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

Öz

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.

Anahtar Kelimeler

• : Endüstriyel Kaza
• Hava Kirliliği
• Endüstriyel Kaza Riski
• Teknolojik Afetler
• Hava Kalitesi

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

Öz

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.

Anahtar Kelimeler

• Industrial Accident
• Air Pollution
• Industrial Accident Risk
• Technological Disaster
• Technological Disaster
• Air Quality

Kaynakça

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