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)

----#: . 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)

Dose-response

For the dose-response

• 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: [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 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

• 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: [3]
  • Inhalable particles, RR 1.27
  • Fine particles, RR 1.26

• 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] 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

----#: . 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)

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) (type: truth; paradigms: science: comment)

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%)

⇤--#: . 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)

Formula

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

+ Show code- Hide code library(OpasnetUtils) library(xtable) library(ggplot2) breathing.rate <- 20 # m3 /d activity <- 0 emission.factor <- 0 intake.fraction <- new("ovariable", name = "intake.fraction", data = data.frame(Pollutant = "PM2.5", Result = 0.5e-6)) 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( "eH8rXRhTwNBAv9Oq", # Activity [[Energy balance in Kuopio]] "QEq4AnzY04LXx3gX", # Emission factors [[Emission factors for burning processes]] "", # Intake fraction given above "", # Breathing rate given above "", # Population size data missing "", # Disease burden data missing "" # Exposure-response function data missing ) ) 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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