Fine particulate matter (PM2.5) association with peripheral artery disease admissions in northeastern United States

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Cell-free circulating mitochondrial DNA: An emerging biomarker for airborne particulate matter associated with cardiovascular diseases

The association of airborne particulate matter exposure with the deteriorating function of the cardiovascular system is fundamentally driven by the impairment of mitochondrial-nuclear crosstalk orchestrated by aberrant redox signaling. The loss of delicate balance in retrograde communication from mitochondria to the nucleus often culminates in the methylation of the newly synthesized strand of mitochondrial DNA (mtDNA) through DNA methyl transferases. In highly metabolic active tissues such as the heart, mtDNA's methylation state alteration impacts mitochondrial bioenergetics. It affects transcriptional regulatory processes involved in biogenesis, fission, and fusion, often accompanied by the integrated stress response. Previous studies have demonstrated a paradoxical role of mtDNA methylation in cardiovascular pathologies linked to air pollution. A pronounced alteration in mtDNA methylation contributes to systemic inflammation, an etiological determinant for several co-morbidities, including vascular endothelial dysfunction and myocardial injury. In the current article, we evaluate the state of evidence and examine the considerable promise of using cell-free circulating methylated mtDNA as a predictive biomarker to reduce the more significant burden of ambient air pollution on cardiovascular diseases.

The Impact of Fine Particulate Matter 2.5 on the Cardiovascular System: A Review of the Invisible Killer

Air pollution exerts several deleterious effects on the cardiovascular system, with cardiovascular disease (CVD) accounting for 80% of all premature deaths caused by air pollution. Short-term exposure to particulate matter 2.5 (PM_2.5) leads to acute CVD-associated deaths and nonfatal events, whereas long-term exposure increases CVD-associated risk of death and reduces longevity. Here, we summarize published data illustrating how PM_2.5 may impact the cardiovascular system to provide information on the mechanisms by which it may contribute to CVDs. We provide an overview of PM_2.5, its associated health risks, global statistics, mechanistic underpinnings related to mitochondria, and hazardous biological effects. We elaborate on the association between PM_2.5 exposure and CVD development and examine preventive PM_2.5 exposure measures and future strategies for combating PM_2.5-related adverse health effects. The insights gained can provide critical guidelines for preventing pollution-related CVDs through governmental, societal, and personal measures, thereby benefitting humanity and slowing climate change.

Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces

Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations. However, previous studies on this issue have reported mixed results. This study analyzed the spatio-temporal characteristics of NO₂, PM_2.5 and O₃ in Fujian Province, Southeast China in 2015. In order to reduce uncertainties in the conclusions drawn, the effects landscape metrics describing green spaces have on air pollutants have been analyzed using Pearson correlation analysis at six different spatial scales for the four seasons, considering the influence of meteorological conditions. The results show that PM_2.5 and O₃ are major pollutants whose relative importance varies with the seasons. Significant differences in pollutant concentrations were observed in suburban and urban areas, highlighting the importance of ensuring a reasonable spatial distribution of monitoring stations. Moreover, significant correlations between air pollutants and green space landscape patterns during the four seasons were found, revealing increased air pollutant concentrations with increasing landscape fragmentation and reduced connectivity and aggregation. This probably indicates that interconnected green spaces have the potential to improve air quality. Utilizing green space function regulations can alleviate NO₂ and PM_2.5 pollution effectively, but it is still difficult to reduce O₃ concentrations because green spaces are likely to not only serve as sinks for O_3, but can also promote O₃ formation.

An 18 year data-linkage study on the association between air pollution and acute limb ischaemia

