Related Experiment Video
Updated: Aug 3, 2025

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level
Published on: June 6, 2018
Estimating atmospheric radon deviation using statistical coefficients: Sulaymaniyah city, Iraq, as a case of study
Adil M Hussein1, Kamal O Abdullah1, Aziz H Fattah1
1Physics Department, College of Science, University of Sulaimani, Ministry of High Education, Sulaymaniyah city, Kurdistan Region, Iraq.
Abstract:
The authors studied the atmospheric radon concentration with associated meteorological parameters variation during the dust events from July to November 2017. We obtained the meteorological parameters data in weather station of Sulaymaniyah city, Iraq. In the environmental monitoring plan, the atmospheric radon fluctuated from 15 to 48 Bq m-3 around the mean value of 31.5 ± 7 Bq m-3 within the summer. In autumn, varied from 22 to 46 Bq m-3 with a mean value of 34 ± 12 Bq m-3. We employed this to determine the radon level anomalously. Using the modified statistical coefficients, such as the residual deviation (RD), residual fluctuation ratio (RFR), F-test, and p-value coefficients. Among the atmospheric radon fluctuation values, particularly one anomalous (42 Bq m-3) on 25 July was determined because the excessive value of the RD was 1.9 σ, and the RFR value was 66 %. Corresponding to our coefficients criteria, the minimum level of atmospheric radon (22 Bq m-3) does not consider anomalous because of increasing wind speed. Based on this, our method for determining the atmospheric radon anomalies that are influenced by the missed factors beyond the mentioned meteorological parameters is accurate.
More Related Videos
06:11Employing the Forced Oscillation Technique for the Assessment of Respiratory Mechanics in Adults
Published on: February 9, 2022
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
Related Concept Videos
Measurement of Air Content in Concrete
The pressure method,...
Statistical Methods for Analyzing Epidemiological Data
Estimating Population Standard Deviation
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Calculating and Interpreting the Linear Correlation Coefficient
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...