Microenvironmental Exposures Research Articles

Assessing the Radon Exposure Variability and Lifetime Health Effects across Indoor Microenvironments and Sub-Populations

To assess the health impacts of radon exposure over a lifetime, in the present study, the annual effective dose (AED) and cumulative excess lifetime cancer risk (ELCR-C) were evaluated by considering various indoor microenvironmental exposures based on age-specific time–activity patterns using Monte Carlo simulations. Significant regional variations in indoor radon concentrations across the Republic of Korea were observed, with the highest levels found in schools and single detached houses. Based on the standard annual total of 8760 h spent indoors and outdoors, the AED varied by age group and dwelling type, with the ELCR-C for single detached houses being approximately 1.36 times higher than that for apartments on average. The present study highlights the importance of comprehensive health risk assessments that consider differences across indoor environments and age groups, indicating that limited evaluations of specific sites or areas may distort actual exposure levels.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Modeling the COVID-19 epidemic in Croatia: a comparison of three analytic approaches

AimTo facilitate the development of a COVID-19 predictive model in Croatia by analyzing three different methodological approaches.MethodWe used the historical data to explore the fit of the extended SEIRD compartmental model, the Heidler function, an exponential approximation in analyzing electromagnetic phenomena related to lightning strikes, and the Holt-Winters smoothing (HWS) for short-term epidemic predictions. We also compared various methods for the estimation of R0.ResultsThe R0 estimates for Croatia varied from 2.09 (95% CI 1.77-2.40) obtained by using an empirical post-hoc method to 2.28 (95% CI 2.27-2.28) when we assumed an exponential outbreak at the very beginning of the COVID-19 epidemic in Croatia. Although the SEIRD model provided a good fit for the early epidemic stages, it was outperformed by the Heidler function fit. HWS achieved accurate short-term predictions and depended the least on model entry parameters. Neither model performed well across the entire observed period, which was characterized by multiple wave-form events, influenced by the re-opening for the tourist season during the summer, mandatory masks use in closed spaces, and numerous measures introduced in retail stores and public places. However, an extension of the Heidler function achieved the best overall fit.ConclusionsPredicting future epidemic events remains difficult because modeling relies on the accuracy of the information on population structure and micro-environmental exposures, constant changes of the input parameters, varying societal adherence to anti-epidemic measures, and changes in the biological interactions of the virus and hosts.

Does real-time and perceived environmental exposure to air pollution and noise affect travel satisfaction? evidence from Beijing, China

Poor air quality and noise pollution have become major environmental risks in urban China. However, the effect of personal exposure to air pollution and noise in various traffic microenvironments on individuals’ daily travel satisfaction has rarely been investigated. Drawing upon real-time data collected using portable sensors and GPS tracking devices in Beijing from December 2017 to February 2018, this paper explores the variations in the co-exposure to real-time air pollution and noise among various transportation modes. It then employs structural equation models to investigate whether and how the objectively measured and subjectively perceived exposure to air pollution and noise in different traffic microenvironments influences individuals’ daily travel satisfaction. Findings show that travel satisfaction and co-exposure to real-time air pollution and noise vary greatly among different transportation modes. People travelling by public transport experience high personal noise exposure and tend to have lower levels of travel satisfaction. While perceived air pollution and noise have significant direct effects on travel experiences, the pathways between objective pollution and travel satisfaction differ for air pollution and noise. Objective air pollution negatively affects travel satisfaction indirectly by influencing perceived air pollution, whereas objective noise has a positive and direct effect on travel satisfaction. Moreover, people with different socio-economic backgrounds tend to bear unequal burdens of air pollution and noise during their daily travels. These findings indicate that more research should be conducted to enhance our understanding of the relationships between personal micro-environmental exposures and subjective wellbeing.

