Individual PM2 Research Articles

The association between cumulative exposure to PM2.5 and DNA methylation measured using methyl-capture sequencing among COPD patients

BackgroundParticulate matter with a diameter of < 2.5 μm (PM2.5) influences gene regulation via DNA methylation; however, its precise mechanism of action remains unclear. Thus, this study aimed to examine the connection between personal PM2.5 exposure and DNA methylation in CpG islands as well as explore the associated gene pathways.MethodsA total of 95 male patients with chronic obstructive pulmonary disease (COPD) were enrolled in this study. PM2.5 concentrations were measured for 12 months, with individual exposure recorded for 24 h every 3 months. Mean indoor and estimated individual PM2.5 exposure levels were calculated for short-term (7 days), mid-term (35 days), and long-term (90 days). DNA methylation analysis was performed on the blood samples, which, after PCR amplification and hybridization, were finally sequenced using an Illumina NovaSeq 6000 system. Correlation between PM2.5 exposure and CpG methylation sites was confirmed via a mixed-effects model. Functional enrichment analysis was performed on unique CpG methylation sites associated with PM2.5 exposure to identify the relevant biological functions or pathways.ResultsThe number of CpG sites showing differential methylation was 36, 381, and 182 for the short-, mid-, and long-term indoor models, respectively, and 3, 98, and 28 for the short-, mid-, and long-term estimated exposure models, respectively. The representative genes were TMTC2 (p = 1.63 × 10-3, R2 = 0.656), GLRX3 (p = 1.46 × 10-3, R2 = 0.623), DCAF15 (p = 2.43 × 10-4, R2 = 0.623), CNOT6L (p = 1.46 × 10-4, R2 = 0.609), BSN (p = 2.21 × 10-5, R2 = 0.606), and SENP6 (p = 1.59 × 10-4, R2 = 0.604). Functional enrichment analysis demonstrated that the related genes were mostly associated with pathways related to synaptic transmission in neurodegenerative diseases and cancer.ConclusionA significant association was observed between PM2.5 exposure and DNA methylation upon short-term exposure, and the extent of DNA methylation was the highest upon mid-term exposure. Additionally, various pathways related to neurodegenerative diseases and cancer were associated with patients with COPD.ClinicalTrials.gov identifierNCT04878367.

Air pollutants and the risk of incident hepatobiliary diseases: A large prospective cohort study in the UK Biobank

The association between air pollutants and hepatobiliary pancreatic diseases remains inconclusive. This study analyzed up to 247,091 participants of White European ancestry (aged 37 to 73 years at recruitment) from the UK Biobank, a large-scale prospective cohort with open access. An air pollution score was utilized to assess the combined effect of PM2.5, PM2.5–10, PM10, NO2, and NOX on total hepatobiliary pancreatic diseases, liver diseases, cholecyst diseases, and pancreatic diseases. Cox proportional hazard models were employed to evaluate the relationships between air pollutants and the incidence of these diseases. Restricted cubic spline regressions were used to examine the dose-response association between air pollutants and the risk of hepatobiliary pancreatic diseases. We identified 4865 cases of total hepatobiliary pancreatic diseases, over a median follow-up of 10.86 years. The air pollution scores were moderately associated with increased liver disease risk (HR = 1.009, 95 % CI: 1.004, 1.014), but not with cholecyst and pancreatic diseases. Among the individual air pollutants, PM2.5 (HR = 1.069, 95 % CI: 1.025, 1.115) and PM10 (HR = 1.036, 95 % CI: 1.011, 1.061) significantly increased liver disease risk. Males showed a higher risk of liver diseases with PM2.5 (HR = 1.075, 95 % CI: 1.015, 1.139). Additionally, individuals with overweight (HR = 1.125, 95 % CI: 1.052, 1.203), age ≥ 60 and ≤73 (HR = 1.098, 95 % CI: 1.028, 1.172), and alcohol intake ≥ 14 unit/week (HR = 1.078, 95 % CI: 1.006, 1.155) had a higher risk of developing liver diseases at high expose to PM2.5. This study suggests that prolonged exposure to ambient air pollutants may elevate the risk of liver diseases.

Associations of insulin sensitivity and immune inflammatory responses with child blood lead (Pb) and PM2.5 exposure at an e-waste recycling area during the COVID-19 lockdown.

