Several studies have reported an association between residential surrounding particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅) and coronary heart disease (CHD). However, the underlying biological mechanism remains unclear. To fill this research gap, this study enrolled a residentially stable sample of 942 patients with CHD and 1723 controls. PM₂.₅ concentration was obtained from satellite-based annual global PM₂.₅ estimates for the period 1998–2019. MicroRNA microarray and pathway analysis of target genes was performed to elucidate the potential biological mechanism by which PM₂.₅ increases CHD risk. The results showed that individuals exposed to high PM₂.₅ concentrations had higher risks of CHD than those exposed to low PM₂.₅ concentrations (odds ratio = 1.22, 95% confidence interval: 1.00, 1.47 per 10 μg/m³ increase in PM₂.₅). Systolic blood pressure mediated 6.6% of the association between PM₂.₅ and CHD. PM₂.₅ and miR-4726-5p had an interaction effect on CHD development. Bioinformatic analysis demonstrated that miR-4726-5p may affect the occurrence of CHD by regulating the function of RhoA. Therefore, individuals in areas with high PM₂.₅ exposure and relative miR-4726-5p expression have a higher risk of CHD than their counterparts because of the interaction effect of PM₂.₅ and miR-4726-5p on blood pressure.

PM₂.₅ (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM₂.₅ distribution is very helpful for PM₂.₅ pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM₂.₅ estimation model in China. Hourly cross-validation results indicated that estimated PM₂.₅ values were consistent with the site observation values, with an R² range of 0.82–0.91 and root mean square error (RMSE) of 8.79–14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM₂.₅ showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00–11:00, and then began to decline. High-resolution PM₂.₅ concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi ‘an, Wuhan, and Chengdu. Our model can also capture the direction of PM₂.₅, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM₂.₅ pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM₂.₅ concentrations typically occur near cities.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM₂.₅) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM₂.₅ were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM₂.₅ were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Ambient particles with aerodynamic diameter <0.1 μm (PM₀.₁) have been suggested to have significant health impact. However, studies on the association between long-term PM₀.₁ exposure and human blood lipid metabolism are still limited. This study was aimed to evaluate such association based on multiple lipid biomarkers and dyslipidemia indicators. We matched the 2006–2009 average PM₀.₁ concentration simulated using the neural-network model following the WRF-Chem model with the clinical and questionnaire data of 15,477 adults randomly recruited from 33 communities in Northeast China in 2009. After controlling for social demographic and behavior confounders, we assessed the association of PM₀.₁ concentration with multiple lipid biomarkers and dyslipidemia indicators using generalized linear mixed-effect models. Effect modification by various social demographic and behavior factors was examined. We found that each interquartile range increase in PM₀.₁ concentration was associated with a 5.75 (95% Confidence interval, 3.24–8.25) mg/dl and a 6.05 (2.85–9.25) mg/dl increase in the serum level of total cholesterol and LDL-C, respectively. This increment was also associated with an odds ratio of 1.25 (1.10–1.42) for overall dyslipidemias, 1.41 (1.16, 1.73) for hypercholesterolemia, and 1.90 (1.39, 2.61) for hyperbetalipoproteinemia. Additionally, we found generally greater effect estimates among the younger participants and those with lower income or with certain behaviors such as high-fat diet. The deleterious effect of long-term PM₀.₁ exposure on lipid metabolism may make it an important toxic chemical to be targeted by future preventive strategies.

Environmentally persistent free radicals (EPFRs) have aroused widespread concern due to their potential adverse health effects. Research on EPFRs in road dust is still very limited. In this study, 86 road dust samples were collected using vacuum sampling in a rapidly developing city in central China. The pollution characterization and health risk of EPFRs in the urban road dust were then systematically analyzed. The results showed the average concentrations of EPFRs in urban road dust and fraction of particle with aerodynamic diameters lower than 10 μm (PM₁₀) were 2.24 × 10¹⁷ to 3.72 × 10¹⁹ spins·g⁻¹ and 6.02 × 10¹⁷ to 1.41 × 10²⁰ spins g⁻¹, respectively. The concentrations of EPFRs in dust from expressways, arterial roads, and secondary trunk roads were significantly higher than those found in the remaining road types. The g-factors of 2.0032–2.0039 indicated that the EPFRs have consisted of oxygen-centered and carbon-centered radicals or carbon-centered radicals with nearby oxygen or halogen atoms. Moreover, three decay patterns of EPFRs were observed: a fast decay followed by a slow decay, a single slow decay, and the slowest decay. In addition, a comparative evaluation was made for probabilistic risk assessments of exposure to the EPFRs in road dust and the PM₁₀ fraction. Compared with road dust, the probability of the number of equivalent cigarettes to exceed the 100 and 200 cigarettes for inhaling EPFRs in the PM₁₀ fraction increased by 27.0% and 25.0%, respectively. The simulation results showed the PM₁₀ fraction were primarily deposited in the upper respiratory tract regions (57.1%) and pulmonary regions (28.8%). The findings of this study suggest a potential risk of EPFRs in inhalable particles and provide a new insight for further exploration of the EPFRs in fine particles of road dust.

