Air pollution exposure during pregnancy and lung function in childhood: The LUIS study.

BACKGROUND: The adverse effects of high air pollution levels on childhood lung function are well-known. Limited evidence exists on the effects of moderate exposure levels during early life on childhood lung function. We investigated the association of exposure to moderate air pollution during pregnancy, infancy, and preschool time with lung function at school age in a Swiss population-based study. METHODS: Fine-scale spatiotemporal model estimates of particulate matter with a diameter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) were linked with residential address histories. We compared air pollution exposures within different time windows (whole pregnancy, first, second, and third trimester of pregnancy, first year of life, preschool age) with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) measured cross-sectionally using linear regression models adjusted for potential confounders. RESULTS: We included 2182 children, ages 6-17 years. Prenatal air pollution exposure was associated with reduced lung function at school age. In children aged 12 years, per 10 µg·m-3 increase in PM2.5 during pregnancy, FEV1 was 55 mL lower (95% CI -84 to -25 mL) and FVC 62 mL lower (95% CI -96 to -28 mL). Associations were age-dependent since they were stronger in younger and weaker in older children. PM2.5 exposure after birth was not associated with reduced lung function. There was no association between NO2 exposure and lung function. CONCLUSION: In utero lung development is most sensitive to air pollution exposure, since even modest PM2.5 exposure during the prenatal time was associated with reduced lung function, most prominent in younger children.

Distribution Characteristics of Atmospheric Microplastics in Typical Desert Agricultural Regions.

We examined the distribution characteristics of atmospheric microplastics in typical desert agricultural regions, with a focus on the agricultural areas surrounding the Taklamakan Desert, Xinjiang, China. We collected samples of total suspended particulate matter (TSP), atmospheric deposition, and atmospheric dust using both active and passive collection methods. The chemical composition, particle size, shape, and color of atmospheric microplastics were examined using a stereomicroscope and a Fourier-transform infrared spectrometer to analyze their characteristics. The results showed that the primary chemical compositions of microplastics included polypropylene (PP), polyethylene, polyethylene terephthalate, polymethylmethacrylate, and cellophane. Particle sizes were mainly within the range of 0 to 1000 µm. Fibrous microplastics constituted the majority of the TSP and atmospheric deposition, whereas film-like microplastics constituted the largest proportion of atmospheric dustfall. The deposition flux of atmospheric microplastics in the first quarter was measured at 103.21 ± 22.12 particles/m2/day, which was lower than that observed in conventional agricultural areas. The abundance of microplastics in atmospheric dustfall was found to be 1.36 particles/g. The proportion of PP microplastics in atmospheric dustfall can be as high as 35%. Through a comparison of microplastic content in TSP during dust storms and under normal weather conditions, it was found that dust storms can lead to an increase in the abundance of microplastics within the atmospheric TSP. The present study provides a scientific basis for understanding the distribution of atmospheric microplastics in typical desert agricultural regions. Environ Toxicol Chem 2024;00:1-14. © 2024 SETAC.

Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways.

Air pollution induced by fine particulate matter with diameter ≤ 2.5 µm (PM2.5) poses a significant challenge for global air quality management. Understanding how factors such as climate change, land use and land cover change (LULCC), and changing emissions interact to impact PM2.5 remains limited. To address this gap, we employed the Community Earth System Model and examined both the individual and combined effects of these factors on global surface PM2.5 in 2010 and projected scenarios for 2050 under different Shared Socioeconomic Pathways (SSPs). Our results reveal biomass-burning and anthropogenic emissions as the primary drivers of surface PM2.5 across all SSPs. Less polluted regions like the US and Europe are expected to experience substantial PM2.5 reduction in all future scenarios, reaching up to ~5 µg m-3 (70 %) in SSP1. However, heavily polluted regions like India and China may experience varied outcomes, with a potential decrease in SSP1 and increase under SSP3. Eastern China witness ~20 % rise in PM2.5 under SSP3, while northern India may experience ~70 % increase under same scenario. Depending on the region, climate change alone is expected to change PM2.5 up to ±5 µg m-3, while the influence of LULCC appears even weaker. The modest changes in PM2.5 attributable to LULCC and climate change are associated with aerosol chemistry and meteorological effects, including biogenic volatile organic compound emissions, SO2 oxidation, and NH4NO3 formation. Despite their comparatively minor role, LULCC and climate change can still significantly shape future air quality in specific regions, potentially counteracting the benefits of emission control initiatives. This study underscores the pivotal role of changes in anthropogenic emissions in shaping future PM2.5 across all SSP scenarios. Thus, addressing all contributing factors, with a primary focus on reducing anthropogenic emissions, is crucial for achieving sustainable reduction in surface PM2.5 levels and meeting sustainable pollution mitigation goals.

