Air pollution and health

Impact of improved air quality on lung function and blood pressure of middle-aged and older population in China

Long-term exposure to fine particulate matters (PM2.5) has been associated with respiratory and cardiovascular diseases and the burden are potentially higher in China experiencing heavy air pollution. In this study, we established the exposure-response association between long-term exposures to PM2.5 and lung function and blood pressure in Chinese middle-aged and older adults using linear mixed-effects and generalized additive mixed models based on 3 waves longitudinal health outcomes data by enrolling 19,988 participants from 121 cities across the mainland of China. We also assessed the effect of Clean Air Policy (CAP) based on a quasi-experimental difference-in-differences (DID) design. A 10 µg/m3 increase in PM2.5 concentration was associated with a 7.18 (95 % confidence interval [CI]: -8.35, -6.02) L/min decrease in PEF (peak expiratory flow) and a 0.72 (95 % [CI]: 0.53, 0.90) and a 0.30 (95 % [CI]: 0.18, 0.42) mmHg increase in systolic and diastolic blood pressure, respectively. The associations were more pronounced in males and rural areas for PEF, but similar across subgroups for blood pressure. DID results suggested that the effect of CAP on health outcomes were sensitive to magnitudes of reduction in PM2.5. A 5 µg/m3 reduction in PM2.5 or more generally led to 18.70 (95 % [CI]: 0.79, 36.61) higher PEF and -2.05 (95 % [CI]: -3.87, -0.23) lower diastolic blood pressure, respectively, compared to no reduction or increase in exposure. However, the effects were significant only in rural areas. Our analysis support CAP aiming to benefit public health and provides insights to inform future control policy for efficiently decreasing air pollution exposure burden.

Evaluating the impact of roadside vegetation barriers on urban air pollution using low-cost mobile sensors

Urban population growth has led to an increase in the number of people living near major roads and highways, increasing exposure to roadside air pollution. This has raised significant public health concerns and driven efforts to regulate air quality in these micro-environments. Solutions such as the implementation of vegetation barriers can reduce exposure to traffic-related emissions by influencing pollutant dispersion patterns. Three primary methods are commonly used to characterize pollutant distribution in complex urban environments: (i) geostatistical analysis using remote sensing, (ii) high-precision measurements across physical barriers, and (iii) dispersion models, particularly computational fluid dynamics (CFD) models. Although numerous studies have investigated the role of vegetation in mitigating traffic-related air pollution, most have relied on small-scale assessments or modeled data. This study presents a comprehensive workflow for evaluating the effectiveness of vegetation barriers in improving urban air quality. It utilizes real-world data collected over two years (May 2015-December 2017) using low-cost mobile sensors in Pamplona, Spain -a medium-sized European city representative of 80 % of urban areas in Europe- within the framework of the LIFE + Respira project. Seven pollutants (CO, NO, NO2, O3, PM1, PM2.5, and PM10) were analyzed. Results revealed significant reductions in CO, NO, and NO2 levels behind vegetation barriers, while O3 increased. Findings for PMx were mixed, suggesting that barrier effectiveness depends on particle size and vegetation characteristics. These results are consistent with previous research validating the methodology. Future studies could refine this approach, assess long-term vegetation impacts, and explore additional environmental factors influencing urban air pollution dynamics.

Non-carcinogenic health risks assessment of bioaerosols

Bioaerosols, pose potential health risks, yet quantitative assessments of non-carcinogenic risks from bioaerosol inhalation are limited. This study introduces a novel approach for assessing non-carcinogenic health risks using bioaerosol exposure data. The method employs the Average Daily Dose and Hazard Quotient (HQ) metrics, adapted from US Environmental Protection Agency guidelines, with the Reference Dose (RfD) based on thresholds from the National Institute of Occupational Safety and Health and the American Conference of Governmental Industrial Hygienists. This study utilizes a time-weighted approach, considering age-specific inhalation rates and body weights, to enhance the precision of lifetime exposure assessments. This methodology was applied to data collected over one year across multiple locations in Qatar, assessing seasonal and site-specific variations in risk. Results indicate generally low health risks, with HQ values below 1 for most sites and seasons. However, the study identified elevated HQ values at highly active sites during the dry summer, suggesting potential health concerns that need urgent attention. The proposed framework offers a replicable approach for evaluating bioaerosol-related health risks across diverse environments.•Novel adaptation of HQ-based risk assessment for bioaerosols in Qatar, incorporating a time-weighted approach.•Evaluation of seasonal and site-specific exposure dynamics.•Designed for replicability in different environmental conditions.

Highly Efficient Removal of VOCs at Low Concentrations by Low-Cost Biochar-Based Zr-MOF Adsorbents

Despite the low concentrations of volatile organic compounds (VOCs) indoors, they still pose significant health concerns. Therefore, there is an urgent need to prepare adsorbents to remove low-concentration VOCs indoors. In this study, biochar-based MOF composites (UIO-66@PSB and UN-66@PSB) were efficaciously prepared and optimized by a straightforward one-pot method. After being optimized, UIO-66@PSB and UN-66@PSB were characterized by SEM-EDX, XRD, FTIR, XPS, and BET. Toluene and acetaldehyde, as typical indoor pollutants, were chosen as the adsorbates to examine the adsorption performances of UIO-66@PSB and UN-66@PSB adsorbents. The results demonstrated that both UIO-66@PSB and UN-66@PSB exhibited excellent adsorption ability for low-concentration toluene and acetaldehyde while retaining effective adsorption performance even under conditions of high humidity. The PSO model and the Freundlich model were effective in characterizing the adsorption characteristics of toluene, while the PSO model and the Langmuir model were similarly effective in characterizing the adsorption performance of acetaldehyde. Additionally, after 5 adsorption-desorption cycles, UIO-66@PSB and UN-66@PSB can still maintain good adsorption stability. The DFT results revealed that the π-π interaction plays a pivotal role in the adsorption of toluene, with the presence of an amino group shown to augment this phenomenon. Conversely, the electrostatic interaction emerges as the dominant force in the adsorption of acetaldehydes. When the materials were placed in the simulated chamber to observe the adsorption property for the real sample, the removal rates of toluene by UIO-66@PSB and UN-66@PSB could reach 90.85 and 95.73%, respectively. This work indicated that biochar-based MOF composites, which are readily recyclable, have great potential to absorb low concentrations of VOCs in rooms or vehicles.

Long-term monitoring of particulate matter in an Asian community using research-grade low-cost sensors

Particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5) poses significant health risks, necessitating comprehensive exposure assessment. Long-term community monitoring can provide representative exposure levels for environmental epidemiological studies. This study deployed nine research-grade low-cost sensors (AS-LUNG-O) for 3.5 years of street-level PM2.5 monitoring in an Asian community, evaluating temporospatial variations, hotspots, and emission sources. The hourly mean PM2.5 concentrations from December 2017 to July 2021 were 24.3 ± 14.1 µg/m3. PM2.5 levels were typically higher in winter, on weekends, and during religious events compared to summer, weekdays, and typical days, with some peak concentrations occurring randomly. Daytime PM2.5 levels generally exceeded nighttime background levels by 30-50%, with certain religious activities causing up to 80% increases. Spatial analysis identified temples and markets as pollution hotspots. Using a generalized additive mixed model, we found that the COVID-19 pandemic shutdown and higher wind speeds negatively impacted PM concentrations. Religious events, traffic, and vendors were significant PM sources, continually influencing community air quality throughout the 3.5-year monitoring period. This study demonstrates the value of long-term PM monitoring in capturing unexpected peaks, identifying critical sources, and revealing intricate temporospatial distributions. Research-grade low-cost sensor networks complement traditional monitoring stations by facilitating source identification in targeted communities and providing representative PM exposure data for long-term environmental epidemiological research.

Morphometric effects of particulate air pollution on an optically trapped single red blood cell

Particulate air pollution is associated with excess deaths and increases in hospital admissions because of cardiovascular and respiratory diseases. Several scientific studies and assessments have linked particulate pollution to a variety of health problems. In this paper, we provide a single cell in vitro analysis for the effect of the particles, which can enter into blood stream, on red blood cells (RBCs). The RBCs under experiment are incubated with [Formula: see text] particle as the most abundant air pollutants in big cities. The self-referencing digital holographic microscopy (DHM) in Gates' arrangement as a vibration-immune methodology is considered here for live visualization and quantitative analysis of the cells. DHM is a label-free and noninvasive method, therefore, suitable for quantitative and morphometric imaging of biological specimens in arbitrary time scales and at video rates. Single RBCs are immobilized by a blinking multiple optical trapping system integrated to the DHM system. Through post-process numerical reconstruction of the recorded digital holograms, the morphology changes of the pollution-exposed RBCs are tracked and expressed in terms of volume and several statistical morphometry parameters.

