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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 children's exposures and improved health.

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.

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.

Background: 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., Methods: 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

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 microm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subject's 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 microg/m3 for the outdoor samples, 4 and 5.3 microg/m3 for the indoor samples, and 3.8 and 3.4 microg/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.

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 (Finf) were combined with time-location data to estimate an ambient contribution fraction (alpha, mean = 0.66+/-0.21) for each subject. The mean alpha 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 alpha 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 alpha 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.

In this article we present results from a 2-year comprehensive exposure assessment study that examined the particulate matter (PM) exposures and health effects in 108 individuals with and without chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), and asthma. The average personal exposures to PM with aerodynamic diameters < 2.5 microm (PM2.5) were similar to the average outdoor PM2.5 concentrations but significantly higher than the average indoor concentrations. Personal PM2.5 exposures in our study groups were lower than those reported in other panel studies of susceptible populations. Indoor and outdoor PM2.5, PM10 (PM with aerodynamic diameters < 10 microm), and the ratio of PM2.5 to PM10 were significantly higher during the heating season. The increase in outdoor PM10 in winter was primarily due to an increase in the PM2.5 fraction. A similar seasonal variation was found for personal PM2.5. The high-risk subjects in our study engaged in an equal amount of dust-generating activities compared with the healthy elderly subjects. The children in the study experienced the highest indoor PM2.5 and PM10 concentrations. Personal PM2.5 exposures varied by study group, with elderly healthy and CHD subjects having the lowest exposures and asthmatic children having the highest exposures. Within study groups, the PM2.5 exposure varied depending on residence because of different particle infiltration efficiencies. Although we found a wide range of longitudinal correlations between central-site and personal PM2.5 measurements, the longitudinal r is closely related to the particle infiltration efficiency. PM2.5 exposures among the COPD and CHD subjects can be predicted with relatively good power with a microenvironmental model composed of three microenvironments. The prediction power is the lowest for the asthmatic children.