Emission factors for burning processes: Difference between revisions

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This page is a knowledge crystal of subtype method. The page identifier is Op_en2791
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Question

What are the emission factors for burning processes and how to estimate emissions based on them? The focus is on the situation in Finland.

Answer

An example code for downloading and using the variable.

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## This code is Op_en2719/ on page [[Emission factors for burning processes]].

library(OpasnetUtils)
library(ggplot2)

objects.latest("Op_en2791", code_name = "emissionstest")
objects.latest("Op_en2791", code_name = "emissionFactors")

oprint(summary(EvalOutput(emissionFactors)))

Rationale

HSY emission factors

This section describes the use of HSY emission factors used for CO2 emission estimates. This should be merged with the other data, but it is first described as such.

These emission factors are derived from the Helsinki Region Environmental Services (HSY) climate data [3] on 27th Nov 2018. Emission factors were initially calculated for every entry in the data file (4180 rows in total). Then, emission sectors were compared along timeline for each city separately. We noticed that several sectors shared practically the same emission factors with few differences, which are not plausible as real differences (Table Sector classification). So, we took a mean of the values to represent all sectors in the same EFclass group.

Then, we compared results from different cities. There were minor changes that may be due to some real differences in data, and also a some changes that looked like artefact. However, there were two sectors, namely district heating and fuels that clearly differed between cities in a plausible way. Therefore, we used city-specific emission factors for these two sectors and those of Helsinki for all other sectors (because Helsinki seemed to have the least amount of artefact in the data).

Sector classification(-)
Obs	Sector	Sektori	EFclass	PKluokka	dummy
1	metro	metrot	electricity	sähkö	1
2	trams	raitiovaunut	electricity	sähkö	1
3	local trains	lähijunat	electricity	sähkö	1
4	consumer electricity	kulutussähkö	electricity	sähkö	1
5	passenger ships	matkustajalaivat	ships	laivat	1
6	cargo ships	rahtilaivat	ships	laivat	1
7	buses	linja-autot	diesel machines	dieselkoneet	1
8	vans	pakettiautot	diesel machines	dieselkoneet	1
9	machinery	työkoneet	diesel machines	dieselkoneet	1
10	trucks	kuorma-autot	diesel machines	dieselkoneet	1
11	leasure boats	huviveneet	boats	veneet	1
12	professional boats	ammattiveneet	boats	veneet	1
13	district heating	kaukolämpö	district heating	kaukolämpö	1
14	oil heating	öljylämmitys	oil heating	öljylämmitys	1
15	electric heating	sähkölämmitys	electric heating	sähkölämmitys	1
16	geothermal heating	maalämpö	geothermal heating	maalämpö	1
17	fuels	polttoaineet	fuels	polttoaineet	1
18	processes	prosessit	processes	prosessit	1
19	private cars	henkilöautot	private cars	henkilöautot	1
20	motor cycles	moottoripyörät	motor cycles	moottoripyörät	1

Sector hierarchy(-)
Obs	Class	Subclass	Luokka	Alaluokka	dummy
1	heating	district heating	lämmitys	kaukolämpö	1
2	heating	oil heating	lämmitys	öljylämmitys	1
3	heating	electric heating	lämmitys	sähkölämmitys	1
4	heating	geothermal heating	lämmitys	maalämpö	1
5	electricity	consumer electricity	sähkö	kulutussähkö	1
6	transport	road transport	liikenne	tieliikenne	1
7	transport	rail transport	liikenne	raideliikenne	1
8	transport	shipping	liikenne	laivaliikenne	1
9	industry and machinery	machinery	teollisuus ja työkoneet	työkoneet	1
10	industry and machinary	fuels	teollisuus ja työkoneet	polttoaineet	1
11	industry and machinery	processes	teollisuus ja työkoneet	prosessit	1
12	waste management	landfill	jätteiden käsittely	kaatopaikka	1
13	waste management	biowaste composting	jätteiden käsittely	biojätteen kompostointi	1
14	waste management	waste water treatment	jätteiden käsittely	jäteveden käsittely	1
15	waste management	waste water sludge composting	jätteiden käsittely	jätevesilietteen kompostointi	1
16	agriculture	fields	maatalous	pellot	1
17	agriculture	farm animals	maatalous	kotieläimet	1
18	road transport	private cars	tieliikenne	henkilöautot	1
19	road transport	motor cycles	tieliikenne	moottoripyörät	1
20	road transport	vans	tieliikenne	pakettiautot	1
21	road transport	trucks	tieliikenne	kuorma-autot	1
22	road transport	buses	tieliikenne	linja-autot	1
23	rail transport	local trains	raideliikenne	lähijunat	1
24	rail transport	metro	raideliikenne	metrot	1
25	rail transport	trams	raideliikenne	raitiovaunut	1
26	shipping	leasure boats	laivaliikenne	huviveneet	1
27	shipping	professional boats	laivaliikenne	ammattiveneet	1
28	shipping	passenger ships	laivaliikenne	matkustajalaivat	1
29	shipping	cargo ships	laivaliikenne	rahtilaivat	1

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# This is code Op_en2791/HSYdata on page [[Emission factors for burning processes]]
library(OpasnetUtils)

HSYdata <- Ovariable("HSYdata",ddata="Op_en2791",subset="HSY CO2 emission and energy consumption")

EFclass <- Ovariable("EFclass", ddata="Op_en2791", subset="Sector classification")

objects.store(HSYdata, EFclass)
cat("Ovariables HSYdata, EFclass stored.\n")

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# This code is Op_en2791/emfactor on page [[Emission factors for burning processes]]
library(OpasnetUtils)

emfactor <- Ovariable(
  "emfactor",
  dependencies=data.frame(
    Name=c("HSYdata","EFclass"),
    Ident=c("Op_en2791/HSYdata","Op_en2791/HSYdata")
  ),
  formula=function(...) {
    
    dat <- HSYdata
    dat$Year <- as.numeric(as.character(dat$Year))
    dat$CO2 <- as.numeric(as.character(dat$CO2))
    result(dat) <- dat$CO2 / result(dat)
    levels(dat$Sector) <- tolower(levels(dat$Sector))
    levels(dat$Consumer) <- tolower(levels(dat$Consumer))
    
    ### Combine sectors that have essentially the same emission factor.
    
    colnames(dat@output)[colnames(dat@output)=="Sector"] <- "Sektori" # The data is in Finnish but titles in English
    
    dat <- dat * EFclass
    
    dat <- dat[!(is.infinite(result(dat)) | is.nan(result(dat)) | result(dat)<0.01),]
    
    dat2 <- dat[dat$City == "Helsinki" | dat$Sector %in% c("district heating","fuels") , ]
    dat2 <- oapply(dat2,INDEX=c("City","Year","EFclass"),FUN=mean)
    
    if(FALSE) {
      
      # The figures illustrate the reasons for merging some sectors under the same emission factor.  
      print(ggplot(dat@output,
                   aes(x=Year, y=result(dat),colour=City,group=paste(City,Sector)))+geom_line()+
              facet_wrap(~ EFclass, scales="free_y")+geom_point(aes(shape=City))+
              labs(y="CO2 emission factor (kg/kWh)")
      )
      
      print(ggplot(dat2@output,
                   aes(x=Year, y=result(dat2),colour=City,group=City))+geom_line()+
              facet_wrap(~ EFclass, scales="free_y")+geom_point(aes(shape=City))+
              labs(y="CO2 emission factor (kg/kWh)")
      )
    }
    