From Inhalation to Neurodegeneration: Air Pollution as a Modifiable Risk Factor for Alzheimer's Disease.

Air pollution, a growing concern for public health, has been linked to various respiratory and cardiovascular diseases. Emerging evidence also suggests a link between exposure to air pollutants and neurodegenerative diseases, particularly Alzheimer's disease (AD). This review explores the composition and sources of air pollutants, including particulate matter, gases, persistent organic pollutants, and heavy metals. The pathophysiology of AD is briefly discussed, highlighting the role of beta-amyloid plaques, neurofibrillary tangles, and genetic factors. This article also examines how air pollutants reach the brain and exert their detrimental effects, delving into the neurotoxicity of air pollutants. The molecular mechanisms linking air pollution to neurodegeneration are explored in detail, focusing on oxidative stress, neuroinflammation, and protein aggregation. Preclinical studies, including in vitro experiments and animal models, provide evidence for the direct effects of pollutants on neuronal cells, glial cells, and the blood-brain barrier. Epidemiological studies have reported associations between exposure to air pollution and an increased risk of AD and cognitive decline. The growing body of evidence supporting air pollution as a modifiable risk factor for AD underscores the importance of considering environmental factors in the etiology and progression of neurodegenerative diseases, in the face of worsening global air quality.

Year-long and seasonal differences of PM2.5 chemical characteristics and their role in the viability of human lung epithelial cells (A549).

Fine particulate matters-PM2.5 in the air can have considerable negative effects on human health and the environment. Various human cell-based studies examined the effect of PM2.5 on human health in different cities of the world using various chemical parameters. Unfortunately, limited information is available regarding the relationship between toxicity and chemical characteristics of PM2.5 collected in Istanbul, Türkiye, located in one of the most populated cities in the world. To investigate the chemical characteristics and cytotoxicity of PM2.5 in Istanbul, samples were collected for 12 months, then potentially toxic metals, oxidative potential, and particle indicators (e.g., functional groups and elements) were determined, and the cytotoxicity of PM2.5 on human A549 lung alveolar epithelial cells was examined. The mean PM2.5 mass concentration was 24.0 ± 17.4 µg m-3 and higher in cold months compared to other seasons. Moreover, the results of the metals, elemental, and functional groups indicated that seasonal and monthly characteristics were influenced by the regional anthropogenic sources and photochemistry input. The cytotoxicity results also showed that the viability of A549 cells was reduced with the exposure of PM2.5 (30-53%) and higher cytotoxicity was obtained in summer compared to the other seasons due to the impact of the metals, elements, and oxidative characteristics of PM2.5.

Interaction between Extreme Temperature Events and Fine Particulate Matter on Cardiometabolic Multimorbidity: Evidence from Four National Cohort Studies.

Accumulating evidence linked extreme temperature events (ETEs) and fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM); however, it remained unknown if and how ETEs and PM2.5 interact to trigger CMM occurrence. Merging four Chinese national cohorts with 64,140 free-CMM adults, we provided strong evidence among ETEs, PM2.5 exposure, and CMM occurrence. Performing Cox hazards regression models along with additive interaction analyses, we found that the hazards ratio (HRs) of CMM occurrence associated with heatwave and cold spell were 1.006-1.019 and 1.063-1.091, respectively. Each 10 µg/m3 increment of PM2.5 concentration was associated with 17.9% (95% confidence interval: 13.9-22.0%) increased risk of CMM. Similar adverse effects were also found among PM2.5 constituents of nitrate, organic matter, sulfate, ammonium, and black carbon. We observed a synergetic interaction of heatwave and PM2.5 pollution on CMM occurrence with relative excess risk due to the interaction of 0.999 (0.663-1.334). Our study provides novel evidence that both ETEs and PM2.5 exposure were positively associated with CMM occurrence, and the heatwave interacts synergistically with PM2.5 to trigger CMM.

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India.

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4 µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3 µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Inhalational exposure history is associated with differential sinonasal gene expression profiles and clinical outcomes in chronic rhinosinusitis patients: A pilot study.

KEY POINTS: Inhalational exposure (IE) history assessment is important and may guide chronic rhinosinusitis disease management. Combined exposure status was the most significant factor across differential gene expression analyse IE history was associated with pro-inflammatory transcriptome changes and worse clinical outcomes.

Non-communicable airway disease and air pollution in three African Countries: Benin, Cameroon and The Gambia.

