L.-J. Sally Liu

Modeling the residential infiltration of outdoor PM2.5 in the multi-ethnic study of atherosclerosis and air pollution (MESA Air)

Background: Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM2.5)] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (Finf) will affect epidemiologic study results. Objective: We aimed to develop models to predict Finf for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM2.5 exposure, subclinical cardiovascular disease, and clinical outcomes. Methods: We collected 526 two-week, paired indoor–outdoor PM2.5 filter samples from a subset of study homes. PM2.5 elemental composition was measured by X-ray fluorescence, and Finf was estimated as the indoor/outdoor sulfur ratio. We regressed Finf on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R2 and root mean square error (RMSE) from a 10-fold cross-validation. Results: The mean ± SD Finf across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. Finf was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week Finf, with an RMSE of 0.13. Conclusions: We developed intuitive models that can predict Finf using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential Finf into an exposure assessment.

Transportation noise and blood pressure in a population-based sample of adults

Background: There is some evidence for an association between traffic noise and ischemic heart disease; however, associations with blood pressure have been inconsistent, and little is known about health effects of railway noise. Objectives: We aimed to investigate the effects of railway and traffic noise exposure on blood pressure; a secondary aim was to address potentially susceptible subpopulations. Methods: We performed adjusted linear regression analyses using data from 6,450 participants of the second survey of the Swiss Study on Air Pollution and Lung Disease in Adults (SAPALDIA 2) to estimate the associations of daytime and nighttime railway and traffic noise (A-weighted decibels) with systolic blood pressure (SBP) and diastolic blood pressure (DBP; millimeters of mercury). Noise data were provided by the Federal Office for the Environment. Stratified analyses by self-reported hypertension, cardiovascular disease (CVD), and diabetes were performed. Results: Mean noise exposure during the day and night was 51 dB(A) and 39 dB(A) for traffic noise, respectively, and 19 dB(A) and 17 dB(A) for railway noise. Adjusted regression models yielded significant effect estimates for a 10 dB(A) increase in railway noise during the night [SBP β = 0.84; 95% confidence interval (CI): 0.22, 1.46; DBP β = 0.44; 95% CI: 0.06, 0.81] and day (SBP β = 0.60; 95% CI: 0.07, 1.13). Additional adjustment for nitrogen dioxide left effect estimates almost unchanged. Stronger associations were estimated for participants with chronic disease. Significant associations with traffic noise were seen only among participants with diabetes. Conclusion: We found evidence of an adverse effect of railway noise on blood pressure in this cohort population. Traffic noise was associated with higher blood pressure only in diabetics, possibly due to low exposure levels. The study results imply more severe health effects by transportation noise in vulnerable populations, such as adults with hypertension, diabetes, or CVD.

Source Apportionment of Indoor, Outdoor, and Personal PM2.5 in Seattle, Washington, Using Positive Matrix Factorization

Abstract As part of a large exposure assessment and health-effects panel study, 33 trace elements and light-absorbing carbon were measured on 24-hr fixed-site filter samples for particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subjects residence, inside each residence, and on each subject (personal sample). Both two-way (PMF2) and three-way (PMF3) positive matrix factorization were used to deduce the sources contributing to PM2.5. Five sources contributing to the indoor and outdoor samples were identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed more PM2.5 mass on average than any other source in all microenvironments, with average values estimated by PMF2 and PMF3, respectively, of 7.6 and 8.7 μg/m3 for the outdoor samples, 4 and 5.3 μg/m3 for the indoor samples, and 3.8 and 3.4 μg/m3 for the personal samples. Personal exposure to the combustion-related particles was correlated with outdoor sources, whereas exposure to the crustal and chlorine-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the child subjects.

Investigation of the Concentration of Bacteria and Their Cell Envelope Components in Indoor Air in Two Elementary Schools

ABSTRACT Bacterial cell envelope components are widely distributed in airborne dust, where they act as inflammatory agents causing respiratory symptoms. Measurements of these agents and other environmental factors are assessed in two elementary schools in a southeastern city in the United States. Muramic acid (MA) was used as a marker for bacterial peptidoglycan (PG), and 3-hydroxy fatty acids (3-OH FAs) were used as markers for Gram-negative bacterial lipopolysaccharide (LPS). Culturable bacteria were collected using an Andersen sampler with three different culture

Characterization of Source-Specific Air Pollution Exposure for a Large Population-Based Swiss Cohort (SAPALDIA)

