Air pollution and daily mortality in three U.S. counties

I used generalized additive models to analyze the time-series of daily total nonaccidental and cause-specific (cardiovascular, cerebrovascular, and chronic obstructive pulmonary disease) deaths over the period 1987-1995 in three major U.S. metropolitan areas: Cook County, Los Angeles County, and Maricopa County. In all three counties I had monitoring information on particulate matter [less than/equal to] 10 microm (PM(10)), carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. In Los Angeles, monitoring information on particulate matter [less than/equal to] 2.5 microm (PM(2.5)) was available as well. I present the results of both single and multi-pollutant analyses. Air pollution was associated with each of the mortality end points. With respect to the individual components of the pollution mix, the results indicate considerable heterogeneity of air pollution effects in the different geographic locations. In general, the gases, particularly CO, but not ozone, were much more strongly associated with mortality than was particulate matter. This association was particularly striking in Los Angeles County.     

Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: analyses of RIOPA data

The Relationship of Indoor, Outdoor and Personal Air (RIOPA) study was designed to investigate residential indoor, outdoor and personal exposures to several classes of air pollutants, including volatile organic compounds, carbonyls and fine particles (PM2.5). Samples were collected from summer, 1999 to spring, 2001 in Houston (TX), Los Angeles (CA) and Elizabeth (NJ). Indoor, outdoor and personal PM2.5 samples were collected at 212 nonsmoking residences, 162 of which were sampled twice. Some homes were chosen due to close proximity to ambient sources of one or more target analytes, while others were farther from sources. Median indoor, outdoor and personal PM2.5 mass concentrations for these three sites were 14.4, 15.5 and 31.4 microg/m3, respectively. The contributions of ambient (outdoor) and nonambient sources to indoor and personal concentrations were quantified using a single compartment box model with measured air exchange rate and a random component superposition (RCS) statistical model. The median contribution of ambient sources to indoor PM2.5 concentrations using the mass balance approach was estimated to be 56% for all study homes (63%, 52% and 33% for California, New Jersey and Texas study homes, respectively). Reasonable variations in model assumptions alter median ambient contributions by less than 20%. The mean of the distribution of ambient contributions across study homes agreed well for the mass balance and RCS models, but the distribution was somewhat broader when calculated using the mass balance model with measured air exchange rates.     

A population exposure model for particulate matter: case study results for PM(2.5) in Philadelphia, PA.

A population exposure model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, has been developed and applied in a case study of daily PM(2.5) exposures for the population living in Philadelphia, PA. SHEDS-PM is a probabilistic model that estimates the population distribution of total PM exposures by randomly sampling from various input distributions. A mass balance equation is used to calculate indoor PM concentrations for the residential microenvironment from ambient outdoor PM concentrations and physical factor data (e.g., air exchange, penetration, deposition), as well as emission strengths for indoor PM sources (e.g., smoking, cooking). PM concentrations in nonresidential microenvironments are calculated using equations developed from regression analysis of available indoor and outdoor measurement data for vehicles, offices, schools, stores, and restaurants/bars. Additional model inputs include demographic data for the population being modeled and human activity pattern data from EPA's Consolidated Human Activity Database (CHAD). Model outputs include distributions of daily total PM exposures in various microenvironments (indoors, in vehicles, outdoors), and the contribution from PM of ambient origin to daily total PM exposures in these microenvironments. SHEDS-PM has been applied to the population of Philadelphia using spatially and temporally interpolated ambient PM(2.5) measurements from 1992-1993 and 1990 US Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM(2.5) exposures for the population of Philadelphia (median=20 microg/m(3); 90th percentile=59 microg/m(3)). Variability in human activities, and the presence of indoor-residential sources in particular, contributed to the observed variability in total PM(2.5) exposures. The uncertainty in the estimated population distribution for total PM(2.5) exposures was highest at the upper end of the distribution and revealed the importance of including estimates of input uncertainty in population exposure models. The distributions of daily microenvironmental PM(2.5) exposures (exposures due to time spent in various microenvironments) indicated that indoor-residential PM(2.5) exposures (median=13 microg/m(3)) had the greatest influence on total PM(2.5) exposures compared to the other microenvironments. The distribution of daily exposures to PM(2.5) of ambient origin was less variable across the population than the distribution of daily total PM(2.5) exposures (median=7 microg/m(3); 90th percentile=18 microg/m(3)) and similar to the distribution of ambient outdoor PM(2.5) concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM(2.5) exposures as was observed for exposure to other PM(2.5) sources. For most of the simulated population, exposure to PM(2.5) of ambient origin contributed a significant percent of the daily total PM(2.5) exposures (median=37.5%), especially for the segment of the population without exposure to environmental tobacco smoke in the residence (median=46.4%). Development of the SHEDS-PM model using the Philadelphia PM(2.5) case study also provided useful insights into the limitations of currently available data for use in population exposure models. In addition, data needs for improving inputs to the SHEDS-PM model, reducing uncertainty and further refinement of the model structure, were identified.     

