Health impacts of energy consumption in Kuopio

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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

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 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: [2]. 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: [3] 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: [4]
- 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)

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

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 a given sized power plant (µg/m3)
Q = PM2.5 emission from a given sized power plant (t/a)
iF = fraction of PM2.5 emission from a given sized power plant 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

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

PAF = (RR’ – 1) / RR’

Calculation of 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) (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)

Keywords

References

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