Issues Related to Combining Multiple Speciated PM2.5 Data Sources in Spatio-Temporal Exposure Models for Epidemiology: The NPACT Case Study

Background: Regulatory monitoring data have been the most common exposure data resource in studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological study. Objectives: We aimed to explore three important features of the PM2.5 component monitoring data obtained from multiple sources to combine all available data for developing spatio-temporal prediction models in the National Particle Component and Toxicity (NPACT) study. Methods: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participants. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine three features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing a spatio-temporal prediction model given: 1) all available data; 2) NPACT data only; and 3) NPACT data with temporal trends estimated from other pollutants. Results: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and the protocol differences for sampling resulted in incompatible measurements between networks. Given these features, we determined that it was preferable to develop our spatio-temporal model using only the NPACT data and under simplifying assumptions. Conclusions: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into exposure model development

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM in Cohort Studies Before the 1999 Implementation of Widespread Monitoring

Introduction: Recent cohort studies use exposure prediction models to estimate the association between long-term residential concentrations of PM2.5 and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999. We evaluated a novel statistical approach to produce high quality exposure predictions from 1980-2010 for epidemiological applications. Methods: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. The model consists of a spatially-varying long-term mean, a spatially-varying temporal trend, and spatially-varying and temporally-independent spatio-temporal residuals structured using a universal kriging framework. Temporal trends in annual averages of PM2.5 before 1999 were estimated by using a) extrapolation based on PM2.5 data for 1999-2010 in FRM/IMPROVE, b) PM2.5 sulfate data for 1987-2010 in the Clean Air Status and Trends Network, and c) visibility data for 1980-2010 across the Weather-Bureau-Army-Navy network. We validated the resulting models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Southern California Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN). Results: The PM2.5 prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2= 0.84–0.91). Model performance using CARB dichot and IPN data was worse than those in IMPROVE most likely due to inconsistent sampling methods and smaller numbers of monitoring sites. Discussion: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods of up to 30 years

Particulate matter components and subclinical atherosclerosis: common approaches to estimating exposure in a Multi-Ethnic Study of Atherosclerosis cross-sectional study

Abstract Background Concentrations of outdoor fine particulate matter (PM2.5) have been associated with cardiovascular disease. PM2.5 chemical composition may be responsible for effects of exposure to PM2.5. Methods Using data from the Multi-Ethnic Study of Atherosclerosis (MESA) collected in 2000–2002 on 6,256 US adults without clinical cardiovascular disease in six U.S. metropolitan areas, we investigated cross-sectional associations of estimated long-term exposure to total PM2.5 mass and PM2.5 components (elemental carbon [EC], organic carbon [OC], silicon and sulfur) with measures of subclinical atherosclerosis (coronary artery calcium [CAC] and right common carotid intima-media thickness [CIMT]). Community monitors deployed for this study from 2007 to 2008 were used to estimate exposures at baseline addresses using three commonly-used approaches: (1) nearest monitor (the primary approach), (2) inverse-distance monitor weighting and (3) city-wide average. Results Using the exposure estimate based on nearest monitor, in single-pollutant models, increased OC (effect estimate [95% CI] per IQR: 35.1 μm [26.8, 43.3]), EC (9.6 μm [3.6,15.7]), sulfur (22.7 μm [15.0,30.4]) and total PM2.5 (14.7 μm [9.0,20.5]) but not silicon (5.2 μm [−9.8,20.1]), were associated with increased CIMT; in two-pollutant models, only the association with OC was robust to control for the other pollutants. Findings were generally consistent across the three exposure estimation approaches. None of the PM measures were positively associated with either the presence or extent of CAC. In sensitivity analyses, effect estimates for OC and silicon were particularly sensitive to control for metropolitan area. Conclusion Employing commonly-used exposure estimation approaches, all of the PM2.5 components considered, except silicon, were associated with increased CIMT, with the evidence being strongest for OC; no component was associated with increased CAC. PM2.5 chemical components, or other features of the sources that produced them, may be important in determining the effect of PM exposure on atherosclerosis. These cross-sectional findings await confirmation in future work employing longitudinal outcome measures and using more sophisticated approaches to estimating exposure.http://deepblue.lib.umich.edu/bitstream/2027.42/112668/1/12940_2013_Article_651.pd

Exhaled Nitric Oxide in Children with Asthma and Short-Term PM(2.5) Exposure in Seattle

The objective of this study was to evaluate associations between short-term (hourly) exposures to particulate matter with aerodynamic diameters < 2.5 μm (PM(2.5)) and the fractional concentration of nitric oxide in exhaled breath (Fe(NO)) in children with asthma participating in an intensive panel study in Seattle, Washington. The exposure data were collected with tapered element oscillation microbalance (TEOM) PM(2.5) monitors operated by the local air agency at three sites in the Seattle area. Fe(NO) is a marker of airway inflammation and is elevated in individuals with asthma. Previously, we reported that offline measurements of Fe(NO) are associated with 24-hr average PM(2.5) in a panel of 19 children with asthma in Seattle. In the present study using the same children, we used a polynomial distributed lag model to assess the association between hourly lags in PM(2.5) exposure and Fe(NO) levels. Our model controlled for age, ambient NO levels, temperature, relative humidity, and modification by use of inhaled corticosteroids. We found that Fe(NO) was associated with hourly averages of PM(2.5) up to 10–12 hr after exposure. The sum of the coefficients for the lag times associated with PM(2.5) in the distributed lag model was 7.0 ppm Fe(NO). The single-lag-model Fe(NO) effect was 6.9 [95% confidence interval (CI), 3.4 to 10.6 ppb] for a 1-hr lag, 6.3 (95% CI, 2.6 to 9.9 ppb ) for a 4-hr lag, and 0.5 (95% CI, −1.1 to 2.1 ppb) for an 8-hr lag. These data provide new information concerning the lag structure between PM(2.5) exposure and a respiratory health outcome in children with asthma

