External cost of electricity generation systems

Y. Matsuki, D.Sc.Professor, Department of

Mathematical Method for System Analysis, IASA, NTUU “KPI”

August 10, 2010

How can you calculate the externalities?

How to calculate the monetary value of the health impacts

D= ∫ ρ(x)·f(x,C(x,Q))·Uv(x) dximpact of Area

D: damage cost (Euro, US dollars, UAH)ρ(x): population density (person/m2)f(x,C(x,Q)): Exposure-Response Function

cases/(year.person.μg/m3)

Uv(x): unit cost (Euro/cases)C(x,Q): Concentration of the pollution (μg/m3)Q: Emission of the pollution (μg/year)x: Distance from the emission source (m)

What’s new on the public health issue?

• Loss of life expectancy for chronic mortality from air pollution

• [Dockery et al 1993, Pope et al 1995] have found positive correlations between exposure to particles and total mortality

Epidemiology of Acute Health Effects

• Table of contents- Introduction

- Studies of air pollution episodes- Health effects at low levels of air pollution

- Acute Morbidity- Daily time-series mortality studies- Slope of the mortality exposure-response relationship and lead-lag

relationships- Acute Morbidity

- Hospital usage- Exacerbation of asthma- Respiratory symptoms- Lung function- Restricted activity

Introduction

• It’s about human health and pollution.• Where in the world is this story about?• When did this story start?• What changed for the last 10-15 years?

Air pollution episodes• Where the most dramatic episode occurred and

when?• What happened?• Mortality and morbidity• How?• Respiratory and cardiovascular• Cardiopulmonary disease• What was the level of particle and SO2?• 500 μg/m3 – 2 mg/m3

• If not old days, where do these levels exist?

Health effects at low level of air pollution

• What was the primary interest to air pollution policy among the most developed nations for the last 20 years?

• To determine the lowest level• The length of exposure to cause health

impacts• Threshold was often assumed.

Health effects at low level of air pollution

• With improved air monitoring, is threshold proven?

• No threshold, or bellow ambient level in the US in 1996.

• Many of the studies suggest linear model.• What is necessary to prove it?• Number of time-series studies

What are the health effects?

• Mortality• Hospitalization for respiratory and heart

disease• Aggravation of asthma• Incidence and duration of respiratory

symptoms• Lung function• Restricted activity

Acute MortalityDaily time-series mortality studies

• US EPA reviewed Ostro 1993, Schwartz 1994c, Dockery & Pope 1994, Pope et al. 1995b.

• What these studies observed?• Changes in daily death counts

associated with short-term changes in particulate air pollution.a near linear function.

What do you see from those table and figures?

• Consistency in estimated effects• Statistically significant effect• Estimated Range?• 0.5 percent – 1.6 percent in daily mortality

for each 10 μg/m3 increase in PM10 concentration.

• Weighted mean?• About 0.8 percent

Percent increase in Mortality per 10 μg/m3 increase in PM10

• Mortality Cases/ μg/m3 ?• 0.5 percent – 1.6 percent in daily mortality

for each 10 μg/m3 increase in PM10 concentration.

• 5 x 10-4 – 1.5 x 10-3 Cases/ μg/m3

• Weighted mean about 0.8 percent • 8 x 10-4

Mortality by respiratory disease and cardiovascular disease

• Large effect on respiratory disease mortality

• Also cardiovascular disease causing death

Shape of the mortality exposure-response relationship and lead-lag relationships

• PM10 concentration in typical US cities• 10 to 120 μg/m3

• Max 365 μg/m3 in the Utah Valley• How is in Ukraine?

Shape of the mortality exposure-response relationship and lead-lag relationships

• What does it say?• Typically near linear or log-linear• Three possibilities:

(1) no threshold (2) threshold is bellow existing pollution levels(3) looking more linear than it really is.

