Fine Particulate Matter Air Research Articles

57 Particles in Outdoor Air: are they all Equal?

Abstract Statement from the Committee on the Medical Effects of Air Pollutants (COMEAP) on the differential toxicity of particulate matter according to source or constituents Particulate matter in outdoor air comes from a wide range of different sources and contains many different chemical components. Understanding which of these sources and components are particularly harmful would help to focus policies and interventions to reduce impacts on health. COMEAP recently (July 2022) published an updated statement on this topic, drawing on recent reviews of the available evidence. It noted that particulate matter, and constituents of particulate matter, associated with combustion and road traffic have received the most research attention. However, there was insufficient coherence in the epidemiological evidence to conclude that these sources of particulate matter play a significantly greater role in the health effects of particulate air pollution than other sources or constituents. A comprehensive review of the evidence reveals that toxicity has been attributed to a wide variety of constituents and sources, possibly acting by a number of different mechanisms. However, the complexity of sources, sizes and chemical composition of particulate matter makes it extremely difficult to identify which characteristics of particles might make them less harmful or more toxic than others. Future research assessing the influence of chemical composition of particulate matter should offer greater insights into toxicity. But, at present, PM2.5 (the mass concentration of fine particulate matter in air) remains the most suitable metric for evaluating health impacts and regulating particulate air pollution.

Air Pollution Exposure Is Associated With Cognitive Performance: Results From the Einstein Aging Study

Prior research has established that those exposed to higher levels of fine particulate matter (PM1, PM2.5) air pollution have higher levels of accumulated amyloid-beta (Aβ) and tau in frontal cortex at autopsy, higher error rates on cognitive function assessments, and lower scores on memory and both verbal and non-verbal intelligence assessments. We explored the relationship between regional air quality monitoring measures (EPA AirData) and baseline cognitive performance of 312 older adults, from the Einstein Aging Study (EAS, NIA P01AG003949). Participants completed neuropsychological assessments at baseline and each followup wave (i.e., delayed free recall and total recall; Trails A & B, Digit Symbol substitution task (DSST), MoCA). For each participant, based on their zipcode, we computed average PM2.5 exposure at various exposure windows (1-15, 30-60, 60-90, 90-120 days prior to baseline). Adjusting for age, education, and gender across all models, mean of daily particulate matter exposure at various exposure windows (30-60, 60-90 days) was significantly related to performance on the MoCA and Trails A & B, in expected directions (i.e., higher pollution, worse cognitive performance - more error, slower speed). Models with memory performance as the outcome indicated that only distant time horizons were related to memory performance (i.e., 60-90, 90-120 days prior). These findings suggest that particulate matter air pollution likely affects different cognitive domains at different timescales. This methodology cannot address contributions from indoor air quality and mobility - an exposure misclassification likely resulting in significant biases towards the null in the estimation of the effects of air pollution.

Magnetism and Grain-Size Distribution of Particles Deposited on the Surface of Urban Trees in Lanzhou City, Northwestern China.

Studies on the variation in the particulate matter (PM) content, Saturation Isothermal Remanent Magnetization (SIRM), and particle grain-size distribution at a high spatial resolution are helpful in evaluating the important role of urban forests in PM removal. In this study, the trees located in dense urban forests (T0) retained more PM than trees located in open spaces (T1–T4); the SIRM and PM weight of T0 were 1.54–2.53 and 1.04–1.47 times more than those of T1–T4, respectively. In addition, the SIRM and PM weight decreased with increasing distance to the road, suggesting that distance from pollution sources plays a key role in reducing the air concentration of PM. The different grain-size components were determined from frequency curve plots using a laser particle-size analyzer. A unimodal spectrum with a major peak of approximately 20 μm and a minor peak between 0.1 and 1 μm was observed, indicating that a large proportion of fine air PM was retained by the needles of the study trees. Additionally, more <2.5 μm size fraction particles were observed at the sampling site near the traffic source but, compared to a tree in a row of trees, the percentage of the >10 μm size fraction for the tree in the dense urban forest was higher, indicating that the particles deposited on the needle surface originating from traffic sources were finer than those from natural atmospheric dust. The exploration of the variation in the PM weight, SIRM, and grain size of the particles deposited on the needle surface facilitates monitoring the removal of PM by urban forests under different environmental conditions (e.g., in closed dense urban forests and in open roadside spaces), different distances to roads, and different sampling heights above the ground.

