Штучний інтелект

Анотація: Air pollution poses a significant threat to public health, ecosystems, and the global climate. Accurate prediction and effective management of air quality are of paramount importance, which, in turn, rely on sophisticated air quality models. These models integrate a variety of atmospheric parameters to simulate the dispersion of pollutants in the complex urban atmosphere, yet the influence of specific input parameters on predicted pollution concentrations has not been fully elucidated. This comprehensive study assesses how variations in model input parameters can lead to divergent pollution concentration outputs, with the goal of identifying those that are most critical to model accuracy. Using observational data from air quality monitoring stations in conjunction with meteorological records, the study explores the sensitivity of forecasted pollutant concentrations to fluctuations in model inputs such as emission source strength, atmospheric stability, wind speed and direction, diurnal heating patterns, chemical reaction rates, and boundary layer dynamics. Dispersion models are evaluated across different spatial and temporal scales to gauge their response to environmental variables and topographic features. The performance of these models is also assessed against satellite-derived pollutant measurements to encompass a broader geographical context. Through the application of numerical simulations and statistical analyses, the study quantifies the relative impact of each parameter. Cross-validation techniques, along with uncertainty quantification methods, are applied to ensure the reliability of the conclusions drawn. The research also incorporates the use of machine learning tools to identify complex patterns in the environmental data that may be missed by traditional modeling approaches. The abstract concludes that a detailed understanding of influential model parameters is essential for refining air quality predictions. Improvements in the accuracy of dispersion models will enable policymakers and urban planners to make better-informed decisions regarding air pollution control and mitigation strategies. This work forms the foundation for future advancements in the field of atmospheric sciences and encourages continued exploration into the interaction between anthropogenic activities, meteorological phenomena, and air quality outcomes.

Ключові слова: air quality modelling, pollutant dispersion, environmental parameters, model sensitivity, observational data, statistical analysis, meteorological influences, machine learning

Посилання:

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