Progress on Exposure Assessment

• Estimating exposures in CRA 2000• Estimating exposures to IAP in CRA 2005

– Addressing limitations of 2000 round estimates– Progress on data availability – household energy– Improving ventilation factor: India example– Potential use of concentration-response data from ambient air

pollution epidemiology: GAINS model results

Health Effects

• Relative risk estimates from epidemiologic studies based on crude exposure classifications of exposure (whether solid fuels used for cooking or heating)– Similar method used in most studies of ETS health effects

• Why not use actual pollutant exposures, as done in outdoor air pollution?– Limited information: indoor concentrations in solid fuel using

households and non-solid fuel using households – Difficulty of extrapolating from developed countries exp-resp

studies outdoors

Estimating Exposures to IAP for Burden of

Disease Estimation

Exposed: – Households using solid fuels (wood, dung,

agricultural residues, coal, charcoal) as primary source of energy for cooking

• Unexposed: – Households not using solid fuels as primary

cooking fuel (counterfactual level)

Estimating Household Fuel Use - 2000 CRA

• Household Fuels Database– 52 countries in 10 WHO regions– Sources of data include: national population and housing censuses,

household energy surveys, FAO– Estimates for each region are population weighted averages of available

data• Filling in the Gaps: Fuel Prediction Model

– Stepwise linear regression– Model parameters: stable over a several year period, available on a

national level for most countries across most regions, routinely updated, from a reliable source

• Model assumption: as countries develop, people gradually shift up an energy ladder from solid fuels to cleaner fuels and/or cleaner technologies– Brazil (1999 GNP per capita of $4420) richest country in the database

with significant percentage of household solid fuel use. – All countries with 1999 GNP per capita > $5000 assumed to have made

complete transition to clean household cooking systems

Development indicators entered in the

prediction model

Solid Fuel Use (Dependent Variable) Gini coefficient

Adult Female Illiteracy GNP per capita

Average annual growth rate ln(GNP per capita)

Dummy variables for all WHO regions Petroleum Use per capita

Electricity Consumption, per capita Ln (Petroleum Use per capita)

Fuelwood production per capita Rural Population (Percent)

Fuelwood Production Traditional Fuel Use (National)

Population

Final Fuel Prediction Model

• Parameters:

• Model Summary:– R: 0.8637– R2: 0.7460– Adjusted R2: 0.7244– Standard Error of the Estimate: 0.1891

• Model meets assumptions of normalcy, constant variance.• Collinearity and Tolerance also assessed.

StandardizedCoefficients

95% ConfidenceInterval for B

Beta t Sig. Lower Upper(Constant) 3.1926 0.0025 0.4135 1.8223RURAL 0.3527 3.0938 0.0033 0.2312 1.0908EMR -0.2838 -3.4968 0.0010 -0.3904 -0.1053LNGNP -0.2646 -2.5648 0.0136 -0.1852 -0.0224per capita PetroleumUse -0.2244 -2.5454 0.0143 -0.0006 -0.0001

ln GNP/cap Percent Rural Petroleum use/cap Eastern Mediter.

CRA-2000 (Smith et al, 2004)

~500 million stoves

Exposure to IAP from Solid Fuel Use:

Solid fuels and Poor Ventilation / Inefficient stoves

Scaling Up to the Regional Level

Assigning ventilation factors

Ventilation is a function of climate and development.Formerly Socialist Economies of Europe and Soviet Union• long history of household solid fuel use, under cold

climatic conditions and relatively high standards of living

• ventilation factor set to 0.2China• national improved stove program (NISP) since 1983 • ventilation factor set to 0.5 for adult, 0.25 for childrenAll other countries (45% of world population)• Ventilation factor set to 1.0