BACKGROUND: Air pollution exposure can increase the risk of development and exacerbation of chronic airway disease (CAD). We set out to assess CAD patients in Benin, Cameroon and The Gambia and to compare their measured exposures to air pollution. METHODOLOGY: We recruited patients with a diagnosis of CAD from four clinics in the three countries. We collected epidemiological, spirometric and home air pollution data. RESULTS: Of the 98 adults recruited, 56 were men; the mean age was 51.6 years (standard deviation ±17.5). Most (69%) patients resided in cities and ever smoking was highest in Cameroon (23.0%). Cough, wheeze and shortness of breath were reported across the countries. A diagnosis of asthma was present in 74.0%; 16.3% had chronic obstructive pulmonary disease and 4.1% had chronic bronchitis. Prevalence of airflow obstruction was respectively 77.1%, 54.0% and 64.0% in Benin, Cameroon, and Gambia. Across the sites, 18.0% reported >5 exacerbations. The median home particulate matter less than 2.5 µm in diameter (PM2.5) was respectively 13.0 µg/m3, 5.0 µg/m3 and 4.4 µg/m3. The median home carbon monoxide (CO) exposures were respectively 1.6 parts per million (ppm), 0.3 ppm and 0.4 ppm. Home PM2.5 differed significantly between the three countries (P < 0.001) while home CO did not. CONCLUSION: Based on these results, preventive programmes should focus on ensuring proper spirometric diagnosis, good disease control and reduction in air pollution exposure.

CONTEXTE: L'exposition à la pollution de l'air peut accroître le risque de développement et d'aggravation des maladies chroniques des voies respiratoires (CAD). Nous avons entrepris d'évaluer les patients atteints de CAD au Bénin, au Cameroun et en Gambie et de comparer les niveaux d'exposition à la pollution de l'air qu'ils ont subis. MÉTHODOLOGIE: Nous avons sélectionné des patients ayant reçu un diagnostic de CAD dans quatre cliniques de ces trois pays. Nous avons collecté des informations épidémiologiques, des mesures spirométriques ainsi que des données sur la pollution de l'air à leur domicile. RÉSULTATS: En total, 98 individus adultes ont été sélectionnés pour cette étude. Parmi eux, 56 étaient de sexe masculin. L'âge moyen de ces participants était de 51,6 ans, avec un écart-type de ±17,5. La majorité des patients (69%) résidaient en milieu urbain, tandis que le taux de tabagisme le plus élevé était observé au Cameroun (23,0%). Les symptômes de toux, de respiration sifflante et d'essoufflement ont été rapportés dans tous les pays. Parmi les patients, 74% ont reçu un diagnostic d'asthme, 16,3% souffraient de maladie pulmonaire obstructive chronique et 4,1% de bronchite chronique. L'obstruction des voies respiratoires était présente respectivement chez 77,1%, 54,0% et 64,0% des cas au Bénin, au Cameroun et en Gambie. Sur l'ensemble des sites, 18,0% ont signalé plus de cinq exacerbations. La médiane des PM2.5 à domicile était de 13,0 µg/m3, 5,0 µg/m3 et 4,4 µg/m3, respectivement. Les expositions médianes au monoxyde de carbone (CO) à domicile étaient de 1,6 ppm, 0,3 ppm et 0,4 ppm respectivement. Les PM2,5 à domicile présentaient des différences significatives entre les trois pays (P < 0,001), contrairement au CO à domicile. CONCLUSION: En se basant sur ces résultats, il est recommandé que les programmes de prévention se focalisent sur un dépistage spirométrique adéquat, une gestion efficace de la maladie et une diminution de l'exposition à la pollution atmosphérique.

Ambient Particulate Matter Induces In Vitro Toxicity to Intestinal Epithelial Cells without Exacerbating Acute Colitis Induced by Dextran Sodium Sulfate or 2,4,6-Trinitrobenzenesulfonic Acid.

Inflammatory bowel disease (IBD) is an immunologically complex disorder involving genetic, microbial, and environmental risk factors. Its global burden has continued to rise since industrialization, with epidemiological studies suggesting that ambient particulate matter (PM) in air pollution could be a contributing factor. Prior animal studies have shown that oral PM10 exposure promotes intestinal inflammation in a genetic IBD model and that PM2.5 inhalation exposure can increase intestinal levels of pro-inflammatory cytokines. PM10 and PM2.5 include ultrafine particles (UFP), which have an aerodynamic diameter of <0.10 µm and biophysical and biochemical properties that promote toxicity. UFP inhalation, however, has not been previously studied in the context of murine models of IBD. Here, we demonstrated that ambient PM is toxic to cultured Caco-2 intestinal epithelial cells and examined whether UFP inhalation affected acute colitis induced by dextran sodium sulfate and 2,4,6-trinitrobenzenesulfonic acid. C57BL/6J mice were exposed to filtered air (FA) or various types of ambient PM reaerosolized in the ultrafine size range at ~300 µg/m3, 6 h/day, 3-5 days/week, starting 7-10 days before disease induction. No differences in weight change, clinical disease activity, or histology were observed between the PM and FA-exposed groups. In conclusion, UFP inhalation exposure did not exacerbate intestinal inflammation in acute, chemically-induced colitis models.

