Intake fractions of PM

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Question

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

Answer

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. Therefore, they have been successfully used in many assessments.

Intake fraction is defined as

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\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.

+ Show code- Hide code 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

+ Show code- Hide code ###This code is Op_en5813/exposure on page [[Intake fractions of PM]]. library(OpasnetUtils) exposure <- Ovariable("exposure", dependencies = data.frame( Name = c( "emissions", # is in ton /a "iF", # conc (g /m3) * pop (#) * BR (m3 /s) / emis (g /s) <=> conc = emis * iF / BR / pop # conc is the exposure concentration "population" ), Ident = c( NA, "Op_en5813/iFHumbert", # [[Intake fractions of PM]] NA ) ), formula = function(...) { BR <- 20 # Nominal breathing rate (m^3 /d) BR <- BR / 24 / 3600 # m^3 /s out <- 1E+12 / 365 / 24 / 3600 # Emission scaling from ton /a to ug /s. out <- (emissions * out) * iF / BR / population # the actual equation out <- unkeep(out, prevresults = TRUE, sources = TRUE) out@output <- out@output[!out@output$Pollutant %in% c("CO2", "CO2official") , ] colnames(out@output)[colnames(out@output) == "Pollutant"] <- "Exposure_agent" out <- oapply(out, cols = c("Renovation"), FUN = sum) return(out) } ) objects.store(exposure) cat("Ovariable exposure stored.\n")

Data

These data come from

Pollutants:

• PM10-2.5: Primary PM10 - primary PM2.5
• PM2.5: Primary PM2.5
• SO2: Secondary PM2.5 derived from SO2 (in practice, SO_4)
• NOx: Secondary PM2.5 derived from NOx (in practice, NO_3)
• NH3: Secondary PM2.5 derived from NH3 (in practice, NH4)

Intake fractions of PM(ppm)

Obs	Pollutant	Emission height	Urban	Rural	Remote	Average	Description
1	PM10-2.5	High	8.8	0.7	0.04	5.0
2	PM10-2.5	Low	13	1.1	0.04	7.5
3	PM10-2.5	Ground	40	3.7	0.04	23
4	PM10-2.5	Average	37	3.4	0.04	21
5	PM2.5	High	11	1.6	0.1	6.8
6	PM2.5	Low	15	2.0	0.1	6.8
7	PM2.5	Ground	44	3.8	0.1	25
8	PM2.5	Average	26	2.6	0.1	15
9	SO2		0.99	0.79	0.05	0.89
10	NOx		0.2	0.17	0.01	0.18
11	NH3		1.7	1.7	0.1	1.7

+ Show code- Hide code ## This is code Op_en5813/iFHumbert on page [[Intake fractions of PM]]. library(OpasnetUtils) iF <- Ovariable("iF", ddata = "Op_en5813", subset = "Intake fractions of PM") # [[Intake fractions of PM]] Humbert et al 2011 data colnames(iF@data) <- gsub("[ \\.]", "_", colnames(iF@data)) iF@data$iFResult <- iF@data$iFResult * 1E-6 objects.store(iF) cat("Ovariable iF (Humbert et al 2011) stored.\n")

Data not used

Intake fractions of PM(per million)

Obs	Geographical area	Year	PM type	Source category	Subcategory	Result	Description of sub-category	Specification	Description
1	Finland	2000	Anthropogenic PM2.5	Power plants	Large power plants	0.18-0.37	Emission from large (>50 MW) power plants (n=117)	mode; min-max	Tainio et al. (2010): 0.28 (0.18-0.37)
2	Finland	2000	Anthropogenic PM2.5	Power plants	Small power plants	0.27-0.44	Emission from small (<50 MW) power plants	mode; min-max	Tainio et al. (2010): 0.34 (0.27-0.44)
3	Northern Europe	-	Anthropogenic PM2.5	Power plants	Major power plants	0.50	-	mean of all seasons	Tainio et al. (2009)

iF for Primary PM (ppm)(-)

Obs	Soucre	Sector	iF	Description
1	Electricity plants	Energy production	1.6
2	CHP Plants	Energy production	11.0
3	Heat plants	Energy production	15.0
4	Blast furnaces	Energy production	8.9
5	Gas works	Energy production	11.0
6	Oil refineries	Energy production	8.9
7	Coal transformation	Energy production	15.0
8	Petrochemical plants	Energy production	8.9
9	Coke/pat. fuel/BKB plants	Energy production	6.8
10	Other transformation	Energy production	8.9
11	Energy industry own use	Energy production	6.8
12	Iron and steel	Industry	6.8
13	Chemical and petrochem	Industry	6.8
14	Non-ferrous metals	Industry	6.8
15	Non-metallic minerals	Industry	6.8
16	Transport equipment	Industry	8.9
17	Machinery	Industry	8.9
18	Mining and quarrying	Industry	3.8
19	Food and tobacco	Industry	6.8
20	Paper, pulp & printing	Industry	6.8
21	Wood and wood products	Industry	8.9
22	Construction	Industry	44.0
23	Textile and leather	Industry	15.0
24	Non-specified	Industry	8.9
25	Domestic aviation	Transport	2.0
26	Road computed from transport data	Transport	25.0
27	Public transport (busses)	Transport	44.0
28	Automobile	Transport	25.0
29	Bicycle	Transport
30	Pedestrian	Transport
31	Freight transport	Transport	25.0
32	Rail transport	Transport	25.0
33	Of which person transport	Transport	25.0
34	Of which freight transport	Transport	3.8
35	Pipeline transport	Transport	3.8
36	Domestic navigation	Transport	3.8
37	Non-specified	Transport	10.0
38	Residential	Other	25.0
39	Commercial (& public services)	Other	25.0
40	Street lighting	Other
41	Public utilities (power, heat, water&waste)	Other	8.9
42	Agriculture & forestry	Other	3.8
43	Fishing	Other	0.1
44	Non-specified	Other	8.9

Calculations for Tainio

+ Show code- Hide code 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")

See also

• Apte research group: Mapping air pollution with Google Street View Cars [1]

References

• PILTTI final report