    #### Fill other EFclasses than district heating and fuels with city-unspecific values
    
    dat2$City <- as.factor(ifelse(dat2$EFclass %in% c("district heating","fuels"), as.character(dat2$City),NA))
    dat2@output <- fillna(dat2@output, "City")
    
    #### Make geothermal energy and electric heating dependent on consumer electricity by factors 0.5 and 1.5, respectively.
    
    elfact <- Ovariable(output=data.frame(
      EFclass=c("consumer electricity","geothermal heating","electric heating"),
      Result=c(1,0.5,1.5)),
      marginal=c(TRUE,FALSE)
    )
    
    dat3 <- dat2[!dat2$EFclass %in% c("geothermal heating","electric heating"),]
    tmp <-  dat2[dat2$EFclass %in% c("electricity"),]
    tmp$EFclass <- NULL
    dat3 <- combine(dat3, elfact*tmp) * EFclass
    
    return(dat3)
  }
)

objects.store(emfactor)
cat("Ovariable emfactor stored.\n")

Emission factors in Helsinki(ton/GWh)
Obs	Sector	2015	2030	2035
1	Consumer electricity	121.5	70.6	45
2	Electric heating	234.2	138.5	88.3
3	District heating	189.7	128.8	49.1
4	Natural gas	198	198	198
5	Light fuel oil	261	261	261
6	Coal	341	341	341

The data above comes from Gaia report [4].

Inputs and calculations

**Variables needed for calculating emissions.**
Dependencies	Measure	Indices
fuelUse (from Energy balance or other relevant source)	Amount of fuel used per timepoint.	Required indices: Fuel. Typical indices: Plant
emissionsLocations (case-specific knowledge from e.g. Helsinki energy production)	Tells how where emissions occur and from how high a stack.	Required indices: - . Typical indices: Plant
emissionFactors (generic information, but may be cultural differences. E.g. Emission factors for burning processes ##	emissions per unit of energy produced (g / J or similar unit)	Required indices: Pollutant, Fuel. Typical indices: Burner.

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###This code is Op_en2791/emissionstest on page [[Emission factors for burning processes]].

library(OpasnetUtils)

####### Calculate emissions

emissions <- Ovariable("emissions", 
	dependencies = data.frame(
		Name = c(
			"fuelUse", # Use of fuels (in MJ)
			"emissionFactors", # Emissions of different pollutants per energy (mg /MJ)
			"emissionLocations" # Locations where the emissions are emitted, indexed by City_area or Plant
		),
		Ident = c(
			NA, # typically from [[Energy balance]] but there are other ways to calculate
			"Op_en2791/emissionFactors", # [[Emission factors for burning processes]]
			NA # Case-specific data on locations of emissions.
		)
	),
	formula = function(...) {

		# convert from mg to ton emitted.
		out <- fuelUse * emissionFactors * 1E-9 

		if(!"City_area" %in% colnames(out@output)) {
			out <- out * Ovariable(
				output = data.frame(City_area = "Not known", Result = 1),
				marginal = c(TRUE, FALSE)
			)
		}
		out <- unkeep(out * emissionLocations, sources = TRUE, prevresults = TRUE)
	
		out@output$Emission_site <- as.factor(ifelse(
			out@output$Emission_site == "At site of consumption",
			as.character(out@output$City_area),
			as.character(out@output$Emission_site)
		))

		out <- oapply(
			out,
			cols = c("Burner", "City_area"),
			FUN = sum
		)
		return(out)
	}
)

objects.store(emissions)
cat("Ovariable emissions stored.\n")

Emission factors for heating

Emission factors of energy production(mg /MJ)
Obs	Burner	Fuel	PM2.5	CO2direct	CO2trade	CO2eq	Description
1	Household	Wood	140 (65.8-263)	74200	0	8333	Other stoves and ovens. Karvosenoja et al. 2008
2	Household	Biofuel	140 (65.8-263)	74200	0	8333	Other stoves and ovens. Karvosenoja et al. 2008
3	Household	Light oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
4	Household	Oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
5	Household	Other sources	0-10	74200	74200	74200	Same as oil.
6	Household	Coal	0-10	74200-87222	74200	87222
7	Household	Geothermal	0-10	74200-87222	74200	87222
8	Household	Gas	0-3	55650	55650	55650	For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
9	Household	Fuel oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
10	Domestic	Wood	140 (65.8-263)	74200	0	8333	Other stoves and ovens. Karvosenoja et al. 2008 Just repeat the previous rows to match different wording of burners.
11	Domestic	Biofuel	140 (65.8-263)	74200	0	8333	Other stoves and ovens. Karvosenoja et al. 2008
12	Domestic	Light oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
13	Domestic	Oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
14	Domestic	Other sources	0-10	74200	74200	74200	Same as oil.
15	Domestic	Coal	0-10	74200-87222	74200	87222
16	Domestic	Geothermal	0-10	74200-87222	74200	87222
17	Domestic	Gas	0-3	55650	55650	55650	For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
18	Domestic	Fuel oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
19	Diesel engine	Fuel oil	0-10	74200-87222	74200	87222	Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
20	Diesel engine	Light oil	0-10	74200-87222	74200	87222
21	Diesel engine	Biofuel	0-10	74200-87222	74200	87222
22	Large fluidized bed	Gas	0-3	55650	55650	55650	For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
23	Large fluidized bed	Coal	2-20	106000	106000	106000	Same as peat.
24	Large fluidized bed	Wood	2-20	74200	0	74200	Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
25	Large fluidized bed	Biofuel	2-20	74200	0	74200	Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
26	Large fluidized bed	Waste	2-20	74200	0	-50000	CO2trade same as wood. CO2eq is guesswork but it is negative because without burning it would produce methane in landfill
27	Large fluidized bed	Peat	2-20	106000	106000	107500	Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
28	Large fluidized bed	Heavy oil	8-22	91111-106000	106000	91111	Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
29	Large fluidized bed	Fuel oil	8-22	91111-106000	106000	91111	Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
30	Grid	Electricity	1-10	53000	212000	53000	50 % of large-scale burning (because of nuclear and hydro). Heavy oil 279 kg /MWh. Officially, electricity is not CHP but requires a double amount of coal to produce it.
31	None	Electricity_taxed	1-10	53000	212000	53000	50 % of large-scale burning (because of nuclear and hydro). Heavy oil 279 kg /MWh. Officially, electricity is not CHP but requires a double amount of coal to produce it. These emissions are assumed when power plants buy electricity from the grid.
32	None	Electricity	0	0	0	0	We might want to keep these locations in the model, but we assume that emissions are zero.
33	None	Heat	0	0	0	0	We might want to keep these locations in the model, but we assume that emissions are zero.
34	None	Cooling	0	0	0	0	We might want to keep these locations in the model, but we assume that emissions are zero.

