Section: Methodology and research methods. Models and forecasts
Industrial and transport emissions are the main sources of air pollution in large cities, causing significant risks to human health. Minimizing risks requires information on the distribution and physico-chemical characteristics of emissions. Spatial and temporal detailed data are required because the intensity and composition of emissions varies greatly with time of day and local variations in wind, traffic composition and flow. There are modern mathematical models that simulate the behavior of emissions from industrial plants and traffic flows with a high degree of resolution. The chemistry of the simulated emissions has also been largely resolved by taking into account photochemical reactions as well as dry and wet deposition processes. This review present concepts of urban air pollution monitoring, and analyses and summarizes new insights of real-time air pollutants concentrations. This research is expected to open a door for creating smart cities and digital twins for effective management of environmental risks in an urbanized area. Fifty nine articles were included and studies were classified by various modeling approaches such as statistical and analytical models which give the best prediction results. We find that air pollution monitoring and assessment techniques for calculating air concentrations were successfully used to study temporal and spatial changes in pollutant concentrations. In the same time, it is impossible to create a universal analytical model for predicting the concentrations of pollutants anywhere and for any condition. It should be noted that the mathematical models are constantly being scaled to validate, optimize and expand experimental data. The outcome of this study will help engineers and researchers develop air pollution forecasts concept.
Keywords: mathematical models, air pollution, types of pollutants, environmental monitoring methods, air quality.