Th17-Immune Response in Patients With Membranous Nephropathy Is Associated With Thrombosis and Relapses.

Membranous nephropathy (MN) is a rare autoimmune kidney disease. Most autoimmune diseases are associated with a pro-inflammatory Th17-immune response, but little is known about immune dysregulation in MN. In China, MN was associated with exposure to fine air particulate matter (PM2.5) that could act as a danger signal and redirect immune response toward the Th2 or Th17 pathway. We aimed to analyze the cytokine profile of MN patients and to study the possible environmental factors involved in this immune reorientation, as well as the consequences on the prognosis of the disease. In this prospective study, 59 MN patients filled a comprehensive lifestyle questionnaire. Peripheral blood cells from MN patients were stimulated in vitro to measure the cytokines produced in supernatant. Cytokine profiles of MN patients were compared to 28 healthy donors and analyzed regarding individual PM2.5 exposure. Compared to healthy donors, MN patients had higher serum levels of Th17 and Th2 cytokines IL-17A (62 pg/ml [IQR, 16–160] versus 31 [IQR, 13–51], P=0.035), IL-6 (66767 pg/ml [IQR, 36860–120978] versus 27979 [IQR, 18672–51499], P=0.001), and IL-4 (12 pg/ml [IQR, 0–33] versus 0 pg/ml [IQR, 0–0], P=0.0003), respectively, as well as a deficiency of Th1 and regulatory T cell cytokines IFN-γ (5320 pg/ml [IQR, 501–14325] versus 18037 [IQR, 4889–31329], P=0.0005) and IL-10 (778 pg/ml [IQR, 340–1247] versus 1102 [IQR, 737–1652], P=0.04), respectively. MN patients with high IL-17A levels lived in areas highly exposed to PM2.5: 51 μg/m3 versus 31 μg/m3 for patients with low IL-17A levels (P=0.002) while the World Health Organization recommends an exposition below 10 μg/m3. MN patients with Th17-mediated inflammation had more venous thromboembolic events (P=0.03) and relapsed more often (P=0.0006). Rituximab treatment induced Th1 and regulatory T cell cytokines but did not impact Th17 cytokines. MN patients with Th17-mediated inflammation which appears to be related to an urban environment have worse prognosis. Alternative strategies targeting dysregulated cytokine balance could be considered for these patients at high risk of relapse.

Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Clinical impact of fine particulate matter (PM2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837–0.853]) than existing traditional regression models using CHA2DS2-VASc (0.654 [0.646–0.661]), CHADS2 (0.652 [0.646–0.657]), or HATCH (0.669 [0.661–0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM2.5 as a highly important variable, we applied PM2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM2.5 (c-indices: boosted ensemble ML, 0.954 [0.949–0.959]; PM-CHA2DS2-VASc, 0.859 [0.848–0.870]; PM-CHADS2, 0.823 [0.810–0.836]; or PM-HATCH score, 0.849 [0.837–0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM2.5 data was found to predict incident AF better than models without PM2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.

Prenatal air pollution influences neurodevelopment and behavior in autism spectrum disorder by modulating mitochondrial physiology

