Indoor air pollution from biomass fuels is considered as a potential environmental risk factor in developing countries
of the world. Exposure to these fuels have been associated to many respiratory and other ailments such as acute lower
respiratory infection, chronic obstructive pulmonary disease, asthma, lung cancer, cataract, adverse pregnancy
outcomes, etc. The use of biomass fuels is found to be nearly zero in the developed countries but widespread in the
developing countries including Nepal. Women and children are the most vulnerable group since they spend a lot of
time inside smoky kitchens with biomass fuel burning, inefficient stove and poor ventilation particularly in rural
households of Nepal. Measurements of indoor air pollution through monitoring equipment such as high volume
sampler, laser dust monitor, etc are expensive, thus not affordable and practicable to use them frequently. In this
context, it becomes imperative to use statistical models instead for predicting air pollution concentrations in household
kitchens. The present paper has attempted to contribute in this regard by developing some statistical models specifically
categorical regression models with optimal scaling for predicting indoor particulate air pollution and carbon monoxide
concentrations based upon a cross-sectional survey data of Nepalese households. The common factors found
significant for prediction are fuel type, ventilation situation and house types. The highest estimated levels are found
to be for those using solid biomass fuels with poor ventilation and Kachhi houses. The estimated PM10 and CO levels
are found to be 3024 µg/m3 and 24115 µg/m3 inside kitchen at cooking time which are 5.2 and 40.40 times higher than
the lowest predicted values for those using LPG / biogas and living in Pakki houses with improved ventilation,
respectively.