The current state of environmental safety requires the introduction of the latest technologies for monitoring and analyzing environmental data. Air pollution with fine particles (PM2.5, PM10) from local emission sources creates significant computational challenges due to insufficient data and the dynamic nature of pollution propagation processes.
Objective. The goal of the work is to solve these problems by developing and integrating modern methods and tools for modeling and intelligent analysis of air pollution characteristics. A prototype hybrid algorithmic pipeline is proposed that integrates an adapted Gaussian model with optimized machine learning and neural network models. The method utilizes a Mamdani-type fuzzy logic system to determine the Atmospheric Stability Class based on continuous meteorological inputs. Additionally, Bayesian inverse modeling using Markov Chain Monte Carlo (MCMC) methods is applied to estimate unknown source emission intensity. The approach is implemented using cloud technologies (Azure Data Lake) and edge computing systems (Nvidia Jetson Nano). A large-scale comparative analysis of deep neural network architectures (Bidirectional LSTM, CNN) and ensemble models (XGBoost, CatBoost) was conducted. The Bidirectional LSTM provided the best overall performance (MSE=0.521, R2=0.985). The integration of fuzzy stability inputs reduced the MSE by 16%. The experiments conducted and numerical modeling confirmed the effectiveness of the proposed methods and the operability of the developed neuro-controller system. The results allow recommending the integrated approach for real-time environmental monitoring and decision support in data-scarce environments.