Background Although the dispersion model approach has been used in some epidemiologic studies to examine health effects of traffic-specific air pollution, no study has evaluated the model predictions vigorously. Methods We evaluated total and traffic-specific particulate matter < 10 and < 2.5 μm in aero-dynamic diameter (PM10, PM2.5), nitrogren dioxide, and nitrogen oxide concentrations predicted by Gaussian dispersion models against fixed-site measurements at different locations, including traffic-impacted, urban-background, and alpine settings between and across cities. The model predictions were then used to estimate individual subjects’ historical and cumulative exposures with a temporal trend model. Results Modeled PM10 and NO2 predicted at least 55% and 72% of the variability of the measured PM10 and NO2, respectively. Traffic-specific pollution estimates correlated with the NOx measurements (R2 ≥0.77) for background sites but not for traffic sites. Regional background PM10 accounted for most PM10 mass in all cities. Whereas traffic PM10 accounted for < 20% of the total PM10, it varied significantly within cities. The modeling error for PM10 was similar within and between cities. Traffic NOx accounted for the majority of NOx mass in urban areas, whereas background NOx accounted for the majority of NOx in rural areas. The within-city NO2 modeling error was larger than that between cities. Conclusions The dispersion model predicted well the total PM10, NOx, and NO2 and traffic-specific pollution at background sites. However, the model underpredicted traffic NOx and NO2 at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM10 are suitable for examining individual exposures and health effects within and between cities.

Changes in Lung Function and Airway Inflammation Among Asthmatic Children Residing in a Woodsmoke-Impacted Urban Area

Fine particulate matter (PM2.5) is associated with respiratory effects, and asthmatic children are especially sensitive. Preliminary evidence suggests that combustion-derived particles play an important role. Our objective was to evaluate effect estimates from different PM2.5 exposure metrics in relation to airway inflammation and lung function among children residing in woodsmoke-impacted areas of Seattle. Nineteen children (ages 6–13 yr) with asthma were monitored during the heating season. We measured 24-h outdoor and personal concentrations of PM2.5 and light-absorbing carbon (LAC). Levoglucosan (LG), a marker of woodsmoke, was also measured outdoors. We partitioned PM2.5 exposure into its ambient-generated (Eag) and nonambient (Ena) components. These exposure metrics were evaluated in relation to daily changes in exhaled nitric oxide (FENO), a marker of airway inflammation, and four lung function measures: midexpiratory flow (MEF), peak expiratory flow (PEF), forced expiratory volume in the first second (FEV1), and forced vital capacity (FVC). Eag, but not Ena, was correlated with combustion markers. Significant associations with respiratory health were seen only among participants not using inhaled corticosteroids. Increases in FENO were associated with personal PM2.5, personal LAC, and Eag but not with ambient PM2.5 or its combustion markers. In contrast, MEF and PEF decrements were associated with ambient PM2.5, its combustion markers, and Eag, but not with personal PM2.5 or personal LAC. FEV1 was associated only with ambient LG. Our results suggest that lung function may be especially sensitive to the combustion-generated component of ambient PM2.5, whereas airway inflammation may be more closely related to some other constituent of the ambient PM2.5 mixture.

Spatial Characteristics of Fine Particulate Matter: Identifying Representative Monitoring Locations in Seattle, Washington

Abstract This study investigates how PM2.5 varies spatially and how these spatial characteristics can be used to identify potential monitoring sites that are most representative of the overall ambient exposures to PM2.5 among susceptible populations in the Seattle, WA, area. Data collected at outdoor sites at the homes of participants of a large exposure assessment study were used in this study. Harvard impactors (HIs) were used at 40 outdoor sites throughout the Seattle metropolitan area. Up to six sites at a time were monitored for 10 consecutive 24-hr average periods. A fixed-effect analysis of variance (ANOVA) model that included date and location effects was used to analyze the spatial variability of outdoor PM2.5 concentrations. Both date and location effects were shown to be highly significant, explaining 92% of the variability in outdoor PM2.5 measurements. The day-to-day variability was 10 times higher than the spatial variability between sites. The site mean square was more than twice the error mean square, showing that differences between sites, while modest, are potentially an important contribution to measurement error. Variances of the model residuals and site effects were examined against spatial characteristics of the monitoring sites. The spatial characteristics included elevation, distance from arterials, and distance from major PM2.5 point sources. Results showed that the most representative PM2.5 sites were located at elevations of 80–120 m above sea level, and at distances of 100–300 m from the nearest arterial road. Location relative to industrial PM2.5 sources is not a significant predictor of residential outdoor PM2.5 measurements. Additionally, for sites to be representative of the average population exposures to PM2.5 among those highly susceptible to the health effects of PM2.5, areas of high elderly population density were considered. These representative spatial characteristics were used as multiple, overlapping criteria in a Geographic Information System (GIS) analysis to determine where the most representative sites are located.

