Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques

Abstract

Particulate matter (PM2.5) concentrations are a serious human health concern and global models are the common methods for PM2.5 particle estimation disregarding the local changes and factors. In this study, a polynomial model for PM2.5 particles prediction was proposed to examine the correlations among PM2.5, PM10, and meteorological parameters. The study was carried out in the north of Iraq including two provinces; Kirkuk and Sulaymaniyah. The data gathered from different sources. Two datasets have been used, collected during July 2019 and February 2020. To test our methodology, the model was applied on a small subset of the study area (5.6 km2) inside the Kirkuk province. Datasets (observation and ground truth) were utilized to examine the model. Based on the July 2019 dataset, the mean local R2 values were estimated at 0.98 and 0.97 in the north part of Iraq, and inside the Kirkuk province (the small subset), respectively. While based on the February 2020 dataset, the mean local R2 values were estimated at 0.98 inside the Kirkuk province. High values of prediction accuracies were obtained by 82% and 96% in July and February, respectively. Moreover, our findings highlighted that the health impacts and air quality varied from moderate to unhealthy in the region.

Keywords

• pm2.5
• pm10
• polynomial model
• meteorological parameters
• health impact