Fine particulate air pollution and its components in association with cause-specific emergency admissions

Inhalation of particulate matter containing environmentally persistent free radicals induces endothelial dysfunction mediated via AhR activation at the air-blood interface

Particulate matter (PM) containing environmentally persistent free radicals (EPFR) is formed by the incomplete combustion of organic wastes, resulting in the chemisorption of pollutants to the surface of PM containing redox-active transition metals. In prior studies in mice, EPFR inhalation impaired endothelium-dependent vasodilation. These findings were associated with aryl hydrocarbon receptor (AhR) activation in the alveolar type-II (AT-II) cells that form the air-blood interface in the lung. We thus hypothesized that AhR activation in AT-II cells promotes the systemic release of mediators that promote endothelium dysfunction peripheral to the lung. To test our hypothesis, we knocked down AhR in AT-II cells of male and female mice and exposed them to 280 µg/m3 EPFR lo (2.7e + 16 radicals/g) or EPFR (5.5e + 17 radicals/g) compared with filtered air for 4 h/day for 1 day or 5 days. AT-II-AhR activation-induced EPFR-mediated endothelial dysfunction, reducing endothelium-dependent vasorelaxation by 59%, and eNOS expression by 50%. It also increased endothelin-1 mRNA levels in the lungs and peptide levels in the plasma in a paracrine fashion, along with soluble vascular cell adhesion molecule-1 and iNOS mRNA expression, possibly via NF-kB activation. Finally, AhR-dependent increases in antioxidant response signaling, coupled to increased levels of 3-nitrotyrosine in the lungs of EPFR-exposed littermate control but not AT-II AhR KO mice suggested that ATII-specific AhR activation promotes oxidative and nitrative stress. Thus, AhR activation at the air-blood interface mediates endothelial dysfunction observed peripheral to the lung, potentially via release of systemic mediators.

Global associations between long-term exposure to PM2.5 constituents and health: A systematic review and meta-analysis of cohort studies

Existing studies on the most impactful component remain controversial, hindering the optimization of future air quality standards that concerns particle composition. We aimed to summarize the health risk associated with PM2.5 components and identify those components with the greatest health risk. We performed a meta-analysis to quantify the combined health effects of PM2.5 components, and used the meta-smoothing to produce the pooled concentration-response (C-R) curves. Out of 8954 initial articles, 80 cohort studies met the inclusion criteria, including a total of 198.08 million population. The pooled C-R curves demonstrated approximately J-shaped association between total mortality and exposure to BC, and NO3-, but U-shaped and inverted U-shaped relationship withSO42- and OC, respectively. In addition, this study found that exposure to various elements, including BC,SO42-NO3-, NH4+, Zn, Ni, and Si, were significantly associated with an increased risk of total mortality, with Ni presenting the largest estimate. And exposure to NO3-, Zn, and Si was positively associated with an increased risk of respiratory mortality, while exposure to BC, SO42-, and NO3- showed a positive association with risk of cardiovascular mortality. For health outcome of morbidity, BC was notably associated with a higher incidence of asthma, type 2 diabetes and stroke. Subgroup analysis revealed a higher susceptibility to PM2.5 components in Asia compared to Europe and North America, and females showed a higher vulnerability. Given the significant health effects of PM2.5 components, governments are advised to introduce them in regional monitoring and air quality control guidelines. ENVIRONMENTAL IMPLICATION: PM2.5 is a complex mixture of chemical components from various sources, and each component has unique physicochemical properties and uncertain toxicity, posing significant threat to public health. This study systematically reviewed cohort studies on the association between long-term exposure to 13 PM2.5 components and the risk of morbidity and mortality. And we applied the meta-smoothing approach to establish the pooled concentration-response associations between PM2.5 components and mortality globally. Our findings will provide strong support for PM2.5 components monitoring and the improvement of air quality-related regulations. This will aid in helping to enhance health intervention strategies and mitigating public exposure to detrimental particulate matter.

Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

Low-level PM2.5 induces the occurrence of early pulmonary injury by regulating circ_0092363

The significant correlation between particulate matter with aerodynamic diameters of ≤ 2.5 µm (PM2.5) and the high morbidity and mortality of respiratory diseases has become the consensus of the research. Epidemiological studies have clearly pointed out that there is no safe concentration of PM2.5, and mechanism studies have also shown that exposure to PM2.5 will first cause pulmonary inflammation. Therefore, the purpose of this study is to explore the mechanism of early lung injury induced by low-level PM2.5 from the perspective of epigenetics. Based on the previous results of population samples, combined with an in vitro/vivo exposure model of PM2.5, it was found that low-level PM2.5 promoted the transport of circ_0092363 from intracellular to extracellular spaces. The decreased expression of intracellular circ_0092363 resulted in reduced absorption of miR-31-5p, leading to inhibition of Rho associated coiled-coil containing protein kinase 1 (ROCK1) and the subsequent abnormal expression of tight junction proteins such as Zonula occludens protein 1 (ZO-1) and Claudin-1, ultimately inducing the occurrence of early pulmonary injury. Furthermore, this study innovatively introduced organoid technology and conducted a preliminary exploration for a study of the relationship among environmental exposure genomics, epigenetics and disease genomics in organoids. The role of circ_0092363 in early pulmonary injury induced by low-level PM2.5 was elucidated, and its value as a potential diagnostic biomarker was confirmed.

Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study

OBJECTIVE

To estimate exposure-response associations between chronic exposure to fine particulate matter (PM2.5) and risks of the first hospital admission for major cardiovascular disease (CVD) subtypes.

DESIGN

Population based cohort study.

SETTING

Contiguous US.

PARTICIPANTS

59 761 494 Medicare fee-for-service beneficiaries aged ≥65 years during 2000-16. Calibrated PM2.5 predictions were linked to each participant's residential zip code as proxy exposure measurements.

MAIN OUTCOME MEASURES

Risk of the first hospital admission during follow-up for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, valvular heart disease, thoracic and abdominal aortic aneurysms, or a composite of these CVD subtypes. A causal framework robust against confounding bias and bias arising from errors in exposure measurements was developed for exposure-response estimations.

RESULTS

Three year average PM2.5 exposure was associated with increased relative risks of first hospital admissions for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, and thoracic and abdominal aortic aneurysms. For composite CVD, the exposure-response curve showed monotonically increased risk associated with PM2.5: compared with exposures ≤5 µg/m3 (the World Health Organization air quality guideline), the relative risk at exposures between 9 and 10 µg/m3, which encompassed the US national average of 9.7 µg/m3 during the study period, was 1.29 (95% confidence interval 1.28 to 1.30). On an absolute scale, the risk of hospital admission for composite CVD increased from 2.59% with exposures ≤5 µg/m3 to 3.35% at exposures between 9 and 10 µg/m3. The effects persisted for at least three years after exposure to PM2.5. Age, education, accessibility to healthcare, and neighborhood deprivation level appeared to modify susceptibility to PM2.5.

CONCLUSIONS

The findings of this study suggest that no safe threshold exists for the chronic effect of PM2.5 on overall cardiovascular health. Substantial benefits could be attained through adherence to the WHO air quality guideline.

