Results of Intake Fraction Studies

From Opasnet
Jump to: navigation, search


Scope

This variable presents the results of published intake fraction studies.

Definition

Data

Source of information is published articles from various journals. The source of each value are listed in the references.

Unit

-

See also

http://en.opasnet.org/w/Intake_fraction

References

id description
1 Evans, J.S.; Wolff, S.K.; Phonboon, K.; Levy, J.I.; Smith, K.R. (2002) Exposure efficiency: an idea whose time has come? Chemosphere. 49, 1075–1091. (Phonboon, K., 1996. Risk Assessment of Environmental Effects in Developing Countries, Doctoral Thesis, Harvard School of Public Health, Boston, Massachussetts)
2 Evans, J.S.; Wolff, S.K.; Phonboon, K.; Levy, J.I.; Smith, K.R. (2002) Exposure efficiency: an idea whose time has come? Chemosphere. 49, 1075–1091. (Smith, K.R. (1993). Fuel combustion, air pollution exposure, and health: The situation in developing countries, Annual reviews of Energy and Environment 18, 529-566.)
3 Evans, J.S.; Wolff, S.K.; Phonboon, K.; Levy, J.I.; Smith, K.R. (2002). Exposure efficiency: an idea whose time has come? Chemosphere. 49, 1075–1091. (Original: Wolff, S.K., 2000. evaluation of fine particle exposures, health risks and control options, Doctoral Thesis, Department of Environmental Health, Harvard School of Public Health)
4 Bennett, D.H.; Margni, M.D.; McKone, T.E.; Jolliet, O. (2002). Intake fractions for multimedia pollutants: A tool for life cycle analysis and comparative risk assessment. Risk Analysis 22, 905-918
5 Evans, J.S.; Thompson, K.M.; Hattis, D. (2000). Exposure efficiency: Concept and application to perchloroethylene exposure from dry cleaners, Journal of Air & Waste Management Association 50, 1700-1703.
6 Greco, S.L.; Wilson, A.M.; Spengler, J.D.; Levy, J.I. (2007). Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States, Atmospheric Environment 41, 1011-1025.
7 Hao, J.M.; Li, J.; Ye, X.M.; Zhu, T.L. (2003). Estimating health damage cost from secondary sulfate particles - a case study of Hunan Province, China, Journal of Environmental Sciences-China 15, 611-617.
8 Heath, G.A.; Granvold, P.W.; Hoats, A.S.; Nazaroff, W.W. (2006). Intake fraction assessment of the air pollutant exposure implications of a shift toward distributed electricity generation, Atmospheric Environment 40, 7164–7177.
10 Hirai, Y.; Sakai. S.I.; Watanabe, N.; Takatsuki, H. (2004). Congener-specific intake fractions for PCDDs/DFs and Co-PCBs: modeling and validation, Chemosphere 54, 1383–1400.
11 Huijbregts, M.A.J.; Struijs, J.; Goedkoop, M.; Heijungs, R.; Hendriks, A.J.; van de Meent, D. (2005). Human population intake fractions and environmental fate factors of toxic pollutants in life cycle impact assessment, Chemosphere 61, 1495–1504.
12 Klepeis, N.E.; Nazaroff, W.W. (2006). Modeling residential exposure to secondhand tobacco smoke, Atmospheric Environment 40 (2006) 4393–4407.
13 Lai, A.C.K.; Thatcher, T.L.; Nazaroff, W.W. (2000). Inhalation transfer factors for air pollution health risk assessment, Journal of Air & Waste Management Association 50, 1688-1699
