Models and Algorithms for the Comprehensive Assessment and Forecasting of Environmental Conditions, Using the Example of Carbon Monoxide Air Pollution and Noise Pollution Associated with Road Traffic in Cities

This paper presents a set of algorithms and models for the comprehensive assessment of the environmental situation in urbanised areas subject to anthropogenic impacts from industrial and transport facilities. The proposed mechanism combining fuzzy logic and neural network predictive models, with subsequent spatial visualization of the results using GIS technologies, enabling a shift from traditional component-by-component assessments to a holistic spatio-temporal characterisation of the environmental situation, with the possibility of formulating well-founded management measures. An algorithm for generating management scenarios has been developed, ensuring a closed-loop cycle: monitoring—integrated assessment—forecasting—development of environmental protection measures. The algorithm was tested using field environmental monitoring data from urban areas, collected at 82 road sections (2023–2025), for which motor vehicle traffic was identified as the primary source of anthropogenic pressure. Forty-three mathematical models were developed based on an artificial neural network, which achieved a coefficient of determination of 0.75–0.82 on a test data sample. Spatial visualisation of the results was implemented in the ArcMap 10.8 GIS system, whilst modelling was carried out in the MatLab environment. The objective of this study is to develop a set of tools for a comprehensive assessment of the environmental conditions in urbanized areas. The proposed set of tools can be expanded to include other components, however, testing is currently limited to two components (CO and noise) in a single city (Oryol, Russia).