# Intake fractions of PM

## Question

How to calculate exposure based on intake fractions of airborne particulate matter for different emission sources and locations?

Intake fraction (iF) is the fraction of an emission that is ultimately breathed by someone in the target population. With fine particles, it is often in the range of one in a million, but variation is large. It can be used as a shortcut for calculating exposures in a situation where actual atmospheric fate and transport modelling is not feasible. For fine particles, there is fairly good understanding of the magnitudes of intake fractions in different situations. [1] Therefore, they have been successfully used in many assessments.

Intake fraction is defined as

$\displaystyle iF = \frac{c * P * BR}{E},$ where

• iF = intake fraction (unitless after proper unit conversions)
• c = exposure concentratíon of the population (µg/m3)
• P = population size
• BR = breating rate, usually a nominal value 20 m3/d is used
• E = emission of fine particles (g/s)

In an assessment, exposure concentration c is solved from the equation and used as exposure in health impact modelling.

 library(OpasnetUtils) library(ggplot2) objects.latest("Op_en5813", code_name="exposure") emissions <- 1 population <- 500000 exposure <- EvalOutput(exposure) cat("Intake fractions as parts per million.\n") oprint(summary(EvalOutput(iF) * 1E+6)) #oggplot(iF, x = "Area", fill = "Pollutant") + facet_wrap(~ Emission_height) 

## Rationale

### Inputs and calculations

Variables used to calculate exposure in an assessment model (in µg/m3 in ambient air average concentration).
Variable Measure Indices Missing data
emissions (from the model) is in ton /a Required indices: - . Typical indices: Time, City_area, Exposure_agent, Emission_height.
iF (generic data but depends on population density, emission height etc) conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration Required indices: - . Typical indices: Emission_height, Area
population Amount of population exposed. Required indices: - . Typical indices: Time, Area

### Data not used

Intake fractions of PM: Difference between revisions(per million)
ObsGeographical areaYearPM typeSource categorySubcategoryResultDescription of sub-categorySpecificationDescription
1Finland2000Anthropogenic PM2.5Power plantsLarge power plants0.18-0.37Emission from large (>50 MW) power plants (n=117)mode; min-maxTainio et al. (2010): 0.28 (0.18-0.37)
2Finland2000Anthropogenic PM2.5Power plantsSmall power plants0.27-0.44Emission from small (<50 MW) power plants mode; min-maxTainio et al. (2010): 0.34 (0.27-0.44)
3Northern Europe-Anthropogenic PM2.5Power plantsMajor power plants0.50-mean of all seasonsTainio et al. (2009)
iF for Primary PM (ppm)(-)
ObsSoucreSectoriFDescription
1Electricity plants Energy production 1.6
2CHP Plants Energy production 11.0
3Heat plants Energy production 15.0
4Blast furnaces Energy production 8.9
5Gas works Energy production 11.0
6Oil refineries Energy production 8.9
7Coal transformation Energy production 15.0
8Petrochemical plants Energy production 8.9
9Coke/pat. fuel/BKB plants Energy production 6.8
10Other transformation Energy production 8.9
11Energy industry own use Energy production 6.8
12Iron and steel Industry 6.8
13Chemical and petrochem Industry 6.8
14Non-ferrous metals Industry 6.8
15Non-metallic minerals Industry 6.8
16Transport equipment Industry 8.9
17Machinery Industry 8.9
18Mining and quarrying Industry 3.8
19Food and tobacco Industry 6.8
20Paper, pulp & printing Industry 6.8
21Wood and wood products Industry 8.9
22Construction Industry 44.0
23Textile and leather Industry 15.0
24Non-specified Industry 8.9
25Domestic aviation Transport 2.0
26Road computed from transport data Transport 25.0
27Public transport (busses) Transport 44.0
28Automobile Transport 25.0
29Bicycle Transport
30Pedestrian Transport
31Freight transport Transport 25.0
32Rail transport Transport 25.0
33Of which person transport Transport 25.0
34Of which freight transport Transport 3.8
35Pipeline transport Transport 3.8
37Non-specified Transport 10.0
38Residential Other 25.0
39Commercial (& public services) Other 25.0
40Street lighting Other
41Public utilities (power, heat, water&waste) Other 8.9
42Agriculture & forestry Other 3.8
43Fishing Other 0.1
44Non-specified Other 8.9

### Calculations for Tainio

 library(OpasnetUtils) iF.PM2.5 <- Ovariable("iF.PM2.5", data = opbase.data("Op_en5813")) objects.store(iF.PM2.5) cat("Object iF.PM2.5 (Tainio et al 2010) stored.\n")