Fine particular matter (PM2.5) and lead (Pb) exposure can induce insulin resistance, elevating the likelihood of diabetes onset. Nonetheless, the underlying mechanism remains ambiguous. Consequently, we assessed the association of PM2.5 and Pb exposure with insulin resistance and inflammation biomarkers in children. A total of 235 children aged 3-7years in a kindergarten in e-waste recycling areas were enrolled before and during the Corona Virus Disease 2019 (COVID-19) lockdown. Daily PM2.5 data was collected and used to calculate the individual PM2.5 daily exposure dose (DED-PM2.5). Concentrations of whole blood Pb, fasting blood glucose, serum insulin, and high mobility group box 1 (HMGB1) in serum were measured. Compared with that before COVID-19, the COVID-19 lockdown group had lower DED-PM2.5 and blood Pb, higher serum HMGB1, and lower blood glucose and homeostasis model assessment of insulin resistance (HOMA-IR) index. Decreased DED-PM2.5 and blood Pb levels were linked to decreased levels of fasting blood glucose and increased serum HMGB1 in all children. Increased serum HMGB1 levels were linked to reduced levels of blood glucose and HOMA-IR. Due to the implementation of COVID-19 prevention and control measures, e-waste dismantling activities and exposure levels of PM2.5 and Pb declined, which probably reduced the association of PM2.5 and Pb on insulin sensitivity and diabetes risk, but a high level of risk of chronic low-grade inflammation remained. Our findings add new evidence for the associations among PM2.5 and Pb exposure, systemic inflammation and insulin resistance, which could be a possible explanation for diabetes related to environmental exposure.

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Abstract. Observation-based data of primary and secondary organic carbon in ambient particulate matter (PM) are essential for model evaluation, climate and air quality research, health effect assessments, and mitigation policy development. Since there are no direct measurement tools available to quantify primary organic (POC) and secondary organic carbon (SOC) as separate quantities, their estimation relies on inference approaches using relevant measurable PM constituents. In this study, we measured hourly carbonaceous components and major ions in PM2.5 for a year and a half in suburban Hong Kong from July 2020 to December 2021. We differentiated POC and SOC using a novel Bayesian inference approach. The hourly POC and SOC data allowed us to examine temporal characteristics varying from diurnal and weekly patterns to seasonal variations, as well as their evolution characteristics during individual PM2.5 episodes. A total of 65 city-wide PM2.5 episodes were identified throughout the entire study period, with SOC contributions during individual episodes varying from 10 % to 66 %. In summertime typhoon episodes, elevated SOC levels were observed during daytime hours, and high temperature and NOx levels were identified as significant factors contributing to episodic SOC formation. Winter haze episodes exhibited high SOC levels, likely due to persistent influences from regional transport originating from the northern region to the sampling site. Enhanced SOC formation was observed with increase in the nocturnal NO3 radical (indicated by the surrogate quantity of [NO2][O3]) and under conditions characterized by high water content and strong acidity. These results suggest that both NO3 chemistry and acid-catalyzed aqueous-phase reactions likely make notable contributions to SOC formation during winter haze episodes. The methodology employed in this study for estimating POC and SOC provides practical guidance for other locations with similar monitoring capabilities in place. The availability of hourly POC and SOC data is invaluable for evaluating and improving atmospheric models, as well as understanding the evolution processes of PM pollution episodes. This, in turn, leads to more accurate model predictions and a better understanding of the contributing sources and processes.

Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation

BackgroundThe individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear. MethodA multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively. ResultPM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP. ConclusionOur study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

Unpacking the factors contributing to changes in PM2.5-associated mortality in China from 2013 to 2019

From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13–2.88) to 1.94 (1.62–2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.