Spatial indices quantifying exposure to swine farming in North Carolina

Introduction

Proximity to swine farms is often used as a surrogate in exposure assessments, allowing for the relative quantification of potential pollutant dispersion, odor intensity, and health impacts on neighboring communities. However, defining exposure is complex, and the resulting risk profiles can vary depending on the definition used.

Methods

To quantify the spatially based exposure of surrounding communities to swine farms in North Carolina, three spatially explicit metrics were developed at the census tract-level: IDx1: number of households within 1-mile from a hog farm, IDx2: Co-kriging using the number of hogs and manure lagoons, and IDx3: hog density per square mile. Then, the correlation between these indices and Centers for Disease Control and Prevention (CDC)'s Social Vulnerability Index (SVI) and Environmental Justice Index (EJI), which are generalized vulnerability measures, was evaluated to assess direct impact from swine farms versus multiple stressors.

Results

The three indices differed visually, with IDx3 strongly correlated with IDx1 (0.8) and moderately correlated with IDx2 (0.4). CDC EJI and SVI were not prominently correlated with any of the swine-farm specific indices (≤0.3) indicating limited overlap. The correlation between swine-farm-specific indices and CDC SVI was slightly pronounced in rural areas indicating socially vulnerable populations are more likely to live near swine farming areas in rural census tracts. Having swine farm-specific indices offers a more tailored and nuanced understanding of the potential health and environmental risks. However, the differences between the maps and the varying correlations underscored how different definitions of exposure can yield distinct narratives about which neighborhoods are at risk. Defining and measuring potential exposure, considering factors like proximity, duration, frequency, vulnerability, and cumulative impact, is highly challenging.

Discussion

The study emphasizes the need for a hierarchical framework to quantify and compare environmental exposures, addressing risk-modifying factors and individual-level exposure across space and time before implying direct exposure risks. This approach enables more informed planning for targeted solutions and fosters collaboration among stakeholders, facilitating critical discussions on integrated One Health solutions.

A study on the spatial distribution of life expectancy and its air pollution factors in China based on geographically weighted regression

Background

Life expectancy in China has demonstrated a consistent upward trend, yet significant disparities persist across provinces. Addressing these regional imbalances necessitates a comprehensive investigation into the determinants of life expectancy. Previous research has largely overlooked the critical role of spatial heterogeneity, which is essential for understanding the underlying mechanisms driving these disparities. By incorporating spatial analysis, this study aims to identify and address the factors contributing to the uneven distribution of life expectancy across China, thereby providing a more nuanced understanding of regional health inequalities.

Methods

Therefore, this study investigated the spatial distribution characteristics and patterns of life expectancy across 31 provinces in China in 2020 by conducting descriptive and spatial autocorrelation analyses, utilizing life expectancy data alongside key air pollution indicators (PM2.5, SO2, NO2, and PM10). To address spatial heterogeneity, the geographically weighted regression (GWR) model was applied to assess the regional variations in the impact of air pollutants on life expectancy. This approach allows for the incorporation of geographic coordinates into the regression coefficients, capturing localized effects and providing a more nuanced understanding of the relationship between air pollution and life expectancy across different regions.

Results

The findings revealed that in 2020, life expectancy in China exhibited a distinct east-to-west decreasing trend, demonstrating significant spatial autocorrelation that was predominantly characterized by two aggregation patterns: high-high and low-low clusters. The analysis demonstrated that air pollutants, including SO2, NO2, and PM10, exerted significant influences on life expectancy, albeit with regional variations. Specifically, SO2 exhibited a more pronounced negative impact on life expectancy in southern cities, while NO2 demonstrated a stronger effect in northwestern regions. Notably, PM10 showed a significant influence limited to Yunnan Province, highlighting the spatial heterogeneity in the relationship between air pollution and life expectancy across China.

Conclusion

These findings highlight the imperative for local governments to develop and implement region-specific air pollution control measures, taking into account the unique environmental and socio-economic conditions of their respective areas.

Air pollution and hypertension in rural versus urban children: Lipidomic insights into PM2.5 impacts

Fine particulate matter and its impact on blood pressure (BP) in children remain a concern, with the role of lipid metabolism as a potential mediator not fully elucidated. We conducted a cohort study of 2239 urban subjects and 2194 rural subjects at baseline in China from 2014 to 2024 and a nested case-control study with lipidomics analyses. Analysis results showed that higher fine particulate matter smaller than 2.5 μm (PM2.5) exposure associated with high density lipoprotein cholesterol (HDL-C), non-HDL-C, and higher systolic blood pressure (SBP), partially mediated by HDL-C/non-HDL-C changes. Mediation analysis indicated a significant mediating effect of HDL-C on the PM2.5-DBP and PM2.5-MAP (DBP, diastolic blood pressure; MAP, mean arterial pressure) association in urban subjects, while no mediation effect was found in rural subjects. For non-HDL-C, significant mediating effects were observed in both urban and rural subjects. Further analyses revealed distinct urban-rural lipidomic patterns, with specific phosphatidylethanolamine (PEs) mediating PM2.5-related hypertension in rural subjects, while lactosylceramides (LacCer) played this role in urban youth. These patterns extended to other BP indices as well. In the urban area, PG(44:11), LacCer(d45:1), were identified as playing significant mediating roles in the association between PM2.5 exposure and hypertension while for rural subjects, PEs including PE(16:0/16:0) and PE(18:0/18:2) showed significant mediating effects. Our findings underscore the impact of PM2.5 exposure on lipid profiles and BP risk in children, suggesting area-specific mechanisms and the potential for lipidomic-based interventions to mitigate environmental health risks.

Association between weather conditions and migraine: a systematic review and meta-analysis

BACKGROUND

Previous studies have linked migraine to weather conditions, but variations in the factors examined and inconsistent focus have complicated comparisons. This underscores the need for a more comprehensive and clearer analysis.

METHODS

Studies published before December 2024 on the association between weather and migraine were searched from PubMed, Embase, Web of Science, and Cochrane. Meta-analyses based on effect sizes were performed using Review Manager version 5.4.1.

RESULTS

A total of 31 studies were included in the meta-analyses. It revealed a significant association between migraine attack and weather changes reported as a trigger factor (RD = 0.47, 95% CI = 0.40-0.54). Additionally, specific weather factors, such as temperature (OR = 1.15, 95% CI = 1.02-1.29) and ambient pressure (OR = 1.07, 95% CI = 1.01-1.15), were significantly associated with migraine attacks, while humidity (OR = 1.04, 95% CI = 0.97-1.11) did not show a significant association. Moreover, increased levels of air pollutants, including PM10 (OR = 1.07, 95% CI = 1.03-1.11), PM2.5 (OR = 1.04, 95% CI = 1.01-1.06), NO2 (OR = 1.08, 95% CI = 1.03-1.14), CO (OR = 1.08, 95% CI = 1.01-1.16), and O3 (OR = 1.12, 95% CI = 1.03-1.21), were significantly associated with an increased risk of migraine clinical visits, whereas SO2 (OR = 1.02, 95% CI = 1.00-1.04) was not.

CONCLUSIONS

This meta-analysis revealed that weather changes are significant trigger factors for migraine, with temperature and ambient pressure playing notable roles in this association. Additionally, increased levels of air pollutants are linked to a higher risk of migraine attacks. These findings could lead to new interventions for patients who are weather-sensitive and offer fresh perspectives for future research into the pathogenesis of migraine.

Climate, Air Quality and Their Contribution to Cardiovascular Disease Morbidity and Mortality in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis

Background

Increasing exposure to climatic features is strongly linked to various adverse health outcomes and mortality. While the link between these features and cardiovascular outcomes is well established, most studies are from high-income countries.

Objectives

This review synthesizes evidence as well as research gaps on the relationship between climate indicators, household/ambient air pollution, and all-cause cardiovascular disease (CVD) morbidity and mortality in low- and middle-income countries (LMICs).

Methods

Seven electronic databases were searched up to June 15, 2024. Articles were included if they focused on LMICs, addressed all-cause CVD morbidity and/or mortality, and studied climate or environmental exposures. Studies were selected using ASReview LAB, extracted and analyzed with random effect meta-analysis performed if sufficient articles were identified.

