[Show abstract][Hide abstract] ABSTRACT: Recent studies have linked acute respiratory and cardiovascular outcomes to measurements or estimates of traffic-related air pollutants at homes or schools. However, few studies have evaluated these outdoor measurements and estimates against personal exposure measurements. We compared measured and modeled home outdoor concentrations with personal measurements of traffic-related air pollutants in the Los Angeles air basin (Whittier and Riverside). Personal exposure of 63 children with asthma and 15 homes were assessed for particulate matter with an aerodynamic diameter less than 2.5 μm (PM(2.5)), elemental carbon (EC), and organic carbon (OC) during sixteen 10-day monitoring runs. Regression models to predict daily home outdoor PM(2.5), EC, and OC were constructed using home outdoor measurements, geographical and meteorological parameters, as well as CALINE4 estimates at outdoor home sites, which represent the concentrations from local traffic sources. These home outdoor models showed the variance explained (R(2)) was 0.97 and 0.94 for PM(2.5), 0.91 and 0.83 for OC, and 0.76 and 0.87 for EC in Riverside and Whittier, respectively. The PM(2.5) outdoor estimates correlated well with the personal measurements (Riverside R(2) = 0.65 and Whittier R(2) = 0.69). However, excluding potentially inaccurate samples from Riverside, the correlation between personal exposure to carbonaceous species and home outdoor estimates in Whittier was moderate for EC (R(2) = 0.37) and poor for OC (R(2) = 0.08). The CALINE4 estimates alone were not correlated with personal measurements of EC or other pollutants. While home outdoor estimates provide good approximations for daily personal PM(2.5) exposure, they may not be adequate for estimating daily personal exposure to EC and OC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11869-010-0099-y) contains supplementary material, which is available to authorized users.
Air Quality Atmosphere & Health 09/2012; 5(3):335-351. · 1.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM(2.5))] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (F(inf)) will affect epidemiologic study results.
We aimed to develop models to predict F(inf) for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM(2.5) exposure, subclinical cardiovascular disease, and clinical outcomes.
We collected 526 two-week, paired indoor-outdoor PM(2.5) filter samples from a subset of study homes. PM(2.5) elemental composition was measured by X-ray fluorescence, and F(inf) was estimated as the indoor/outdoor sulfur ratio. We regressed F(inf) 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.
The mean ± SD F(inf) 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. F(inf) 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 F(inf), with an RMSE of 0.13.
We developed intuitive models that can predict F(inf) using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential F(inf) into an exposure assessment.
Environmental Health Perspectives 02/2012; 120(6):824-30. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
We aimed to investigate the effects of railway and traffic noise exposure on blood pressure; a secondary aim was to address potentially susceptible subpopulations.
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.
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.
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.
Environmental Health Perspectives 09/2011; 120(1):50-5. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.
[Show abstract][Hide abstract] ABSTRACT: Exposures of occupants in school buses to on-road vehicle emissions, including emissions from the bus itself, can be substantially greater than those in outdoor settings. A dual tracer method was developed and applied to two school buses in Seattle in 2005 to quantify in-cabin fine particulate matter (PM2.5) concentrations attributable to the buses' diesel engine tailpipe (DPMtp) and crankcase vent (PMck) emissions. The new method avoids the problem of differentiating bus emissions from chemically identical emissions of other vehicles by using a fuel-based organometallic iridium tracer for engine exhaust and by adding deuterated hexatriacontane to engine oil. Source testing results showed consistent PM:tracer ratios for the primary tracer for each type of emissions. Comparisons of the PM:tracer ratios indicated that there was a small amount of unburned lubricating oil emitted from the tailpipe; however, virtually no diesel fuel combustion products were found in the crankcase emissions. For the limited testing conducted here, although PMck emission rates (averages of 0.028 and 0.099 g/km for the two buses) were lower than those from the tailpipe (0.18 and 0.14 g/km), in-cabin PMck concentrations averaging 6.8 microg/m3 were higher than DPMtp (0.91 microg/m3 average). In-cabin DPMtp and PMck concentrations were significantly higher with bus windows closed (1.4 and 12 microg/m3, respectively) as compared with open (0.44 and 1.3 microg/m3, respectively). For comparison, average closed- and open-window in-cabin total PM2.5 concentrations were 26 and 12 microg/m3, respectively. Despite the relatively short in-cabin sampling times, very high sensitivities were achieved, with detection limits of 0.002 microg/m3 for DPMtp and 0.05 microg/m3 for PMck.