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM₂.₅) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM₂.₅ samples, all of which are the important components of PM₂.₅. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM₂.₅ concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM₂.₅ concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM₂.₅ and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM₂.₅, offering a new perspective for atmospheric ecology and pollution control.

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM₂.₅ (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM₂.₅ using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM₂.₅ personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM₂.₅ concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM₂.₅ during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM₂.₅ of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM₂.₅, which vary by season.

The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 μm) than in coarse PM (aerodynamics between 2.1 and 10 μm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 μm and 2.1–3.3 μm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10⁻⁵). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 μm particle size segment have a greater influence on the uncertainty of probabilistic risk.

Children’s respiratory health are particularly vulnerable to outdoor air pollution, but evidence is lacking on the very acute effects of air pollution on the risk of acute upper respiratory infections (AURI) and acute lower respiratory infections (ALRI) in children. This study aimed to evaluate the risk of cause-specific AURI and ALRI, in children within 24 h of exposure to air pollution. We obtained data on emergency cases, including 11,091 AURI cases (acute pharyngitis, acute tonsillitis, acute obstructive laryngitis and epiglottitis, and unspecified acute upper respiratory infections) and 11,401 ALRI cases (pneumonia, acute bronchitis, acute bronchiolitis, unspecified acute lower respiratory infection) in Brisbane, Australia, 2013–2015. A time-stratified case-crossover analysis was used to examine the hourly association of AURI and ALRI with high concentration (95th percentile) of four air pollutants (particulate matters with aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm (PM₂.₅), ozone (O₃), nitrogen dioxide (NO₂)). We observed increased risk of acute tonsillitis associated with PM₂.₅ within 13–24 h (odds ratio (OR), 1.45; 95% confidence interval [CI], 1.02–2.06) and increased risk of unspecified acute upper respiratory infections related to O₃ within 2–6 h (OR, 1.38, 95%CI, 1.12–1.70), NO₂ within 1 h (OR, 1.19; 95%CI, 1.01–1.40), and PM₂.₅ within 7–12 h (OR, 1.21; 95%CI, 1.02–1.43). Cold season and nigh-time air pollution has greater effects on AURI, whereas greater risk of ALRI was seen in warm season and daytime. Our findings suggest exposures to particulate and gaseous air pollution may transiently increase risk of AURI and ALRI in children within 24 h. Prevention measures aimed at protecting children’s respiratory health should consider the very acute effects of air pollution.

Biomass burning tracers have been widely used to identify biomass burning types, but such tools can sometimes cause large uncertainties in the source attribution studies of PM₂.₅ (particles with an aerodynamic diameter of smaller than 2.5 μm). To quantify the relative importance of the major biomass burning tracers in PM₂.₅ released from biofuels combusted in the North China Plain, combustion experiments under the smoldering and flaming combustion conditions were conducted using nine types of typical household biofuels including two types of agricultural wastes, five types of hardwoods, one softwood, and one mixed wood briquette. PM₂.₅ samples were collected from the combustion experiments and source profiles of PM₂.₅ were thus determined for various biofuels under the two different combustion conditions. Carbonaceous species including organic carbon (OC) and elemental carbon (EC) were the major chemical components of the PM₂.₅ released from combustion of all the tested biofuels, with mass fractions of 37–45% and 4–7% under the smoldering condition and 11–25% and 7–29% under the flaming condition, respectively. Higher mass fractions of water-soluble inorganic ions (WSIIs, e.g., K⁺ and Cl⁻) in PM₂.₅ were observed under the flaming than smoldering combustion condition, while anhydrosugars (levoglucosan (LG) and mannosan (MN)) presented in an opposite pattern. The average LG/MN ratio in PM₂.₅ changed significantly with biofuel type (20–55 for agricultural wastes, 10–22 for hardwoods (except elm) and 3–6 for softwood), but varied little with combustion condition. In contrast, the K⁺/LG ratio in PM₂.₅ varied significantly between smoldering (<0.2) and flaming (>0.6) combustion conditions for all the biofuel types except softwood. Results from this study suggested that the ratio LG/MN was the best tracer for identifying the biofuel types and the ratio K⁺/LG is suitable for identifying the combustion conditions in this region.