Particulate Matter Air Pollution Research Articles

Association between long-term exposure to fine particulate air pollution and risk of death attributed to esophageal cancer in Taiwan

ABSTRACT The International Agency for Research on Cancer (IARC) classifies exposure to fine particulate matter (PM2.5) air pollution as carcinogenic to humans (Group I), most frequently associated with lung cancer. Airborne air pollutants may be associated with other sites of cancer, although few studies have examined this avenue of research. Esophageal cancer mortality rates vary substantially across townships in Taiwan, a fact that suggests environment influence. Therefore, the aim of this study was to investigate the association between long-term exposure to ambient PM2.5 and deaths attributed to esophageal cancer in 66 municipal areas across Taiwan. To conduct this study, annual PM2.5 levels were determined taking into account age-standardized esophageal cancer mortality rates in male and female residents of these municipalities from 2012 to 2021. The annual PM2.5 levels of each municipality were divided into tertiles and computed adjusted risk ratio (RR) using weighted-multiple regression analyses controlling for municipal lung cancer deaths, urbanization level, and physician density. Men residing in those areas with intermediate PM2.5 tertile levels (18.96–25.19 ug/m3) were found to have an adjusted RR of 1.22 (1.15–1.30) and those of residing areas with the highest tertiles levels (25.20–29.48 ug/m3) exhibited an RR of 1.11 (1.051.18). However, in women in the same municipalities, a significant inverse association was found between PM2.5 levels and mortality attributed to esophageal cancer, 0.82 (95% CI = 0.65-1.04) and 0.61 (95% CI = 0.47-0.79), respectively. These findings suggest that long-term exposure to PM2.5 increases the risk of developing esophageal cancer in men in Taiwan.

Influence of Oil and Gas End-Use on Summertime Particulate Matter and Ozone Pollution in the Eastern US.

The influence of oil and gas end-use activities on ambient air quality is complex and understudied, particularly in regions where intensive end-use activities and large biogenic emissions of isoprene coincide. In these regions, vehicular emissions of nitrogen oxides (NOx≡NO + NO2) modulate the oxidative fate of isoprene, a biogenic precursor of the harmful air pollutants ozone, formaldehyde, and particulate matter (PM2.5). Here, we investigate the direct and indirect influence of the end-use emissions on ambient air quality. To do so, we use the GEOS-Chem model with focus on the eastern United States (US) in summer. Regional mean end-use NOx of 1.4 ppb suppresses isoprene secondary organic aerosol (OA) formation by just 0.02 μg m-3 and enhances abundance of the carcinogen formaldehyde by 0.3 ppb. Formation of other reactive oxygenated volatile organic compounds is also enhanced, contributing to end-use maximum daily mean 8-h ozone (MDA8 O3) of 8 ppb. End-use PM2.5 is mostly (67%) anthropogenic OA, followed by 20% secondary inorganic sulfate, nitrate and ammonium and 11% black carbon. These adverse effects on eastern US summertime air quality suggest potential for severe air quality degradation in regions like the tropics with year-round biogenic emissions, growing oil and gas end-use and limited environmental regulation.

Gene expression changes in mouse lung induced by subacute inhalation of PM10-rich particulate matter

Introduction Particulate matter (PM) air pollution is associated with an increased incidence of lung diseases, but the underlying mechanisms have not been fully elucidated. In this study, a mouse model of subacute lung inflammation was employed to investigate the cellular responses and gene expression changes induced by exposure to natural ambient air pollution. Methods C57BL/6J mice were exposed to road dust (primarily PM10) at 150 µg/m³ for 21 days (8 h/day) through a nose-only inhalation exposure system. Lung tissues were analyzed for the expression of proinflammatory signaling, oxidative stress, and fibrosis markers. RNA-sequencing analysis was conducted to identify differentially expressed genes (DEGs). A gene ontology over-representation analysis was performed to identify the altered genetic pathways. Results Elevated levels of proinflammatory cytokines, including IL-1β, IL-6, and TNF-α, and an increase in phosphorylated MAPK were determined in the road dust exposure group compared to the control group. Histopathological examinations revealed more severe lung inflammation and damage in the exposed mice, including fibrosis and bronchiolar hyperplasia. Gene expression profiling identified 108 DEGs, with decreases in most except genes such as Krt15 and Reg3g. The protein–protein interaction network analysis together with text-mining identified 18 key hub genes, associated with fatty acid oxidation, lipid metabolism, and peroxisomes. Conclusion This study identified key genes, signaling pathways, and cellular responses in mouse lung affected by road dust exposure. These findings contribute to a deeper understanding of the transcriptional and cellular responses induced by subacute exposure to the PM in road dust.

