# A Regional Multi-Agent Air Monitoring Platform
## 4 The ACreM Platform
### Air Monitor Agent
Using properties such as hasLimitValue and hasDuration ensures semantic clarity and allows for automated reasoning about compliance. For example, the ontology can determine whether observed pollutant levels adhere to these thresholds and flag non-compliance for further action. This structured representation facilitates integration with real-time sensor data, allowing applications to dynamically monitor, analyze, and report air quality violations. By incorporating such temporal and quantitative constraints, the ontology provides a robust foundation for reasoning about air quality and supporting decision-making in environmental management. The axioms created in the ontology are associated with threshold values for various air pollutants as defined by the World Health Organization (WHO) standards. These values, measured in milligrams per cubic meter (mg/m\({}^{3}\)), typically represent average daily concentrations, although some thresholds may also include annual averages or shorter-term values. The threshold values may vary across different regions depending on their specific characteristics, such as geographic location, the presence of industrial zones, heating methods, traffic levels, and other environmental or socioeconomic factors. To address this variability, we introduced individuals within the class RegionPollutants_(City). Each individual in this class represents a specific city or region and is defined using data properties that link the individual to its corresponding threshold values. For example, the thresholds for air pollutants in the region represented by the individual Plovdiv are detailed in Figure 10.1  
The ontology facilitates the modeling of region-specific pollutant thresholds by integrating both universal guidelines (e.g., WHO standards) and context-sensitive data tailored to local environments. The RegionPollutants_(City) class provides a framework to capture the unique air quality characteristics of specific regions, while maintaining consistency with global benchmarks. Figure 10.2 SubClass Of axiom for the Nitrogen_Dioxid class. For each region (e.g., Plovdiv), the ontology defines the following:  
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* Thresholds for pollutants such as PM10, PM2.5, SO2, NO2, CO, and O3. * All values are expressed in mg/m3. * Thresholds correspond to specific durations, such as daily averages (e.g., 24 h periods) or annual means. * Attributes that account for local factors, such as dominant industrial activities, population density, and prevalent weather patterns, are presented as annotation properties for the concrete region (Figure 18). The Air Pollution Ontology provides a robust framework for understanding and monitoring air quality. This ontology-based approach enables data-driven decision-making, helping to identify hotspots, predict pollution events, and develop tailored interventions to improve air quality in Plovdiv. The ontology incorporates the standard threshold values for air pollutants defined by the World Health Organization (WHO), ensuring a globally recognized baseline for air quality assessment. Simultaneously, its architecture is designed to be extensible, allowing for the addition of specific data for individual regions. For example, the region of Plovdiv is included with detailed pollutant thresholds based on its unique geographic, industrial, and climatic characteristics. Furthermore, in cases where specific regional data is unavailable, the ontology's flexibility ensures that the WHO standard thresholds can be applied universally, serving as default values for any region. This dual capability makes the ontology both adaptable to local contexts and reliable for broader applications. By facilitating the integration of region-specific attributes alongside global standards, the ontology supports effective air  
Figure 10.3 Individual Plovdiv and data properties for different pollutions. Figure 10.4 Attributes that account for local factors of the Plovdiv region. quality management, compliance monitoring, and sustainable urban development across diverse geographic areas.