Background: There is limited information regarding the effects of air pollutants, such as nitrogen oxides (NO_x), nitric oxide (NO₂), nitrous oxide (NO) and particulate matter with a diameter smaller than 10 μm (PM10), on acute limb ischaemia (ALI), a peripheral arterial disease (PAD) often with a poor clinical outcome. Patients and methods: We conducted an 18-year retrospective cohort study using routinely collected healthcare records from Ninewells Hospital, Dundee, and Perth Royal Infirmary, in Tayside, Scotland, UK from 2000 to 2017. ALI hospitalisation events and deaths were linked to daily NO_x, NO₂, NO and PM10 levels extracted from publicly available data over this same time period. Distributed lag models were used to estimate risk ratios for ALI hospitalisation and for ALI mortality, adjusting for temperature, humidity, day of the week, month and public holiday. Results: 5,608 hospital admissions in 2,697 patients were identified over the study period (mean age 71.2 years, ±11.1). NO_x and NO were associated with an increase of ALI hospital admissions on days of exposure to pollutant (p=.018), while PM10 was associated with a cumulative (lag 0-9 days) increase (p=.027) of ALI hospital admissions in our study. There was no increase of ALI mortality associated with pollution levels. Conclusions: ALI hospital admissions were positively associated with ambient NO_x and NO on day of high measured pollution levels and a cumulative effect was seen with PM10.

Forgotten but not gone: Particulate matter as contaminations of mucosal systems

A decade ago, environmental issues, such as air pollution and the contamination of the oceans with microplastic, were prominently communicated in the media. However, these days, political topics, as well as the ongoing COVID-19 pandemic, have clearly taken over. In spite of this shift in focus regarding media representation, researchers have made progress in evaluating the possible health risks associated with particulate contaminations present in water and air. In this review article, we summarize recent efforts that establish a clear link between the increasing occurrence of certain pathological conditions and the exposure of humans (or animals) to airborne or waterborne particulate matter. First, we give an overview of the physiological functions mucus has to fulfill in humans and animals, and we discuss different sources of particulate matter. We then highlight parameters that govern particle toxicity and summarize our current knowledge of how an exposure to particulate matter can be related to dysfunctions of mucosal systems. Last, we outline how biophysical tools and methods can help researchers to obtain a better understanding of how particulate matter may affect human health. As we discuss here, recent research has made it quite clear that the structure and functions of those mucosal systems are sensitive toward particulate contaminations. Yet, our mechanistic understanding of how (and which) nano- and microparticles can compromise human health via interacting with mucosal barriers is far from complete.

Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke

INTRODUCTION

Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.

METHODS

Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.

RESULTS

Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.

CONCLUSION

Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.

Environmental determinants of cardiovascular disease: lessons learned from air pollution

Air pollution is well recognized as a major risk factor for chronic non-communicable diseases and has been estimated to contribute more to global morbidity and mortality than all other known environmental risk factors combined. Although air pollution contains a heterogeneous mixture of gases, the most robust evidence for detrimental effects on health is for fine particulate matter (particles ≤2.5 µm in diameter (PM2.5)) and ozone gas and, therefore, these species have been the main focus of environmental health research and regulatory standards. The evidence to date supports a strong link between the risk of cardiovascular events and all-cause mortality with PM2.5 across a range of exposure levels, including to levels below current regulatory standards, with no 'safe' lower exposure levels at the population level. In this comprehensive Review, the empirical evidence supporting the effects of air pollution on cardiovascular health are examined, potential mechanisms that lead to increased cardiovascular risk are described, and measures to reduce this risk and identify key gaps in our knowledge that could help address the increasing cardiovascular morbidity and mortality associated with air pollution are discussed.

Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

Effects of PM2.5 on Cardio-Pulmonary Function Injury in Open Manganese Mine Workers

Exposure to fine particulate matter 2.5 (PM_2.5) is associated with adverse health effects, varying by its components. The health-related effects of PM_2.5 exposure from ore mining may be different from those of environment pollution. The aim of this study was to investigate the effects of different concentrations of PM_2.5 exposure on the cardio-pulmonary function of manganese mining workers. A total of 280 dust-exposed workers who were involved in different types of work in an open-pit manganese mine were randomly selected. According to the different concentrations of PM_2.5 in the working environment, the workers were divided into an exposed group and a control group. The electrocardiogram, blood pressure, and multiple lung function parameters of the two groups were measured and analyzed. The PM_2.5 exposed group had significantly lower values in the pulmonary function indexes of forced expiratory volume in one second (FEV1.0), maximum mid expiratory flow (MMEF), peak expiratory flow rate (PEFR), percentage of peak expiratory flow out of the overall expiratory flow volume (PEFR%), forced expiratory flow at 25% and 75% of forced vital capacity (FEF 25, FEF75), forced expiratory flow when 25%, 50%, and 75% of forced vital capacity has been exhaled (FEF25%,FEF50%, FEF75%), and FEV1.0/FVC% (the percentage of the predicted value of forced vital capacity) than the control group (all p < 0.05). Both groups had mild or moderate lung injury, most of which was restrictive ventilatory disorder, and there was significant difference in the prevalence rate of restrictive respiratory dysfunction between the two groups (41.4% vs. 23.6%, p = 0.016). Electrocardiogram (ECG) abnormalities, especially sinus bradycardia, were shown in both groups, but there was no statistical difference of the prevalence rate between the two groups (p > 0.05). Also, no significant difference of the prevalence rate of hypertension was observed between the PM2.5 exposure and control groups (p > 0.05). PM_2.5 exposure was associated with pulmonary function damage of the workers in the open-pit manganese mine, and the major injury was restrictive ventilatory disorder. The early effect of PM_2.5 exposure on the cardiovascular system was uncertain at current exposure levels and exposure time.

