Assessing the impact of Kirkuk Cement Plant emissions on land cover by modelling Gaussian Plume with Python and QGIS - Mahmood Ajaj, Qayssar; Shafri, Helmi Zulhaidi Mohd; Wayayok, Aimrun; Firuz Ramli, Mohammad (Elsevier, 2023)
This research uses the Python language to model the Gaussian Plume equation in Quantum Geographic Information System (QGIS) to estimate the contaminants released from the cement plant. Spline interpolation and the maximum likelihood (ML) classification process are used to extract wind speeds and land cover classes. The primary and secondary directions were weighed in perspective of their exposure to cement plant emissions in all seasons of 2020 using an Analytic Hierarchy Process (AHP). The values of wind speeds of all seasons were between 3.07 and 4.35 (m/s). Sand (barren land) is the most common land category with 75.75 of the studied area. Water has the lowest amount, accounting for only 4.67 study area. Approximately 13.35 area was covered by vegetation. Finally, the urban class, which compose 7.97 of the sample area. The overall accuracy and Kappa coefficient of ML were 98.2143 and 0.9736 respectively. The outcomes of risk pollution are classified into four classes: very high, high medium, and low. Very high risk pollution records the highest value from 52.428 to 1264.332 lg/m3 in spring season and lowest value ranged between 0 and 0.017 lg/m3 for winter season 2020. The most polluted urban areas were 8.573 km2 in the summer. Plantation areas with the highest levels of pollution were 5 km2 in the summer. Summer contaminated sand areas were 60.974 km2 . Water body contaminated areas were 2.667 km2 in the summer. The created tool identifies the contaminants emitted from the cement plant in high-resolution distribution pattern.