analysed, revealing that the cyclone redistributed aerosols over the Bengal basin region (mainly over 'Kolkata') and three other nearby cities along the track of the cyclone (i.e., Bhubaneswar (Odisha) Agartala (Tripura) and Shillong (Meghalaya) respectively).

Short-term exposure to ambient fine particulate matter constituents and myocardial infarction mortality.

Short-term ambient fine particulate matter (PM2.5) exposure has been related to an increased risk of myocardial infarction (MI) death, but which PM2.5 constituents are associated with MI death and to what extent remain unclear. We aimed to explore the associations of short-term exposure to PM2.5 constituents with MI death and evaluate excess mortality. We conducted a time-stratified case-crossover study on 237,492 MI decedents in Jiangsu province, China during 2015-2021. Utilizing a validated PM2.5 constituents grid dataset at 1 km spatial resolution, we estimated black carbon (BC), organic carbon (OC), sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) exposure by extracting daily concentrations grounding on the home address of each subject. We employed conditional logistic regression models to evaluate the exposure-response relationship between PM2.5 constituents and MI death. Overall, per interquartile range (IQR) increase of BC (lag 06-day; IQR: 1.75 µg/m3) and SO42- (lag 04-day; IQR: 5.06 µg/m3) exposures were significantly associated with a 3.91% and 2.94% increase in odds of MI death, respectively, and no significant departure from linearity was identified in the exposure-response curves for BC and SO42-. If BC and SO42- exposures were reduced to theoretical minimal risk exposure concentration (0.89 µg/m3 and 1.51 µg/m3), an estimate of 4.55% and 4.80% MI deaths would be avoided, respectively. We did not find robust associations of OC, NO3-, NH4+, and Cl- exposures with MI death. Individuals aged ≥80 years were more vulnerable to PM2.5 constituent exposures in MI death (p for difference <0.05). In conclusion, short-term exposure to PM2.5-bound BC and SO42- was significantly associated with increased odds of MI death and resulted in extensive excess mortality, notably in older adults. Our findings emphasized the necessity of reducing toxic PM2.5 constituent exposures to prevent deaths from MI and warranted further studies on the relative contribution of specific constituents.

Accurate PM2.5 Urban Air Pollution Forecasting using Multivariate Ensemble Learning Accounting for Evolving Target Distributions.

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-hour PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Mathematical models for traffic-source PM2.5 dispersion in an urban street canyon considering the capture capability of roadside trees.

Rapid urbanization increases the densely built-up blocks, the population and vehicles. Large amounts of particulate matter (PM), especially PM2.5 (PM with an aerodynamic diameter of 2.5µm or less), from vehicle exhaust are critical to human health. In typical street canyons in hot and humid regions, traffic-source PM usually diffuses to the densely built-up blocks through roadside trees. Roadside trees are a double-edged sword, serving as "guards" to absorb PM2.5 while may lead to PM2.5 gathering in street levels, thereby influencing the PM2.5 dispersion in the densely built-up blocks. To quantify the dispersion process, this study proposed traffic-source PM2.5 dynamic dispersion models considering the capture capability of roadside trees and built-up blocks based on the OSPM model. Due to the difficulty in obtaining the adsorption and deposition rate of the proposed models, the numerical simulations by ENVI-met software were used to solve and obtain the relationship between capture capability and characteristic index of roadside trees. Subsequently, The accuracy and effectiveness of the proposed traffic-source PM2.5 dynamic dispersion models were verified through field experimental data. Results show that the calculated PM2.5 concentration significantly linearly increased with the measured values with the determined coefficient (R2) of 0.98, and the first-order coefficient close to 1. It indicates that the proposed traffic-source PM2.5 dispersion model accurately quantified the impact of roadside trees on PM2.5 and its concentration dispersion process to the built-up blocks. This study provides suggestions for designing characteristic indexes of roadside trees and built-up blocks to improve the air quality of urban street canyons.

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 µm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM2.5, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

Parental Report of Indoor Air Pollution is Associated with Respiratory Morbidities In Bronchopulmonary Dysplasia.