Commuter exposure to smoke and particulate matter air pollution in Auckland during the 2019 New Zealand International Convention Centre fire

ABSTRACT Human mobility typically exhibits temporal and spatial predictability. However, during hazardous events, roadways, footpaths and public transport networks can be disrupted by detours, closures and congestion. Urban fires, exemplified by the October 2019 New Zealand International Convention Centre (NZICC) fire in Auckland, New Zealand, are on the rise, posing threats to life, property and mobility. In this study, we employ geospatial analyses to investigate the impact of the NZICC fire on human mobility, encompassing both driving and walking. We generate predicted surfaces of particulate matter (PM2.5 and PM10) from seven local fixed air monitoring stations to estimate network-based exposure and inhalation dosage during travel. High levels of air pollution during the fire exceeded baseline concentrations, surpassing National Environmental Standards for Air Quality (NESAQ) and World Health Organization (WHO) limits for both PM2.5 and PM10. Private car commuters faced delays and congestion due to road closures, prolonging smoke exposure. Pedestrians, including those accessing essential public transportation infrastructure, experienced unavoidable exposure to smoke along the fastest routes from NZICC to places like Britomart train station and the Downtown Ferry. We recommend increasing the availability and dissemination of air pollutant monitoring data to enhance public awareness of the health risks associated with smoke exposure.

Adverse impacts of particulate matter air pollution on female and male reproductive function.

Particulate matter (PM) air pollution consists of liquid and solid particles, which are categorized by size as less than 10 (PM10) μm, 2.5 (PM2.5) μm, or 0.1μm (PM0.1 or ultrafine) in aerodynamic diameter and which vary in composition depending on the sources. PM exposure is ubiquitous and has been associated with many adverse health effects. This narrative review focuses on epidemiological and experimental studies that investigated the effects of PM exposure on female and male reproduction and on pregnancy.μ.

Toxicity prediction based on PAHs in fruits and vegetables − A machine learning approach

This study analyses the toxicity levels of fruits and vegetables by identifying the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in areas affected by industrial and vehicle pollution. The presence of PAHs on plant surfaces, caused mostly by air pollution particulate matter, raises serious concerns about the nutritional value of these foods. Traditional techniques for measuring PAH levels include Gas Chromatography/Mass Spectrometry (GC/MS) and High-Performance Liquid Chromatography (HPLC). Although successful, these procedures are expensive and time-consuming. Toxicity detection is often based on expert knowledge or experimental analysis relative to the European Food Safety Authority’s (EFSA) limits. In response to these challenges, this work uses artificial intelligence approaches to determine toxicity levels using 16 PAHs. Data on PAH concentrations in fruits and vegetables were collected from a variety of sources, classified as safe or harmful, and verified using statistical analysis. The validated dataset was subsequently classified using several machine learning techniques. The level of toxicity is scaled and compared to the expected outputs using the results obtained from the neural network. An experimental investigation validates the promising findings of level of toxicity classification using artificial intelligence approaches, which are then evaluated statistically. The study shows that machine learning algorithms obtained classification accuracies of over 90 %, including the evaluation of harmfulness. The accuracy for the two-class (Safe and Unsafe) category was 98.27 %, while the accuracy for the four-class (No harm, Low harm, Moderate Harm, and Severe harm) category was 98.68 %. These findings highlight the potential of artificial intelligence in improving food safety and public health by presenting an innovative multidisciplinary method to assessing the impacts of environmental pollutants on the edibility of fruits and vegetables in the context of climate change.

Ambient Air Pollution Exposure and Outcomes in Patients Receiving Lung Transplant