14 Levy, J.I.; Wilson, A.M.; Evans, J.S.; Spengler, J.D. (2003). Estimation of primary and secondary particulate matter intake fractions for power plants in Georgia, Envrion. Sci. Technol. 37, 5528-5536
15 Li, J.; Hao, J.M. (2003). Application of intake fraction to population exposure estimates in Hunan Province of China, Journal of Environmental Science and Health Part A – Toxic/hazardous Substances & Environmental Engineering 38, 1041-1054.
16 MacLeod, M.; Bennett, D.H.; Perem, M.; Maddalena, R; McKone, T.; Mackay, D. (2004). Dependence of intake fraction on release location in a multimedia framework: A case study of four contaminants in North America. J. Indus. Ecol. 8 (3), 89-102.
17 Margni, M.; Pennington, D.W.; Amman, C.; Jolliet, O. (2004). Evaluating multimedia/multipathway model intake fraction estimates using POP emission and monitoring data, Environmental Pollution 128, 263–277.
18 Marshall, J.D. Teoh, S.K.; Nazaroff, W.W. (2005). Intake fraction of nonreactive vehicle emissions in US urban areas, Atmospheric Environment 39, 1363–1371.
19 Marshall, J.D.; Behrentz (2005). Vehicle self-pollution intake fraction: Children’s exposure to school bus emissions, Environ. Sci. Technol. 39, 2559-2563.
20 Marshall, J.D.; Riley, W.J.; McKone, T.E.; Nazaroff, W.W. (2003). Intake fraction of primary pollutants: motor vehicle emissions in the South Coast Air Basin, Atmospheric Environment 37, 3455–3468.
21 Spadaro, J.V.; Rabl, A. (2004). Pathway Analysis for Population-Total Health Impacts of Toxic Metal Emissions, Risk Analysis 24, 1121-1141.
22 Wang, S.; Hao, J.; Ho, M.S.; Li, J.; Lu, Y. (2006). Intake fractions of industrial air pollutants in China: Estimation and application, Science of the Total Environment 354, 127– 141.
23 Ilacqua, V. (2004). EXPOLIS-INDEX Work Package 3 Final Report, Part II Intake Fractions. In Project Final Report to Cefic, pp24 –56.
24 Zhou, Y.; Levy, J.I.; Evans, J.S.; Hammitt, J.K. (2006). The influence of geographic location on population exposure to emissions from power plants throughout China, Environment International 32, 365 – 373.
25 Zhou, Y.; Levy, J.I.; Hammitt, J.K.; Evans, J.S. (2003). Estimating population exposure to power plant emissions using CALPUFF: a case study in Beijing, China, Atmospheric Environment 37, 815–826.
26 Nazaroff, W.W. (2008). Inhalation intake fraction of pollutants from episodic indoor emissions, Building and Environment 43, 269–277.
27 Zhou Ying;Levy,J.I. (2008). The impact of urban street canyons on population exposure to traffic-related primary pollutants, Atmospheric Environment 42, 3087-3098.
28 Nigge, K-M. (2001) Generic spatial classes for human health impacts, part I :methodology; Generic Spatial Classes for Human Helath Impacts, Part I
29 Heath, G.A. and Nazaroff, W.W. (2007) Intake-to-delivered-energy ratios for central station and distributed electricity generation in California; Atmospheric Environment 41, 9159-9172

