Ambient PM2.5 Chemical Composition and Cardiovascular Disease Hospitalizations in China

Ambient PM2.5 exposure, physical activity, and cardiovascular dysfunction: Analysis of CHARLS data and experimental study in mice

Previous studies have confirmed ambient fine particulate matter (PM2.5) as a major environmental risk factor for cardiovascular diseases (CVDs), yet the specific molecular pathways remain poorly understood. Furthermore, while physical activity benefits cardiovascular health, its protective effects against PM2.5-induced damage need further explored. We aimed to investigate the relationship between long-term PM2.5 exposure, physical activity, and cardiovascular health, and explore the potential molecular mechanisms. This research combined epidemiological and experimental approaches. The epidemiological study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations among long-term PM2.5 exposure, physical activity, and CVDs. For the experimental study, C57BL/6 male mice were assigned to either regular physical activity or sedentary behavior, and were exposed to PM2.5 or filtered air (FA) for 2, 4, and 6 months. We observed that long-term PM2.5 exposure significantly increased cardiovascular disease risk, while physical activity exhibited protective effects and can partially mitigate the adverse impacts of PM2.5 on heart disease and dyslipidemia. In animal study, mice with long-term exposure to PM2.5 demonstrated elevated blood pressure, disrupted adipokine levels, altered lipid profiles, and mitochondrial damage. Regular physical activity partially mitigated these adverse effects. Lipidomics and proteomics analyses revealed that PM2.5 exposure disrupted lipid metabolism networks and protein regulatory pathways, while regular physical activity mitigated these perturbations through the modulation of lipid metabolism, the coagulation cascade, and immune responses. These findings underscore the importance of regular physical activity in public health strategies, while prioritizing PM2.5 reduction measures for cardiovascular disease prevention.

Quantifying regional transport contributions to PM2.5-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach

Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. This study analyzed PM2.5-bound trace elements based on one year of sampling data collected from a low-PM2.5 island in southeastern coastal China. A de-weathered model based on the eXtreme Gradient Boosting (XGBoost) algorithm was applied to remove meteorological influences and estimate local baseline pollutant concentrations. By combining backward air mass trajectories with de-weathered concentrations, we quantified the variation in transport contributions among different trajectory types. Results indicated that meteorological factors reduced PM2.5 and anthropogenic trace element concentrations by 36.7 %-58.4 % in summer, but increased them by 6.4 %-26.0 % in winter. In contrast, elements related to shipping emissions showed an opposite trend. Positive matrix factorization (PMF) identified industrial and shipping emissions as the two main sources of trace elements, originating from distinct regions. Shipping emissions contributed greatly health risks in summer, while industrial emissions dominated in other seasons. The non-carcinogenic risk (NCR) remained within acceptable levels, whereas carcinogenic risks (CR) exceeded recommended thresholds. Marine airflows (MA), inland airflows (IA), and local airflows (LA) altered trace element concentrations by -3.7 %, +6.4 %, and -5.4 %, respectively. These airflow types changed NCR by -16.4 %, +8.2 %, and -13.5 %, and CR by -4.1 %, +4.7 %, and -28.9 %, respectively. These findings underscore the substantial impact of regional transport on trace elements and the critical need for coordinated regional air quality management, offering new insights into pollutant sources and their associated health risks in relatively less polluted coastal regions.

Deep transfer learning for spatiotemporal mapping of PM2.5 nitrate across China: Addressing small data challenges in environmental machine learning

The proportion of nitrate in PM2.5 is increasing in China, leading to rising health risks. However, due to the lack of a publicly accessible nationwide monitoring network, small data challenges persist in research on the spatiotemporal distribution of nitrate concentrations. Here, we employed a novel transfer learning method to compensate for data scarcity in reconstructing PM2.5 nitrate concentrations across China. Firstly, a deep neural network (Source model) was pre-trained with extensive nitrate observations from the conterminous United states. Then, the Source model was fine-tuned with limited nitrate observations from China to yield the Transfer model. Compared to previous machine learning models constrained by insufficient nitrate observations, the Transfer model demonstrated improved generalizability through various validation strategies, with cell-based cross-validation R2= 0.72 and RMSE= 7.5 μg/m3. The results suggested that the prior knowledge learned from the large United States dataset has enhanced the generalizability of the Transfer model. Through the Transfer model, this study generated a 10 km gridded daily nitrate dataset for 2005-2020. The predictions revealed the most severe nitrate pollution in the North China Plain and major economic zones. Overall, nitrate concentrations across China showed a fluctuating upward trend from 2005 to 2014 by 0.4 μg/m3/year, followed by reductions from 2015 to 2020 by 0.6 μg/m3/year, corresponding with the implementation of air pollution control policies. This study demonstrates the effectiveness of transfer learning for addressing small data challenges and data disparity, which is particularly valuable for developing countries and regions with limited resources for environmental management and research.

