It has been widely accepted that human influenced emission of green house gases are largely responsible for the climate change which we are experiencing currently. To produce detail information as a guide line for long term future climate change evolution arising from global warming, a projection approach is generally adopted to study the changes in regional climate. Regional climate modeling, as a part of down scaling techniques, to enhance the regional details due to geographical forcing is a very useful technique for over coming insufficient aspects of the GCMs such as narrow mountain ridges, complex terrain and complicated land sea contrasts. Several studies have mentioned that over the mountainous regions, the nested approach with a high resolution regional climate could improve simulation of physio-graphically relevant fields. By increasing resolution, the local effects play a significant role in the regions characterized by complex physio-graphics and are better resolved and more realistically simulated. This book comprises of three chapters. Detail descriptions and findings are given in chapters.

The study was carried out in Chiraigaon Community Development Block of Varanasi District for a period of two years.The study area is located in the state of Uttar Pradesh in India. A Community based cross sectional research design was adopted for this work. Sample size estimation was based on prevalence of at least one risk factor at 90%; with permissible error of 5%. In all two hundred and one subjects were included in this study. The study subjects were drawn following a standard statistical procedure. The primary tool in the study was modified proforma based on WHO- STEP wise approach. The other tools in the study were weighing machine, steel anthropometry rod, measuring tape and sphygmomanometer. Prevalence and pattern of risk factor were assessed by interviewing the subjects and subjecting them to anthropometric assessment and biochemical investigation. Weight, height, Waist circumference and hip circumference recording were done following standard technique. In order to pinpoint correlates of risk factors of NCDs, univariate and logistic regression analysis was done. An attempt was made to identify attributes of subjects at higher risk of non communicable diseases.