Method details

id description
1 For the local analysis, concentration estimates were computed using ISCLT2, a sector-averaged Gaussian model. For the regional analysis, concentrations were estimated using a simple spreadsheet-based sector-averaged Gaussian model. In both models, first-order losses were computed using half-lives of 8 h for particles, 4 h for SO2 and 48 h for benzene. Approximately 5 million persons live within 20 km of the source (3997 persons per km2), 7 million live within 50 km (923 persons per km2) and 160 million live within 1000 km (52 persons per km2). Breathing rates appropriate for the body weights of the Thai population were used (age- specific rates between 4.4 m3/d (infants) and 11.8 m3/d (young adults), which yield an average breathing rate for the entire Thai population on the order of 9.5 /d––about half of the nominal value of 20 m3/d). Exposure efficiency (intake fraction) did not increase significantly beyond 50 km. This was attributed to the relatively short half-lives of the pollutants, the extremely high population densities in the immediate vicinity of the source and the relatively low stack heights at the refinery.
2 The estimated 1985 US population of each of the 243 air quality regions was used to derive population-weighted exposure estimates. Using the Pacific Northwest Laboratory long-range transport model, it's been estimated that in the US each English ton(2000 lb) of annual emissions resulted in approximately 100 person ug/m3 year of exposure to particles. This analysis considered only primary particles, but accounted for both dry deposition (using a terminal settling velocity of 0.2 cm/s (characteristic of a 10 lm particle)) and wet deposition (by means of an empirical relationship which accounts for intensity of rainfall). Using a breathing rate of 28 m3/d this estimate was converted to exposure efficiency (i.e. intake fraction).
3 This exposure efficiency (intake fraction) estimate for coalfired power plants in LDC was derived by multiplying corresponding US value by a factor of 9 intended to account for the greater population densities typical of developing countries.
4 Calculation relies on Dockery and Spenglers observation that smoking 1 cigarette per day indoors results in a 1 lg/m3 increase in indoor concentrations of particulate matter (Dockery and Spengler, 1979). In the calculation of exposure efficiency (intake fracton) an emissions factor of 24 mg/cigarette and an indoor occupancy of 2.5 persons was used.
5 Hourly meteorological data for the year 1990 were obtained from EPA. CALPUFF was used to estimate the source related increment in ambient concentration in each of 448 geographic cells, each 100km x 100 km, covering a region1600 km (N–S) by 2800 km (W–E) around the source. These hourly values were averaged to determine the impact of the souce on the annual mean concentration experienced in each cell. Populationdata for each cell were derived using ArcView 3.2 Geographic Information System and includes all of the population of the US and portions of southern Canada and northern Mexico.
6 An open environmental system with landscape and climate parameters reflecting U.S. averages and population-based lifetime average exposure parameters (brething rates, diet, activity patterns, etc.). Mixing height 700m, simulated continuos release and multimedia dispersion lasts for 100 years, with exposure occuring during the last 70 years of the release. Do not account the variation of pollutant concentrations and population densities, and relationships between releases and food production regions or the movement of food between regions.
7 Calculated based on measurements (1995)
8 Global exposure efficiency (i.e. intake fraction) for perchloroethylene emissions from dry cleaners in the Northernhemisphere was evalueted with box model using mixing height of 11km, box volume of 1.6x109 km and population of 3.8x109.
9 For each chosen site, local exposure efficiency was computed using the EPAs HumanExposure Model, HEM, version II. HEM is a sector-averaged Gaussian dispersion model coupled with block level populationdata from the 1990 US Census. It relies on meteorological data from the nearest stability array (STAR) site. A nominal breathing rate of 20 m3/d was used to convert ambient concentrations to intake.
