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Journals / Ankara Üniversitesi Çevrebilimleri Dergisi / 2025 / Cilt: 12 Sayı: 2

Prediction of Air Pollution Risk Resulting from Industrial Accidents: A Systematic Literature Review

Endüstriyel Kazalar Sonucu Oluşan Hava Kirliliği Riskinin Tahmini: Sistematik Literatür İncelemesi

Mehmet Fatih Şen(Afet ve Acil Durum Yönetimi Başkanlığı)0000-0002-4777-3938
Saliha Çetinyokuş(GAZİ ÜNİVERSİTESİ)0000-0001-9955-6428
Pages
25–45
Publication Date
2025-12-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that the field lacks a standardized approach, with significant differences in method selection, indicator systematization, and weighting approaches.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study systematically examines the methodologies used in predicting air pollution risk following industrial accidents.
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Key Findings & Data:In the search conducted across Scopus, TRDizin, National Thesis Center, ResearchGate, and PubMed databases covering the period 2000-2025 (June), 94 publications were evaluated, and 22 studies were selected for detailed analysis due to specific scope considerations.
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Conclusion & Impact:The findings indicate that the field lacks a standardized approach, with significant differences in method selection, indicator systematization, and weighting approaches.
🔬Design:Modeling & Simulation
📍Scope:2000-2025 Verileri
⚙️Variables:: Endüstriyel Kaza · Hava Kirliliği · Endüstriyel Kaza Riski

Abstract

This study systematically examines the methodologies used in predicting air pollution risk following industrial accidents. In the search conducted across Scopus, TRDizin, National Thesis Center, ResearchGate, and PubMed databases covering the period 2000-2025 (June), 94 publications were evaluated, and 22 studies were selected for detailed analysis due to specific scope considerations. For the compilation of historical accidents, 271 accident reports downloaded from ARIA and CSB accident databases were analyzed using an artificial intelligence model (Anthropic Claude 3.5 Sonnet) and manually validated. The studies were classified into three categories: environmental risk assessment of industrial facilities independent of air pollution, air pollution risk assessment following industrial accidents, and accident consequence prediction using machine learning. The findings indicate that the field lacks a standardized approach, with significant differences in method selection, indicator systematization, and weighting approaches. While index-based methods offer mathematical simplicity, probabilistic methods are superior in uncertainty analysis. Machine learning applications demonstrate success rates of 80-96% using Random Forest and XGBoost algorithms. For future research, standardization among accident databases, integration of machine learning with probabilistic methods, and strengthening interdisciplinary collaboration are recommended.

Özet

Bu çalışma, endüstriyel kaza sonrası hava kirliliği riskinin tahmininde kullanılan metodolojileri sistematik olarak incelemektedir. Scopus, TRDizin, Ulusal Tez Merkezi, ResearchGate ve PubMed veri tabanlarında 2000-2025 (Haziran) aralığında gerçekleştirilen taramada, 94 yayın değerlendirilmiş ve spesifik kapsam nedeniyle 22 çalışma detaylı analize alınmıştır. Tarihsel kazaların derlenmesinde ARIA ve CSB kaza veri tabanlarından indirilen 271 kaza raporu, yapay zekâ modeli (Anthropic Claude 3.5 Sonnet) ile analiz edilmiş ve manuel olarak doğrulanmıştır. Çalışmalar üç kategoride sınıflandırılmıştır: hava kirliliğinden bağımsız endüstriyel kuruluşların çevresel risk değerlendirmeleri, endüstriyel kaza kaynaklı hava kirliliği risk değerlendirmeleri, makine öğrenmesi ile kaza sonuç tahmini. Bulgular, alanın standart yaklaşımdan yoksun olduğunu ve yöntem seçimi, belirteç sistematiği ile ağırlıklandırma yaklaşımlarında ciddi farklılıklar bulunduğunu göstermektedir. İndeks tabanlı yöntemler matematiksel basitlik sunarken, olasılıksal yöntemler belirsizlik analizinde üstündür. Makine öğrenmesi uygulamaları, Rastgele Ormanlar ve XGBoost algoritmaları ile %80-96 başarı oranları göstermektedir. Gelecek araştırmalar için kaza veritabanları arasında standardizasyonun sağlanması, makine öğrenmesinin olasılıksal yöntemlerle entegrasyonu ve disiplinler arası iş birliğinin güçlendirilmesi önerilmektedir.