Epigenome-wide association study on ambient PM2.5 exposure in Han Chinese, the NSPT study

Ambient PM2.5 exposure has been recognized as a major health risk and related to aging, cardiovascular, respiratory and neurologic diseases, and cancer. However, underlying mechanism of epigenetic alteration and regulated pathways still remained unclear. The study on methylome effect of PM2.5 exposure was quite limited in Chinese population, and cohort-based study was absent. The study included blood-derived DNA methylation for 3365 Chinese participants from the NSPT cohort. We estimated individual PM2.5 exposure level of short-medium-, medium- and long-term, based on a validated prediction model. We preformed epigenome-wide association studies to estimate the links between PM2.5 exposure and DNA methylation change, as well as stratification and sensitive analysis to examined the robustness of the association models. A systematic review was conducted to obtain the previously published CpGs and examined for replication. We also conducted comparison on the DNA methylation variation corresponding to different time windows. We further conducted gene function analysis and pathway enrichment analysis to reveal related biological response. We identified a total of 177 CpGs and 107 DMRs associated with short-medium-term PM2.5 exposure, at a strict genome-wide significance (P < 5 × 10−8). The effect sizes on most CpGs tended to cease with the exposure of extended time scale. Associated markers and aligned genes were related to aging, immunity, inflammation and carcinogenesis. Enriched pathways were mostly involved in cell cycle and cell division, signal transduction, inflammatory pathway. Our study is the first EWAS on PM2.5 exposure conducted in large-scale Han Chinese cohort and identified associated DNA methylation change on CpGs and regions, as well as related gene functions and pathways.

Maternal PM2.5 exposure is associated with preterm birth and gestational diabetes mellitus, and mitochondrial OXPHOS dysfunction in cord blood

Maternal exposure to fine particulate matter (PM2.5) is associated with adverse pregnancy and neonatal health outcomes. To explore the mechanism, we performed mRNA sequencing of neonatal cord blood. From an ongoing prospective cohort, Air Pollution on Pregnancy Outcome (APPO) study, 454 pregnant women from six centers between January 2021 and June 2022 were recruited. Individual PM2.5 exposure was calculated using a time-weighted average model. In the APPO study, age-matched cord blood samples from the High PM2.5 (˃15 ug/m3; n = 10) and Low PM2.5 (≤ 15 ug/m3; n = 30) groups were randomly selected for mRNA sequencing. After selecting genes with differential expression in the two groups (p-value < 0.05 and log2 fold change > 1.5), pathway enrichment analysis was performed, and the mitochondrial pathway was analyzed using MitoCarta3.0. The risk of preterm birth (PTB) increased with every 5 µg/m3 increase of PM2.5 in the second trimester (odds ratio 1.391, p = 0.019) after adjusting for confounding variables. The risk of gestational diabetes mellitus (GDM) increased in the second (odds ratio 1.238, p = 0.041) and third trimester (odds ratio 1.290, p = 0.029), and entire pregnancy (odds ratio 1.295, p = 0.029). The mRNA-sequencing of cord blood showed that genes related to mitochondrial activity (FAM210B, KRT1, FOXO4, TRIM58, and FBXO7) and PTB-related genes (ADIPOR1, YBX1, OPTN, NFkB1, HBG2) were upregulated in the High PM2.5 group. In addition, exposure to high PM2.5 affected mitochondrial oxidative phosphorylation (OXPHOS) and proteins in the electron transport chain, a subunit of OXPHOS. These results suggest that exposure to high PM2.5 during pregnancy may increase the risk of PTB and GDM, and dysregulate PTB-related genes. Alterations in mitochondrial OXPHOS by high PM2.5 exposure may occur not only in preterm infants but also in normal newborns. Further studies with larger sample sizes are required.

Quantifying Individual PM2.5 Exposure with Human Mobility Inferred from Mobile Phone Data

Treatment of air pollution and health impacts are both crucial components of long-term sustainability. Measuring individual exposure to air pollution is significant to evaluating public health risks. In this paper, we introduce a big data analytics framework to quantify individual PM2.5 exposure by combining residents’ mobility traces and PM2.5 concentration at a 1-km grid level. Diverging from traditional approaches reliant on population data, our methodology can accurately estimate the hourly PM2.5 exposure at the individual level. Taking Shanghai as an example, we model one million anonymous users’ mobility behavior based on 60 billion Call Detail Records (CDRs) data. By integrating users’ stay locations and high-resolution PM2.5 concentration, we quantify individual PM2.5 exposure and find that the average PM2.5 exposure of residences in Shanghai is 60.37 ug·h·m−3 during the studied period. Further analysis reveals the unbalance of the spatiotemporal distribution of PM2.5 exposure in Shanghai. Our PM2.5 exposure estimation method provides a reliable evaluation of environmental hazards and public health predicaments confronted by residents, facilitating the formulation of scientific policies for environmental control, and thus advancing the realization of sustainable development.