Fine organic particulate matter dominates indoor-generated PM2.5 in RIOPA homes

Residential indoor and outdoor fine particle (PM(2.5)) organic (OC) and elemental carbon (EC) concentrations (48 h) were measured at 173 homes in Houston, TX, Los Angeles County, CA, and Elizabeth, NJ as part of the Relationship of Indoor, Outdoor and Personal Air (RIOPA) study. The adsorption of organic vapors on the quartz fiber sampling filter (a positive artifact) was substantial indoors and out, accounting for 36% and 37% of measured OC at the median indoor (8.2 microg C/m(3)) and outdoor (5.0 microg C/m(3)) OC concentrations, respectively. Uncorrected, adsorption artifacts would lead to substantial overestimation of particulate OC both indoors and outdoors. After artifact correction, the mean particulate organic matter (OM=1.4 OC) concentration indoors (9.8 microg/m(3)) was twice the mean outdoor concentration (4.9 microg/m(3)). The mean EC concentration was 1.1 microg/m(3) both indoors and outdoors. OM accounted for 29%, 30% and 29% of PM(2.5) mass outdoors and 48%, 55% and 61% of indoor PM(2.5) mass in Los Angeles Co., Elizabeth and Houston study homes, respectively. Indirect evidence provided by species mass balance results suggests that PM(2.5) nitrate (not measured) was largely lost during outdoor-to-indoor transport, as reported by Lunden et al. This results in dramatic changes with outdoor-to-indoor transport in the mass and composition of ambient-generated PM(2.5) at California homes. On average, 71% to 76% of indoor OM was emitted or formed indoors, calculated by (1) Random Component Superposition (RCS) model and (2) non-linear fit of OC and air exchange rate data to the mass balance model. Assuming that all particles penetrate indoors (P=1) and there is no particle loss indoors (k=0), a lower bound estimate of 41% of indoor OM was indoor-generated (mean). OM appears to be the predominant species in indoor-generated PM(2.5), based on species mass balance results. Particulate OM emitted or formed indoors is substantial enough to alter the concentration, composition and behavior of indoor PM(2.5). One interesting effect of increased indoor OM concentrations is a shift in the gas-particle partitioning of polycyclic aromatic hydrocarbons (PAHs) from the gas to the particle phase with outdoor-to-indoor transport.     

Indoor/outdoor PM10 and PM2.5 in Bangkok, Thailand

Twenty-four-hour averaged PM10 and PM2.5 concentrations were obtained by using 4-liter-per-minute-pumps and impactors in microenvironments of a busy shopping district and a university hospital campus. In both areas, most people live directly adjacent to their worksites--minimizing the need to measure commuting exposure as part of total daily exposure. Co-located samplers were set in indoor microenvironments, the near-ambient zone of the households, and at nearby streetside central ambient monitoring stations. Smoking and use of other indoor PM sources were recorded daily via questionnaires. Consistent with previous studies, smoking and the use of charcoal stoves increased indoor particulate matter levels. The sampled air-conditioned hospital area had substantially lower particle concentrations than outdoors. A simple total exposure model was used to estimate the human exposure. The averaged ratios of co-located PM2.5/PM10 concentrations in various microenvironments are reported for each location. A single daily indoor average PM10 concentration for all households measured in a given sampling day is calculated for correlation analysis. Results showed that day-to-day fluctuations of these calculated indoor PM10 levels correlated well with near-ambient data and moderately well with ambient data collected at the nearby central monitoring site. This implies that ambient monitors are able to capture the daily variations of indoor PM levels or even personal exposure and may help explain the robust association of ambient PM levels and health effects found in many epidemiological studies. Absolute PM exposures, however, were substantially underestimated by ambient monitors in the shopping district, probably because of strong local sources.     

Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study

To provide additional insight into factors affecting exposure to airborne particulate matter and the resultant health effects, we developed a method to estimate the ambient and nonambient components of total personal exposure. The ambient (or outdoor) component of total personal exposure to particulate matter (PM) (called ambient exposure) includes exposure to the ambient PM concentration while outdoors and exposure while indoors to ambient PM that has infiltrated indoors. The nonambient component of total personal exposure to PM (called nonambient exposure) refers to exposure to PM generated by indoor sources and an individual's personal activity. We used data collected from a personal monitoring study in Vancouver, Canada to demonstrate the methodology. In this study, ambient PM(2.5) exposure was 71% of the measured ambient PM(2.5) concentration and was responsible for 44% of the measured total personal PM(2.5) exposure. Regression analysis of the pooled data sets for ambient and total exposure against outdoor concentrations yielded similar slopes (0.76 for ambient and 0.77 for total) but a higher coefficient of determination for ambient exposure (R(2)=0.62) than for total exposure (R(2)=0.072). As expected, the nonambient exposure was not related to the ambient concentration (R(2)<10(-6)). For longitudinal analyses of the relationship between measured personal exposure and ambient concentrations for individual subjects, the correlation of total personal exposure with ambient concentration yielded values of Pearson's r from 0.83 to -0.68 with an average of 0.36. The relationship was statistically significant for only five of the 16 subjects. In contrast, the correlation of the estimated ambient exposure with ambient concentration yielded values of Pearson's r from 0.92 to 0.77 with an average of 0.88; 14 were significant. An example, taken from an epidemiologic analysis using the exposure data from this paper, demonstrates the usefulness of separating total exposure into its ambient and nonambient components.     

Exposures to multiple air toxics in New York City

Efforts to assess health risks associated with exposures to multiple urban air toxics have been hampered by the lack of exposure data for people living in urban areas. The TEACH (Toxic Exposure Assessment, a Columbia/Harvard) study was designed to characterize levels of and factors influencing personal exposures to urban air toxics among high school students living in inner-city neighborhoods of New York City and Los Angeles, California. This present article reports methods and data for the New York City phase of TEACH, focusing on the relationships between personal, indoor, and outdoor concentrations in winter and summer among a group of 46 high school students from the A. Philip Randolph Academy, a public high school located in the West Central Harlem section of New York City. Air pollutants monitored included a suite of 17 volatile organic compounds (VOCs) and aldehydes, particulate matter with a mass median aerodynamic diameter 相似文献   

Exposure assessment of ultrafine particles in epidemiologic time-series studies

The health effects of fine-particulate air pollution (PM2.5, aerodynamic diameter <2.5 microm) observed in epidemiologic time-series studies may partly be due to the high number concentration of ultrafine particles (aerodynamic diameter <0.1 microm) in urban air. The key uncertainty is how well daily variations in the ultrafine particle concentration measured at a central site correlate with the variations in average personal exposure. Due to a lack of research data, this correlation has been estimated indirectly in this review on the basis of studies on the sources, ambient air levels, spatial variability, indoor air levels, and lung deposition of ultrafine particles. It is concluded that central site monitoring may give a somewhat worse proxy for human exposure to ultrafine particles than to PM2.5 in time-series studies.     

Personal exposure to particles in Banská Bystrica, Slovakia

Epidemiological studies have associated adverse health impacts with ambient concentrations of particulate matter (PM), though these studies have been limited in their characterization of personal exposure to PM. An exposure study of healthy nonsmoking adults and children was conducted in Banska Bystrica, Slovakia, to characterize the range of personal exposures to air pollutants and to determine the influence of occupation, season, residence location, and outdoor and indoor concentrations on personal exposures. Twenty-four-hour personal, at-home indoor, and ambient measurements of PM10, PM2.5, sulfate (SO4(2-)) and nicotine were obtained for 18 office workers, 16 industrial workers, and 15 high school students in winter and summer. Results showed that outdoor levels of pollutants were modest, with clear seasonal differences: outdoor PM10 summer/winter mean = 35/45 microg/m3; PM2.5 summer/winter mean = 22/32 microg/m3. SO4(2-) levels were low (4-7 microg/m3) and relatively uniform across the different sample types (personal, indoor, outdoor), areas, and occupational groups. This suggests that SO4(2-) may be a useful marker for combustion mode particles of ambient origin, although the relationship between personal exposures and ambient SO4(2-) levels was more complex than observed in North American settings. During winter especially, the central city area showed higher concentrations than the suburban location for outdoor, personal, and indoor measures of PM10, PM2.5, and to a lesser extent for SO4(2-), suggesting the importance of local sources. For PM2.5 and PM10, ratios consistent with expectations were found among exposure indices for all three subject groups (personal>indoor>outdoor), and between work type (industrial>students>office workers). The ratio of PM2.5 personal to indoor exposures ranged from 1.0 to 3.9 and of personal to outdoor exposures from 1.6 to 4.2. The ratio of PM10 personal to indoor exposures ranged from 1.1 to 2.9 and the ratio of personal to outdoor exposures from 2.1 to 4.1. For a combined group of office workers and students, personal PM10/PM2.5 levels were predicted by statistically significant multivariate models incorporating indoor (for PM2.5) or outdoor (for PM10) PM levels, and nicotine exposure (for PM10). Small but significant fractions of the overall variability, 15% for PM2.5 and 17% for PM10, were explained by these models. The results indicate that central site monitors underpredict actual human exposures to PM2.5 and PM10. Personal exposure to SO4(2-) was found to be predicted by outdoor or indoor SO4(2-) levels with 23-71% of the overall variability explained by these predictors. We conclude that personal exposure measurements and additional demographic and daily activity data are crucial for accurate evaluation of exposure to particles in this setting.     

