Emission factors for burning processes: Difference between revisions

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[[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]].]]
[[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 ==
== 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 ===
=== Inputs and calculations ===
:''See discussions with the statements on the discussion page.{{disclink|Discussions of emission factors}}


{| {{prettytable}}
{| {{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]] ##
| 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)
| 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>
</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.
*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:
* 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.
*; 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)'>
<rcode name='emissionFactors' embed=1 label='Initiate emissionFactors (only for developers)'>
Line 1,939: Line 2,110:
** [[RAINS emission factors]]
** [[RAINS emission factors]]
** [[WebFIRE]]
** [[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)}}
{{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 />
<references />


== Related files ==
</noinclude>
</noinclude>

Latest revision as of 10:17, 6 December 2018


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

Example of the use of emission factors: CO2 and fine particle emissions in Helsinki. Scenarios are based on Helsinki energy decision 2015.

An example code for downloading and using the variable.

+ Show code

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(-)
ObsSectorSektoriEFclassPKluokkadummy
1metrometrotelectricitysähkö1
2tramsraitiovaunutelectricitysähkö1
3local trainslähijunatelectricitysähkö1
4consumer electricitykulutussähköelectricitysähkö1
5passenger shipsmatkustajalaivatshipslaivat1
6cargo shipsrahtilaivatshipslaivat1
7buseslinja-autotdiesel machinesdieselkoneet1
8vanspakettiautotdiesel machinesdieselkoneet1
9machinerytyökoneetdiesel machinesdieselkoneet1
10truckskuorma-autotdiesel machinesdieselkoneet1
11leasure boatshuviveneetboatsveneet1
12professional boatsammattiveneetboatsveneet1
13district heatingkaukolämpödistrict heatingkaukolämpö1
14oil heatingöljylämmitysoil heatingöljylämmitys1
15electric heatingsähkölämmityselectric heatingsähkölämmitys1
16geothermal heatingmaalämpögeothermal heatingmaalämpö1
17fuelspolttoaineetfuelspolttoaineet1
18processesprosessitprocessesprosessit1
19private carshenkilöautotprivate carshenkilöautot1
20motor cyclesmoottoripyörätmotor cyclesmoottoripyörät1
Sector hierarchy(-)
ObsClassSubclassLuokkaAlaluokkadummy
1heatingdistrict heatinglämmityskaukolämpö1
2heatingoil heatinglämmitysöljylämmitys1
3heatingelectric heatinglämmityssähkölämmitys1
4heatinggeothermal heatinglämmitysmaalämpö1
5electricityconsumer electricitysähkökulutussähkö1
6transportroad transportliikennetieliikenne1
7transportrail transportliikenneraideliikenne1
8transportshippingliikennelaivaliikenne1
9industry and machinerymachineryteollisuus ja työkoneettyökoneet1
10industry and machinaryfuelsteollisuus ja työkoneetpolttoaineet1
11industry and machineryprocessesteollisuus ja työkoneetprosessit1
12waste managementlandfilljätteiden käsittelykaatopaikka1
13waste managementbiowaste compostingjätteiden käsittelybiojätteen kompostointi1
14waste managementwaste water treatmentjätteiden käsittelyjäteveden käsittely1
15waste managementwaste water sludge compostingjätteiden käsittelyjätevesilietteen kompostointi1
16agriculturefieldsmaatalouspellot1
17agriculturefarm animalsmaatalouskotieläimet1
18road transportprivate carstieliikennehenkilöautot1
19road transportmotor cyclestieliikennemoottoripyörät1
20road transportvanstieliikennepakettiautot1
21road transporttruckstieliikennekuorma-autot1
22road transportbusestieliikennelinja-autot1
23rail transportlocal trainsraideliikennelähijunat1
24rail transportmetroraideliikennemetrot1
25rail transporttramsraideliikenneraitiovaunut1
26shippingleasure boatslaivaliikennehuviveneet1
27shippingprofessional boatslaivaliikenneammattiveneet1
28shippingpassenger shipslaivaliikennematkustajalaivat1
29shippingcargo shipslaivaliikennerahtilaivat1

+ Show code

+ Show code

Emission factors in Helsinki(ton/GWh)
ObsSector201520302035
1Consumer electricity121.570.645
2Electric heating234.2138.588.3
3District heating189.7128.849.1
4Natural gas198198198
5Light fuel oil261261261
6Coal341341341

The data above comes from Gaia report [4].

