Personal Exposure Model Research Articles

Cardiopulmonary effects of short-term ambient PM2.5 exposure: central air quality monitor data versus personal exposure models

Cardiopulmonary effects of short-term ambient PM2.5 exposure: central air quality monitor data versus personal exposure models

The PRECISE-DYAD protocol: linking maternal and infant health trajectories in sub-Saharan Africa.

PRECISE-DYAD is an observational cohort study of mother-child dyads running in urban and rural communities in The Gambia and Kenya. The cohort is being followed for two years and includes uncomplicated pregnancies and those that suffered pregnancy hypertension, fetal growth restriction, preterm birth, and/or stillbirth. The PRECISE-DYAD study will follow up ~4200 women and their children recruited into the original PRECISE study. The study will add to the detailed pregnancy information and samples in PRECISE, collecting additional biological samples and clinical information on both the maternal and child health.Women will be asked about both their and their child's health, their diets as well as undertaking a basic cardiology assessment. Using a case-control approach, some mothers will be asked about their mental health, their experiences of care during labour in the healthcare facility. In a sub-group, data on financial expenditure during antenatal, intrapartum, and postnatal periods will also be collected. Child development will be assessed using a range of tools, including neurodevelopment assessments, and evaluating their home environment and quality of life. In the event developmental milestones are not met, additional assessments to assess vision and their risk of autism spectrum disorders will be conducted. Finally, a personal environmental exposure model for the full cohort will be created based on air and water quality data, combined with geographical, demographic, and behavioural variables. The PRECISE-DYAD study will provide a greater epidemiological and mechanistic understanding of health and disease pathways in two sub-Saharan African countries, following healthy and complicated pregnancies. We are seeking additional funding to maintain this cohort and to gain an understanding of the effects of pregnancies outcome on longer-term health trajectories in mothers and their children.

The PRECISE-DYAD protocol: linking maternal and infant health trajectories in sub-Saharan Africa

Background: PRECISE-DYAD is an observational cohort study of mother-child dyads running in urban and rural communities in The Gambia and Kenya. The cohort is being followed for two years and includes uncomplicated pregnancies and those that suffered pregnancy hypertension, fetal growth restriction, preterm birth, and/or stillbirth. Methods: The PRECISE-DYAD study will follow up ~4200 women and their children recruited into the original PRECISE study. The study will add to the detailed pregnancy information and samples in PRECISE, collecting additional biological samples and clinical information on both the maternal and child health. Women will be asked about both their and their child’s health, their diets as well as undertaking a basic cardiology assessment. Using a case-control approach, some mothers will be asked about their mental health, their experiences of care during labour in the healthcare facility. In a sub-group, data on financial expenditure during antenatal, intrapartum, and postnatal periods will also be collected. Child development will be assessed using a range of tools, including neurodevelopment assessments, and evaluating their home environment and quality of life. In the event developmental milestones are not met, additional assessments to assess vision and their risk of autism spectrum disorders will be conducted. Finally, a personal environmental exposure model for the full cohort will be created based on air and water quality data, combined with geographical, demographic, and behavioural variables. Conclusions: The PRECISE-DYAD study will provide a greater epidemiological and mechanistic understanding of health and disease pathways in two sub-Saharan African countries, following healthy and complicated pregnancies. We are seeking additional funding to maintain this cohort and to gain an understanding of the effects of pregnancies outcome on longer-term health trajectories in mothers and their children.

Model development and validation of personal exposure to PM2.5 among urban elders

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM2.5 exposure by 46–70%. The model incorporating home indoor PM2.5 can achieve the highest agreement (R2 = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM2.5 determined by LUR model with a machine learning technique can improve the prediction (R2 = 0.592) of personal PM2.5 exposure, compared with the prediction of the traditional LUR model (R2 = 0.385).