The association between long-term exposure to PM2.5 constituents and ischemic stroke in the New York City metropolitan area

Numerous studies linked fine particulate matter (PM2.5) to ischemic stroke. However, only a few investigated the differential associations with specific PM2.5 components and sources. We utilized electronic health records (EHR) from the Mount Sinai Health System in the New York City metropolitan area during 2011-2019 and assessed the associations of PM2.5 components and sources with ischemic stroke. We used mixed-effect Poisson survival regressions to assess the single-exposure associations with the chemical components. We used multivariable regression to assess the simultaneous associations with source-apportioned PM2.5 exposures estimated using non-negative matrix factorization. Then, we assessed the sensitivity of our results to different specifications of EHR data continuity: (1) using a less strict definition of censorship year, (2) adjusting the model for EHR data continuity index, a validated algorithm measuring EHR-data continuity based on indicators of primary care service utilization. We observed higher risks for ischemic stroke (Risk ratio [95 % confidence intervals] per interquartile range increase) associated with higher exposure to nickel (1.080 [1.045; 1.116]), vanadium (1.070 [1.033; 1.109]), zinc (1.076 [1.031; 1.122]), and nitrate (1.084 [1.039; 1.132]). In the multivariate models we found higher risk for ischemic stroke associated with exposure to oil combustion sourced PM2.5 (1.061 [1.012; 1.113]). The results remained consistent under different model specifications accounting for EHR data continuity. In conclusion, we found an increased risk of ischemic stroke associated with specific PM2.5 components and sources. These findings were robust to different specifications of EHR-data continuity. Our findings can inform policy and interventions aimed at reducing cardiovascular disease burden.

Cardiovascular Emergency Hospitalization Risks of PM2.5 Transition Metals: A Time-Stratified Case-Crossover Study

PM2.5 pollution poses significant health risks in urban areas, yet the specific cardiovascular impacts of its hazardous components, especially transition metals, remain insufficiently understood. This study evaluated the associations of PM2.5 components on acute myocardial infarction (AMI) and acute aortic dissections (AAD) emergency hospitalizations (n = 9985) using a time-stratified case-crossover between 2017 and 2023 in Xiamen, China. We collected comprehensive data on daily air pollutants, PM2.5 components (water-soluble ions, carbon components, metals, and other elements), and meteorological variables. Conditional logistic regressions were used to estimate odds ratios (OR) per the interquartile range (IQR) of exposures. Our finding reveals significant short-term associations of exposures to air pollutants and PM2.5 components with increased cardiovascular emergency hospitalizations. The strongest associations were observed between cumulative 3-day lagged (lag 0-3) PM2.5 transition metals including Mn [odds ratio, OR = 1.106 (95% CI: 1.032-1.186)], Fe [OR = 1.078, (95% CI: 1.015-1.145)], V [OR = 1.117 (95% CI: 1.024-1.219)], and Zn [OR = 1.08, (95% CI: 1.005-1.161)] exposure with AMI. These associations were stronger among older (age >65 years), male patients, and during colder seasons. Our study highlights the underexplored subacute cardiovascular risks of PM2.5 transition metals, underscoring the need to integrate them into urban air quality management to promote environmental sustainability.