OBJECTIVE: To determine the association between indoor air pollution and respiratory morbidities in children with bronchopulmonary dysplasia recruited from the multicenter Bronchopulmonary Dysplasia (BPD) Collaborative. STUDY DESIGN: A cross-sectional study was performed among participants less than 3 years old in the BPD Collaborative Outpatient Registry. Indoor air pollution was defined as any reported exposure to tobacco or marijuana smoke, electronic cigarette emissions, gas stoves, and/or wood stoves. Clinical data included acute care use and chronic respiratory symptoms in the past 4 weeks. RESULTS: A total of 1,011 subjects born at a mean gestational age of 26.4 ± 2.2 weeks were included. Most (66.6%) had severe BPD. Over 40% of subjects were exposed to at least one source of indoor air pollution. The odds of reporting an emergency department visit (OR 1.7 [1.18, 2.45], antibiotic use (OR 1.9 [1.12, 3.21]), or a systemic steroid course (OR 2.18 [1.24, 3.84]) were significantly higher in subjects reporting exposure to secondhand smoke (SHS) compared with those without SHS exposure. Subjects reporting exposure to air pollution (not including SHS) also had a significantly greater odds (OR 1.48 [1.08, 2.03]) of antibiotic use as well. Indoor air pollution exposure (including SHS) was not associated with chronic respiratory symptoms or rescue medication use. CONCLUSION: Exposure to indoor air pollution, especially SHS, was associated with acute respiratory morbidities, including ED visits, antibiotics for respiratory illnesses, and systemic steroid use.

A case-crossover study of ST-elevation myocardial infarction and organic carbon and source-specific PM2.5 concentrations in Monroe County, New York.

Background: Previous work reported increased rates of cardiovascular hospitalizations associated with increased source-specific PM2.5 concentrations in New York State, despite decreased PM2.5 concentrations. We also found increased rates of ST elevation myocardial infarction (STEMI) associated with short-term increases in concentrations of ultrafine particles and other traffic-related pollutants in the 2014-2016 period, but not during 2017-2019 in Rochester. Changes in PM2.5 composition and sources resulting from air quality policies (e.g., Tier 3 light-duty vehicles) may explain the differences. Thus, this study aimed to estimate whether rates of STEMI were associated with organic carbon and source-specific PM2.5 concentrations. Methods: Using STEMI patients treated at the University of Rochester Medical Center, compositional and source-apportioned PM2.5 concentrations measured in Rochester, a time-stratified case-crossover design, and conditional logistic regression models, we estimated the rate of STEMI associated with increases in mean primary organic carbon (POC), secondary organic carbon (SOC), and source-specific PM2.5 concentrations on lag days 0, 0-3, and 0-6 during 2014-2019. Results: The associations of an increased rate of STEMI with interquartile range (IQR) increases in spark-ignition emissions (GAS) and diesel (DIE) concentrations in the previous few days were not found from 2014 to 2019. However, IQR increases in GAS concentrations were associated with an increased rate of STEMI on the same day in the 2014-2016 period (Rate ratio [RR] = 1.69; 95% CI = 0.98, 2.94; 1.73 µg/m3). In addition, each IQR increase in mean SOC concentration in the previous 6 days was associated with an increased rate of STEMI, despite imprecision (RR = 1.14; 95% CI = 0.89, 1.45; 0.42 µg/m3). Conclusion: Increased SOC concentrations may be associated with increased rates of STEMI, while there seems to be a declining trend in adverse effects of GAS on triggering of STEMI. These changes could be attributed to changes in PM2.5 composition and sources following the Tier 3 vehicle introduction.

Assessing the effectiveness of PM2.5 pollution control from the perspective of interprovincial transport and PM2.5 mitigation costs across China.

