Author's Name: Ahmed F. Hassoon & Zainab M. Abbood
Subject Area: Science and Engineering
Subject Other
Section Research Paper


Atmospheric elements, CO concentration column, multiple leaner regressions, normality and Constant of variance test.


In this research we used the data of carbon monoxide column concentration , atmospheric elements , temperature at 2m (T), dew-point 2m (Td), wind speed 10m (U),total cloud cover (TCC) and relative humidity (RH%)(calculated from T and Td) , and other elements included boundary layer height (in meter)(BLH) , sensible heat flux (SSLHF) in units ( J.s/m2 ), from ( ECMWF) over Basra province and at grid point 30.375° latitude and 47.25° longitude. We concentrated on the relationship between these atmospheric elements and the CO column concentration by used hourly data recorded every 3 hour at 00,03,06,09,12,15,18,21 ,in four months ( Jan, April, July, Oct.) from 2012. Hourly analysis of Maximum and minimum data contour line CO is plotted graphically over Iraq map by satellite image, maximum values of CO is at July month 1.16*10-3 kg/m2 , while lowest minimum data recorded at January 6.95*10-5 kg/m2 . The statically simple linear regression and multiple linear regression is reported , in simple linear regression the effect of these element on CO is don?t clear specifically at months Jan and Oct. , while in the April and July the correlation is significant specifically at the element Td , RH% , SSLHF at April and T , U , TCC, BLH SSLHF at July. Where the simple correlation coefficient have values 0.512, 0.468, 0.507, 0.482 and 0.44 respectively. Multiple linear regression used several tests method such as normality, constant variance test and power of performed to examine the correlation between dependent variable and independent variable ( atmospheric elements) , all of these methods is based on the probity values P , Overall correlation in multiple linear regressions between dependent variable (CO concentration column) and independent variable became better because we take completed effected integrated, thus R became 0.55, 0.688, 0.789 and 0.756 for months Jan, April ,July and Oct. respectively

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