The threshold effects of bus micro-environmental exposures on passengers’ momentary mood

The bus plays a crucial role in the daily travel of urban residents. Previous research on bus micro-environmental exposures and their adverse impacts on passengers’ health and comfort has attracted considerable attention. However, few studies to date have explored the correlation between bus micro-environmental exposures and passengers’ momentary mood, examined the thresholds of various micro-environmental factors, and analyzed the optimal micro-environmental exposure levels for passengers. To this end, both micro-environmental exposures and questionnaire survey data are simultaneously collected from 543 passengers in randomly selected buses operating on the 6 routes in Guangzhou, China. Multiple regression models are then applied to explore some research gaps in the existing literature. The results suggest that in-bus noise and passenger load have negative overall correlations with passengers’ momentary mood, whereas temperature and relative humidity have positive overall correlations with it. Passengers’ momentary mood is subject to the threshold effects of these four micro-environmental indicators. The thresholds of in-bus noise, temperature, and relative humidity identified in this study are 65–79 dB, 30–34 °C, and 50–65% respectively. In terms of passenger load, 8–18 is a threshold below which momentary mood gets worse, and 19–29 is the other threshold beyond which momentary mood deteriorates considerably. Further, the optimal exposure levels of these bus micro-environmental indicators are 0–65 dB, 22–28 °C, 41–50%, and 18–19 respectively. These threshold effects provide valuable implications for transportation planners and bus operators to achieve desirable and user-friendly in-bus micro-environment based on a better understanding of the changes in passengers’ momentary mood.

Investigation of Microenvironmental Exposures to Particle-Bound Polycyclic Aromatic Hydrocarbons for Elementary School Children.

Polycyclic aromatic hydrocarbons (PAHs) are formed when organic matters incompletely combust and get distributed into the air in the form of vapor or the particular phase of absorption or condensation on the surface of respirable particles. Certain PAHs are considered as carcinogenic and mutagenic, and are primarily associated with the particulate phase. Therefore, the characterization of exposure to particle-bound PAHs (p-PAHs) is critical to assessing the health risks in our daily life. A panel study was conducted during the years 2004 and 2005 to assess microenvironmental exposures to p-PAHs for elementary school children living in Taipei metropolitan area. During the study, integrated filter samples were collected by a dust monitor (model 1.108, Grimm) for 17 p-PAH species analysis using gas chromatography with mass spectrometry (GC/MS). The sampling durations were five days. Overall, 52 samples for children’s microenvironmental exposures were included in the data analysis. Results showed that geometric mean (GM) levels (and geometric standard deviation) of p-PAH exposures were 4.443 (3.395) ng/m3 for children. The top three highest proportions of p-PAH components were indeno[1,2,3-cd]pyrene (IND) (21.7%), benzo[g,h,i]perylene (BghiP) (18.5%), and dibenz[a,h]anthracene (DBA) (9.1%), all of which are 5- or 6-ring p-PAHs. In addition, results from diagnostic ratios and principal component analysis (PCA) found that traffic pollution, incense burning, and cooking emission were the major p-PAH exposure sources for children. The total benzo[a]pyrene equivalent (BaPeq) concentration was 1.07 ± 0.80 ng/m3 (mean ± standard deviation), with a GM of 0.84 ng/m3. The GM value of the inhalation carcinogenic risk was 7.31 × 10−5 with the range of 2.23 × 10−5 to 3.11 × 10−4, which was higher than the U.S. Environmental Protection Administration guideline limit of 10−6. DBA accounted for 45.1% of the excess cancer risk, followed by benzo[a]pyrene (BaP) (33.5%) and IND (10.7%). In conclusion, the current study demonstrated that inhalational cancer risk due to the p-PAH exposures for children is not negligible, and more efficient technical and management policies should be adopted to reduce the PAH pollutant sources.