Due to the transboundary nature of air pollutants, a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area. The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work, such as financial fund allocation and subsidy policy formulation. Herein, we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs. We found that approximately 60% of the PM2.5 pollution was from local sources, while the remaining 40% originated from outside provinces. Furthermore, about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013-2020, accounting for 41.2% of the total abatement costs. Provinces with lower unit improvement costs for PM2.5, such as Jiangsu, Hebei, and Shandong, were major contributors, while Guangdong, Guangxi, and Fujian, bearing higher unit costs, were among the main beneficiaries. Our study identifies provinces that contribute to air quality improvement in other provinces, have high economic efficiency, and provide a quantitative framework for determining inter-provincial compensations. This study also reveals the uneven distribution of pollution abatement costs (PM2.5 improvement/abatement costs) due to transboundary PM2.5 transport, calling for adopting inter-provincial economic compensation policies. Such mechanisms ensure equitable cost-sharing and effective regional air quality management.

piR-27222 mediates PM2.5-induced lung cancer by resisting cell PANoptosis through the WTAP/m6A axis.

PM2.5 pollution has been associated with the incidence of lung cancer, but the underlying mechanism is still unclear. PIWI-interacting RNAs (piRNAs), initially identified in germline cells, have emerged as a novel class of small non-coding RNAs (26 - 32 nucleotides) with diverse functions in various diseases, including cancer. However, the role and mechanism of piRNAs in the development of PM2.5-induced lung cancer remain to be clarified. In the presented study, we used a PM2.5-induced malignant transformation cell model to analyze the change of piRNA profiles. Among the disturbed piRNAs, piR-27222 was identified as an oncogene that inhibited cell death in a m6A-dependent manner. Mechanistically, we found that piR-27222 could deubiquitinate and stabilize eIF4B by directly binding to eIF4B and reducing its interaction with PARK2. The enhanced expression of eIF4B, in turn, promoted the expression of WTAP, leading to increased m6A modification in the Casp8 transcript. Consequently, the stability of Casp8 transcripts was reduced, rendering lung cancer cells resistant to PANoptosis. Collectively, our findings reveal that PM2.5 exposure up-regulated piR-27222 expression, which could affect EIF4B/WTAP/m6A axis, thereby inhibiting PANoptosis of cells and promoting lung cancer. Our study provides new insights into understanding the epigenetic mechanisms underlining PM2.5-induced lung cancer.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City.

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400 µg/mL PM2.5-treated groups. We also found 400 µg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400 µg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400 µg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400 µg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400 µg/mL PM2.5 groups, and the SOD activity of cells in the 400 µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400 µg/mL PM2.5 groups and the levels of IL-1 were higher in the 400 µg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.

Comparison of the rate of healthcare encounters for influenza from source-specific PM2.5 before and after tier 3 vehicle standards in New York state.

BACKGROUND: Influenza healthcare encounters in adults associated with specific sources of PM2.5 is an area of active research. OBJECTIVE: Following 2017 legislation requiring reductions in emissions from light-duty vehicles, we hypothesized a reduced rate of influenza healthcare encounters would be associated with concentrations of PM2.5 from traffic sources in the early implementation period of this regulation (2017-2019). METHODS: We used the Statewide Planning and Research Cooperative System (SPARCS) to study adult patients hospitalized (N = 5328) or treated in the emergency department (N = 18,247) for influenza in New York State. Using a modified case-crossover design, we estimated the excess rate (ER) of influenza hospitalizations and emergency department visits associated with interquartile range increases in source-specific PM2.5 concentrations (e.g., spark-ignition emissions [GAS], biomass burning [BB], diesel [DIE]) in lag day(s) 0, 0-3 and 0-6. We then evaluated whether ERs differed after Tier 3 implementation (2017-2019) compared to the period prior to implementation (2014-2016). RESULTS: Each interquartile range increase in DIE in lag days 0-6 was associated with a 21.3% increased rate of influenza hospitalization (95% CI: 6.9, 37.6) in the 2014-2016 period, and a 6.3% decreased rate (95% CI: -12.7, 0.5) in the 2017-2019 period. The GAS/influenza excess rates were larger in the 2017-2019 period than the 2014-2016 period for emergency department visits. We also observed a larger ER associated with increased BB in the 2017-2019 period compared to the 2014-2016 period. IMPACT STATEMENT: We present an accountability study on the impact of the early implementation period of the Tier 3 vehicle emission standards on the association between specific sources of PM2.5 air pollution on influenza healthcare encounters in New York State. We found that the association between gasoline emissions and influenza healthcare encounters did not lessen in magnitude between periods, possibly because the emissions standards were not yet fully implemented. The reduction in the rates of influenza healthcare encounters associated with diesel emissions may be reflective of past policies to reduce the toxicity of diesel emissions. Accountability studies can help policy makers and environmental scientists better understand the timing of pollution changes and associated health effects.