Journal of the Air & Waste Management Association (1995) 05/2011; 61(5):494-503. · 1.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In the Seattle Air Toxics Monitoring Pilot Program, we measured 15 hazardous air pollutants (HAPs) at 6 sites for more than a year between 2000 and 2002. Spatial-temporal variations were evaluated with random-effects models and principal component analyses. The potential health risks were further estimated based on the monitored data, with the incorporation of the bootstrapping technique for the uncertainty analysis. It is found that the temporal variability was generally higher than the spatial variability for most air toxics. The highest temporal variability was observed for tetrachloroethylene (70% temporal vs. 34% spatial variability). Nevertheless, most air toxics still exhibited significant spatial variations, even after accounting for the temporal effects. These results suggest that it would require operating multiple air toxics monitoring sites over a significant period of time with proper monitoring frequency to better evaluate population exposure to HAPs. The median values of the estimated inhalation cancer risks ranged between 4.3 × 10⁻⁵ and 6.0 × 10⁻⁵, with the 5th and 95th percentile levels exceeding the 1 in a million level. VOCs as a whole contributed over 80% of the risk among the HAPs measured and arsenic contributed most substantially to the overall risk associated with metals.
Environment international 01/2011; 37(1):11-7. · 6.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Emission from field burning of crop residue, a common practice in many parts of the world today, has potential effects on air quality, atmosphere and climate. This study provides a comprehensive size and compositional characterization of particulate matter (PM) emission from rice straw (RS) burning using both in situ experiments (11 spread field burning) and laboratory hood experiments (3 pile and 6 spread burning) that were conducted during 2003-2006 in Thailand. The carbon balance and emission ratio method was used to determine PM emission factors (EF) in the field experiments. The obtained EFs varied from field to hood experiments reflecting multiple factors affecting combustion and emission. In the hood experiments, EFs were found to be depending on the burning types (spread or pile), moisture content and the combustion efficiency. In addition, in the field experiments, burning rate and EF were also influenced by weather conditions, i.e. wind. Hood pile burning produced significantly higher EF (20±8 g kg(-1) RS) than hood spread burning (4.7±2.2 g kg(-1) RS). The majority of PM emitted from the field burning was PM(2.5) with EF of 5.1±0.7 g m(-2) or 8.3±2.7 g kg(-1) RS burned. The coarse PM fraction (PM(10-2.5)) was mainly generated by fire attention activities and was relatively small, hence the resulting EF of PM(10) (9.4±3.5 g kg(-1) RS) was not significantly higher than PM(2.5). PM size distribution was measured across 8 size ranges (from <0.4 μm to >9.0 μm). The largest fractions of PM, EC and OC were associated with PM(1.1). The most significant components in PM(2.5) and PM(10) include OC, water soluble ions and levoglucosan. Relative abundance of some methoxyphenols (e.g., acetylsyringone), PAHs (e.g., fluoranthene and pyrene), organochlorine pesticides and PCBs may also serve as additional signatures for the PM emission. Presence of these toxic compounds in PM of burning smoke increases the potential toxic effects of the emission. For illustration, an estimation of the annual RS field burning in Thailand was made using the obtained in situ field burning EFs and preliminary burning activity data.