Shape of the mortality exposure-response relationship and lead-lag relationships

• Increased mortality occurred concurrently or within 1-5 days following an increase in air pollution.

Acute Morbidity Hospital usage

• What happened in the Utah Valley during the winter of 1986-1987?

• A labor dispute resulted in the closure of the local steel mill, the largest single source of particulate emission.

• This winter PM10 ave. 51 μg/m3 , max. 113 μg/m3

• Previous year ave. 90 μg/m3 , max 365 μg/m3

• Children hospital admission for respiratory disease dropped 50 percent.

Acute Morbidity Hospital usage

• What was the argument by Lamm et al. (1994)?

• Not closure of the steel mill, but Respiratory Syncytial Virus (RSV)

• What was the argument by Pope (1991)?• Not by the virus.

Acute Morbidity Hospital usage

• What is the exposure-response of the hospital admission of all respiratory diseases?

• 0.8 – 3.4 % increase per 10 μg/m3 by PM10

• 8 x 10-4 – 3.4 x 10-3 Cases/ μg/m3 by PM10

Acute Morbidity Hospital usage

• Emergency department visit % increase by 10 μg/m3 increase of PM10

• 0.5 – 3.4 (ave. 1.0) % increase/ 10 μg/m3

Exacerbation of asthmaRespiratory symptoms

• Asthma, Bronchodilator• Cough

Exacerbation of asthma• What is the exposure-response relation of

asthmatic attack?• 3 % increase in asthmatic attacks with 10 μg/m3

increase of PM10 • 3 x 10-3 cases/ μg/m3

• What is the exposure-response relation of bronchodilator use?

• 1.1 – 12 % (ave. 3.0) increase with 10 μg/m3

increase of PM10 • 3.0 x 10-3 cases/ μg/m3

Respiratory symptoms• Lower Respiratory symptoms

– Wheezing, dry cough, phlegm, shortness of breath, chest discomfort/pain

• What is the exposure-response relation of lower respiratory symptoms?

• Ave. 3.0 % increase in lower respiratory symptoms with 10 μg/m3 increase of PM10

• 3.0 x 10-3 cases/ μg/m3

• Upper Respiratory symptoms– Runny nose, stuffy nose, sinusitis, sore throat, wet cough,

head cold, hay fever, red eyes• Statistically insignificant association observed.

Key words

• Pneumonia• COPD: chronic obstructive pulmonary

(lung) disease• Coronary Artery Disease• Disrythmias (such as slow heart rate)• Congestive Heart Failure

Lung function

• FEV: forced expiratory volume (a measure of lung function)

• FVC: forced vital capacity• PEF: Peak expiratory flow

Epidemiology of Chronic Health Effects

• Table of contents- Introduction- Mortality Studies

- Population-based (ecologic) mortality studies- Research needs for improved study designs- Prospective Cohort Mortality Studies- Harvard six-cities study- Implication of prospective cohort mortality results

- Chronic Health Effects; Morbidity- Chronic differences in lung function- Chronic respiratory symptoms and disease

Introduction

• What is the difference between the acute effects and the chronic effects?

• Acute: associated with short term (day to day change)

• Chronic = long-term: a long time + cumulative effects of repeated exposure

• If acute effect exists, is there also chronic effect by the same pollutant?

• Not automatically

Mortality StudiesPopulation-based mortality studies

• What is the summary of the population-based cross-sectional study?

• Average mortality is higher in cities with higher fine particulate and sulfate particulates.

• How the other risks were controlled?• Smoking rate, education levels, income levels, poverty

rates, housing density, etc were included in the regression models.

• What is the coefficients of air pollution related mortality?• About 3 % per 10 μg/m3

• 4 x 10-3 per μg/m3

What are limitations of Population-based Studies?

• Systematic and/or analytical bias– Study designs– Data sets– Analytic techniques– Regression analysis– Hypothesis testing– Controlling some other factors

• Size of the estimated association– Comparison with the current pollution level to the chronic

mortality is not appropriate, – Because now the pollution level is lower than years ago.