Results & Conclusion

Out of 7,306 articles, 58 met the inclusion criteria: 26 on morbidity, 29 on mortality, and 3 on both. Exposures included PM10, PM2.5, NO2, SO2, BC, O3, CO, solid fuel usage, and temperature variation. Short-term exposure to PM2.5 was significantly associated with CVD morbidity (RR per 10 µg/m3 increase:1.006, 95% CI 1.003-1.009) and mortality (RR:1.007, 95% CI 1.002-1.012). Short-term exposure to NO2 and O3 also increased CVD mortality risk. Long-term exposure to PM2.5 elevated CVD morbidity (RR per 10 µg/m3 increase:1.131, 95% CI 1.057-1.210) and mortality (RR:1.092, 95% CI 1.030-1.159). High and low temperatures and long-term solid fuel use were linked to CVD deaths. The bulk of studies were from mainland China (72%), which may not accurately reflect the situation in other LMICs. Sub-Saharan Africa was particularly lacking, representing a major research gap.

Does outdoor air pollution cause poor semen quality? A systematic review and meta-analysis

INTRODUCTION

It is hypothesized that air pollutants could be associated with semen parameters. This systematic review and meta-analysis of observational studies was conducted to reach a firm conclusion regarding the possible association between outdoor air pollution and semen parameters among the adult population.

METHODS

PubMed, Scopus, and ISI Web of Science were systematically searched using the text keywords and MeSH terms, including "air pollution" and "semen parameters,". The population, Intervention, Comparison, and Outcome (PICO) framework was used as follows: P (Adult men), I (Individuals with the highest exposure to the air pollutants), C (Individuals with the lowest exposure to the air pollutants), O [Semen parameters, including semen volume, total sperm count, sperm concentration, total motility, progressive motility, normal morphology rate, and DNA fragmentation index (DFI)]. The overall effect was presented as a weighted mean difference (WMD) and 95% confidence interval (CI) analyzed via a fixed (inverse-variance) or random (DerSimonian-Laird) weighted model. Low, moderate, and high heterogeneity were defined as I2 index < 40, 40-75, and > 75%, respectively.

RESULTS

Seventeen studies covering 24,065 participants were enrolled in this systematic review and meta-analysis. Higher exposure to outdoor air pollution was associated with significant decreases in semen volume (WMD: -0.13 mL; 95% CI, -0.21 to -0.05; P = 0.001; I2 = 32.1%), sperm concentration (WMD: -12.41 × 106/mL; 95% CI, -23.29 to -1.53; P = 0.03; I2 = 98.7%), total motility (WMD: -5.96%; 95% CI, -10.76 to -1.16; P = 0.01; I2 = 96.2%), progressive motility (WMD: -4.89%; 95% CI, -9.23 to -0.55; P = 0.03; I2 = 98.0%), normal morphology rate (WMD: -2.64%; 95% CI, -4.36 to -0.92; P = 0.003; I2 = 94.6%), and significant increases in DNA fragmentation index (WMD: 5.41%; 95% CI, 3.24 to 7.59; P < 0.001; I2 = 70.4%).

CONCLUSION

Based on the results, it can be stated that air pollution can impair sperm parameters. Further prospective cohort studies are needed to illuminate this issue and clarify the underlying mechanisms.

Can nutraceuticals counteract the detrimental effects of the environment on male fertility? A parallel systematic review and expert opinion

INTRODUCTION

Male fertility relies on a complex physiology that may be negatively influenced by lifestyle, diet, and environment. The beneficial effect of nutraceuticals on male fertility is a debated claim. The aim of this study was to assess if the positive effect of nutraceuticals can counteract the negative effects of the environment on male fertility.

EVIDENCE ACQUISITION

PubMed®/MEDLINE®, Embase and Cochrane Database were searched (September-October 2023), along with crosschecking of references and search for ongoing studies of the effects of the environment and nutraceuticals on male fertility, in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

EVIDENCE SYNTHESIS

Several environmental factors such as microplastic and other endocrine-disrupting chemicals and climate changes may affect the sperm quality in terms of reduction of sperm count number, mobility and altered morphology and thereby reduce male fertility. On the other hand, new evidence demonstrates that a balanced diet rich in antioxidants and essential nutrients, together with minimized exposure to environmental toxins, may improve male fertility and reproductive health. Several nutraceutical compounds proved a protective role against negative environmental effects on male fertility.

CONCLUSIONS

Available evidence confirms that the environment may negatively impact male fertility, and this impact is estimated to rise in the forthcoming years. On the other hand, new data indicate that nutraceuticals may have a protective role against the negative impact of environmental factors on male fertility. The need for future studies to monitor and explore these aspects of men's health cannot be underestimated.

Development and evaluation of rapid, national-scale outdoor air pollution modelling and exposure assessment: Hybrid Air Dispersion Exposure System (HADES)

Improvements in computer processing power are facilitating the development of more detailed environmental models with greater geographical coverage. We developed a national-scale model of outdoor air pollution (Hybrid Air Dispersion Exposure System - HADES) for rapid production of concentration maps of nitrogen dioxide (NO2) and ozone (O3) at very high spatial resolution (10m). The model combines dispersion modelling with satellite-derived estimates of background concentrations, land cover, and a 3-D representation of buildings, in a statistical calibration framework. We developed an emissions inventory covering England and Wales to implement the model and tested its performance using concentration data for the years 2018-2019 from fixed-site monitoring locations. In 10,000 Monte Carlo cross-validation iterations, hourly-annual average R2 values for NO2 were 0.77-0.79 (RMSE: root mean squared error of 5.3-5.7 µg/m3), and 0.87-0.89 for O3 (RMSE = 3.6-3.8 µg/m3) at the 95% confidence interval. The annual average R2 was 0.80 for NO2 (RMSE = 4.9 µg/m3) and 0.86 for O3 (RMSE = 3.2 µg/m3) from aggregating the hourly-annual estimates. The air pollution surfaces are freely available for non-commercial use. In using these surfaces for exposure assessment, all residential locations, and neighbourhoods in urban areas, are unlikely to be below the 2021 World Health Organisation Air Quality Guidelines threshold (10 µg/m3) for annual average NO2 concentrations (10 µg/m3). Rural and suburban areas are likely to exceed the peak-season 8-hour daily maximum O3 threshold (60 µg/m3).

Enhanced Nitrogen Dioxide Detection Using Resistive Graphene-Based Electronic Sensors Modified with Polymers of Intrinsic Microporosity

In this study, we report on the fabrication and evaluation of gas sensing performance for 3 × 3 graphene pixel array sensors coated with polymers of intrinsic microporosity (PIM-1 and PIM-EA-TB) and Matrimid, a commercial polyimide, for the detection of nitrogen dioxide (NO2). The polymer films, with thicknesses of only 9-11 nm, significantly enhanced the gas sensing performance, demonstrating responses as high as -25.7% compared to a bare graphene response of -10.8%. The gas sensing performance was evaluated in real-time by exposing the sensors to NO2 concentrations from 1 to 50 ppm, along with selectivity tests using ammonia (NH3), nitric oxide (NO), methane (CH4), and carbon dioxide (CO2). In addition to their high sensitivity, the sensors exhibited reduced response times by 56 s. They also demonstrated high selectivity for NO2, with minimal cross-sensitivity to other gases. Furthermore, the polymer membranes exhibited rapid recovery times (114-153 s) and limits of detection in the low parts per billion range, with PIM-EA-TB achieving a detection limit of 0.7 ppb. These features highlight their potential as promising candidates for real-time environmental monitoring of toxic gases, showcasing the potential use of PIMs to enhance the sensitivity and selectivity of graphene-based gas sensors and providing a foundation for further development of cost-effective and reliable NO2 detection systems.