Astaxanthin alleviates fine particulate matter (PM2.5)-induced lung injury in rats by suppressing ferroptosis and apoptosis

Objectives: Fine particulate matter (PM2.5), a small molecule particulate pollutant, can reach the lungs via respiration and cause lung damage. Currently, effective strategies and measures are lacking to prevent and treat the pulmonary toxicity of PM2.5. Astaxanthin (ASX), a natural xanthophyll carotenoid, has attracted attention due to its unique biological activity. Our research aims to probe into the prevention and treatment of ASX on PM2.5-induced lung injury and clarify its potential mechanism. Methods: Sprague-Dawley (SD) rats were given olive oil and different concentrations of ASX orally daily for 21 days. PM2.5 suspension was instilled into the trachea of rats every two days for one week to successfully develop the PM2.5 exposure model in the PM2.5-exposed and ASX-treated groups of rats. The bronchoalveolar lavage fluid (BALF) was collected, and the content of lung injury-related markers was detected. Histomorphological changes and expression of markers associated with oxidative stress, inflammation, iron death, and apoptosis were detected in lung tissue. Results: PM2.5 exposure can cause changes in lung histochemistry and increase the expression levels of TP, AKP, ALB, and LDH in the BALF. Simultaneously, inflammatory responses and oxidative stress were promoted in rat lung tissue after exposure to particulate matter. Additionally, ASX preconditioning can alleviate histomorphological changes, oxidative stress, and inflammation caused by PM2.5 and reduce PM2.5-related ferroptosis and apoptosis. Conclusion: ASX preconditioning can alleviate lung injury after PM2.5 exposure by inhibiting ferroptosis and apoptosis.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

The Short-Term Effects and Burden of Ambient Air Pollution on Hospitalization for Type 2 Diabetes: Time-Stratified Case-Crossover Evidence From Sichuan, China

Type 2 diabetes mellitus (T2DM), a complicated metabolic disease, might be developed or exacerbated by air pollution, resulting in economic and health burden to patients. So far, limited studies have estimated associations between short-term exposure to air pollution and disease burden of T2DM in China. Hence, we aimed to estimate the associations and burden of ambient air pollutants (NO2, PM10, PM2.5, SO2, and CO) on hospital admissions (HAs) for T2DM using a time-stratified case-crossover design. Data on HAs for T2DM during 2017-2019 were collected from hospital electronic health records in nine cities in Sichuan Province using conditional poisson regression. Totally, 92,381 T2DM hospitalizations were recorded. There were significant short-term effects of NO2, PM10, PM2.5, SO2 and CO on HAs for T2DM. A 10 μg/m3 increment of NO2, PM10, PM2.5, SO2 and CO as linked with a 3.39% (95% CI: 2.26%, 4.54%), 0.33% (95% CI: 0.04%, 0.62%), 0.76% (95% CI: 0.35%, 1.16%), 12.68% (95% CI: 8.14%, 17.42%) and 79.00% (95% CI: 39.81%, 129.18%) increase in HAs for T2DM at lag 6. Stratified analyses modified by age, sex, and season showed old (≥65 years) and female patients linked with higher impacts. Using WHO's air quality guidelines of NO2, PM10, PM2.5, and CO as the reference, the attributable number of T2DM HAs exceeding these pollutants exposures were 786, 323, 793, and 2,127 during 2017-2019. Besides, the total medical costs of 25.83, 10.54, 30.74, and 67.78 million China Yuan were attributed to NO2, PM10, PM2.5, and CO. In conclusion, short-term exposures to air pollutants were associated with higher risks of HAs for T2DM.

Health risk assessment of aerosol particles (PM2.5 and PM10) during winter crop at the agricultural site of Delhi, India

For the last few decades, air pollution in developing country like India is increasing, and it is a matter of huge concern due to its associated human health impacts. In this region, the burgeoning population, escalating urbanization and industrialization, has been cited as the major reason for such a high air pollution. The present study was carried out for health risk assessment of aerosol particles (PM10 and PM2.5) and its associated heavy metals of an agriculture farm site at Indian Agricultural Research Institute (IARI) considered to be green urban area in Delhi, India. The concentrations of both PM10 and PM2.5 varied significantly from 136 to 177 µg/m3 and 56 to 162 µg/m3, respectively at the site. In the present case, the highest PM10 and PM2.5 levels were reported in January, followed by December. The levels of ambient PM10 and PM2.5 are influenced by wind prevailing meteorology. These levels of PM10 and PM2.5 are more than the permissible limits of WHO guidelines of 15 and 5 µg/m3, respectively, thereby leading to high aerosol loadings specifically in winters. The PM concentration of the atmosphere was found to be negatively correlated with temperature during the sampling period. The concentrations of surface ozone O3 and NOx in the present study were observed to be high in February and March, respectively. The increasing air pollution in the city of Delhi poses a great risk to the human health, as the particulate matter loaded with heavy metals can enter humans via different pathways, viz., ingestion, inhalation, and absorption through skin. The mean hazard index for metals (Zn, Pb, Cd, As, Cr, and Ni) was observed within the acceptable limit (HI < 1), thereby indicating negligible non-carcinogenic effects to residing population. The carcinogenic risk assessment was conducted for Cd, Pb, and As only, as the concentrations for other metals were found to be quite low. The carcinogenic risk values were also within the limits of USEPA standards, indicating no carcinogenic risks to the health of children and adults residing near the site. This information about the PM pollution at the agricultural site and health risk assessment will serve as a baseline data in assessment of human health impacts due to air pollution at the local scale and can be used for development of mitigation strategies for tackling air pollution.

Short-term PM2.5 exposure induces transient lung injury and repair

Exposure to fine atmospheric particulate matter (PM) is known to induce lung inflammation and injury; however, the way in which sophisticated endogenous lung repair and regenerative programs respond to this exposure remains unknown. In this study, we established a whole-body mouse exposure model to mimic real scenarios. Exposure to fine PM (PM with an aerodynamic diameter ≤ 2.5 µm [PM2.5]; mean 1.05 mg/m3) for 1-month elicited inflammatory infiltration and epithelial alterations in the lung, which were resolved 6 months after cessation of exposure. Immune cells that responded to PM2.5 exposure mainly included macrophages and neutrophils. During PM2.5 exposure, alveolar epithelial type 2 cells initiated rapid repair of alveolar epithelial mucosa through proliferation. However, the reparative capacity of airway progenitor cells (club cells) was impaired, which may have been related to the oxidative production of neutrophils or macrophages, as suggested in organoid co-cultures. These data suggested that the pulmonary toxic effects of short-term exposure to fine atmospheric PM at a certain dosage could be overcome through tissue reparative mechanisms.

Research progress of different components of PM2.5 and ischemic stroke

PM2.5 is a nonhomogeneous mixture of complex components produced from multiple sources, and different components of this mixture have different chemical and biological toxicities, which results in the fact that the toxicity and hazards of PM2.5 may vary even for the same mass of PM2.5. Previous studies on PM2.5 and ischemic stroke have reached different or even opposing conclusions, and considering the heterogeneity of PM2.5 has led researchers to focus on the health effects of specific PM2.5 components. However, due to the complexity of PM2.5 constituents, assessing the association between exposure to specific PM2.5 constituents and ischemic stroke presents significant challenges. Therefore, this paper reviews and analyzes studies related to PM2.5 and its different components and ischemic stroke, aiming to understand the composition of PM2.5 and identify its harmful components, elucidate their relationship with ischemic stroke, and thus provide some insights and considerations for studying the biological mechanisms by which they affect ischemic stroke and for the prevention and treatment of ischemic stroke associated with different components of PM2.5.

Revealing Long-Term Indoor Air Quality Prediction: An Intelligent Informer-Based Approach

Indoor air pollution is an urgent issue, posing a significant threat to the health of indoor workers and residents. Individuals engaged in indoor occupations typically spend an average of around 21 h per day in enclosed spaces, while residents spend approximately 13 h indoors on average. Accurately predicting indoor air quality is crucial for the well-being of indoor workers and frequent home dwellers. Despite the development of numerous methods for indoor air quality prediction, the task remains challenging, especially under constraints of limited air quality data collection points. To address this issue, we propose a neural network capable of capturing time dependencies and correlations among data indicators, which integrates the informer model with a data-correlation feature extractor based on MLP. In the experiments of this study, we employ the Informer model to predict indoor air quality in an industrial park in Changsha, Hunan Province, China. The model utilizes indoor and outdoor temperature, humidity, and outdoor particulate matter (PM) values to forecast future indoor particle levels. Experimental results demonstrate the superiority of the Informer model over other methods for both long-term and short-term indoor air quality predictions. The model we propose holds significant implications for safeguarding personal health and well-being, as well as advancing indoor air quality management practices.

Retrospective cohort study investigating synergism of air pollution and corticosteroid exposure in promoting cardiovascular and thromboembolic events in older adults

OBJECTIVE

To evaluate the synergistic effects created by fine particulate matter (PM2.5) and corticosteroid use on hospitalisation and mortality in older adults at high risk for cardiovascular thromboembolic events (CTEs).

