Health impacts of energy consumption in Kuopio: Difference between revisions

From Opasnet
Jump to navigation Jump to search
mNo edit summary
 
(20 intermediate revisions by 7 users not shown)
Line 2: Line 2:
[[Category:Energy]]
[[Category:Energy]]
[[Category:Climate change]]
[[Category:Climate change]]
[[Category:Code under inspection]]
{{variable|moderator=|stub=Yes}}
{{variable|moderator=|stub=Yes}}


Line 8: Line 9:
What are the health impacts of energy use in Kuopio?
What are the health impacts of energy use in Kuopio?


== Answer ==
{{attack|# |the R-code does not run|--[[User:Phatman|emmanuel]] 15:41, 29 April 2013 (EEST)}}== Answer ==


This code gets the ovariable of this page and calculates some basic results.
<rcode graphics="1">
library(OpasnetUtils) # A package with Opasnet functionalities
library(xtable)      # A package with table formats
objects.latest("Op_en5809", "calculations") # Get the latest ovariables from code calculations on page Op_en5809.
healthimpactsofenergyconsumptioninKuopio <- EvalOutput(healthimpactsofenergyconsumptioninKuopio)
print(xtable(healthimpactsofenergyconsumptioninkuopio@output), type = 'html') # Show a result table
</rcode>


== Rationale ==
== Rationale ==
Line 33: Line 48:
**H biogas (4.18 GWh/a)
**H biogas (4.18 GWh/a)
**H oil (79.39 GWh/a)
**H oil (79.39 GWh/a)
====Emission factors (Ef) for CO2 and PM====
*Data: http://en.opasnet.org/w/Emission_factors_for_burning_processes
Haapaniemi: Fuel power 245MW.
*Main fuel is peat (84 %), others: oil (12%) and biomass (4%)
*EF for CO2
**Peat 382 kg /MWh (row 23)
**Biomass/wood 0 kg /MWh (row 12)
**Heavy oil 279 kg /MWh (row 1)
*EF for PM 
**Peat2-20 mg /MJ (row 26)
**Biomass/wood 1-15 mg /MJ (row 22)
**Heavy oil 8-22 mg/MJ (row 32)
{{attack|# |It would be relevant to know the actual capacity and filtering techniques used in Haapaniemi to decide on the proper EFs. Now we are just assuming that both combustion units in Haapaniemi are big and have efficient filtering techniques.|--[[User:Virpi Kollanus|Virpi Kollanus]] 09:22, 14 September 2012 (EEST)}}{{attack|# |Are the different fuels used interchangeably or simultaneously and what is the combined potential emission|--[[User:Phatman|emmanuel]] 15:59, 29 April 2013 (EEST)}}
Small power plants
*EF for CO2
**Heavy oil: 279 kg /MWh (row 1)
**Biogas: 0
*EF for PM
**Heavy oil: 4-38 mg/MJ (row 29)
**Biogas: 0
{{attack|# |You have linked a page here. Describe in more detail which pieces of data on that page is used (e.g. which rows in the table).|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}
{{comment|# |Now the emission factors for all fuels are the same. Do we keep it that way, or can you find fuel-specific emission factors?|--[[User:Jouni|Jouni]] 13:06, 13 September 2012 (EEST)}}
====Emissions====
Calculation of PM2.5 emission
'''Q = A * 3.6 * 10^6 * EF / 10^9
*Q = annual PM2.5 emission (t/a)
*A = annual amount of energy (GWh) produced in a given type of combustion process (power plant size, fuel, technique), converted to energy unit compatible with the PM2.5 emission factor (MJ) (1 GWh = 3.6 * 10^6 MJ)
*EF = PM2.5 unit emission factor for the combustion process (mg/MJ)
Emissions from combustion of different fuels are added up to get total emissions from different sized power plants
*Capacity > 50 MW
*Capacity < 50 MW
====Intake fraction (iF)====
Definition:
*The intake fraction (iF) has been defined as the integrated incremental intake of a pollutant released from a source category or region summed over all exposed individuals. (Tainio et al., 2009)
Formula:
*iF = [concentration (µg/m3) * population * breathing rate (m3/s)] / emission rate (g/s)
Result:
*See [[Intake fractions of PM]]
References:
*Tainio et al. (2009, 2010)
====Exposure====
Calculation of population exposure to PM2.5
'''E = Q * iF / BR * 10^12
*E = average population exposure to PM2.5 in Finland due to emission from given sized power plants (µg/m3)
*Q = PM2.5 emission from a given sized power plant (t/a)
*iF = fraction of PM2.5 emission from given sized power plants inhaled by the population of Finland
*BR = amount of air inhaled by the population of Finland in one year (20 m3/d * 365 d * Population)
Exposures due to emissions from different sized power plants are added up to get total exposure resulting from energy production.