Assessment of Microenvironmental Exposures to Ultrafine Particles among Adolescent Schoolchildren

Background: Assessing personal exposure to ultrafine particles (UFPs) is challenging due to multiple indoor and outdoor sources and high spatiotemporal variation. Our objective is to characterize personal UFP exposures experienced by adolescents enrolled in the Ecological Momentary Assessment and Personal Particle Exposure Study (EcoMAPPE). Methods: Participants (ages 13-17) complete a seven-day sampling period using a personal UFP monitor for 3 hours each day that simultaneously records GPS-based location at 1-second resolution. Measured UFP concentrations are assigned to microenvironments based on speed and distance from participant specified locations: schools (400m), homes (100m), and other (100m) locations. UFP concentrations measured outside these areas exhibiting speeds greater than 2 m/s are categorized as occurring while in transit. We define the percent contribution of each microenvironment to a participant’s overall exposure as the summation of UFP concentration in a specific microenvironment divided by the summation of all recorded UFP concentrations. Lastly, we derive exposure-time ratios by dividing the percent contribution of each microenvironment by the percentage of time spent within each microenvironment. Results: Complete sampling and location data is available for 21 participants, representing 378.5 hours of UFP data. The overall median UFP concentration is lowest at home (4,500 particles/cm3), while school/other (5,990), and transit concentrations (7,960) are higher. Most UFP measurements are recorded at home (70%), followed by school/other locations (23%), and transit (7%). Participants experience median exposure-time ratios of 0.99 (Q1: 0.90; Q3: 1.04) at home, 0.82 (0.45; 1.21) at other locations, and 1.10 (0.62; 1.73) in transit.Conclusion: Despite the low percentage of time spent in transit, most participants experience UFP concentrations during transit that contribute more to their overall exposure in comparison to other microenvironments.

Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults.

BackgroundPrevious human exposure studies of traffic-related air pollutants have demonstrated adverse health effects in human populations by comparing areas of high and low traffic, but few studies have utilized microenvironmental monitoring of pollutants at multiple traffic locations while looking at a vast array of health endpoints in the same population. We evaluated inflammatory markers, heart rate variability (HRV), blood pressure, exhaled nitric oxide, and lung function in healthy participants after exposures to varying mixtures of traffic pollutants.MethodsA repeated-measures, crossover study design was used in which 23 healthy, non-smoking adults had clinical cardiopulmonary and systemic inflammatory measurements taken prior to, immediately after, and 24 hours after intermittent walking for two hours in the summer months along three diverse roadways having unique emission characteristics. Measurements of PM2.5, PM10, black carbon (BC), elemental carbon (EC), and organic carbon (OC) were collected. Mixed effect models were used to assess changes in health effects associated with these specific pollutant classes.ResultsMinimal associations were observed with lung function measurements and the pollutants measured. Small decreases in BP measurements and rMSSD, and increases in IL-1β and the low frequency to high frequency ratio measured in HRV, were observed with increasing concentrations of PM2.5 EC.ConclusionsSmall, acute changes in cardiovascular and inflammation-related effects of microenvironmental exposures to traffic-related air pollution were observed in a group of healthy young adults. The associations were most profound with the diesel-source EC.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-015-0049-0) contains supplementary material, which is available to authorized users.

Modeling population exposure to ultrafine particles in a major Italian urban area.

Average daily ultrafine particles (UFP) exposure of adult Milan subpopulations (defined on the basis of gender, and then for age, employment or educational status), in different exposure scenarios (typical working day in summer and winter) were simulated using a microenvironmental stochastic simulation model. The basic concept of this kind of model is that time-weighted average exposure is defined as the sum of partial microenvironmental exposures, which are determined by the product of UFP concentration and time spent in each microenvironment. In this work, environmental concentrations were derived from previous experimental studies that were based on microenvironmental measurements in the city of Milan by means of personal or individual monitoring, while time-activity patterns were derived from the EXPOLIS study. A significant difference was observed between the exposures experienced in winter (W: 28,415 pt/cm3) and summer (S: 19,558 pt/cm3). Furthermore, simulations showed a moderate difference between the total exposures experienced by women (S: 19,363 pt/cm3; W: 27,623 pt/cm3) and men (S: 18,806 pt/cm3; W: 27,897 pt/cm3). In addition, differences were found as a function of (I) age, (II) employment status and (III) educational level; accordingly, the highest total exposures resulted for (I) 55–59 years old people, (II) housewives and students and (III) people with higher educational level (more than 10 years of scholarity). Finally, significant differences were found between microenvironment-specific exposures.

