Reply to the Discussion Started for the Article Titled as “Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression (Journal of Natural Hazards and Environment, January 2019, 5(1):1-10)”
In this study, the author gave the following information in response to the discussion initiated for the article titled “Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression”. In the writing process of the article, the author only obtained the import...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Artvin Coruh University
2019-07-01
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| Series: | Doğal Afetler ve Çevre Dergisi |
| Subjects: | |
| Online Access: | http://dacd.artvin.edu.tr/tr/download/article-file/776171 |
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| Summary: | In this study, the author gave the following information in response to the discussion initiated for the article titled “Bitlis Air Pollution Emission Inventory and Estimation of Health Effects by Multiple Linear Regression”. In the writing process of the article, the author only obtained the imported coal data and calculated the regression analysis according to the data obtained. SOx, NOx, CO and PM10 emission inventories and regression analysis were recalculated from the pollutant parameters. In the article, primary pollutants are determined by calculation, not by measurement. According to the data obtained from the Bitlis Environmental Status Report, the total amount of imported coal in Bitlis province in 2015 is 55118 tons and the Domestic Coal (Social Assistance Foundation) is 15000 tons and the total coal amount is 70.118 tons. In 2015, SOx, NOx, PM10 and CO parameters of primary pollutants in air pollution from domestic coal were calculated to be discharged to the atmosphere at 538 kg, 48 kg, 57 kg and 601 kg, respectively. Number of patients who applied to hospitals due to respiratory and lung diseases obtained from Bitlis Health Provincial Directorate of 2015 were determined. According to these data, classic multiple linear regression method was used to estimate the number of patients in the next year. The real data and the estimation data were compared again to ensure that R2 = 0.81. |
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| ISSN: | 2528-9640 2528-9640 |