We investigate the role of the mitochondrion, an organelle highly sensitive to environmental agents, in the influence of prenatal air pollution exposure on neurodevelopment and behavior in 96 children with autism spectrum disorder (ASD) [45 with neurodevelopmental regression (NDR); 76% Male; mean (SD) age 10 y 9 m (3 y 9 m)]. Mitochondrial function was assessed using the Seahorse XFe96 in fresh peripheral blood mononuclear cells. Second and third trimester average and maximal daily exposure to fine air particulate matter of diameter ≤2.5 µm (PM2.5) was obtained from the Environmental Protection Agency’s Air Quality System. Neurodevelopment was measured using the Vineland Adaptive Behavior Scale 2nd edition and behavior was assessed using the Aberrant Behavior Checklist and Social Responsiveness Scale. Prenatal PM2.5 exposure influenced mitochondrial respiration during childhood, but this relationship was different for those with (r = 0.25–0.40) and without (r = −0.07 to −0.19) NDR. Mediation analysis found that mitochondrial respiration linked to energy production accounted for 25% (SD = 2%) and 10% (SD = 2%) of the effect of average prenatal PM2.5 exposure on neurodevelopment and behavioral symptoms, respectively. Structural equation models estimated that PM2.5 and mitochondrial respiration accounted for 34% (SD = 4%) and 36% (SD = 3%) of the effect on neurodevelopment, respectively, and that behavior was indirectly influenced by mitochondrial respiration through neurodevelopment but directly influenced by prenatal PM2.5. Our results suggest that prenatal exposure to PM2.5 disrupts neurodevelopment and behavior through complex mechanisms, including long-term changes in mitochondrial respiration and that patterns of early development need to be considered when studying the influence of environmental agents on neurodevelopmental outcomes.

Temporal trend of arsenic in outdoor air PM2.5 in Wuhan, China, in 2015-2017 and the personal inhalation of PM-bound arsenic: implications for human exposure.

Arsenic in fine air particulate matter (PM2.5) has been identified as an important factor responsible for the morbidity of lung cancer, which has increased sharply in many regions of China. Some reports in China have shown that arsenic in the air exceeds the ambient air quality standard value, while long-term airborne arsenic concentrations in central China and human exposure via inhalation of PM-bound arsenic (inhalable airborne PM) have not been well characterized. In this study, 579 outdoor air PM2.5 samples from Wuhan, a typical city in central China, were collected from 2015 to 2017, and arsenic was measured by inductively coupled plasma-mass spectrometry. Personal exposure to PM-bound arsenic via inhalation and urinary arsenic concentration were also measured. The concentrations of arsenic in PM2.5 were in the range of 0.42-61.6ng/m3 (mean 8.48ng/m3). The average concentration of arsenic in 2015 (10.7ng/m3) was higher than that in 2016 (6.81ng/m3) and 2017 (8.18ng/m3), exceeded the standard value. The arsenic concentrations in spring and winter were higher than those in summer and autumn. No significant differences (p > 0.05) were found among different sites. The daily intake of arsenic inhalation based on PM10 samples collected by personal samplers (median, 10.8ng/m3) was estimated. Urban residents inhaled higher levels of PM-bound arsenic than rural residents. Daily intake of arsenic via inhalation accounted for a negligible part (< 1%) of the total daily intake of arsenic (calculated based on excreted urinary arsenic); however, potential associations between the adverse effects (e.g., lung adenocarcinoma) and inhaled PM-bound arsenic require more attention, particularly for those who experience in long-term exposure. This study is the first report of a 3-year temporal trend of airbornePM2.5-bound arsenic in central China.

A combined microextraction procedure for isolation of polycyclic aromatic hydrocarbons in ambient fine air particulate matter with determination by gas chromatography-tandem mass spectrometry

A novel combined vortex-assisted dispersive liquid-liquid microextraction (VA-DLLME)–micro-solid-phase extraction (μ˗SPE) procedure for polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter of aerodynamic diameter of ≤ 2.5 µm (PM2.5), was developed. PM2.5 was collected on polytetrafluoroethylene (PTFE)-coated glass fiber filter, that was first processed by ultrasonicating in aqueous solution. Then, VA-DLLME, followed by µ-SPE, were applied to the solution using 1-octanol and graphene oxide as extractant and sorbent, respectively for extracting the PAHs. The PAHs were determined by gas chromatography–tandem mass spectrometry (GC–MS/MS). Parameters affecting the extraction efficiency including selection of the sorbent, duration of DLLME and µ-SPE, desorption solvent type, and ultrasonication-assisted desorption time were investigated. Under the optimum extraction conditions, the method exhibited linear ranges of 0.5–50 μg/L and 0.5–100 μg/L depending on the analytes. The limits of detection were in the range of 0.013 μg/L–0.135 μg/L. The developed method was applied to determine the PAHs in genuine PM2.5 samples collected on PTFE glass fiber filters. The concentration levels in the atmospheric PM2.5 were determined to be from below the limit of quantification to 0.135 ng/m3. The relative recoveries obtained from spiked concentrations at 1 μg/L and 5 μg/L were in the range 57˗88% with relative standard deviations <14%.