====Mortality====
====Mortality====
*Natural mortality in Finland in population aged over 30 in 2010: 46440. '''This data is used in the assessment'''
*Natural mortality in Finland in population aged over 30 in 2010: 46440. '''This data is used in the assessment'''


Line 51: Line 136:


*[[Population of Finland]]
*[[Population of Finland]]
**All = 5 351 427 '''This is the data used in the assessment when calculating the total annual breathing rate in Finland'''
**All = 5 351 427 '''This data is used in the assessment when calculating the total annual breathing rate in Finland'''
**over (and equal) 30 years old = 3 481 672 in 2010[http://pxweb2.stat.fi/database/StatFin/vrm/vaerak/vaerak_fi.asp]
**over (and equal) 30 years old = 3 481 672 in 2010[http://pxweb2.stat.fi/database/StatFin/vrm/vaerak/vaerak_fi.asp]


====Dose-response====
====Dose-response====
For the dose-response we use RR 1.06 (all-cause RR, average), reference Pope et al, 2002 (below). This is a widely and most commonly used value for RR.
For the dose-response we use RR 1.06 (all-cause RR, average), reference Pope et al, 2002 (below). This is a widely and most commonly used value for RR.


Line 82: Line 168:
{{comment|# |Which of these ERFs is used, or all of them? Do you add them to the page [[:op_fi:Seturi/annosvaste|Seturi/annosvaste]]?|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}
{{comment|# |Which of these ERFs is used, or all of them? Do you add them to the page [[:op_fi:Seturi/annosvaste|Seturi/annosvaste]]?|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}


====Intake fraction (iF)====
====Mortality impacts====


Definition:
Calculation of population mortality impacts due to PM2.5 exposure
*The intake fraction (iF) has been defined as the integrated incremental intake of a pollutant released from a source category or region summed over all exposed individuals. (Tainio et al., 2009)


Formula:
Relative risk exposure-response function (ERF) is adjusted to the level of exposure caused by the PM2.5 emission from the energy production:
*iF = [concentration (µg/m3) * population * breathing rate (m3/s)] / emission rate (g/s)


Result:
'''RR’ = Exp(ln(RR) / U * E)
*Major power plants: iF for primary PM2.5 = 0.5*10<sup>-6</sup> (mean of all seasons)
*RR’ = adjusted relative risk
*RR = relative risk exposure-response function from an epidemiological study for the mortality endpoint of interest
*U = unit of exposure the ERF relates to
*E = average population exposure to PM2.5 due to energy production


References:
Calculation of the fraction of population mortality risk attributable to the PM2.5 exposure (PAF):
*Tainio et al. (2009)


{{comment|# |This number was taken into the code. However, you can also make a new page about iFs of PM.|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}
'''PAF = (RR’ – 1) / RR’


{{attack|# |This iF is for Northern Europe. iFs for Finnish PM2.5 emissions and Finnish population are:
Calculation of annual deaths attributable to the PM2.5 exposure (AD):
*Large power plants (>50 MW) 0.28 * 10^-6
*Small power plants (<50 MW) 0.34 * 10^-6
|--[[User:Virpi Kollanus|Virpi Kollanus]] 15:16, 13 September 2012 (EEST)}}


