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Air pollution health studies of fine particulate matter (diameter ≤2.5 µm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 µg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 µg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

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Continuous, long-term, and time-resolved measurement of outdoor air pollution has been limited by logistical hurdles and resource constraints. Measuring air pollution in more places is desired to address community concerns regarding local air quality impacts related to proximate sources, to provide data in areas lacking regional air monitoring altogether, or to support environmental awareness and education. This study integrated commercially available technologies to create the Village Green Project (VGP), a durable, solar-powered air monitoring park bench that measures real-time ozone, PM2.5, and meteorological parameters. The data are wirelessly transmitted via cellular modem to a server, where automated quality checks take place before data are provided to the public nearly instantaneously. Over 5500 h of data were successfully collected during the first ten months of pilot testing in Durham, North Carolina, with about 13 days (5.5%) of downtime because of low battery power. Additional data loss (4-14% depending on the measurement) was caused by infrequent wireless communication interruptions and instrument maintenance. The 94.5% operational time via solar power was within 1.5% of engineering calculations using historical solar data for the location. The performance of the VGP was evaluated by comparing the data to nearby air monitoring stations operating federal equivalent methods (FEM), which exhibited good agreement with the nearest benchmark FEMs for hourly ozone (r(2) = 0.79) and PM2.5 (r(2) = 0.76).

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Patients with non-cystic fibrosis bronchiectasis (NCFB) share many of the respiratory symptoms and the disease progression of cystic fibrosis (CF). As there are no approved therapies for the management of NCFB, an approach has been to use therapies similar to those used to treat CF. In many cases, however, this is ineffective or detrimental. The reason for this has not been determined, but it may be due to key differences in pathogenesis. The questions arising from this have spurred dedicated investigation into the effective management of NCFB. Patients with NCFB have chronic bacterial infection, and bacterial load correlates with symptoms and quality of life. Treatment with systemic antibiotics decreases bacterial load and can have a favorable effect on outcomes. Chronic or frequent use of systemic antibiotics, however, is impractical and sometimes unsafe, so aerosol as a means of delivery is seen as an attractive alternative. The clinical response to and tolerability of inhaled antibiotics have differed significantly between NCFB and CF. New delivery technology, novel antibiotic formulations and a better understanding of the bacterial burden of NCFB are now changing the approach to disease management.

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Patients with tracheostomies, those requiring mechanical ventilation, and those too small or compromised for conventional devices, are realizing the benefits of increasingly sophisticated aerosol delivery systems. New medicines and novel aerosol formulations, have enhanced our ability to treat lung disease, and are opening the doors for therapy to treat diseases like diabetes, pulmonary hypertension, and cancer. Progress in the aerosol delivery of drugs has been spurred by the significant benefits, including ease of use, patient comfort, greater selectivity of effect, and the potential to decrease side effects.

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A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.

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The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.

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A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time. The AER, which is the airflow into and out of a building, is a primary mechanism for entry of outdoor air pollutants and removal of indoor source emissions. The mechanistic Lawrence Berkeley Laboratory (LBL) AER model was linked to a leakage area model to predict AER from questionnaires and meteorology. The LBL model was also extended to include natural ventilation (LBLX). Using literature-reported parameter values, AER predictions from LBL and LBLX models were compared to data from 642 daily AER measurements across 31 detached homes in central North Carolina, with corresponding questionnaires and meteorological observations. Data was collected on seven consecutive days during each of four consecutive seasons. For the individual model-predicted and measured AER, the median absolute difference was 43% (0.17 h(-1)) and 40% (0.17 h(-1)) for the LBL and LBLX models, respectively. Additionally, a literature-reported empirical scale factor (SF) AER model was evaluated, which showed a median absolute difference of 50% (0.25 h(-1)). The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.

