Health impacts of energy consumption in Kuopio

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Question

What are the health impacts of energy use in Kuopio?

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

• Mortality in Pohjois-Savo (per 100 000 person) at the 2009: 1092
• Mortality by reasons (in Pohjois-Savo, per 100 000 person at the 2009)(should be known which of these can be caused by PM)
  • Cancer (lung, larynx, trachea) 38,7
  • Cardiovascular diseases 455,1
    • Inc.
    • Ischemic heart diseases 285,1
    • Other, except rheumatic and alcoholic heart diseases 31,0
  • Respiratory diseases 41,9
  • Inc.
    • influenza 0,8 (NO!)
    • pneumonia 11,3
    • bronchitis and pulmonary emphysema 17,3
    • asthma 4,0
    • other respirotary deseases 8,5

[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)

Dose-response

• 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

• 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.
  • Inhalable particles, RR 1.27
  • Fine particles, RR 1.26

• 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. Adjusted mortality relative risk associated with a 10-μg/m3 change in fine particles measuring less than 2.5μm in diameter:
  • all-cause, RR 1.04 – 1.06
  • cardiopulmonary, RR 1.06 – 1.09
  • lung cancer, RR 1.08 – 1.14
  • all other cause, RR 1.01

• 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. 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

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)

Emission factors (Ef) for CO2 and PM

• Data: http://en.opasnet.org/w/Emission_factors_for_burning_processes
  • Ef for Co2 is 382 kg /MWh
  • Ef for PM is 2-20 mg /MJ
  • Haapaniemi: Fuel power 245MW.
  • Main fuel is peat (84 %), others: oil (12%) and biomass (4%)

Formula

+ Show code- Hide code library(OpasnetUtils) library(xtable) library(ggplot2) breathing.rate <- 20 # m3 /d activity <- 0 emission.factor <- 0 intake.fraction <- 0 breathing.rate <- 0 population.size <- 0 exposure.response.function <- 0 disease.burden <- 0 dependencies <- data.frame( Name = c( "activity", "emission.factor", "intake.fraction", "breathing.rate", "population.size", "disease.burden", "exposure.response.function" ), Key = c("muRtYjhkphoNPQJI") ) formula <- function(dependencies, ...) { ComputeDependencies(dependencies, ...) out <- 0 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(out@output), type = "html") objects.put(health.impact)

Related files

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