Assessing the Impact of Air Quality and Socioeconomic Conditions on Respiratory Disease Incidence

Background and Objective: Air pollution poses significant risks to global public health and has well-established links to respiratory diseases. This study investigates the associations between air pollution markers-Air Quality Index (AQI), ambient ozone, and nitrogen dioxide (NO2)-and the incidence of chronic obstructive pulmonary disease (COPD), asthma, and tuberculosis. It also examines how socioeconomic factors such as gross domestic product (GDP) per capita, tobacco prevalence, and healthcare expenditure influence these relationships. This study includes data from 27 countries, thereby offering a global perspective to inform public health interventions and policy reforms. Methods: Data on average air pollution levels, respiratory disease incidence, and socioeconomic factors were collected from publicly available sources spanning four years. The 27 countries included in the study were selected to represent a broad range of pollution levels, income brackets, and geographical regions. Statistical analyses were performed using Python 3.12.0 to explore the relationships between these variables. Key Findings: AQI and NO2 levels were significantly associated with increased incidences of COPD and tuberculosis, with rates rising especially during periods of heightened pollution. Conversely, ambient ozone exhibited inconsistent relationships with respiratory diseases, heavily influenced by socioeconomic factors. Higher GDP per capita and healthcare expenditure were linked to improved management of infectious diseases like tuberculosis, though they also corresponded with higher reporting of chronic conditions such as COPD. Tobacco smoking emerged as a critical risk factor for COPD across all regions. Conclusions: This study underscores the strong associations between air pollutants and respiratory diseases, particularly tuberculosis and COPD, with socioeconomic factors significantly influencing these relationships. Reducing air pollution and improving healthcare systems, particularly in low-income regions, are essential to mitigating the global burden of respiratory diseases.

Air pollution-related disease and economic burden in China, 1990-2050: A modelling study based on Global burden of disease

The disease burden of air pollution (AP) has been well-documented, yet few studies have explored its economic burden. Retrieving disease burden data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, this study constructed a cost-of-illness (COI) model to estimate China's economic burden of AP in 1990 and 2021 and to identify national and provincial differences, then, an age-period-cohort (APC) model was adopted to estimate trends to 2050. Results demonstrated a serious load of AP, emphasizing the necessity for intervention. Nationwide, 2021 witnessed a decline in the disease burden compared to 1990, while economic burden increased during the same period. Evidence has shown that the significant reduction in the disease and economic burden of household air pollution from solid fuels (HAP) was attributed to the adoption of clean energy, offering insights into air pollution control for low- and middle-income countries (LMICs). While ambient particulate matter pollution (AMP) emerged as the leading cause of disease and economic burden in 2021, it is predicted to endure, particularly in provinces like Heilongjiang, Hebei, and Liaoning. Northern and inland regions bear a higher burden than southern and coastal do. Population aging exacerbates these challenges, urging stricter controls on AMP and protections for the elderly, thereby mitigating health and economic impacts caused by AP.

Making Accessible and Attractive Porosities in Block Copolymer Nanofibers for Highly Permeable and Durable Air Filtration

Submicron particulate matter (PM) can penetrate deeply into human tissue, posing a serious threat to human health. However, the electrostatic charge of commercial respirators is easily dissipated, making it difficult to maintain long-term filtration. Herein, a hierarchically porous filter based on nanofibers with accessible porosity and particulate-attractive surfaces, achieving significant filtration performance is developed through polarity-driven interactions. This is achieved by selective swelling of electrospun nanofibers of the block copolymer of polysulfone and poly(ethylene glycol) (PSF-b-PEG), in which the originally solid nanofibers are 3D perforated with the PEG chains lined along the pore walls. Thus-produces nanofiber filters exhibit a long-term continuous filtration with an efficiency of over 95% for PM0.3 and a low pressure drop of only 40 Pa. In particular, it maintains superior filtration performance even under high particle concentrations and high humidity conditions. Additionally, the filter exhibits high air permeability (10814 m3 m-2 h-1 kPa-1) and water vapor transmission rate (3707 g m-2 d-1). This work provides new strategies and understandings on the development of porous structures simultaneously exhibiting high gas permeability and efficient particulate rejection.

Joint exposure to multiple air pollutants and residual cardiovascular risk in hypertension

BACKGROUND

Despite the widespread availability of antihypertensive medications, residual cardiovascular risk of hypertension remained high. Limited studies have investigated the link between air pollution, particularly joint exposure to multiple air pollutants, with residual cardiovascular risk of hypertension.

METHODS

1981 hypertensive patients (≥ 18 years) from an ongoing longitudinal cohort in China, were enrolled between 2013 and 2019. Using high-quality datasets from China, the ground-level air pollutants concentrations, including PM2.5, PM2.5-10, SO2, O3, CO and NO2, at each participant's residence were calculated. The relationships between individual and multiple air pollutants with the residual cardiovascular risk were assessed by Cox proportional hazards models, air pollution score analyses and Bayesian Kernel Machine Regression model.

RESULTS

Over an average follow-up period of 2.24 years (SD, 1.25), 706 hypertensive patients developed cardiovascular disease. In the single-pollutant analysis, higher concentrations of PM2.5, PM2.5-10, SO2 and CO were linked to increased residual cardiovascular risk. The air pollution score analyses and Bayesian kernel machine regression suggested that combined exposure to multiple air pollutants had a positive association with the residual cardiovascular risk, and NO2 played a dominant role. With higher NO2 concentrations, the hazard ratio of individual pollutants to residual cardiovascular risk increased.

CONCLUSIONS

Prolonged exposure to a mixture of various air pollutants is linked to elevated residual cardiovascular risk in individuals with hypertension. Apart from taking antihypertensive medication and adopting healthy lifestyle behaviors, hypertensive patients should lower air pollutant exposure to decrease residual cardiovascular risk.

From smog to scarred hearts: unmasking the detrimental impact of air pollution on myocardial ischemia-reperfusion injury

Air pollution is a global environmental health hazard associated with elevated cardiovascular morbidity and mortality. Emerging evidence suggests that exposure to various air pollutants, specifically particulate matter (PM), ultrafine particulate matter (UFPM), and diesel exhaust particles, may exacerbate myocardial ischemia-reperfusion (I/R) injury. PM exposure can directly impair cardiomyocyte survival under ischemic conditions by inducing inflammation, oxidative stress, apoptosis, and dysregulation of non-coding RNAs. Moreover, exposure to PM, UFPM, and diesel exhaust particles can increase infarct size, worsen cardiac function, and exacerbate inflammation, oxidative stress and mitochondrial dysfunction in I/R injury. Evidence indicates that the severity of these effects depends on the specific pollutant, exposure duration, and animal model used. In clinical studies, long-term exposure to air pollution, or even high-dose exposure over a short duration, especially to PM2.5 and PM10, was found to be a risk factor for myocardial infarction. Several interventions targeting the pathways involved in air pollution-induced cardiac I/R injury have shown benefits in preclinical studies, including Cyclosporin A, vanillic acid, and β1-adrenoreceptor antagonists, TRPV1 antagonists, GSK-3β inhibitor, and indomethacin. This review comprehensively summarizes the detrimental impacts of air pollutants on cardiac I/R injury from in vitro and in vivo reports to preclinical investigations, highlighting the complex interplay between pollutant type, exposure duration, and cardiovascular outcomes. The detrimental impact of air pollution through multiple pathways, including oxidative stress, inflammation, mitochondrial dysfunction, and apoptosis on cardiac I/R injury is also discussed, emphasizing the urgence for targeted interventions and public health strategies to mitigate the cardiovascular consequences of pollutant exposure.

[The effects of air pollution on health, state of epidemiological knowledge]

The health effects of air pollution have been recognized for many years. However, this area of research continues to receive increasing attention from both the scientific community and civil society. The aim of this article is to review the main epidemiological findings on the effects of outdoor air pollution. First, we will define outdoor air pollution, then describe how the exposure to different pollutants is estimated in epidemiology. Finally, we will present three examples of epidemiological studies on the effects of air pollution.

Triboelectric Nanogenerators for Self-Powered Degradation of Chemical Pollutants

Environmental and human health is severely threatened by wastewater and air pollution, which contain a broad spectrum of organic and inorganic pollutants. Organic contaminants include dyes, volatile organic compounds (VOCs), medical waste, antibiotics, pesticides, and chemical warfare agents. Inorganic gases such as CO2, SO2, and NO x are commonly found in polluted water and air. Traditional methods for pollutant removal, such as oxidation, physicochemical techniques, biotreatment, and enzymatic decomposition, often prove to be inefficient, costly, or energy-intensive. Contemporary solutions like nanofiber-based filters, activated carbon, and plant biomass also face challenges such as generating secondary contaminants and being time-consuming. In this context, triboelectric nanogenerators (TENGs) are emerging as promising alternatives. These devices harvest ambient mechanical energy and convert it to electrical energy, enabling the self-powered degradation of chemical pollutants. This Review summarizes recent progress and challenges in using TENGs as self-powered electrochemical systems (SPECs) for pollutant degradation via photocatalysis or electrocatalysis. The working principles of TENGs are discussed, focusing on their structural flexibility, operational modes, and ability to capture energy from low-frequency mechanical stimuli. The Review concludes with perspectives and suggestions for future research in this field, hoping to inspire further interest and innovation in developing TENG-based SPECs, which represent sustainable and eco-friendly solutions for pollutant treatment.