Large fluidized bed (Peat) CO₂-eq value from Väisänen, Sanni: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology^[1]
Other CO₂-eq values from EKOREM: Sähkölämmitys ja lämpöpumput sähkönkäyttäjinä ja päästöjen aiheuttajina Suomessa.
Classes of climate emissions:
CO2direct

Direct CO₂ emissions from the stack

CO2trade

CO₂ emissions as they are defined in the emission trade. Non-trade sectors have emission 0.

CO2eq

CO₂ emissions as equivalents (i.e. includes methane, N₂O and other climate emissions based on life cycle impacts.

In Finland there are about 700 kettles that have under 5MW fuel power. Same amount is between 5 to 50 MW kettles and over 50 MW kettles there are 200 in Finland. One heating power plant can have several kettles. Many 5-50 MW power plants have also less than 5 MW a kettle. ^[2]

See further discussions about emission factors of wood burning and other topics on the discussion page.D↷

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# This is code Op_en2791/emissionFactors on page [[Emission factors for burning processes]].
library(OpasnetUtils)

emissionFactors <- Ovariable(
	name = 'emissionFactors',
	ddata = 'Op_en2791', # [[Emission factors for burning processes]]
	subset = 'Emission factors of energy production'
)

objects.store(emissionFactors)
cat("Objects emissionFactors initiated!\n")

Other data

This is other important data that wasn't in the end used in the model's calculations. These include for example emission factors for wood heating, emission types for different kinds of plants, kettles and fuels, and energy and sulphur contents of different fuels.

Show details

These data are used in Urgenche.

Emission factors for wood heating(PJ /a; mg /MJ)
Obs	Type	Activity in Finland	PM2.5 emission factor	Description
1	Residential buildings	34.2 (30.8-37.6)		Karvosenoja et al. 2008
2	Primary wood-heated residential buildings	20.2 (16.6-23.9)		Karvosenoja et al. 2008
3	Manual feed boilers with accumulator tank	5.42 (3.89-7.22)	80.0 (37.6-150)	Karvosenoja et al. 2008
4	Manual feed boilers without accumulator tank	2.67 (1.67-3.87)	700 (329-1310)	Karvosenoja et al. 2008
5	Automatic feed wood chip boilers	1.46 (1.01-2)	50.0 (23.5-93.9)	Karvosenoja et al. 2008
6	Automatic feed pellet boilers	0.102 (0.0693-0.142)	30.0 (14.1-56.3)	Karvosenoja et al. 2008
7	Iron stoves	0.142 (0.0976-0.196)	700 (329-1310)	Karvosenoja et al. 2008
8	Other stoves and ovens	10.2 (7.86-12.8)	140 (65.8-263)	Karvosenoja et al. 2008
9	Low-emission stoves	0	80 (37.6-150)	Karvosenoja et al. 2008
10	Open fireplaces	0.163 (0.111-0.224)	800 (376-1500)	Karvosenoja et al. 2008
11	Supplementary wood-heated residential buildings	14.0 (10.7-17.4)		Karvosenoja et al. 2008
12	Iron stoves	0.212 (0.135-0.316)	700 (329-1310)	Karvosenoja et al. 2008
13	Other stoves and ovens	13.6 (10.4-16.9)	140 (65.8-263)	Karvosenoja et al. 2008
14	Low-emission stoves	0	80 (37.6-150)	Karvosenoja et al. 2008
15	Open fireplaces	0.222 (0.14-0.332)	800 (376-1500)	Karvosenoja et al. 2008
16	Recreational buildings	5.00 (4.50-5.50)		Karvosenoja et al. 2008
17	Iron stoves	0.782 (0.372-1.37)	700 (329-1310)	Karvosenoja et al. 2008
18	Other stoves and ovens	3.96 (3.19-4.59)	140 (65.8-263)	Karvosenoja et al. 2008
19	Open fireplaces	0.262 (0.118-0.477)	800 (376-1500)	Karvosenoja et al. 2008

Karvosenoja et al. 2008 ^[3]

Table 1. Describing emission types in different kind of plants and kettles with different fuels and fuel power.

Emission factors for burning processes: Difference between revisions(kg /MWh or mg /MJ)
Obs	Type of plant or kettle	Fuel power (MW)	Fuel	Emission type	PM separator	Unit	Result	Description
1	-	-	Heavy oil	CO2	-	kg /MWh	279	-
2	Poltinpoltto	<5	Heavy oil	PM	partly syklone	mg /MJ	20-90	Number of plants: 300
3	Poltinpoltto	5-15	Heavy oil	PM	partly syklone	mg /MJ	10-70	200
4	Poltinpoltto	15-50	Heavy oil	PM	partly syklone	mg /MJ	5-40	100
5	-	>50	Heavy oil	TSP	-	mg /MJ	15 (5-30)	Savolahti M., Personal communication
6	-	>50	Heavy oil	PM2.5	-	mg /MJ	5 (1.5-9)	Savolahti M., Personal communication
7	-	-	Light oil	CO2	-	kg /MWh	267	-
8	Poltinpoltto	<5	Light oil	PM	-	mg /MJ	0-10	20
9	Poltinpoltto	5-15	Light oil	PM	-	mg /MJ	0-10	20
10	Poltinpoltto	15-50	Light oil	PM	-	mg /MJ	0-10	10
11	Diesel/kaasumoottori	20-40	Heavy oil	PM	-	mg /MJ	30-60	2
12	-	-	Wood	CO2	-	kg /MWh	0	-
13	Kerrosleijukattila	5-10	Wood	PM	electric	mg /MJ	10-70	8
14	Kerrosleijukattila	10-50	Wood	PM	electric	mg /MJ	5-30	10
15	Kiertoleijukattila	5-10	Wood	PM	electric	mg /MJ	5-30	4
16	Puuarinakattila	<5	Wood	PM	syklone/electric	mg /MJ	20-150	120
17	Puuarinakattila	5-10	Wood	PM	syklone/electric	mg /MJ	20-150	50
18	Puuarinakattila	10-50	Wood	PM	syklone/electric	mg /MJ	10-150	35
19	Kaasutuskattila	2-7	Wood	PM	syklone	mg /MJ	50-150	5
20	Leijupoltto	20-100	Wood	PM2.5	(multisyklone+) electric	mg /MJ	2-20	Reference: VTT 2005
21	Leijupoltto	20-100	Biomass	PM2.5	(multisyklone+) electric	mg /MJ	2-20	Reference: VTT 2005;wood > biomass
22	Leijupoltto	100-300	Wood	PM2.5	(multisyklone+) electric	mg /MJ	1-15	VTT 2005
23	-	-	Peat	CO2	-	kg /MWh	382	-
24	Leijupoltto	5-20	Peat	PM2.5	(multisyklone+) electric	mg /MJ	5-25	VTT 2005
25	Leijupoltto	20-100	Peat	PM2.5	(multisyklone+) electric	mg /MJ	4-23	VTT 2005
26	Leijupoltto	100-300	Peat	PM2.5	(multisyklone+) electric	mg /MJ	2-20	VTT 2005
27	Leijupoltto	300-500	Peat	PM2.5	electric	mg /MJ	2-15	VTT 2005
28	Leijupoltto	>500	Peat	PM2.5	electric	mg /MJ	2	VTT 2005
29		5-20	Heavy oil	PM2.5	-	mg /MJ	4-38	VTT 2005
30		20-100	Heavy oil	PM2.5	-	mg /MJ	8-53	VTT 2005
31		20-100	Heavy oil	PM2.5	cyclone/multicyclone	mg /MJ	5-24	VTT 2005
32		100-300	Heavy oil	PM2.5	-	mg /MJ	8-22	VTT 2005
33		100-300	Heavy oil	PM2.5	cyclone/multicyclone	mg /MJ	4-13	VTT 2005