DESIGN AND SETTING

A retrospective cohort study using a US nationwide administrative healthcare claims database.

PARTICIPANTS

A 50% random sample of participants with high-risk conditions for CTE from the 2008-2016 Medicare Fee-for-Service population.

EXPOSURES

Corticosteroid therapy and seasonal-average PM2.5.

MAIN OUTCOME MEASURES

Incidences of myocardial infarction or acute coronary syndrome (MI/ACS), ischaemic stroke or transient ischaemic attack, heart failure (HF), venous thromboembolism, atrial fibrillation and all-cause mortality. We assessed additive interactions between PM2.5 and corticosteroids using estimates of the relative excess risk due to interaction (RERI) obtained using marginal structural models for causal inference.

RESULTS

Among the 1 936 786 individuals in the high CTE risk cohort (mean age 76.8, 40.0% male, 87.4% white), the mean PM2.5 exposure level was 8.3±2.4 µg/m3 and 37.7% had at least one prescription for a systemic corticosteroid during follow-up. For all outcomes, we observed increases in risk associated with corticosteroid use and with increasing PM2.5 exposure. PM2.5 demonstrated a non-linear relationship with some outcomes. We also observed evidence of an interaction existing between corticosteroid use and PM2.5 for some CTEs. For an increase in PM2.5 from 8 μg/m3 to 12 μg/m3 (a policy-relevant change), the RERI of corticosteroid use and PM2.5 was significant for HF (15.6%, 95% CI 4.0%, 27.3%). Increasing PM2.5 from 5 μg/m3 to 10 μg/m3 yielded significant RERIs for incidences of HF (32.4; 95% CI 14.9%, 49.9%) and MI/ACSs (29.8%; 95% CI 5.5%, 54.0%).

CONCLUSION

PM2.5 and systemic corticosteroid use were independently associated with increases in CTE hospitalisations. We also found evidence of significant additive interactions between the two exposures for HF and MI/ACSs suggesting synergy between these two exposures.

Public health benefits of zero-emission electric power generation in Virginia

Curbing the worst impacts of global climate change will require rapidly transitioning away from fossil fuel across all sectors of the economy. This transition will also yield substantial co-benefits, as fossil fuel combustion releases harmful pollutants into the air. In this article, we present an analysis of the co-benefits to health and health-care costs related from decarbonization of the power sector, using the Virginia Clean Economy Act (VCEA) as a case study. Using a model that combines a source-response matrix approach to pollutant concentration modelling tied to health impact functions, our analysis shows that, by 2045, the VCEA will save up to 32 lives per year across the state, and avoid up to $355 million per year in health-related costs. Fossil-fuel free generation will also help the most disadvantaged communities, as counties in the highest poverty rate quintile also avoid the most pollutant-related deaths.

Healthcare utilization of lung cancer patients associated with exposure to fine particulate matter: A Korean cohort study

BACKGROUND

Higher concentrations of particulate matter (PM) have been shown to cause deterioration of the symptoms of respiratory and cardiovascular disease in several regional studies. Here, we aimed to investigate the healthcare utilization of lung cancer patients associated with short-term exposure to PM at the national level in Korea.

METHODS

We extracted the data of 210 558 subjects over a period of 3 years (2015-2017), who were diagnosed with lung cancer before 2015 and benefited from the National Health Insurance Sharing Service. We performed the interpolation method using the geographic information system to calculate the estimated mean PM2.5 and PM10 concentrations by regions and classified three groups as high (upper 10%), intermediate (10%-90%), and low (bottom 10%) based on the mean PM mass concentrations of the month.

RESULTS

The monthly average number of outpatient visits was significantly increased in high PM2.5 urban areas (46.296 vs. 50.646, p = 0.015). In high PM2.5 nationwide regions, the monthly average number of emergency admission was significantly increased (0.528 vs. 0.785, p = 0.001). The outpatient visits tended to change with PM2.5 concentration and correlated with PM10 /PM2.5 concentrations in urban and nationwide areas. In high PM2.5 urban regions, there was a significant increase in bronchodilator prescriptions (3.102 vs. 3.758, p = 0.008). Concerning high PM2.5 nationwide regions, there were significantly increased prescriptions of antibiotics, steroids, bronchodilators, antihistamines, and mucolytics.

CONCLUSIONS

This study suggests that exposure to PM2.5 is significantly associated with hospital utilization and drug prescription in lung cancer patients.

Acute effects of air pollution on type II diabetes mellitus hospitalization in Lanzhou, China

Studies on the effects of short-term air pollution exposure on hospitalization for type 2 diabetes mellitus (T2DM) are relatively scarce in developing regions. The time-series study was used to explore the acute effects of air pollutants on hospitalization for T2DM in Lanzhou, China. A distribution lag nonlinear model based on the generalized additive model was used to analyze the hospitalization impact of air pollution on T2DM. Stratified analysis by gender, age and season was obtained. The results were indicated as the relative risk (RR) with 95% confidence interval (CI) for single-day lags (from lag0 to lag7) and cumulative lag days (from lag0-1 to lag0-7). The strongest correlations (RR, 95% CI) of hospitalization for T2DM and PM10 (RR = 1.003, 95% CI 1.000, 1.001) at lag7 and NO2 (RR = 1.022, 95% CI 1.000, 1.045) at lag0-4 were observed for an increase of 10 µg/m3 in the concentrations and CO (RR = 1.091, 95% CI 1.017, 1.170) at lag0-4 for an increase of 1 mg/m3 in the concentration. The hazardous impacts of PM10, NO2 and CO were greater for females, people aged ≥ 65 years and in the cold season. However, there was no significant association between PM2.5, SO2 and O38h and the number of hospitalizations for T2DM.

Effect of Air Pollution on Heart Failure: Systematic Review and Meta-Analysis

BACKGROUND

Heart failure (HF) poses a significant global disease burden. The current evidence on the impact of air pollution on HF remains inconsistent.

OBJECTIVES

We aimed to conduct a systematic review of the literature and meta-analysis to provide a more comprehensive and multiperspective assessment of the associations between short- and long-term air pollution exposure and HF from epidemiological evidences.

METHODS

Three databases were searched up to 31 August 2022 for studies investigating the association between air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO, O 3 ) and HF hospitalization, incidence, or mortality. A random effects model was used to derive the risk estimations. Subgroup analysis was conducted by geographical location, age of participants, outcome, study design, covered area, the methods of exposure assessment, and the length of exposure window. Sensitivity analysis and adjustment for publication bias were performed to test the robustness of the results.

RESULTS

Of 100 studies covering 20 countries worldwide, 81 were for short-term and 19 were for long-term exposure. Almost all air pollutants were adversely associated with the risk of HF in both short- and long-term exposure studies. For short-term exposures, we found the risk of HF increased by 1.8% [relative risk ( RR ) = 1.018 , 95% confidence interval (CI): 1.011, 1.025] and 1.6% (RR = 1.016 , 95% CI: 1.011, 1.020) per 10 - μ g / m 3 increment of PM 2.5 and PM 10 , respectively. HF was also significantly associated with NO 2 , SO 2 , and CO, but not O 3 . Positive associations were stronger when exposure was considered over the previous 2 d (lag 0-1) rather than on the day of exposure only (lag 0). For long-term exposures, there were significant associations between several air pollutants and HF with RR (95% CI) of 1.748 (1.112, 2.747) per 10 - μ g / m 3 increment in PM 2.5 , 1.212 (1.010, 1.454) per 10 - μ g / m 3 increment in PM 10 , and 1.204 (1.069, 1.356) per 10 -ppb increment in NO 2 , respectively. The adverse associations of most pollutants with HF were greater in low- and middle-income countries than in high-income countries. Sensitivity analysis demonstrated the robustness of our results.

DISCUSSION

Available evidence highlighted adverse associations between air pollution and HF regardless of short- and long-term exposure. Air pollution is still a prevalent public health issue globally and sustained policies and actions are called for to reduce the burden of HF. https://doi.org/10.1289/EHP11506.