Modeling personal particle-bound polycyclic aromatic hydrocarbon (pb-pah) exposure in human subjects in Southern California

BackgroundExposure to polycyclic aromatic hydrocarbon (PAH) has been linked to various adverse health outcomes. Personal PAH exposures are usually measured by personal monitoring or biomarkers, which are costly and impractical for a large population. Modeling is a cost-effective alternative to characterize personal PAH exposure although challenges exist because the PAH exposure can be highly variable between locations and individuals in non-occupational settings. In this study we developed models to estimate personal inhalation exposures to particle-bound PAH (PB-PAH) using data from global positioning system (GPS) time-activity tracking data, traffic activity, and questionnaire information.MethodsWe conducted real-time (1-min interval) personal PB-PAH exposure sampling coupled with GPS tracking in 28 non-smoking women for one to three sessions and one to nine days each session from August 2009 to November 2010 in Los Angeles and Orange Counties, California. Each subject filled out a baseline questionnaire and environmental and behavior questionnaires on their typical activities in the previous three months. A validated model was used to classify major time-activity patterns (indoor, in-vehicle, and other) based on the raw GPS data. Multiple-linear regression and mixed effect models were developed to estimate averaged daily and subject-level PB-PAH exposures. The covariates we examined included day of week and time of day, GPS-based time-activity and GPS speed, traffic- and roadway-related parameters, meteorological variables (i.e. temperature, wind speed, relative humidity), and socio-demographic variables and occupational exposures from the questionnaire.ResultsWe measured personal PB-PAH exposures for 180 days with more than 6 h of valid data on each day. The adjusted R2 of the model was 0.58 for personal daily exposures, 0.61 for subject-level personal exposures, and 0.75 for subject-level micro-environmental exposures. The amount of time in vehicle (averaging 4.5% of total sampling time) explained 48% of the variance in daily personal PB-PAH exposure and 39% of the variance in subject-level exposure. The other major predictors of PB-PAH exposures included length-weighted traffic count, work-related exposures, and percent of weekday time.ConclusionWe successfully developed regression models to estimate PB-PAH exposures based on GPS-tracking data, traffic data, and simple questionnaire information. Time in vehicle was the most important determinant of personal PB-PAH exposure in this population. We demonstrated the importance of coupling real-time exposure measures with GPS time-activity tracking in personal air pollution exposure assessment.

Ambient and microenvironmental particles and exhaled nitric oxide before and after a group bus trip.

ObjectivesAirborne particles have been linked to pulmonary oxidative stress and inflammation. Because these effects may be particularly great for traffic-related particles, we examined associations between particle exposures and exhaled nitric oxide (FENO) in a study of 44 senior citizens, which involved repeated trips aboard a diesel bus.MethodsSamples of FENO collected before and after the trips were regressed against microenvironmental and ambient particle concentrations using mixed models controlling for subject, day, trip, vitamins, collection device, mold, pollen, room air nitric oxide, apparent temperature, and time to analysis. Although ambient concentrations were collected at a fixed location, continuous group-level personal samples characterized microenvironmental exposures throughout facility and trip periods.ResultsIn pre-trip samples, both microenvironmental and ambient exposures to fine particles were positively associated with FENO. For example, an interquartile increase of 4 μg/m3 in the daily microenvironmental PM2.5 concentration was associated with a 13% [95% confidence interval (CI), 2–24%) increase in FENO. After the trips, however, FENO concentrations were associated pre-dominantly with microenvironmental exposures, with significant associations for concentrations measured throughout the whole day. Associations with exposures during the trip also were strong and statistically significant with a 24% (95% CI, 15–34%) increase in FENO predicted per interquartile increase of 9 μg/m3 in PM2.5. Although pre-trip findings were generally robust, our post-trip findings were sensitive to several influential days.ConclusionsFine particle exposures resulted in increased levels of FENO in elderly adults, suggestive of increased airway inflammation. These associations were best assessed by microenvironmental exposure measurements during periods of high personal particle exposures.