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Cite This Article

Şen, M. F., Çetinyokuş, S. (2025). Prediction of Air Pollution Risk Resulting from Industrial Accidents: A Systematic Literature Review. Ankara Üniversitesi Çevrebilimleri Dergisi, Cilt: 12 Sayı: 2, 25–45. https://doi.org/10.62163/aucevrebilim.1814514

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References (57)

  1. Achour, M. H. Haroun, A. E. Schult, C. J. And Gasem, K. A M. 2005. A new method to assess the environmental risk of a chemical process. Chemical Engineering and Processing, 44: 901-909. doi:10.1016/j.cep.2004.10.003
  2. Anonim. 2017. Key Lessons for Preventing Inadvertent Mixing During Chemical Unloading Operations, Chemical Reaction and Release in Atchison, Kansas, No. 2017-01-I-KS. https://www.csb.gov/file.aspx?DocumentId=6047 (Erişim Tarihi: 10.10.2025).
  3. Anonim. 2018. FCC Unit Explosion and Asphalt Fire at Husky Superior Refinery Investigation Report, No. 2018-02-I-WI. https://www.csb.gov/assets/1/6/Husky_Superior_Refinery_Report_2022-12-23_(1).pdf (Erişim Tarihi: 10.10.2025).
  4. Anonim. 2018. The World’s Worst Industrial Disaster Is Still Unfolding. https://www.theatlantic.com/science/archive/2018/07/the-worlds-worst-industrial-disaster-is-still-unfolding/560726/ (Erişim Tarihi: 10.10.2025).
  5. Anonim. 2022. Announcing the results of the investigation into the Aqaba port accident and referring the investigation file to the Public Prosecution. https://www.arabiaweather.com/en/content/announcing-the-results-of-the-investigation-into-the-aqaba-port-accident-and-referring-the-investigation-file-to-the-public-prosecution (Erişim Tarihi: 20.10.2025).
  6. Anonim. 2022.Chlorine gas leak at Aqaba port causes 13 deaths, 250 injuries. https://thearabweekly.com/chlorine-gas-leak-aqaba-port-causes-13-deaths-250-injuries (Erişim Tarihi: 20.10.2025).
  7. Anonim. 2025. The Aria Industrial Accident Database. https://www.aria.developpement-durable.gouv.fr/?lang=en (Erişim Tarihi: 10.10.2025).
  8. Anonim. 202). https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (Erişim tarihi: 01.09.2025
  9. Anonim. 2025. Report: Catastrophic Explosions at Bandar Abbas Port Reveal Iran’s Regime Negligence and Economic Fragility. https://www.ncr-iran.org/en/news/economy/report-catastrophic-explosions-at-bandar-abbas-port-reveal-regime-negligence-and-economic-fragility/ (Erişim Tarihi: 20.10.2025).
  10. Anonim. 2025. Gujarat: 5 dead, 57 injured as explosion rocks Dahej pesticide company in Bharuch district. https://timesofindia.indiatimes.com/city/surat/gujarat-5-dead-32-injured-in-dahej-pesticide-company-explosion/articleshow/76175766.cms (Erişim tarihi: 10.10.2025).
  11. Casal J. 2008. Evaluation of the Effects and Consequences of Major Accidents in Industrial Plants. Industrial Safety Series, 8
  12. Chebila, M. 2021. Predicting the consequences of accidents involving dangerous substances using machine learning. Ecotoxicology and Environmental Safety, 208: 111470. https://doi.org/10.1016/j.ecoenv.2020.111470
  13. Chai, H., Dong, K., Liang, Y., Han, Z. and He, R. 2025. Machine learning-based accidents analysis and risk early warning of hazardous materials transportation. Journal of Loss Prevention in the Process Industries, 95: 105594. https://doi.org/10.1016/j.jlp.2025.105594
  14. Chen, Q. Jia, Q. Yuan, Z. Huang, L. 2014. Environmental risk source management system for thepetrochemical industry. Process Safety and Environmental Protection, 9: 251-260. Doi:10.1016/j.psep.2013.01.004