Environmental Pollutants as Emerging Concerns for Cardiac Diseases: A Review on Their Impacts on Cardiac Health

Comorbidities related to cardiovascular disease (CVD) and environmental pollution have emerged as serious concerns. The exposome concept underscores the cumulative impact of environmental factors, including climate change, air pollution, chemicals like PFAS, and heavy metals, on cardiovascular health. Chronic exposure to these pollutants contributes to inflammation, oxidative stress, and endothelial dysfunction, further exacerbating the global burden of CVDs. Specifically, carbon monoxide (CO), ozone, particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), heavy metals, pesticides, and micro- and nanoplastics have been implicated in cardiovascular morbidity and mortality through various mechanisms. PM2.5 exposure leads to inflammation and metabolic disruptions. Ozone and CO exposure induce oxidative stress and vascular dysfunction. NO2 exposure contributes to cardiac remodeling and acute cardiovascular events, and sulfur dioxide and heavy metals exacerbate oxidative stress and cellular damage. Pesticides and microplastics pose emerging risks linked to inflammation and cardiovascular tissue damage. Monitoring and risk assessment play a crucial role in identifying vulnerable populations and assessing pollutant impacts, considering factors like age, gender, socioeconomic status, and lifestyle disorders. This review explores the impact of cardiovascular disease, discussing risk-assessment methods, intervention strategies, and the challenges clinicians face in addressing pollutant-induced cardiovascular diseases. It calls for stronger regulatory policies, public health interventions, and green urban planning.

Absence of a Causal Link between Elemental Carbon Exposure and Short-Term Respiratory Toxicity in Human-Derived Organoids and Cellular Models

Black carbon or elemental carbon (EC) in the atmosphere plays an ambiguous role in acute respiratory toxic effects. Here, we evaluate the contribution of EC to the short-term toxicity (including cytotoxicity and oxidative stress potency) of fine particulate matter (PM2.5) on the human respiratory tract using in vitro airway organoids and cell lines. The toxic potency of EC per unit mass, including char and soot, is more than 2 orders of magnitude lower than that of polycyclic aromatic hydrocarbons (PAHs), which are coemitted from incomplete combustion. EC contributes approximately 1 order of magnitude less to PM2.5 toxicity than PAHs, despite its positive associations with PM2.5-induced toxic potency (p < 0.0001). Furthermore, PAHs contribute 71.9 ± 12.2% and 61.9 ± 32.8% of the overall toxic potency of PM2.5 emitted from typical incomplete burning of solid and liquid fuels, respectively, while the PM2.5 toxicity significantly correlates with PAHs content (r = 0.94, p = 0.002). Hence, EC is not a cause of inducing acute toxicity, likely attributed to coemitted PAHs. These findings provide causal evidence for understanding the respiratory health risks associated with exposure to PM2.5 and further benefit to establishing efficient air pollution control policies.

The effects of PM2.5 components on the cardiovascular disease admissions in Shanghai City, China: a multi- region study

BACKGROUND

The burden of cardiovascular disease (CVD) is severe worldwide. Although many studies have investigated the association of particulate pollution with CVD, the effect of finer particulate pollution components on CVD remains unclear. This study aimed to explore the effect of five PM2.5 components ([Formula: see text], sulfate; [Formula: see text], nitrate; [Formula: see text], ammonium; OM, organic matter; BC, carbon black) on CVD admission in Shanghai City, identify the susceptible population, and provide clues for the prevention and control of particulate pollution.

METHODS

Daily PM2.5 components data during 2013-2019 in three districts of Shanghai were obtained from Tracking Air Pollution in China. We obtained CVD daily admissions data from relevant departments of Tongji Hospital, including basic information (sex, age, time of admissions, ICD code of root cause of admissions, etc.). First, generalized additive model (GAM) and distributed lag non-linear (DLNM) model were used to evaluate the individual effects of PM2.5 components on CVD admission in three districts of Shanghai. Then, the three regions were pooled for analysis using either a random-effects model or a fixed-effects model.

RESULTS

Overall, all five PM2.5 components had significant effects on CVD admission risk. BC and OM were strongly associated with daily CVD admissions, with increasing interquartile range of the concentrations, the maximum values of cumulative RR (95% CI) were 1.318 (95%CI: 1.222-1.415) and 1.243 (95%CI: 1.164-1.322), respectively. The elderly (≥ 65 years old) was more sensitive to the four PM2.5 components than the young population. [Formula: see text] and BC were strongest associated with CVD admissions in the elderly than in younger people, with increasing interquartile range of the concentrations, the maximum cumulative RR (95% CI) was 1.567 (95% CI: 1.116-2.019) and 1.534 (95% CI: 1.104-1.963), respectively.

CONCLUSIONS

This study found that five PM2.5 components were significant risk factors for CVD admissions and specific CVD diseases in Shanghai City. The elderly were susceptible to [Formula: see text],[Formula: see text], OM, and BC.