Population exposure model

Occupational and toxicological studies have demonstrated adverse health effects from exposure to toxic air contaminants. Data on outdoor levels of toxic air contaminants have not been available for most communities in the United States, making it difficult to assess the potential for adverse human health effects from general population exposures. Models and new experiments provide a great amount of new data (Woodruff et al., 1998). 

Burke JM, Zufall MJ, Ozkaynak H (2001) A population exposure model for particulate matter case study results for PM2.5 in Philadelphia, PA. J Expo Anal Environ Epidemiol 11 470-489... 

By brainstorming about potential disaster scenarios and the scope of resources anticipated to be needed under each scenario, the intensity and duration of the mental health response can also be anticipated. The U.S. Department of Health and Human Services (2004) has developed a population exposure model that planners can use to estimate the psychological impact of mass violence and terrorism and, therefore, the resources that might he needed. The model s underlying principle is that individuals who are most personally, physically, and psychologically exposed to trauma and the disaster scene are likely to he affected the most (Figure 5.1). 

Fig. 1.1 Extended FUMAPEX scheme of Urban Air Quality Information Forecasting System (UAQIFS) including feedbacks. Improvements of meteorological forecasts (NWP) in urban areas, interfaces and integration with UAP and population exposure models following the off-line or online integration (Baklanov 2005 after EMS-PUMAPEX 2005)...

US Environmental Protection Agency (US ERA) (2003b). SHEDS-PM A population exposure model for predicting distributions of PM exposure and dose from both outdoor and indoor sources. Available from http //cfpub.epa.gov/si/si public record Report.cfm dirEntrylD=63055 (accessed October 17, 2008). 

CASRAM predicts discharge fractions, flash-entrainment quantities, and liquid pool evaporation rates used as input to the model s dispersion algorithm to estimate chemical hazard population exposure zones. The output of CASRAM is a deterministic estimate of the hazard zone (to estimate an associated population health risk value) or the probability distributions of hazard-zones (which is used to estimate an associated distribution population health risk). 

Droz PO, Wu MM, Cumberland WG. 1989a. Variability in biological monitoring of organic solvent exposure. II. Application of a population physiological model. BrJ Ind Med 46 547-558. 

Receptor Exposure. Exposure modeling should produce a statistically representative profile of pollutant intake by a set of receptors. This is done by combining the space/time distribution of pollutant concentrations with that of receptor populations (whether they be people, fish, ducks or property made of some material that is vulnerable to pollutant damage). The accuracy and resolution of the exposure estimates are chosen to be consistent with the main purposes of decision making. These purposes include the following ... 

The purpose of an Exposure Route and Receptor Analysis is to provide methods for estimating individual and population exposure. The results of this step combined with the output of the fate models serve as primary input to the exposure estimation step. Unlike the other analytic steps, the data prepared in this step are not necessarily pollutant-specific. The two discrete components of this analysis are (1) selection of algorithms for estimating individual intake levels of pollutants for each exposure pathway and (2) determination of the regional distribution of study area receptor populations and the temporal factors and behavioral patterns influencing this distribution. 

For a limited number of exposure pathways (primarily inhalation of air in the vicinity of sources), pollutant fate and distribution models have been adapted to estimate population exposure. Examples of such models include the SAI and SRI methodologies developed for EPA s Office of Air Quality Planning and Standards (1,2), the NAAQS Exposure Model (3), and the GEMS approach developed for EPA s Office of Toxic Substances (4). In most cases, however, fate model output will serve as an independent input to an exposure estimate. 

This inverse relationship between equilibrium factor and "unattached" fraction and their relationship to the resulting dose is important in considering how to most efficiently and effectively monitor for exposure. This inverse relationship suggests that it is sufficient to determine the radon concentration. However, it is not clear how precisely this relationship holds and if the dose models are sufficiently accurate to fully support the use of only radon measurements to estimate population exposure and dose. 

Under this project, an IPCS Harmonization Project Document on the Principles of Characterizing and Applying Human Exposure has been published (WHO/IPCS 2005). This document sets out the characteristics of exposure assessment models that should be described to aid in model selection by exposure assessors. The document summarizes current practice in exposure modeling and principles for evaluating exposure models, but does not provide a comprehensive list of existing exposure models. The focus of the document is on the discussion of general properties of exposure models and how they should be described. The characteristics of different modeling frameworks are examined, and 10 principles are recommended for characterization, evaluation, and use of exposure models in order to help model users select and apply the most appropriate models. The report also discusses issues such as validation, input data needs, time resolution, and extrapolation of the model results to different populations and scenarios. 

When the project was started in 2002, European exposure factor data were scattered within numerous national and international institutions. ExpoFacts has created no new data, but instead compiled the existing data into one Internet database, where it can be easily found, screened, and downloaded from. Data were collected from the EU countries, candidate countries to EU, and EFTA countries. As a result, the ExpoFacts database contains data from 30 European countries. In addition to the population time use patterns and exposure route information, e.g., dietary statistics, the database contains socio-demographic and physiologic information to enable database use as a tool for population-wide exposure modeling and risk assessment. 

USEPA performed post hoc cost-benefit calculations and sensitivity analyses for several regulatory options (41). Population exposure calculations were prepared for pre- and postregulation scenarios, and risks were calculated by using the CAG multistage model. A cost of 200,000 per cancer case avoided was assumed on the basis of both earnings and social value. 

This chapter will focus on PM ambient concentrations, which are key variables for exposure models, and are generally obtained by direct measurements in air quality monitoring stations. However, depending on the location and dimension of the region to be studied, monitoring data could not be sufficient to characterise PM levels or to perform population exposure estimations. Numerical models complement and improve the information provided by measured concentration data. These models simulate the changes of pollutant concentrations in the air using a set of mathematical equations that translate the chemical and physical processes in the atmosphere. 

PM concentration fields coming from air quality models are estimations of outdoor microenvironments that combined with gridded population and microenvironments information can be used for exposure modelling and estimation of doses and health effects, integrating the source to dose assessment chain (recall Fig. 1). 

Kousa et al. [20] classified exposure models as statistical, mathematical and mathematical-stochastic models. Statistical models are based on the historical data and capture the past statistical trend of pollutants [21]. The mathematical modelling, also called deterministic modelling, involves application of emission inventories, combined with air quality and population activity modelling. The stochastic approach attempts to include a treatment of the inherent uncertainties of the model [22],... 

Mathematical exposure models applied to urban areas have been presented by Jensen [23], Kousa et al. [20] and Wu et al. [24]. The model presented by Jensen [23] is based on the use of traffic flow computations and the operational street pollution model (OSPM) for evaluating outdoor air pollutants concentrations in urban areas. The activity patterns of the population have been evaluated using... 

Mathematical construction of physical/ chemical processes that predict the range and probability density distribution of an exposure model outcome (e.g. predicted distribution of personal exposures within a study population)... 

As shown along this chapter, a reliable air quality model is a valuable tool for human exposure studies, once modelled concentrations at different spatial scales and time resolutions allow to better characterising the air quality at the microenvironments visited by a target population, rather than monitoring values that are site and time specific. Moreover, air quality and exposure modelling approach considers the contribution of indoor environments, where people spend most of their time, to the exposure estimation. 

Kousa A, Kukkonen J, Karppinen A, Aamio P, Koskentalo T (2002) A model for evaluating the population exposure to ambient air pollution in an urban area. Atmos Environ 36 2109-2119... 

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