Exogenous 8-Hydroxydeoxyguanosine Attenuates PM2.5-Induced Inflammation in Human Bronchial Epithelial Cells by Decreasing NLRP3 Inflammasome Activation

Particulate matter 2.5 (PM_2.5) induces lung injury by increasing the generation of reactive oxygen species (ROS) and inflammation. ROS aggravates NLRP3 inflammasome activation, which activates caspase-1, IL-1β, and IL-18 and induces pyroptosis; these factors propagate inflammation. In contrast, treatment with exogenous 8-hydroxydeoxyguanosine (8-OHdG) decreases RAC1 activity and eventually decreases dinucleotide phosphate oxidase (NOX) and ROS generation. To establish modalities that would mitigate PM_2.5-induced lung injury, we evaluated whether 8-OHdG decreased PM_2.5-induced ROS generation and NLRP3 inflammasome activation in BEAS-2B cells. CCK-8 and lactate dehydrogenase assays were used to determine the treatment concentration. Fluorescence intensity, Western blotting, enzyme-linked immunosorbent assay, and immunoblotting assays were also performed. Treatment with 80 μg/mL PM_2.5 increased ROS generation, RAC1 activity, NOX1 expression, NLRP3 inflammasome (NLRP3, ASC, and caspase-1) activity, and IL-1β and IL-18 levels in cells; treatment with 10 μg/mL 8-OHdG significantly attenuated these effects. Furthermore, similar results, such as reduced expression of NOX1, NLRP3, ASC, and caspase-1, were observed in PM_2.5-treated BEAS-2B cells when treated with an RAC1 inhibitor. These results show that 8-OHdG mitigates ROS generation and NLRP3 inflammation by inhibiting RAC1 activity and NOX1 expression in respiratory cells exposed to PM_2.5.

Onco-Pathogen Mediated Cancer Progression and Associated Signaling Pathways in Cancer Development

Infection with viruses, bacteria, and parasites are thought to be the underlying cause of about 8-17% of the world's cancer burden, i.e., approximately one in every five malignancies globally is caused by an infectious pathogen. Oncogenesis is thought to be aided by eleven major pathogens. It is crucial to identify microorganisms that potentially act as human carcinogens and to understand how exposure to such pathogens occur as well as the following carcinogenic pathways they induce. Gaining knowledge in this field will give important suggestions for effective pathogen-driven cancer care, control, and, ultimately, prevention. This review will mainly focus on the major onco-pathogens and the types of cancer caused by them. It will also discuss the major pathways which, when altered, lead to the progression of these cancers.

Profiling Microbiota from Multiple Sites in the Respiratory Tract to Identify a Biomarker for PM2.5 Nitrate Exposure-Induced Pulmonary Damages

The microbiota present in the respiratory tract (RT) responds to environmental stimuli and engages in a continuous interaction with the host immune system to maintain homeostasis. A total of 40 C57BL/6 mice were divided into four groups and exposed to varying concentrations of PM_2.5 nitrate aerosol and clean air. After 10 weeks of exposure, assessments were conducted on the lung and airway microbiome, lung functions, and pulmonary inflammation. Additionally, we analyzed data from both mouse and human respiratory tract (RT) microbiomes to identify possible biomarkers for PM_2.5 exposure-induced pulmonary damages. On average, 1.5 and 13.5% inter-individual microbiome variations in the lung and airway were explained by exposure, respectively. In the airway, among the 60 bacterial OTUs (operational taxonomic units) > 0.05% proportion, 40 OTUs were significantly affected by PM_2.5 exposure (FDR ≤ 10%). Further, the airway microbiome was associated with peak expiratory flow (PEF) (p = 0.003), pulmonary neutrophil counts (p = 0.01), and alveolar 8-OHdG oxidative lesions (p = 0.0078). The Clostridiales order bacteria showed the strongest signals. For example, the o_Clostridiales;f_;g_ OTU was elevated by PM_2.5 nitrate exposure (p = 4.98 × 10^-5) and negatively correlated with PEF (r = -0.585 and p = 2.4 × 10^-4). It was also associated with the higher pulmonary neutrophil count (p = 8.47 × 10^-5) and oxidative lesion (p = 7.17 × 10^-3). In human data, we confirmed the association of airway Clostridiales order bacteria with PM_2.5 exposure and lung function. For the first time, this study characterizes the impact of PM_2.5 exposure on the microbiome of multiple sites in the respiratory tract (RT) and its relevance to airflow obstructive diseases. By analyzing data from both humans and mice, we have identified bacteria belonging to the Clostridiales order as a promising biomarker for PM_2.5 exposure-induced decline in pulmonary function and inflammation.

Wildfires and the Changing Landscape of Air Pollution-related Health Burden in California

Rationale: Wildfires are a growing source of pollution including particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5), but associated trends in health burden are not well characterized. Objectives: We investigated trends and disparities in PM2.5-related cardiorespiratory health burden (asthma, chronic obstructive pulmonary disease, and all-cause respiratory and cardiovascular emergency department [ED] visits and hospital admissions) for all days and wildfire smoke-affected days across California from 2008 to 2016. Methods: Using residential Zone Improvement Plan code and daily PM2.5 exposures, we estimated overall and subgroup-specific (age, gender, race and ethnicity) associations with cardiorespiratory outcomes. Health burden trends and disparities were evaluated on the basis of relative risk, attributable number, and attributable fraction by demographic and geographic factors and over time. Measurements and Main Results: PM2.5-attributed burden steadily decreased, whereas the fraction attributed to wildfire smoke varied by fire season intensity, constituting up to 15% of the annual PM2.5-burden. The highest relative risk and PM2.5-attributed burden (92 per 100,000 people) was observed for respiratory ED visits, accounting for 2.2% of the respiratory annual burden. Disparities in overall morbidity in the oldest age, Black, and "other" race groups were also reflected in PM2.5-attributed burden, whereas Asian populations had the highest risk rate in respiratory outcomes and thus the largest fraction of the total burden attributed to the exposure. In contrast, high wildfire PM2.5-attributed burden rates in rural, central, and northern California populations occurred because of differential exposure. Conclusions: In California, wildfires' impact on air quality offset the public health gains achieved through reductions in nonsmoke PM2.5. Disproportionate effects could be attributed to differences in subpopulation susceptibility, relative risk, and differential exposure.

Assessment of trace elements directly from archived total suspended particulate filters by laser ablation ICP-MS: A case study of South Carolina

Background

Exposure to particulate air pollution is one of the greatest environmental risk factors for adverse human health outcomes. However, the constituents that may be responsible for such adverse health effects have not been fully studied.

Methods

Total suspended particulates filters collected every 6 days in 2011 from three South Carolina locations were used in this case study. An inductively coupled plasma mass spectrometer interfaced with a laser ablation system (LA-ICP-MS) was used to directly analyze 41 inorganic elemental species on air pollution filters. Then, machine learning and multivariate statistical methods was employed to identify combinatorial patterns in the data and classify sites based on their elemental composition.

Results

Forty-one elements were assessed and 33 were used in subsequent analysis. Correlations between United States Environmental Protection Agency (US EPA)'s chemical analysis dataset and data from the current study revealed significant associations between 7/15 elements with enough variation for comparison (r between 0.28 to 0.66, p<0.05). Subsequent multivariate analyses revealed four distinct patterns in the distribution of elements by sample location throughout the year.

Conclusion

The different airborne elements may need to be assessed to understand combinations of elements which occur together over space and/or time. Such information can be helpful in planning effective counter measures and strategies to control particulate air pollution.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000-2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and warm-months ozone (O3) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 μg/m3 increase in PM2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO2, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O3. Both for NO2 and PM2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO2 ≤ 36 ppb, PM2.5 ≤ 8.2 μg/m3). Furthermore, associations remained in the restricted low-level cohorts. The O3-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM2.5 and NO2 are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000-2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and warm-months ozone (O₃) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 μg/m³ increase in PM_2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO₂, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O₃. Both for NO₂ and PM_2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO₂ ≤ 36 ppb, PM_2.5 ≤ 8.2 μg/m³). Furthermore, associations remained in the restricted low-level cohorts. The O₃-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM_2.5 and NO₂ are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Association of long-term exposure to PM2.5 constituents with glucose metabolism in Chinese rural population

BACKGROUND

Evidence on the associations of fine particulate matter (PM_2.5) constituents and glucose metabolism is limited in resource-limited areas. This study aimed to explore the associations of PM_2.5 constituents with glucose metabolism in rural areas, and to further specify the most responsible constituent.