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender

ABSTRACT This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk factors of acute respiratory diseases were identified in Beijing, China. We conducted age and gender stratified deep neural network models in air pollution epidemiology. We ranked risk factors of respiratory diseases in stratified populations and conducted quantitative comparison. People ≥50 years were more sensitive to PM2.5 pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM10. Young women <50 years were more sensitive to general air pollutants such as SO2 and NO2 than <50 years young men. Meteorological factors such as wind speed and precipitation could promote the diffusion of fine particulate matter and general air pollutants (SO2, NO2, etc.), which could help to reduce the incidence of acute respiratory diseases. This study represents a quantitative analysis of environmental health risk factors identification related to acute respiratory diseases based on deep neural network method. The results of this study could help people to improve their awareness of acute respiratory diseases prevention.

Children's exposure to sized-fractioned particulate matter and black carbon in an urban environment

Fine air particulate matter (PM2.5) is currently one of the major environmental issues influencing people's health. The first step for assessing the health effects of inhaled PM2.5 is based on the evaluation of the human exposure levels. The main objective of this study is to quantify children's daily exposure to sized-fractioned PM2.5 and Black Carbon (BC) in an urban environment. Children from Lisbon metropolitan area carried portable monitoring equipment during three days and recorded the time spent in their activities and respective microenvironments (MEs). The average exposure to PM2.5 (19 μg/m3) was higher than that registered by the nearest fixed urban background station (11 μg/m3), evidencing the importance of assessing the personal daily exposure. The average exposure to PM1, PM0.5 and PM0.25 was 14 μg/m3, 11 μg/m3, and 7.7 μg/m3, respectively. Time-activity pattern records showed that children spent more than 80% of their time indoors, especially at home (55%) and in the classroom (22%), where they received 44% of the daily BC dose. Although commuting only accounted for 5.0% of the daily time, children inhaled 23% of their daily BC dose when travelling in rush hours to school. Time series analysis of the BC concentrations showed an average of 1.3 μg/m3, with high peak levels in underground parking lots (63 μg/m3), during charcoal grills (53 μg/m3), and when candles were burning (6.6 μg/m3). This work highlights the importance of urban planning to reduce children's exposure to traffic emissions, combined with awareness-raising actions for citizens concerning the impact of indoor sources.

Chemical Analysis of Air Particulate Matter Trapped by a Porous Material, Synthesized from Silica Fume and Sodium Alginate

This work shows the ability of a new porous material (SUNSPACE), obtained by industrial by-products, to sequestrate air particulate matter (PM). This activity allows introducing the azure chemistry approach, devoted to better link new remediation strategies and sustainability. In particular, SUNSPACE is synthesized from silica fume and sodium alginate; it can be shaped in a porous solid, and it looks promising for environmental application as nanoparticle sequestration. Studies to evaluate the sequestration capability of SUNSPACE are performed in different environments, with and without anthropogenic sources of PM. Solid SUNSPACE disc samples are used as passive samplers and exposed for one and two months, in vertical and horizontal positions, indoor, and outdoor. Total reflection X-ray fluorescence technique is employed to perform elemental chemical analysis of the entrapped PM. Two sample preparation strategies to evaluate the composition of PM are considered: sample sonication in Milli-Q water and total sample mineralization by microwave acid digestion. These two options are proposed to analyse different PM fractions: in particular, sonication allows removing the coarse PM, entrapped on external material surface pores; on the contrary, digestion can offer information on fine and ultrafine PM, trapped in internal pores. Results confirm the ability of the porous material to sequestrate air PM and the differences in the sample preparation, supported by elemental analysis, and show the difference in the coarse and fine air particulate matter composition. In summary, the new material results as very promising for applications requiring nanoparticle sequestration.

Analysis of the Temporal and Spatial Variation of PM2.5 in China Based on the LUR Model

The land use regression (LUR) model is one of the most important systematic methods to simulate the temporal and spatial differentiation of the atmospheric pollutant concentration. To explore the adaptability of the LUR model to the simulation of air pollutants at the national scale in China and the temporal and spatial variation characteristics of fine air particulate matter (PM2.5) in China in 2015 and its correlation with different geographical elements, we built a LUR model. The LUR model is based on a geographically weighted algorithm using PM2.5 data acquired from the national control monitoring site in 2015 as the dependent variable and applying factors such as the type of land use, altitude, population, road traffic, and meteorological elements as independent variables. Based on model regression mapping, we obtained the distributions of monthly and annual PM2.5 concentrations nationwide in 2015 and analyzed the temporal and spatial variation characteristics of PM2.5 concentrations using the Hu line as a reference line. The results indicate that introducing the geographically weighted algorithm can significantly reduce the residual Moran's Ⅰ of the LUR model, weaken the spatial autocorrelation of residuals, and improve the coefficient of determination R2, which is better to reveal the complex relationship between the spatial distribution and impact factors of PM2.5. Cropland, forest, grass and urban industrial and residential land, and meteorological elements and major roads noticeably impact the PM2.5 concentration. Different spatial distributions of different geographical elements have distinct effects on PM2.5. The PM2.5 shows distinct temporal and spatial differences on both sides of the Hu line. The PM2.5 concentration is relatively high in developed cities with a large population and high industrialization levels. The concentration of PM2.5 is higher in winter and gradually decreases in autumn, spring, and summer.

A model investigating environmental factors that play a role in female fecundity or birth rate.

ObjectiveOver 12% of women in the United States have reduced fertility and/or fecundity. Environmental factors, such as temperature, and socioeconomic factors have been implicated in reducing female fecundity. The purpose of this study is to investigate the effect of environmental factors coupled with socioeconomic factors on birth rate at the country-level. We use birth rate as a proxy for female fecundity. This will enable us to identify the most important factors affecting female fecundity.MethodsUsing country-specific data from 182 countries, we constructed a regression model of the effects of environmental and socioeconomic factors on birth rate at the country-level. Our model assesses the role of temperature, Gross Domestic Product (GDP) per capita, fine air particulate matter (PM 2.5), and prevalence of male and female Body Mass Index (BMI) > = 25 (age-standardized) on birth rate per country. Because many of these factors are inter-dependent, we include all possible two-way interaction terms to assess the role of individual factors and interactions between multiple factors in the model.ResultsIn the full regression model, we found that GDP per capita along with 5 interaction terms were significant after adjusting for multiple testing. Female BMI was only nominally significant. GDP per capita was independently associated with birth rate (adjusted p-value <0.001). Prevalence of BMI > = 25 age-standardized in males and females were also significant when interacting with air pollution or GDP on female fecundity (birth rate). Temperature did not affect birth rate either independently or as an interaction unless BMI was removed from the model.ConclusionA country’s economic wealth was the most significant factor in predicting birth rate in a statistical model that includes environmental and socioeconomic variables. This is important for future studies investigating environmental factors involved in increasing or decreasing female fecundity.

Reactive Oxygen Species Formed by Secondary Organic Aerosols in Water and Surrogate Lung Fluid.

Reactive oxygen species (ROS) play a central role in adverse health effects of air pollutants. Respiratory deposition of fine air particulate matter can lead to the formation of ROS in epithelial lining fluid, potentially causing oxidative stress and inflammation. Secondary organic aerosols (SOA) account for a large fraction of fine particulate matter, but their role in adverse health effects is unclear. Here, we quantify and compare the ROS yields and oxidative potential of isoprene, β-pinene, and naphthalene SOA in water and surrogate lung fluid (SLF). In pure water, isoprene and β-pinene SOA were found to produce mainly OH and organic radicals, whereas naphthalene SOA produced mainly H2O2 and O2•-. The total molar yields of ROS of isoprene and β-pinene SOA were 11.8% and 8.2% in water and decreased to 8.5% and 5.2% in SLF, which can be attributed to ROS removal by lung antioxidants. A positive correlation between the total peroxide concentration and ROS yield suggests that organic (hydro)peroxides may play an important role in ROS formation from biogenic SOA. The total molar ROS yields of naphthalene SOA was 1.7% in water and increased to 11.3% in SLF. This strong increase is likely due to redox reaction cycles involving environmentally persistent free radicals (EPFR) or semiquinones, antioxidants, and oxygen, which may promote the formation of H2O2 and the adverse health effects of anthropogenic SOA from aromatic precursors.