Elevated ambient fine particulate matter (PM2.5) air pollution exposure has been associated with poor health outcomes across several domains, but its associated outcomes among lung transplant recipients are poorly understood. To investigate whether greater PM2.5 exposure at the zip code of residence is associated with a higher hazard for mortality and graft failure in patients with lung transplants. This retrospective cohort study used panel data provided by the United Network for Organ Sharing, which includes patients receiving transplants across all active US lung transplant programs. Adult patients who received lung transplants between May 2005 and December 2016 were included, with a last follow-up of September 10, 2020. Data were analyzed from September 2022 to May 2023. Zip code-level annual PM2.5 exposure was constructed using previously published North American estimates. The primary outcome was time to death or lung allograft failure after lung transplant. A gamma shared frailty Cox proportional hazards model was used to produce unadjusted and adjusted hazard ratios (HRs) to estimate the association of zip code PM2.5 exposure at the time of transplant with graft failure or mortality. Among 18 265 lung transplant recipients (mean [SD] age, 55.3 [13.2] years; 7328 female [40.2%]), the resident zip code's annual PM2.5 exposure level was greater than or equal to the Environmental Protection Agency (EPA) standard of 12μg/m3 for 1790 patients (9.8%) and less than the standard for 16 475 patients (90.2%). In unadjusted analysis, median graft survival was 4.87 years (95% CI, 4.57-5.23 years) for recipients living in high PM2.5 areas and 5.84 years (95% CI, 5.71-5.96 years) for recipients in the low PM2.5 group. Having an annual PM2.5 exposure level greater than or equal to the EPA standard 12 μg/m3 was associated with an increase in the hazard of death or graft failure (HR, 1.11; 95% CI, 1.05-1.18; P < .001) in the unadjusted analysis and after adjusting for covariates (HR, 1.08; 95% CI, 1.01-1.15; P = .02). Each 1 μg/m3 increase in exposure was associated with an increase in the hazard of death or graft failure (adjusted HR, 1.01; 95% CI, 1.00-1.02; P = .004) when treating PM2.5 exposure as a continuous variable. In this study, elevated zip code-level ambient PM2.5 exposure was associated with an increased hazard of death or graft failure in lung transplant recipients. Further study is needed to better understand this association, which may help guide risk modification strategies at individual and population levels.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Effect of air pollutants particulate matter (PM2.5, PM10), sulfur dioxide (SO2) and ozone (O3) on cognitive health

Impaired cognitive health is the leading cause of various disabilities and disorders. Air pollution has been dramatically increasing over the last few decades and has been identified as a potential risk factor for impaired cognitive health. This study investigates the effect of air pollutants, particulate matter (PM2.5, PM10), sulfur dioxide (SO2), and ground-level ozone, on global cognitive health. The data on environmental pollutants and cognitive health were recorded from PubMed, Web of Science, Scopus, and Google Scholar. Initially, 790 articles were identified after screening for duplicates and applying the inclusion and exclusion criteria, 21 studies were included, and data was synthesized to get a pooled result. The overall results revealed that increased exposure to PM2.5 was positively and significantly associated with cognitive decline (OR 1.49; 95% CI 1.11, 1.99; p = 0.01). The risk of cognitive impairment due to PM10 (OR 1.30; 95% CI 1.00–1.70, p = 0.05), and SO2 (OR 1.39; 95% CI 1.27–1.51; p < 0.01) exposure were also significantly heightened. The study findings show that overall exposure to particulate matter PM2.5, PM10, and SO2 was associated with an increased risk of a decrease in global cognitive functions. The findings suggest that reducing levels of air pollutants could be a strategic approach to mitigate cognitive health risks in populations worldwide.

Sex disparities in the burden of air particulate matter-related unhealthy years and life-years lost in Asia-Pacific countries, 1990–2019

Air quality, particularly in the Asia-Pacific region (APAC), significantly impacts human health and mortality. This study aims to quantify and compare the effects of ambient particulate matter (APM) and household air pollution (HAP) from solid fuels on unhealthy years and life-years lost in APAC from 1990 to 2019. It also examines factors influencing unhealthy years among females due to HAP. Our findings show that females were more vulnerable to HAP, whereas males were at a higher mortality risk from APM. Pacific islands encountered the greatest burden from HAP, while West Asia was most affected by APM. Over the studied period, the impact of APM on unhealthy years and life-years lost increased, primarily affecting the elderly and adults more than children and youth. Conversely, health impacts from HAP declined across all age groups in the Pacific Islands, though less so compared to other subregions. Key predictors of female unhealthy years from HAP included pollution, demographics, and women's empowerment, with no significant economic influences. Understanding these impacts, along with age and gender differences, is crucial for developing targeted environmental health policies and interventions.

Particulate matter air pollution components and cancer risk

Particulate matter air pollution components and cancer risk

Preoperative Exposure to Fine Particulate Matter and Risk of Postoperative Complications: A Single Center Observational Cohort Bayesian Analysis.