METHODS

A total of 38,442 adults were recruited from the Henan Rural Cohort Study during 2015-2017. Three-year averaged concentrations of PM_2.5 mass and its constituents (black carbon (BC), ammonium (NH₄⁺), nitrate (NO₃^-), organic matter (OM), inorganic sulfate (SO₄^2-), soil particles (SOIL) and sea salt (SS)) were estimated by a hybrid satellite-based model. Generalized linear model was applied to explore the associations of PM_2.5 mass and its constituents with type 2 diabetes mellitus (T2DM), fasting blood glucose (FBG), insulin, and HOMA-β. Proportion and residual analyses were employed to specify the most responsible constituent.

RESULTS

The adjusted odds ratio (OR) for T2DM associated with 1 μg/m³ increase was 1.02 for PM_2.5 mass, 1.28 for BC, 1.15 for NH₄⁺, 1.08 for NO₃^-, 1.10 for OM, 1.11 for SO₄^2-, and 1.12 for SOIL. Significant associations of PM_2.5 mass and its constituents with elevated FBG, decreased insulin and HOMA-β were also observed. Proportion and residual analyses indicated that BC was the most responsible constituent, in which 1 percentage increment in the proportion of BC in PM_2.5 corresponded with 1.51-fold risk for T2DM, 0.17 mmol/L increase in FBG, 2.18 μU/mL decrease in insulin, and 38.26 % decrease in HOMA-β; and 1 μg/m³ increment in the PM_2.5-adjusted BC corresponded with 1.59-fold risk for T2DM, 0.53 mmol/L increase in FBG, 4.79 μU/mL decrease in insulin, and 91.32 % decrease in HOMA-β.

CONCLUSIONS

PM_2.5 mass and its constituents (BC, NH₄⁺, NO₃^-, OM, SO₄^2-, SOIL) were associated with T2DM, increased FBG, decreased insulin and HOMA-β, of which BC was most responsible for these associations.

TRIAL REGISTRATION

The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015. http://www.chictr.org.cn/showproj.aspx?proj=11375.

Association between short-term exposure to ambient air pollution and heart failure: An updated systematic review and meta-analysis of more than 7 million participants

Introduction

Exposure to air pollution has been linked to the mortality of heart failure. In this study, we sought to update the existing systematic review and meta-analysis, published in 2013, to further assess the association between air pollution and acute decompensated heart failure, including hospitalization and heart failure mortality.

Methods

PubMed, Web of Science, EMBASE, and OVID databases were systematically searched till April 2022. We enrolled the studies regarding air pollution exposure and heart failure and extracted the original data to combine and obtain an overall risk estimate for each pollutant.

Results

We analyzed 51 studies and 7,555,442 patients. Our results indicated that heart failure hospitalization or death was associated with increases in carbon monoxide (3.46% per 1 part per million; 95% CI 1.0233-1.046, P < 0.001), sulfur dioxide (2.20% per 10 parts per billion; 95% CI 1.0106-1.0335, P < 0.001), nitrogen dioxide (2.07% per 10 parts per billion; 95% CI 1.0106-1.0335, P < 0.001), and ozone (0.95% per 10 parts per billion; 95% CI 1.0024-1.0166, P < 0.001) concentrations. Increases in particulate matter concentration were related to heart failure hospitalization or death (PM_2.5 1.29% per 10 μg/m³, 95% CI 1.0093-1.0165, P < 0.001; PM₁₀ 1.30% per 10 μg/m³, 95% CI 1.0102-1.0157, P < 0.001).

Conclusion

The increase in the concentration of all pollutants, including gases (carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone) and particulate matter [(PM_2.5), (PM₁₀)], is positively correlated with hospitalization rates and mortality of heart failure.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021256241.

Ambient air pollution and hospitalization for type 2 diabetes in China: A nationwide, individual-level case-crossover study

Scarce evidence is available on the short-term association between air pollution and type 2 diabetes (T2D). We aimed to evaluate the associations between short-term exposure to six criteria air pollutants and hospitalization for T2D based on a national registry. We conducted an individual-level, time-stratified case-crossover study among inpatients with a primary diagnosis of T2D from 153 hospitals across 20 provincial regions in China (2013-2021). Daily concentrations of fine particulate matter (PM_2.5), inhalable particle (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and carbon monoxide (CO), and ozone were collected from the nearest monitoring stations. T2D patients were separated into those admission for T2D with and without complications. Distributed lag non-linear models combined with conditional logistic regressions were used to estimate the associations. A total of 88,904 patients were hospitalized for T2D. Short-term exposures to all six air pollutants above except for ozone were significantly associated with the risk of hospitalization for T2D and both subclasses. An interquartile range increase in the concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO at lag 0-2 d was associated with higher hospitalization risk of T2D by 1.71% (95%CI: 0.56%, 2.87%), 2.08% (0.88%, 3.29%), 4.85% (3.29%, 6.44%), 2.44% (1.22%, 3.67%) and 2.55% (1.24%, 3.88%), respectively. The associations of T2D hospitalizations were stronger in cold season than in warm season. Air pollutants had more acute and stronger associations with T2D with complications. The exposure-response relationship curves showed no thresholds, and the slopes were larger for T2D with complications. This nationwide individual-level, case-crossover study provides the first comprehensive evidence that short-term exposure to multiple criteria air pollutants may increase the risk of hospitalizations for T2D, especially for T2D with complications.

A comparison of fine particulate matter (PM2.5) in vivo exposure studies incorporating chemical analysis

The complex, variable mixtures present in fine particulate matter (PM_2.5) have been well established, and associations between chemical constituents and human health are expanding. In the past decade, there has been an increase in PM_2.5 toxicology studies that include chemical analysis of samples. This investigation is a crucial component for identifying the causal constituents for observed adverse health effects following exposure to PM_2.5. In this review, investigations of PM_2.5 that used both in vivo models were explored and chemical analysis with a focus on respiratory, cardiovascular, central nervous system, reproductive, and developmental toxicity was examined to determine if chemical constituents were considered in the interpretation of the toxicity findings. Comparisons between model systems, PM_2.5 characteristics, endpoints, and results were made. A vast majority of studies observed adverse effects in vivo following exposure to PM_2.5. While limited, investigations that explored connections between chemical components and measured endpoints noted significant associations between biological measurements and a variety of PM_2.5 constituents including elements, ions, and organic/elemental carbon, indicating the need for such analysis. Current limitations in available data, including relatively scarce statistical comparisons between collected toxicity and chemical datasets, are provided. Future progress in this field in combination with epidemiologic research examining chemical composition may support regulatory standards of PM_2.5 to protect human health.

Comparing the Therapeutic Efficacies of Lung Cancer: Network Meta-Analysis Approaches

BACKGROUND

In recent years, reduction of nuclear power generation and the use of coal-fired power for filling the power supply gap might have increased the risk of lung cancer. This study aims to explore the most effective treatment for different stages of lung cancer patients.

METHODS

We searched databases to investigate the treatment efficacy of lung cancer. The network meta-analysis was used to explore the top three effective therapeutic strategies among all collected treatment methodologies.

RESULTS

A total of 124 studies were collected from 115 articles with 171,757 participants in total. The results of network meta-analyses showed that the best top three treatments: (1) in response rate, for advanced lung cancer were Targeted + Targeted, Chemo + Immuno, and Targeted + Other Therapy with cumulative probabilities 82.9, 80.8, and 69.3%, respectively; for non-advanced lung cancer were Chemoradio + Targeted, Chemoradi + Immuno, and Chemoradio + Other Therapy with cumulative probabilities 69.0, 67.8, and 60.7%, respectively; (2) in disease-free control rate, for advanced lung cancer were Targeted + Others, Chemo + Immuno, and Targeted + Targeted Therapy with cumulative probabilities 93.4, 91.5, and 59.4%, respectively; for non-advanced lung cancer were Chemo + Surgery, Chemoradio + Targeted, and Surgery Therapy with cumulative probabilities 80.1, 71.5, and 43.1%, respectively.