A modern, flexible cloud-based database and computing service for real-time analysis of vehicle emissions data

In response to the demand for advanced tools in environmental monitoring and policy formulation, this work leverages modern software and big data technologies to enhance novel road transport emissions research. This is achieved by making data and analysis tools more widely available and customisable so users can tailor outputs to their requirements. Through the novel combination of vehicle emissions remote sensing and cloud computing methodologies, these developments aim to reduce the barriers to understanding real-driving emissions (RDE) across urban environments. The platform demonstrates the practical application of modern cloud-computing resources in overcoming the complex demands of air quality management and policy monitoring. This paper shows the potential of modern technological solutions to improve the accessibility of environmental data for policy-making and the broader pursuit of sustainable urban development. The web-application is publicly and freely available at https://cares-public-app.azurewebsites.net.

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission

A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the mechanism of air pollution and its effect on the spread of diseases. The key is to characterize the intrinsic correlation between the disease infection and the change in air pollutant concentration. In this paper, we establish a coupled discrete susceptible-exposed-infectious-susceptible (SEIS) model with demography to characterize the transmission of disease, and the change in the concentration of air pollutants is described in the form of the Beverton-Holt (BH) model with a time-varying inflow rate of air pollutants. Considering the periodic variation characteristics of data, time-varying parameters are defined as specific functional forms. We estimate the change point at which the parameters switch and the parameter values within the switching interval based on Bayesian statistical theory. The data fitting of the model can reflect the seasonal peaks and annual growth trends of values of air quality index (AQI) and the number of influenza-like illnesses (ILI) cases. However, the bias in data fitting indicates a more complex correlation pattern between disease and pollutant concentration changes. To explore unknown mechanisms, we propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining deep learning with difference equations and obtain the curves of the transmission rate and inflow rate functions over time. The results show that neural network models can help us determine time-varying parameters in the model, thereby better reflecting the trend of data changes.

Effects of combined exposure to PM2.5, O3, and NO2 on health risks of different disease populations in the Beijing-Tianjin-Hebei region

Air pollution adversely affects people's health. Under the current background of compound air pollution in China, the emission reduction potential of air pollution control has significantly decreased, and there are few studies on multi-pollutant emission reduction and synergistic effects. PM2.5, O3, and NO2 have caused the enormous disease burden and health risks. This study evaluated the single and combined health effects of pollutants in the Beijing-Tianjin-Hebei region, and discussed the differences in susceptibility among disease populations. Identified the interactions of multiple pollutants and evaluated current environmental policies. This study provided evidence of the interactive effect of combined PM2.5, O3, and NO2 exposure on the health risks of different disease populations. Among them, the interaction between PM2.5 and O3 posed the most significant health risks (Odds Ratio of 3.026) and had the greatest impact on the health of people with cardiovascular diseases (Odds Ratio of 3.136). The excess deaths affected by combined exposure exceeded 40 % of the total excess deaths. The assessment of environmental policies indicated that compliance with the AQG 2021 guideline values would reduce ambient air pollution-related deaths in the Beijing-Tianjin-Hebei region alone by about 30,000 per year. Our national standards were still far from the benchmarks given by the World Health Organization, especially for NO2. In the future, attentions should also be paid to the control of NO2 and other reaction precursors while coordinating the control of PM2.5 and O3.

Burning of municipal waste in household furnaces and the health of their owners

The aim of the study was to determine the scale of emission and airborne dispersion of selected pollutants (PM2.5, PM10, TVOC, HCHO) associated with the combustion of various types of municipal waste (MW), its mixed stream and separate fractions, in a household furnace, as compared to conventional (CF) and alternative (AF) fuels. We demonstrated that each type of fuel (AF, CF, AFw) combusted in a household furnace is a significant source of air pollutants, especially fine PM2.5 particles, whose concentrations exceeded the limit values (3.1-17.2 times for PM2.5 and 0.5-7.4 times for PM10). The combustion of MW in household furnaces generated higher levels of PM2.5 (up to 345 µg/m3) and PM10 (up to 369 µg/m3) than AF or CF, at the same time being a significant source of TVOC (up to 0.3 mg/m3) and HCHO (0.4 mg/m3). The analysis showed that according to the Polish and European classification, air quality (AQI) during the combustion of all the materials analyzed is very poor (n = 12) or extremely poor (n = 19). The combustion of such materials as polystyrene, rubber and upholstery foam in household furnaces generates drastically high health risk to local inhabitants. We found that the combustion of polystyrene generated the highest Cancer Risk (CR) values of 1.04E-01 (children) and 2.60E-02 (adults), exceeding the acceptable level multiple times (CR > 10-6). Inhalation exposure to very poor air quality can lead to health problems, such as disorders of the respiratory, cardiovascular and immune systems. Additional risk is posed by solid fuel combustion in rural areas, which may be a significant factor deteriorating the chemical condition of soils, especially those used for agricultural purposes.

Assessing the accuracy of various statistical models for forecasting PM 2.5 : a case study from diverse regions of Gandhinagar and Ahmedabad

PM2.5 is the most hazardous air pollutant due to its smaller size, which allows deeper bodily penetration. Three diverse regions from Gujarat, India, namely Sector 10, Maninagar, and Vatva, which have green space, high population concentration, and industries, respectively, were chosen to forecast PM2.5 concentration for the next day. Four statistical models, including Multiple Linear Regression (MLR), Principal Component Regression (PCR), Simple Exponential Smoothing (SES), and Autoregressive Integrated Moving Average (ARIMA), were chosen to forecast PM2.5 levels. For this study, data of various pollutants and meteorological parameters were collected from February 2019 to September 2023. Analysis of the seasonal patterns of PM2.5 revealed elevated concentrations during post-monsoon and winter, in contrast to reduced levels during summer and monsoon. Statistical analysis revealed that the concentration of PM2.5 in Sector 10 is much lower than in the other two regions. The analysis of the test results, utilising various accuracy measures like RMSE, MAE, MAPE, IA, and others, indicated that Sector 10 achieved the highest precision in its results. While assessing the models' accuracy on the test data, the ARIMA model demonstrated the highest level of precision. The average RMSE, MAE, and MAPE values for the ARIMA model were 12.63, 8.59, and 0.24, respectively. In the comparison of the performance between these statistical models and the neural network-based Multilayer Perceptron (MLP) model, it was observed that the statistical model demonstrated superior performance over the MLP model.

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution

Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.

Optimizing air quality monitoring spatial layout by maximizing the coverage of the population in Beijing-Tianjin-Hebei and surrounding areas

The spatial layout of the air quality monitoring network (AQMN) is crucial for objective, accurate, and comprehensive air quality assessment. The current technical standard specified the minimum quantity requirements for air quality monitoring sites, but there were no standards to specify the spatial of monitoring sites. This study proposed a novel framework to evaluate and optimize the spatial layout of AQMN. First, this study proposed three indicators to evaluate the performance of the current AQMN. They were monitoring area repetition rate, population coverage rate, and correlations. The assessment of AQMN in Beijing-Tianjin-Hebei and surroundings areas (BTHs) showed the overall monitoring area repetition rate and population coverage rate was 81.07 % and 35.5 %, respectively, which means the current AQMN in BTHs has very high monitoring repeatability and limited population coverage. Secondly, a large-scale linear programming model was built to optimize the spatial layout and determine the spatial location of 279 newly added monitoring sites in BTHs according to the Environmental Monitoring 14th Five-Year Plan of China. The optimization results showed that the optimized AQMN covered 97 million additional people, and the population coverage rate increased to 49.5 %. The proposed framework provided a valuable tool to evaluate and optimize AQMN and could be a potential solution for developing new technical standards of AQMN.

Associations of exposure to PM2.5 and its compounds with carotid intima-media thickness among middle-aged adults

BACKGROUND

Exposure to fine particulate matter (PM2.5) has been linked to an increased risk of atherosclerosis. However, it remains unclear whether specific compounds within PM2.5, rather than the overall mass, serve as a better indicator of adverse cardiovascular health outcomes associated with air pollution.