Personal exposure of PM2.5 and metabolic syndrome markers of pregnant women in South Korea: APPO study

We examined the association between exposure to PM2.5, focused on individual exposure level, and metabolic dysfunction during pregnancy. APPO study (Air Pollution on Pregnancy Outcome) was a prospective, multicenter, observational cohort study conducted from January 2021 to March 2023. Individual PM2.5 concentrations were calculated using a time-weighted average model. Metabolic dysfunction during pregnancy was assessed based on a modified definition of metabolic syndrome and its components, accounting for pregnancy-specific criteria. Exposure to PM2.5 during pregnancy was associated with worsened metabolic parameters especially glucose metabolism. In comparison to participants exposed to the low PM2.5 group, those exposed to high PM2.5 levels exhibited increased odds of gestational diabetes mellitus (GDM) after adjusting for confounding variables in different adjusted models. Specifically, in model 1, the adjusted odds ratio (aOR) was 3.117 with a 95% confidence interval (CI) of 1.234–7.870; in model 2, the aOR was 3.855 with a 95% CI of 1.255–11.844; in model 3, the aOR was 3.404 with a 95% CI of 1.206–9.607; and in model 4, the aOR was 2.741 with a 95% CI of 0.712–10.547. Exposure to higher levels of PM2.5 during pregnancy was associated with a tendency to worsen metabolic dysfunction markers specifically in glucose homeostasis. Further research is needed to investigate the mechanisms underlying the effects of ambient PM2.5 on metabolic dysfunction during pregnancy.

Air quality disparities mapper: An open-source web application for environmental justice

Exposure to fine particulate matter (PM2.5) is linked to increased health risks and disproportionately affects minority and low-income communities. Current research lacks assessment of exposure disparities to PM2.5 components and effective communication of these disparities. We developed an interactive, web-based air pollution mapper, combining high-resolution predictions of PM2.5 components for 2010 across the contiguous United States with U.S. Census demographic data. The interface, hosted at https://disparitiesmapper.github.io/, allows users to visualize the relationship between variables, highlighting the racial, socioeconomic, and geographic disparities in exposure. By mapping individual PM2.5 components, it provides insights into specific sources driving PM2.5 concentrations, aiding in designing targeted reduction programs. This tool makes air quality data more accessible and informs policy outcomes.

Long-term effects of PM2.5 constituents on metabolic syndrome and mediation effects of serum uric acid

Exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) was associated with the risk for metabolic syndrome (MetS) in the general population, but the contributions of individual PM2.5 constituents to this association and the potential pathway between PM2.5 constituents and MetS risk are not well elaborated. This study aimed to investigate associations between PM2.5 constituents and MetS in general populations, relative importance of PM2.5 constituents to and mediation effects of serum uric acid (SUA) on those associations. The 48,148 participants from a provincially representative cohort established in southwest China were included. The 3-year average concentrations of PM2.5 and its constituents (nitrate [NO3−], sulfate [SO42−], ammonium [NH4+], organic matter [OM], and black carbon [BC]) were estimated using a series of machine-learning models. Multivariate logistic regression and weighted quantile sum regression were used to estimate effects of independent PM2.5 constituents on MetS and their contributions to the joint effect. Mediation analysis examined the potential mediation effects of SUA on the associations between PM2.5 constituents and MetS. Each interquartile range (IQR) increase in the concentration of PM2.5 constituents was all positively associated with the increased MetS odds, including SO42− (OR = 1.15 [1.11, 1.19]]), NO3− (OR = 1.12 [1.08, 1.16]), NH4+ (OR = 1.13 [1.09, 1.17]), OM (OR = 1.09 [1.06, 1.13]), and BC (OR = 1.09 [1.06, 1.13]). Their joint associations on MetS were mainly attributed to SO42− (weight=46.1%) and NH4+ (44.0%). The associations of PM2.5 constituents with abnormal MetS components were mainly attributed to NH4+ for elevated BP (51.6%) and reduced HDL-C (97.0%), SO42− for elevated FG (68.9%), NO3- for elevated TG (51.0%), and OM for elevated WC (63.0%). Percentages mediated by SUA for the associations of PM2.5, SO42−, NO3−, and BC with MetS were 13.6%, 13.1%, 10.6%, and 11.1%, respectively. Long-term exposure to PM2.5 constituents, mainly NH4+ and SO42−, was positively associated with MetS odds, partially mediated by SUA.