Evaluation of environmental health benefits of China's building energy conservation policies: an integrated assessment on national and provincial levels

This article evaluates the effect of energy conservation policies and carbon mitigation efforts on reducing health damage in China's building sector, which has been long ignored. The study bases on both national and provincial levels. To evaluate the health damage effect, we use domestic data by region to ensure the reliability of the evaluation. Results show that in the co-control scenario, the GDP loss saved from health benefit in 2020, 2030, and 2050 is 0.13 %, 0.16 %, and 0.23 %, respectively, compared to reference scenario. At a provincial level, extra health benefit of 16, 16, 33, 25, and 23 RMB/person can be observed for Beijing, Heilongjiang, Guangdong, Henan, and Qinghai owing to energy conservation, even with the strictest end-of-pipe control measures. The results confirm the significant effect of energy conservation efforts on reducing the health damage in China's building sector at both national and provincial levels.

Function of PM2.5 in the pathogenesis of lung cancer and chronic airway inflammatory diseases

Previous research has identified that air pollution is associated with various respiratory diseases, but few studies have investigated the function served by particulate matter 2.5 (PM2.5) in these diseases. PM2.5 is known to cause epigenetic and microenvironmental alterations in lung cancer, including tumor-associated signaling pathway activation mediated by microRNA dysregulation, DNA methylation, and increased levels of cytokines and inflammatory cells. Autophagy and apoptosis of tumor cells may also be detected in lung cancer associated with PM2.5 exposure. A number of mechanisms are involved in triggering and aggravating asthma and COPD, including PM2.5-induced cytokine release and oxidative stress. The present review is an overview of the underlying molecular mechanisms of PM2.5-induced pathogenesis in lung cancer and chronic airway inflammatory diseases.

Chemical characterization and quantitativ e assessment of source-specific health risk of trace metals in PM1.0 at a road site of Delhi, India

This study presents the concentration of submicron aerosol (PM_1.0) collected during November, 2009 to March, 2010 at two road sites near the Indian Institute of Technology Delhi campus. In winter, PM_1.0 composed 83% of PM_2.5 indicating the dominance of combustion activity-generated particles. Principal component analysis (PCA) proved secondary aerosol formation as a dominant process in enhancing aerosol concentration at a receptor site along with biomass burning, vehicle exhaust, road dust, engine and tire tear wear, and secondary ammonia. The non-carcinogenic and excess cancer risk for adults and children were estimated for trace element data set available for road site and at elevated site from another parallel work. The decrease in average hazard quotient (HQ) for children and adults was estimated in following order: Mn > Cr > Ni > Pb > Zn > Cu both at road and elevated site. For children, the mean HQs were observed in safe level for Cu, Ni, Zn, and Pb; however, values exceeded safe limit for Cr and Mn at road site. The average highest hazard index values for children and adults were estimated as 22 and 10, respectively, for road site and 7 and 3 for elevated site. The road site average excess cancer risk (ECR) risk of Cr and Ni was close to tolerable limit (10^-4) for adults and it was 13-16 times higher than the safe limit (10^-6) for children. The ECR of Ni for adults and children was 102 and 14 times higher at road site compared to elevated site. Overall, the observed ECR values far exceed the acceptable level.