Estimating Chemical Concentrations of Dust PM2.5 in Iraq: A Climatic Perspective Using Polynomial Model and Remote Sensing Technology

Authors

  • Huda Jaml Jumaah

    Environment and Pollution Engineering Department, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
  • Maha Adnan Dawood

    Department of Fuel and Energy Engineering, College of Oil and Gas Techniques Engineering Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
  • Shakeel Mahmood

    Department of Geography, GC University Lahore, Lahore, Punjab, 54000, Pakistan

DOI:

https://doi.org/10.30564/jasr.v7i3.6381
Received: 22 April 2024; Revised: 26 June 2024; Accepted: 30 June 2024; Published Online: 3 July 2024

Abstract

Air pollution and climate change are interrelated issues, with air pollution levels in Iraq currently exceeding World Health Organization standards. This study aimed to evaluate air quality in Iraq by utilizing climatic data, such as temperature, humidity, and gaseous pollutants for assessing the health effects based on processed and estimated data. The research was conducted between August and November 2020, using remotely sensed images and geographical information techniques. Two methods; Geographic Information Systems GIS-based multiple regression and a polynomial model, were employed to estimate PM2.5 levels in the study area. The results showed a significant influence of climatic variables on air pollution in Iraq, with varying effects on PM2.5 estimation. The health impact ranged from good to unhealthy, with most provinces experiencing poor air quality. Southern parts of Iraq exhibited PM2.5 levels surpassing the healthy threshold. The predictive linear and polynomial model's accuracy was assessed through regression, yielding high correlation coefficients (R2 ) of 0.89, 0.95, 0.98, and 0.96 for August to November, respectively. While model validation accuracy ranged between 85–94 %. The study emphasizes the vital role of climate data in understanding the dispersion of air pollutants and their significant impacts on the environment. Addressing air pollution and climate change, as per the SGS-13 "Climate Action", are interconnected and require comprehensive strategies for mitigation.

Keywords:

Dust PM2.5; Advanced remote sensing; Polynomial model; Health impact; GIS

References

[1] Akbari M., Zahmatkesh H., Eftekhari M., 2021. A GIS-Based system for real-time air pollution monitoring and alerting based on OGC Sensors web enablement standards. Pollution. 7, 25–41. DOI: https://doi.org/10.22059/poll.2020.296938.741

[2] Jaafarzadeh, N., Nouhjah, S., Shahbazian, H., et al., 2024. The relationship between hot spots of air pollution and the incidence of gestational diabetes based on spatial analysis: A study on one of the most air-polluted metropolis of Iran. Environmental Health Engineering And Management Journal. 11(1), 83–92.

[3] Tella A., Balogun A., 2021. Prediction of ambient PM10 concentration in Malaysian cities using geostatistical analyses. Journal of Advanced Geospatial Science & Technology. 1, 115–127.

[4] Wu I., Liao S., Lai S., Wong K., 2021. The respiratory impacts of air pollution in children. Global and domestic (Taiwan) situation. Biomedical journal. 1(1), 115–127. DOI: https://doi.org/10.1016/j.bj.2021.12.004

[5] Jumaah, H. J., Ameen, M. H., Kalantar, B., et al., 2019. Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia. Geomatics, Natural Hazards and Risk. 10(1), 2185–2199. DOI: https://doi.org/10.1080/19475705.2019.1683084

[6] Jumaah, H.J., Ameen, M.H., Mahmood, S. et al., 2023. Study of air contamination in Iraq using remotely sensed Data and GIS. Geocarto International. 38(1), 2178518. DOI: https://doi.org/10.1080/10106049.2023.2178518

[7] Usmani, R.S.A., Saeed, A., Abdullahi, A.M., et al., 2020. Air pollution and its health impacts in Malaysia: a review. Air Quality, Atmosphere & Health. 13(9), 1093–1118. DOI: https://doi.org/10.1007/s11869-020-00867-x

[8] Jumaah, H.J., Mansor, S., Pradhan, B. et al., 2018. UAV-Based PM2.5 monitoring for small-scale urban areas. International Journal of Geoinformatics. 14(4), 61–69.

[9] Jumaah, H.J., Jasim, A., Rashid, A., et al., 2023. Air pollution risk assessment using GIS and remotely sensed data in Kirkuk City, Iraq. Journal of Atmospheric Science Research. 6(3), 41–51. DOI: https://doi.org/10.30564/jasr.v6i3.5834

[10] Ameen, M.H., Jumaah, H.J., Kalantar, B., et al., 2021. Evaluation of PM2.5 particulate matter and noise pollution in Tikrit university based on GIS and statistical modeling. Sustainability. 13, 1–14. DOI: https://doi.org/10.3390/su13179571

[11] Balogun, A., Tella, A., Baloo, L., et al., 2021. A review of the inter-correlation of climate change, air pollution, and urban sustainability using novel machine learning algorithms and spatial information science. Urban Climate. 40, 100989. DOI: https://doi.org/10.1016/j.uclim.2021.100989

[12] Mota, B., Albergaria, M., Pereira, H., et al., 2021. Climatization and luminosity optimization of buildings using genetic algorithm, random forest, and regression models. Energy Informatics. 4, 1–18. DOI: https://doi.org/10.1186/s42162-021-00151-x

[13] Somvanshi, S., Vashisht, A., Chandra, U., et al., 2019. Delhi air pollution modeling using remote sensing technique. Handbook of Environmental Materials Management. 1–27. DOI: https://doi.org/10.1007/978-3-319-58538-3_174-1

[14] Lakshmi, K., Mahaboob, B., Rajaiah, M., et al., 2021. Ordinary least squares estimation of parameters of linear model. Journal of Mathematical and Computational Science. 11, 2015–2030.