Commuter Exposures to Vocs in Various Transportation Modes

MS7-05 Abstract: Because it can be challenging to measure personal exposure on large population scales, modeling exposure using stochastic methods to characterize microenvironmental exposures can be a more efficient way of estimating population exposure. However, there is little data on exposures to contaminants such as volatile organic compounds (VOCs) in different microenvironments away from the home. As part of a study to quantify exposure to volatile organic compounds (VOCs) in different nonhome microenvironments, multiple modes of transportation were sampled simultaneously during morning and evening rush hours in the summer of 2003 (once each time of day, with n = 10 for VOCs and n = 10 for aldehydes) and the winter of 2004 (twice for each time of day, with n = 14 for VOCs and n = 15 for aldehydes). The VOCs include hydrocarbons and chlorinated compounds sampled using thermal desorption tubes and aldehydes sampled using dinitrophenylhydrazine (DNPH) -coated silica cartridges. Modes sampled were walking, bus, train/subway, urban and highway car. The walking, bus, and urban modes followed a similar route, whereas the train and highway car traveled between a major U.S. urban metropolitan area and an outlying suburban town. The walking route was not sampled in the winter. Summary statistics were calculated and time of day, seasons, and mode were compared. Overall coefficient of variations (CVs) for all compounds, except for 1,3-butadiene (CV = 2.0), were below 1. Each mode was compared with the train mode as a reference on a day-by-day basis. Relative to the train route, the bus and car routes had higher concentrations in the summer, but in the winter, there were less consistent patterns. Summer had higher concentrations except for benzene, in which winter mean concentrations were higher (3.1 μg/m3 in winter vs 2.3 μg/m3 in summer). Formaldehyde was significantly higher in the summer, with a mean of 12.8 μg/m3 versus 8.3 μg/m3 in winter. Overall, exposures to VOCs in transportation modes tend to be lower than that of indoor environments in homes, workplaces, or shops and restaurants. Given the small sample size of this study, it is difficult to describe the full temporal or modal variability; however, results suggest that when developing stochastic models for personal exposure, it may be reasonable to assume a single distribution for transportation as a microenvironment rather than mode- or time-specific distribution.

Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research

The National Children’s Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research that may enhance the National Children’s Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial–temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) “Usual” temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children’s Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period.

WINTER AIR POLLUTION, COAL HOME HEATING, AND THE INCIDENCE OF PEDIATRICIAN-REPORTED LOWER RESPIRATORY ILLNESSES IN CHILDREN FROM BIRTH TO THREE YEARS