{{defend|# |I will make a new page for iF of PM tomorrow morning.|--[[User:Marjo|Marjo]] 17:05, 13 September 2012 (EEST)}}
'''AD = PAF * D
 
*D = Baseline annual deaths for the mortality endpoint of interest in the target population
====Emission factors (Ef) for CO2 and PM====
 
*Data: http://en.opasnet.org/w/Emission_factors_for_burning_processes
 
Haapaniemi: Fuel power 245MW.
*Main fuel is peat (84 %), others: oil (12%) and biomass (4%)
*EF for CO2
**Peat 382 kg /MWh (row 23)
**Biomass/wood 0 kg /MWh (row 12)
**Heavy oil 279 kg /MWh (row 1)
*EF for PM 
**Peat2-20 mg /MJ (row 26)
**Biomass 2-20 mg /MJ (row 21) {{attack|# |This EF is for a power plant sized 20-100 MW. Other EFs used for Haapaniemi are for power plants sized 100-300 MW. The EF for wood combustion in 100-300 MW plant is 1-15 mg/MJ. Perhaps we should use this to be consistent.|--[[User:Virpi Kollanus|Virpi Kollanus]] 09:16, 14 September 2012 (EEST)}}
**Heavy oil 8-22 mg/MJ (row 32)
{{attack|# |It would be relevant to know the actual capacity and filtering techniques used in Haapaniemi to decide on the proper EFs. Now we are just assuming that both combustion units in Haapaniemi are big and have efficient filtering techniques.|--[[User:Virpi Kollanus|Virpi Kollanus]] 09:22, 14 September 2012 (EEST)}}
 
Small power plants
*EF for CO2
**Heavy oil: 279 kg /MWh (row 1)
**Biogas: 0
*EF for PM
**Heavy oil: 4-38 mg/MJ (row 29)
**Biogas: 0
 
 
 
{{attack|# |You have linked a page here. Describe in more detail which pieces of data on that page is used (e.g. which rows in the table).|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}
 
{{comment|# |Now the emission factors for all fuels are the same. Do we keep it that way, or can you find fuel-specific emission factors?|--[[User:Jouni|Jouni]] 13:06, 13 September 2012 (EEST)}}


===Formula===
===Formula===


Default run: [http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=EQ2RROPDt7l1fEIQ] EQ2RROPDt7l1fEIQ
<rcode  
<rcode  
label="Initiate functions"  
label="Initiate functions"  
Line 146: Line 201:


breathing.rate <- 20 # m3 /d
breathing.rate <- 20 # m3 /d
intake.fraction <- new("ovariable", name = "intake.fraction", data = data.frame(Pollutant = "PM2.5", Result = 0.5e-6))
exposure.response.function <- 1.006
exposure.response.function <- 1.06
disease.burden <- 46000 # Number of non-accidental deaths in Finland per year
disease.burden <- 1


dependencies <- data.frame(
dependencies <- data.frame(
Name = c(
Name = c(
"energy.balance.Kuopio",
"energy.balance",
"emission.factor",
"emission.factor",
"intake.fraction",
"intake.fraction",
Line 161: Line 215:
),
),
Key = c(
Key = c(
"pOm8Zz4T6xfFLvU0", # Activity [[Energy balance in Kuopio]]
"QZNNQL9ClAHO3COB", # Activity [[Energy balance in Kuopio]]. Contains also Matrix_page, Additions, and Solutions.
"QEq4AnzY04LXx3gX", # Emission factors [[Emission factors for burning processes]]
"QEq4AnzY04LXx3gX", # Emission factors [[Emission factors for burning processes]]
"", # Intake fraction given above
"lYBeQDZGDxOIjMjp", # Intake fraction [[Intake fractions of PM]]
"", # Breathing rate given above
"", # Breathing rate given above
"mzGRDt4VreHPefKI", # Population size [[Population of Finland]]
"mzGRDt4VreHPefKI", # Population size [[Population of Finland]]
"", # Disease burden data missing
"", # Disease burden given above
""  # Exposure-response function data missing
""  # Exposure-response function given above but could be [[:op_fi:Seturi/annosvaste]]
)
)
)
)


formula <- function(dependencies, ...) {
formula <- function(dependencies, intermediates = FALSE, ...) {
Fetch2(dependencies)
ComputeDependencies(dependencies, ...)
ComputeDependencies(dependencies, ...)