Community composition and seasonal changes of archaea in coarse and fine air particulate matter

Abstract. Archaea are ubiquitous in terrestrial and marine environments and play an important role in biogeochemical cycles. Although air acts as the primary medium for their dispersal among different habitats, their diversity and abundance is not well characterized. The main reason for this lack of insight is that archaea are difficult to culture, seem to be low in number in the atmosphere, and have so far been difficult to detect even with molecular genetic approaches. However, to better understand the transport, residence time, and living conditions of microorganisms in the atmosphere as well as their effects on the atmosphere and vice versa, it is essential to study all groups of bioaerosols. Here we present an in-depth analysis of airborne archaea based on Illumina sequencing of 16S rRNA genes from atmospheric coarse and fine particulate matter samples and show seasonal dynamics and discuss anthropogenic influences on the diversity, composition, and abundance of airborne archaea.The relative proportions of archaea to bacteria, the differences of the community composition in fine and coarse particulate matter, and the high abundance in coarse matter of one typical soil related family, the Nitrososphaeraceae, point to local phyllosphere and soil habitats as primary emission sources of airborne archaea.We found comparable seasonal dynamics for the dominating Euryarchaeota classes and Crenarchaeota orders peaking in summer and fall. In contrast, the omnipresent Cenarchaeales and the Thermoplasmata occur only throughout summer and fall. We also gained novel insights into archaeal composition in fine particulate matter (&lt; 3 µm), with Cenarchaeaceae, Nitrososphaeraceae, Methanosarcinales, Thermoplasmata, and the genus Nitrosopumilus as the dominating taxa.The seasonal dynamics of methanogenic Euryarchaeota point to anthropogenic activities, such as fertilization of agricultural fields with biogas substrates or manure, as sources of airborne archaea. This study gains a deeper insight into the abundance and composition of archaea in the atmosphere, especially within the fine particle mode, which adds to a better understanding of the overall atmospheric microbiome.

Feasibility study of a candidate reference material for ions in PM2.5: does commutability matter also for inorganic matrices?

The existing Air Quality Directive 2008/50/EC establishes within the European Union (EU) member states limit values for fine air particulate matter (PM2.5) including the possibility to discount natural sources of pollution when assessing compliance with the legislation. In proving this, EU member states shall determine, amongst others, the rural background concentration of some anions (Cl−, NO3− and SO42−) and cations (Na+, NH4+, K+, Ca2+ and Mg2+). To deliver reliable data and to comply with the data quality objectives of the legislation, environmental control laboratories should use certified reference materials (CRMs) to validate or verify the performance of their analytical methods. Since no CRMs for anions and cations in PM2.5 are presently available, we present the commutability issues encountered during the first attempt to develop such a material. We demonstrate that a dust, collected in a road tunnel and previously used for the production of two CRMs of a PM10-like material, does not behave as an authentic fine particulate matter collected according to EN12341:2014 when measured by an established method proposed by the European Committee for Standardization (CEN/TR 16269:2011). The water-soluble fractions of SO42−, NH4+, K+, Ca2+ and Mg2+ in a PM2.5-like candidate CRM produced from that road tunnel dust are only fully extracted after 3 h of sonication and not after 30 min, as stated in the method. Moreover, we found that the particle size of the test material influenced the extraction yield of K+, Ca2+ and Mg2+, suggesting that these ionic species were incorporated in the core of the particles and inaccessible to the extraction procedure. These particular features make the material unsuitable for the measurements of ions with the CEN method. The difference in the extraction time can be seen as a commutability issue and the candidate CRM should be considered as not commutable with routine samples. This demonstrates that commutability studies should not only be considered for clinical CRMs, but also for inorganic CRMs when they are intended to be used to quantify operationally defined analytes.

Influence of aging in the modulation of epigenetic biomarkers of carcinogenesis after exposure to air pollution.

