Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS
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 lan...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Egyptian Journal of Remote Sensing and Space Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110982322001132 |
| _version_ | 1797854532966809600 |
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| author | Qayssar Mahmood Ajaj Helmi Zulhaidi Mohd Shafri Aimrun Wayayok Mohammad Firuz Ramli |
| author_facet | Qayssar Mahmood Ajaj Helmi Zulhaidi Mohd Shafri Aimrun Wayayok Mohammad Firuz Ramli |
| author_sort | Qayssar Mahmood Ajaj |
| collection | DOAJ |
| description | 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 µg/m3 in spring season and lowest value ranged between 0 and 0.017 µg/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. |
| first_indexed | 2024-04-09T20:08:41Z |
| format | Article |
| id | doaj.art-64dfcc29a6d741849542131dfaa75686 |
| institution | Directory Open Access Journal |
| issn | 1110-9823 |
| language | English |
| last_indexed | 2024-04-09T20:08:41Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Journal of Remote Sensing and Space Sciences |
| spelling | doaj.art-64dfcc29a6d741849542131dfaa756862023-04-01T08:45:45ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232023-02-01261116Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGISQayssar Mahmood Ajaj0Helmi Zulhaidi Mohd Shafri1Aimrun Wayayok2Mohammad Firuz Ramli3Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia; Department of Surveying Techniques Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk 36001, IraqDepartment of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia; Corresponding author.Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Environmental Sciences, Universiti Putra Malaysia, Serdang 43400, MalaysiaThis 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 µg/m3 in spring season and lowest value ranged between 0 and 0.017 µg/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.http://www.sciencedirect.com/science/article/pii/S1110982322001132Air PollutionEnvironmental riskQGISLand coverWind speed |
| spellingShingle | Qayssar Mahmood Ajaj Helmi Zulhaidi Mohd Shafri Aimrun Wayayok Mohammad Firuz Ramli Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS Egyptian Journal of Remote Sensing and Space Sciences Air Pollution Environmental risk QGIS Land cover Wind speed |
| title | Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS |
| title_full | Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS |
| title_fullStr | Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS |
| title_full_unstemmed | Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS |
| title_short | Assessing the Impact of Kirkuk Cement Plant Emissions on Land cover by Modelling Gaussian Plume with Python and QGIS |
| title_sort | assessing the impact of kirkuk cement plant emissions on land cover by modelling gaussian plume with python and qgis |
| topic | Air Pollution Environmental risk QGIS Land cover Wind speed |
| url | http://www.sciencedirect.com/science/article/pii/S1110982322001132 |
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