1. 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

2. 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

3. 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)

4. 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

5. Development indicators entered in the prediction model Solid Fuel Use (Dependent Variable)Gini coefficient Adult Female IlliteracyGNP per capita Average annual growth rateln(GNP per capita) Dummy variables for all WHO regions Petroleum Use per capita Electricity Consumption, per capitaLn (Petroleum Use per capita) Fuelwood production per capitaRural Population (Percent) Fuelwood ProductionTraditional Fuel Use (National) Population

6. Final Fuel Prediction Model Parameters: Model Summary: –R: 0.8637 –R 2 : 0.7460 –Adjusted R 2 : 0.7244 –Standard Error of the Estimate: 0.1891 Model meets assumptions of normalcy, constant variance. Collinearity and Tolerance also assessed. ln GNP/cap Percent Rural Petroleum use/cap Eastern Mediter.

7. CRA-2000 (Smith et al, 2004) ~500 million stoves

8. Exposure to IAP from Solid Fuel Use: Solid fuels and Poor Ventilation / Inefficient stoves Scaling Up to the Regional Level

9. 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 0.2 China national improved stove program (NISP) since 1983 ventilation factor set to 0.5 for adult, 0.25 for children All other countries (45% of world population) Ventilation factor set to 1.0

10. From Solid Fuel Use to IAP Exposures

11. 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

12. 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

13. 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

14. Fuel Use Questions on World Health Surveys

15. 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

16. 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)!

17. 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.

18. Questionnaire example: current MICS core questionnaire HC6. What type of fuel does your household mainly use for cooking? Electricity Liquid Propane Gas (LPG) Natural gas Biogas Kerosene Coal / Lignite Charcoal Wood Straw/shrubs/grass Animal dung Agricultural crop residue Other (specify) HC7. In this household, is food cooked on an open fire, an open stove or a closed stove? Probe for type. Open fire Open stove Closed stove Other (specify) HC7a. Does the fire/stove have a chimney or a hood? Yes No HC8. Is the cooking usually done in the house, in a separate building, or outdoors? In the house In a separate building Outdoors Other (specify)

19. 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.

20. Other National Sources Census surveys National household surveys National sample surveys, e.g., India

21. Total Pop % covered Million Missing AsiaCentral7739%47 East135098%27 South146098%29 SE574100%0 Caribbean3945%21 EuropeCentral12049%61 East21198%4 Latin AmericaAndean50100%0 Central21986%31 Tropical193100%0 N Africa/Mideast41547%220 Oceania968%3 SubSah AfricaCentral8375%21 East31598%6 South27324%207 West29098%6 88% Coverage of Nationally Representative Household Surveys With Questions on Fuel Use Covered: 4994 million Missing: 684 million [still looking] Low and Middle-Income

22. 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.)

23. 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 WHOSample draft output from WHO

24. 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

25. PM 2.5 concentrations for 2000 computed with GAINS/TM5 Population-weighted annual mean concentrations (µg/m 3 ) PM 2.5 from anthropogenic primary PM emissions and secondary inorganic aerosols. Natural sources are excluded! Source: Markus Amman, IIASA

26. No forest fires Total 6600 gigagrams in 2000 BC Campaign Data

27. Household Fuel Use Basic Demographic Information Indoor Air Pollution Stove, kitchen, ventilation Socio-economic Development Expenditures, income, Outdoor Emissions (climate and health) Emission Factors BC, PM, etc Groups Interested in Good Household Fuel Databases

28. 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

29. Daily average concentrations of PM10 in Andhra Pradesh Simple average 485 Concentrations in 3 districts Nizamabad ( 493) Rangareddy (378) Warangal (514) Kitchen types Wood / Wood chipsAgri residuesDung With partition Concentration in Kitchen area 666.12474.00157.00 Concentration in Living area 602.48312.40115.00 Without partition Concentration in Kitchen area 656.17563.29596.38 Concentration in Living area 385.82196.29345.00 Separate outdoor Concentration in Kitchen area 548.48596.40305.83 Concentration in Living area 295.04324.00255.33 Open cooking Concentration in Kitchen area 303.61209.50312.00 Concentration in Living area 192.61346.00980.00 Weighted by proportion of households in each district by fuel and kitchen type and extrapolated to other districts 534

30. 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

31. [to framing slides]