Population Exposure Models Research Articles

Low levels of per- and polyfluoroalkyl substances (PFAS) detected in drinking water in Norway, but elevated concentrations found near known sources

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous contaminants which are also found in drinking water. Concentration levels in drinking water vary widely and range from a very low contribution to total daily exposure for humans to being the major source of uptake of PFAS. PFAS concentrations in Norwegian drinking water has been rarely reported. We investigated concentrations of 31 PFAS in 164 water samples, representing both source water (i.e., before drinking water treatment) and finished drinking water. Samples were taken from 18 different water bodies across Norway. The 17 waterworks involved supply drinking water to 41 % of the Norwegian population. Only four of the waterworks utilised treatment involving activated carbon which was able to significantly reduce PFAS from the source water. Samples of source water from waterworks not employing activated carbon in treatment were therefore considered to represent drinking water with regards to PFAS (142 samples). All samples from one of the water bodies exceeded the environmental quality standard (EQS) for perfluorooctane sulfonic acid (PFOS) according to the water framework directive (0.65 ng/L). No concentrations exceeded the sum of (20) PFAS (100 ng/L) specified in the EU directive 2020/2184 for drinking water. Several EU countries have issued lower guidelines for the sum of the four PFAS that the European Food Safety Authority (EFSA) has established as the tolerable weekly intake (TWI) for PFOS, perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorohexane sulfonic acid (PFHxS). Denmark and Sweden have guidelines specifying 2 and 4 ng/L for the sum of these PFAS. Only one of the 142 drinking water samples exceeded the Danish TWI and contained a sum of 6.6 ng/L PFAS. A population exposure model, for individuals drinking water from the investigated sources, showed that only 0.5 % of the population was receiving PFAS concentrations above the Danish limit of 2 ng/L.

Impact of traffic policy on travel population exposure during heavy pollution episodes: A Chengdu case study

Previous studies on exposure have often omitted the impact of travelers' response to policies during air pollution, particularly for multi-modal traffic system. To address this gap, we investigate the population exposure of three modes (cars, buses, and metro) during morning rush hour in Chengdu, China, amidst the implementation of contingent traffic-related policies during heavy pollution episodes with elevated background pollutant concentrations of PM2.5. We introduce a trip-based average population exposure model to evaluate travel population exposure. This model allows to calculate the exposure for all modes, and assess its changes owing to modal shifts. Additionally, we propose a traffic-modal exposure coefficient to quantify PM2.5 exposure disparities among traffic modals. The results indicate that: the population exposure of total travelers experiences a growth effect, influenced not only by increased pollutant concentration but also by modal shift. Lastly, the inside-to-outside ratio, bus waiting time, and attractiveness of active traveler are discussed for more policy implications. This study not only offers a quantitative model to uncover potential overestimation of the effect of policies on reducing exposure if failure to consider modal shift, but also provides valuable insights for managing exposure growth to safeguard health of urban residents and promote sustainable development of cities.

Multi-Hazard Population Exposure in Low-Elevation Coastal Zones of China from 1990 to 2020

China’s low-elevation coastal zone (LECZ) is characterized by multiple hazards and high impacts. How to quantitatively portray the spatiotemporal characteristics of the exposed population to multi-hazards in the LECZ is an important subject of risk reduction. In this study, the overall characteristics, spatial patterns, and main impact hazard in the LECZ from 1990 to 2020 were investigated using a multi-hazard population exposure model, spatial autocorrelation method, and principal component analysis (PCA) method. The results show that among the four hazards (earthquake, tropical cyclones (TCs), flood, and storm surge), TCs cover the largest area, accounting for 90.1% of the total LECZ area. TCs were also the hazard with the largest average annual growth rate of the exposed population (2.36%). The central region of China’s LECZ is the cluster of exposed populations and the main distribution area with the largest increase in exposed populations. Therefore, the central region is a hotspot for multi-hazard risk management. Additionally, flood contributes the most to the multi-hazard population exposure index; thus, flood is a key hazard of concern in the LECZ. This study identifies the hotspot areas and priority hazards of multi-hazard exposed populations in the LECZ and provides important policy recommendations for multi-hazard risk management in the LECZ, which is important for LECZ to enhance the resilience of hazards.