10 The S–R matrix is a regression-based derivation of output from the Climatological Regional Dispersion Model (CRDM) which uses assumptions similar to the Industrial Source Complex Short Term model (ISCST3). The underlying model, CRDM, incorporates terms for wet and dry deposition of primary and secondary species that constitute PM2.5 and uses meteorological summaries (annual average mixing heights and joint frequency distributions of wind speed and direction) from 100 upper air meteorological sites throughout North America. Additionally, CRDM uses Turner’s sector-average approach, a probabilistic method where relative frequencies of occurrence of combinations of wind and stability conditions at the emissions source are used to calculate the relative frequencies of transport in various sectors. A set of county-specific calibration factors for PM2.5 was used to calibrate the S–R matrix model to ambient air quality data. This model covers the 48 contiguous US states and treats all mobile source emissions in each of 3080 US counties as area sources. It is important to note that mobile source emissions impact the county where the emissions originated in addition to downwind counties, and that the national iF incorporates exposure in all counties. The nominal population breathing rate of 20m3/d was used with county-level population projections for year 2007, estimated from 1990 Census data, and with mobile source PM2.5 and precursor emissions from each county were based upon EPA National Emissions Inventory information.
11 Gaussian plume dispersion modelling with a GIS-based exposure assessment to estimate the annual-average population inhaaltion intake of primary polluntants emitted from electricity generation facilities in California. For each case, the plume of primary air pollutant emissions is modelled for 8760 h using data that represent a typical annual meteorological cycle for that region of California. Finely resolved concentration fields are combined with population-average volumetric breathing rates and census tract-level population density (year 2000) to yield hourly estimates of inhalation intake of emitted pollutants by the exposed population within 100 km of the source. A constant breathing rate equal to the estimated lifetime population-average value of 12m3/d is used and temporal variability in population density was not considered.
12 Measurement-based iFs were calculated by dividing the congener-specific dioxin intakes by the dioxin emission inventory for 1 year period. The congener-specific intakes through food intake, water consumption, inhalation, and soil ingestion were compiled from total diet studies of Japan, and the measured concentrations in air and soil were from Environment Agency of Japan, 1999. The total diet studies included the intakes through both food and drinking water. The inhalation rate and the soil ingestion rate were the same as those used in the USES model. Congener-specific emissions of dioxins were calculated on the basis of the 7.5-kg TEQ/year total emission to air measured in 1997 (Environment Agency of Japan, 1998) and the congener profile of air deposition measured in 1998 (Environment Agency of Japan, 1999).
13 Compartment-specific environmental fate model based on Simplebox. All assumptions and calculations are made in global scale
14 Model tracks the individual minute-by-minute location of a smoker and a nonsmoker as they move among rooms of a house during a single day. The model incorporates key aspects of the house configuration, including time-dependent door and window positions and the operation of a central air handling system. Model calculates room-by-room contaminant concentrations and smoker and non-smoker exposure and intake. Pollutants are asummed to be well mixed within each room and inhalation rate of 7.8 m3/d is used. iFs were calculated for varying heat and air conditioning situations and for individuals with different behaviour. Human activity patterns were obtained from USEPA’s National Human Activity Pattern Survey (NHAPS). These data provide minute-by-minute information about the daily duration and sequence of time spent by Americans in various locations, including the rooms of their home.
15 Uniform population distribution was assumed with breathing rate of 0.73 m3/hr and wind speed between 1 and 10 m/sec were used.
16 Steady release of nonreactive pollutants was assumed and time averaged downwind concentrations were calculated with source height of 30 and 100m. Breathing rate of 0.73 m3/hr and wind speed between 1 and 10 m/sec were used.
17 Concentrations were calculated for steady-stade conditions in well-mixed air basin. Uniform population distribution was assumed and breathing rate of 0.73 m3/hr was used. Mixing height was 300m and wind speed was varied between 1 and 10 m/sec
18 Different wind speeds with variations in fan and window settings were used.
19 Different traveling speeds, with varying wind speeds and variations in fan and window settings were used.
20 CALMET version 5.2, CALPUFF version 5.5, and CALPOST version 5.2 were used. Given available data for 1990, upper-air data were taken from the National Center for Atmospheric Research/National Centers for Environmental Prediction global reanalysis, which provided spatial resolution of 2.5° x 2.5° (roughly 250 km) at 19 vertical levels with 6-h time resolution. Surface observations were taken from the SAMSON dataset from the National Climatic Data Center, providing hourly observations from 262 stations across the United States. In CALPUFF runs the default wet and dry deposition model routines and the MESOPUFF II chemical transformation mechanism for SO2 and NOx. Dry deposition rates vary as a function of terrain as well as meteorological conditions and pollutant, while wet deposition is modeled using constant pollutant-specific scavenging coefficients applied when any precipitation occurs (liquid or frozen). Sulfate oxidation is a nonlinear function of ozone concentrations, solar radiation, stability, and relative humidity during daytime hours, with a constant rate of 0.2%/hour at night. Nitrate oxidation is similarly related in a nonlinear fashion to ozone andNOx concentrations as well as stability, with a nighttime rate of 2%/hour.