While exposure to fine particulate matter air pollution (PM 2.5 ) is known to cause adverse health effects, its impact on postoperative outcomes in US adults remains understudied. Perioperative exposure to PM 2.5 may induce inflammation that insidiously interacts with the systemic inflammatory response after surgery, leading to higher postoperative complications. We conducted a single center, retrospective cohort study using data from 64,313 surgical patients living along Utah's Wasatch Front and undergoing elective surgical procedures at a single academic medical center from 2016-2018. Patients' addresses were geocoded and linked to daily Census-tract level PM 2.5 estimates preoperatively. We hypothesized that elevated PM 2.5 concentrations in the seven days prior to surgery would be associated with an increase in a bundle of major postoperative complications. A hierarchical Bayesians regression model was fit adjusting for age, sex, season, neighborhood disadvantage, and the Elixhauser index of comorbidities. Postoperative complications increased in a dose-dependent manner with higher concentrations of PM 2.5 exposure, with a relative increase of 7% in the odds of complications for every 10ug/m3 increase in the highest single-day 24-hr PM 2.5 exposure during the 7 days prior to surgery. The association persisted after controlling for comorbidities and potential confounders; our inferences were robust to modeling choices and sensitivity analysis. In this large Utah cohort, exposure to elevated PM 2.5 concentrations in the week before surgery was associated with increased postoperative complications in a dose-dependent manner, suggesting a potential impact of air pollution on surgical outcomes. These findings merit replication in larger datasets to identify populations at risk and to define the interaction and impact of different pollutants. PM 2.5 exposure is a potential perioperative risk factor and, given the unmitigated air pollution in urban areas, a global health concern.

Ambient particulate matter and chronic obstructive pulmonary disease mortality: a nationwide, individual-level, case-crossover study in China

Short-term exposure to particulate matter air pollution has been associated with the exacerbations of COPD, but its association with COPD mortality was not fully elucidated. We aimed to assess the association between short-term particulate matter exposure and the risk of COPD mortality in China using individual-level data. We derived 2.26 million COPD deaths from a national death registry database in Chinese mainland between 2013 and 2019. Exposures to fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) were assessed by satellite-based models of a 1×1km resolution and assigned to each individual based on residential address. The associations of PM2.5 and PM2.5-10 with COPD mortality were examined using a time-stratified case-crossover design and conditional logistic regressions with distributed lag models. We further conducted stratified analyses by age, sex, education level, and season. Short-term exposures to both PM2.5 and PM2.5-10 were associated with increased risks of COPD mortality. These associations appeared and peaked on the concurrent day, attenuated and became nonsignificant after 5 or 7 days, respectively. The exposure-response curves were approximately linear without discernible thresholds. An interquartile range increase in PM2.5 and PM2.5-10 concentrations was associated with 4.23% (95% CI: 3.75%, 4.72%) and 2.67% (95% CI: 2.18%, 3.16%) higher risks of COPD mortality over lag 0-7d, respectively. The associations of PM2.5 andPM2.5-10 attenuated slightly but were still significant in the mutual-adjustment models. A larger association of PM2.5-10 was observed in the warm season. This individual-level, nationwide, case-crossover study suggests that short-term exposure to PM2.5 and PM2.5-10 might act as one of the environmental risk factors for COPD mortality. This study is supported by the National Key Research and Development Program of China (2023YFC3708304 and 2022YFC3702701), the National Natural Science Foundation of China (82304090 and 82030103), the 3-year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (GWVI-11.2-YQ31), and the Science and Technology Commission of Shanghai Municipality (21TQ015).

Improvement of Air Quality During Lockdown Period of COVID-19 Pandemic in Kathmandu Valley, Nepal

The Kathmandu Valley is currently Nepal's most hazardous region for human health and environmental sustainability because of increased air pollution and high particulate matter levels. According to multiple studies and observations, Nepal's urban districts saw an improvement in air quality during the COVID-19 lockdown. The Air Quality Index (AQI) is a standard method of informing the public about air pollution levels. The AQI is based on the quantitative value of pollutants' constituent parts, such as particulate matter (PM). Thus, the focus of this study was on how the AQI values change in the Ratna Park suburb of the Kathmandu district in the Kathmandu Valley and how those fluctuations are related to lockdown and non-lockdown times during the COVID-19 pandemic and comparing them to identify crucial information’s. This study shows that during the lockdown, the air quality in the Kathmandu Valley dramatically improved, with average AQI values for PM2.5 and PM10 rising to 21.34 and 64.42 from 50.74 and 106.51, respectively. Furthermore, we investigate the positive correlation between relative humidity, a meteorological component, and air quality.