ISEE-444 Abstract: We undertook a cohort study to evaluate the relationship between ambient air pollution and early childhood respiratory morbidity in an eastern European country during the years 1994-1999. We examined the associations between winter (November through March) measures of fine- and coarse-fraction particulate matter (PM2.5 and PM10-2.5), sulfur dioxide (SO2), nitrogen oxides (NOX), and polycyclic aromatic hydrocarbons (PAHs) and the incidence of lower respiratory illnesses (LRI) in children from birth to three years of age. Study sites were located in two districts, one with high outdoor air pollution from power plants and coal home heating emissions, and the other an area with lower average outdoor air pollution. Morbidity and questionnaire information at three years was available for 452 children. All sick- and well-child visit diagnoses during the first three years of life were abstracted from pediatric records using ICD-10 classifications and categorized into broad illness groupings. Analyses reported here made use of an LRI grouping that included bronchitis and bronchiolitis (979 diagnoses) but excluded pneumonia (70 diagnoses). Multivariable analyses applied generalized estimating equation methods and adjusted for temperature, day of the week, district of residence, and year. In single-pollutant models, we found that LRI incidence increased with increasing concentrations of PM2.5, PM10-2.5, PAHs and NOX, but not with SO2. Though effect estimates were imprecise, an overall pattern emerged, with LRI incidence increasing with elevated air pollutant levels on the same day and the one or two days preceding diagnosis. For an interquartile range (IQR) increase in pollutant concentration, LRI incidence increased 12% (95% CI: −2.4%, 28%) for same-day PM2.5, 3.5% (95% CI: −1.6%, 8.9%) for same-day PM10-2.5, 8.1% (95% CI: −4.6%, 22%) for previous-day total PAHs, and 8.5% (95% CI: −6.1%, 25%) for three-day average NOX. Among children whose homes were heated primarily with coal, we observed much stronger pollutant-LRI associations. Per IQR-increase in three-day average pollutant levels, LRI incidence increased 84% (95% CI: 22%, 176%) for PM2.5, 24% (95% CI: 5.9%, 45%) for PM10-2.5, 33% (95% CI: 9.7%, 62%) for PAHs, and 22% (95% CI: −14%, 73%) for NOX. These findings underscore the importance of considering indoor and microenvironmental exposures in air pollution health studies conducted in a setting similar to this eastern European location, where local household emissions significantly impact outdoor pollutant concentrations.

Validation of personal exposure models for sulfate and aerosol strong acidity.

Personal exposure models for sulfates (SO4 =) and aerosol strong acidity (H+) were previously developed using concentration and activity pattern data collected from a personal monitoring study conducted in Uniontown, Pennsylvania, during the summer of 1990. Models were constructed based on time-weighted micro-environmental exposures. For SO4 =, the "best-fit" model included a correction factor, while for H+, it included both a correction factor and a neutralization term. In this paper, we present the validation of these models using data collected in a personal monitoring study conducted in State College, Pennsylvania, during the summer of 1991. Indoor and outdoor concentration and activity pattern data collected in this study were used as inputs for the "best-fit" models for SO4 = and H+. Predicted personal exposures subsequently were compared to the measured personal exposures from State College to determine their accuracy and precision. Predicted personal exposures for both SO4 = and H+ were in excellent agreement with measured personal exposures from State College. The models explained 91 and 62 percent of the variability in personal SO4 = and H+ exposures, respectively, and were able to estimate personal exposures substantially better than outdoor concentrations alone. Validation results suggest that the models' correction and neutralization factors are not site specific and support the models' future application as a technique to assess the personal acid aerosol exposures of children living in similar rural and semi-rural communities.

A comparative study of respirable particulate microenvironmental concentrations and personal exposures

Personal monitoring (PM) for respirable suspended particulate matter (RSP) of thirty subjects was performed as part of an air pollution health effects study conducted in Houston, Texas. Parallel RSP measurements were performed in the study subjects' homes and two fixed site monitoring stations. The participants' daytime activities were independently recorded by study techicians. These data were used to characterize RSP concentrations in each microenvironment visited by the participants. Four estimates of daytime exposure to RSP were calculated based on two different microenvironmental models, and home and fixed site mean daytime RSP concentrations. These estimates were compared to mean daytime personal exposure from PM. Hourly estimates of exposure were calculated from a microenvironmental model and mean hourly home RSP concentrations and compared to hourly PM data. The results of the study indicate that, as in the case of NO2, it is important to characterize indoor microenvironmental RSP concentrations according to location, sources, and concurrent activities, both qualitatively and quantitatively. Stratification of concentrations according to sources present and self-reported activity can lead to misclassification of exposures. For RSP and, probably, other pollutants with indoor sources and with short exposure integration times, adequate measures of exposure can only be obtained with very detailed and complex microenvironmental models or comprehensive personal monitoring.