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.Key words: Biomass fuel; Categorical regression; Indoor air pollution; Optimal scaling; Respiratory ailmentsDOI: 10.3126/njst.v10i0.2962Nepal Journal of Science and Technology Vol. 10, 2009 Page: 205-211
Population‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models