Transformational IoT sensing for air pollution and thermal exposures

Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

Personal exposures to traffic-related air pollution in three Canadian bus transit systems: the Urban Transportation Exposure Study

BackgroundExposure to traffic-related air pollution (TRAP) is associated with increased incidence of several cardiopulmonary diseases. The elevated TRAP exposures of commuting environments can result in significant contributions to daily exposures.ObjectivesTo assess the personal TRAP exposures (UFPs, BC, PM2.5, and PM10) of the bus transit systems of Toronto, Ottawa, and Vancouver, Canada. Personal exposure models estimated the contribution of bus commuting to daily TRAP exposures. Associations between bus type and riding exposures and bus stop/station type and waiting exposures were estimated.ResultsBus commuting (4.6% of the day) contributed ~59%(SD = 15%), 60%(SD = 20%), and 57%(SD = 18%) of daily PM2.5-Ba and 70%(SD = 19%), 64%(SD = 15%), and 70%(SD = 15%) of daily PM2.5-Fe, in Toronto, Ottawa, and Vancouver, respectively. Enclosed bus stations were found to be hotspots of PM2.5 and BC. Buses with diesel particulate filters (DPFs) and hybrid diesel/electric propulsion were found to have significantly lower in-bus PM2.5, UFP, and BC relative to 1983–2003 diesel buses in each city with the exception of UFP in Vancouver.SignificancePersonal exposures for traffic-related air pollutants were assessed for three Canadian bus transit systems. In each system, bus commuting was estimated to contribute significantly toward daily exposures of fine-fraction Ba and Fe as well as BC. Exposures while riding were associated with bus type for several pollutants in each city. These associations suggest the use of hybrid diesel/electric buses equipped with diesel particulate filters have improved air quality for riders.

EXPLUME v1.0: a model for personal exposure to ambient O<sub>3</sub> and PM<sub>2.5</sub>

Abstract. This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

Residential exposure to pesticides and health effects in the Netherlands

S17: Residential exposure to pesticides and health effects; what we know and what we should know, Room 217, Floor 2, August 28, 2019, 1:30 PM - 3:00 PM Approximately 5% of the Dutch population, living in less urbanized areas (<1500 addresses per km2), resides within 50 meters of agricultural fields. This number increases to about 25% of the population when considering 250 m to agricultural fields. Concerns have been raised about the exposures to pesticides of these residents and possible associated health effects. Due to the lack of information on exposure levels of the Dutch (rural) population in relation to pesticide use on agricultural fields, the OBO study was initiated. The OBO study aimed to assess the exposure to pesticides of residents living within 250 m from an agricultural field. To address this aim, a study design was applied that combines measurements of environmental concentrations (outdoor and indoor air, dust from the doormat, vacuumed floor dust and soil from the garden), personal exposure (urine and hand wipes) and exposure models. We successfully measured 14 application and 9 off-season periods at 9 bulb-growing locations covering a variety of applications with varying pesticides. In addition, we sampled from control households (n=16) that had no agricultural fields in the direct vicinity (>500m). We successfully measured 46 different pesticides in indoor dust concentrations of 58 residential and 16 control homes and repeated 24hr air concentrations at 71 residential and 16 control homes. In addition, urinary measurements of 5 selected pesticides (i.e. asulam, carbendazim, chlorpropham, prochloraz and tebuconazole) were performed among the 192 residents from the homes in the study. Results of both the environmental- and bio-monitoring were analyzed for patterns over the application season, between residents and controls, and with distance to the fields. Empirical results were corroborated with a novel model framework allowing the estimation of environmental concentrations and personal exposures out and inside the home. Detailed results of these comparisons, currently embargoed, will be presented.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BackgroundIt has been widely reported that gestational exposure to fine particulate matters (PM2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM2.5 concentrations and personal PM2.5 exposure would significantly affect the estimation of exposure-response relationship. ObjectiveOur study aimed to predict gestational personal exposure to PM2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants. MethodWe collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model. ResultsOur ambient PM2.5 model yielded a cross-validation R2 of 0.96. Personal PM2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R2 of 0.76. ConclusionWe established a prediction model for gestational personal exposure to PM2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models.

