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 children's exposures and improved health.

CHARACTERIZATION OF PARTICULATE MATTER EMISSION FROM OPEN BURNING OF RICE STRAW.

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.

Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

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.

Measuring in-cabin school bus tailpipe and crankcase PM2.5: a new dual tracer method.

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.

Reduced exposure to PM10 and attenuated age-related decline in lung function.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Quantification of Self Pollution from Two Diesel School Buses using Three Independent Methods.

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.

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 >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. RESULTS: 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. 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.

Predicting Airborne Particle Levels Aboard Washington State School Buses.

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.

Changes in lung function and airway inflammation among asthmatic children residing in a woodsmoke-impacted urban area.

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.

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 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. RESULTS: 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. CONCLUSIONS: 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.

Source apportionment of PM(2.5) and selected hazardous air pollutants in Seattle.

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.

Effect of particulate air pollution on lung function in adult and pediatric subjects in a Seattle panel study.

STUDY OBJECTIVE: 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. PARTICIPANTS: 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. RESULTS: FEV(1) decrements were associated with 1-day lagged central site PM

Exposure assessment in the National Children's Study: introduction.

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.

Exposure and measurement contributions to estimates of acute air pollution effects.

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.

Evaluation and quality control of personal nephelometers in indoor, outdoor and personal environments.

Personal nephelometers provide useful real-time measurements of airborne particulate matter (PM). Recent studies have applied this tool to assess personal exposures and related health effects. However, a thorough quality control (QC) procedure for data collected from such a device in a large-scale exposure assessment study is lacking. We have evaluated the performance of a personal nephelometer (personal DataRAM or pDR) in the field. We present here a series of post hoc QC procedures for improving the quality of the pDR data. The correlations and the ratios between the pDRs and the collocated gravimetric measurements were used as indices of the pDR data quality. The pDR was operated in four modes: passive (no pump), active (with personal sampling pumps), active with a heated inlet, and a humidistat. The pDRs were worn by 21 asthmatic children, placed at their residences indoors and outdoors, as well as at a central site. All fixed-site pDRs were collocated with Harvard Impactors for PM2.5 (HI2.5). By examining the differences between the time-weighted average concentrations calculated from the real-time pDRs' readings and recorded internally by the pDRs, we identified 9.1% of the pDRs' measurements suffered from negative drifts. By comparing the pDRs' daily base level with the HI2.5 measurements, we identified 5.7% of the pDRs' measurements suffered from positive drifts. High relative humidity (RH) affected outdoor pDR measurements, even when a heater was used. Results from a series of chamber experiments suggest that the heated air stream cooled significantly after leaving the heater and entering the pDR light-scattering chamber. An RH correction equation was applied to the pDR measurements to remove the RH effect. The final R2 values between the fixed-site pDRs and the collocated HI2.5 measurements ranged between 0.53 and 0.72. We concluded that with a carefully developed QC procedure, personal nephelometers can provide high-quality data for assessing PM exposures on subjects and at fixed locations. We also recommend that outdoor pDRs be operated in the active mode without a heater and that the RH effect be corrected with an RH correction equation.

Exposure assessment of particulate matter for susceptible populations in Seattle.

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.

Association of FEV1 in asthmatic children with personal and microenvironmental exposure to airborne particulate matter.

Exposure to particulate matter (PM) air pollution has been shown to exacerbate children's asthma, but the exposure sources and temporal characteristics are still under study. Children's exposure to PM is likely to involve both combustion-related ambient PM and PM related to a child's activity in various indoor and outdoor microenvironments. Among 19 children with asthma, 9-17 years of age, we examined the relationship of temporal changes in percent predicted forced expiratory volume in 1 sec (FEV1) to personal continuous PM exposure and to 24-hr average gravimetric PM mass measured at home and central sites. Subjects were followed for 2 weeks during either the fall of 1999 or the spring of 2000, in a southern California region affected by transported air pollution. FEV(subscript)1(/subscript) was measured by subjects in the morning, afternoon, and evening. Exposure measurements included continuous PM using a passive nephelometer carried by subjects; indoor, outdoor home, and central-site 24-hr gravimetric PM2.5 (PM of aerodynamic diameter < 2.5 microm) and PM10; and central-site hourly PM10, nitrogen dioxide, and ozone. Data were analyzed with linear mixed models controlling for within-subject autocorrelation, FEV1 maneuver time, and exposure period. We found inverse associations of FEV1 with increasing PM exposure during the 24 hr before the FEV1 maneuver and with increasing multiday PM averages. Deficits in percent predicted FEV1 (95% confidence interval) for given PM interquartile ranges measured during the preceding 24-hr were as follows: 128 microg/m3 1-hr maximum personal PM, -6.0% (-10.5 to -1.4); 30 microg/m3 24-hr average personal PM, -5.9% (-10.8 to -1.0); 6.7 microg/m3 indoor home PM2.5, -1.6% (-2.8 to -0.4); 16 microg/m3 indoor home PM10, -2.1% (-3.7 to -0.4); 7.1 microg/m3 outdoor home PM2.5, -1.1% (-2.4 to 0.1); and 7.5 microg/m3 central-site PM2.5, -0.7% (-1.9 to 0.4). Stronger associations were found for multiday moving averages of PM for both personal and stationary-site PM. Stronger associations with personal PM were found in boys allergic to indoor allergens. FEV1 was weakly associated with NO2 but not with O3. Results suggest mixed respiratory effects of PM in asthmatic children from both ambient background exposures and personal exposures in various microenvironments.

The U.S. Environmental Protection Agency Particulate Matter Health Effects Research Centers Program: a midcourse report of status, progress, and plans.

In 1998 Congress mandated expanded U.S. Environmental Protection Agency (U.S. EPA) health effects research on ambient air particulate matter (PM) and a National Research Council (NRC) committee to provide research oversight. The U.S. EPA currently supports intramural and extramural PM research, including five academically based PM centers. The PM centers in their first 2.5 years have initiated research directed at critical issues identified by the NRC committee, including collaborative activities, and sponsored scientific workshops in key research areas. Through these activities, there is a better understanding of PM health effects and scientific uncertainties. Future PM centers research will focus on long-term effects associated with chronic PM exposures. This report provides a synopsis of accomplishments to date, short-term goals (during the next 2.5 years) and longer-term goals. It consists of six sections: biological mechanisms, acute effects, chronic effects, dosimetry, exposure assessment, and the specific attributes of a coordinated PM centers program.

Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington.

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.

Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization.

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.