Estimated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington

Abstract Epidemiological studies of particulate matter (PM) routinely use concentrations measured with stationary outdoor monitors as surrogates for personal exposure. Despite the frequently reported poor correlations between ambient concentrations and total personal exposure, the epidemiologic associations between ambient concentrations and health effects depend on the correlation between ambient concentrations and personal exposure to ambient-generated PM. This paper separates personal PM exposure into ambient and nonambient components and estimates the outdoor contribution to personal PM exposures with continuous light scattering data collected from 38 subjects in Seattle, WA. Across all subjects, the average exposure encountered indoors at home was lower than in all other microenvironments. Cooking and being at school were associated with elevated levels of exposure. Previously published estimates of particle infiltration (F inf) were combined with time–location data to estimate an ambient contribution fraction (α, mean = 0.66 ± 0.21) for each subject. The mean α was significantly lower for subjects monitored during the heating season (0.55 ± 0.16) than for those monitored during the nonheating season (0.80 ± 0.17). Our modeled α estimates agreed well with those estimated with the sulfur-tracer method (slope = 1.08; R2 = 0.67). We modeled exposure to ambient and nonambient PM with both continuous light scattering and 24-hr gravimetric data and found good agreement between the two methods. On average, ambient particles accounted for 48% of total personal exposure (range = 21–80%). The personal activity exposure was highly influenced by time spent away from monitored microenvironments. The median hourly longitudinal correlation between central site concentrations and personal exposures was 0.30. Although both a and the nonambient sources influence the personal–central relationship, the latter seems to dominate. Thus, total personal exposure may be poorly predicted by stationary outdoor monitors, particularly among persons whose PM exposure is dominated by nonambient exposures, for example, those living in tightly sealed homes, those who cook, and children.

Differences in Heart Rate Variability Associated with Long-Term Exposure to NO2

Background Heart rate variability (HRV), a measure of cardiac autonomic tone, has been associated with cardiovascular morbidity and mortality. Short-term studies have shown that subjects exposed to higher traffic-associated air pollutant levels have lower HRV. Objective Our objective was to investigate the effect of long-term exposure to nitrogen dioxide on HRV in the Swiss cohort Study on Air Pollution and Lung Diseases in Adults (SAPALDIA). Methods We recorded 24-hr electrocardiograms in randomly selected SAPALDIA participants ≥ 50 years of age. Other examinations included an interview investigating health status and measurements of blood pressure, body height, and weight. Annual exposure to NO2 at the address of residence was predicted by hybrid models (i.e., a combination of dispersion predictions, land-use, and meteorologic parameters). We estimated the association between NO2 and HRV in multivariable linear regression models. Complete data for analyses were available for 1,408 subjects. Results For women, but not for men, each 10-μg/m3 increment in 1-year averaged NO2 level was associated with a decrement of 3% (95% CI, −4 to −1) for the standard deviation of all normal-to-normal RR intervals (SDNN), −6% (95% CI, −11 to −1) for nighttime low frequency (LF), and −5% (95% CI, −9 to 0) for nighttime LF/high-frequency (HF) ratio. We saw no significant effect for 24-hr total power (TP), HF, LF, or LF/HF or for nighttime SDNN, TP, or HF. In subjects with self-reported cardiovascular problems, SDNN decreased by 4% (95% CI, −8 to −1) per 10-μg/m3 increase in NO2. Conclusions There is some evidence that long-term exposure to NO2 is associated with cardiac autonomic dysfunction in elderly women and in subjects with cardiovascular disease.

Adopting Clean Fuels and Technologies on School Buses. Pollution and Health Impacts in Children

RATIONALE More than 25 million American children breathe polluted air on diesel school buses. Emission reduction policies exist, but the health impacts to individual children have not been evaluated. METHODS Using a natural experiment, we characterized the exposures and health of 275 school bus riders before, during, and after the adoption of clean technologies and fuels between 2005 and 2009. Air pollution was measured during 597 trips on 188 school buses. Repeated measures of exhaled nitric oxide (FeNO), lung function (FEV1, FVC), and absenteeism were also collected monthly (1,768 visits). Mixed-effects models longitudinally related the adoption of diesel oxidation catalysts (DOCs), closed crankcase ventilation systems (CCVs), ultralow-sulfur diesel (ULSD), or biodiesel with exposures and health. MEASUREMENTS AND MAIN RESULTS Fine and ultrafine particle concentrations were 10-50% lower on buses using ULSD, DOCs, and/or CCVs. ULSD adoption was also associated with reduced FeNO (-16% [95% confidence interval (CI), -21 to -10%]), greater changes in FVC and FEV1 (0.02 [95% CI, 0.003 to 0.05] and 0.01 [95% CI, -0.006 to 0.03] L/yr, respectively), and lower absenteeism (-8% [95% CI, -16.0 to -0.7%]), with stronger associations among patients with asthma. DOCs, and to a lesser extent CCVs, also were associated with improved FeNO, FVC growth, and absenteeism, but these findings were primarily restricted to patients with persistent asthma and were often sensitive to control for ULSD. No health benefits were noted for biodiesel. Extrapolating to the U.S. population, changed fuel/technologies likely reduced absenteeism by more than 14 million/yr. CONCLUSIONS National and local diesel policies appear to have reduced childrens exposures and improved health.