energy.balance.Kuopio@output <- energy.balance.Kuopio@output[energy.balance.Kuopio@output$Energy.class %in% c("CHP peat", "CHP renewable", "CHP oil", "H biogas", "H oil"), ]
fuel.energy.class <- data.frame(
Energy.class = c("CHP peat", "CHP renewable", "CHP oil", "H biogas", "H oil"),  
Fuel = c("Peat", "Biomass", "Heavy oil", "Biomass", "Heavy oil"),
Fuel.power..MW. = c("100-300", "20-100", "15-50", "20-100", "15-50"),
Subcategory = c(rep("Large power plants", 3), rep("Small power plants", 2))
)


fuel.energy.class <- data.frame(
energy.balance@output <- merge(fuel.energy.class, energy.balance@output)
Energy.class = c("CHP peat", "CHP renewable", "CHP oil", "H biogas", "H oil"),
Fuel = c("Peat", "Renewables", "Heavy oil", "Renewables", "Heavy oil"),
Fuel.power..MW. = c("100-300", "20-100", ">50", "20-100", ">50")#,
# fuel.energy.classResult = 1
)
# fuel.energy.class <- EvalOutput(new("ovariable", name = "fuel.energy.class", data = fuel.energy.class))
cat("Fuel.energy.class\n")
print(xtable(fuel.energy.class), type = 'html')


cat("Energy balance\n")
out <- energy.balance * 3.6E6 * emission.factor * 1E-9 # Emission (ton /a)
print(xtable(energy.balance.Kuopio@output), type = 'html')
if(intermediates) {
cat("Energy balance\n")
print(xtable(energy.balance@output), type = 'html')
cat("Emission factors\n")
print(xtable(emission.factor@output[emission.factor@output$Iter == 1, ]), type = 'html')
cat("Emission\n")
print(xtable(out@output[out@output$Iter == 1, ]), type = "html")
}


# print(xtable(emission.factor@output), type = 'html')
population@output <- population@output[population@output$Age == "All", colnames(population@output) != "Age"]
out <- out * intake.fraction * 1E-6 / (breathing.rate * 365 * population) * 10^12 # Exposure concentration (ug /m3)
out@output <- out@output[ , c("Fuel", "Energy.class", "Emission.type", "Subcategory", "Iter", "Result")]


fuels <- data.frame(
if(intermediates) {
Emission.type = c("CO2", "CO2", "CO2", "PM2.5", "PM2.5", "PM2.5"),
cat("Exposure\n")
Fuel = c("Heavy oil", "Biomass", "Peat", "Heavy oil", "Biomass", "Peat"),
print(xtable(out@output[out@output$Iter == 1, ], digits = 9), type = "html")
Fuel.power..MW. = c("-", "-", "-", ">50", "20-100", "100-300")
cat("Exposure-response function\n")
)
print(exposure.response.function)
}


# print(xtable(fuels), type = 'html')
out <- (exposure.response.function - 1) * out + 1 # RR' Actually, exp(log(exposure.response.function) * out)
 
out <- disease.burden * (out - 1) / out # Number of additional deaths /a
# emission.factor@output <- merge(emission.factor@output, fuels)
 
# print(xtable(emission.factor@output), type = 'html')


energy.balance.Kuopio@output <- merge(fuel.energy.class, energy.balance.Kuopio@output)
if(intermediates) {
# out <- fuel.energy.class * energy.balance.Kuopio # * 3.6e6 * emission.factor / 10^9 # Emission (ton /a)
cat("Health impact\n")
print(xtable(energy.balance.Kuopio@output), type = 'html')
print(xtable(out@output[out@output$Iter == 1, ], digits = 9), type = "html")
}