Gas–particle partitioning of organophosphate esters in indoor and outdoor air and its implications for individual exposure

The extensive utilization of organophosphate esters (OPEs) has resulted in their widespread presence in the environment, raising concerns about potential human health risks. In this study, 13 OPEs were analyzed in both gas and particle phases as well as in indoor and outdoor atmospheric environments. Moreover, human exposure to OPEs were investigated within a university environment, focusing on forehead contact and individual PM2.5 inhalation. The results showed similar distribution patterns of OPEs indoors and outdoors, although higher concentrations were found indoors. The average atmospheric concentration of ∑OPEs (combining particle and gaseous OPEs) was 1575 pg/m3 in the outdoor environment and 6574 pg/m3 ∑OPEs in the indoor microenvironments. The overwhelming majority of OPEs exhibit a pronounced propensity to adsorb onto PM2.5 particles. Notably, the concentration of OPEs on the forehead differed significantly from that in the atmospheric environment, whereas individual PM2.5 exposure was consistent with the concentration of indoor PM2.5. Intriguingly, some OPEs with high octanol–water partition coefficient (log Kow) were not detected in the environment but found on human foreheads. Gas-particle partitioning was predicted using the Harner–Bidleman and Li–Ma–Yang models and the results were in agreement with the monitoring data for approximately half of the OPE monomers. Correlations between OPEs exposure and gas-particle partitioning were found to be more significant for novel OPEs. No non-cancer risk to humans through individual exposure to OPEs was identified via forehead exposure or inhalation. The previously unreported relationship between individual exposure and the environmental occurrence of traditional and novel OPEs demonstrated in this study highlights the importance of evaluating the potential health risks associated with actual OPE exposure.

PM2.5-bound metals and blood metals are associated with pulmonary function and Th17/Treg imbalance: A panel study of asthmatic adults

Growing research has demonstrated that exposure to fine particulate matter (PM2.5) was associated with decreased pulmonary function and obvious inflammatory response. However, few pieces of research focus on the effects of PM2.5-bound metals on people with asthma. Here, we assessed whether PM2.5 and PM2.5-bound metals exposure could worsen pulmonary function in asthmatic patients and further elucidate the possible mechanisms. Thirty-four asthmatic patients were recruited to follow up for one year with eight visits in 2019–2020 in Taiyuan City, China. The index of pulmonary function was detected and blood and nasal epithelial lining fluid (ELF) samples were acquired for biomarkers measurement at each follow-up. Linear mixed-effect (LME) models were used to evaluate the relations between PM2.5, PM2.5-bound metals, and blood metals with lung function and biomarkers of Th17/Treg balance. The individual PM2.5 exposure concentration varied from 37 μg/m3 to 194 μg/m3 (mean: 59.63 μg/m3) in the present study. An interquartile range (IQR) increment of PM2.5 total mass was associated with a faster decline in maximal mid-expiratory flow (MMEF) and higher interleukin-23 (IL-23). PM2.5-bound metals [e.g. copper (Cu), nickel (Ni), manganese (Mn), titanium (Ti), and zinc (Zn)] were significantly associated with IL-23 (Cu: 5.1126%, 95% CI: 9.3708, 0.8544; Mn: 14.7212%, 95% CI: 27.926, 1.5164; Ni: 1.0269%, 95% CI: 2.0273, 0.0264; Ti: 16.7536%, 95% CI: 31.6203, 1.8869; Zn: 24.5806%, 95% CI: 46.609, 2.5522). Meanwhile, blood lead (Pb) and Cu were associated with significant declines of 0.382–3.895% in MMEF and maximum ventilatory volume (MVV). Blood Pb was associated with descending transforming growth factor β (TGF-β). In conclusion, exposure to PM2.5-bound metals and blood metals is a risk factor for decreased pulmonary function, especially in small airways. These alterations might be partially attributed to the imbalance of Th17/Treg.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places.