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BACKGROUND: The mechanisms for the relationship between particulate air pollution and cardiac disease are not fully understood. Air pollution-induced myocardial ischemia is one of the potentially important mechanisms. METHODS: We investigate the acute effects and the time course of fine particulate pollution (PM2.5) on myocardium ischemic injury as assessed by ST-segment height in a community-based sample of 106 healthy non-smokers. Twenty-four hour beat-to-beat electrocardiogram (ECG) data were obtained using a high resolution 12-lead Holter ECG system. After visually identifying and removing all the artifacts and arrhythmic beats, we calculated beat-to-beat ST-height from ten leads (inferior leads II, III, and aVF; anterior leads V3 and V4; septal leads V1 and V2; lateral leads I, V5, and V6,). Individual-level 24-hour real-time PM2.5 concentration was obtained by a continuous personal PM2.5 monitor. We then calculated, on a 30-minute basis, the corresponding time-of-the-day specific average exposure to PM2.5 for each participant. Distributed lag models under a linear mixed-effects models framework were used to assess the regression coefficients between 30-minute PM2.5 and ST-height measures from each lead; i.e., one lag indicates a 30-minute separation between the exposure and outcome. RESULTS: The mean (SD) age was 56 (7.6) years, with 41% male and 74% white. The mean (SD) PM2.5 exposure was 14 (22) µg/m3. All inferior leads (II, III, and aVF) and two out of three lateral leads (I and V6), showed a significant association between higher PM2.5 levels and higher ST-height. Most of the adverse effects occurred within two hours after PM2.5 exposure. The multivariable adjusted regression coefficients ß (95% CI) of the cumulative effect due to a 10 µg/m3 increase in Lag 0-4 PM2.5 on ST-I, II, III, aVF and ST-V6 were 0.29 (0.01-0.56) µV, 0.79 (0.20-1.39) µV, 0.52 (0.01-1.05) µV, 0.65 (0.11-1.19) µV, and 0.58 (0.07-1.09) µV, respectively, with all p < 0.05. CONCLUSIONS: Increased PM2.5 concentration is associated with immediate increase in ST-segment height in inferior and lateral leads, generally within two hours. Such an acute effect of PM2.5 may contribute to increased potential for regional myocardial ischemic injury among healthy individuals.

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Regression models are developed to describe the relationship between ambient PM2.5 (particulate matter [PM] < or = 2.5 microm in aerodynamic diameter) mass concentrations measured at a central-site monitor with those at residential outdoor monitors. Understanding the determinants and magnitude of variability and uncertainty in this relationship is critical for understanding personal exposures in the evaluation of epidemiological data. The repeated measures regression models presented here address temporal and spatial characteristics of data measured in the 2004-2007 Detroit Exposure and Aerosol Research Study, and they take into account missing data and other data features. The models incorporate turbulence kinetic energy and planetary boundary layer height, meteorological data that are not routinely considered in models that relate central-site concentrations to exposure to health effects. It was found that turbulence kinetic energy was highly statistically significant in explaining the relationship of PM2.5 measured at a particular stationary outdoor air monitoring site with PM2.5 measured outside nearby residences for the temporal coverage of the data.

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Inflammation is associated with various pulmonary diseases and contributes to the pathogenesis of acute lung injury. We previously identified a proinflammatory signaling pathway triggered by G protein-coupled receptors (GPCRs) in which stimulation of G(q)-coupled GPCRs results in activation of the transcription factor NF-kappaB. Because damage to the lung causes the release of multiple mediators acting through G(q)-coupled GPCRs, this signaling pathway is likely to contribute to inflammatory processes in the injured lung. In an effort to identify novel inhibitors of lung inflammation, the National Institutes of Health Clinical Collection, a library of 446 compounds, was screened for inhibitory activity toward production of IL-8 induced by stimulation of the G(q)-coupled tachykinin 1 receptor with substance P in A549 cells. Twenty-eight compounds that significantly inhibited substance P-induced IL-8 production were identified. The most potent inhibitor was triptolide, a diterpenoid compound from Tripterygium wilfordii Hook F, a vine used in traditional Chinese medicine for the treatment of autoimmune diseases. Triptolide inhibited IL-8 production induced by substance P with an IC(50) of 2.3 x 10(-8) M and inhibited NF-kappaB activation in response to an agonist of the protease-activated receptor 2 with an IC(50) of 1.4 x 10(-8) M. Anti-inflammatory effects of triptolide were assessed in vivo using a chlorine gas lung injury model in mice. Triptolide inhibited neutrophilic inflammation and the production of KC (Cxcl1) in the lungs of chlorine-exposed mice. The results demonstrate that triptolide exhibits anti-inflammatory activity in cultured lung cells and in an in vivo model of acute lung injury.