[15] Jasim, O., Hamed, N., Abid, M., 2020. Urban air quality assessment using integrated artificial intelligence algorithms and geographic information system modeling in a highly congested area Iraq. Journal of Southwest Jiaotong University. 55(1). DOI: https://doi.org/10.35741/issn.0258-2724.55.1.16

[16] Ajaj, Q.M., Shareef, M.A., Hassan, N.D., et al., 2018. GIS based spatial modeling to mapping and estimation relative risk of different diseases using inverse distance weighting (IDW) interpolation algorithm and evidential belief function (EBF) (Case study: Minor Part of Kirkuk City, Iraq). International Journal of Engineering & Technology. 7(4.37), 185–191. DOI: https://doi.org/10.14419/ijet.v7i4.37.24098

[17] Kalantar, B., Ameen, M.H., Jumaah, H.J., et al., 2020. Zab River (IRAQ) sinuosity and meandering analysis based on the remote sensing data. The International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. XLIII-B3-2020, 91–95. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-91-2020

[18] Jumaah, H.J., Kalantar, B., Ueda, N., et al., 2021. The effect of war on land use dynamics in Mosul Iraq using remote sensing and GIS techniques. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium. 6476–6479. DOI: https://doi.org/10.1109/igarss47720.2021.9553165

[19] Jumaah, H.J., Abbas, W.H., Khalaf, Z.A., et al., 2023. Applications of Remote Sensing and GIS in Assessing Climate Change and Forecasting Air Quality in Iraq. Journal of Engineering and Technology Development. 1(1), 1–7.

[20] Kandel, A., Pokhrel, K., 2024. Study of urban sprawl and its impact on vegetation, land surface temperature and air pollution using remote sensing and GIS in Kathmandu Valley From 2015 to 2020. Journal of Geoscience and Environment Protection. 12(3), 28–53. DOI: https://doi.org/10.4236/gep.2024.123003

[21] Freedman, D., 2009. Statistical Models: Theory and Practice. 2nd ed. Cambridge University Press: UK. DOI: https://doi.org/10.1017/CBO9780511815867

[22] Hamed, H.H., Jumaah, H.J., Kalantar, B., et al., 2021. Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards, and Risk. 12(1), 1778–1796. DOI: https://doi.org/10.1080/19475705.2021.1946602

[23] Jasim A., Awchi T., 2020. Regional meteorological drought assessment in Iraq. Arabian Journal of Geosciences. 13(284), 1–16. DOI: https://doi.org/10.1007/s12517-020-5234-y

[24] EOSDIS Worldview, 2013 [Internet]. NASA EOSDIS [cited 2024 Mar 15] Available from: https://worldview.earthdata.nasa.gov/

[25] Ostertagová, E., 2012. Modelling using polynomial regression. Procedia Engineering. 48, 500–506. DOI: https://doi.org/10.1016/j.proeng.2012.09.545

[26] Olufemi, A.C., Mji, A., Mukhola, M.S., 2019. Health risks of exposure to air pollutants among students in schools in the vicinities of coal mines. Energy Exploration & Exploitation. 37(6), 1638–1656. DOI: https://doi.org/10.1177/0144598718765489

[27] Mahmood, M.R., Jumaah, H.J., 2023. NBR Index-Based fire detection using sentinel-2 images and gis: A case study in Mosul Park, Iraq. International Journal of Geoinformatics. 19(3), 67–74. DOI: https://doi.org/10.52939/ijg.v19i3.2607

[28] ‏ Pongpiachan, S., Wang, Q., Apiratikul, et al., 2024. Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements. Plos One. 19(3), e0287187. DOI: https://doi.org/10.1371/journal.pone.0287187

[29] Mahmood, S., Ali, A., Jumaah, H.J., 2024. Geo-visualizing the hotspots of smog-induced health effects in district Gujranwala, Pakistan: A community perspective. Environmental Monitoring and Assessment. 196(5), 1–14. DOI: https://doi.org/10.1007/s10661-024-12619-w

Downloads

How to Cite

Jaml Jumaah, H., Adnan Dawood, M., & Mahmood, S. (2024). Estimating Chemical Concentrations of Dust PM2.5 in Iraq: A Climatic Perspective Using Polynomial Model and Remote Sensing Technology. Journal of Atmospheric Science Research, 7(3), 44–56. https://doi.org/10.30564/jasr.v7i3.6381

Issue

Vol. 7 , Iss. 3 (July 2024)

Article Type

Article

License

Copyright © 2024 Huda Jaml Jumaah, Maha Adnan Dawood, Shakeel Mahmood

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC