Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5.

Current air quality standards for particulate matter (PM) use the PM mass concentration [PM with aerodynamic diameters ≤ 10 μm (PM(10)) or ≤ 2.5 μm (PM(2.5))] as a metric. It has been suggested that particles from combustion sources are more relevant to human health than are particles from other sources, but the impact of policies directed at reducing PM from combustion processes is usually relatively small when effects are estimated for a reduction in the total mass concentration

Contrasts in Oxidative Potential and Other Particulate Matter Characteristics Collected Near Major Streets and Background Locations

Background: Measuring the oxidative potential of airborne particulate matter (PM) may provide a more health-based exposure measure by integrating various biologically relevant properties of PM into a single predictor of biological activity

Can We Identify Sources of Fine Particles Responsible for Exercise-Induced Ischemia on Days with Elevated Air Pollution? The ULTRA Study

Epidemiologic studies have shown that ambient particulate matter (PM) has adverse effects on cardiovascular health. Effective mitigation of the health effects requires identification of the most harmful PM sources. The objective of our study was to evaluate relative effects of fine PM [aerodynamic diameter ≤ 2.5 μm (PM(2.5))] from different sources on exercise-induced ischemia. We collected daily outdoor PM(2.5) samples between autumn 1998 and spring 1999 in Helsinki, Finland. The mass of PM(2.5) was apportioned between five sources. Forty-five elderly nonsmoking persons with stable coronary heart disease visited a clinic biweekly for submaximal exercise testing, during which the occurrence of ST segment depressions was recorded. Levels of PM(2.5) originating from local traffic and long-range transport were associated with ST segment depressions > 0.1 mV, with odds ratios at 2-day lag of 1.53 [95% confidence interval (CI), 1.19–1.97] and 1.11 (95% CI, 1.02–1.20) per 1 μg/m(3), respectively. In multipollutant models, where we used indicator elements for sources instead of source-specific PM(2.5), only absorbance (elemental carbon), an indicator of local traffic and other combustion, was associated with ST segment depressions. Our results suggest that the PM fraction originating from combustion processes, notably traffic, exacerbates ischemic heart diseases associated with PM mass

Determinants of the Proinflammatory Action of Ambient Particulate Matter in Immortalized Murine Macrophages

Background: Proximity to traffic-related pollution has been associated with poor respiratory health in adults and children. Objectives: We wished to test the hypothesis that particulate matter (PM) from high-traffic sites would display an enhanced capacity to elicit inflammation. Methods: We examined the inflammatory potential of coarse [2.5–10 μm in aerodynamic diameter (PM2.5–10)] and fine [0.1–2.5 μm in aerodynamic diameter (PM0.1–2.5)] PM collected from nine sites throughout Europe with contrasting traffic contributions. We incubated murine monocytic-macrophagic RAW264.7 cells with PM samples from these sites (20 or 60 μg/cm2) and quantified their capacity to stimulate the release of arachidonic acid (AA) or the production of interleukin-6 and tumor necrosis factor-α (TNFα) as measures of their inflammatory potential. Responses were then related to PM composition: metals, hydrocarbons, anions/cations, and endotoxin content. Results: Inflammatory responses to ambient PM varied markedly on an equal mass basis, with PM2.5–10 displaying the largest signals and contrasts among sites. Notably, we found no evidence of enhanced inflammatory potential at high-traffic sites and observed some of the largest responses at sites distant from traffic. Correlation analyses indicated that much of the sample-to-sample contrast in the proinflammatory response was related to the content of endotoxin and transition metals (especially iron and copper) in PM2.5–10. Use of the metal chelator diethylene triamine pentaacetic acid inhibited AA release, whereas recombinant endotoxin-neutralizing protein partially inhibited TNFα production, demonstrating that different PM components triggered inflammatory responses through separate pathways. Conclusions: We found no evidence that PM collected from sites in close proximity to traffic sources displayed enhanced proinflammatory activity in RAW264.7 cells. Key words: copper, endotoxin, inflammation, iron, macrophages, metals, particulate matter, polyaromatic hydrocarbons. Environ Health Perspect 118:1728–1734 (2010). doi:10.1289/ehp.1002105 [Online 27 July 2010

Associations between PM2.5 and Heart Rate Variability Are Modified by Particle Composition and Beta-Blocker Use in Patients with Coronary Heart Disease

BACKGROUND: It has been hypothesized that ambient particulate air pollution is able to modify the autonomic nervous control of the heart, measured as heart rate variability (HRV). Previously we reported heterogeneous associations between particulate matter with aerodynamic diameter < 2.5 mu m (PM2.5) and HRV across three study centers. OBJECTIVE: We evaluated whether exposure misclassification, effect modification by medication, or differences in particle composition could explain die inconsistencies. METHODS: Subjects with coronary heart disease visited clinics biweekly in Amsterdam, the Netherlands; Erfurt, Germany; and Helsinki, Finland for 6-8 months. The standard deviation (SD) of NN intervals on an electrocardiogram (ECG; SDNN) and high frequency (HF) power of HRV was measured with ambulatory ECG during paced breathing. Outdoor levels of PM2.5 were measured at a central site. In Amsterdam and Helsinki, indoor and personal PM2.5 were measured during the 24 hr preceding the clinic visit. PM2.5 was apportioned between sources using principal component analyses. We analyzed associations of indoor/personal PM2.5 elements of PM2.5 and source-specific PM2.5 With HRV using linear regression. RESULTS: Indoor and personal PM2.5 were not associated with HRV. Increased outdoor PM2.5 was associated with decreased SDNN and HF at lags of 2 and 3 days only among persons not using beta-blocker medication. Traffic-related PM2.5 was associated with decreased SDNN, and long-range transported PM2.5 with decreased SDNN and HF, most strongly among persons not using beta blockers. Indicators for PM2.5 from traffic and long-range transport were also associated with decreased HRV. CONCLUSIONS: Our results suggest that differences in the composition of particles, beta-blocker use, and obesity of study subjects may explain some inconsistencies among previous studies on HRV