Personal exposure is sensitive to the personal features and behavior of the individual, and including interpersonal variability will improve the health and quality of life evaluations. Participatory sensing assesses the spatial and temporal variability of environmental indicators and is used to quantify this interpersonal variability. Transferring the participatory sensing information to a specific study population is a basic requirement for epidemiological studies in the near future. We propose a methodology to reduce the void between participatory sensing and health research. Instantaneous microscopic land-use regression modeling (µLUR) is an innovative approach. Data science techniques extract the activity-specific and route-sensitive spatiotemporal variability from the data. A data workflow to prepare and apply µLUR models to any mobile population is presented. The µLUR technique and data workflow are illustrated with models for exposure to traffic related Black Carbon. The example µLURs are available for three micro-environments; bicycle, in-vehicle, and indoor. Instantaneous noise assessments supply instantaneous traffic information to the µLURs. The activity specific models are combined into an instantaneous personal exposure model for Black Carbon. An independent external validation reached a correlation of 0.65. The µLURs can be applied to simulated behavioral patterns of individuals in epidemiological cohorts for advanced health and policy research.

PM exposure variations due to different time activity profile simulations within a single dwelling

This paper describes the combination of a physical pollutant model IAPPEM with a time activity profile, to create an air pollutant personal exposure model. A previous study has already demonstrated the capability of IAPPEM in predicting indoor Particulate Matter (PM) concentrations in a residential environment. The present work investigates personal exposure variability between individuals and, doors opened vs closed scenarios, by examining the movements of four individuals through the same dwelling. The results of the simulations highlight the distinction between personal exposure and indoor concentrations, providing computational validation to well-documented experimental differences that exist between indoor concentrations and personal exposure. Using IAPPEM, personal exposure is calculated according to the percentage contribution from the time spent in each room, illustrating that the periods of presence, in relation to the times of indoor source emission, are as important as the durations spent in each room.This study provides a comparison between two approaches for evaluating personal exposure: a time-weighted average and a time activity profile. The results showed that a time-weighted averaged profile is a poor substitute when compared with personal exposure calculated based on a time activity profile, as, in each simulated scenario, exposures were under-predicted using the time-weighted approach, in some cases by up to 135%.

Personal Air Pollution Monitoring in the MADRES Cohort of Pregnant Women: Challenges and Lessons Learned

Introduction: Increasing rates of maternal overweight and obesity before, during and after pregnancy pose serious health concerns for both mothers and children. In California, childbearing women from disadvantaged minority and low-income groups are disproportionately exposed to multiple environmental chemicals. Urban air pollution is a ubiquitous exposure that has been implicated in the pathogenesis of obesity, diabetes and related metabolic and cardiovascular disease. Most air pollution studies assign exposures based on residential location. Methods: Given that pregnant women may regularly travel to non-residential locations, we propose to use a personal exposure assessment approach that includes time-activity patterns in key microenvironments to reduce exposure measurement error in Project 2 of the recently established “Maternal and Developmental Risks from Environmental and Social Stressors” (MADRES) pregnancy cohort. Personal PM2.5 monitoring is currently being conducted using a microPEM, along with GPS, tri-axis accelerometry and Ecological Momentary Assessment (EMA) prompts on smartphones to capture geolocation, physical activity, and context-specific information, respectively, in 60 low income, predominantly Hispanic women living in East Los Angeles. Results: Data will be presented to illustrate and describe main challenges encountered in pilot testing, in terms of developing algorithms for evaluating participant compliance using a combination of sensors and self-report tools. Lessons learned for ensuring data quality while optimizing battery life and reducing participant burden will be presented. Conclusions: Recent advances in sensor technologies are making personal exposure assessment in health studies more feasible, presenting unique opportunities for capturing key contextual information and reducing exposure measurement error in personal exposure models.

Estimation of chronic personal exposure to airborne polycyclic aromatic hydrocarbons

BackgroundPolycyclic aromatic hydrocarbons (PAH) exposure from solid fuel burning represents an important public health issue for the majority of the global population. Yet, understanding of individual-level exposures remains limited. ObjectivesTo develop regionally adaptable chronic personal exposure model to pro-carcinogenic PAH (c-PAH) for the population in Kraków, Poland. MethodsWe checked the assumption of spatial uniformity in eight c-PAH using the coefficients of divergence (COD), a marker of absolute concentration differences. Upon successful validation, we developed personal exposure models for eight pro-carcinogenic PAH by integrating individual-level data with area-level meteorological or pollutant data. We checked the resulting model for accuracy and precision against home outdoor monitoring data. ResultsDuring winter, COD of 0.1 for Kraków suggest overall spatial uniformity in the ambient concentration of the eight c-PAH. The three models that we developed were associated with index of agreement approximately equal to 0.9, root mean square error<2.6ng/m3, and 90th percentile of absolute difference ≤4ng/m3 for the predicted and the observed concentrations for eight pro-carcinogenic PAH. ConclusionsInexpensive and logistically feasible information could be used to estimate chronic personal exposure to PAH profiles, in lieu of costly and labor-intensive personal air monitoring at wide scale. At the same time, thorough validation through direct personal monitoring and assumption checking are critical for successful model development.

