Health impacts of energy consumption in Kuopio

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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- Hide code 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

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

• 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)

⇤--#: . 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:

• 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

• 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- Hide code library(OpasnetUtils) library(xtable) library(ggplot2) breathing.rate <- 20 # m3 /d exposure.response.function <- 1.006 disease.burden <- 46000 # Number of non-accidental deaths in Finland per year dependencies <- data.frame( Name = c( "energy.balance", "emission.factor", "intake.fraction", "breathing.rate", "population", "disease.burden", "exposure.response.function" ), Key = c( "QZNNQL9ClAHO3COB", # Activity [[Energy balance in Kuopio]]. Contains also Matrix_page, Additions, and Solutions. "QEq4AnzY04LXx3gX", # Emission factors [[Emission factors for burning processes]] "lYBeQDZGDxOIjMjp", # Intake fraction [[Intake fractions of PM]] "", # Breathing rate given above "mzGRDt4VreHPefKI", # Population size [[Population of Finland]] "", # Disease burden given above "" # Exposure-response function given above but could be [[:op_fi:Seturi/annosvaste]] ) ) formula <- function(dependencies, intermediates = FALSE, ...) { ComputeDependencies(dependencies, ...) 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)) ) energy.balance@output <- merge(fuel.energy.class, energy.balance@output) out <- energy.balance * 3.6E6 * emission.factor * 1E-9 # Emission (ton /a) 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") } population@output <- population@output[population@output$Age == "All", colnames(population@output) != "Age"] out <- out * intake.fraction * 1E-6 / (breathing.rate * 365 * population) * 10^12 # Exposure concentration (ug /m3) out@output <- out@output[ , c("Fuel", "Energy.class", "Emission.type", "Subcategory", "Iter", "Result")] if(intermediates) { cat("Exposure\n") print(xtable(out@output[out@output$Iter == 1, ], digits = 9), type = "html") cat("Exposure-response function\n") print(exposure.response.function) } 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 if(intermediates) { cat("Health impact\n") print(xtable(out@output[out@output$Iter == 1, ], digits = 9), type = "html") } return(out) } health.impact <- new("ovariable", name = "health.impact", formula = formula, dependencies = dependencies ) out <- EvalOutput(health.impact, intermediates = TRUE) print(xtable(out@output[out@output$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)

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