21 Contains county-to-county transfer factors across the United States for primary particles (both PM2.5 and PM10) and secondary particles (including sulfates and nitrates), based on an adjusted version of the Climatological Regional Dispersion Model (CRDM).CRDM uses simple climatological summaries based on 1990 meteorological data (annual average mixing heights and joint frequency distributions of wind speed and direction) along with a sector-averaged dispersion model across 16 wind directions. Deposition and chemical conversion are incorporated at each receptor location with relatively simple assumptions, although it should be noted that these assumptions are used to develop “first guess” ambient concentration estimates, which are then calibrated based on ambient concentration monitoring data. The model assumes dry deposition rates of 0.1 cm/s for particles, 0.5 cm/s for SO2, and 1 cm/s for NOx, gaseous nitrate, and ammonia. Wet deposition rates (in cm/s) are calculated as a function of the annual precipitation rate in each county in inches (P): 0.08P for particles, 0.008P for SO2, 0.014P for ammonia, and 0.025P for NOx. Chemical conversion is incorporated by assuming sulfate oxidation to be a function of relative humidity and nitrate oxidation to be a constant 2%/hour.
22 Ambient concentrations of fine particles were simulated with CALPUFF, and the GIS technology was used to generate a population distribution database from county-level population statistical data. An integrated computer program package was developed to carry out numerical integration of dispersion results over the population data, and produce intake fractions.
23 The model is built on a framework that links contaminant fate models of individual regions, and is generally applicable to large, spatially heterogeneous areas. The North American environment is modeled as 24 ecological regions, within each region contaminant fate is described using a 7 compartment multimedia fugacity model including a vertically segmented atmosphere, freshwater, freshwater sediment, soil, coastal water and vegetation compartments. Inter-regional transport of contaminants in the atmosphere, freshwater and coastal water is described using a database of hydrological and meteorological data compiled with Geographical Information Systems (GIS) techniques. Steady-state and dynamic solutions to the 168 mass balance equations make up the linked model for North America. Regionally segmented models such as BETR North America can provide a critical link between evaluative models of long-range transport potential and contaminant concentrations observed in remote regions. The continent-scale mass balance calculated by the model provides a sound basis for evaluating long-range transport potential of organic pollutants, and formulation of continent-scale management and regulatory strategies for chemicals. Inhalation, ingestion and dermal pathways are considered. Ingestion pathways include consumption of four classes of vegetation (grains, protected produce, leafy vegetables and root crops), five classes of animal products (beef and poultry, eggs, fish, and dairy products), potable water consumption and incidental soil ingestion. Dermal exposure is from contaminated water during bathing and recreation, and contact with soil. More information: MacLeod, M.; Woodfine, D.G.; Mackay, D.; McKone, T.; Bennett, D.; Maddalena, R. (2001). BETR North America: a regionally segmented multimedia contaminant fate model for North America. Environ. Sci. Pollut. Res. Int. 8 (3), 156-63.
24 Each exposure estimate is first multiplied by the population of each single country and summed over all countries. The result is then divided by the sum of the total population over all countries. Total intake over all Western Europe is then obtained assuming an average body weight of 70 kg, and a Western European population of 431 million inhabitants. iF is then calculated by dividing the total intake from risk assessment estimates by the emitted quantity. This is termed iF(estimated).
25 iF is predicted with IMPACT2002 for five selected congeners. Results are then multiplied to their respective TEF and summed over a single TEQ value. iF is determined by accounting for the single congener contribution to the overall air emission. A comparison between the three iFs on the single TEQ value is then made as an overall fate and exposure model evaluation.