Validation of modeled daily erythemal exposure along tropical and subtropical shipping routes by ship‐based and satellite‐based measurements

AbstractThe Personal ERythemal EXposure (PEREX) model for seafarers working on decks of vessels has been developed to be used for retrospective estimates of personal occupational erythemal exposure in dependence of work profile, time period, and sea route. Extremely high UV index values up to 22 and daily erythemal exposure up to 89 standard erythemal dose have been derived from ship‐based measurements in tropical oceans. Worldwide climatological maps of daily solar erythemal exposure derived from 10 year (2004–2013) hourly grid point radiative transfer model calculations for both cloudless sky and cloudy sky serve as the database of PEREX. The PEREX database is compared with ship‐based measurements taken along four routes of merchant vessels, continuous UV radiation measurements taken on the research vessel Meteor on its mainly tropical and subtropical routes for 2 years, daily cloudless‐sky erythemal exposure derived from 10 min LibRadtran radiative transfer model calculations, and 2 years of satellite‐based erythemal exposure data of the Ozone Monitoring Instrument on the Aura satellite along the ship routes. Systematic differences between PEREX model data, ship‐based data, and satellite‐based daily erythemal exposure for all‐sky conditions are only 1 to 3%, while short‐term variations of cloudiness result in standard deviations of differences around 30%. Measured ratios between cloudless‐sky erythemal radiation at vertical to horizontal incidence decrease with decreasing solar zenith angle, while clouds flatten their diurnal course.

Predicting Chronic Personal Exposure to Airborne Polycyclic Aromatic Hydrocarbon ?Mixture in Krakow, Poland

BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) are a globally distributed class of genotoxin and carcinogens. Inhalation PAHs, in particular, is an important route of exposure. Yet, in absence of directly monitoring of personal exposure (PE), estimating chronic PE to a mixture of airborne PAHs typically relies on proxy predictor variables. Here, we examined the performances of three prediction models of chronic PE. METHODS: In an on-going prospective birth cohort study, the pregnant women were directly monitored for their PE either once during the gestational period to eight carcinogenic PAHs and pyrene (n=266) or repeatedly, once during eachtrimester (n=78). Additional monitoring of the same PAHs in indoor and outdoor setting were also conducted. Three PE models were developed. Under the most resource-restricted scenario (model 1), we considered self-reported indoor and outdoor sources as proxy variables of PE. In the intermediate-resource scenario (model 2), we posited that the citywide demand for heating (unit, °C) is a proxy variable for PE to PAHs. Under the most resource abundant scenario (model 3), we posited that infiltrated indoor concentration of directly monitored outdoor mean concentrations represents a proxy of PE. RESULTS: Based on the Model 1, proxy variables for the pregnancy period as independent predictors explained ? 70% variability in PE to each of the nine PAHs (all Ps &lt; 0.0001). Based on the Model 2, a single unit increase (°C) in citywide demand for heating was associated with 10% increase in PE to each of nine PAHs (all Ps &lt; 0.0001). In addition, single unit increase in the wind speed (m/s) was associated with 30—40% reduction in PE to each of nine PAHs. Based on the Model 3, pooled 48-hour mean outdoor concentration for each of eight species explained 76—87% of total variability in PE to the same species. CONCLUSION: In Krakow, Poland, accurate and precise prediction of PE of nine PAHs is possible using inexpensive data sources.

