Autonomous System for Air Quality Monitoring on the Campus of the University of Ruse: Implementation and Statistical Analysis

Maciej Kozłowski (), Asen Asenov, Velizara Pencheva, Sylwia Agata Bęczkowska, Andrzej Czerepicki and Zuzanna Zysk
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Maciej Kozłowski: Faculty of Transport, Warsaw University of Technology, 00-661 Warszawa, Poland
Asen Asenov: Faculty of Transport, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Velizara Pencheva: Faculty of Transport, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Sylwia Agata Bęczkowska: Faculty of Transport, Warsaw University of Technology, 00-661 Warszawa, Poland
Andrzej Czerepicki: Faculty of Transport, Warsaw University of Technology, 00-661 Warszawa, Poland
Zuzanna Zysk: Faculty of Transport, Warsaw University of Technology, 00-661 Warszawa, Poland

Sustainability, 2025, vol. 17, issue 14, 1-18

Abstract: Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO 2 , NO 2 , SO 2 , PM 1 , PM 2.5 , and PM 10 , along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models.

Keywords: air quality monitoring; IoT sensors; particulate matter; gaseous pollutants; statistical analysis; Spearman correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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