CONCLUSION

The therapeutic strategies with the best effectiveness will be different depending on the stage of lung cancer patients.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R² 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R²: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM_2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort

BACKGROUND

Epidemiological evidence linking type 2 diabetes mellitus (T2DM) with air pollution is discrepant and most are restricted to the influences of air-pollutant mass concentration. This study aims to explore the effects of long-term exposure to air pollution and its metal constituents on T2DM prevalence in China.

METHODS

We used data on 10,253 adult residents from the baseline survey of Wuhan Chronic Disease Cohort in 2019. Ambient PM_2.5, PM₁₀ and NO₂ exposure were estimated at residences based on Chinese Air Quality Reanalysis Dataset. Concentrations of 10 metal constituents were measured by 976 PM_2.5 filter samples collected from four monitoring stations. Logistic regression models were employed to examine associations of T2DM prevalence with 3-year mean concentrations of each air pollutant and PM_2.5 metal constituents prior to the baseline investigation.

RESULTS

A total of 673 T2DM cases (6.6%) were identified. The 3-year mean exposures to PM_2.5, PM₁₀ and NO₂ were 50.89 μg/m³, 82.86 μg/m³, and 39.79 μg/m³, respectively. And interquartile range (IQR) of 10 metals in PM_2.5 varied from 0.03 ng/m³ to 78.30 ng/m³. For 1 μg/m³ increment in PM_2.5, PM₁₀ and NO₂, the odds of T2DM increased by 7.2% (95%CI: 1.026, 1.136), 3.1% (95%CI: 1.013, 1.050), and 2.1% (95%CI: 1.005, 1.038) after adjusting for potential confounders. Cd and Sb in PM_2.5 were significant risk factors to T2DM with odds ratios of 1.350 (95%CI: 1.089, 1.673) and 1.389 (95%CI: 1.164, 1.658) for per IQR increase, respectively. Stratification analyses indicated that males and those aged ≥45 years were more susceptive to long-term air pollution.

CONCLUSIONS

Long-term exposure to PM_2.5, PM₁₀ and NO₂ increased T2DM prevalence in a Wuhan population, especially for men and middle-aged and elderly people. Moreover, T2DM was significantly associated with Cd and Sb in PM_2.5. Further research to validate these results and to clarify the underlying mechanisms is warranted.

The Physiological Effects of Air Pollution: Particulate Matter, Physiology and Disease

Nine out of 10 people breathe air that does not meet World Health Organization pollution limits. Air pollutants include gasses and particulate matter and collectively are responsible for ~8 million annual deaths. Particulate matter is the most dangerous form of air pollution, causing inflammatory and oxidative tissue damage. A deeper understanding of the physiological effects of particulate matter is needed for effective disease prevention and treatment. This review will summarize the impact of particulate matter on physiological systems, and where possible will refer to apposite epidemiological and toxicological studies. By discussing a broad cross-section of available data, we hope this review appeals to a wide readership and provides some insight on the impacts of particulate matter on human health.

A Novel Tree Ensemble Model to Approximate the Generalized Extreme Value Distribution Parameters of the PM2.5 Maxima in the Mexico City Metropolitan Area

We introduce a novel spatial model based on the distribution of generalized extreme values (GEVs) and tree ensemble models to analyze the maximum concentrations levels of particulate matter with a diameter of less than 2.5 microns (PM2.5) in the Mexico City metropolitan area during the period 2003–2021. Spatial trends were modeled through a decision tree in the context of a non-stationary GEV model. We used a tree ensemble model as a predictor of GEV parameters to approximate nonlinear trends. The decision tree was built by using a greedy stagewise approach, the objective function of which was the log-likelihood. We verified the validity of our model by means of the likelihood and Akaike’s information criterion (AIC). The maps of the generalized extreme value parameters on the spatial plane show the existence of differentiated local trends in the extreme values of PM2.5 in the study area. The results indicated strong evidence of an increase in the west–east direction of the study area. A spatial map of risk with maximum concentration levels of PM2.5 in a period of 25 years was built.

Associations of Exposure to Fine Particulate Matter Mass and Constituents with Systemic Inflammation: A Cross-Sectional Study of Urban Older Adults in China

Systemic inflammation is a key mechanism in the development of cardiovascular diseases induced by exposure to fine particles (particles with aerodynamic diameter ≤2.5 μm [PM_2.5]). However, little is known about the effects of chemical constituents of PM_2.5 on systemic inflammation. In this cross-sectional study, filter samples of personal exposure to PM_2.5 were collected from community-dwelling older adults in Tianjin, China, and the chemical constituents of PM_2.5 were analyzed. Blood samples were collected immediately after the PM_2.5 sample collection. Seventeen cytokines were measured as targets. A linear regression model was applied to estimate the relative effects of PM_2.5 and its chemical constituents on the measured cytokines. A positive matrix factorization model was employed to distinguish the sources of PM_2.5. The calculated source contributions were used to estimate their effects on cytokines. After adjusting for other covariates, higher PM_2.5-bound copper was significantly associated with increased levels of interleukin (IL)1β, IL6, IL10, and IL17 levels. Source analysis showed that an increase in PM_2.5 concentration that originated from tire/brake wear and cooking emissions was significantly associated with enhanced levels of IL1β, IL6, tumor necrosis factor alpha (TNFα), and IL17. In summary, personal exposure to some PM_2.5 constituents and specific sources could increase systemic inflammation in older adults. These findings may explain the cardiopulmonary effects of specific particulate chemical constituents of urban air pollution.

COVID-19 persuaded lockdown impact on local environmental restoration in Pakistan

Coronavirus disease 2019 (COVID-19) pandemic adversely affected human beings. The novel coronavirus has claimed millions of lives all over the globe. Most countries around the world, including Pakistan, restricted people's social activities and ordered strict lockdowns throughout the country, to control the fatality of the novel coronavirus. The persuaded lockdown impact on the local environment was estimated. In the present study, we assessed air quality changes in four cities of Pakistan, namely Islamabad, Karachi, Lahore, and Peshawar, based on particulate matter (PM_2.5), using "Temtop Airing 1000," which is capable of detecting and quantifying PM_2.5. The Air Quality Index (AQI) was evaluated in three specific time spans: the COVID-19 pandemic pre- and post-lockdown period (January 1, 2020 to March 20, 2020, and May 16, 2020 to June 30, 2020 respectively), and the COVID-19 pandemic period (March 21 2020 to May 15, 2020). We compared land-monitored AQI levels for the above three periods of time. For validation, air quality was navigated by the Moderate Resolution Imaging Spectrometer (MODIS) satellite during the first semester (January 1 to June 30) of 2019 and 2020. It is seen that the concentration of PM_2.5 was considerably reduced in 2020 (more than 50%), ranging from ~ 0.05 to 0.3 kg⋅m³, compared to the same period in 2019. The results revealed that the AQI was considerably reduced during the lockdown period. This finding is a very promising as the inhabitants of the planet Earth can be guaranteed the possibility of a green environment in the future.

A Bayesian Multi-Outcome Analysis of Fine Particulate Matter and Cardiorespiratory Hospitalizations

BACKGROUND

Short-term fine particulate matter (PM2.5) exposure is positively associated with acute cardiovascular and respiratory events. Understanding whether this association varies across specific cardiovascular and respiratory conditions has important biologic, clinical, and public health implications.

METHODS

We conducted a time-stratified case-crossover study of hospitalizations from 2000 through 2014 among United States Medicare beneficiaries aged 65+. The outcomes were hospitalizations with any of 57 cardiovascular and 32 respiratory discharge diagnoses. We estimated associations with two-day moving average PM2.5 as a piecewise linear term with a knot at PM2.5 = 25 g/m3. We used Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS) to identify de novo groups of related diseases such that PM2.5 associations are: (1) similar within outcome groups; but (2) different between outcome groups. We adjusted for temperature, humidity, and individual-level characteristics. We introduce an R package, moretrees.