From Solid Fuel Use to IAP Exposures

AFR ESEAR D

WPR B

AFR D

SEAR B

EMR DAMR D

AMR BEUR C

EMR B

AMR A EUR A WPR A

EUR B

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Household Solid Fuel Use

Exposure Equivalent

Addressing Limitations in 2005 Round

• Uncertainty in solid fuel use model– Enough data available in nearly all regions to avoid

modeling (~100 countries)– Urban vs. rural estimates of household energy use

now available

• Rough adjustment for ventilation can be improved– Quantitative data on cooking location, stove type and

pollution level in India– Perhaps can be used to develop exposure estimates

based on household survey parameters

Increase in Access to Routinely Available Data

• Systematic inclusion of questions on household energy, cooking practices in WHS, LSMS, DHS, and MICS core questionnaires

• In progress: development of comprehensive database on solid fuel use

World Health Survey(World Health Organization)

• National in scope with target sample size of 5,000– Probability sample (generally multi-stage cluster sampling)

• Detailed questions on household energy:– Main fuel used for cooking– Open fire or stove, presence of chimney/hood, closed stove

with chimney– Location of cooking – living area, separate kitchen, outdoors– Main fuel used for heating– Type of heating stove

• Available for download from WHO website on country-by-country basis

• Detailed questions on household energy included in 2003 data from 51 countries available

Fuel UseQuestions

on World Health

Surveys

Living Standard Measurement Study(World Bank)

• National in scope with samples typically between 3,000 and 10,000

• Sometimes multiple years but not the same HHs• Detailed questions on income and expenditures• Energy questions:

– What is main fuel used for cooking (pick one)– Expenditures on LPG and kerosene– No questions on fuel collection time– May have question on stove chimney and stove location

(Main cooking stove)• Available for download from World Bank website, but

no table queries. Must work with raw data.• Extensive country coverage

Demographic and Health Surveys(USAID implemented by MACRO Intl)

• National in scope with samples typically between 3,000 and 10,000

• Sometimes multiple years (but same HHs)• No questions in income and expenditures, but

detailed questions on household assets and amenities• Energy Questions virtually the same as LSMS

surveys. • Available for download from website; some

information is available from a query format and also raw data is available

• Extensive country coverage: results of > 200 surveys now available (multiple years in some countries)!

Multiple Indicator Cluster Survey (MICS) - UNICEF

• MICS programme assists countries in filling data gaps for monitoring the situation of children and women through statistically sound, internationally comparable estimates of socioeconomic and health indicators. The household survey programme is the largest source of statistical information on children.

Questionnaire example: current MICS core questionnaire

HC6. What type of fuel does your household mainly use for cooking?

ElectricityLiquid Propane Gas (LPG)Natural gasBiogasKeroseneCoal / LigniteCharcoalWood Straw/shrubs/grassAnimal dungAgricultural crop residueOther (specify)

HC7. In this household, is food cooked on an open fire, an open stove or a closed stove?

Probe for type.

Open fireOpen stoveClosed stoveOther (specify)

HC7a. Does the fire/stove have a chimney or a hood?

YesNo

HC8. Is the cooking usually done in the house, in a separate building, or outdoors?

In the houseIn a separate buildingOutdoorsOther (specify)

Specialized Energy Surveys• Regional scope with samples typically between 3,000 and

6,000• Two with multiple years and the same HHs (Bangladesh and

Vietnam)• Detailed questions on income and/or expenditures• Energy questions on all fuels for all purposes:

– What source of energy does HH use (pick Mulitple)– Always questions on energy expenditures and collection time– Many times questions on energy use– May have question on stove chimney and stove location

• Not available for download from World Bank website, and government permission is required for use unless part of World Bank work.

• Limited Country Coverage: Recent countries are Bangladesh, Peru, Vietnam.