• Cannot control for individual differences in cigarette smoking, and other risk factors.

What are limitations of Population-based Studies?

• Age, poverty, health care, occupations, cigarette smoking, housing quality, cooking fuels vary among cities and potentially could be confounding the apparent air pollution associations.

Improved study designs

• What are 2 important issues 1970s –1980s

• Threshold• Study design – what evidence needed?• If threshold, what will become easier?• To establish the acceptable goal for

pollution control

Prospective Cohort Mortality Studies

• 3 cohort mortality studies• With improved study design

Prospective Cohort Mortality Studies

• Not on the data available for the population as a whole,

• But, it analyzes the incidence of health effects in a sample of individuals.

• Negative aspect: • It relies on community-based air pollution

monitoring.• Costly and time-consuming

Harvard six-cities study• 14-16 follow up of 8,111 adults living in 6 cities of the US• TSP, PM10, PM2.5, SO4, H+, SO2, NO2 and O3 levels were

monitored.• What is most strongly associated with mortality risk?• Smoking• But, after controlling for individual differences (age, sex,

smoking, body mass, education, occupational exposure),• Differences in relative mortality risks across 6 cities were

strongly associated with difference in pollution levels in those cities.

• PM10, PM2.5, SO4 than TSP and SO2, H+, or ozone.

Shape of the figure

• Mortality risk and fine particulate• Nearly linear• No threshold

Implications of prospective cohort mortality results

• The increased risk from air pollution bigger or smaller than cigarette smoking?

• Small• But, there is a correlation.

Summary

• Mortality • Acute exposure Total 0.5-1.5 %/10μg/m3

• 5 x 10-4 – 1.5 x 10-3 (cases/μg/m3)• Chronic exposure 3 – 9 %/10μg/m3

• 3 x 10-3 – 9 x 10-3 (cases/μg/m3)

Exposure-Response Function f(r,C(r,Q)) PM10 and Nitrates

7.8E-2, 1.0E-1

Asthmatic children Cardiovascular, Respiratory

2.78E-5 Infant Mortality

6.00E-2

1.63E-1

Asthmatic adultsBronchodilator Lower respiratory symptoms

6.00E-5, 2.56E-6

Hospital Admissions Cardiovascular, Respiratory

1.0E-2 Work Days Lost

5.0E-2 Restricted Activity Days

7.65E-5 Chronic Bronchitis

2.60E-4 Long-term Mortality cases/(year.person.μg/m3)Health impact

f(r,C(r,Q)) SO2

2.84E-6 Hospital Admissions Admissions

2.30E-6 Short-term Mortality

cases/(year.person.μg/m3)Health impact

Source: Rabl 2001

Plant

Trypilska Power Station

Trypilska Power Station, Emissions in 2006

564.363Carbon oxide

40 909.568Sulfur oxide and other sulfur compound

11 108.921Nitrogen compounds

21 951.11610 975.560

Total suspended particles (TSP): PM10

22.087Metals and their compounds

74 605.000Total

Emissions, tons/yearName of the pollutant

Technical characteristics of the Trypilska Power Station

413Released gas temperature, K

14Flow rate from the stack, m/s

9.6Diameter of the stack, m

700(because of hot air and gas

flow)

Effective release height, m

180Stack height, m

Value of the parametersParameters

Cities around the Trypilska power plant

SE32,25013,757KagarlykSE29,2508,447RzhyschivNE,ENE28,250107,950BoryspilN29,50086,839BrovaryNNW36,2502,611,327KyivNW37,50034,465VyshneveWNW37,50035,968BoyarkaW30,75039,722VasylkivWSW9,50032,776ObukhivSSW, SW42,50026,434Uzyn

Prevailed down wind direction

Down wind distance, km

Population, personsName of the city

Note: The down wind distances were measured from the Power Station to the centers of the cities