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

A panel study on the effect of atmospheric PM2.5 exposure on the gut microbiome in healthy elderly people aged 60-69 years old

Objective: To analyze the short-term effect of individual atmospheric PM2.5 exposure on the diversity, enterotype, and community structure of gut microbiome in healthy elderly people in Jinan, Shandong province. Methods: The present panel study recruited 76 healthy elderly people aged 60-69 years old in Dianliu Street, Lixia District, Jinan, Shandong Province, and followed them up five times from September 2018 to January 2019. The relevant information was collected by questionnaire, physical examination, precise monitoring of individual PM2.5 exposure, fecal sample collection and gut microbiome 16S rDNA sequencing. The Dirichlet multinomial mixtures (DMM) model was used to analyze the enterotype. Linear mixed effect model and generalized linear mixed effect model were used to analyze the effect of PM2.5 exposure on gut microbiome α diversity indices (Shannon, Simpson, Chao1, and ACE indices), enterotype and abundance of core species. Results: Each of the 76 subjects participated in at least two follow-up visits, resulting in a total of 352 person-visits. The age of 76 subjects was (65.0±2.8) years old with BMI (25.0±2.4) kg/m2. There were 38 males accounting for 50% of the subjects. People with an educational level of primary school or below accounted for 10.5% of the 76 subjects, and those with secondary school and junior college or above accounting for 71.1% and 18.4%. The individual PM2.5 exposure concentration of 76 subjects during the study period was (58.7±53.7) μg/m3. DMM model showed that the subjects could be divided into four enterotypes, which were mainly driven by Bacteroides, Faecalibacterium, Lachnospiraceae, Prevotellaceae, and Ruminococcaceae. Linear mixed effects model showed that different lag periods of PM2.5 exposure were significantly associated with a lower gut α diversity index (FDR<0.05 after correction). Further analysis showed that PM2.5 exposure was significantly associated with changes in the abundances of Firmicutes (Megamonas, Blautia, Streptococcus, etc.) and Bacteroidetes (Alistipes) (FDR<0.05 after correction). Conclusion: Short-term PM2.5 exposure is significantly associated with a decrease in gut microbiome diversity and changes in the abundance of several species of Firmicutes and Bacteroidetes in the elderly. It is necessary to further explore the underlying mechanisms between PM2.5 exposure and the gut microbiome, so as to provide a scientific basis for promoting the intestinal health of the elderly.

Exposure to ambient air pollution and elevated blood levels of gamma-glutamyl transferase in a large Austrian cohort

Gamma glutamyl transferase (GGT) is related to oxidative stress and an indicator for liver damage. We investigated the association between air pollution and GGT in a large Austrian cohort (N = 116,109) to better understand how air pollution affects human health.Data come from voluntary prevention visits that were routinely collected within the Vorarlberg Health Monitoring and Prevention Program (VHM&PP). Recruitment was ongoing from 1985 to 2005. Blood was drawn and GGT measured centralized in two laboratories. Land use regression models were applied to estimate individuals' exposure at their home address for particulate matter (PM) with a diameter of <2.5 μm (PM2.5), <10 μm (PM10), fraction between 10 μm and 2.5 μm (PMcoarse), as well as PM2.5 absorbance (PM2.5abs), NO2, NOx and eight components of PM. Linear regression models, adjusting for relevant individual and community-level confounders were calculated.The study population was 56 % female with a mean age of 42 years and mean GGT was 19.0 units. Individual PM2.5 and NO2 exposures were essentially below European limit values of 25 and 40 μg/m3, respectively, with means of 13.58 μg/m3 for PM2.5 and 19.93 μg/m3 for NO2. Positive associations were observed for PM2.5, PM10, PM2.5abs, NO2, NOx, and Cu, K, S in PM2.5 and PM10 fractions and Zn mainly in PM2.5 fraction. The strongest association per interquartile range observed was an increase of serum GGT concentration by 1.40 % (95 %-CI: 0.85 %; 1.95 %) per 45.7 ng/m3 S in PM2.5. Associations were robust to adjustments for other biomarkers, in two-pollutant models and the subset with a stable residential history.We found that long-term exposure to air pollution (PM2.5, PM10, PM2.5abs, NO2, NOx) as well as certain elements, were positively associated with baseline GGT levels. The elements associated suggest a role of traffic emissions, long range transport and wood burning.

