Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model

Al-Mustansiriya University, College of Basic Education, Department of Geography, Baghdad, Iraq

^* Corresponding author: ghufraaneng2017@uomustansiriyah.edu.iq

Abstract

Particulate matter (PM2.5) concentrations are a serious concern for human health. In this study, a multinomial model was proposed to predict PM2.5 concentrations, and the relationship between PM2.5, PM10, and atmospheric parameters was studied. The study was conducted in northern Iraq, including Kirkuk Governorate. Data were collected from different sources and two datasets collected in February 2020 and July 2022 were used. The model was applied to the study area within Kirkuk Governorate: Based on the July 2022 dataset, the average value of the coefficient of determination R² within Kirkuk Governorate was estimated to be 0.97; Based on the February 2020 dataset, the average value of R² within Kirkuk Governorate was estimated to be 0.98, and the prediction accuracy was 82% for July and 96% for February. Moreover, the health impacts and air quality in the area were found to range from moderate to unhealthy. The aim of this study was to develop a multinomial model to predict PM2.5 concentrations using PM10, humidity, temperature and wind speed as independent variables. The results showed a high correlation coefficient (R² = 0.98) between predicted and measured PM2.5 concentrations. The outputs of the (IDW) model indicate that the predicted PM2.5 concentration ranges between (35.92-47.65) μg/m3, which is an unhealthy air quality for sensitive groups in Kirkuk Governorate. The results of the study highlighted the impact of industrial areas and recommended monitoring and reducing exposure to particulate matter and pollutants emitted from factories.