RESULTS

Our dataset included 16,007,293 cardiovascular and 8,690,837 respiratory hospitalizations. Of 57 cardiovascular diseases, 51 were grouped and positively associated with PM2.5. We observed a stronger positive association for heart failure, which formed a separate group. We observed negative associations for groups containing the outcomes other aneurysm and intracranial hemorrhage. Of 32 respiratory outcomes, 31 were grouped and were positively associated with PM2.5. Influenza formed a separate group with a negative association.

CONCLUSIONS

We used a new statistical approach, MOReTreeS, to uncover variation in the association between short-term PM2.5 exposure and hospitalizations for cardiovascular and respiratory causes controlling for patient characteristics, time trends, and environmental confounders.

Air pollution and hospital admissions for critical illness in emergency department: a tertiary-center research in Changsha, China, 2016-2020

We aimed to comprehensively investigate the associations of air pollutants with hospital admissions for critical illness in ED. Patients with critical illness including level 1 and level 2 of the Emergency Severity Index admitted in ED of Changsha Central Hospital from January 2016 to December 2020 were enrolled. Meteorological and air pollutants data source were collected from the National Meteorological Science Data Center. A Poisson generalized linear regression combined with a polynomial distributed lag model (PDLM) was utilized to explore the effect of air pollution on hospital admissions for critical illness in ED. Benchmarks as references (25th) were conducted for comparisons with high levels of pollutant concentrations (75th). At first, lagged effects of all different air pollutants were analyzed. Then, based on the most significant factor, analyses in subgroups were performed by gender (male and female), age (< 45, 45-65, and > 65), disorders (cardiovascular, neurological, respiratory), and seasons (spring, summer, autumn, and winter). A total of 47,290 patients with critical illness admitted in ED were included. The effects of air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃ and CO) on critical illness ED visits were statistically significant. Strong collinearity between PM_2.5 and PM₁₀ (r = 0.862) was found. Both single-day lag and cumulative-day lag day models showed that PM_2.5 had the strongest effects (lag 0, RR = 1.025, 95% CI 1.008-1.043, and lag 0-14, RR = 1.067, 95% CI 1.017-1.120, respectively). In both PM_2.5 and PM₁₀, the risks of critical illness in male, > 65 ages, respiratory diseases, and winter increased the most significant. Air pollutants, especially PM_2.5 and PM₁₀ exposure, could increase the risk of critical illness admission.

Black Carbon Emissions and Associated Health Impacts of Gas Flaring in the United States

Gas flaring from oil and gas fields is a significant source of black carbon (BC) emissions, a component of particulate matter that damages health and warms the climate. Observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instrument indicate that approximately 17.2 billion cubic meters (bcm) of gas was flared from upstream oil and gas operations in the United States in 2019. Based on an emissions factor equation that accounts for the higher heating value of the gas, that corresponded to nearly 16,000 tons of BC emitted, though estimates vary widely across published emissions factors. In this study, we used three reduced-form air quality and health effect models to estimate the health impacts from the flaring-emitted BC particulate matter in the United States. The three models—EASIUR, AP3, and InMAP—predict 26, 48, and 53 premature deaths, respectively, in 2019. The mortality range expands from 5 to 360 deaths annually if alternative emission factors are used. This study shows that reduced-form models can be useful to estimate the impacts of numerous dispersed emissions sources such as flares, and that further research is needed to better quantify BC emissions factors from flares.

A nationwide study of air pollution from particulate matter and daily hospitalizations for respiratory diseases in Italy

BACKGROUND/AIM

The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Our aim was to assess the short-term effects of PM10 and PM2.5 on respiratory admissions in the whole country of Italy during 2006-2015.

METHODS

We estimated daily PM concentrations at the municipality level using satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). Meta-analysis of province-specific estimates obtained by time-series models, adjusting for temperature, humidity and other confounders, was applied to extrapolate national estimates for each outcome. At last, we tested for effect modification by sex, age, period, and urbanization score. Analyses for PM_2.5 were restricted to 2013-2015 cause the goodness of fit of exposure estimation.

RESULTS

A total of 4,154,887 respiratory admission were registered during 2006-2015, of which 29% for LRTI, 12% for COPD, 6% for URTI, and 3% for asthma. Daily mean PM₁₀ and PM_2.5 concentrations over the study period were 23.3 and 17 μg/m³, respectively. For each 10 μg/m³ increases in PM₁₀ and PM_2.5 at lag 0-5 days, we found excess risks of total respiratory diseases equal to 1.20% (95% confidence intervals, 0.92, 1.49) and 1.22% (0.76, 1.68), respectively. The effects for the specific diseases were similar, with the strongest ones for asthma and COPD. Higher effects were found in the elderly and in less urbanized areas.

CONCLUSIONS

Short-term exposure to PM is harmful for the respiratory system throughout an entire country, especially in elderly patients. Strong effects can be found also in less urbanized areas.

Air quality and health-related impacts of traditional and alternate jet fuels from airport aircraft operations in the U.S

Aviation emissions from landing and takeoff operations (LTO) can degrade local and regional air quality leading to adverse health outcomes in populations near airports and downwind. In this study we aim to quantify the air quality and health-related impacts from commercial LTO emissions in the continental U.S. for two recent years' inventories, 2011 and 2016. We quantify the LTO-attributable PM_2.5, O₃, and NO₂ concentrations and health outcomes for mortality and multiple morbidity health endpoints. We also quantify the impacts from two scenarios representing a nation-wide implementation of 5% or 50% blends of sustainable alternative jet fuels. We estimate 80 (68-93) and 88 (75-100) PM_2.5-attributable and 610 (310-920) and 1,100 (570-1,700) NO₂-attributable premature mortalities in 2011 and 2016, respectively. We estimate a net decrease of 28 (14-56) and 54 (27-110) in O₃-attributable premature mortalities across the U.S. in 2011 and 2016, respectively due to the large O₃ titration effects near the airports. We also find that the asthma exacerbations due to NO₂ exposures from LTO emissions increase from 100,000 (2,500-200,000) in 2011 to 170,000 (4,400-340,000) in 2016. Implementing a 5% or 50% blend of sustainable alternative jet fuel in 2016 results in a 1% or 18% reduction, respectively in PM_2.5-attributable premature mortalities. Monetizing the value of avoided total premature mortalities, we find that a 50%-blended sustainable alternative jet fuel results in a 19% decrease in PM_2.5 damages per ton of fuel burned and a 2% decrease in total damages per ton of fuel burned as compared to damages from traditional jet fuel. We also quantify health impacts by state and find California to be the most impacted by LTO emissions. We find that LTO-attributable PM_2.5 and NO₂ premature mortalities increase by 10% and 80%, respectively from 2011 to 2016 and that NO₂-attributable premature mortalities are responsible for 91% of total LTO-attributable premature mortalities in both 2011 and 2016. And since we find LTO-attributable NO₂ to be unaffected by the implementation of sustainable alternative jet fuels, additional approaches focused on NO_X reductions in the combustor are needed to mitigate the air quality-related health impacts from LTO emissions.

Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event

Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire-related smoke PM2.5 fields and variability in concentration-response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire-attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration-response parameters derived from studies of ambient and wildfire-specific PM2.5 are used to assess health-related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health-related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire-related decision-making.

Assessing short-term impacts of PM2.5 constituents on cardiorespiratory hospitalizations: Multi-city evidence from China

Apart from concentrations of particulate mass, PM_2.5-associated effects on health may largely depend on its chemical components. However, little is known regarding the underlying effects of specific PM_2.5 constituents. The study included nearly 1 million hospital admissions from five Chinese cities during 2015-2017. Based on the modified Community Multiscale Air Quality model, our study simulated daily concentrations of PM_2.5 and five main components. We used a time-stratified case-crossover design with conditional logistic regression models to estimate short-term effects of PM_2.5 constituents on cause-specific hospital admissions. Per interquartile range increase in exposure to PM_2.5, elemental carbon, organic carbon, nitrate, sulfate and ammonium at lag 04-day was related to an excess risk (ER%) for non-accidental admissions of 1.6% [95% confidence interval: 1.1-2.0], 1.9% [1.3-2.4], 1.0% [0.5-1.6], 1.2% [0.4-2.0], 1.2% [0.9-1.5] and 1.4% [0.9-1.9], respectively. Great heterogeneities of constituents-admission associations existed in diverse causes and constituents. This study provided multi-center high-quality evidence that hospital admissions, particularly those for ischemic heart disease (ER% ranging from 2.3 to 5.4% at lag 04-day) and pneumonia (1.9-5.1% at lag 4-day), could be triggered by short-term exposures to ambient PM_2.5 constituents. Relatively stronger constituents-admission associations were found among females for respiratory causes and the elderly for cardiovascular causes.