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BACKGROUND: Exposure to fine airborne particulate matter [< or =2.5 microm in aerodynamic diameter (PM(2.5))] has been associated with cardiovascular and hematologic effects, especially in older people with cardiovascular disease. Some epidemiologic studies suggest that adults with diabetes also may be a particularly susceptible population. OBJECTIVES: The purpose of this study was to analyze the short-term effects of ambient PM(2.5) on markers of endothelial function in diabetic volunteers. METHODS: We conducted a prospective panel study in 22 people with type 2 diabetes mellitus in Chapel Hill, North Carolina (USA), from November 2004 to December 2005. We acquired daily measurements of PM(2.5) and meteorologic data at central monitoring sites. On 4 consecutive days, we measured endothelial function by brachial artery ultrasound in all participants and by pulsewave measurements in a subgroup. Data were analyzed using additive mixed models with a random participant effect and adjusted for season, day of the week, and meteorology. RESULTS: Flow-mediated dilatation decreased in association with PM(2.5) during the first 24 hr, whereas small-artery elasticity index decreased with a delay of 1 and 3 days. These PM(2.5)-associated decrements in endothelial function were greater among participants with a high body mass index, high glycosylated hemoglobin A1c, low adiponectin, or the null polymorphism of glutathione S-transferase M1. However, high levels of myeloperoxidase on the examination day led to strongest effects on endothelial dysfunction. CONCLUSIONS: These data demonstrate that PM(2.5) exposure may cause immediate endothelial dysfunction. Clinical characteristics associated with insulin resistance were associated with enhanced effects of PM on endothelial function. In addition, participants with greater oxidative potential seem to be more susceptible.

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Exposure to fine airborne particulate matter (PM(2.5)) is associated with cardiovascular events and mortality in older and cardiac patients. Potential physiologic effects of in-vehicle, roadside, and ambient PM(2.5) were investigated in young, healthy, nonsmoking, male North Carolina Highway Patrol troopers. Nine troopers (age 23 to 30) were monitored on 4 successive days while working a 3 P.M. to midnight shift. Each patrol car was equipped with air-quality monitors. Blood was drawn 14 hours after each shift, and ambulatory monitors recorded the electrocardiogram throughout the shift and until the next morning. Data were analyzed using mixed models. In-vehicle PM(2.5) (average of 24 microg/m(3)) was associated with decreased lymphocytes (-11% per 10 microg/m(3)) and increased red blood cell indices (1% mean corpuscular volume), neutrophils (6%), C-reactive protein (32%), von Willebrand factor (12%), next-morning heart beat cycle length (6%), next-morning heart rate variability parameters, and ectopic beats throughout the recording (20%). Controlling for potential confounders had little impact on the effect estimates. The associations of these health endpoints with ambient and roadside PM(2.5) were smaller and less significant. The observations in these healthy young men suggest that in-vehicle exposure to PM(2.5) may cause pathophysiologic changes that involve inflammation, coagulation, and cardiac rhythm.

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BACKGROUND: Exposure to fine particulate matter air pollutants (PM2.5) affects heart rate variability parameters, and levels of serum proteins associated with inflammation, hemostasis and thrombosis. This study investigated sources potentially responsible for cardiovascular and hematological effects in highway patrol troopers. RESULTS: Nine healthy young non-smoking male troopers working from 3 PM to midnight were studied on four consecutive days during their shift and the following night. Sources of in-vehicle PM2.5 were identified with variance-maximizing rotational principal factor analysis of PM2.5-components and associated pollutants. Two source models were calculated. Sources of in-vehicle PM2.5 identified were 1) crustal material, 2) wear of steel automotive components, 3) gasoline combustion, 4) speed-changing traffic with engine emissions and brake wear. In one model, sources 1 and 2 collapsed to a single source. Source factors scores were compared to cardiac and blood parameters measured ten and fifteen hours, respectively, after each shift. The "speed-change" factor was significantly associated with mean heart cycle length (MCL, +7% per standard deviation increase in the factor score), heart rate variability (+16%), supraventricular ectopic beats (+39%), % neutrophils (+7%), % lymphocytes (-10%), red blood cell volume MCV (+1%), von Willebrand Factor (+9%), blood urea nitrogen (+7%), and protein C (-11%). The "crustal" factor (but not the "collapsed" source) was associated with MCL (+3%) and serum uric acid concentrations (+5%). Controlling for potential confounders had little influence on the effect estimates. CONCLUSION: PM2.5 originating from speed-changing traffic modulates the autonomic control of the heart rhythm, increases the frequency of premature supraventricular beats and elicits pro-inflammatory and pro-thrombotic responses in healthy young men.