Region-oriented simultaneously joint two-pollutant control strategies are required to substantially reduce deaths attributed to both PM2.5 and ozone pollution in China

China is confronting serious air pollution of fine particulate matter (PM2.5) and ozone, which routinely exceed air quality standards. PM2.5 concentrations decreased by 30–40% while ozone increased by 15–20%, across China during 2015–2019. This could be attributed to targeted clean air policies that have focused more on controlling particulate matter since 2013. The deaths attributable to PM2.5 and ozone exposure over China changed from 1.45 to 0.13 million in 2015 to 1.04 and 0.21 million in 2019, respectively. The changes in mortality, PM2.5 and ozone present spatially heterogeneous patterns across cities and regions. The ozone production regimes (NOx -limited, volatile organic compounds (VOCs)-limited or transition between them), the responses of P(O3) to the change in NOx and VOCs, the reactions of HO2, OH, and RO2, and meteorological conditions, are regionally dependent and spatially heterogeneous. Our results have important implications for developing a smarter, region-oriented, two-pollutant coordinated control policy for VOCs and NOx in China.

Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data

Monitoring particulate matter (PM) air pollution in terms of both concentration and composition, is very important due to its effects on human health and climate. In the PRIMARY project we aim at retrieving the aerosol composition from space using the hyperspectral observations from the Italian Space Agency's PRISMA mission. To this end, we are developing a machine learning algorithm trained with synthetic top-of-atmosphere reflectances and underlying aerosol fields. As part of this process, we plan to use the global forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) as the core to generate this synthetic dataset. However, to proceed in this direction, a preliminary assessment of the reliability of this model-based dataset when compared to observations is necessary, also to bias correct the output if needed. With this aim, we assess the representation of the aerosol chemical composition and the related optical properties at selected globally distributed sites in CAMS, comparing the simulations with near-surface aerosol chemical analyses from the SPARTAN network and column sun-photometer observations from the AERONET network. We found that CAMS forecasts skills changed over time due to updates in the modelling system, with the latter two version cycles (46 and 47) being similar. Generally, they reproduce the aerosol composition within a factor of 2. We found a substantial overestimation of organic matter (OM) by a factor of 3. Applying a correcting factor to OM (constant at the global level) warrants a much more realistic representation of PM2.5 total mass and relative fraction of single species in CAMS. From the so derived CAMS aerosol-speciated profiles, we calculate aerosol optical properties, needed for subsequent use in a radiative transfer model. Comparison against AERONET indeed shows that OM bias correction resulted in improvements in Extinction Ångstrom Exponent (α440nm870nm). Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (g) simulations resulted slightly degraded, confirming the possibility of using CAMS as the base for a synthetic retrieval training dataset.

O-075 Particulate matter (PM2.5 and PM10) exposure prior to oocyte collection is associated with decreased live birth rates in subsequent frozen embryo transfers

Abstract Study question Does exposure to particulate matter air pollution prior to oocyte collection (OPU) or frozen embryo transfer (FET) impact FET live birth rates? Summary answer Increasing PM10 and PM2.5 exposure prior to OPU is associated with decreased odds of live birth in subsequent FET, independent of the conditions at FET. What is known already Ambient air pollution is increasing, and exposure is associated with adverse reproductive health outcomes, including infertility, miscarriage, preterm birth and low birth weight. Animal studies have shown that air pollutants can impair spermatogenesis, oogenesis, ovarian reserve and embryogenesis. These findings are corroborated by human epidemiological studies. The impact of ambient air pollution on IVF outcomes is uncertain. Additionally, whether pollutants exert a greater impact during oocyte development or early pregnancy is uncertain. Therefore, studies of FETs offer an opportunity to delineate different phases of reproduction that may be adversely affected by pollution. Study design, size, duration A retrospective analysis of 3,657 FETs in a single Australian centre from January 2013 – December 2021. All FETs during the study period were included; data were collected from the clinic database. Air pollutant concentrations for each day during the study were obtained from the Governmental monitoring body, including SO2, CO, O3, NO2, PM10 and PM2.5. Evaluation of FETs allowed separation of environmental effects during oocyte development and during early pregnancy. Participants/materials, setting, methods Quartiles were created for pollutants averaged over 24 hours, 2 weeks, 4 weeks and 3 months prior to OPU and prior to FET. Multi-pollutant models were created to account for co-exposures and were adjusted for variables at the time of OPU and FET. Bonferroni correction was made to account for the 4 timepoints tested on each outcome. Linear trends were assessed for each model, and pairwise comparisons against the first quartile were performed. Main results and the role of chance Overall, air quality was excellent, with pollutant levels within published safe limits on &amp;gt; 99% of days during the study period. Increasing PM10 exposure in the 2 weeks prior to OPU was associated with decreased odds of livebirth (linear trend p = 0.009); the odds of livebirth decreased by 38% when PM10 concentrations were in the highest quartile compared with the lowest (OR 0.62, p = 0.010). Increasing PM2.5 exposure in the 3 months prior to OPU was also associated with decreased odds of livebirth (linear trend p = 0.011). No significant linear trends were observed between livebirths and other pollutants. The summer seasonal association with livebirth that we have previously demonstrated consistently held in the multi-pollutant models. No significant linear trends were observed between air pollutants and biochemical pregnancy, clinical pregnancy, miscarriage, gestation at birth or embryological outcomes. Limitations, reasons for caution The retrospective design is a limitation. Whilst corrections were made for multiple pollutant co-exposures, other environmental conditions, patient factors and repeated cycles, we were unable to account for all potential covariates. To reduce the risk of type 1 error due to the multiple timeframes, we utilised a Bonferroni correction. Wider implications of the findings Even when PM2.5 and PM10 exposures are within so-called “safe” ranges, increasing exposure in the weeks and months prior to OPU are associated with decreasing livebirth rates, independent of the conditions at the time of FET. Trial registration number 2022/ET000980

Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors.

There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration-response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2-55.9%) to 49.9% (95% confidence interval 47.8-52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.

Particulate Matter Capture and Air Pollution Tolerance of Six Roadside Plants in Cheongju, South Korea

Particulate matter (PM), a highly hazardous air pollutant with known adverse health effects, has been proven to be effectively mitigated by using plants. This study aimed to evaluate the capacity of various plants in capturing PM and their tolerance to air pollution. This will facilitate the selection of suitable species for roadside planting. Accordingly, this research quantified the accumulation of particulate matter in six different plant species. Four biochemical parameters [total chlorophyll, leaf pH extract, relative water content, and ascorbic acid] were evaluated to determine the Air Pollution Tolerance Index (APTI), PM accumulation results varied among the plant species. The tolerance level was categorized into four groups: tolerant, moderately tolerant, intermediate, and sensitive. Ligustrum obtusifolium demonstrated the highest PM accumulation and tolerance index values. The presence of leaf hair and roughness on leaf surfaces was observed to facilitate higher PM accumulation in certain plants. PM accumulation on leaves was significantly correlated with biochemical parameters and APTI of plants. Based on these outcomes, Ligustrum obtusifolium, Hibiscus syriacus, and Chamaecyparis obtusa were identified as suitable for roadside planting to mitigate atmospheric particulate matter.

Long–term exposure to particulate matter pollution and incidence of ischemic and hemorrhagic stroke: A prospective cohort study in Eastern China

Although epidemiological studies have demonstrated significant associations of long–term exposure to particulate matter (PM) air pollution with stroke, evidence on the long–term effects of PM exposure on cause–specific stroke incidence is scarce and inconsistent. We incorporated 33,282 and 33,868 individuals aged 35–75 years without a history of ischemic or hemorrhagic stroke at the baseline in 2014, who were followed up till 2021. Residential exposures to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) and particulate matter with an aerodynamic diameter less than 10 μm (PM10) for each participant were predicted using a satellite–based model with a spatial resolution of 1 × 1 km. We employed time-varying Cox proportional hazards models to assess the long–term effect of PM pollution on incident stroke. We identified 926 cases of ischemic stroke and 211 of hemorrhagic stroke. Long–term PM exposure was significantly associated with increased incidence of both ischemic and hemorrhagic stroke, with almost 2 times higher risk on hemorrhagic stroke. Specifically, a 10 μg/m³ increase in 3-year average concentrations of PM2.5 was linked to a hazard ratio (HR) of 1.35 (95% confidence interval (CI): 1.18–1.54) for incident ischemic stroke and 1.79 (95% CI: 1.36–2.34) for incident hemorrhagic stroke. The HR related to PM10, though smaller, remained statistically significant, with a HR of 1.25 for ischemic stroke and a HR of 1.51 for hemorrhagic stroke. The excess risks are larger among rural residents and individuals with lower educational attainment. The present cohort study contributed to the mounting evidence on the increased risk of incident stroke associated with long–term PM exposures. Our results further provide valuable evidence on the heightened sensitivity of hemorrhagic stroke to air pollution exposures compared with ischemic stroke.