out <- energy.balance.Kuopio * 3.6e6 * emission.factor / 10^9 # Emission (ton /a)
cat("Emission\n")
print(out)
#print(xtable(out@output[out@output@Iter == 1]), type = 'html')
out <- out * intake.fraction / (breathing.rate * 365 * population) * 10^12 # Exposure concentration (ug /m3)
cat("Exposure\n")
print(out)
#print(xtable(out@output[out@output@Iter == 1]), type = 'html')
out <- (exposure.response.function - 1) * out + 1 # RR' Actually, exp(ln(exposure.response.function) * out)
print(out)
#print(xtable(out@output[out@output@Iter == 1]), type = 'html')
out <- disease.burden * (out - 1) / out # Number of additional deaths /a
print(out)
#print(xtable(out@output[out@output@Iter == 1]), type = 'html')
return(out)
return(out)
}
}
data <- tidy(op_baseGetData("opasnet_base", "Op_en5675"), "health.impact") # This data comes from [[Training assessment]]; should be changed.


health.impact <- new("ovariable",
health.impact <- new("ovariable",
name        = "health.impact",
name        = "health.impact",
formula      = formula,
formula      = formula,
dependencies = dependencies,
dependencies = dependencies
data        = data
)
)


out <- EvalOutput(health.impact, N = 10)
out <- EvalOutput(health.impact, intermediates = TRUE)
 
print(xtable(head(out@output)), type = "html")


print(xtable(out@output[out@output$Iter == 1, ]), type = "html")
ggplot(out@output [out@output$health.impactSource == "Formula", ],
aes(x = health.impactResult,
fill = Subcategory)) + geom_density(alpha = 0.2)
#objects.put(health.impact)
#objects.put(health.impact)


</rcode>
</rcode>
'''Modelling steps in health impact assessment
1) Calculation of PM2.5 emission
'''Q = A * 3.6 * 10^6 * EF / 10^9
*Q = annual PM2.5 emission (t/a)
*A = annual amount of energy (GWh) produced in a given type of combustion process (power plant size, fuel, technique), converted to energy unit compatible with the PM2.5 emission factor (MJ) (1 GWh = 3.6 * 10^6 MJ)
*EF = PM2.5 unit emission factor for the combustion process (mg/MJ)
Emissions from combustion of different fuels are added up to get total emissions from different sized power plants
*Capacity > 50 MW
*Capacity < 50 MW
2) Calculation of population exposure to PM2.5
'''E = Q * iF / BR * 10^12
*E = average population exposure to PM2.5 in Finland due to emission from given sized power plants (µg/m3)
*Q = PM2.5 emission from a given sized power plant (t/a)
*iF = fraction of PM2.5 emission from given sized power plants inhaled by the population of Finland
*BR = amount of air inhaled by the population of Finland in one year (20 m3/d * 365 d * Population)
Exposures due to emissions from different sized power plants are added up to get total exposure resulting from energy production.
3) Calculation of population mortality impacts due to PM2.5 exposure
Relative risk exposure-response function (ERF) is adjusted to the level of exposure caused by the PM2.5 emission from the energy production:
'''RR’ = Exp(ln(RR) / U * E)
*RR’ = adjusted relative risk
*RR = relative risk exposure-response function from an epidemiological study for the mortality endpoint of interest
*U = unit of exposure the ERF relates to
*E = average population exposure to PM2.5 due to energy production
Calculation of the fraction of population mortality risk attributable to the PM2.5 exposure (PAF):
'''PAF = (RR’ – 1) / RR’
Calculation of annual deaths attributable to the PM2.5 exposure (AD):
'''AD = PAF * D
*D = Baseline annual deaths for the mortality endpoint of interest in the target population
{{attack|# |The pseudocode about how to actually calculate the health impacts based on the data above is missing.|--[[User:Jouni|Jouni]] 12:52, 13 September 2012 (EEST)}}


==See also==
==See also==

Latest revision as of 10:56, 26 August 2013



Question

What are the health impacts of energy use in Kuopio?

⇤--#: . the R-code does not run --emmanuel 15:41, 29 April 2013 (EEST) (type: truth; paradigms: science: attack)== Answer ==

This code gets the ovariable of this page and calculates some basic results.