METHODS

In this cross-sectional study, we included 3257 participants (aged 37-51 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Exposure to PM2.5 and its constituent compounds, black carbon (BC), ammonium, nitrate, organic matter, sulfate, mineral dust, and sea salt were included in the analyses. Carotid intima media thickness (cIMT; the average of common, bulb, and internal carotid) was measured by carotid ultrasonography. We assessed the cross-sectional associations of one-year exposure to PM2.5 and its compounds with mean cIMT using linear regression models adjusting for participants' demographics, individual- and neighborhood-level socioeconomic status, behavioral components, and health conditions. We also adopted Bayesian kernel machine regression (BKMR) models to investigate the association between the PM2.5 compound mixture and cIMT as well as the contribution of each compound to the association.

RESULTS

Greater exposure to BC was associated with higher cIMT (mm) (β =0.034, 95 % CI = 0.019-0.049, per IQR increase [0.56 μg/m3] of BC) among participants with a mean age of 45.0, consisting of 45.9 % Black and 54.1 % White males and females. The association was generally consistent across participants' demographic characteristics. In our BKMR analysis, BC exhibited a dose-response association with cIMT with a high contribution to the association of cIMT with PM2.5 compound as a mixture (posterior inclusion probability [PIP]: 1.00).

CONCLUSIONS

Our findings suggest that certain compounds of PM2.5, such as BC, may offer more reliable indications of the impact of air pollution on cardiovascular health.

The associations between exposure to ambient air pollution and coagulation markers and the potential effects of DNA methylation

Previous studies have illustrated the pivotal role of coagulation biomarkers in the link between air pollution and cardiovascular disease (CVD). However, inconsistencies remain in the conclusions, with limited studies conducted in rural areas of China. We conducted a panel study in rural areas of Henan Province, China. Considering the potential effect modifications of atherosclerotic cardiovascular disease (ASCVD) risks, 104 participants were enrolled, comprising two matched groups: 52 with high ASCVD risks and 52 with low ASCVD risks. DNA methylation at CpG sites and coagulation indices were measured for all participants. Linear mixed-effect regression models were used to evaluate the associations between ambient air pollution, coagulation biomarkers, and DNA methylation. We observed that for every 5-day standard deviation (SD) increment of PM2.5 (11.91 μg/m³) and PM10 (13.65 μg/m³), fibrinogen increased by 7.70 % (95 %CI: 2.27, 13.12) and 8.50 % (95 %CI: 2.46, 14.55), respectively. SO2 (6.95 μg/m³) was associated with 40.25 % (95 %CI: 14.83, 65.67) increase in plasminogen activator inhibitor-1 (PAI-1). Decreased methylation at CpG sites was associated with exposure to air pollution. However, DNA methylation did not mediate the association between ambient air pollution and coagulation. Our study revealed the harmful impact of ambient air pollution on coagulation function but found no significant mediation effects of DNA methylation.

Shape-dependent aerosol dynamics in indoor environments: Penetration, deposition, and dispersion

Particle shape exerts a significant influence on their dynamic behavior, and it is imperative to elucidate these effects given the potential for severe environmental toxicity associated with shaped particles. Despite extensive research on the dynamical processes of spherical particles, the behaviors of non-spherical particles have been insufficiently investigated. In this study, we have developed a suite of computation-based models that account for particle shape and have reported on the typical dynamical behaviors of non-spherical particles within indoor environments. We have explored three typical scenarios, i.e., particle penetration into indoor spaces through building cracks, indoor particle deposition, and indoor particle dispersion. The shape-induced deviations are associated with dynamical processes, showing a decrease trend among penetration, deposition, and dispersion of the non-spherical particles. The maximum discrepancy due to particle shape during the penetration process exceeds 1000 %, observed with particles of approximately 0.02 μm in diameter interacting with straight cracks 4.5 cm in length and 0.25 mm in height. Moreover, there is a discrepancy of more than 70 % in the deposition of particles with a diameter of approximately 10 μm on side walls when using side air supply ventilation. Similarly, a discrepancy of nearly 11 % is noted for particles around 0.02 μm in diameter during dispersion under displacement ventilation within indoor settings. The interaction between shape-related particle dynamics, particularly their diffusion characteristics, and the properties of the flow field leads to these shape-dependent dynamical discrepancies. These findings offer a comprehensive understanding of how the shape of particles affects their indoor dynamic behavior, thereby supporting the control of hazardous particles in indoor environments.

Exposure of RAW264.7 macrophages to exhaust emissions (gases and PAH) and non-exhaust emissions (tire particles) induces additive or synergistic TNF-α production depending on the tire particle size

Road traffic is a major contributor to air pollution and consequently negatively affects human health. Car pollution originates both from exhaust emissions (EE) and non-exhaust emissions (NEE, such as tire and brake wear particles, erosion of road surfaces and resuspension of road dust). While the toxicity of EE and NEE has been characterized separately, their combined effects are poorly documented. However, we are constantly exposed to a mixture of pollutants and their interactions should not be neglected as they may significantly impact their toxicological profile resulting in additive, synergistic or antagonistic effects. To fill this gap, we investigated in vitro the combined toxicity of exhaust gases and benzo[a]pyrene (representative of EE) and tire particles (representative of NEE). Macrophages from the RAW264.7 cell line were exposed for 24 h to tire particles (TP) of variable size (6-113 µm), alone or in combination with exhaust gases (CO2, CO, NO, NO2) and benzo[a]pyrene (B[a]P) as an archetype of polycyclic aromatic hydrocarbon (PAH). The cell response was assessed in terms of cytotoxicity, proinflammatory response and oxidative stress. TP, gases and B[a]P, alone or in combination triggered neither cytotoxicity nor oxidative stress. On the contrary, a proinflammatory response was elicited with two different profiles depending on the size of the TP: TNF-α production was either slightly (with the finest TP) or strongly (with coarse TP) increased in the presence of gases and B[a]P, suggesting that the effects of TP, gases and B[a]P were either additive or synergistic, depending on TP size.

Construction of a minute ventilation model to address inter-individual inhaled dose variability within identical exposure scenarios using wearable devices

Inhaled dose is crucial for accurately assessing exposure to air pollution, determined by pollutant concentration and minute ventilation (VE). However, the VE predictive models and its application to assess the health effects of air pollution are still lacking. In this study, we developed VE predictive models using machine learning techniques, utilizing data obtained from eighty participants who underwent a laboratory cardiopulmonary exercise test (CPET). VE predictive models were developed using generalized additive model (GAM), random forest model (RF) and extreme gradient boosting (XGBoost) and analyzed for explanation of input variables. The Random Forest model, cross-validated, exhibited outstanding performance with an R2 of 0.986 and a MAE of 1.816 L/min. The median difference between the measured VE and the predicted VE was 0.18 L/min, and the median difference between the black carbon (BC) inhaled dose based on predicted VE and measured VE was 0.02 ng. Employing explainable machine learning, the results showed that metabolic equivalent (METs), heart rate, and body weight are the three top important variables, emphasizing the significance of incorporating METs variables when constructing VE models. Through multiple linear regression models and an adjusted stratified analysis model, the significant adverse association between BC concentration and inhaled dose on diastolic blood pressure (DBP) was only observed in female. The disparity in the effect of BC inhaled dose compared to BC concentration on DBP reached up to 115 %. This study is the first to explore the ability of different machine learning algorithms to construct VE prediction models and directly apply the models to assess health effects of an example pollutant. This study contributes to the accurate assessment of air pollution exposure leveraging wearable devices, an approach useful for environmental epidemiology studies.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

Scalable pilot production of highly efficient 5-ply respiratory masks enhanced by bacterial cellulose nanofibers

This study presents the pilot-scale production of highly efficient real respiratory masks enhanced by bacterial cellulose nanofibers (BCNFs). The BCNFs suspension was deposited onto tissue paper substrates using fog spray technique with three BCNFs grammage levels of 0.5, 1, and 2 g/m2, followed by freeze drying. Also, two continuous and batch welding processes have been used to construct the core structure of the masks. Field emission scanning electron microscopy (FE-SEM) confirmed the uniform distribution and size of fog-sprayed BCNFs and their pore networks. With increase in BCNFs grammage, the adsorption efficiency of masks increased in both continuous and batch production methods. The mask produced through batch processing showed the highest efficiency of 99.2 % (N99) for the particulate matter of 0.3 μm, while the maximum corresponding efficiency value in continuous processing was 95.4 % (N95). The pressure drops of the masks increased with the increase in BCNFs grammage in both methods. The maximum pressure drops of N95 and N99 masks obtained were 112 ± 10 Pa and 128 ± 8 Pa, respectively. Notably, the filtration efficacy of masks was preserved when subjected to relative humidity fluctuations ranging from 30 % to 70 %. The successful findings of this study offer significant promise for future air filtration applications.

