IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9571-d621765.html
   My bibliography  Save this article

Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling

Author

Listed:
  • Mohammed Hashim Ameen

    (Department of Environmental Engineering, Engineering College, Tikrit University, Tikrit 34001, Iraq)

  • Huda Jamal Jumaah

    (Department of Environment and Pollution Engineering, Technical Engineering College-Kirkuk, Northern Technical University, Kirkuk 36001, Iraq)

  • Bahareh Kalantar

    (RIKEN Center of Advanced Intelligence Project, The Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 970804, Japan)

  • Naonori Ueda

    (RIKEN Center of Advanced Intelligence Project, The Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 970804, Japan)

  • Alfian Abdul Halin

    (Department of Multimedia, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia)

  • Abdullah Saeb Tais

    (Department of Civil Engineering, Engineering College, Tikrit University, Tikrit 34001, Iraq)

  • Sarah Jamal Jumaah

    (Department of Physics, College of Education for Pure Sciences, University of Kirkuk, Kirkuk 36001, Iraq)

Abstract

In this paper, we assess the extent of environmental pollution in terms of PM2.5 particulate matter and noise in Tikrit University, located in Tikrit City of Iraq. The geographic information systems (GIS) technology was used for data analysis. Moreover, we built two multiple linear regression models (based on two different data inputs) for the prediction of PM2.5 particulate matter, which were based on the explanatory variables of maximum and minimum noise, temperature, and humidity. Furthermore, the maximum prediction coefficient R 2 of the best models was 0.82, with a validated (via testing data) coefficient R 2 of 0.94. From the actual total distribution of PM2.5 particulate values ranging from 35–58 μg/m 3 , our best model managed to predict values between 34.9–60.6 μg/m 3 . At the end of the study, the overall air quality was determined between moderate and harmful. In addition, the overall detected noise ranged from 49.30–85.79 dB, which inevitably designated the study area to be categorized as a noisy zone, despite being an educational institution.

Suggested Citation

  • Mohammed Hashim Ameen & Huda Jamal Jumaah & Bahareh Kalantar & Naonori Ueda & Alfian Abdul Halin & Abdullah Saeb Tais & Sarah Jamal Jumaah, 2021. "Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9571-:d:621765
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9571/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/17/9571/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohan Sarkar & Anupam Das & Sutapa Mukhopadhyay, 2021. "Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 8613-8642, June.
    2. Rezzy Eko Caraka & Yusra Yusra & Toni Toharudin & Rung-Ching Chen & Mohammad Basyuni & Vilzati Juned & Prana Ugiana Gio & Bens Pardamean, 2021. "Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan," Sustainability, MDPI, vol. 13(11), pages 1-12, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matara Caroline Mongina & Nyambane Simpson Osano & Yusuf Amir Okeyo & Ochungo Elisha Akech & Khattak Afaq, 2024. "Classification of Particulate Matter (PM2.5) Concentrations Using Feature Selection and Machine Learning Strategies," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 15(1), pages 85-96.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kadir Diler Alemdar & Ömer Kaya & Antonino Canale & Muhammed Yasin Çodur & Tiziana Campisi, 2021. "Evaluation of Air Quality Index by Spatial Analysis Depending on Vehicle Traffic during the COVID-19 Outbreak in Turkey," Energies, MDPI, vol. 14(18), pages 1-15, September.
    2. Ana Catarina T. Silva & Pedro T. B. S. Branco & Sofia I. V. Sousa, 2022. "Impact of COVID-19 Pandemic on Air Quality: A Systematic Review," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
    3. Aladayleh Khaled Jameel & Qudah Shatha Mustafa Abdallah Al & Bargues José Luis Fuentes & Gisbert Pablo Ferrer, 2023. "Global trends of the research on COVID-19 risks effect in sustainable facility management fields: a bibliometric analysis," Engineering Management in Production and Services, Sciendo, vol. 15(1), pages 12-28, March.
    4. Toni Toharudin & Resa Septiani Pontoh & Rezzy Eko Caraka & Solichatus Zahroh & Panji Kendogo & Novika Sijabat & Mentari Dara Puspita Sari & Prana Ugiana Gio & Mohammad Basyuni & Bens Pardamean, 2021. "National Vaccination and Local Intervention Impacts on COVID-19 Cases," Sustainability, MDPI, vol. 13(15), pages 1-17, July.

    More about this item

    Keywords

    PM2.5; air quality; noise; silence zone area; GIS; linear regression;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9571-:d:621765. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.