[Show abstract][Hide abstract] ABSTRACT: We monitored two Seattle school buses to quantify the buses' self pollution using the dual tracers (DT), lead vehicle (LV), and chemical mass balance (CMB) methods. Each bus drove along a residential route simulating stops, with windows closed or open. Particulate matter (PM) and its constituents were monitored in the bus and from a LV. We collected source samples from the tailpipe and crankcase emissions using an on-board dilution tunnel. Concentrations of PM(1), ultrafine particle counts, elemental and organic carbon (EC/OC) were higher on the bus than the LV. The DT method estimated that the tailpipe and the crankcase emissions contributed 1.1 and 6.8 mug/m(3) of PM(2.5) inside the bus, respectively, with significantly higher crankcase self pollution (SP) when windows were closed. Approximately two-thirds of in-cabin PM(2.5) originated from background sources. Using the LV approach, SP estimates from the EC and the active personal DataRAM (pDR) measurements correlated well with the DT estimates for tailpipe and crankcase emissions, respectively, although both measurements need further calibration for accurate quantification. CMB results overestimated SP from the DT method but confirmed crankcase emissions as the major SP source. We confirmed buses' SP using three independent methods and quantified crankcase emissions as the dominant contributor.
[Show abstract][Hide abstract] ABSTRACT: The risk estimates calculated from the conventional risk assessment method usually are compound specific and provide limited information for source-specific air quality control. We used a risk apportionment approach, which is a combination of receptor modeling and risk assessment, to estimate source-specific lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to first quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty analysis. The results showed that the overall cancer risk was 6.09 x 10(-5), with the background (1.61 x 10(-5)), diesel (9.82 x 10(-6)) and wood burning (9.45 x 10(-6)) sources being the primary risk sources. The PM(2.5) mass concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other than marine and soil were higher than 110(-6). It was also found that the diesel and wood burning sources presented similar cancer risks although the diesel exhaust contributed less to the PM(2.5) mass concentration than the wood burning. This highlights the additional value from such a risk apportionment approach that could be utilized for prioritizing control strategies to reduce the highest population health risks from exposure to HAPs.
Environment international 12/2008; 35(3):516-22. · 6.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: School buses contribute substantially to childhood air pollution exposures yet they are rarely quantified in epidemiology studies. This paper characterizes fine particulate matter (PM(2.5)) aboard school buses as part of a larger study examining the respiratory health impacts of emission-reducing retrofits.To assess onboard concentrations, continuous PM(2.5) data were collected during 85 trips aboard 43 school buses during normal driving routines, and aboard hybrid lead vehicles traveling in front of the monitored buses during 46 trips. Ordinary and partial least square regression models for PM(2.5) onboard buses were created with and without control for roadway concentrations, which were also modeled. Predictors examined included ambient PM(2.5) levels, ambient weather, and bus and route characteristics.Concentrations aboard school buses (21 mug/m(3)) were four and two-times higher than ambient and roadway levels, respectively. Differences in PM(2.5) levels between the buses and lead vehicles indicated an average of 7 mug/m(3) originating from the bus's own emission sources. While roadway concentrations were dominated by ambient PM(2.5), bus concentrations were influenced by bus age, diesel oxidative catalysts, and roadway concentrations. Cross validation confirmed the roadway models but the bus models were less robust.These results confirm that children are exposed to air pollution from the bus and other roadway traffic while riding school buses. In-cabin air pollution is higher than roadway concentrations and is likely influenced by bus characteristics.
[Show abstract][Hide abstract] ABSTRACT: 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.
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).
We recorded 24-hr electrocardiograms in randomly selected SAPALDIA participants >or= 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.
For women, but not for men, each 10-microg/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-microg/m3 increase in NO2.
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.
Environmental Health Perspectives 10/2008; 116(10):1357-61. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Fine particulate matter (PM(2.5)) 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 PM(2.5) 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 PM(2.5) and light-absorbing carbon (LAC). Levoglucosan (LG), a marker of woodsmoke, was also measured outdoors. We partitioned PM(2.5) exposure into its ambient-generated (E(ag)) and nonambient (E(na)) components. These exposure metrics were evaluated in relation to daily changes in exhaled nitric oxide (FE(NO)), a marker of airway inflammation, and four lung function measures: midexpiratory flow (MEF), peak expiratory flow (PEF), forced expiratory volume in the first second (FEV(1)), and forced vital capacity (FVC). E(ag), but not E(na), was correlated with combustion markers. Significant associations with respiratory health were seen only among participants not using inhaled corticosteroids. Increases in FE(NO) were associated with personal PM(2.5), personal LAC, and E(ag) but not with ambient PM(2.5) or its combustion markers. In contrast, MEF and PEF decrements were associated with ambient PM(2.5), its combustion markers, and E(ag), but not with personal PM(2.5) or personal LAC. FEV(1) was associated only with ambient LG. Our results suggest that lung function may be especially sensitive to the combustion-generated component of ambient PM(2.5), whereas airway inflammation may be more closely related to some other constituent of the ambient PM(2.5) mixture.