[Management of adult bronchiectasis - Consensus-based Guidelines for the German Respiratory Society (DGP) e. V. (AWMF registration number 020-030)]

Bronchiectasis is an etiologically heterogeneous, chronic, and often progressive respiratory disease characterized by irreversible bronchial dilation. It is frequently associated with significant symptom burden, multiple complications, and reduced quality of life. For several years, there has been a marked global increase in the prevalence of bronchiectasis, which is linked to a substantial economic burden on healthcare systems. This consensus-based guideline is the first German-language guideline addressing the management of bronchiectasis in adults. The guideline emphasizes the importance of thoracic imaging using CT for diagnosis and differentiation of bronchiectasis and highlights the significance of etiology in determining treatment approaches. Both non-drug and drug treatments are comprehensively covered. Non-pharmacological measures include smoking cessation, physiotherapy, physical training, rehabilitation, non-invasive ventilation, thoracic surgery, and lung transplantation. Pharmacological treatments focus on the long-term use of mucolytics, bronchodilators, anti-inflammatory medications, and antibiotics. Additionally, the guideline covers the challenges and strategies for managing upper airway involvement, comorbidities, and exacerbations, as well as socio-medical aspects and disability rights. The importance of patient education and self-management is also emphasized. Finally, the guideline addresses special life stages such as transition, family planning, pregnancy and parenthood, and palliative care. The aim is to ensure comprehensive, consensus-based, and patient-centered care, taking into account individual risks and needs.

Air-Pollution-Mediated Microbial Dysbiosis in Health and Disease: Lung-Gut Axis and Beyond

Growing evidence suggests physiological and pathological functions of lung and gut microbiomes in various pathologies. Epidemiological and experimental data associate air pollution exposure with host microbial dysbiosis in the lungs and gut. Air pollution through increased reactive oxygen species generation, the disruption of epithelial barrier integrity, and systemic inflammation modulates microbial imbalance. Microbiome balance is crucial in regulating inflammation and metabolic pathways to maintain health. Microbiome dysbiosis is proposed as a potential mechanism for the air-pollution-induced modulation of pulmonary and systemic disorders. Microbiome-based therapeutic approaches are increasingly gaining attention and could have added value in promoting lung health. This review summarizes and discusses air-pollution-mediated microbiome alterations in the lungs and gut in humans and mice and elaborates on their role in health and disease. We discuss and summarize the current literature, highlight important mechanisms that lead to microbial dysbiosis, and elaborate on pathways that potentially link lung and lung microbiomes in the context of environmental exposures. Finally, we discuss the lung-liver-gut axis and its potential pathophysiological implications in air-pollution-mediated pathologies through microbial dysbiosis.

Low-Temperature Reduction of NOx by NH3 with Unity Conversion on Nanofilament MnO2/Activated Semi-Coke Catalyst

Selective catalytic reduction of nitrogen oxides with NH3 at low temperatures remains a crucial goal for industrial applications. However, effective catalysts operating at 70-90 °C are rarely reported, limiting SCR scenarios to high-temperature conditions. Herein, we report a unique MnO2 nanofilament catalyst grown on activated semi-coke synthesized via a one-step in situ hydrothermal approach, which exhibits a stable and marked 100 % conversion rate of NO to N2 with 100 % selectivity at 90 °C, superior to the other prepared structures (nanowires, nanorods, and nanotubes). Temperature-programmed desorption shows a large number of acid sites on MnO2(NFs)/ASC, benefiting the formation of NH4 + ions. Meanwhile, diffuse reflectance infrared Fourier transform spectroscopy reveals the activation of NO with O2 to form bidentate nitrate/bridging nitrate NO2 intermediates via bidentate nitrate species, triggering the Fast SCR with NH3 at low temperatures. Such an effective, easy-to-prepare, and low-cost catalyst paves a new pathway for low-temperature SCR for a wide range of application scenarios.

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ

Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ's 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC's daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3's potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution's detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution's adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.

Exposure to coal dust exacerbates cognitive impairment by activating the IL6/ERK1/2/SP1 signaling pathway

Coal dust (CD) is a common pollutant, and epidemiological surveys indicate that long-term exposure to coal dust not only leads to the occurrence of pulmonary diseases but also has certain impacts on cognitive abilities. However, there is little open-published literature on the effects and specific mechanisms of coal dust exposure on the cognition of patients with Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). An animal model has been built in this study with clinical population samples to explore the changes in neuroinflammation and cognitive abilities with coal dust exposure. In the animal model, compared to C57BL/6 mice, APP/PS1 mice exposed to coal dust exhibited more severe cognitive impairment, accompanied by significantly elevated levels of neuroinflammatory factors Apolipoprotein E4 (AOPE4) and Interleukin-6 (IL6) in the hippocampus, and more severe neuronal damage. In clinical sample sequencing, it was found that there is significant upregulation of AOPE4, neutrophils, and IL6 expression in the peripheral blood of MCI patients compared to normal individuals. Mechanistically, cell experiments revealed that IL6 could promote the phosphorylation of ERK1/2 and enhance the expression of transcription factor SP1, thereby promoting AOPE4 expression. The results of this study suggest that coal dust can promote the upregulation of IL6 and AOPE4 in patients, exacerbating cognitive impairment.

Neighborhood-Level Nitrogen Dioxide Inequalities Contribute to Surface Ozone Variability in Houston, Texas

In Houston, Texas, nitrogen dioxide (NO2) air pollution disproportionately affects Black, Latinx, and Asian communities, and high ozone (O3) days are frequent. There is limited knowledge of how NO2 inequalities vary in urban air quality contexts, in part from the lack of time-varying neighborhood-level NO2 measurements. First, we demonstrate that daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 tropospheric vertical column densities (TVCDs) resolve a major portion of census tract-scale NO2 inequalities in Houston, comparing NO2 inequalities based on TROPOMI TVCDs and spatiotemporally coincident airborne remote sensing (250 m × 560 m) from the NASA TRacking Aerosol Convection ExpeRiment-Air Quality (TRACER-AQ). We further evaluate the application of daily TROPOMI TVCDs to census tract-scale NO2 inequalities (May 2018-November 2022). This includes explaining differences between mean daily NO2 inequalities and those based on TVCDs oversampled to 0.01° × 0.01° and showing daily NO2 column-surface relationships weaken as a function of observation separation distance. Second, census tract-scale NO2 inequalities, city-wide high O3, and mesoscale airflows are found to covary using principal component and cluster analysis. A generalized additive model of O3 mixing ratios versus NO2 inequalities reproduces established nonlinear relationships between O3 production and NO2 concentrations, providing observational evidence that neighborhood-level NO2 inequalities and O3 are coupled. Consequently, emissions controls specifically in Black, Latinx, and Asian communities will have co-benefits, reducing both NO2 disparities and high O3 days city wide.

Unraveling Cross-Organ Impacts of Airborne Pollutants: A Multiomics Study on Respiratory Exposure and Gastrointestinal Health

Poor air quality is increasingly linked to gastrointestinal diseases, suggesting a potential correlation with human intestine health. However, this relationship remains largely unexplored due to limited research. This study used a controlled mouse model exposed to cooking oil fumes (COFs) and metagenomics, transcriptomics, and metabolomics to elucidate interactions between intestine microbiota and host metabolism under environmental stress. Our findings reveal that short-term COF inhalation induces pulmonary inflammation within 3 days and leads to gastrointestinal disturbances, elucidating a pathway connecting respiratory exposure to intestinal dysfunction. The exposure intensity significantly correlates with changes in intestinal tissue integrity, microbial composition, and metabolic function. Extended exposure of 7 days disrupts intestine microbiota and alters tryptophan metabolism, with further changes observed after 14 days, highlighting an adaptive response. These results highlight the vulnerability of intestinal health to airborne pollutants and suggest a pathway through which inhaled pollutants may affect distant organ systems.

Association Between Asian Dust Exposure and Pneumonia Hospitalization in Western Japan: A Case-Crossover Study

Epidemiological studies have reported that Asian dust (AD), a type of desert dust, has harmful effects on human health. This study aimed to examine the association between AD exposure and hospitalization due to pneumonia. Data on patients in Western Japan admitted for pneumonia were included from a real-world database derived from electronic medical records. We used the meteorological observatory data of the most populous city in each prefecture, in which AD event was defined as a loss of visibility from a distance ≤10 km. A case-crossover design and conditional logistic regression model were used. Overall, 12 938 patients were included, and AD exposure events were observed for 557 days. Exposure to an AD event five days prior to hospitalization was significantly associated with hospitalization for pneumonia after adjusting for weather variables (odds ratio = 1.17). These findings suggest that AD exposure is associated with an increased rate of admission for pneumonia in Western Japan.