A comprehensive SERS, SEM and EDX study of individual atmospheric PM2.5 particles in Chengdu, China

Characterization of atmospheric fine particulate matter (PM2.5) in large cities has important implications for the study of their sources and formation mechanisms, as well as in developing effective measures to control air pollution. Herein, we report a holistic physical and chemical characterization of PM2.5 by combining surface-enhanced Raman scattering (SERS) with scanning electron microscopy (SEM) and electron-induced X-ray spectroscopy (EDX). PM2.5 particles were collected in a suburban area of Chengdu, a large city in China with a population over 21 million. A special SERS chip composed of inverted hollow Au cone (IHAC) arrays was designed and fabricated to allow direct loading of PM2.5 particles. SERS and EDX were used to reveal the chemical composition, and particle morphologies were analyzed from SEM images. SERS data of atmospheric PM2.5 indicated qualitatively the presence of carbonaceous particulate matter, sulfate, nitrate, metal oxides and bioparticles. The EDX showed the presence of the elements C, N, O, Fe, Na, Mg, Al, Si, S, K, and Ca in the collected PM2.5. Morphology analysis showed that the particulates were mainly in the form of flocculent clusters, spherical, regular crystal shaped or irregularly shaped particles. Our chemical and physical analyses also revealed that the main sources of PM2.5 are automobile exhaust, secondary pollution caused by photochemical reactions in the air, dust, emission from nearby industrial exhaust, biological particles, other aggregated particles, and hygroscopic particles. SERS and SEM data collected during three different seasons showed that carbon-containing particles are the principal sources of PM2.5. Our study demonstrates that the SERS based technique, when combined with standard physicochemical characterization methods, is a powerful analytical tool to determine the sources of ambient PM2.5 pollution. Results obtained in this work may be valuable to the prevention and control of PM2.5 pollution in air.

The effect of ambient PM2.5 exposure on survival of lung cancer patients after lobectomy

Exposure to fine particulate matter (PM2.5) is linked to lung cancer incidence and mortality. However, the impact of PM2.5 exposure on lung cancer patients after lobectomy, which remains the primary treatment for early-stage lung cancer, is unknown. Therefore, we investigated the correlation between PM2.5 exposure and the survival of lung cancer patients after lobectomy. This study included 3,327 patients with lung cancer who underwent lobectomy procedures. We converted residential addresses into coordinates and estimated individual patients' daily PM2.5 and O3 exposure levels. A Cox multivariate regression model was used to analyze the specific monthly association between PM2.5 exposure and lung cancer survival. Every 10 μg/m3 increase in monthly PM2.5 concentration in the first and second months after lobectomy increased the risk of death (hazard ratio [HR]: 1.043, 95% confidence interval [CI]: 1.019–1.067 and HR: 1.036, 95% CI: 1.013–1.060, respectively). Non-smokers, younger patients, and patients with longer hospitalization durations had worse survival rates when exposed to greater concentrations of PM2.5. High postoperative PM2.5 exposure immediately after lobectomy reduced the survival of patients with lung cancer. Patients living in areas with high PM2.5 should be offered the opportunity to transfer to areas with better air quality after undergoing lobectomies, to prolong their survival times.

Long-term impact of the 2014 Hazelwood coal mine fire on emergency department presentations in Australia

BackgroundIn 2014, wildfires ignited a coal mine in Australia, burning for 6 weeks, releasing large amounts of fine particulate matter ≤2.5 μm in diameter (PM2.5). We investigated the association between individual PM2.5 exposure and emergency department presentations (EDPs) within 5 years post-fire. MethodsSurvey and exposure data for 2725 residents from an exposed and unexposed town were linked with ED administrative data from 2009 to 2019. The association between individual PM2.5 and EDPs was assessed using recurrent survival analysis. ResultsA 10 μg/m3 increase in PM2.5 was associated with a 10% increase in respiratory EDPs (HR = 1.10; 95%CI:1.00–1.22) over 5 years post-fire. Increased risks of EDPs for ischaemic heart disease (HR = 1.39; 95%CI:1.12–1.73), atherothrombotic disease (HR = 1.27; 95%CI:1.08–1.50), and cardiovascular disease (HR = 1.10, 95%CI:0.99–1.22) were evident within 2.5 years. ConclusionPM2.5 exposure from a 6-week mine fire increased the 5-year risk of respiratory conditions. An increased risk of CVD within 2.5 years post-fire subsided after this time.