Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

Effect of acute PM2.5 exposure on PTGS2 and RNA m6A modification

Particulate matter 2.5 (PM2.5) is a prevalent risk factor in many diseases, but its molecular mechanism remains ambiguous and may be diverse. RNA m6A is an important epigenetic modification that regulates gene expression at the post-transcriptional level. Some previous animal exposure studies found that PM2.5 exposure up-regulated m6A RNA methylation in the lung, but there is no research on m6A RNA methylation in humans from PM2.5 exposure now. Here, in the present experiment, we performed a panel study of 65 students at the Chinese research academy of environmental sciences (CRAES) with 3 rounds of follow-up visits from August 2021 to January 2022. We examined m6A RNA modification profiles of peripheral blood mononuclear cells (PBMCs) from subjects after low and high concentrations of ambient PM2.5 exposure. We applied a linear mixed-effect (LME) model to investigate the association between PM2.5 exposure and global m6A RNA methylation and PTGS2 level in peripheral blood. We found that increased levels of global m6A RNA methylation and PTGS2 level were associated with higher PM2.5 exposure. Among the methylated mRNAs, PTGS2 was hyper-methylated after high concentrations of PM2.5 exposure, which coincided with the increased expression of PTGS2 mRNA. In the present study, we determined that PM2.5 exposure promoted RNA m6A modification, and PTGS2 in peripheral blood could serve as a novel regulatory factor of inflammation induced by PM2.5 exposure. Furthermore, RNA m6A modification may contribute to the altered expression of PTGS2 induced by PM2.5 exposure. Our finding provided a new perspective for the prevention and treatment of PM2.5 exposure-induced adverse health effects.

New Homogeneous Spatial Areas Identified Using Case-Crossover Spatial Lag Grid Differences between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Hospitalizations

Optimal use of Hierarchical Bayesian Model (HBM)-assembled aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM2.5 fused surfaces and four respiratory–cardiovascular hospital events in 12 km2 grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included five grids (lag grids 04; 720 km2) and the smallest HOSA contained two grids (lag grids 01; 288 km2). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM2.5 concentration levels, population density, and poverty percentages than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory–cardiovascular chronic diseases in persons residing in rural areas was discussed.

Characterization of Atmospheric PM2.5 Inorganic Aerosols Using the Semi-Continuous PPWD-PILS-IC System and the ISORROPIA-II

A semi-continuous monitoring system, a parallel plate wet denuder and particle into liquid sampler coupled with ion chromatography (PPWD-PILS-IC), was used to measure the hourly precursor gases and water-soluble inorganic ions in ambient particles smaller than 2.5 µm in diameter (PM2.5) for investigating the thermodynamic equilibrium of aerosols using the ISORROPIA-II thermodynamic equilibrium model. The 24-h average PPWD-PILS-IC data showed very good agreement with the daily data of the manual 5 L/min porous-metal denuder sampler with R2 ranging from 0.88 to 0.98 for inorganic ions (NH4+, Na+, K+, NO3−, SO42−, and Cl−) and 0.89 to 0.98 for precursor gases (NH3, HNO3, HONO, and SO2) and slopes ranging from 0.94 to 1.17 for ions and 0.87 to 0.95 for gases, respectively. In addition, the predicted ISORROPIA-II results were in good agreement with the hourly observed data of the PPWD-PILS-IC system for SO42− (R2 = 0.99 and slope = 1.0) and NH3 (R2 = 0.97 and slope = 1.02). The correlation of the predicted results and observed data was further improved for NH4+ and NO3− with the slope increasing from 0.90 to 0.96 and 0.95 to 1.09, respectively when the HNO2 and NO2− were included in the total nitrate concentration (TN = [NO3−] + [HNO3] + [HONO] + [NO2−]). The predicted HNO3 data were comparable to the sum of the observed [HNO3] and [HONO] indicating that HONO played an important role in the thermodynamic equilibrium of ambient PM2.5 aerosols but has not been considered in the ISORROPIA-II thermodynamic equilibrium model.