Exposure to ambient and nonambient components of particulate matter: a comparison of health effects

BACKGROUND: Numerous epidemiologic studies report associations between outdoor concentrations of particles and adverse health effects. Because personal exposure to particles is frequently dominated by exposure to nonambient particles (those originating from indoor sources), we present an approach to evaluate the relative impacts of ambient and nonambient exposures. METHODS: We developed separate estimates of exposures to ambient and nonambient particles of different size ranges (PM2.5, PM10-2.5 and PM10) based on time-activity data and the use of particle sulfate measurements as a tracer for indoor infiltration of ambient particles. To illustrate the application of these estimates, associations between cardiopulmonary health outcomes and the estimated exposures were compared with associations computed using measurements of personal exposures and outdoor concentrations for a repeated-measures panel study of 16 patients with chronic obstructive pulmonary disease conducted in the summer of 1998 in Vancouver. RESULTS: Total personal fine particle exposures were dominated by exposures to nonambient particles, which were not correlated with ambient fine particle exposures or ambient concentrations. Although total and nonambient particle exposures were not associated with any of the health outcomes, ambient exposures (and to a lesser extent ambient concentrations) were associated with decreased lung function, decreased systolic blood pressure, increased heart rate, and increased supraventricular ectopic heartbeats. Measures of heart rate variability showed less consistent relationships among the various exposure metrics. CONCLUSIONS: These results demonstrate the usefulness of separating total personal particle exposures into their ambient and nonambient components. The results support previous epidemiologic findings using ambient concentrations by demonstrating an association between health outcomes and ambient (outdoor origin) particle exposures but not with nonambient (indoor origin) particle exposures.     

Real-time measurement of indoor particulate matter originating from environmental tobacco smoke: a pilot study

Short-term measurement of suspended particulate matter has been recently made possible since the release of laser-operating portable instruments. Data of a pilot study of field evaluation of environmental tobacco smoke (ETS) with a portable instrument are reported. We analysed the concentrations of total suspended particle (TSP) and of the fine particles PM10, PM7, PM2.5 and PM1 released indoor from a single cigarette, and their levels inside smoking- and non-smoking-areas of a restaurant. The results indicate that ETS creates high level indoor particulate pollution, with concentrations of PM10 exceeding air quality standards. This kind of field evaluation could allow a more careful assessing of short-term exposure to ETS and its relevance to public health.     

Childhood exposure to PM10: relation between personal, classroom, and outdoor concentrations.

OBJECTIVES: To investigate the validity of outdoor concentrations of particulate matter < 10 microns diameter (PM10) as a measure of exposure in time series studies, and to study the extent to which differences between personal and outdoor PM10 concentrations can be explained. METHODS: Four to eight repeated measurements of personal and outdoor PM10 concentrations were conducted for 45 children, aged 10-12 years, from four schools in Wageningen and Amsterdam, The Netherlands. Repeated PM10 measurements in the classrooms were conducted in three of the schools. Averaging time was 24 hours for the personal and outdoor measurements, and eight hours (daytime) and 24 hours for the classroom measurements. For each child separately, personal exposures were related to outdoor concentrations in a regression analysis. The distribution of the individual correlation and regression coefficients was investigated. Information about factors that might influence personal exposures was obtained by questionnaire. RESULTS: Median Pearson's correlations between personal and outdoor concentrations were 0.63 for children with parents who did not smoke and 0.59 for children with parents who smoked. For children with parents who did not smoke, excluding days with exposure to environmental tobacco smoke (ETS) improved the correlation to a median R of 0.73. The mean personal PM10 concentration was 105 micrograms/m3; on average 67 micrograms/m3 higher than the corresponding outdoor concentrations. The main part of this difference could be attributed to exposure to ETS, to high PM10 concentrations in the classrooms, and to (indoor) physical activity. CONCLUSIONS: The results show a reasonably high correlation between repeated personal and outdoor PM10 measurements within children, providing support for the use of fixed site measurements as a measure of exposure to PM10 in epidemiological time series studies. The large differences between personal and outdoor PM10 concentrations probably result from a child's proximity to particle generating sources and particles resuspended by personal activities.     