  15. Demirarslan, K.O. Doğruparmak, Ç ve Karademir, A. 2017. Evaluation of three pollutant dispersion models for the environmental assessment of a district in Kocaeli, Turkey. Global NEST Journal, 19, No 1: 37-48
  16. Dominski, F.H., Branco, J.H.L., Buonanno, G., Stabile, L., Da Silva, M.G. and Andrade, A. 2021. Effects of air pollution on health: a mapping review of systematic reviews and meta-analyses. Environmental Research, 201: 11487. https://doi.org/10.1016/j.envres.2021.111487
  17. Eckerman, I. 2005. The Bhopal Saga – Causes and Consequences of the World’s Largest Industrial Disaster. Universities Press (India) Private Limited.
  18. Effatpanah, M., Effatpanah, H., Jalali, S., Parseh, I., Goudarzi, G., Barzegar, G., Geravandi, S., Darabi, F., Ghasemian, N. and Mohammadi, M. 2020. Hospital admission of exposure to air pollution in Ahvaz megacity during 2010–2013. Clinical Epidemiology and Global Health, 8: 550-556. https://doi.org/10.1016/j.cegh.2019.12.001
  19. Ghannam, K and El-Fadel, M. 2013. Emissions Characterization and Regulatory Compliance at An Industrial Complex: An Integrated MM5/Calpuff Approach. Atmospheric Environment Elsevier Journal, 69:156-169. https://doi.org/10.1016/j.atmosenv.2012.12.022
  20. Gümrükçüoğlu, M. 2012. Determination of Industrial Sülfür Dioxide Emissions and Mapping by Geographic Information System, https://acikerisim.sakarya.edu.tr/handle/20.500.12619/50115.
  21. Hanna, S.R., Egan, B.A., Purdum, J. And Wagler, J. 2005. Comparison of AERMOD, ISC3, and ADMS Model Performance with Five Field Data Sets. International Journal of Environment and Pollution, 16: 301-314.
  22. Homberger, E., Reggiani, G., Sambeth, J. and Wipf, H. 1979. The Seveso Accident: Its Nature, Extent and Consequences. The Annals of Occupational Hygiene, 22: 327-330. https://doi.org/10.1093/annhyg/22.4.327
  23. Huang, W., Xu, H., Wu, J., Ren, M., Ke, Y. and Qiao, J. 2024. Toward cleaner air and better health: Current state, challenges, and priorities. Science Journal, 385: 386-390. https://doi.org/10.1126/science.adp7832
  24. Hunsaker, C., Graham, R. L., Suter, G.W., O'neill, R.V., Barnthouse, L.W. and Gardner, R.H. 1990. Assessing Ecological Risk on a Regional Scale. Environmental Management, 14: 325-332.
  25. Jianfeng, L., Bin, Z., Yang, W. and Mao, L. 2009. The unfolding of ‘12.23’ Kaixian blowout accident in China. Safety Science, 47: 1107-1117. https://doi.org/10.1016/j.ssci.2008.12.005
  26. Lamba, V. 2022. Sitapura Industrial Disaster in Jaipur and Its Management. International Journal of Trend in Scientific Research and Development, 6: 240-249. https://journals.indexcopernicus.com/api/file/viewByFileId/1567713
  27. Landrigan, P., Fuller, R., Acosta, N.J.R., Adeyi, O., Arnold, R., Basu, N., Balde, A.B., Bertollini, R., Bose-O’Reilly, S., Boufford, J.I., Breysse, PN. and Zhong, M. 2024. The Lancet Commission on pollution and health. Lancet, 391: 462-512. http://dx.doi.org/10.1016/S0140-6736(17)32345-0
  28. Liu, R., Liu, J., Zhang, Z., Borthwick, A., Cai, Y., Dong, L. and Du, X. 2018. Risks of airborne pollution accidents in a major conurbation: Case study of Zhangjiakou, a host city for the 2022 Winter Olympics. Stochastic Environmental Research and Risk Assessment, 32: 3257-3272. Doi: 10.1007/s00477-018-1590-5