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 µg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Differential effects of long- and short-term exposure to PM2.5 on accelerating telomere shortening: from in vitro to epidemiological studies.

Exposure to air pollutants has been associated with DNA damage and increases the risks of respiratory diseases, such as asthma and COPD; however short- and long-term effects of air pollutants on telomere dysfunction remain unclear. We investigated the impact of short- and long-term exposure to fine particulate matter with an aerodynamic diameter below 2.5 µm (PM2.5) on telomere length in human bronchial epithelial BEAS-2B cells, and assessed the potential correlation between PM2.5 exposure and telomere length in the LIGHTS childhood cohort study. We observed that long-term, but not short-term, PM2.5 exposure was significantly associated with telomere shortening, along with the downregulation of human telomerase reverse transcriptase (hTERT) mRNA and protein levels. Moreover, long-term exposure to PM2.5 induced proinflammatory cytokine secretion, notably interleukin 6 (IL-6) and IL-8, triggered subG1 cell cycle arrest, and ultimately caused cell death. Long-term exposure to PM2.5 upregulated the LC3-II/ LC3-I ratio but led to p62 protein accumulation in BEAS-2B cells, suggesting a blockade of autophagic flux. Moreover, consistent with our in vitro findings, our epidemiological study found significant association between annual average exposure to higher PM2.5 and shortening of leukocyte telomere length in children. However, no significant association between 7-day short-term exposure to PM2.5 and leukocyte telomere length was observed in children. By combining in vitro experimental and epidemiological studies, our findings provide supportive evidence linking potential regulatory mechanisms to population level with respect to long-term PM2.5 exposure to telomere shortening in humans.

Joint Effect of Short-Term Exposure to Fine Particulate Matter and Ozone on Mortality: A Time Series Study in 272 Chinese Cities.

Short-term exposure to PM2.5 or O3 can increase mortality risk; however, limited studies have evaluated their interaction. A multicity time series study was conducted to investigate the synergistic effect of PM2.5 and O3 on mortality in China, using mortality data and high-resolution pollutant predictions from 272 cities in 2013-2015. Generalized additive models were applied to estimate associations of PM2.5 and O3 with mortality. Modification and interaction effects were explored by stratified analyses and synergistic indexes. Deaths attributable to PM2.5 and O3 were evaluated with or without modification of the other pollutant. The risk of total nonaccidental mortality increased by 0.70% for each 10 µg/m3 increase in PM2.5 when O3 levels were high, compared to 0.12% at low O3 levels. The effect of O3 on total nonaccidental mortality at high PM2.5 levels (1.26%) was also significantly higher than that at low PM2.5 levels (0.59%). Similar patterns were observed for cardiovascular or respiratory diseases. The relative excess risk of interaction and synergy index of PM2.5 and O3 on nonaccidental mortality were 0.69% and 1.31 with statistical significance, respectively. Nonaccidental deaths attributable to short-term exposure of PM2.5 or O3 when considering modification of the other pollutant were 28% and 31% higher than those without considering modification, respectively. Our results found synergistic effects of short-term coexposure to PM2.5 and O3 on mortality and suggested underestimations of attributable risks without considering their synergistic effects.

Nature-based solutions for monitoring the impact of vehicular particulate matter and for the preventive conservation of the Palatine Hill archaeological site in Rome, Italy.

Magnetic and chemical biomonitoring methodologies were applied to the southern slopes of the Palatine Hill archaeological area in Rome, Italy. Plant leaves and lichen transplants were respectively sampled and exposed between July 2022 and June 2023 to assess the impact of vehicular particulate matter from Via dei Cerchi, a trafficked road coasting Circus Maximus, towards the archaeological area upon the Palatine Hill. The magnetic properties of leaves and lichens, inferred from magnetic susceptibility, hysteresis loops and first order reversal curves, were combined with the concentration of trace elements. It was demonstrated that the bioaccumulation of magnetite-like particles, associated with tracers of vehicular emissions, such as Ba and Sb, decreased with longitudinal distance from the road, without any important influence of elevation from the ground. Lichens demonstrated to be more efficient biomonitors of airborne PM than leaves, irrespective of the plant species. Conversely, leaves intercepted and accumulated all PM fractions, including road dusts and resuspended soil particles. Thus, plant leaves are suitable for providing preventive conservation services that limit the impact of particulate pollution on cultural heritage sites within busy metropolitan contexts.