Inputs and calculations

Variables needed for calculating emissions.
Dependencies Measure Indices Missing data
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.

+ Show code

Emission factors for heating

Emission factors of energy production(mg /MJ)
ObsBurnerFuelPM2.5CO2directCO2tradeCO2eqDescription
1HouseholdWood140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
2HouseholdBiofuel140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
3HouseholdLight oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
4HouseholdOil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
5HouseholdOther sources0-10742007420074200Same as oil.
6HouseholdCoal0-1074200-872227420087222
7HouseholdGeothermal0-1074200-872227420087222
8HouseholdGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
9HouseholdFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
10DomesticWood140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008 Just repeat the previous rows to match different wording of burners.
11DomesticBiofuel140 (65.8-263)7420008333Other stoves and ovens. Karvosenoja et al. 2008
12DomesticLight oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
13DomesticOil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
14DomesticOther sources0-10742007420074200Same as oil.
15DomesticCoal0-1074200-872227420087222
16DomesticGeothermal0-1074200-872227420087222
17DomesticGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
18DomesticFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
19Diesel engineFuel oil0-1074200-872227420087222Light oil <5 MW Emission factors for burning processes. Light oil 267 kg /MWh
20Diesel engineLight oil0-1074200-872227420087222
21Diesel engineBiofuel0-1074200-872227420087222
22Large fluidized bedGas0-3556505565055650For PM2.5: one third of that of oil. For CO2: 3/4 of that of oil.
23Large fluidized bedCoal2-20106000106000106000Same as peat.
24Large fluidized bedWood2-2074200074200Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
25Large fluidized bedBiofuel2-2074200074200Leijupoltto 100-300 MW Emission factors for burning processes. Karvosenoja et al., 2008
26Large fluidized bedWaste2-20742000-50000CO2trade same as wood. CO2eq is guesswork but it is negative because without burning it would produce methane in landfill
27Large fluidized bedPeat2-20106000106000107500Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
28Large fluidized bedHeavy oil8-2291111-10600010600091111Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
29Large fluidized bedFuel oil8-2291111-10600010600091111Leijupoltto 100-300 MW Emission factors for burning processes. Peat 382 kg /MWh
30GridElectricity1-10530002120005300050 % 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.
31NoneElectricity_taxed1-10530002120005300050 % 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.
32NoneElectricity0000We might want to keep these locations in the model, but we assume that emissions are zero.
33NoneHeat0000We might want to keep these locations in the model, but we assume that emissions are zero.
34NoneCooling0000We 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↷

+ Show code


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.

Dependencies

  • Plant/kettle type
  • Power output
  • Efficiency

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

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

See also

  • SMALL-SCALE PELLET BOILER EMISSIONS – CHARACTERIZATION AND COMPARISON TO OTHER COMBUSTION UNITS

HEIKKI LAMBERG. REPORT SERIES IN AEROSOL SCIENCE N:o 156 (2014). [7]

⇤--#: . Links 7-10 (From Tissari to Motiva) say the page doesn't exist. --Heta (talk) 15:45, 31 August 2015 (UTC) (type: truth; paradigms: science: attack)

Urgenche research project 2011 - 2014: city-level climate change mitigation
Urgenche pages

Urgenche main page · Category:Urgenche · Urgenche project page (password-protected)

Relevant data
Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings

Relevant methods
Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎

City Kuopio
Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing

City Basel
Buildings in Basel (password-protected)

Energy balances
Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou


References