Study of air contamination in Iraq using remotely sensed Data and GIS

Abstract

This study is an effort to spatially assess the air quality in Iraq. A statistical model was applied to predict air quality and determine the relationships of air pollutants with the Air Quality Index (AQI) in 2020. Remotely sensed and ground station data were used in this study. Three methods have been applied; spatial interpolation, Least Square (LS) statistical method, and Geographically Weighted Regression (GWR) model. Analysis revealed that air quality was unhealthy in Iraq. The northern part showed moderate air quality whereas contamination levels were high in most of the southern part of Iraq. Predictive (LS), and (GWR) models were developed. The resulting correlation coefficient R2 was equal to 0.88 and 0.85 for LS and GWR respectively. Accuracy evaluations were 95% and 94%. The results of the study have the potential to assist the related government departments and disaster management authorities in decision-making and urban air contamination risk reduction.

Keywords

• air quality index
• least square
• geographically weighted regression
• remotely sensed data
• spatial interpolation