VTT 2005 ^[4]

Emission factors vary greatly depending on kettle or plant type, fuel type and fuel power. Type of emission and possible PM separator also affect to EF number.

Data been used in this page is from many different study and sources to get wide data base for EF caculations.

Emission factors from Matti's conceptual model

This data comes from File:UrgencheConceptualModel.xlsx.

Table 2.

**Emission factors (t/TJt)**
		CO2 (+CO)	CH4	BC	Fine PM	SO2	NO
Power plant/industry scale	Coal	101	0.001	0.02	5	0.7	0.3
Power plant/industry scale	Oil (residual)	82	0.003	0.02	0.01	0.5	0.1
Power plant/industry scale	NatGas	59	0.001	0.005	0	0	0.06
Power plant/industry scale	Biomass (ww+agr)	130	0.025	0.05	1	0	0.2
Power plant/industry scale	Municipal waste	100	0.029	0.05	1	0	0.2
Local heating	Coal	103	0.301	14	8	1	0.1
Local heating	Oil (distillate)	78	0.010	0.2	0	0.5	0.04
Local heating	NatGas	59	0.005	0.01	0	0	0.02
Local heating	Biomass (wood)	148	0.253	10	1	0	0.7
Transport	Petrol	67	0.001		0.01
Transport	Diesel	69	0.001		0.028	EURO-III (CRT traps)
Transport	LNG	70	0.001		0.007
Transport	Liq biofuel	64	0.010		0.03

Table 3.

**Energy contents**
C2H5OH	29.700	MJ/kg
C2	24.000	molecular weight
H6	6.000	molecular weight
O	16.000	molecular weight
Petrol	43	MJ/kg
Diesel	45	MJ/kg
Liquefied natural gas (LNG)	56	MJ/kg
Gas	49,00	MJ/kg
Solid biofuel	7,00	MJ/kg
Liquid biofuel	29,00	MJ/kg
Coal	25,60	MJ/kg
Crude oil	43,0	MJ/kg
1 kg ethanol =	0.522	kg C
produces	1.913	kg CO2
1MJ ethanol produces	0.064	kg CO2

Table 4.

**Sulphur contents**
	%
Coal S	1
Residual oil S	2.5
Distillate oil S	0.5
Diesel S	0.1
Petrol S	0.05

Table x

This data comes from File:Energy supply in Europe.xls.

**Fuel ash and sulfur contents of fuels (%)**
Fuel	Ash	Sulfur
Coal and peat	11,0	1,1
Crude oil	0,4	2,5
Petrochem products	0,04	1,5
Biofuel and waste	0,5	-

Table 5.

This data comes from File:Energy supply in Europe.xls.

**Removal rates (%)**
	Coal and peat	Petrochem products	Biofuel and waste
Power generation fly ash removal	99,5	90,0	99,0
Heat/power fly ash removal	99,5	90,0	95,0
District heating fly ash removal	95	90	90
Power generation SO2 removal	45	45
Heat/power SO2 removal	0
District heating SO2 removal	0
Power generation NOx removal	0
Heat/power NOx removal	0
District heating NOx removal	0

Table X

This data comes from File:Energy supply in Europe.xls.

**CO₂ emissions from combustion (MtCO₂/Mtoe)**
Coal & peat	Crude oil	Oil	Gas	Biofuels & waste
3,86	3,13	3,13	2,27	4,79

Table X

This data comes from File:Energy supply in Europe.xls.

**EC+OC emission factor (t/Mtoe)**
Type of plant	Coal & peat	Crude oil	Petrochem products	Gas	Biofuels & waste
Electricity plants	260		300	170	300
CHP Plants	610		400	240	800
Heat plants	1000		600	400	3000
Blast furnaces	3000
Gas works	3000	1000		100
Oil refineries		400	200	100
Petrochemical plants	1000	300
Coke/pat. fuel/BKB plants	600
Liquefication plants	600

Table 6.

**Energy efficiencies**
Fuel	Power only	CHP Power	CHP Heat	Heat only
Nuclear	0.37	0.32	0.50	1.00
Hydro
Wind, solar, geothermal
Coal/peat	0.45	0.35	0.55	0.90
Crude oil	0.50	0.40	0.50	0.90
Natural gas	0.53	0.40	0.50	0.90
Biomass	0.40	0.33	0.55	0.90
Municipal waste	0.40	0.33	0.55	0.90

Table 7.

**Relative GHG potency**
Time	CO2	CH4
20 a.	1	72
100 a.	1	25

Emission factors for PM

Table 8. Emission factors for small power plants in Finland.^[5]

Type of plant or kettle	Fuel power (MW)	Fuel	PM separator	Emission factor (mg/MJ)	Number of plants
Poltinpoltto	<5	Heavy oil	partly syklone	20-90	300
Poltinpoltto	5-15	Heavy oil	partly syklone	10-70	200
Poltinpoltto	15-50	Heavy oil	partly syklone	5-40	100
Poltinpoltto	<5	Light oil	-	<10	20
Poltinpoltto	5-15	Light oil	-	<10	20
Poltinpoltto	15-50	Light oil	-	<10	10
Diesel/kaasumoottori	20-40	Heavy oil	-	30-60	2
Kerrosleijukattila	5-10	Wood	electric	10-70	8
Kerrosleijukattila	10-50	Wood	electric	5-30	10
Kiertoleijukattila	20-40	Wood	electric	5-30	4
Puuarinakattila	<5	Wood	syklone/electric	20-150	120
Puuarinakattila	5-10	Wood	syklone/electric	20-150	50
Puuarinakattila	10-50	Wood	syklone/electric	10-150	35
Kaasutuskattila	2-7	Wood	syklone	50-150	5

("Taulukoissa esitetyt ominaispäästökertoimet edustavat vaihteluväliä, joiden sisällä suurin osa yksittäisten laitosten arvoista on. Vaihteluvälit perustuvat käytettävissä olevien laitostietokantojen arvoihin (VAHTI-tiedot) ja pienten polttolaitosten päästömittaustuloksiin.")