[Show abstract][Hide abstract] ABSTRACT: Air pollution has been associated with impaired health, including reduced lung function in adults. Moving to cleaner areas has been shown to attenuate adverse effects of air pollution on lung function in children but not in adults.
We conducted a prospective study of 9651 adults (18 to 60 years of age) randomly selected from population registries in 1990 and assessed in 1991, with 8047 participants reassessed in 2002. There was complete information on lung volumes and flows (e.g., forced vital capacity [FVC], forced expiratory volume in 1 second [FEV1], FEV1 as a percentage of FVC, and forced expiratory flow between 25 and 75% of the FVC [FEF25-75]), smoking habits, and spatially resolved concentrations of particulate matter that was less than 10 microm in aerodynamic diameter (PM10) from a validated dispersion model assigned to residential addresses for 4742 participants at both the 1991 and the 2002 assessments and in the intervening years.
Overall exposure to individual home outdoor PM10 declined over the 11-year follow-up period (median, -5.3 mug per cubic meter; interquartile range, -7.5 to -4.2). In mixed-model regression analyses, with adjustment for confounders, PM10 concentrations at baseline, and clustering within areas, there were significant negative associations between the decrease in PM10 and the rate of decline in FEV1 (P=0.045), FEV1 as a percentage of FVC (P=0.02), and FEF25-75 (P=0.001). The net effect of a decline of 10 microg of PM10 per cubic meter over an 11-year period was to reduce the annual rate of decline in FEV1 by 9% and of FEF25-75 by 16%. Cumulative exposure in the interval between the two examinations showed similar associations.
Decreasing exposure to airborne particulates appears to attenuate the decline in lung function related to exposure to PM10. The effects are greater in tests reflecting small-airway function.
New England Journal of Medicine 01/2008; 357(23):2338-47. · 54.42 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The potential benefits of combining the speciated PM(2.5) and VOCs data in source apportionment analysis for identification of additional sources remain unclear. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine (ME-2) to quantify source contributions to the mixture of hazardous air pollutants (HAPs). We used the 'missing mass', defined as the concentration of the measured total particle mass minus the sum of all analyzed species, as an additional variable and implemented an auxiliary equation to constrain the sum of all species mass fractions to be 100%. Regardless of whether the above constraint was implemented and/or the additional VOCs data were included with the PM(2.5) data, the models identified that wood burning (24%-31%), secondary sulfate (20%-24%) and secondary nitrate (15%-20%) were the main contributors to PM(2.5). Using only PM(2.5) data, the model distinguished two diesel features with the 100% constraint, but identified only one diesel feature without the constraint. When both PM(2.5) and VOCs data were used, one additional feature was identified as the major contributor (26%) to total VOC mass. Adding VOCs data to the speciated PM(2.5) data in source apportionment modeling resulted in more accurate source contribution estimates for combustion related sources as evidenced by the less 'missing mass' percentage in PM(2.5). Using the source contribution estimates, we evaluated the validity of using black carbon (BC) as a surrogate for diesel exhaust. We found that BC measured with an aethalometer at 370 nm and 880 nm had reasonable correlations with the estimated concentrations of diesel particulate matters (r>0.7), as well as with the estimated concentrations of wood burning particles during the heating seasons (r=0.56-0.66). This indicates that the BC is not a unique tracer for either source. The difference in BC between 370 and 880 nm, however, correlated well exclusively with the estimated wood smoke source (r=0.59) and may be used to separate wood smoke from diesel exhaust. Thus, when multiple BC related sources exist in the same monitoring environment, additional data processing or modeling of the BC measurements is needed before these measurements could be used to represent the diesel exhaust.