+ Show code

Rationale

Dependencies

Heat and electricity production in Kuopio

What is the amount of energy produced (GWh/a) from different fuels in different types of power plants?

  • Power plants in Kuopio:
    • Combined heat and power plant in Haapaniemi (CHP)
      • Total capacity: 100-300 MW
      • Technique: leijupoltto
      • Fuels: peat, renewable, heavy oil
    • Small heat plants (H)
      • Capacity: <20 MW (?)
      • Fuels: biogas, heavy oil
  • Data: Energy balance in Kuopio, relevant parameters:
    • CHP peat (1497.31 GWh/a)
    • CHP renewable (75.12 GWh/a)
    • CHP oil (75.12 GWh/a)
    • H biogas (4.18 GWh/a)
    • H oil (79.39 GWh/a)

Emission factors (Ef) for CO2 and PM

Haapaniemi: Fuel power 245MW.

  • Main fuel is peat (84 %), others: oil (12%) and biomass (4%)
  • EF for CO2
    • Peat 382 kg /MWh (row 23)
    • Biomass/wood 0 kg /MWh (row 12)
    • Heavy oil 279 kg /MWh (row 1)
  • EF for PM
    • Peat2-20 mg /MJ (row 26)
    • Biomass/wood 1-15 mg /MJ (row 22)
    • Heavy oil 8-22 mg/MJ (row 32)

⇤--#: . It would be relevant to know the actual capacity and filtering techniques used in Haapaniemi to decide on the proper EFs. Now we are just assuming that both combustion units in Haapaniemi are big and have efficient filtering techniques. --Virpi Kollanus 09:22, 14 September 2012 (EEST) (type: truth; paradigms: science: attack)⇤--#: . Are the different fuels used interchangeably or simultaneously and what is the combined potential emission --emmanuel 15:59, 29 April 2013 (EEST) (type: truth; paradigms: science: attack)

Small power plants

  • EF for CO2
    • Heavy oil: 279 kg /MWh (row 1)
    • Biogas: 0
  • EF for PM
    • Heavy oil: 4-38 mg/MJ (row 29)
    • Biogas: 0


⇤--#: . You have linked a page here. Describe in more detail which pieces of data on that page is used (e.g. which rows in the table). --Jouni 12:52, 13 September 2012 (EEST) (type: truth; paradigms: science: attack)

----#: . Now the emission factors for all fuels are the same. Do we keep it that way, or can you find fuel-specific emission factors? --Jouni 13:06, 13 September 2012 (EEST) (type: truth; paradigms: science: comment)

Emissions

Calculation of PM2.5 emission

Q = A * 3.6 * 10^6 * EF / 10^9

  • Q = annual PM2.5 emission (t/a)
  • A = annual amount of energy (GWh) produced in a given type of combustion process (power plant size, fuel, technique), converted to energy unit compatible with the PM2.5 emission factor (MJ) (1 GWh = 3.6 * 10^6 MJ)
  • EF = PM2.5 unit emission factor for the combustion process (mg/MJ)

Emissions from combustion of different fuels are added up to get total emissions from different sized power plants

  • Capacity > 50 MW
  • Capacity < 50 MW

Intake fraction (iF)

Definition:

  • The intake fraction (iF) has been defined as the integrated incremental intake of a pollutant released from a source category or region summed over all exposed individuals. (Tainio et al., 2009)

Formula:

  • iF = [concentration (µg/m3) * population * breathing rate (m3/s)] / emission rate (g/s)

Result:

References:

  • Tainio et al. (2009, 2010)

Exposure

Calculation of population exposure to PM2.5

E = Q * iF / BR * 10^12

  • E = average population exposure to PM2.5 in Finland due to emission from given sized power plants (µg/m3)
  • Q = PM2.5 emission from a given sized power plant (t/a)
  • iF = fraction of PM2.5 emission from given sized power plants inhaled by the population of Finland
  • BR = amount of air inhaled by the population of Finland in one year (20 m3/d * 365 d * Population)

Exposures due to emissions from different sized power plants are added up to get total exposure resulting from energy production.