Spatio-Temporal Patterns of Global Population Exposure Risk of PM2.5 from 2000–2016

A high level of fine particulate matter (PM2.5) has become one of the greatest threats to human health. Based on multi-source remote sensing data, the pollutant population exposure model, accompanied by the Theil–Sen Median and Mann–Kendall methods, was used to analyze the spatio-temporal patterns of global population exposure risk of PM2.5 from 2000 to 2016. The population distribution patterns of high-risk exposure areas have been accurately identified; the variation trend and stability of global population exposure risk of PM2.5 have also been analyzed. According to the results, the average concentration of PM2.5 is correlated with the total population. The average concentration of PM2.5 for countries from high to low are Asia (14.7 μg/m3), Africa (8.1 μg/m3), Europe (8.03 μg/m3), South America (5.69 μg/m3), North America (4.41 μg/m3), and Oceania (1.27 μg/m3). In addition, the global average population exposure risk of PM2.5 is decreasing annually. Specifically, China, India, Southeast Asia, and other regions have higher exposure risks. Less developed mountainous regions, cold regions, deserts and tropical rainforest regions have lower exposure risks. Moreover, Oceania, North America, South America and other regions have relatively stable exposure, whereas areas with relatively unstable exposure risk of PM2.5 are mainly concentrated in Asia, India, and eastern China, followed by Southeast Asia, Europe, and Africa. Furthermore, Asia has the largest population of all the continents, followed by Africa and Europe. Countries with increased populations are mainly distributed in Africa, whereas the countries with a declining population are mainly distributed in Europe. Based on this, it is important to identify the relationship between PM2.5 concentration and population exposure risk to improve human settlements and environmental risk assessment.

A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Outdoor concentrations of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) are often used as a surrogate for population exposure to PM2.5 in epidemiological studies. However, people spend most of their daily activities indoors; therefore, the relationship between indoor and outdoor PM2.5 concentrations should be considered in the estimation of population exposure to PM2.5. In this study, a population exposure model was developed to predict seasonal population exposure to PM2.5 in Seoul, Korea. The input data for the population exposure model comprised 3984 time-location patterns, outdoor PM2.5 concentrations, and the microenvironment-to-outdoor PM2.5 concentrations in seven microenvironments. A probabilistic approach was used to develop the Korea simulation exposure model. The determinants for the population exposure group were identified using a multinomial logistic regression analysis. Population exposure to PM2.5 varied significantly among the three seasons (p < 0.01). The mean ± standard deviation of population exposures to PM2.5 was 21.3 ± 4.0 μg/m3 in summer, 9.8 ± 2.7 μg/m3 in autumn, and 29.9 ± 10.6 μg/m3 in winter. Exposure to PM2.5 higher than 35 μg/m3 mainly occurred in winter. Gender, age, working hours, and health condition were identified as significant determinants in the exposure groups. An “unhealthy” health condition was the most significant determinant. The high PM2.5 exposure group was characterized as a higher proportion of males of a lower age with longer working hours. The population exposure model for PM2.5 could be used to implement effective interventions and evaluate the effectiveness of control policies to reduce exposure.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM(2.5) and its components (elemental carbon (EC), SO(4)), O(3), carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NO(x), and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM(2.5), SO(4), and O(3)); (ii) for all pollutants except NO(x), temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

Estimating the contribution of traffic-related pollutants to urban air quality for applying a hybrid modeling approach

Traffic-related pollutants such as NO, NO2, PM and O3 which is indirectly produced through photochemical reaction are widely known as a serious hazard for human health. In this study, we estimate the contribution of traffic-related pollutants to ambient concentrations using CMAQ model and examine the “double count” of the same traffic emissions for applying a hybrid modeling approach, which is to combine the regional-scale air quality model and local dispersion model for linking the model-predicted gridded concentrations with population exposure model. Busan, the target area in this study, is the second largest metropolis located on the southeastern tip of the Korean Peninsula with a population of around 3.6 million. The main emission sources in the city are on-road mobile sources (50.5%), followed by non-road mobile sources such as shipping around port areas (17.6%) and solvent utilization (17.4%). In this study, to evaluate the impact of the traffic-related pollutants on outdoor ambient concentrations were quantified by analyzing the difference between model-predicted concentrations derived from two sets of simulation scenarios : (1) the WRF-SMOKE-CMAQ simulation for a 1 month (May, 2011) with (i.e., BASE case) and (2) without the traffic emissions (i.e., CNTL case). To evaluate the model validation for the BASE case, a statistical comparison was made between the observed and simulated values of both meteorological variables (air temperature, wind speed and direction) and air pollutants (O3 and NO2) at several monitoring sites. Overall, the simulated values show good correlation with the observations and indicate that O3-NO2 dynamics are generally well captured by the CMAQ (MBE of -7.4 ppb and 3.4 ppb for NO2 and O3, respectively). The differences between two simulations (BASE case minus CNTL case) are the range from -4 ppb to 60 ppb for NO2 and -44 ppb to 17 ppb for O3. This result reveals that traffic-related pollutants contribute negatively to O3 concentrations due to NOx increase under the NOx-saturated environment.

Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta

Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration-response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.

Analysis of potential mercury policies: The impact of stream basin characteristics on susceptible populations

Human exposure to Hg through fish consumption from local waterways is an ongoing concern to regulatory decision makers. Previously described population exposure and bioaccumulation models were combined to analyze the impact of potential policies on susceptible populations. The combined model simulated the problem of Hg exposure by examining the system from the point of Hg in the water column to its concentration in population biomarkers. Evaluated policy scenarios included the protectiveness of fish consumption advisories, total maximum daily load changes, and watershed management strategies. Simulations indicated that the characteristics of a basin combined with the unique pattern of intake rates of susceptible populations determine the risk associated with fish consumption from a given waterway. Each population had a unique pattern of biomarker response to changes in fish tissue Hg. Management strategies that lowered bioaccumulation rates also reduced ecosystem services. Reducing fish tissue contamination through reductions in Hg loading to watersheds is a long-term solution. For the short-term, fish consumption advisories should be used to protect populations from adverse exposures. The combination of characteristics of the basin and the populations that fish from the waterway should be the determinant for setting advisories.

Exposure analysis of accidental release of mercury from compact fluorescent lamps (CFLs)

Mercury release after breakage of compact fluorescent lamps (CFLs) has recently become an issue of public health concern, especially in the case of early life infants. Preliminary, screening type calculations have indicated that there is potential for increased intake of mercury vapor by inhalation after breakage of a CFL.Several experimental and computational studies have shown that, when modeling the breakage of a CFL, the room space must be segregated into different zones, according to the potential of mercury vapor to accumulate in them after accidental release. In this study, a detailed two-zone model that captures the physicochemical processes that govern mercury vapor formation and dispersion in the indoor environment was developed. The mercury fate model was coupled to a population exposure model that accounts for age and gender-related differences in time–activity patterns, as well as country differences in body weight and age distribution. The parameters above are used to determine the intake through inhalation (gas phase and particles) and non-dietary ingestion (settled dust) for each age, gender group and ethnicity.Results showed that the critical period for intake covers the first 4h after the CFL breaks and that room air temperature significantly affects the intake rate. Indoor air concentration of mercury vapor may exceed toxicological thresholds of concern such as the acute Reference Exposure Limit (REL) for mercury vapor set by the Environmental Protection Agency of California. Ingestion intake through hand-to-mouth behavior is significant for infants and toddlers, counting for about 20% of the overall intake. Simple risk reduction measures including increased indoor ventilation followed by careful clean-up of the accident site, may limit dramatically the estimated health risk.

Importance of the Population Exposure Model in the Impact of PM and Daily Mortality

Importance of the Population Exposure Model in the Impact of PM and Daily Mortality

Subgroups exposed to systematically different elemental compositions of PM 2.5

Identification of exposure subgroups is important for both health-based assessments where health effects are linked to the elemental composition of PM 2.5 mixture to which participants are exposed, and for development of population exposure models where population exposures to PM 2.5 mass are modeled generally using fixed site ambient monitoring. Here we demonstrate that workplace sources dominate PM 2.5 mass in the upper end of the distribution for EXPOLIS participants in Athens, Basel, Helsinki and Oxford, resulting in poor performance of models that use ambient concentrations to predict exposures when predicting higher exposures, where adverse health impacts would be more likely. Further, since different microenvironments reflect differing contributions from local PM 2.5 sources, personal PM 2.5 exposures for participants whose exposures are dominated by different microenvironments show systematically different elemental personal compositions. Perhaps a more significant complication for epidemiologic associations is that the proportion of participants whose exposures are dominated by each microenvironment varies across the exposure distribution to PM 2.5. Participants exposed predominantly in the outdoor or personal microenvironments are a greater fraction of the lower end of the PM 2.5 exposure distribution while participants with dominant workplace environments are a greater fraction of the upper end of the distribution, with corresponding differences in elemental compositions of PM 2.5 exposures across the exposure distribution.