Tracking personal exposure to particulate diesel exhaust in a diesel freight terminal using organic tracer analysis

Personal exposure to particle-phase molecular markers was measured at a trucking terminal in St Louis, MO, as part of a larger epidemiologic project aimed at assessing carbonaceous fine particulate matter (PM) exposure in this occupational setting. The integration of parallel personal exposure, ambient worksite area and ambient urban background (St Louis Supersite) measurements provided a unique opportunity to track the work-related exposure to carbonaceous fine PM in a freight terminal. The data were used to test the proposed personal exposure model in this occupational setting: To accurately assess the impact of PM emission sources, particularly motor vehicle exhaust, and organic elemental carbon (OCEC) analysis and nonpolar organic molecular marker analysis by thermal desorption-gas chromatography/mass spectrometry (TD-GCMS) were conducted on all of the PM samples. EC has been used as a tracer for diesel exhaust in urban areas, however, the emission profile for diesel exhaust is dependent upon the operating conditions of the vehicle and can vary considerably within a fleet. Hopanes, steranes, polycyclic aromatic hydrocarbons and alkanes were measured by TD-GCMS. Hopanes are source-specific organic molecular markers for lubricating oil present in motor vehicle exhaust. The concentrations of OC, EC and the organic tracers were averaged to obtain average profiles to assess differences in the personal, worksite area and urban background samples, and were also correlated individually by sample time to evaluate the exposure model presented above. Finally, a chemical mass balance model was used to apportion the motor vehicle and cigarette-smoke components of the measured OC and EC for the average personal exposure, worksite area and urban background samples.

Measured and Modeled Personal Exposures to and Risks from Volatile Organic Compounds

We developed a personal exposure model using volatile organic compound data collected for teachers and office workers as part of the Boston Exposure Assessment in Microenvironments (BEAM) study. We included participant-specific time-activity and concentration measurements of residential outdoor, residential indoor, and workplace microenvironments, along with average concentrations in various dining, retail, and transportation microenvironments. We used a series of time-weighted personal exposure models to compare measured personal concentrations using median regression models, with bias estimates representing the difference between measured and modeled personal exposures. Incorporating only the outdoor microenvironment results in an unbiased estimate of personal exposure only for carbon tetrachloride. Adding the residential indoor microenvironment provides an unbiased estimate for trichloroethene as well. A model incorporating residential outdoor, indoor, and workplace microenvironments provides an unbiased estimate for the above compounds and chloroform, 1,4-dichlorobenzene, benzene, and alpha-pinene, and adding the transportation microenvironment adds ethylbenzene. A fully saturated model, including outdoor, indoor, workplace, transportation, and all other microenvironments, provides an unbiased estimate for the previously listed compounds along with tetrachloroethene and styrene. MTBE, toluene, o-xylene, d-limonene, formaldehyde, and acetaldehyde were not fully characterized even in the saturated model, emphasizing that additional time-activity and concentration information would more fully characterize personal exposure.

How to Use A Spatial–Temporal Population Exposure Model—Views of a Local Authority

MS8 Symposium Title: Population Exposure Modeling and Urban Air Quality Management in Europe Symposium Organizers: Otto Hänninen and Matti Jantunen. MS8-01 The EXPAND model has been developed for the determination of human exposure to ambient air pollution in an urban area. The model is able to treat various environments such as homes, workplaces, and various traffic modes. The latest version includes also various population subgroups (eg, adults, elderly, and infants). The model combines the predicted hourly concentrations of pollutants and the information on people's time use at different locations. The computed results are processed and visualized using the geographic information system, which illustrates, eg, the most problematic areas and time periods. We have used this model to evaluate the population exposure to NO2 and PM2.5 in the different scenarios of a transportation system plan. From a local authority's point of view, there are some open questions that need to be addressed. Compared with dispersion models, the exposure model requires a lot of additional input data, which is difficult to get. Up-to-date time-use studies are not readily available; they do not cover all important subgroups such as infants, and they are not planned to give information on the locations where people spend their time. The evaluation of exposure is hindered due to the lack of information on indoor/outdoor ratios. At present also, the information on the exposures in different traffic modes is inadequate. The advantage of EXPAND model is that it describes the spatial and temporal variation of population exposures over the whole metropolitan area. On the other hand, the interpretation of the results for decision- and policy-makers is difficult because it does not give information on personal exposures, which is needed for the evaluation of health effects. On the other hand, the personal exposure models, in which individuals are followed for the whole day, are complex in evaluating the exposures for the whole metropolitan area, and the probabilistic models do not give the spatial and temporal variation of the exposures. Finally, the air-quality legislation is based on the limit values that are given for the concentrations of pollutants in ambient air, not for exposures. Thus, the abatement strategies tend to aim at reducing concentrations, even if it would sometimes be more cost-efficient to reduce the exposures.

