Air Pollution Risk Assessment Using GIS and Remotely Sensed Data in Kirkuk City, Iraq

Authors

  • Huda Jamal Jumaah

    Environment and Pollution Engineering Department, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
  • Abed Jasim

    Department of Surveying Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
  • Aydin Rashid

    Department of Surveying Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
  • Qayssar Ajaj

    Department of Surveying Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq

DOI:

https://doi.org/10.30564/jasr.v6i3.5834
Received: 7 July 2023 | Revised: 15 August 2023 | Accepted: 22 August 2023 | Published Online: 23 August 2023

Abstract

According to World Health Organization (WHO) estimates and based on a world population review, Iraq ranks tenth among the most air-polluted countries in the world. In this study, the authors tried to evaluate the outdoor air of Kirkuk City north of Iraq. The authors relied on two types of data: field measurements and remotely sensed data. Fifteen air quality points were determined in the study region representing the monthly average measurements implemented for the one-year dataset. Geographic information systems (GIS) based geo-statistic and geo-processing techniques have been applied to collected data. Spatial distribution data related to Air Quality Index (AQI), and Particulate Matter (PM10 and PM2.5) were obtained by mapping collected records. Remotely sensed data of PM2.5 were analyzed and compared with the collected data. Health impacts were assessed per each air pollutant determined in the study. Spatial distribution maps revealed the hazardous air type in the study area. Overall AQI ranged between 300 and 472 µg/m3 referring to unhealthy, very unhealthy, and hazardous classes of pollution. Also, PM10 ranged between 300 and 570 µg/m3 indicating the same class of air pollution from unhealthy to hazardous. While PM2.5 ranged between 40 and 60 µg/m3 which represents unhealthy air for sensitive persons and unhealthy air. The remotely sensed data revealed different air types for the study period ranging from 14.5 to 52.5 µg/m3 represented in moderate and unhealthy air for sensitive persons. Significant correlations were obtained where the mean local R2 (coefficient of determination) was obtained as 0.83. The assessed data were within high air pollution that requires immediate intervention for controlling causes and eliminating their effects.

Keywords:

Air pollution risk; AQI; GIS; Particulate matter; Remote sensing

References

[1] Al-Hasnawi, S., Hussain, H.M., Al-Ansari, N., et al., 2016. The effect of the industrial activities on air pollution at Baiji and its surrounding areas, Iraq. Engineering. 8(1), 34-44.

[2] Kadhem, J.A., Reza, K.S., Ahmed, W.K., 2017. Alternative fuel use in Iraq: A way to reduce air pollution. European Journal of Engineering and Technology Research. 2(5), 20-30.

[3] 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

[4] Alwaely, A.A., Al-qaralocy, H.N., Al-Asadi, K.A., et al., 2015. The environmental aftermath resulted from chemical bombardment of Halabja Territory for the period 1988-2014. International Journal of Scientific & Engineering Research. 6(9), 40-44.

[5] Alwaeli, A.A., Chaichan, K., Kazem, H.A., 2014. Effect of dust on photovoltaic utilization in Iraq: Review Article. Renewable and Sustainable Energy Reviews. 37, 734-749.

[6] Jumaah, H.J., Kalantar, B., Halin, A.A., et al., 2021. Development of UAV-based PM2.5 monitoring system. Drones. 5(3), 60. DOI: https://doi.org/10.3390/drones5030060

[7] 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(17), 9571.

[8] Al-Kasser, M.K., 2021. Air pollution in Iraq sources and effects. IOP Conference Series: Earth and Environmental Science. 790(1), 012014.

[9] 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

[10] 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

[11] Wilkinson, P., Smith, K.R., Davies, M., et al., 2009. Public health benefits of strategies to reduce greenhouse-gas emissions: Household energy. The Lancet. 374(9705), 1917-1929.

[12] Zhang, Q., Zheng, Y., Tong, D., et al., 2019. Drivers of improved PM2. 5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences. 116(49), 24463-24469.

