Штучний інтелект

Анотація: Atmospheric pollution constitutes a substantial environmental and public health burden globally. Effective mitigation necessitates rigorous quantitative characterization of pollutant concentrations and their complex temporal dynamics, particularly concerning emissions from localized sources. Conventional analytical approaches often exhibit limitations when confronted with the high dimensionality, non-linearity, and stochasticity inherent in atmospheric dispersion processes. This work reviews and evaluates synergistic methodologies integrating atmospheric dispersion modeling with intelligent data analysis to elucidate quantitative air pollution characteristics. The modeling component specifically addresses advanced Gaussian plume formalisms designed for single-point emission sources (industrial funnels/stacks), incorporating complex physical phenomena such as plume rise dynamics, deposition mechanisms, and potential chemical decay. These physics-based models are employed alongside and often integrated with, intelligent analytical systems leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms for predictive modeling, anomaly detection, pattern recognition, and the assimilation of heterogeneous data streams. The synergistic integration of sophisticated Gaussian models for point sources with AI/ML techniques facilitates enhanced predictive capabilities for downwind pollutant concentrations and deposition fields. Discussion focuses on demonstrable improvements in forecast accuracy compared to baseline models, the ability to resolve complex plume behaviors under varying meteorological regimes, refined source term estimation capabilities, and the robust evaluation of emission control scenarios specifically targeting point sources. The fusion of deterministic dispersion physics, as captured by complex Gaussian formulations, with adaptive, data-driven AI/ML methodologies yields a more potent and nuanced analytical framework than achievable with either approach in isolation. The integrated application of advanced atmospheric dispersion models, exemplified by complex Gaussian treatments for single-funnel emissions, coupled with intelligent data analysis techniques, represents a significant advancement in the quantitative assessment and prediction of localized air pollution events. This paradigm provides essential tools for scientifically robust impact assessment, regulatory compliance verification, and the optimization of air quality management strategies pertaining to point-source emissions.

Ключові слова: modeling, gaussian dispersion, point source analysis, machine learning, predictive analytics, intelligent systems, quantitative analysis

Посилання:

1. Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (3rd ed.). Wiley.
2. Kahl J. D. W., Chapman H. L. Atmospheric stability characterization using the Pasquill method: A critical evaluation. Atmospheric Environment. 2018. Vol. 187. P. 196–209. URL: https://doi.org/10.1016/j.atmosenv.2018.05.058
3. AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization / A. J. Cimorelli et al. Journal of Applied Meteorology. 2005. Vol. 44, no. 5. P. 682–693. URL: https://doi.org/10.1175/jam2227.1
4. Lohmann L. The Dyson effect: carbon ''offset'' forestry and the privatisation of the atmosphere. International Journal of Environment and Pollution. 2001. Vol. 15, no. 1. P. 51. URL: https://doi.org/10.1504/ijep.2001.000591
5. Real-time on-site monitoring of soil ammonia emissions using membrane permeation-based sensing probe / M. Zhou et al. Environmental Pollution. 2021. Vol. 289. P. 117850. URL: https://doi.org/10.1016/j.envpol.2021.117850
6. Long-term trends in atmospheric Quercus pollen related to climate change in southern Spain: A 25-year perspective / R. López-Orozco et al. Atmospheric Environment. 2021. Vol. 262. P. 118637. URL: https://doi.org/10.1016/j.atmosenv.2021.118637
7. Martin R. V. Satellite remote sensing of surface air quality. Atmospheric Environment. 2008. Vol. 42, no. 34.P. 7823–7843. URL: https://doi.org/10.1016/j.atmosenv.2008.07.018
8. Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2 / M. Crippa et al. Earth System Science Data. 2018. Vol. 10, no. 4. P. 1987–2013. URL: https://doi.org/10.5194/essd-10-1987-2018
9. Fully coupled “online” chemistry within the WRF model / G. A. Grell et al. Atmospheric Environment. 2005. Vol. 39, no. 37. P. 6957–6975. URL: https://doi.org/10.1016/j.atmosenv.2005.04.027
10. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1 / K. W. Appel et al. Geoscientific Model Development. 2017. Vol. 10, no. 4. P. 1703–1732. URL: https://doi.org/10.5194/gmd-10-1703-2017
11. Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines / S. Kecorius et al. Atmospheric Environment. 2017. Vol. 170. P. 169–183. URL: https://doi.org/10.1016/j.atmosenv.2017.09.037
12. Raissi M., Perdikaris P., Karniadakis G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics. 2019. Vol. 378. P. 686–707. URL: https://doi.org/10.1016/j.jcp.2018.10.045
13. Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds / S. Li et al. Environmental Modeling & Software. 2021. Vol. 143. P. 105083. URL: https://doi.org/10.1016/j.envsoft.2021.105083