Modeling Daily Exposure of Childrenʼs Exposure to PM, Ozone, NO2, and Biologic Agents in the Fresno Asthmatic Childrenʼs Environment Study (FACES)

SS6-06 Abstract: A multipollutant personal exposure model has been developed for assigning daily exposures to children participating in the Fresno Asthmatic Children's Environment Study (FACES). This presentation describes the methods and results for estimating children's personal exposure to ozone, NO2, PM2.5 mass, elemental carbon (EC), organic carbon, PM10 mass, endotoxin, agricultural fungi, Cladosporium, Alternaria, total fungal spores, and total pollens grains. The modeling approach relies on a central air monitoring station measurements to characterize the day-to-day temporal variations in outdoor concentrations, spatial mapping of pollutant concentrations measured (at various times) at 85 locations in the community to characterize intraurban gradients in ambient concentrations, and indoor–outdoor measurements made in 80 homes to guide selection of parameters for the mass balance equation used to estimate indoor concentrations Subjects: Specific home survey and daily human activity questionnaire data are used in the microenvironmental exposure model. The principal findings are: On most days, personal exposure estimates vary between subjects by a factor of 2 for PM2.5 mass and by a factor of 3 or more for other pollutants considered here. The large variations between subjects in estimated exposure to pollutants of ambient origin suggest that the use of central site ambient concentrations alone for individual exposure assignments in FACES may result in considerable exposure misclassification. The between-subject variations in personal exposure estimates are generally greater for biologic agents than conventional pollutants and greater for primary pollutants (such as EC) than secondary (ozone) or combined primary/secondary pollutants (PM2.5). For example, the personal exposure estimates may range from 100 to 800 total pollen grains/m3 and from 10 to 250 Alternaria spores/m3 in one day, which are much larger ranges than are estimated for conventional pollutants. The mean estimated personal exposure concentrations of pollutants of ambient origin are consistently lower than the ambient concentrations measured at the FACES central monitoring station. On average, the mean personal exposure concentrations range from 15% of central site ambient concentrations for total pollen grains to 59% of central site ambient concentrations for PM2.5 mass. The pollutant ranking (from highest to lowest) for mean ratio of personal exposure to central site ambient concentrations is PM2.5 mass, endotoxin, EC, agricultural fungi, NO2, PM coarse, Alternaria, ozone, Cladosporium, and total pollen grains. The systematic variations in personal exposure levels relative to ambient concentrations are primarily a result of lower indoor than outdoor concentrations and secondarily a result of spatial differences in ambient concentrations within the community.

Comment on “Derivation of numerical values for the World Health Organization guidelines for recreational waters”

The subject paper describes a procedure for adjusting a risk model based upon a measure of personal exposure (the “UK personal exposure model”) in order to attribute an expected rate of gastroenteritis among a group of swimmers to a mean recreational water quality value (enterococci per 100 mL). We term the resulting model for group risk the “UK ecologic exposure model.” The distinction is essential to establishing recreational water quality guidelines because exposures of individual bathers are not known from a water monitoring program, the only assessment available being some form of ecologic exposure such as a mean log indicator density. While the authors of the subject paper solved the UK ecologic exposure model for only a single point (that value of mean log 10 enterococcus density which is expected to result in five extra cases of gastroenteritis per 100 swimmers), we extend their model to show the entire curve over a relevant range of densities. The resulting exposure–response curve is seen to not differ substantially from the existing USEPA model for “highly credible gastrointestinal illness” in marine waters. However, particularly since such correspondence is not guaranteed for future studies or for other existing epidemiological studies, we recommend the direct approach to evaluating ecologic exposure, such as used in the USEPA studies, rather than the indirect approach of the UK ecologic exposure model, given the number of untested assumptions that are necessary for accomplishing the latter.