Open Access

In utero and early life exposure to diesel exhaust air pollution increases adult susceptibility to heart failure in mic

Associations between Health Effects and Particulate Matter and Black Carbon in Subjects with Respiratory Disease

We measured fractional exhaled nitric oxide (FE(NO)), spirometry, blood pressure, oxygen saturation of the blood (SaO(2)), and pulse rate in 16 older subjects with asthma or chronic obstructive pulmonary disease (COPD) in Seattle, Washington. Data were collected daily for 12 days. We simultaneously collected PM(10) and PM(2.5) (particulate matter ≤10 μm or ≤2.5 μm, respectively) filter samples at a central outdoor site, as well as outside and inside the subjects’ homes. Personal PM(10) filter samples were also collected. All filters were analyzed for mass and light absorbance. We analyzed within-subject associations between health outcomes and air pollution metrics using a linear mixed-effects model with random intercept, controlling for age, ambient relative humidity, and ambient temperature. For the 7 subjects with asthma, a 10 μg/m(3) increase in 24-hr average outdoor PM(10) and PM(2.5) was associated with a 5.9 [95% confidence interval (CI), 2.9–8.9] and 4.2 ppb (95% CI, 1.3–7.1) increase in FE(NO), respectively. A 1 μg/m(3) increase in outdoor, indoor, and personal black carbon (BC) was associated with increases in FE(NO) of 2.3 ppb (95% CI, 1.1–3.6), 4.0 ppb (95% CI, 2.0–5.9), and 1.2 ppb (95% CI, 0.2–2.2), respectively. No significant association was found between PM or BC measures and changes in spirometry, blood pressure, pulse rate, or SaO(2) in these subjects. Results from this study indicate that FE(NO) may be a more sensitive marker of PM exposure than traditional health outcomes and that particle-associated BC is useful for examining associations between primary combustion constituents of PM and health outcomes

Karst of the Driftless Area of Jo Daviess County, Illinois

Ope

Declining specific capacity of high-capacity wells in the Mahomet aquifer : mineralogical and biological factors

Ope

Pulmonary Effects of Indoor- and Outdoor-Generated Particles in Children with Asthma

Most particulate matter (PM) health effects studies use outdoor (ambient) PM as a surrogate for personal exposure. However, people spend most of their time indoors exposed to a combination of indoor-generated particles and ambient particles that have infiltrated. Thus, it is important to investigate the differential health effects of indoor- and ambient-generated particles. We combined our recently adapted recursive model and a predictive model for estimating infiltration efficiency to separate personal exposure (E) to PM(2.5) (PM with aerodynamic diameter ≤2.5 μm) into its indoor-generated (E(ig)) and ambient-generated (E(ag)) components for 19 children with asthma. We then compared E(ig) and E(ag) to changes in exhaled nitric oxide (eNO), a marker of airway inflammation. Based on the recursive model with a sample size of eight children, E(ag) was marginally associated with increases in eNO [5.6 ppb per 10-μg/m(3) increase in PM(2.5); 95% confidence interval (CI), −0.6 to 11.9; p = 0.08]. E(ig) was not associated with eNO (−0.19 ppb change per 10μg/m(3)). Our predictive model allowed us to estimate E(ag) and E(ig) for all 19 children. For those combined estimates, only E(ag) was significantly associated with an increase in eNO (E(ag): 5.0 ppb per 10-μg/m(3) increase in PM(2.5;) 95% CI, 0.3 to 9.7; p = 0.04; E(ig): 3.3 ppb per 10-μg/m(3) increase in PM(2.5); 95% CI, −1.1 to 7.7; p = 0.15). Effects were seen only in children who were not using corticosteroid therapy. We conclude that the ambient-generated component of PM(2.5) exposure is consistently associated with increases in eNO and the indoor-generated component is less strongly associated with eNO

Extent and Duration of the 2003 Cascadia Slow Earthquake

Inversion of continuous GPS measurements from the Pacific Northwest show the 2003 Cascadia slow earthquake to be among the largest of ten transients recognized here. Twelve stations bracketing slow slip indicate transient slip propagated bi-directionally from initiation in the southern Puget basin, reaching 300 km along-strike over a period of seven weeks. This event produced, for the first time, resolvable vertical subsidence, and horizontal displacement reaching six mm in southern Washington State. Inverted for non-negative thrust slip, a maximum of 3.8 cm of slip is inferred, centered at 28 km depth near the sharp arch in the subducting Juan de Fuca plate. Nearly all slip lies shallower than 38 km. Inverted slip shows a total moment release equal to Mw= 6.6 and a high degree of spatial localization rather than near-uniform slip. This suggests rupture concentrated along asperities holds for slow earthquakes as well as conventional events