Science of The Total Environment 11/2007; 386(1-3):42-52. · 3.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
We evaluated total and traffic-specific particulate matter < 10 and < 2.5 microm in aero-dynamic diameter (PM(10), PM(2.5)), 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.
Modeled PM(10) and NO(2) predicted at least 55% and 72% of the variability of the measured PM(10) and NO(2), respectively. Traffic-specific pollution estimates correlated with the NO(x) measurements (R(2) >or=0.77) for background sites but not for traffic sites. Regional background PM(10) accounted for most PM(10) mass in all cities. Whereas traffic PM(10) accounted for < 20% of the total PM(10), it varied significantly within cities. The modeling error for PM(10) was similar within and between cities. Traffic NO(x) accounted for the majority of NO(x) mass in urban areas, whereas background NO(x) accounted for the majority of NO(x) in rural areas. The within-city NO(2) modeling error was larger than that between cities.
The dispersion model predicted well the total PM(10), NO(x), and NO(2) and traffic-specific pollution at background sites. However, the model underpredicted traffic NO(x) and NO(2) at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM(10) are suitable for examining individual exposures and health effects within and between cities.
Environmental Health Perspectives 11/2007; 115(11):1638-45. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Swiss Cohort Study on Air Pollution and Lung Diseases in Adults (SAPALDIA), conducted in 1991 (SAPALDIA 1) in eight areas among 9,651 randomly selected adults aged 18-60 years, reported associations among the prevalence of respiratory symptoms, nitrogen dioxide, and particles with an aerodynamic diameter of less than 10 microg/m3. Later, 8,047 subjects reenrolled in 2002 (SAPALDIA 2). The effects of individually assigned traffic exposures on reported respiratory symptoms were estimated, while controlling for socioeconomic and exposure- and health-related factors. The risk of attacks of breathlessness increased for all subjects by 13% (95% confidence interval: 3, 24) per 500-m increment in the length of main street segments within 200 m of the home and decreased in never smokers by 12% (95% confidence interval: 0, 22) per 100-m increment in distance from home to a main street. Living within 20 m of a main street increased the risks of regular phlegm by 15% (95% confidence interval: 0, 31) and wheezing with breathing problems by 34% (95% confidence interval: 0, 79) in never smokers. In 2002, the effects related to road distance were different from those in 1991, which could be due to changes in the traffic pollution mixture. These findings among a general population provide strong confirmation that living near busy streets leads to adverse respiratory health effects.
American Journal of Epidemiology 01/2007; 164(12):1190-8. · 4.78 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To determine whether increased exposure to particulate matter air pollution (PM), measured with personal, residential, or central site monitoring, was associated with pulmonary function decrements in either adults with COPD or children with asthma.
We studied 57 adults with or without COPD and 17 children aged 6 to 13 years with physician-diagnosed asthma in Seattle during a 3-year panel study. Study design and measurements: Indoor and outdoor PM measurements were made at subjects' homes. The subjects wore personal exposure monitors for 10 consecutive 24-h periods, and PM was also measured at a central outdoor location. We assessed the within-subject effect of particulate exposure on FEV(1) and peak expiratory flow (PEF) in adults, and maximal midexpiratory flow (MMEF), PEF, FEV(1), and symptoms in children.
FEV(1) decrements were associated with 1-day lagged central site PM </= 2.5 microm in diameter (PM(2.5)) in adult subjects with COPD. In children not receiving antiinflammatory medication, same day indoor, outdoor, and central site exposures to PM(2.5) were associated with decrements in MMEF, PEF, and FEV(1). Associations with PM(2.5) and lung function decrements were also observed for 1-day lagged indoor (MMEF, PEF, FEV(1)) and personal (PEF only) exposures. Antiinflammatory medication use in children significantly attenuated the PM effect on airflow rates and volumes.
