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Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression

Journal: Dogal Afetler ve Cevre Dergisi (Vol.5, No. 1)

Publication Date:

Authors : ;

Page : 1-10

Keywords : Air Pollution; Calculation of Emission Inventory; Healthy Effects of Pollutants; Multiple Linear Regression Analysis;

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The province of Bitlis has a terrestrial climate due to its topographic structure and its geographical location. In Bitlis, which is one of the lowest temperature averages in our country, winters are quite cold and long. Due to the long cold winter and the heavy weather conditions, the highest air pollution rates are observed during the winter months because of unconscious and excessive consumption of the coal. The pollutant concentrations caused by these fuels used in the winter months do not exceed the air quality limit values but they affect the air quality of Bitlis province negatively by the effect of inversion Those pollutants such as gas or dust that cause air pollution damage the respiratory system. The effects of air pollution on human health vary according to the duration of exposure and the amount of pollutants. In recent years, a number of studies have been conducted on the short-term and long-term effects of air pollution and it has been observed that the disease and mortality rates are increasing due to the cardiovascular system. According to Bitlis Provincial Directorate of Environment and Urban Planning, the total amount of coal entering Bitlis province in 2015 is 55.118,63 kg. In 2015, the SOx, NOx, PM10 and CO parameters from the primary pollutants in the air pollution were calculated to be 1.980 kg, 176 kg, 210 kg and 2.211 kg respectively. In this study, SOx, NOx, CO and PM10 measurement values of pollutant parameters were utilized. The number of patients who applied to hospitals from respiratory and lung diseases was taken from Bitlis Health Directorate. According to this data, the number of patients in the next year was estimated by using multiple linear regression method. The actual data and forecast data were achieved by comparing R2 = 0.98.

Last modified: 2019-08-26 15:02:57