Prediction of PM2.5 concentration based on a CNN-LSTM neural network algorithm.

Fine particulate matter (PM2.5) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM2.5, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM2.5 concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM2.5 time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PM2.5data of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for pre-processing and characteristic analysis. Then, the CNN-LSTM deep learning model was integrated to capture the temporal and spatial features and trends in the data. The CNN layer was used to extract spatial features, while the LSTM layer was used to learn time dependencies. Through comparative experiments and model evaluation, we found that the CNN-LSTM model can achieve excellent PM2.5 prediction performance. The results show that the coefficient of determination (R2) is 0.91, and the root mean square error (RMSE) is 8.216 µg/m3. The CNN-LSTM model achieves better prediction accuracy and generalizability compared with those of the CNN and LSTM models (R2 values of 0.85 and 0.83, respectively, and RMSE values of 11.356 and 14.367, respectively). Finally, we analyzed and explained the predicted results. We also found that some meteorological factors (such as air temperature, pressure, and wind speed) have significant effects on the PM2.5 concentration at ground stations in Qingdao. In summary, by using deep learning methods, we obtained better prediction performance and revealed the association between PM2.5 concentration and meteorological factors. These findings are of great significance for improving the quality of the atmospheric environment and protecting public health.

CircCDR1as orchestrates the advancement of asthma triggered by PM2.5 through the modulation of ferroptosis.

Exposure to fine particulate matter (PM2.5) in the ambient environment augments susceptibility to respiratory ailments. Circular RNAs, a distinctive subclass of endogenous non-coding RNAs, have been acknowledged as pivotal regulators of pathological conditions. Ferroptosis, an innovative iron-dependent form of cellular demise, has emerged as a consequential participant in numerous maladies. Despite the established association between PM2.5 exposure and the exacerbation of asthma, scant investigations have probed into the implication of circRNAs and ferroptosis in PM2.5-induced asthma. Consequently, this inquiry sought to scrutinize the potential involvement of circCDR1as and ferroptosis in PM2.5-induced asthma. Through the formulation of a PM2.5 exposure model in asthmatic mice and an in vitro cellular model, it was discerned that PM2.5 induced ferroptosis, thereby intensifying asthma progression. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) revealed an upregulation of circCDR1as in the PM2.5-stimulated asthma cell model. Molecular biology assays demonstrated that diminished circCDR1as expression hindered the onset of ferroptosis in response to PM2.5 exposure. Notably, Ferrostatin-1 (Fer-1), an inhibitor of ferroptosis, manifested the ability to impede the advancement of asthma. Mechanistically, RNA pull-down and molecular biology experiments substantiated that circCDR1as selectively bound to insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2), thereby modulating the occurrence of ferroptosis. CircCDR1as emerged as a potential orchestrator of asthma progression by regulating ferroptosis under PM2.5 exposure. Additionally, PM2.5 exposure elicited activation of the Wnt/ß-catenin signaling pathway, subsequently influencing the expression of C-myc and Cyclin D1, ultimately exacerbating asthma development. In summation, the interaction between circCDR1as and IGF2BP2 in regulating ferroptosis was identified as a critical facet in the progression of asthma under PM2.5 exposure. This investigation underscores the pivotal roles of circCDR1as and ferroptosis in PM2.5-induced asthma, offering a novel theoretical foundation for the therapeutic and preventive approaches to asthma.