This study was undertaken with a view to developing a methodology to design a model for predicting quantitative exposures to indoor air pollution (IAP) from qualitative information on fuel use and housing characteristics to construct an exposure atlas which can be applied in a larger spatial context in the future.

• Preparation of inventory of existing data sources with information related to household exposures to IAP from the use of solid fuels for cooking and/or heating.
• Conduct of household level surveys to collect qualitative data in approximately 1450 households on housing characteristics, demographic, socio-economic parameters, indicators of IAP such as fuel type, stove type and ventilation conditions etc.
• Collection of quantitative information on IAP by monitoring each household in the same districts for Respirable Particulate Matter (RPM).
• Development of a model equation based on data available from the current study which will be used to build an exposure atlas to predict exposures to IAP in population using data on a partial set of variables available from national level surveys.

           A three-stage cluster-sampling scheme was followed. A mix of random sampling for selection of households for survey and purposive sampling for monitoring activity was adapted. A questionnaire-based household survey was conducted in around 1450 households in the 3 selected districts of Telangana region - Ranga Reddy, Warangal and Nizamabad. The questionnaire captured information on topics related to housing characteristics, demographic parameters, fuel usage, cooking and time activity patterns, and personal habits, all of which are important determinants of exposures to IAP levels. Monitoring activity was conducted in 420 households in the 3 districts by Sri Ramchandra Medical College and Research Institute (SRMC & RI), Chennai, to collect quantitative information on respirable suspended particulate matter (RSPM). The center for occupational & environmental health (COEH), University of Berkeley, California, developed a model for estimating exposures to IAP using data obtained as a result of survey and monitoring activities.

           Socio-economic Characterstics: Majority of the villagers are either small or marginal farmers and up to 20% are landless. Prevalence of education is low in both the samples. Approximately 23% of the households have not even completed one year of schooling, while 49 % and 44 % of the households in monitored and non-monitored hhs had five years of schooling as the highest education level. 31 % and 36% of the households owned radio and TV respectively.

           Housing and Kitchen Characteristics: Housing characterstics in both monitored and non-monitored households are remarkably similar. In about 48% of the households, roofs are made of tiles and slates, 30% of hhs had leaves, bamboo and thatched roofs. Most of the households had walls made of mud/ dirt (70-77%). A much larger proportion of households cook their food in the open air in monitored and non- monitored households. In contrast, the number of households with indoor kitchen without partitions was smaller (6% in non-monitored versus 25% in monitored). 27.5 - 29% of the households had indoor kitchen with partitions in both monitored and non-monitored households.

           Fuel-use pattern: Biomass fuel use was prevelent in all rural households of the three study districts in A.P. Majority of the households use wood for cooking (72-81%). Mixed fuel usage was 9%, whereas prevalence of clean fuel usage was 12%. Majority of the households were found to be traditional stove users cooking on 3-stone stoves plastered with mud (56% in monitored and 75% in non-monitored). The use of traditional stoves with chimneys is 10.5% in monitored against 8.5% in non-monitored hhs.Usage of improved stoves is negligible.

          With regards to the monitoring of RSPM concentrations, households using mixed fuels have the highest concentrations, mean 24-hr kitchen concentrations in mixed fuel using households is nearly three and half times higher than that of kerosene using households (732 mg/m³ vs. 203 mg/m³). LPG users have the lowest concentrations in both kitchen and living areas. Households with poor kitchen ventilation had more than a two-fold risk of having high kitchen concentrations compared to households with good ventilation.

           The IAPEA study was designed to explore the possibilities of arriving at a simple methodology by which qualitative information on fuel use and housing characteristics could be used to predict quantitative exposures to IAP in rural households of Andhra Pradesh. This was also the first attempt at 24-hour monitoring in exposure surveys in India. Before undertaking this survey, national level surveys were reviewed which revealed that certain household characteristics are not well defined. Hence an attempt was made to collect information on these variables systematically through the current survey. They could also influence the design of large-scale survey instruments, such as the Census or National Sample Survey, by way of introducing questions on the key determinants of exposure with a view to facilitating classification of population sub-groups spatially into exposure sub-categories. This would help in better tailoring and targeting mitigation measures through projects or programs right from the design stage. Hence, the next step needed to facilitate interventions is to develop greater confidence in the key determinants of exposure to indoor air pollution from biomass fuels, and use that to develop robust models that can facilitate exposure classification of population sub-groups.