An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings
Abstract
:1. Introduction
2. Methodology
2.1. Mitigation Measures
2.2. Sampling Site
2.3. Data
2.4. Data Analysis
2.4.1. Bivariate Polar Plot
2.4.2. Machine Learning
2.4.3. Metaheuristics
2.4.4. Explainable Artificial Intelligence
2.4.5. Cluster Analysis of Variable Impacts
3. Results and Discussion
3.1. Implementation of the State of Emergency
3.2. Modelling Results
3.2.1. Environmental Setting E3—Chemical Manufacturing, Combustion, and Petroleum-Related Emissions
3.2.2. Environmental Setting E7—Non-Combustion Emissions, Nocturnal Chemistry, and Meteorological Context
3.2.3. Environmental Setting E4—Local Industrial Processes
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Environmental Setting | Mean Impact [ppb] | Mean Normalized Impact [%] | Mean Absolute Impact [ppb] | Population Percentage [%] | Dominant Inherent Impact | Dominant Inherent Impact Prevalence [%] |
---|---|---|---|---|---|---|
E-1 | 0.04 | 15.76 | 0.37 | 15.9 | ||
E0 | −0.12 | −48.47 | 0.3 | 2.9 | Moderate negative | 36.9 |
E1 | −0.09 | −38.51 | 0.38 | 38.6 | High negative | 22.8 |
Moderate negative | 35.4 | |||||
E2 | 0.02 | 8.89 | 0.27 | 4.0 | Moderate negative | 23.4 |
Moderate positive | 22.9 | |||||
High positive | 29.0 | |||||
E3 | −0.04 | −15.67 | 0.22 | 10.5 | Moderate negative | 29.2 |
Moderate positive | 27.1 | |||||
E4 | −0.01 | −2.33 | 0.25 | 5.4 | Moderate negative | 24.4 |
Moderate positive | 26.5 | |||||
E5 | 0.26 | 107.84 | 0.63 | 3.2 | Moderate positive | 23.7 |
High positive | 25.2 | |||||
E6 | −0.11 | −45.15 | 0.32 | 6.5 | Moderate negative | 27.9 |
Minor | 21.3 | |||||
Moderate positive | 24.6 | |||||
E7 | 0.37 | 151.86 | 0.87 | 7.2 | Moderate positive | 29.6 |
High positive | 25.3 | |||||
E8 | −0.07 | −27.26 | 0.32 | 5.8 | Moderate negative | 26.8 |
Moderate positive | 26.6 |
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Radić, N.; Perišić, M.; Jovanović, G.; Bezdan, T.; Stanišić, S.; Stanić, N.; Stojić, A. An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings. Atmosphere 2025, 16, 231. https://doi.org/10.3390/atmos16020231
Radić N, Perišić M, Jovanović G, Bezdan T, Stanišić S, Stanić N, Stojić A. An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings. Atmosphere. 2025; 16(2):231. https://doi.org/10.3390/atmos16020231
Chicago/Turabian StyleRadić, Nataša, Mirjana Perišić, Gordana Jovanović, Timea Bezdan, Svetlana Stanišić, Nenad Stanić, and Andreja Stojić. 2025. "An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings" Atmosphere 16, no. 2: 231. https://doi.org/10.3390/atmos16020231
APA StyleRadić, N., Perišić, M., Jovanović, G., Bezdan, T., Stanišić, S., Stanić, N., & Stojić, A. (2025). An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings. Atmosphere, 16(2), 231. https://doi.org/10.3390/atmos16020231