Methodology of forming dynamic air quality monitoring models in urban areas based on traffic sensor network optimization algorithms

This scientific article presents a comprehensive theoretical and applied study dedicated to the optimization of atmospheric air monitoring systems in modern cities with populations over 100,000. The relevance of this study stems from the need to transition from traditional passive static monitoring methods to dynamic adaptive models of environmental quality management, a key element of the "smart" and "symbiotic" city concept for minimizing environmental risks. Four major industrial and administrative centers of the Republic of Belarus – Grodno, Pinsk, Lida, and Soligorsk – were selected as the research sites. The scientific novelty of this study lies in the development of an integrated approach combining a mathematical solution to the optimal sensor placement problem (SLP) with a multivariate graphical analysis of anthropogenic impact. A detailed methodology is provided for conducting 20,000 iteration cycles for each transport scheme, which made it possible to identify critical nodes in urban framework networks where the concentration of sensors provides minimal error in calculating transport and environmental parameters. Particular attention is paid to the interdisciplinary consideration of meteorological factors, in particular, the use of a weighting factor for wind rose indicators when prioritizing gas analyzer installation locations. The article systematizes data on traffic load, the hierarchy of street and road networks, and the density of social facilities in the studied cities. Implementation of the proposed model (based on 32 automated posts) allows for the generation of symbolic solutions for assessing the environmental situation in unmonitored network sections. The study's results form the basis for the development of predictive emission management systems capable of adjusting traffic management in real time to prevent exceeding maximum permissible pollutant concentrations. The conclusion outlines promising areas for integrating the resulting models with machine learning and artificial intelligence algorithms within the framework of longterm sustainable urban development programs.

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