## 4 Results and Discussion  
The most likely outcomes from the analysis of the case study are:  
1. The main result of the project is the EU-SENSE system, based on machine learning algorithms that work with data coming from different nodes. The system's advantage is to reduce noise from the environment (false alarm rate). When an anomaly is detected in the environmental noise, inverse dispersion modelling is used for source characterization. As a result, the source position and the source strength are reliably estimated;
2. The EU-SENSE system is a versatile, state-of-the-art solution that will enhance the work of CBRN practitioners. Data on hazardous substances are gathered systemically and more effectively when compared with a situation where CBRN personnel use different devices to detect a chemical hazard. The incoming data are formatted, and, in case of large contamination, are sent to a CBRN warning and reporting centre;
3. Introducing a new system often involves training. In the case of the EU-SENSE system, a training mode was proposed. It will involve a case study approach and simulation;
4. A framework for research and development activities can be applied in other work on developing sensors for testing environments. A problem-solving theory can stipulate other eventual commercial products [49]. Regarding the above, the EU-SENSE system offers significant improvements in the situational awareness of first responders. Based on the application of computational tools, practitioners can better assess the situation and apply countermeasures with an improved coordination. It should be stressed that the EU-SENSE project is closely related to environmental security and safety as it focuses on chemical reconnaissance. Chemical agents or toxic industrial chemicals are often used (or generated as waste) in the technological process of industrial plants. Potential incidents and breakdowns might lead to a contamination threat of the local environment on a large scale [50; 51]. In such scenarios, the EU-SENSE system will help first responders monitor the threat and its dispersion over the area of interest. During the operation, the network will enable real-time data analysis from the deployment site through the situational awareness tool, which serves as the main access point to the system. Moreover, the system can support critical infrastructure resilience. Critical infrastructure is vulnerable to different natural influences, including natural disasters [52] and they may cause the release of chemicals [53; 54]. In such a situation, the system, which relies on data from many sensors, can be a reliable tool for immediate hazards analysis. Critical infrastructure can also be threatened by terrorist activities [55; 56]. In order to combat any human-made threat, sensors can be used in drones or other robotic vehicles. In these contexts, the EU-SENSE system can aid CBRN practitioners in saving the natural environment by reducing potential side effects for human health. The use of the system will also help public services to organise more efficient and better-prepared actions. In addition, any novel system requires training. The training module, which is a part of the situational awareness layer, serves as an example of practices that should be followed. The design of the didactic activities should be intuitive and based on usability attributes [57]. In further research and development works, the instructions could be generated by artificial intelligence [58]. It is vital to stress that NATO countries use uniform procedures in the warning and reporting system (ATP-45). This paves the way for wide dissemination and use of the project results. The introduction of machine learning as part of the analysis can improve the assessment of contamination situations and support CBRN specialists in their work, especially in a large CBRN event (cross-border) and the need to process large amounts of data. Current CBRN information management programs do not have this functionality [59].