This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting information system for integrating data processing, analysis, and visualization to improve environmental monitoring practices is described in this article. The system utilizes machine learning models (ARIMA and BATS) for time series forecasting, automatically selecting the optimal model based on accuracy metrics. Spatial analysis employing inverse distance weighting (IDW) provides insights into pollutant distribution, while correlation analysis identifies relationships between pollutants. The system was tested using retrospective data from the Kremenchuk agglomeration (2007–2024), demonstrating its ability to forecast air quality parameters and identify areas exceeding maximum permissible pollutant concentrations. Results indicate that BATS often outperforms ARIMA for several key pollutants, highlighting the importance of automated model selection. The developed system offers a cost-effective solution for local municipalities, enabling data-driven decision-making, optimized monitoring network placement, and improved alignment with European Union environmental standards.

Today there is a wide variety of similar products in the market that are usually offered by several suppliers. This leads to the need to analyze the market and to solve the problem of finding the best goods supplier that would satisfy all necessary consumer requirements. The study is devoted to the web-based information system development for selecting the optimal supplier among those who are located on the AliExpress trading platform under multicriteria conditions. The optimal supplier selection is carried out with the Analytic hierarchy process method considering the priority of the evaluation criteria for the consumer. The suppliers are evaluated by the complex indicator of supplier’s competitiveness which is calculated by developed mathematical model. The main functions of the proposed information system are the follows: goods selection, the forming a set of alternatives which consist from all suppliers, the evaluation of alternatives by eight independent criteria, the calculation the complex indicator of supplier’s competitiveness, the forming of a good’s supplier’s rating. It allows to find the best goods’ supplier providing data on calculated suppliers rating and current prices.