⇤--#: . Using two languages is not suitable. --Sami Rissanen 11:37, 4 February 2013 (EET) (type: truth; paradigms: science: attack)

Table 9. Emission factors for wood burning in small scale heating (e.g. secondary heating at houses) can be found from PUPO emission and toxicity database

**Table. Emissions of wood burning in different fireplaces.[5]**
Fire place	Number of measurements	Mean (mg/MJ)	Min (mg/MJ)	Max (mg/MJ)
Panospoltto	32	76	14.15	224.27
Varaava tulisija	22	80	27.97	224.27
-Perinteinen	18	91	30.75	224.27
-Moderni	3	32	27.97	36.52
Kamiinat ja muut	5	54	45.8	74.2
Kiukaat	1	168	-	-
Klapikattilat	4	59	14.15	106.14
-Perinteinen	2	102	98.59	106.14
-Moderni	2	16	14.15	17.58
Poltinpoltto	49	20	5.21	89.2
Pellettipoltin	21	21	5.21	89.2
Stokeripoltin	5	15	12.21	18.71

Emission factors for greenhousegases

EFs by fuel based classification

Heljo & Laineen selvityksessä on päädytty seuraaviin kasvihuonekaasupäästökertoimiin (kgCO2/MWh) laskettuna rakennuksen bruttotasolla (rakennukseen hankittu energia)[6]

Table 10.

kgCO2/MWh)
Puu	18
Kevyt polttoöljy	265
Raskas polttoöljy	179
Kaukolämpö, erillistuotanto	231
Kaukolämpö, yhteistuotanto	224
Kaukolämpö keskimäärin (yhteistuotantoa 73 %)	226
Sähkö keskimäärin	204
Sähkön marginaalikulutus	714
Sähkön perustehoalueen kulutus	112
Sähkön välitehoalueen kulutus	459
Sähkön huipputehoalueen kulutus	867
Sähkölämmityksen lämmitysenergia (esimerkkiarvo)	400

Table 11. Fuel classification and emission factors in Finland 2010. ^[6] ^[7]. Definitions for classification of fuels and energy sources can be found here.

Code Heading		Fuel-specific unit	CO₂ default emission factor [t/TJ]		Default net calorific value (as fired) [GJ/unit]		NB
1	Fossil fuels
11	Petroleum products
111	Petroleum gases
1111	Refinery gas	t	54.2	*	49.7	*
1112	LPG (Liquefied petroleum gas)	t	65.0		46.2
112	Light distillates
1121	Naphtha	t	72.7		44.3
1122	Motor gasoline	t	72.9		43.0
1123	Aviation gasoline	t	71.3		43.7
113	Medium distillates
1131	Kerosene (jet fuel)	t	73.2		43.3
1132	Other kerosenes	t	71.5	*	43.1
1133	Diesel oil	t	73.6		42.8
1134	Light fuel oil (heating fuel oil)	t	74.1		42.7
1135	Gasoil (for non-road use)	t	73.6	*	42.8
1139	Other medium distillates	t	74.1		42.7
114	Heavy distillates
1141	Heavy fuel oil, sulphur content < 1%	t	78.8		41.1
1142	Heavy fuel oil, sulphur content ≥ 1%	t	78.8		40.5
1143	Other heavy distillates	t	79.2	*	40.2
115	Petroleum coke	t	97.0		33.5
116	Recycled and waste oils	t	78.8		41.0
119	Other petroleum products	t	78.8		30.0
12	Coal
121	Hard coal
1211	Anthracite	t	98.3	*	33.5
1212	Hard coal, bituminous	t	94.6		24.6	*
122	Other coal
1221	Semi-bituminous coal, brown coal, lignite	t	108.0		20.0
1222	Coal briquettes	t	94.6	*	30.0
1228	Coal tar	t	90.6	*	37.0
1229	Other non-specified coal	t	108.0		10.0
123	Coke	t	108.0		29.3
124	Coke oven gas	1000 m3	41.5		16.7
125	Blast furnace gas	1000 m3	263.7		3.8
13	Natural gas
131	Natural gas	1000 m3	55.04		36.0
2	Peat
21	Peat
211	Milled peat	t	105.9		10.1
212	Sod peat	t	102.0		12.3
213	Peat pellets and briquettes	t	97.0		18.0	*
3	Renewable and mixed fuels
31	Biomass
311	Forest fuelwood
3111	Firewood (stems and split firewood)	t	109,6		14.0	*	BIO
3112	Chips from roundwood	t	109,6		9.5	*	BIO
3113	Forest residue chips	t	109,6		9.0	*	BIO
312	Industrial wood residue						BIO
3121	Bark	t	109,6		7.5	*	BIO
3122	Sawdust, cutter shavings etc.	t	109,6		8.0	*	BIO
3123	Wood residue chips	t	109,6		9.5	*	BIO
3128	Unspecified industrial wood residue	t	109,6		7.5	*	BIO
3129	Other industrial wood residue	t	109,6	*	8.8	*	BIO
313	Black liquor and other concentrated liquors	tka	109,6		11.5	*	BIO
314	Other by-products from wood processing	TJ	109,6				BIO
industry
315	Recovered wood	t	109,6		12.0	*	BIO
316	Wood pellets and briquettes	t	109,6		16.0		BIO
317	Vegetable-based fuels
3171	NEW!!Reed canary grass	t	100.0		14.6		BIO
3172	NEW!!Cereal crops and straw parts	t	109,6		13.5		BIO
3179	NEW!! Other vegetable-based fuels	t	109,6		15.0		BIO
318	Animal-based fuels	t	109.6		30.0	*	BIO
32	Other biofuels and mixed fuels
321	Biogas
3211	Landfill gas	1000 m3	56,1		17.0	*	BIO
3212	Biogas from wastewater treatment	1000 m3	56.1		23.0	*	BIO
3213	Industrial biogas	1000 m3	56.1		20.0	*	BIO
3219	Other biogas	1000 m3	56.1		20.0	*	BIO
322	Liquid biofuels	t	77.4		30..40		BIO
323	Mixed fuels (fossil and non-fossil)
3231	Recovered fuels	t	31.8		20.0		1)
3232	Demolition wood	t	17.0		15.0		1)
3233	Impregnated wood (chemically treated)	t	11.4		12.0		1)
3234	NEW!! Deinking sludge	t	60.0		4.0		1)
3238	NEW!!Municipal waste (unsorted)	t	40.0		10.0		1)
3239	Other mixed fuels	t	110.0		10.0
324	Gasified waste (Product gas)	1000 m3	..		..		2)
325	NEW!!Biosludge	t	109.6		2.5		BIO
4	Other energy sources
41	Nuclear energy
411	Nuclear energy	TJ	–
49	Others
491	Other by-products and wastes used as fuels
4911	Plastics waste	t	74.1		33.0
4912	Rubber waste	t	90.0		33.0
4913	Hazardous waste	t	117.0	*	10.0	*
4919	Other waste	t	75.0		15.0	*
492	Exothermic heat from industry	TJ	–
493	Secondary heat from industry	TJ	–
494	Electricity	MWh	–
495	Steam	MWh	–
497	NEW!!Sulphur	t	0.0		10.5
498	Hydrogen	1000 m3	0.0		10.8
499	Other non-specified energy sources	TJ	..