This study found consistent decrements in MMEF in children with asthma who were not receiving medications. It is notable that effects were observed even though PM exposures were low for an urban area. These findings suggest the need for future larger studies of PM effects in this susceptible population that repeatedly measure spirometry to include MMEF and potentially more sensitive markers of airway inflammation such as exhaled breath condensate and exhaled nitric oxide.
[Show abstract][Hide abstract] ABSTRACT: Past studies of air pollution effects among sensitive subgroups have produced inconsistent results. Our objective was to determine relationships between various measures of air pollution and cardiorespiratory effects in older subjects.
We conducted a study that included repeated measurements of pulmonary function (arterial oxygen saturation) and cardiac function (heart rate and blood pressure) in a panel of 88 subjects (>57 years of age) in Seattle during the years 1999 to 2001. Subjects were healthy or had lung or heart disease. Each subject participated in sessions of 10 consecutive days of exposure monitoring and collection of health outcomes for up to 2 sessions. Associations between health outcomes and indoor, outdoor, and personal measures of particulate matter </=2.5 micrometers (PM2.5) or particulate matter </=10 micrometers (PM10) were evaluated using generalized estimating equations with an exchangeable working correlation matrix and robust standard errors. The model included terms for the within-subject, within-session effect; the within- subject, between-session effect; and an interaction term for medication usage. The model controlled for temperature, relative humidity, body mass index, and age.
Associations between air pollution and health measurements were found primarily in healthy subjects. Healthy subjects taking no medications had decreases in heart rate associated with indoor and outdoor PM2.5 and PM10. Healthy subjects on medication had small increases in systolic blood pressure associated with indoor PM2.5 and outdoor PM10. Heterogeneity analysis found differences among the health groups for associations with particulate air pollution in heart rate but not in blood pressure.
Modest concentrations of air pollutants were associated with small changes in cardiac function.
[Show abstract][Hide abstract] ABSTRACT: The science of exposure assessment is relatively new and evolving rapidly with the advancement of sophisticated methods for specific measurements at the picogram per gram level or lower in a variety of environmental and biologic matrices. Without this measurement capability, environmental health studies rely on questionnaires or other indirect means as the primary method to assess individual exposures. Although we use indirect methods, they are seldom used as stand-alone tools. Analyses of environmental and biologic samples have allowed us to get more precise data on exposure pathways, from sources to concentrations, to routes, to exposure, to doses. They also often allow a better estimation of the absorbed dose and its relation to potential adverse health outcomes in individuals and in populations. Here, we make note of various environmental agents and how best to assess exposure to them in the National Children's Study--a longitudinal epidemiologic study of children's health. Criteria for the analytical method of choice are discussed with particular emphasis on the need for long-term quality control and quality assurance measures.
Environmental Health Perspectives 09/2005; 113(8):1076-82. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Air pollution health effect studies are intended to estimate the effect of a pollutant on a health outcome. The definition of this effect depends upon the study design, disease model parameterization, and the type of analysis. Further limitations are imposed by the nature of exposure and our ability to measure it. We define a plausible exposure model for air pollutants that are relatively nonreactive and discuss how exposure varies. We discuss plausible disease models and show how their parameterizations are affected by different exposure partitions and by different study designs. We then discuss a measurement model conditional on ambient concentrations and incorporate this into the disease model. We use simulation studies to show the impact of a range of exposure model assumptions on estimation of the health effect in the ecologic time series design. This design only uses information from the time-varying ambient source exposure. When ambient and nonambient sources are independent, exposure variation due to nonambient source exposures behaves like Berkson measurement error and does not bias the effect estimates. Variation in the population attenuation of ambient concentrations over time does bias the estimates with the bias being either positive or negative depending upon the association of this parameter with ambient pollution. It is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from nonambient sources that do not contribute information in the time series design. We conclude that general statements about the implications of measurement error need to be conditioned on the health effect study design and the health effect parameter to be estimated.
Journal of Exposure Analysis and Environmental Epidemiology 08/2005; 15(4):366-76. · 2.72 Impact Factor