26 Estimates year-1996 population inhalation of atmospheric emissions in the US. The ASPEN Gaussian plume dispersion model uses meteorological data and the year-1996 National Toxics Inventory to estimate ambient concentrations in all UScensus tracts. Next, a probabilistic exposure model combines (1) ASPENestimated ambient concentrations, (2) time-activity information for 30 hypothetical individuals from each of 10 cohorts (5 age groups, two genders), and (3) estimates of differences between ambient and microenvironment exposure concentrations. The results are summarized as the population average incremental exposure concentration. Because the NATA exposure concentrations are the mean values across census tracts, they are approximately population-weighted values. (Census tracts are sized to contain 4000 people each)
27 The empirical model estimates ambient concentrations of CO, which is a good tracer for nonreactive vehicle emissions, and is based on measured concentrations focusing explicitly on vehicle emissions, incorporating USEPA’s MOBILE5 emission factors (www.epa.gov/otaq/m5.htm). The model offers good predictions of observed data, based on only a few empirically determined parameters.
28 Meteorological data was combined with wind speed and mixing heights with demographic data on urban population and land area with one-compartment model, which is often assumed to be too simple to offer reasonable estimates of ambient concentrations in urban areas, but for conserved or slowly reacting emissions from broadly distributed ground-level sources, the one-compartment model may offer a reasonably accurate estimate of spatially averaged concentrations in an urban area.
29 Tracer gas (sulfur hexafluoride) was measured in several runs in 2002. Buses with differnet manufacturing years were used and some runs were made with windows closed and others with windows open. Breathing rate of 15 l/min and average of 40 children per bus was used in calculations.
30 Attributable exposure concentrations were estimated from ambient concentrations, the time spent in specific microenvironments (i.e., time-activity patterns), and the exposure concentration associated with these microenvironments, motor vehicle emissions estimated by the EMFAC2000 model, used population average breathing rate was 12.2 m3/d.
31 Simple multimedia model considering air, water and food as medias. Time-scale is varying for the totality of the collective dose, and for the collective dose incurred during the first 100 years. Steady-stade is assumed with population-averaged inhalation rate of 20.6 m3/d. Deposition velocity of 1000km is used. The calculation of pollutant concentrations in food begins with an analysis of the concentrations in soil and water.
32 The meteorological data and receptor locations used were specific to each city. Other model param were held constant for all samples: the terrain parameter is set as flat for both sources and receptors and the half-life for exponential decay of SO2 is set at 4 h. Meteorological information was collected for each of the five cities for 1999 and applied to all emission sources within the given city. A geographical information system (GIS) was applied to allocate the population data to the chosen grid system and population was assumed to distribute evenly within each grid cell. People were assumed to be outdoors during the simulation period in this study.
33 A Monte Carlo model simulates a real population by sampling values of the independent variables and calculating the correspondent values of the dependent variables, for a fixed but large number of times. Values of the independent variables are sampled according their specified probability distributions, which constitute the input of the model. In the end, a population of values of the dependent variable is created, that has a frequency distribution similar to that in the actual population. The distributions of intake fraction for indoor air emissions were based on distributions of residence volume, air exchange rates and time-activity data, calculated from the EXPOLIS database, as well as on distributions from the literature. Some approximations were made that are valid for conservative pollutants and continuos sources, such as emissions from building materials, pesticides, molds, as well as for certain non-continuous sources such as cooking or cleaning products. For these categories of sources, intake fractions are approximately independent of the actual indoor concentrations and irrespective of the source.
34 The modeling domain, 3360 x 3360 km, covers all the heavily populated areas in China. Although populations in other countries could be influenced by power plants at selected locations, we restrict the domain to China to provide the information most directly relevant to national policy analyses. Modeling domain is divided into 14,400 grid cells, population in each cell was derived using 1999 county-level population data, population-average breathing rate of 20 m3/d was used. iFs are calculated for 10-day period in each of the four seasons in 1995 and the annual intake fraction estimates for each pollutant obtained by averaging the intake fractions of the four seasons.
