A Regional Investigation of Inverse Distance Weighting Particulate Matter Prediction within Kirkuk City, Iraq

Abstract

It is well known that air polluted with particulate matter (PM) has a negative impact on human health. It is important to monitor and evaluate air quality by revealing the nature of the air and identifying the areas affected by particles. The methods and tools used for this purpose vary. This study aims to predict air quality based on PM data collected using an air pollution measuring device to measure the values of particulate matter (PM) in different sizes. The Inverse Distance Weighted (IDW) approach was used within the Geographic Information Systems (GIS) analysis tools. The tool was applied to measurements collected in Kirkuk city's study area for 2022. Besides, testing PM2.5 data has been collected in 2025 for the validation process. The results showed that there are higher rates of PM than the acceptable standards, which therefore cause health effects. The accuracy value of the prediction data was also calculated for each of the PM1, PM2.5, PM5, and PM10 concentrations. Model validation accuracy results were 80%, 89%, 84%, and 72%, respectively. While cross validation resulted in 82%. The results indicated a good fit for the prediction determined by the analysis. Moreover, the health risks have also been detected from the spatial distribution of each pollutant. Based on our analysis and results, good, moderate, and unhealthy air was detected in the study area.

Keywords: Particulate Matter, IDW, GIS, air quality, health impact