Intake fractions of PM

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Question

What are intake fractions of airborne particulate matter for different emission sources and locations?

Answer

⇤# : The answer should include data from Exposure to PM2.5 in Finland#Intake fractions of PM. --Jouni (talk) 17:16, 8 April 2015 (UTC)

+ Show code- Hide code library(OpasnetUtils) library(ggplot2) objects.latest("Op_en5813", code_name="initiate") iF.PM2.5 <- EvalOutput(iF.PM2.5) ggplot(iF.PM2.5@output, aes(x = iF.PM2.5Result, colour = Subcategory)) + geom_density() + theme_gray(base_size = 24)

Rationale

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)

You have error(s) in your data:

You must define units. Please use syntax: unit="unit"

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

+ 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 stored.\n")

References

• PILTTI final report

Related files

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