Other National Sources

• Census surveys

• National household surveys

• National sample surveys, e.g., India

Total Pop

% covered

Million Missing

Asia Central 77 39% 47

East 1350 98% 27

South 1460 98% 29

SE 574 100% 0

Caribbean 39 45% 21

Europe Central 120 49% 61

East 211 98% 4

Latin America Andean 50 100% 0

Central 219 86% 31

Tropical 193 100% 0

N Africa/Mideast 415 47% 220

Oceania 9 68% 3

SubSah Africa Central 83 75% 21

East 315 98% 6

South 273 24% 207

West 290 98% 6

88% Coverageof

NationallyRepresentative

HouseholdSurveys

WithQuestions on

Fuel Use

Covered: 4994 million

Missing: 684 million

[still looking]

Low and Middle-Income

In progress: development of comprehensive database on solid fuel use estimates

• To provide comprehensive information on household fuel use and cooking practices on a country-by-country basis

• To enable flexible data input from various sources (e.g. household surveys, national censuses)

• To automate flexible data output (e.g. country profiles, solid fuel use on a country-by-country or regional basis)

• Coordinate with other groups interested in household fuel data (IEA, IBRD, WHO, IIASA, etc.)

Database input

cleaner fuels

biomass fuels

solid fuels

Open/closed stove

Presence of chimney/hood

Cooking location

Fuel use disaggregated by maternal education

and household wealth quintiles

Sample draft output from WHO

Estimating contribution of indoor sources to outdoor concentrations

• GAINS model (IIASA)– Used as input to ambient air pollution

concentrations– Provides estimates of anthropogenic

contributions to ambient concentrations– Will enable estimation of indoor ‘share’ of

ambient concentrations, and application of concentration-response functions from ambient air epidemiology

PM2.5 concentrations for 2000 computed with GAINS/TM5Population-weighted annual mean concentrations (µg/m3)

PM2.5 from anthropogenic

primary PM emissions and secondary inorganic aerosols.

Natural sources are excluded!Source: Markus Amman, IIASA

Global Black Carbon Emissions

Household Fuels , 55.9%

Off-road Transport,

7.0%

Power, 0.2%

Ag Burning, 5.5%

Industry, 12.1%

On-road Transport,

19.6%No forest fires Total 6600 gigagrams

in 2000 BC Campaign Data

Household Fuel Use

Basic DemographicInformation

Indoor AirPollution

Stove, kitchen,ventilation

Socio-economicDevelopment

Expenditures,income,

OutdoorEmissions

(climate and health)

Emission FactorsBC, PM, etc

Groups Interested in Good

Household Fuel

Databases

Improving the Ventilation Coefficient: India Example

• 2 – 4 fold difference in 24 hour concentrations between states what ventilation factor / IAP concentrations (using 24 hour average PM10 as an indicator pollutant) should be assigned to solid fuel users in India?

• Limited data on improved stove users• Large-scale studies at state level allows district

level mapping of concentrations by fuel type and kitchen type

• If measurements are available across several states (at least one in each agro-climatic zone), national level exposure estimates can be reliably reconstructed without the need for a measurement in each state

Daily average concentrations of PM10 in Andhra Pradesh

Simple average485

Concentrations in 3 districtsNizamabad ( 493)Rangareddy (378)

Warangal (514)

Kitchen typesWood / Wood

chips Agri residues Dung

With partition

Concentration in Kitchen area

666.12 474.00 157.00

Concentration in Living area

602.48 312.40 115.00

Without partition

Concentration in Kitchen area

656.17 563.29 596.38

Concentration in Living area

385.82 196.29 345.00

Separate outdoor

Concentration in Kitchen area

548.48 596.40 305.83

Concentration in Living area

295.04 324.00 255.33

Open cooking

Concentration in Kitchen area

303.61 209.50 312.00

Concentration in Living area

192.61 346.00 980.00

Weighted by proportionof households

in each district by fuel and kitchen type and extrapolated to

other districts534

Work in progress

• Update of global HH fuel database to include full set of survey results available

• Refining ventilation coefficient– Update of global database of stoves, cooking

locations– Map known concentrations to indirect measures of

exposure in all areas where measurements are available

• Estimating contribution of indoor sources to outdoor concentrations– Validation of GAINS model results (used in ambient

air pollution CRA) with known concentration data

[to framing slides]