NB

BIO = biofuel, whose carbon dioxide emissions are not counted in the total emission amounts of Finland's greenhouse gases, nor are they taken into account in emissions trading.

1) CO₂ factor of mixed fuels is an estimate taking into account only the share of fossil carbon.

2) Gasified waste (Product gas) is reported in the fuel categories of the source materials of gasification.

Calorific values and emission coefficients describe the properties of fuel as fired, inclusive of moisture content (except for 313, where the measurement unit is tonnes of dry matter). Calorific value of natural gas has been specified in normal conditions (0 °C and 1.013 bar).

Emission factors are updated when necessary.

(* Revised values.

Emission factors for district heating is calculated in Motiva CO₂ emission calculation introductions^[8]. For district heating joint production emission factor is 220 kg CO₂/MWh. In this view other GHGs (CH₄ or N₂O) are not taken account as CO₂-ekv! Emission factors by fuel based on Motiva knowledge is in table 2.

Table 12. Emission factors of CO₂ by fuel^[9].

Fuels	kg CO₂/MWh
Heavy oil	279
Light oil	267
Gas	202
Fluid gas	227
Peat	382
Coal	341
Coke	389
Wood	0

CO2 EF for electricity can be classified in two categories: purchase electricity and own production. EF for purchase electricity is the same as own electricity vendor has informed or average Finnish electricity procurement CO2 EF (= 200 kg CO2/MWh). EF for own electricity production can be used EF from table 2 (fuel based EFs). If electricity is produced in combined electricity, heating production EFs are calculated with “benefit division method”. Method is explained in Motiva introduction^[10] (attachment 2).

Table 13. Average emission factors measured with differed gas analyzator (kk) and FTIR analyzator ^[11].

	Ordinary combustion	Distribution (%)	Bad combustion	Distribution (%)	Unit
Temperature of gas	253	1	159	17	Celsius
CO2 (kk)	7.3	5	11	8	%, dry
O2 (kk)	13.3	3	8.7	14	%, dry
H2O (FTIR)	6.5	4	7.7	-	%, wet
CO (kk)	2300	10	8100	29	mg/MJ
CO (FTIR)	2000	11	5700	-	mg/MJ
OGC (kk)	120	17	1700	46	mg C/MJ
OGC (FTIR)	160	24	1500	-	mg C/MJ
CH4 (FTIR)	30	22	350	-	mg C/MJ
C6H6 (FTIR)	20	27	210	-	mg C/MJ

Table 14 and 15 are from Heljo et al 2005^[12]. Coefficient of CO₂-ekv/CO₂ as 1.02 is used in transformation CO₂ emission factors to CO₂-ekv. factors for electricity and district heating.

Table 14. CO₂ emission factors of energy use of buildings (kg CO₂/MWh)

	Gross of building level	Dispersion	Efficiency	Efficienfy energy level
Light oil	267	-	0.85	314
Heavy oil	279	-	0.85	328
Wood	-	-	-	-
District heating
Separate production	226	20-500	-	226
Co-production	220	dispersion	-	220
Electricity production
Average	200	-	-	200
Marginal	700	700-900	-	700
or
Basic capacity	110	-	-	110
Interval capacity	450	-	-	450
Peak capacity	850	-	-	850

Table 15. CO₂-ekv emission factors of energy use of buildings (kg CO₂-ekv/MWh)

	Gross of building level	Dispersion	Efficiency	Efficienfy energy level
Light oil	267	-	0.85	314
Heavy oil	279	-	0.85	328
Wood	18	-	0.6	30
District heating
Separate production	231	20-500	-	231
Co-production	224	dispersion	-	224
Electricity production
Average	204	-	-	204
Marginal	714	700-900	-	714
or
Basic capacity	112	-	-	112
Interval capacity	459	-	-	459
Peak capacity	867	-	-	867

Dependencies

Plant/kettle type
Power output
Efficiency

Data that have another unit than mg/MJ should be changed.

e.g. t/TJ -> mg/MJ

References

[14]

↑ Väisänen S: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat - Estimation of various production chains by using LCA methodology. Lappeenranta University of Technology. 2014. http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2
↑ http://www.ymparisto.fi/download.asp?contentid=3706 ⇤--#: . Link broken. I simply don't know what this is supposed to be. --~~~~ (type: truth; paradigms: science: attack)
↑ Karvosenoja et al. 2008 [1]
↑ http://www.vtt.fi/inf/pdf/tiedotteet/2005/T2300.pdf
↑ http://www.ymparisto.fi/download.asp?contentid=3706
↑ [2]
↑ http://tilastokeskus.fi/tup/khkinv/fi_nir_150110.pdf
↑ http://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf
↑ http://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf
↑ http://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf
↑ http://www.uku.fi/fine/src/raportit/PUPOloppuraportti2007.pdf
↑ http://www.tut.fi/units/rak/rtt/tutkimus/ekorem/EKOREM_Loppuraportti_051214.pdf

[SVäisänen-1] Väisänen S: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat - Estimation of various production chains by using LCA methodology. Lappeenranta University of Technology. 2014. http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2

[2] ttp://www.ymparisto.fi/download.asp?contentid=3706 ⇤--#: . Link broken. I simply don't know what this is supposed to be. --~~~~ (type: truth; paradigms: science: attack)

[3] Karvosenoja et al. 2008 [1]

[4] ttp://www.vtt.fi/inf/pdf/tiedotteet/2005/T2300.pdf

[5] ttp://www.ymparisto.fi/download.asp?contentid=3706

[6] [2]

[7] ttp://tilastokeskus.fi/tup/khkinv/fi_nir_150110.pdf

[8] ttp://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf

[9] ttp://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf

[10] ttp://motiva.fi/files/209/Laskentaohje_CO2_kohde_040622.pdf

[11] ttp://www.uku.fi/fine/src/raportit/PUPOloppuraportti2007.pdf

[12] ttp://www.tut.fi/units/rak/rtt/tutkimus/ekorem/EKOREM_Loppuraportti_051214.pdf