Determinants of personal and indoor PM2.5 and absorbance among elderly subjects with coronary heart disease

Epidemiological studies have established an association between outdoor levels of fine particles (PM2.5) and cardiovascular health. However, there is little information on the determinants of PM2.5 exposures among persons with cardiovascular disease, a potentially susceptible population group. Daily outdoor, indoor and personal PM2.5 and absorbance (proxy for elemental carbon) concentrations were measured among elderly subjects with cardiovascular disease in Amsterdam, the Netherlands, and Helsinki, Finland, during the winter and spring of 1998-1999 within the framework of the ULTRA study. There were 37 non-smoking subjects in Amsterdam and 47 in Helsinki. In Amsterdam, where there were enough exposure events for analyses, exposure to environmental tobacco smoke (ETS) indoors was a major source of between-subject variation in PM2.5 exposures, and a strong determinant of PM2.5 and absorbance exposures. When the days with ETS were excluded, within-subject variation accounted for 89% of the total variation in personal PM2.5 and 97% in absorbance in Amsterdam. The respective figures were 66% and 61% in Helsinki. In both cities, outdoor levels of PM2.5 and absorbance were major determinants of personal and indoor levels. Traffic was also an important determinant of absorbance: living near a major street increased exposure by 22%, and every hour spent in a motor vehicle by 13% in Amsterdam. The respective increases were 37% and 9% in Helsinki. Cooking was associated with increased levels of both absorbance and PM2.5. Our results demonstrate that by using questionnaires in connection with outdoor measurements, exposure estimation of PM2.5 and its combustion originating fraction can be improved among elderly persons with compromised health.     

室内颗粒物污染对儿童哮喘的影响

目的探讨室内大气PM_(10)、PM_(2.5)、PM1污染对儿童哮喘的影响。方法采用1∶1成组病例-对照研究,于2015年10月—2016年5月对石河子市80名哮喘儿童和80名健康对照儿童进行问卷调查与室内颗粒物浓度检测,分析儿童哮喘的危险因素。结果两组合计160名儿童的室内PM_(10)、PM_(2.5)、PM1浓度范围分别为26.57~507.30、12.66~159.00、4.53~77.08μg/m~3,其中PM_(10)超标率为61.9%,PM_(2.5)超标率为6.9%。病例组室内空气中的PM_(10)、PM_(2.5)、PM1浓度中位数均高于对照组,差异有统计学意义(P0.01)。多因素logistic回归分析结果显示,儿童有过敏史(OR=5.171)、有环境烟草烟雾(ETS)暴露(OR=2.429)、PM_(2.5)浓度高于中位数(OR=3.459)是儿童哮喘的危险因素,母乳喂养(OR=0.454)是儿童哮喘的保护因素,均有统计学意义(P0.05)。结论儿童有过敏史、ETS暴露和PM_(2.5)暴露可能增加儿童哮喘风险,同时应提倡母乳喂养,以保护儿童呼吸系统健康。     

Common household activities are associated with elevated particulate matter concentrations in bedrooms of inner-city Baltimore pre-school children

Asthma disproportionately affects inner-city, minority children in the U.S. Outdoor pollutant concentrations, including particulate matter (PM), are higher in inner-cities and contribute to childhood asthma morbidity. Although children spend the majority of time indoors, indoor PM exposures have been less extensively characterized. There is a public health imperative to characterize indoor sources of PM within this vulnerable population to enable effective intervention strategies. In the present study, we sought to identify determinants of indoor PM in homes of Baltimore inner-city pre-school children. Children ages 2-6 (n=300) who were predominantly African-American (90%) and from lower socioeconomic backgrounds were enrolled. Integrated PM(2.5) and PM(10) air sampling was conducted over a 3-day period in the children's bedrooms and at a central monitoring site while caregivers completed daily activity diaries. Homes of pre-school children in inner-city Baltimore had indoor PM concentrations that were twice as high as simultaneous outdoor concentrations. The mean indoor PM(2.5) and PM(10) concentrations were 39.5+/-34.5 and 56.2+/-44.8 microg/m(3), compared to the simultaneously measured ambient PM(2.5) and PM(10) (15.6+/-6.9 and 21.8+/-9.53 microg/m(3), respectively). Common modifiable household activities, especially smoking and sweeping, contributed significantly to higher indoor PM, as did ambient PM concentrations. Open windows were associated with significantly lower indoor PM. Further investigation of the health effects of indoor PM exposure is warranted, as are studies to evaluate the efficacy of PM reduction strategies on asthma health of inner-city children.     

Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator

This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5?μm in aerodynamic diameter (PM(2.5)) and daily mortality in the New York City metropolitan area during the period 2001-2005. Personal PM(2.5) exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10?μg/m(3) increase in personal exposure to PM(2.5) from outdoor sources on the previous day. The corresponding estimates per a 10?μg/m(3) increase in PM(2.5) ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM(2.5) were also higher during the summer months.     

A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode

A novel source-to-dose modeling study of population exposures to fine particulate matter (PM(2.5)) and ozone (O(3)) was conducted for urban Philadelphia. The study focused on a 2-week episode, 11-24 July 1999, and employed the new integrated and mechanistically consistent source-to-dose modeling framework of MENTOR/SHEDS (Modeling Environment for Total Risk studies/Stochastic Human Exposure and Dose Simulation). The MENTOR/SHEDS application presented here consists of four components involved in estimating population exposure/dose: (1) calculation of ambient outdoor concentrations using emission-based photochemical modeling, (2) spatiotemporal interpolation for developing census-tract level outdoor concentration fields, (3) calculation of microenvironmental concentrations that match activity patterns of the individuals in the population of each census tract in the study area, and (4) population-based dosimetry modeling. It was found that the 50th percentiles of calculated microenvironmental concentrations of PM(2.5) and O(3) were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O(3) microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM(2.5). By further examining the modeled estimates of the 24-h aggregated PM(2.5) and O(3) doses, it was found that indoor PM(2.5) sources dominated the contributions to the total PM(2.5) doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O(3) doses due to time spent outdoors dominated the contributions to the total O(3) doses for the upper 5 percentiles. The MENTOR/SHEDS system presented in this study is capable of estimating intake dose based on activity level and inhalation rate, thus completing the source-to-dose modeling sequence. The MENTOR/SHEDS system also utilizes a consistent basis of source characterization, exposure factors, and human activity patterns in conducting population exposure assessment of multiple co-occurring air pollutants, and this constitutes a primary distinction from previous studies of population exposure assessment, where different exposure factors and activity patterns would be used for different pollutants. Future work will focus on incorporating the effects of commuting patterns on population exposure/dose assessments as well as on extending the MENTOR/SHEDS applications to seasonal/annual studies and to other areas in the U.S.     

Air pollution and daily hospital admissions in metropolitan Los Angeles

We used daily time-series analysis to evaluate associations between ambient carbon monoxide, nitrogen dioxide, particulate matter [less than and equal to] 10 microm in aerodynamic diameter (PM(10)), or ozone concentrations, and hospital admissions for cardiopulmonary illnesses in metropolitan Los Angeles during 1992-1995. We performed Poisson regressions for the entire patient population and for subgroups defined by season, region, or personal characteristics, allowing for effects of temporal variation, weather, and autocorrelation. CO showed the most consistently significant (p<0.05) relationships to cardiovascular admissions. A wintertime 25th-75th percentile increase in CO (1.1-2.2 ppm) predicted an increase of 4% in cardiovascular admissions. NO(2), and, to a lesser extent, PM(10) tracked CO and showed similar associations with cardiovascular disease, but O(3) was negatively or nonsignificantly associated. No significant demographic differences were found, although increased cardiovascular effects were suggested in diabetics, in whites and blacks (relative to Hispanics and Asians), and in persons older than 65 years of age. Pulmonary disease admissions associated more with NO(2) and PM(10) than with CO. Pulmonary effects were generally smaller than cardiovascular effects and were more sensitive to the choice of model. We conclude that in Los Angeles, atmospheric stagnation with high primary (CO/NO(2)/PM(10)) pollution, most common in autumn/winter, increases the risk of hospitalization for cardiopulmonary illness. Summer photochemical pollution (high O(3)) apparently presents less risk.