[13] 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.

[14] 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

[15] Najim, A.O., Meteab, M.A., Jasim, A.T., et al., 2023. Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia. The Egyptian Journal of Remote Sensing and Space Science. 26(2), 265-271.

[16] 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.

[17] Fedra, K., 1993. GIS and Environmental Modeling [Internet]. [cited 2023 May 10]. Available from: http://pure.iiasa.ac.at/id/eprint/3730/1/RR-94-02.pdf

[18] Yi, X., Zhang, J., Wang, Z., et al. (editors), 2018. Deep distributed fusion network for air quality prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2018 Aug 19-23; London, UK. p. 965-973.

[19] Kalantar, B., Ueda, N., Al-Najjar, H.A.H., et al., 2019. UAV and Lidar image registration: A SURF-based approach for ground control points selection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 42, 413-418. DOI: https://doi.org/10.5194/isprs-archivesXLII-2-W13-413-2019

[20] 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, 185-191.

[21] Jumaah, H.J., Kalantar, B., Ueda, N., et al. (editors), 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; 2021 Jul 11-16; Brussels, Belgium. New York: IEEE. p. 6476-6479.

[22] 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 and Spatial Information Sciences. 43, 91-95.

[23] Jumaah, H.J., Ameen, M.H., Kalantar, B., 2023. Surface water changes and water depletion of Lake Hamrin, Eastern Iraq, using Sentinel-2 images and geographic information systems. Advances in Environmental and Engineering Research. 4(1), 1-11.

[24] Stillwell, J., Clarke, G., 2004. Applied GIS and spatial analysis. Wiley: Chichester. pp. 254-255.

[25] Hossain, E., Shariff, M.A.U., Hossain, M.S., et al., 2020. A novel deep learning approach to predict air quality index. Advances in Intelligent Systems and Computing. Springer: Singapore. pp. 367-381.

[26] Habbeb, M.G., Sulyman, M.H., Jumaah, H.J., 2022. Modeling water quality index using geographic information systems and weighted arithmetic index in Kirkuk, Iraq. Pollution Research. 41(1), 323-327.

[27] Jumaah, H.J., Ameen, M.H., Mohamed, G.H., et al., 2022. Monitoring and evaluation Al-Razzaza lake changes in Iraq using GIS and remote sensing technology. The Egyptian Journal of Remote Sensing and Space Science. 25(1), 313-321. DOI: https://doi.org/10.1016/j.ejrs.2022.01.013

[28] Hadi, A.M., Mohammed, A.K., Jumaah, H.J., et al., 2022. GIS-based rainfall analysis using remotely sensed data in Kirkuk Province, Iraq: Rainfall analysis. Tikrit Journal of Engineering Sciences. 29(4), 48-55.

[29] EOSDIS Worldview, 2013. NASA EOSDIS [Internet] [cited 2023 Jul 15]. Available from: https://worldview.earthdata.nasa.gov/?v=-1.6482160931174121,-46.54687500000001,202.1794660931174,53.01562500000001&lg=false&t=2023-07-06-T05%3A00%3A53Z

[30] Kirkuk Governorate Real-time Air Quality Index (AQI) & Pollution Report [Internet]. Air Matters. [cited 2023 Jul 15]. Available from: https://air-quality.com/place/iraq/kirkuk-governorate/1edc7885?lang=en&standard=aqi_us

Downloads

How to Cite

Jamal Jumaah, H., Jasim, A., Rashid, A., & Ajaj, Q. (2023). Air Pollution Risk Assessment Using GIS and Remotely Sensed Data in Kirkuk City, Iraq. Journal of Atmospheric Science Research, 6(3), 41–51. https://doi.org/10.30564/jasr.v6i3.5834

Issue

Vol. 6 , Iss. 3 (July 2023)

Article Type

Article

License

Copyright © 2023 Huda Jamal Jumaah, Abed Jasim, Aydin Rashid, Qayssar Ajaj

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