35 The entire domain, 3360km x 3360 km, covers most of China’s area and all its heavily populated regions. County-level population data for the year 1999 with total population of 1.2 billion were used to calculate population exposures. GIS was used to convert the county-level population data to match the concentration data calculated using CALPUFF. A constant breathing rate of 20 m3/d was used in the calculations. iFs were calulated with 10-day simulation, the first 6 days had continuous emissions while the last 4 days had no emissions. The 10-day run length for each season was chosen based on the consideration of the lifetime of pollutants modeled in the atmosphere as well as the capacity limit of the computing facilities available. Preliminary calculations show that a small fraction of the pollutants modeled remained in the atmosphere at the end of the 10-day simulation.
36 Mathematical models are combined with empirical data to explore how intake fraction varies with governing parameters for episodic indoor pollutant releases, such as those from cleaning, cooking, or smoking. In the simple case of the episodic release of a nonreactive pollutant into a well-mixed indoor space with steady occupancy and constant ventilation and breathing rates, the intake fraction is the ratio of the occupants’ volumetric breathing rate to the building’s ventilation flow rate.
37 Concentrations were linked with different subpopulations, including residents, workers, pedestriants, incorporating time-activity patterns and differential breathing rates. Database developed at Los Alamos National Laboratory were used (LANL) was used to estimate the office worker population (1108 persons). Residents that were immediately proximate to the street canyon (100m long) were taken a count (200 residents). Number of pedestrians (1200-3500/h), bikers (40-120/h), and motor vehicle drivers/passangers (average 1000/h) in the street canyon were manually counted in numerous 15 min videos. For the base case scenario, the total iF for a 100 m long street canyon including the contribution of different subpopulations is on the order of 10^-3.
38 A new method for the spatially differentiated assesment of impacts of airborne pollutants on human mealth is presented. The methdod is simply enough to be applied to a large number of emissions within Life Cycle Assessments. Basic idea is to consider how the impact on human health of an emisission of primary airborne pollutant with a linear exposure-response function depends on the population density around the emission site and on the emission height, and how this spatial differentiation can be considered within a Life Cycle Assessment. Spatial differentation of impacts due to emission height and the local population density distribution around emission site has been predicted as has been predicted using a Gaussian plume model. A Gaussian plume model was used to calculate angular average (exposure effiency only depend on the angular average) from the generic meteorological data for 11 different effective emission heights. Equation, that were used suggests the use of the radial population density distribution within a circle of radius 100km around the emission source as the criterion for the definition of generic spatial classes. For German this criterion was operationalized on the basis of an existing official classification of settlement structures (BBR 1998a). The nine settlement structure classess range from large cities within rural distrincts within rural regions. Each of the classes contains between 50 and 3000 municipalitites. For each municipality within a class, the radial population density pi(r) was calculated in increments of ?r = 10km from data provided in (BBR 1998b) and then averaged across the class to yield <pi(r)>. The main difference between classes is the value of <pi(r)> within the first 10km, with a spread by about a factor of 20 between the highest and lowest value. Residence time for diesel particles is 5 days. IF range is evaluated from the barcharts.
39 Gaussian plume dispersion modelling with a GIS-based exposure assessment to estimate the annual-average population inhalation intake of primary polluntants emitted from electricity generation facilities in California. For each case, the plume of primary air pollutant emissions is modelled for 8760 h using data that represent a typical annual meteorological cycle for that region of California. Finely resolved concentration fields are combined with population-average volumetric breathing rates and census tract-level population density (year 2000) to yield hourly estimates of inhalation intake of emitted pollutants by the exposed population within 100 km of the source. A constant breathing rate equal to the estimated lifetime population-average value of 12m3/d is used and temporal variability in population density was not considered.
Retrieved from "http://en.opasnet.org/en-opwiki/index.php?title=Results_of_Intake_Fraction_Studies&oldid=22254"