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

Emission factors for burning processes: Difference between revisions

Latest revision as of 10:17, 6 December 2018

Contents

Question

Answer

Rationale

HSY emission factors

Inputs and calculations

Emission factors for heating

Other data

Dependencies

See also

References

Navigation menu

@@ Line 14: / Line 14: @@
 [[File:Emissions from heating in Helsinki.png|thumb|centre|600px|Example of the use of emission factors: CO<sub>2</sub> and fine particle emissions in Helsinki. Scenarios are based on [[Helsinki energy decision 2015]].]]
+An example code for downloading and using the variable.
+<rcode embed=1>
+## This code is Op_en2719/ on page [[Emission factors for burning processes]].
+library(OpasnetUtils)
+library(ggplot2)
+objects.latest("Op_en2791", code_name = "emissionstest")
+objects.latest("Op_en2791", code_name = "emissionFactors")
+oprint(summary(EvalOutput(emissionFactors)))
+</rcode>
 == Rationale ==
+=== HSY emission factors ===
+''This section describes the use of HSY emission factors used for CO2 emission estimates. This should be merged with the other data, but it is first described as such.
+These emission factors are derived from the Helsinki Region Environmental Services (HSY) climate data [https://www.hsy.fi/fi/asiantuntijalle/ilmastonmuutos/hillinta/seuranta/Sivut/Paastot.aspx] on 27th Nov 2018. Emission factors were initially calculated for every entry in the data file (4180 rows in total). Then, emission sectors were compared along timeline for each city separately. We noticed that several sectors shared practically the same emission factors with few differences, which are not plausible as real differences (Table ''Sector classification''). So, we took a mean of the values to represent all sectors in the same EFclass group.
+Then, we compared results from different cities. There were minor changes that may be due to some real differences in data, and also a some changes that looked like artefact. However, there were two sectors, namely ''district heating'' and ''fuels'' that clearly differed between cities in a plausible way. Therefore, we used city-specific emission factors for these two sectors and those of Helsinki for all other sectors (because Helsinki seemed to have the least amount of artefact in the data).
+<t2b name="Sector classification" index="Sector,Sektori,EFclass,PKluokka" obs=dummy unit="-">
+metro|metrot|electricity|sähkö|1
+trams|raitiovaunut|electricity|sähkö|1
+local trains|lähijunat|electricity|sähkö|1
+consumer electricity|kulutussähkö|electricity|sähkö|1
+passenger ships|matkustajalaivat|ships|laivat|1
+cargo ships|rahtilaivat|ships|laivat|1
+buses|linja-autot|diesel machines|dieselkoneet|1
+vans|pakettiautot|diesel machines|dieselkoneet|1
+machinery|työkoneet|diesel machines|dieselkoneet|1
+trucks|kuorma-autot|diesel machines|dieselkoneet|1
+leasure boats|huviveneet|boats|veneet|1
+professional boats|ammattiveneet|boats|veneet|1
+district heating|kaukolämpö|district heating|kaukolämpö|1
+oil heating|öljylämmitys|oil heating|öljylämmitys|1
+electric heating|sähkölämmitys|electric heating|sähkölämmitys|1
+geothermal heating|maalämpö|geothermal heating|maalämpö|1
+fuels|polttoaineet|fuels|polttoaineet|1
+processes|prosessit|processes|prosessit|1
+private cars|henkilöautot|private cars|henkilöautot|1
+motor cycles|moottoripyörät|motor cycles|moottoripyörät|1
+</t2b>
+<t2b name="Sector hierarchy" index="Class,Subclass,Luokka,Alaluokka" obs="dummy" unit="-">
+heating|district heating|lämmitys|kaukolämpö|1
+heating|oil heating|lämmitys|öljylämmitys|1
+heating|electric heating|lämmitys|sähkölämmitys|1
+heating|geothermal heating|lämmitys|maalämpö|1
+electricity|consumer electricity|sähkö|kulutussähkö|1
+transport|road transport|liikenne|tieliikenne|1
+transport|rail transport|liikenne|raideliikenne|1
+transport|shipping|liikenne|laivaliikenne|1
+industry and machinery|machinery|teollisuus ja työkoneet|työkoneet|1
+industry and machinary|fuels|teollisuus ja työkoneet|polttoaineet|1
+industry and machinery|processes|teollisuus ja työkoneet|prosessit|1
+waste management|landfill|jätteiden käsittely|kaatopaikka|1
+waste management|biowaste composting|jätteiden käsittely|biojätteen kompostointi|1
+waste management|waste water treatment|jätteiden käsittely|jäteveden käsittely|1
+waste management|waste water sludge composting|jätteiden käsittely|jätevesilietteen kompostointi|1
+agriculture|fields|maatalous|pellot|1
+agriculture|farm animals|maatalous|kotieläimet|1
+road transport|private cars|tieliikenne|henkilöautot|1
+road transport|motor cycles|tieliikenne|moottoripyörät|1
+road transport|vans|tieliikenne|pakettiautot|1
+road transport|trucks|tieliikenne|kuorma-autot|1
+road transport|buses|tieliikenne|linja-autot|1
+rail transport|local trains|raideliikenne|lähijunat|1
+rail transport|metro|raideliikenne|metrot|1
+rail transport|trams|raideliikenne|raitiovaunut|1
+shipping|leasure boats|laivaliikenne|huviveneet|1
+shipping|professional boats|laivaliikenne|ammattiveneet|1
+shipping|passenger ships|laivaliikenne|matkustajalaivat|1
+shipping|cargo ships|laivaliikenne|rahtilaivat|1
+</t2b>
+<rcode name="HSYdata" label="Initiate ovariable HSYdata" embed=1>
+# This is code Op_en2791/HSYdata on page [[Emission factors for burning processes]]
+library(OpasnetUtils)
+HSYdata <- Ovariable("HSYdata",ddata="Op_en2791",subset="HSY CO2 emission and energy consumption")
+EFclass <- Ovariable("EFclass", ddata="Op_en2791", subset="Sector classification")
+objects.store(HSYdata, EFclass)
+cat("Ovariables HSYdata, EFclass stored.\n")
+</rcode>
+<rcode name="emfactor" label="Initiate ovariable emfactor" embed=1>
+# This code is Op_en2791/emfactor on page [[Emission factors for burning processes]]
+library(OpasnetUtils)
+emfactor <- Ovariable(
+  "emfactor",
+  dependencies=data.frame(
+    Name=c("HSYdata","EFclass"),
+    Ident=c("Op_en2791/HSYdata","Op_en2791/HSYdata")
+  ),
+  formula=function(...) {
+    dat <- HSYdata
+    dat$Year <- as.numeric(as.character(dat$Year))
+    dat$CO2 <- as.numeric(as.character(dat$CO2))
+    result(dat) <- dat$CO2 / result(dat)
+    levels(dat$Sector) <- tolower(levels(dat$Sector))
+    levels(dat$Consumer) <- tolower(levels(dat$Consumer))
+    ### Combine sectors that have essentially the same emission factor.