Classified as carcinogenic to humans by the IARC in 2013, fine air particulate matter (PM2.5) can be inhaled and retained into the lung or reach the systemic circulation. This can cause or exacerbate numerous pathologies to which the elderly are often more sensitive. In order to estimate the influence of age on the development of early cellular epigenetic alterations involved in carcinogenesis, peripheral blood mononuclear cells sampled from 90 patients from three age classes (25-30, 50-55 and 75-80 years old) were ex vivo exposed to urban PM2.5. Particles exposure led to variations in telomerase activity and telomeres length in all age groups without any influence of age. Conversely, P16INK4A gene expression increased significantly with age after exposure to PM2.5. Age could enhance MGMT gene expression after exposure to particles, by decreasing the level of promoter methylation in the oldest people. Hence, our results demonstrated several tendencies in cells modification depending on age, even if all epigenetic assays were carried out after a limited exposure time allowing only one or two cell cycles. Since lung cancer symptoms appear only at an advanced stage, our results underline the needs for further investigation on the studied biomarkers for early diagnosis of carcinogenesis to improve survival.

Fine Particulate Matter Pollution and Risk of Community-Acquired Sepsis.

While air pollution has been associated with health complications, its effect on sepsis risk is unknown. We examined the association between fine particulate matter (PM2.5) air pollution and risk of sepsis hospitalization. We analyzed data from the 30,239 community-dwelling adults in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort linked with satellite-derived measures of PM2.5 data. We defined sepsis as a hospital admission for a serious infection with ≥2 systemic inflammatory response (SIRS) criteria. We performed incidence density sampling to match sepsis cases with 4 controls by age (±5 years), sex, and race. For each matched group we calculated mean daily PM2.5 exposures for short-term (30-day) and long-term (one-year) periods preceding the sepsis event. We used conditional logistic regression to evaluate the association between PM2.5 exposure and sepsis, adjusting for education, income, region, temperature, urbanicity, tobacco and alcohol use, and medical conditions. We matched 1386 sepsis cases with 5544 non-sepsis controls. Mean 30-day PM2.5 exposure levels (Cases 12.44 vs. Controls 12.34 µg/m3; p = 0.28) and mean one-year PM2.5 exposure levels (Cases 12.53 vs. Controls 12.50 µg/m3; p = 0.66) were similar between cases and controls. In adjusted models, there were no associations between 30-day PM2.5 exposure levels and sepsis (4th vs. 1st quartiles OR: 1.06, 95% CI: 0.85–1.32). Similarly, there were no associations between one-year PM2.5 exposure levels and sepsis risk (4th vs. 1st quartiles OR: 0.96, 95% CI: 0.78–1.18). In the REGARDS cohort, PM2.5 air pollution exposure was not associated with risk of sepsis.

Long-term ambient PM2.5, household air pollution exposure, and blood pressure in the Prospective Urban and Rural Epidemiological (PURE) Study

Introduction: High blood pressure, outdoor fine particulate matter (PM2.5) air pollution, and household air pollution (HAP) from solid fuels are among the leading risk factors for death and disabil...

Online Sources about Atmospheric Fine Particles During the 70th Anniversary of Victory Parade in Shijiazhuang

Robust measures were taken to ensure a good air quality for the parade on the 70th Victory Memorial Day for the Chinese People's War of Resistance against Japanese Aggression (VM Day). During the period, the source of fine particulate matter in air was analyzed with the single particle aerosol mass spectrometer (SPAMS) located at Shijiazhuang air automatic monitoring station of 20 meter. The results indicated that, on VM Day the primary sources of air pollution were vehicle exhaust emission (20.9%) and coal-generated emissions (20.6%), which were also at lower degrees than those on pre-and post-VM Day. It turned out that these air quality-improving measures, especially the vehicle restriction and coal consumption reduction, had a good effect. Particles from the vehicle exhaust and coal combustion source were mainly based on short-chain elements, carbon, manganese and organic carbon. The particles from the industry source were mainly organic carbon and metal. Particles from the dust source were mainly composed of silicate and calcium. When air quality-improving measures were stopped, the concentration of particulate matter rapidly rose again, and the contribution of dust and vehicle exhaust emission was remarkable. It was believed that the interaction of low-pressure static stability of adverse weather conditions and southeast direction of low altitude transmission was a major cause in the deterioration of air quality.