Concentration of the pollution(μg/m3)C(x,Q): Gaussian Plume Model

Q - h2

C = ---------------- exp [--------]21/2 π3/2 uxσz 2σz

2

Some hints for Excel

• =SQRT(3.14, 3)• =EXP(-h**2/2*Sigmaz**2)• =POWER(A1;2)• Q in micro gram/sec

Q - h2

C = ---------------- exp [--------]21/2 π3/2 uxσz 2σz

2

σz

Atmospheric Stability

DDDDC> 6DDDC-DC5-6EDCB-CB3-5FECBA-B2-3

BA-BA< 2

SlightModerateStrongHeavy Cloud

Thinly Overcast or clear sky

Incoming Solar RadiationNightDay

Surface Wind Speed (meter/second)

Weather observation

0900

2100

0900

0800

0700

0600Aug 8

2400

2300

2200

2100

2000

1900

1800Aug 7

Atmospheric Stability(A, B… .F)

Wind DirectionE, ESE, SSE, S…..

Wind Speed (meter/second)

Day

Unit cost Uv for Long-term Mortality

Value of 1 YOLL = v = constantv v v

Uv = v + ---- + ----- + ……+ -----1+r (1+r)2 (1+r)N

r = discount rate of one yearN = years of human life

Exposure-Response

• PM10 long-term mortality• 2,60 × 10 -4 μg/m3

• (Leksell & Rabl, 2001)

Examples of externality studies

Summary of Cost Estimates in mECU/kWh

Canada France GermanyPub. Occ. En

v.Gw.

Pub.

Occ.

Env. Gw.

Pub.

Occ.

Env. Gw.

Coal 2.3 nq 53 nq 0.5 29 8.4Lignite 10.5Oil 69 nq 0.7 16 16.5

Natural Gas

12 nq 0.1 8 3.0

Nuclear

0.01-0.05

2.5 0.07 0 0 3.8

Wind 0.2Hydro

Photo Voltaic

2.7

Pub. public impactsOcc. occupational impactsEnv. Environmental (buildings, crops, ecosystems,…), excluding global warmingGw. Global warmingnr not reportednq not quantified

Summaryof Cost Estimates in mECU/kWh (continued)Greece US Russia

Pub. Occ. Env. Gw.

Pub.

Occ.

Env.

Gw.

Pub.

Occ.

Env.

Gw.

Coal 0.521.1

nr

Lignite 20 0.30 0.66 38Oil 10 0.17 0.95 21 0.15

0.21nr

Natural Gas

2.4 0.17 0.66 5.8 0.011

nr

Nuclear

0.170.26

0.4 - 4

Wind 0.84 0.09 1.2 0.2Hydro 1.2 3.8 0 0.14Photo Voltaic

Pub. public impactsOcc. occupational impactsEnv. Environmental (buildings, crops, ecosystems,…), excluding global warmingGw. Global warmingnr not reportednq not quantified

Germany 1997Damages of fossil fuel cycles

Damages of nuclear fuel cycle

External costs of PV cycle

External costs of wind fuel cycle

Damages of biomass fuel cycle

Biomass 2

Biomass 3

External costs for electricity production in the EU (in EUR-cent per kWh) 2002

AUT: Austria, BE: Belgium, DE: Germany, DK: Denmark, ES: Spain ,FI: Finland, FR: France, GR: Greece, IE: Ireland, IT: Italy, NL: Norway, NO: Netherlands, PT: Portugal, SE: Sweden, UK: United Kingdom

Monetary values used for economic valuation

Results of the coal fuel cycle before NewExt [€-Cent/kWh]

Results of the oil fuel cycle before NewExt [€-Cent/kWh]

Results of the gas fuel cycle before NewExt [€-Cent/kWh]

• DENOX NOx removal system • FGD Flue Gas Desulfurization • SCR Selective catalytic reduction