Effects of Ambient Air Pollution on Hemostasis and Inflammation

BACKGROUND: Air pollution has consistently been associated with increased morbidity and mortality due to respiratory and cardiovascular disease. Underlying biological mechanisms are not entirely clear, and hemostasis and inflammation are suggested to be involved. OBJECTIVES: Our aim was to study the association of the variation in local concentrations of airborne particulate matter (PM) with aerodynamic diameter < 10 mu m, carbon monoxide, nitrogen monoxide, nitrogen dioxide, and ozone with platelet aggregation, thrombin generation, fibrinogen, and C-reactive protein (CRP) levels in healthy individuals. METHODS: From 40 healthy volunteers, we collected 13 consecutive blood samples within a 1-year period and measured light-transmittance platelet aggregometry, thrombin generation, fibrinogen, and CRP. We performed regression analysis using generalized additive models to study the association between the hemostatic and inflammatory variables, and local environmental concentrations 0 air pollutants for time lags within 24 hr before blood sampling or 24-96 hr before blood sampling. RESULTS: In general, air pollutants were associated with platelet aggregation [average, +8% per interquartile range (IQR), p < 0.01] and thrombin generation (average, +1% per IQR, p < 0.015). Platelet aggregation was not affected by in vitro incubation of plasma with PM. We observed no relationship between any of the air pollutants and fibrinogen or CRP levels. CONCLUSIONS:. Air pollution increased platelet aggregation as well as coagulation activity but had no clear effect on systemic inflammation. These prothrombotic effects may partly explain the relationship between air pollution and the risk of ischemic cardiovascular disease

Long-term exposure to low-level air pollution and incidence of chronic obstructive pulmonary disease: The ELAPSE project.

BACKGROUND: Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent. OBJECTIVES: We examined the association between long-term exposure to low-level air pollution and COPD incidence. METHODS: Within the 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in 2010 at participants' baseline residential addresses, which were analysed in relation to COPD incidence using Cox proportional hazards models. RESULTS: Of 98,058 participants, 4,928 developed COPD during 16.6 years mean follow-up. The adjusted hazard ratios (HRs) and 95% confidence intervals for associations with COPD incidence were 1.17 (1.06, 1.29) per 5 µg/m3 for PM2.5, 1.11 (1.06, 1.16) per 10 µg/m3 for NO2, and 1.11 (1.06, 1.15) per 0.5 10-5m-1 for BC. Associations persisted in subset participants with PM2.5 or NO2 levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO2 and BC remained unchanged in two-pollutant models with PM2.5, whereas the HR for PM2.5 was attenuated to unity with NO2 or BC. CONCLUSIONS: Long-term exposure to low-level air pollution is associated with the development of COPD, even below current EU and US limit values and possibly WHO guidelines. Traffic-related pollutants NO2 and BC may be the most relevant

Air pollution and mortality in seven million adults : the Dutch Environmental Longitudinal Study (DUELS)

Long-term exposure to air pollution has been associated with mortality in urban cohort studies. Few studies have investigated this association in large-scale population registries, including non-urban populations.; The aim of the study was to evaluate the associations between long-term exposure to air pollution and nonaccidental and cause-specific mortality in the Netherlands based on existing national databases.; We used existing Dutch national databases on mortality, individual characteristics, residence history, neighborhood characteristics, and national air pollution maps based on land use regression (LUR) techniques for particulates with an aerodynamic diameter ≤ 10 μm (PM10) and nitrogen dioxide (NO2). Using these databases, we established a cohort of 7.1 million individuals ≥ 30 years of age. We followed the cohort for 7 years (2004-2011). We applied Cox proportional hazard models adjusting for potential individual and area-specific confounders.; After adjustment for individual and area-specific confounders, for each 10-μg/m3 increase, PM10 and NO2 were associated with nonaccidental mortality [hazard ratio (HR) = 1.08; 95% CI: 1.07, 1.09 and HR = 1.03; 95% CI: 1.02, 1.03, respectively], respiratory mortality (HR = 1.13; 95% CI: 1.10, 1.17 and HR = 1.02; 95% CI: 1.01, 1.03, respectively), and lung cancer mortality (HR = 1.26; 95% CI: 1.21, 1.30 and HR = 1.10 95% CI: 1.09, 1.11, respectively). Furthermore, PM10 was associated with circulatory disease mortality (HR = 1.06; 95% CI: 1.04, 1.08), but NO2 was not (HR = 1.00; 95% CI: 0.99, 1.01). PM10 associations were robust to adjustment for NO2; NO2 associations remained for nonaccidental mortality and lung cancer mortality after adjustment for PM10.; Long-term exposure to PM10 and NO2 was associated with nonaccidental and cause-specific mortality in the Dutch population of ≥ 30 years of age

Predicting self-perceived general health status using machine learning : an external exposome study

Background: Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status. Methods: Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables. Results: Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852–0.876); 2016: AUC = 0.890 (CI: 0.883–0.896)) and the most important predictors were “Control of own life”, “Physical activity”, “Loneliness” and “Making ends meet”. Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models. Conclusions: This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status