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, walking, and indoors at home; microenvironmental infiltration rates; and pollutant removal rates in indoor environments. Typically, exposure models rely upon ambient air concentration inputs from a sparse network of monitoring stations. Here we present a unique methodology for combining multiple types of air quality models (the Community Multi-Scale Air Quality [CMAQ] chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (the Hazardous Air Pollutant Exposure Model [HAPEM] or the Stochastic Human Exposure and Dose Simulation [SHEDS] model) to enhance estimates of air pollution exposures that vary temporally (annual and seasonal) and spatially (at census-block-group resolution) in an urban area. The results indicate that there is a strong spatial gradient in the predicted mean exposure concentrations near roadways and industrial facilities that can vary by almost a factor of 2 across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district than in the suburbs. This is mostly due to the importance of personal mobility factors whereby individuals living in the central area often move between microenvironments with high concentrations, as opposed to individuals residing at the outskirts of the city. Also, our results indicate 20–30% differences due to commuting patterns and almost a factor of 2 difference because of near-roadway effects. These differences are smaller for the median exposures, indicating the highly variable nature of the reflected ambient concentrations. In conjunction with local data on emission sources, microenvironmental factors, and behavioral and socioeconomic characteristics, the combined source-to-exposure modeling methodology presented in this paper can improve the assessment of exposures in future community air pollution health studies.

Impact and uncertainty of a traffic management intervention: Population exposure to polycyclic aromatic hydrocarbons

In urban areas, road traffic is a major source of carcinogenic polycyclic aromatic hydrocarbons (PAH), thus any changes in traffic patterns are expected to affect PAH concentrations in ambient air. Exposure to PAH and other traffic-related air pollutants has often been quantified in a deterministic manner that disregards the various sources of uncertainty in the modelling systems used. In this study, we developed a generic method for handling uncertainty in population exposure models. The method was applied to quantify the uncertainty in population exposure to benzo[ a]pyrene (BaP) before and after the implementation of a traffic management intervention. This intervention would affect the movement of vehicles in the studied area and consequently alter traffic emissions, pollutant concentrations and population exposure. Several models, including an emission calculator, a dispersion model and a Geographic Information System were used to quantify the impact of the traffic management intervention. We established four exposure zones defined by distance of residence postcode centroids from major road or intersection. A stochastic method was used to quantify the uncertainty in the population exposure model. The method characterises uncertainty using probability measures and propagates it applying Monte Carlo analysis. The overall model predicted that the traffic management scheme would lead to a minor reduction in mean population exposure to BaP in the studied area. However, the uncertainty associated with the exposure estimates was much larger than this reduction. The proposed method is generic and provides realistic estimates of population exposure to traffic-related pollutants, as well as characterises the uncertainty in these estimates. This method can be used within a decision support tool to evaluate the impact of alternative traffic management policies.

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.

Assessing the Health Impact of Air Pollution, Noise, and Accidents as a Function of Transport Policies

MS8-06 Abstract: Transport is an essential activity of modern society and brings about potential for development as well as adverse health implications. The HEARTS (Health Effects and Risks of Transport Systems) project had the main objective of developing tools for the integrated assessment of urban transport impacts on health. Air pollution, noise, and road accidents (including pedestrian accidents) were considered in an attempt to support the identification, development, and implementation of urban transport policies that minimize the health impacts. The project targeted several potential users such as administrations, policy-makers, departments of traffic, environment, health, land use planning, consultants companies, scientific and research institutions, environmental consultants, environmental impact assessment (EIA), and health impact assessment (HIA) practitioners. HEARTS brought together experts and modelers of traffic-related exposure, policy scenarios, and health with the main objectives of: Developing methodologies to assess the health impact of transport-related factors in the urban environment; Integrating different impacts (noise, accidents, air pollution); and Testing the proposed methodology based on policies and scenarios of 3 European cities: Leicester, Lille, and Florence. The project underlined the importance of considering the complexity of human exposure to air pollutants, noise, and traffic accidents in an integrated manner to predict the health impacts of transport policies. Dynamic geographic models were used for the assessment of transport-related exposures to air pollution and noise at the local scale, whereas population exposure models using a probabilistic simulation framework were used for broader-scale population-level exposure assessment. The dynamic geographic model was applied using a collection of specific models under a geographic information system (GIS) platform. Newly developed accident and pedestrian risk models were also included. Ad hoc survey campaigns and an additional collection of data were conducted. In the case study cities, transport policy options and different scenarios were identified and assessed in terms of their health implications.

Overview of the European project FUMAPEX

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

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.

The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants.

Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey (n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.