+    colnames(dat@output)[colnames(dat@output)=="Sector"] <- "Sektori" # The data is in Finnish but titles in English
+    dat <- dat * EFclass
+    dat <- dat[!(is.infinite(result(dat)) | is.nan(result(dat)) | result(dat)<0.01),]
+    dat2 <- dat[dat$City == "Helsinki" | dat$Sector %in% c("district heating","fuels") , ]
+    dat2 <- oapply(dat2,INDEX=c("City","Year","EFclass"),FUN=mean)
+    if(FALSE) {
+      # The figures illustrate the reasons for merging some sectors under the same emission factor.
+      print(ggplot(dat@output,
+                   aes(x=Year, y=result(dat),colour=City,group=paste(City,Sector)))+geom_line()+
+              facet_wrap(~ EFclass, scales="free_y")+geom_point(aes(shape=City))+
+              labs(y="CO2 emission factor (kg/kWh)")
+      )
+      print(ggplot(dat2@output,
+                   aes(x=Year, y=result(dat2),colour=City,group=City))+geom_line()+
+              facet_wrap(~ EFclass, scales="free_y")+geom_point(aes(shape=City))+
+              labs(y="CO2 emission factor (kg/kWh)")
+      )
+    }
+    #### Fill other EFclasses than district heating and fuels with city-unspecific values
+    dat2$City <- as.factor(ifelse(dat2$EFclass %in% c("district heating","fuels"), as.character(dat2$City),NA))
+    dat2@output <- fillna(dat2@output, "City")
+    #### Make geothermal energy and electric heating dependent on consumer electricity by factors 0.5 and 1.5, respectively.
+    elfact <- Ovariable(output=data.frame(
+      EFclass=c("consumer electricity","geothermal heating","electric heating"),
+      Result=c(1,0.5,1.5)),
+      marginal=c(TRUE,FALSE)
+    )
+    dat3 <- dat2[!dat2$EFclass %in% c("geothermal heating","electric heating"),]
+    tmp <-  dat2[dat2$EFclass %in% c("electricity"),]
+    tmp$EFclass <- NULL
+    dat3 <- combine(dat3, elfact*tmp) * EFclass
+    return(dat3)
+  }
+)
+objects.store(emfactor)
+cat("Ovariable emfactor stored.\n")
+</rcode>
+<t2b name="Emission factors in Helsinki" index="Sector,Year" locations="2015,2030,2035" unit="ton/GWh">
+Consumer electricity|121.5|70.6|45
+Electric heating|234.2|138.5|88.3
+District heating|189.7|128.8|49.1
+Natural gas|198|198|198
+Light fuel oil|261|261|261
+Coal|341|341|341
+</t2b>
+The data above comes from Gaia report [http://www.stadinilmasto.fi/2018/06/19/hiilineutraali-helsinki-2035-toimenpideohjelma/].
 === Inputs and calculations ===
-:''See discussions with the statements on the discussion page.{{disclink|Discussions of emission factors}}
 {| {{prettytable}}
-|+'''Variables in the assessment model
+|+'''Variables needed for calculating emissions.
-! Ovariable || Dependencies || Measure || Indices || Missing data
+! Dependencies || Measure || Indices || Missing data
 |----
-| rowspan="3"| emissions (from the model) (emissions in mass per time):
+| fuelUse (from [[Energy balance]] or other relevant source)
-| energyUse (from [[Energy use of buildings]] or other relevant source)
+| Amount of fuel used per timepoint.
-|
+| Required indices: Fuel. Typical indices: Plant
-|
 |
 |----
-| fuelShares (case-specific knowledge from e.g. [[Helsinki energy production]])
+| emissionsLocations (case-specific knowledge from e.g. [[Helsinki energy production]])
-| Tells how much of fuel is used for a certain neating energy need.
+| Tells how where emissions occur and from how high a stack.
-| Required indices: Fuel_type. Typical indices:
+| Required indices: - . Typical indices: Plant
 |
 |----
 | emissionFactors (generic information, but may be cultural differences. E.g. [[Emission factors for burning processes]] ##
 | emissions per unit of energy produced (g / J or similar unit)
-| Required indices: Exposure_agent. Typical indices:  Emission_height.
+| Required indices: Pollutant, Fuel. Typical indices:  Burner.
 |
 |----
@@ Line 135: / Line 305: @@
 </t2b>
-*Large fluidized bed (Peat) CO<sub>2</sub>-eq value from Väisänen, Sanni: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology<ref name="SVäisänen">http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2</ref>
+*Large fluidized bed (Peat) CO<sub>2</sub>-eq value from Väisänen, Sanni: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology<ref name="SVäisänen">Väisänen S: Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat - Estimation of various production chains by using LCA methodology. Lappeenranta University of Technology. 2014. http://www.doria.fi/bitstream/handle/10024/94404/isbn9789522655578.pdf?sequence=2</ref>
 *Other CO<sub>2</sub>-eq values from [http://www.tut.fi/ee/Materiaali/Ekorem/EKOREM_LP_ja_sahko_raportti_051128.pdf EKOREM]: Sähkölämmitys ja lämpöpumput sähkönkäyttäjinä ja päästöjen aiheuttajina Suomessa.
 * Classes of climate emissions:
@@ Line 142: / Line 312: @@
 *; CO2eq: CO<sub>2</sub> emissions as equivalents (i.e. includes methane, N<sub>2</sub>O and other climate emissions based on life cycle impacts.
-In Finland there are about 700 kettles that has under 5MW fuel power. Same amount is between 5 to 50 MW kettles and over 50 MW kettles there are 200 in Finland. One heating power plant can have several kettles. Many 5-50 MW power plants has also less than 5 MW kettle. <ref>http://www.ymparisto.fi/download.asp?contentid=3706 {{attack|# |The page or document cannot be found|--~~~~}}
+In Finland there are about 700 kettles that have under 5MW fuel power. Same amount is between 5 to 50 MW kettles and over 50 MW kettles there are 200 in Finland. One heating power plant can have several kettles. Many 5-50 MW power plants have also less than 5 MW a kettle. <ref> http://www.ymparisto.fi/download.asp?contentid=3706 {{attack|# |Link broken. I simply don't know what this is supposed to be.|--~~~~}}</ref>
-</ref>
+:''See further discussions about emission factors of wood burning and other topics on the discussion page.{{disclink|Discussions of emission factors}}
 <rcode name='emissionFactors' embed=1 label='Initiate emissionFactors (only for developers)'>
@@ Line 1,939: / Line 2,110: @@
 ** [[RAINS emission factors]]
 ** [[WebFIRE]]
+* [https://www.nilu.no/airquis/models_emission.htm NILU emission models]
 {{attack|# |Links 7-10 (From Tissari to Motiva) say the page doesn't exist.|--[[User:Heta|Heta]] ([[User talk:Heta|talk]]) 15:45, 31 August 2015 (UTC)}}
@@ Line 1,950: / Line 2,121: @@
 <references />
-== Related files ==
 </noinclude>