Difference between revisions of "Health impacts of energy consumption in Kuopio"

Revision as of 06:30, 14 September 2012

Question

What are the health impacts of energy use in Kuopio?

Answer

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)

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)

--# : 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)

Population

• Population of Finland
  • All = 5 351 427 This is the data 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)

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:

• Major power plants: iF for primary PM2.5 = 0.5*10^-6 (mean of all seasons)

References:

• Tainio et al. (2009)

--# : This number was taken into the code. However, you can also make a new page about iFs of PM. --Jouni 12:52, 13 September 2012 (EEST)

⇤# : This iF is for Northern Europe. iFs for Finnish PM2.5 emissions and Finnish population are:

--Virpi Kollanus 15:16, 13 September 2012 (EEST)

←# : I will make a new page for iF of PM tomorrow morning. --Marjo 17:05, 13 September 2012 (EEST)

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) ⇤# : 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. --Virpi Kollanus 09:16, 14 September 2012 (EEST)
  • 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)

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)

--# : 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)

Formula

library(OpasnetUtils)
library(xtable)
library(ggplot2)

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 <- 1.06
disease.burden <- 1

dependencies <- data.frame(
	Name = c(
		"energy.balance.Kuopio",
		"emission.factor",
		"intake.fraction",
		"breathing.rate",
		"population",
		"disease.burden",
		"exposure.response.function"
	),
	Key = c(
		"pOm8Zz4T6xfFLvU0", # Activity [[Energy balance in Kuopio]]
		"QEq4AnzY04LXx3gX", # Emission factors [[Emission factors for burning processes]]
		"", # Intake fraction given above
		"", # Breathing rate given above
		"mzGRDt4VreHPefKI", # Population size [[Population of Finland]]
		"", # Disease burden data missing
		""  # Exposure-response function data missing
	)
)

formula <- function(dependencies, ...) {
Fetch2(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", "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")
	print(xtable(energy.balance.Kuopio@output), type = 'html')

#	print(xtable(emission.factor@output), type = 'html')

fuels <- data.frame(
	Emission.type = c("CO2", "CO2", "CO2", "PM2.5", "PM2.5", "PM2.5"), 
	Fuel = c("Heavy oil", "Biomass", "Peat", "Heavy oil", "Biomass", "Peat"), 
	Fuel.power..MW. = c("-", "-", "-", ">50", "20-100", "100-300")
)

#	print(xtable(fuels), type = 'html')

# 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)
#	out <- fuel.energy.class * energy.balance.Kuopio # * 3.6e6 * emission.factor / 10^9 # Emission (ton /a)
	print(xtable(energy.balance.Kuopio@output), 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)
}

data <- tidy(op_baseGetData("opasnet_base", "Op_en5675"), "health.impact") # This data comes from [[Training assessment]]; should be changed.

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

out <- EvalOutput(health.impact, N = 10)

print(xtable(head(out@output)), type = "html")

#objects.put(health.impact)

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

⇤# : The pseudocode about how to actually calculate the health impacts based on the data above is missing. --Jouni 12:52, 13 September 2012 (EEST)

See also

Keywords

References

Related files

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