  29. Liu, H.L., Liu, N. and Shen, F. 2019. Parameter uncertainty analysis in environmental risk assessment caused by hazardous chemical accident. Applied Ecology and Environmental Research, 17(5): 11851-11867
  30. Liu, J., Tan, L. and Ma, Y. 2024). An integrated risk assessment method for urban areas due to chemical leakage accidents. Reliability Engineering and System Safety, 247: 110091. https://doi.org/10.1016/j.ress.2024.110091
  31. Liu, P., Song, H., Wang, T., Wang, F., Li, X., Miao, C. and Zhao, H. 2020. Effects of meteorological conditions and anthropogenic precursors on ground-level ozone concentrations in Chinese cities. Environmental Pollution, 262: 114366
  32. Ma, X., Tsai, Y., Shu, C. and Yang, Y. 2025. Risk evolution analysis of gas leakage accidents based on complex network. Safety Science, 182: 106692. https://doi.org/10.1016/j.ssci.2024.106692
  33. Manisalidis, I., Stavropoulou, E., Stavropoulos, A. and Bezirtzoglou, E. 2020. Environmental and health impacts of air pollution: a review. Public Health, 8: 1-13. https://doi.org/10.3389/fpubh.2020.00014
  34. Manish, S. and Sunil, M. 2013. Emission of Atmospheric Pollutants during IOCL-Sitapur, Jaipur fire 2009,India. Research Journal of Chemical Sciences, 3: 75-79. https://www.isca.me/rjcs/Archives/v3/i7/11.ISCA-RJCS-2013-104.pdf
  35. Martens, D.S., Cox, B., Janssen, B.G., Clemente, D.B.P., Gasparrini, A., Vanpoucke, C., Lefebvre, W., Roels, H.A., Plusquin, M. and Nawrot, T.S. 2017. Prenatal air pollution and newborns’ predisposition to accelerated biological aging. JAMA Pediatrics, 171: 1160-1167. https://doi.org/10.1001/jamapediatrics.2017.3024
  36. Merson, J. R., Migeon, M., Marn, J. and Showstark, Ma. 2025. Mass Casualty Incidents. Physician Assistant Clinics, 10: 771-791. https://doi.org/10.1016/j.cpha.2025.06.009
  37. Nazar, W. and Niedoszytko, M. 2022. Air pollution in Poland: a 2022 narrative review with focus on respiratory diseases. International Journal of Environmental Research and Public Health, 19: 1-20. https://doi.org/10.3390/ijerph19020895
  38. Nyadanu, S.D., Dunne, J., Tessema, G.A., Mullins, B., Boateng, B.K., Bell, M.L., Duko, B. and Pereira, G. 2022. Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses. Environment International, 306: 119465. https://doi.org/10.1016/j.envpol.2022.119465
  39. Obasi, I., Cheng, P., Mikellidou, C., Dimopoulos, C. and Boustra, G. 2025. Machine learning for occupational accident analysis: Applications, challenges, and future directions. Journal of Safety Science and Resilience, 7: 100250. https://doi.org/10.1016/j.jnlssr.2025.100250
  40. Riddle, A., Carruthers, D., Sharpea, A., McHugh, C. and Stocker, J. 2004. Comparisons between FLUENT and ADMS for atmospheric dispersion modelling. Atmospheric Environment, 38 (7), 1029–1038. https://doi.org/10.1016/j.atmosenv.2003.10.052
  41. Romanello, M., Walawender, M., Hsu, S., Moskeland, A., Silva, R., Scamman, D., Ali, Z., Ameli, N., Angelova, D., Basart, S., Beagley, J. and Beggs, P.J. 2024. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. Lancet Review, 404: 1847-1856. https://doi.org/10.1016/S0140-6736(24)01822-1
  42. Sagan, V., Pasken, R., Zarauz, J. and Krotkov, N. 2018. SO2 trajectories in a complex terrain environment using CALPUFF dispersion model, OMI and MODIS data. Int J Appl Earth Obs Geoinformation,69: 99-109. https://doi.org/10.1016/j.jag.2018.03.003