Secondary organic aerosol association with cardiorespiratory disease mortality in the United States

Fine particle pollution, PM2.5, is associated with increased risk of death from cardiorespiratory diseases. A multidecadal shift in the United States (U.S.) PM2.5 composition towards organic aerosol as well as advances in predictive algorithms for secondary organic aerosol (SOA) allows for novel examinations of the role of PM2.5 components on mortality. Here we show SOA is strongly associated with county-level cardiorespiratory death rates in the U.S. independent of the total PM2.5 mass association with the largest associations located in the southeastern U.S. Compared to PM2.5, county-level variability in SOA across the U.S. is associated with 3.5× greater per capita county-level cardiorespiratory mortality. On a per mass basis, SOA is associated with a 6.5× higher rate of mortality than PM2.5, and biogenic and anthropogenic carbon sources both play a role in the overall SOA association with mortality. Our results suggest reducing the health impacts of PM2.5 requires consideration of SOA.

Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models.

Associations between ambient fine particulate matter and child respiratory infection: The role of particulate matter source composition in Dhaka, Bangladesh

Air pollution in the form of fine particulate matter (PM_2.5) has been linked to adverse respiratory outcomes in children. However, the magnitude of this association in South Asia and sources of PM_2.5 that drive adverse health effects are largely unknown. This study evaluates associations between short-term variation in ambient PM_2.5 and incidence of pneumonia and upper respiratory infections among children in Dhaka, Bangladesh. We also perform an exploratory analysis of the PM_2.5 source composition that is most strongly associated with health endpoints. We leveraged data from health surveillance of children less than five years of age between 2005 and 2014 in Kamalapur, Bangladesh, including daily physician-confirmed diagnoses of pneumonia and upper respiratory infection. Twice-weekly source-apportioned ambient PM_2.5 measurements were obtained for the same period, and Poisson regression adjusted for time-varying covariates was used to estimate lagged associations between ambient PM_2.5 and respiratory infection. We use complementary matching and stratification approaches to evaluate whether these associations vary across PM_2.5 source composition. Total PM_2.5 mass was associated with a modest increase in incidence of pneumonia, with a peak effect size two days after exposure (rate ratio = 1.032; 95% confidence interval = 1.008-1.056). We did not identify a significant association between PM_2.5 and upper respiratory infection. Stratified and matching analyses suggested this association was stronger among days when ambient PM_2.5 had a higher mass percent associated with brick kiln and fugitive lead emissions.: This study suggests that elevated ambient PM_2.5 contributes to increased incidence of child pneumonia in urban Dhaka, and that this relationship varies among days with different source composition of PM_2.5.

A preliminary study on household air pollution exposure and health-related factors among rural housewives in Gansu province, northwest China

This study monitored the indoor air PAHs and PM2.5 exposure and their seasonal variations, so as to explore the potential health effects of household air pollution (HAP) on rural women's health in northwest China. It was detected that the average indoor PM2.5 and PAHs concentrations in the heating season were both significantly higher than those in the non-heating season (P<0.01). And they were positively correlated with the urinary 1-OHP levels respectively. Then the PAHs and 1-OHP were both significantly correlated with the urinary 8-OHdG levels (P<0.05). By statistical models, household PM2.5 and PAHs were closely related to urinary 1-OHP levels. Similarly, PM2.5, PAHs and 1-OHP all have significant effects with urinary 8-OHdG (P<0.05). Therefore, housewives in rural northwest China were exposed to higher HAP, and it could improve the risk for oxidative damage.

Health Benefits in California of Strengthening the Fine Particulate Matter Standards

The Clean Air Act requires the United States Environmental Protection Agency to review routinely the National Ambient Air Quality Standards, including fine particulate matter (PM_2.5). A non-governmental Independent Particulate Matter Review Panel recently concluded that the current PM_2.5 standards do not protect public health adequately and recommended revising the daily standard from 35 to 25-30 μg/m³ and the annual standard from 12 to 8-10 μg/m³. To assess the public health implications of adopting the PM_2.5 standards proposed by the panel, the health benefits are quantified from their implementation based on both current (observed) and future (simulated) air quality data for California. The findings indicate that strengthening the standards would provide significant public health benefits valued at $42-$149 billion. Additionally, the stronger standards are shown to benefit environmental justice via health savings that are allocated more within environmentally and socioeconomically disadvantaged communities.

Relationship between fine particulate air pollution and hospital admissions for depression: a case-crossover study in Taipei

There are few apparent studies regarding the association between fine particulate matter (PM_2.5) air pollution and development of depression. Data obtained from epidemiological studies are inconsistent and controversial. The aim of this case-crossover study was to examine the association between short-term exposure to PM_2.5 alone and in combination with other pollutants and frequency of hospitalizations for depression from 2009 to 2013 in Taipei, Taiwan. In the single pollutant model without adjustment for other pollutants, 17% and 4% increase in admissions attributed to depression correlated with interquartile range (IQR) rise in PM_2.5 levels was noted on warm and cool days, respectively. Data were also analyzed using two-pollutant models and it was found that on warm days, the association continued to be significant after including one of the following pollutants: sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃) or carbon monoxide (CO). On cool days, the significance was lost. In conclusion, the relationship between ambient outdoor PM_2.5 exposure and rates of hospitalization for depression appeared to be temperature dependent in Taipei. Further research is needed to verify these observations as well as to distinguish the relative contributions of PM_2.5 and temperature to development for hospital admissions for depression.

Exposure to nanoparticles derived from diesel particulate filter equipped engine increases vulnerability to arrhythmia in rat hearts

Air pollution is well recognized as a central player in cardiovascular disease. Exhaust particulate from diesel engines (DEP) is rich in nanoparticles and may contribute to the health effects of particulate matter in the environment. Moreover, diesel soot emitted by modern engines denotes defective surfaces alongside chemically-reactive sites increasing soot cytotoxicity. We recently demonstrated that engineered nanoparticles can cross the air/blood barrier and are capable to reach the heart. We hypothesize that DEP nanoparticles are pro-arrhythmogenic by direct interaction with cardiac cells. We evaluated the internalization kinetics and the effects of DEP, collected from Euro III (DEPe3, in the absence of Diesel Particulate Filter, DPF) and Euro IV (DEPe4, in the presence of DPF) engines, on alveolar and cardiac cell lines and on in situ rat hearts following DEP tracheal instillation. We observed significant differences in DEP size, metal and organic compositions derived from both engines. DEPe4 comprised ultrafine particles (<100 nm) and denoted a more pronounced toxicological outcome compared to DEPe3. In cardiomyocytes, particle internalization is fastened for DEPe4 compared to DEPe3. The in-vivo epicardial recording shows significant alteration of EGs parameters in both groups. However, the DEPe4-instilled group showed, compared to DEPe3, a significant increment of the effective refractory period, cardiac conduction velocity, and likelihood of arrhythmic events, with a significant increment of membrane lipid peroxidation but no increment in inflammation biomarkers. Our data suggest that DEPe4, possibly due to ultrafine nanoparticles, is rapidly internalized by cardiomyocytes resulting in an acute susceptibility to cardiac electrical disorder and arrhythmias that could accrue from cellular toxicity. Since the postulated transfer of nanoparticles from the lung to myocardial cells has not been investigated it remains open whether the effects on the cardiovascular function are the result of lung inflammatory reactions or due to particles that have reached the heart.