Associations between short-term exposure to ambient air pollution and lung function in adults

BACKGROUND

Evidence of the acute effects of high-level air pollution on small airway function and systemic inflammation in adults is scarce.

OBJECTIVE

To examined the associations of short-term (i.e., daily) exposure to multiple air pollutants with lung function and inflammatory markers.

METHODS

We assessed short-term (daily) effects of air pollutants, including particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) and 10 μm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO), on lung function and peripheral immune cell counts over various lag times using generalized linear regression models.

RESULTS

A total of 4764 adults were included from the general community-dwelling population in Shanghai, China. Exposure to air pollutants and lung function were negatively correlated. Decline in FEF between 25% and 75% of vital capacity (FEF25-75%) were found associated with PM2.5, SO2, and CO, and decline in forced expiratory volume in 3 s (FEV3) to forced vital capacity (FVC) ratio were associated with all examined pollutants, indicating obstruction in small airways. Obstructed airflow in large and middle airways as indicated by decline in FEV1/FVC were also associated with all pollutants. In subgroup analysis, significant negative associations between the five pollutants and SAD parameters were found only in males but not in females. The difference in the associations of SO2 with FEF75% between males and females achieved statistical significance. Additionally, all examined pollutants were significantly associated with lower peripheral neutrophil count.

IMPACT STATEMENT

Acute exposure to air pollutants were associated with airflow-limitation. Both small airways and proximal airways were affected. Acute exposure to air pollutants were accompanied with a lower neutrophil count.

Impact of indoor air pollution on DNA damage and chromosome stability: a systematic review

Indoor air pollution is becoming a rising public health problem and is largely resulting from the burning of solid fuels and heating in households. Burning these fuels produces harmful compounds, such as particulate matter regarded as a major health risk, particularly affecting the onset and exacerbation of respiratory diseases. As exposure to polluted indoor air can cause DNA damage including DNA sd breaks as well as chromosomal damage, in this paper, we aim to provide an overview of the impact of indoor air pollution on DNA damage and genome stability by reviewing the scientific papers that have used the comet, micronucleus, and γ-H2AX assays. These methods are valuable tools in human biomonitoring and for studying the mechanisms of action of various pollutants, and are readily used for the assessment of primary DNA damage and genome instability induced by air pollutants by measuring different aspects of DNA and chromosomal damage. Based on our search, in selected studies (in vitro, animal models, and human biomonitoring), we found generally higher levels of DNA strand breaks and chromosomal damage due to indoor air pollutants compared to matched control or unexposed groups. In summary, our systematic review reveals the importance of the comet, micronucleus, and γ-H2AX assays as sensitive tools for the evaluation of DNA and genome damaging potential of different indoor air pollutants. Additionally, research in this particular direction is warranted since little is still known about the level of indoor air pollution in households or public buildings and its impact on genetic material. Future studies should focus on research investigating the possible impact of indoor air pollutants in complex mixtures on the genome and relate pollutants to possible health outcomes.

Prevalence of Cardiovascular Disease (CAD) due to industrial air pollutants in the proximity of Islamabad Industrial Estate (IEI), Pakistan

Contaminated air quality, in lieu of massive industrial pollution, is severely attributing to health anomalies in the proximity of industrial units. Cardiovascular Disease (CAD) is rising around industrial units in the planned capital city of Pakistan, Pakistan. To study self-reported CAD in the proximity of Industrial Estate Islamabad (IEI) by equating two distinct study groups as 'Band-I': the residence 0-650 meters and 'Band-II' 650-1300 meters radius around the perimeter of IEI. The perimeters were digitized using Google Earth and GIS. Field survey was conducted on deploying 388 (194 in each Band) close-ended (self-administered) questionnaires at the household level, after adjusting the potential confounding variables. The research calculated odds ratios (ORs) of the CAD at 95% CI. The study's findings of the multiple logistic regression for ORs confirmed a significant increase in CAD problems due to industrial affluents in Band-I than in Band-II which were less severe and less life-threatening. Study confirmed high incidences of high blood pressure and breathing issues (up to 67%), due to accumulation of unhealthy affluents thus leading to heart stroke (Band I = 56.20% and Band II = 60.30%). It is aided by smoking that has increased CAD in Band-I. Societal attributes of knowledge, beliefs, attitudes, and preferences fail to safeguard the local residents amid high concentration of harmful pollutants. As a counter measure the affected respondents engaged in highlighting the issue to the concerned public offices, yet there is a high need on part of the capital government to take mitigative measures to immediately halt the disastrous industrial air emissions to save precious lives.

The joint effect of long-term exposure to multiple air pollutants on non-accidental and cause-specific mortality: A longitudinal cohort study

The long-term joint impacts of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on mortality are inconclusive. To bridge this research gap, we included 283,568 adults from the Taiwan MJ cohort between 2005 and 2016 and linked with the mortality data until 31 May 2019. Participants' annual average exposures to PM2.5, NO2, and O3 were estimated using satellite-based spatial-temporal models. We applied elastic net-regularised Cox models to construct a weighted environmental risk score (WERS) for the joint effects of three pollutants on non-accidental, cardiovascular, and cancer mortality and evaluated the contribution of each pollutant. The three pollutants jointly raised non-accidental mortality risk with a WERS hazard ratio (HR) of 1.186 (95% CI: 1.118-1.259) per standard deviation increase in each pollutant and weights of 72.8%, 15.2%, and 12.0% for PM2.5, NO2, and O3, respectively. The WERS increased cardiovascular death risk [HR: 1.248 (1.042-1.496)], with PM2.5 as the first contributor and O3 as the second. The WERS also elevated the cancer death risk [HR: 1.173 (1.083-1.270)], where PM2.5 played the dominant role and NO2 ranked second. Coordinated control of these three pollutants can optimise the health benefits of air quality improvements.

Prenatal PM2.5 Exposure and Its Association with Low Birth Weight: A Systematic Review and Meta-Analysis

Exposure to PM2.5 while pregnant is associated with negative effects on low birth weight (LBW). This study employed a systematic review and meta-analysis to investigate the impact of PM2.5 exposure during pregnancy on LBW. A search of databases such as Scopus, ScienceDirect, and PubMed identified thirteen appropriate studies. This study used a random-effects model to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs) for each trimester. The findings revealed a significant relationship between PM2.5 exposure and LBW in both the first and second trimesters (OR 1.05, 95% CI 1.00-1.09, p < 0.001). There was no significant difference between trimesters (p = 0.704). The results emphasize the persistent influence of PM2.5 on fetal development throughout all stages of pregnancy. Reducing air pollution is critical for improving pregnancy outcomes and decreasing the incidence of LBW. Further study is needed to improve exposure assessments and investigate the underlying biological pathways.

Airborne Exposure to Pollutants and Mental Health: A Review with Implications for United States Veterans

PURPOSE OF REVIEW

Inhalation of airborne pollutants in the natural and built environment is ubiquitous; yet, exposures are different across a lifespan and unique to individuals. Here, we reviewed the connections between mental health outcomes from airborne pollutant exposures, the biological inflammatory mechanisms, and provide future directions for researchers and policy makers. The current state of knowledge is discussed on associations between mental health outcomes and Clean Air Act criteria pollutants, traffic-related air pollutants, pesticides, heavy metals, jet fuel, and burn pits.

RECENT FINDINGS

Although associations between airborne pollutants and negative physical health outcomes have been a topic of previous investigations, work highlighting associations between exposures and psychological health is only starting to emerge. Research on criteria pollutants and mental health outcomes has the most robust results to date, followed by traffic-related air pollutants, and then pesticides. In contrast, scarce mental health research has been conducted on exposure to heavy metals, jet fuel, and burn pits. Specific cohorts of individuals, such as United States military members and in-turn, Veterans, often have unique histories of exposures, including service-related exposures to aircraft (e.g. jet fuels) and burn pits. Research focused on Veterans and other individuals with an increased likelihood of exposure and higher vulnerability to negative mental health outcomes is needed. Future research will facilitate knowledge aimed at both prevention and intervention to improve physical and mental health among military personnel, Veterans, and other at-risk individuals.