Real-time evolution characteristics and potential reactions of contaminants in commuter bus cabin air.

Despite the increasing use of motor vehicles, the impact of airborne pollutants and their health risks inside public transportation, such as commuter buses, is not well understood. This study assessed air quality inside an urban commuter bus by continuously monitoring PM10, PM2.5, and CO concentrations during both driving and parking periods. Our findings revealed that the ventilation system of the bus significantly reduced the infiltration of outdoor particulate matter and water vapor. However, CO concentrations were considerably higher inside the bus than outside, primarily due to vehicular self-emission. The ineffection of the ventilation system to remove CO potentially increases long-term exposure risks for passengers. The study identified ozone as a key oxidant in the cabin. Besides vehicle emissions, C3-C10 saturated aldehydes and carbonyl compounds were detected, including acetone, propanal, and hexanal. The presence of 6-MHO, an oxidation product of squalene, suggests that passengers contribute to VOCs load through direct emissions or skin surface reactions. Additionally, human respiration was found to significantly contribute to isoprene levels, estimated at 81.7 %. This research underscores the need for further investigation into the cumulative effects of stable compounds in cabin air and provides insights for developing healthier public transportation systems.

Validation of the analytical methodology used in obtaining the oral bioavailability of organic target pollutants in atmospheric particulate matter (PM10) applying an in-vitro method.

In recent years, scientists have started evaluating the portion of PM-bound pollutants that may be liberated (bioaccessible fraction) in human fluids and spread through the digestive system ultimately entering systemic circulation (known as the bioavailable fraction). In the current research, an analytical procedure was validated and applied to characterize the oral bioavailable fraction of PM10 samples. The approach encompassed the determination of 49 organic contaminants. The proposed method aims to biomimetic complete mouth-gastric-intestinal system basing on an adaptation of the unified bioaccessibility method (UBM) modified by the inclusion of a dialysis membrane to mimic intestinal absorption and obtain the orally bioavailable fractions. It was followed by a vortex-assisted liquid-liquid extraction (VALLE) step, using gas chromatography-tandem mass spectrometry (GC-MS/MS). Analytical procedure was effectively validated by employing selected reaction monitoring (SRM) mode in MS/MS, matrix-matched calibration, and deuterium-labelled surrogate standards. This approach ensured heightened sensitivity, minimized matrix effects, and compensated for any losses during the process. The validation process covered various aspects, including studying linearity, determining detection and quantification limits, assessing analytical recoveries at three concentration levels, and evaluating precision both within a single day and across multiple days. The validated method was applied to PM10 samples, revealing that polycyclic aromatic hydrocarbons (PAHs) were the most frequently detected, with significant seasonal variations in their concentrations. Organophosphorus flame retardants (OPFRs) like TCPP were also detected in bioavailable fractions, highlighting their potential health impact. Bisphenols, SMCs, and PAEs were not detected, suggesting low levels in the studied urban area. Further research is needed to understand the bioavailability of PM-bound pollutants in different environments.

Air Pollution and Blood Pressure: Evidence From Indonesia.

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures.

PURPOSE: Exposures to hazardous chemicals have been linked to many detrimental health effects and it is therefore critical to have effective biomonitoring methods to better evaluate key environmental exposures that increase the risk of chronic disease and death. Traditional biomonitoring utilizing blood and urine is limited due to the specialized skills and invasiveness of collecting these fluid samples. This systematic review focuses on tear fluid, which is largely under-researched, as a promising complementary matrix to the traditional fluids used for biomonitoring. The objective is to evaluate the practicability of using human tear fluid for biomonitoring environmental exposures, highlighting potential pitfalls and opportunities. RECENT FINDING: Tear fluid biomonitoring represents a promising method for assessing exposures because it can be collected with minimal invasiveness and tears contain exposure markers from both the external and internal environments. Tear fluid uniquely interfaces with the external environment at the air-tear interface, providing a surface for airborne chemicals to diffuse into the ocular environment and interact with biomolecules. Tear fluid also contains molecules from the internal environment that have travelled from the blood to tears by crossing the blood-tear barrier. This review demonstrates that tear fluid can be used to identify hazardous chemicals from the external environment and differentiate exposure groups.