Algorithmic Support for Environmental Impact Assessment of Road Transport Infrastructure on Atmospheric Air in Urban Areas

Introduction. In this study, we develop a system for assessing the environmental impact of road transport on air quality in cities. The research relevance is determined by the need to create improved approaches to assessing air pollution in cities associated with the load of automobile transport. In this respect, a generalized geoinformation model for identifying the urban areas most exposed to pollution is required. This model can be used when developing measures aimed at improving the environmental situation in St Petersburg.Aim. Development of theoretical foundations, as well as software and algorithmic support, with the purpose of creation of a geographic information system for modeling urban air pollution.Materials and methods. The methods of measurement theory mathematics, systems theory mathematics, mathematical modeling, geoinformation data processing, and object-oriented programming were applied.Results. Following the development of theoretical foundations, a software and algorithmic support complex for a system for assessing the level of air pollution in urban environments under the impact of road transport was created. This system includes the modules of initial data preparation, road network spatial modeling, calculation of pollutant emissions by vehicle flows, and modeling of the distribution of pollutant concentrations for each pollutant. A digital model of road transport pollution in the residential areas of St Petersburg was developed.Conclusion. The developed software and algorithmic support can serve as the basis for development of a digital air pollution map. This map can be used when managing problems of urban planning and improving urban environment comfort as part of St Petersburg’s strategic development program. The implementation of the developed digital model in the geographic information system of the city provides opportunities for assessing the impact of road pollution on residential infrastructure. Depending on the source data, including various weather and traffic conditions, the model can be used to identify the most dangerous combinations of pollution factors.