Mortality

  • Natural mortality in Finland in population aged over 30 in 2010: 46440. This data is used in the assessment
  • Mortality by reasons (in Finland at the 2010)
    • Cancer (lung, larynx, trachea) 2 259
    • Cardiovascular diseases 20 549
    • Respiratory diseases 1 982

[1]

⇤--#: . Exposure is calculated for the whole Finland, so we need national numbers also for disease burden. --Jouni 16:44, 12 September 2012 (EEST) (type: truth; paradigms: science: attack)

----#: . Do we use these numbers directly in the code, or do you put the numbers onto a page in Opasnet? Which page? --Jouni 12:52, 13 September 2012 (EEST) (type: truth; paradigms: science: comment)

Population

  • Population of Finland
    • All = 5 351 427 This data is used in the assessment when calculating the total annual breathing rate in Finland
    • over (and equal) 30 years old = 3 481 672 in 2010[2]

Dose-response

For the dose-response we use RR 1.06 (all-cause RR, average), reference Pope et al, 2002 (below). This is a widely and most commonly used value for RR.

  • Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 2002;287:1132–1141. Link: [3]. Table 2. Adjusted mortality relative risk associated with a 10-μg/m3 change in fine particles measuring less than 2.5μm in diameter:
    • all-cause, RR average 1.06
    • cardiopulmonary, RR average 1.09
    • lung cancer, RR average 1.14
    • all other cause, RR average 1.01

Other possible references

  • http://fi.opasnet.org/fi/Seturi/annosvaste
    • Outdoor PM: lung cancer, RR 1.04; 1.14; 1.23
    • Outdoor PM: cardiovascular (sydän- ja hengitystietaudit), RR 1.03; 1.09; 1.16
    • Outdoor PM: total mortality, RR 1.0014; 1.0062; 1.011
  • Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality. American Journal of Respiratory and Critical CareMedicine, 2006; 173:667–672. Link: [4] Table 3. Adjusted proportional hazard mortality rate ratios and 95% CI for a 10-μg/m3 increase in average ambiest PM2.5:
    • total mortality, RR 1.16 – 1.18
    • cardiovascular, RR 1.28
    • respiratory, RR 1.08 – 1.21
    • lung cancer, RR 1.20 – 1,27
    • other, RR 1.02 – 1.05
  • Douglas W. Dockery, C. Arden Pope, Xiping Xu, John D. Spengler, James H. Ware, Martha E. Fay, Benjamin G. Ferris, Jr., and Frank E. Speizer. An Association between Air Pollution and Mortality in Six U.S. Cities.N Engl J Med 1993; 329:1753-1759, December 9, 1993. Link: [5]
    • Inhalable particles, RR 1.27
    • Fine particles, RR 1.26

----#: . Which of these ERFs is used, or all of them? Do you add them to the page Seturi/annosvaste? --Jouni 12:52, 13 September 2012 (EEST) (type: truth; paradigms: science: comment)

Mortality impacts

Calculation of population mortality impacts due to PM2.5 exposure

Relative risk exposure-response function (ERF) is adjusted to the level of exposure caused by the PM2.5 emission from the energy production:

RR’ = Exp(ln(RR) / U * E)

  • RR’ = adjusted relative risk
  • RR = relative risk exposure-response function from an epidemiological study for the mortality endpoint of interest
  • U = unit of exposure the ERF relates to
  • E = average population exposure to PM2.5 due to energy production

Calculation of the fraction of population mortality risk attributable to the PM2.5 exposure (PAF):

PAF = (RR’ – 1) / RR’

Calculation of annual deaths attributable to the PM2.5 exposure (AD):

AD = PAF * D

  • D = Baseline annual deaths for the mortality endpoint of interest in the target population

Formula

Default run: [6] EQ2RROPDt7l1fEIQ

+ Show code

See also

Keywords

References


Related files

<mfanonymousfilelist></mfanonymousfilelist>

Retrieved from "https://en.opasnet.org/index.php?title=Health_impacts_of_energy_consumption_in_Kuopio&oldid=30645"