# A Regional Multi-Agent Air Monitoring Platform
###### Abstract  
Plovdiv faces significant air pollution challenges due to geographic, climatic, and industrial factors, making accurate air quality assessment critical. This study presents a hybrid multi-agent platform that integrates symbolic and sub-symbolic artificial intelligence to improve the reliability of air quality monitoring. The platform features a BDI agent, developed using JaCaMo, for processing real-time sensor measurements and a ReAct agent, implemented with LangChain, to incorporate external data sources and perform advanced analytics. By combining these AI approaches, the platform enhances data integration, detects anomalies, and resolves discrepancies between conflicting air quality reports. Furthermore, its scalable and adaptable architecture lays the foundation for future advancements in environmental monitoring. This research represents the first stage in developing an AI-powered system that supports more objective and data-driven decision-making for air quality management in Plovdiv. air monitoring; intelligent agents; BDI agents; ReAct agents; ontologies +
Footnote †: journal: Future Internet  
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0000-0001-5000-0000]V. T. T.  
and, therefore, air quality assessment has become an important research area. The air quality control procedure is a cycle of interrelated components [3]. Government and regional bodies, as well as non-governmental organizations, research institutes, and universities, are involved in ensuring this procedure. To protect human health, standards for acceptable levels of air pollutants are established and adopted. Air quality administrators use a variety of air monitoring devices, air property modeling, and other assessment tools to fully recognize air quality problems. The air monitoring cycle is a dynamic process involving continuous review and evaluation of goals and strategies based on their effectiveness. All parts of this process must be based on scientific research that provides air quality managers with a basic understanding of how pollutants are emitted, transported, and transformed in the air as well as their impact on human health and the environment. Recently, technologies involving smart components and big data have been increasingly used for air monitoring. Bulgaria is no exception, with several regions with poor air quality, including Plovdiv. Due to its geographical location, Plovdiv is surrounded by multiple pollution sources such as a lead-zinc plant, landfills, and agricultural production. With fires common during summer, the Plovdiv region suffers from heavy particulate pollution. At the same time, Plovdiv is one of the oldest continuously inhabited cities in the world and a major tourist destination, so the topic of air cleanliness is directly related to its prestige and economy. Currently, data and information are obtained from various sources, including government and local authorities, non-governmental organizations, universities, and private initiatives. However, differences between datasets are often the cause of disputes about their reliability and the conditions under which data were collected. There are often various conflicting measurements, assumptions, opinions, and publications about the reliability of the measured data and the location of the measuring instruments. Discrepancies are the cause of ineffective decision-making. For these reasons, air monitoring in the Plovdiv region is a highly sensitive topic. To establish an objective representation of air quality in the region of Plovdiv, this study aims to develop a prototype software platform that delivers the most reliable information by doing the following:  
* Collecting and analyzing measurement data from a previously established sensor network in Plovdiv [4];
* Incorporating additional information from external sources;
* Continuously comparing data to identify deviations and discrepancies;
* Ensuring reliability and accuracy in the measured and obtained data to support objective conclusions and informed decision-making.