  43. Sakti, A.D., Anggraini, T.S., Ihsan, K.T.N., Misra, P., Trang, N.T.Q., Pradhan, B., Wenten, G., Hadi, P.O. and Wikantika, K. 2023. Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products. Science of the Total Environment, 854: 158825. http://dx.doi.org/10.1016/j.scitotenv.2022.158825
  44. Sivaraman, S. and Varadharajan, S. 2021. Investigative consequence analysis: A case study research of beirut explosion accident. Journal of Loss Prevention in the Process Industries, 69: 104837. https://doi.org/10.1016/j.jlp.2020.104387
  45. State of Global Air. 2024. A Special Report On Global Exposure To Air Pollution and Its Health Impacts, With A Focus On Children’s Health. https://www.stateofglobalair.org/resources/report/state-global-air-report-2024
  46. Tahmid, M., Dey, S. and Syeda, S. 2020. Mapping Human Vulnerability and Risk due to Chemical Accidents. Journal of Loss Prevention in the Process Industries, 68: 104289. https://doi.org/10.1016/j.jlp.2020.104289
  47. Tamascelli, N., Solini, R., Paltrinieri, N. and Cozzani, V. 2022. Learning from major accidents: A machine learning approach. Computers and Chemical Engineering, 162: 107786. https://doi.org/10.1016/j.compchemeng.2022.107786
  48. Topuz, E., Talinli, I. ve Aydin, E. 2011. Integration of environmental and human health risk assessment for industries using hazardous materials: A quantitative multi criteria approach for environmental decision makers. Environment International, 37: 393-403
  49. Trivedi, P., Purohit, D. and Soju, A. 2014. Major Industrial Disasters in India. National Institute of Occupational Health, 9: 1-8. https://www.researchgate.net/publication/316284188
  50. Truong, S.C.H., Lee, M., Kim, G., Kim, D., Park, J., Choi, S. and Cho, G. 2016. Accidental benzene release risk assessment in an urban area using an atmospheric dispersion model. Atmospheric Environment, 144: 146-159
  51. Türkcan, B. (2024). Türkiye Afet Yönetiminde Kritik Bileşen Natech: Depremlerin Tetiklediği Endüstriyel Kazalar. Memleket Siyaset Yönetim, 43: 463-490. DOI: 10.56524/msydergi.1533555
  52. Varma, R. and Varma, D.R. 2025. The Bhopal Disaster of 1984. Bulletin of Science, Technology and Society, 25: 37-45. https://doi.org/10.1177/0270467604273822
  53. Yang, M., Shi, L. and Liu, B. 2022. Risks assessment and driving forces of urban environmental accident. Journal of Cleaner Production, 340:130710. https://doi.org/10.1016/j.jclepro.2022.130710
  54. Zhang, H., Liu, R., Liu, J. and Zhang, Z. 2022. Formal Probabilistic Risk Analysis of Accidental Air Pollution in A Development Zone Using Bayesian Networks. Journal of Cleaner Production, 372: 133774. https://doi.org/10.1016/j.jclepro.2022.133774
  55. Zhang, Y., Hu, Y., Talarico, R., Qiu, X., Schwartz, J., Fell, D.B., Oskoui, M., Lavigne, E. and Messerlian, C. 2024. Prenatal exposure to ambient air pollution and cerebral palsy. JAMA Netw. Open, 7: 2420717. https://doi.org/10.1001/jamanetworkopen.2024.20717
  56. Zhao, K.R., Quan, D.Y., Yang, D.Y., Yang, J. and Lin, K. 2010. A System for Identyfing and Analyzing Environmental Accident Risk Sources. International Society for Environmental Information Sciences 2010 Annual Conference (ISEIS), 2010: 1413-1421. doi:10.1016/j.proenv.2010.10.154
  57. Zhou, D., Yang, Y., Zhao, Z., Zhou, K., Zhang, D., Tang, W. and Zhou, M. 2025.Air pollution-related disease and economic burden in China, 1990–2050: A modelling study based on Global burden of disease. Environment International, 196: 109300. https://doi.org/10.1016/j.envint.2025.109300