## 1 Introduction  
In recent decades, society has witnessed the rapid advancement of information and communication technologies, which has significantly transformed safety [1, 2], health [3, 4], and environmental monitoring [5]. Innovations in the Internet of Things (IoT), artificial intelligence (AI)/machine learning (ML), and data analytics have enabled more sophisticated and real-time monitoring capabilities [6, 7]. Wearable devices, environmental sensors,and intelligent infrastructure are now commonplace, collecting vast amounts of data that can be analyzed to improve public safety and individual well-being [8; 9]. Concurrently, the emergence of the Metaverse represents a paradigm shift in how humans interact with digital and physical environments [10]. Extending the traditional IoT to the Internet of Physical-Virtual Things (IoPVT), the Metaverse creates a seamless integration between physical objects and their virtual counterparts. This integration allows for real-time synchronization and interaction within the Metaverse, a collective virtual shared space that merges augmented reality (AR), virtual reality (VR), and the Internet [11; 12]. Using the capabilities of the IoPVT, it becomes possible to create dynamic digital twins (DTs) of physical environments [13]. These digital representations can be used for advanced analytics, predictive modeling, and immersive simulations, opening new horizons in proactive safety and health monitoring [14]. While traditional IoT monitoring systems have significantly improved our ability to collect environmental and infrastructural data in various settings [15; 16], these systems typically function within a limited paradigm, i.e., sensing physical phenomena and transmitting data for analysis in separate, often disconnected computational environments. In contrast, our proposed IoPVT framework represents a fundamental paradigm shift through the seamless integration of physical sensing with virtual representation and interaction. Unlike the traditional IoT, which largely maintains a separation between physical and digital domains, the IoPVT creates a continuous bidirectional flow where physical reality and its digital counterpart are constantly synchronized. This integration enables passive monitoring and active intervention through virtual simulation before physical implementation. Where conventional IoT systems might provide isolated snapshots of conditions, DTs in the IoPVT allow for a holistic, contextual understanding of complex environments while facilitating predictive analytics and immersive visualization that traditional monitoring approaches cannot achieve. Figure 1 highlights the comparison between traditional IoT and IoPVT approaches for outdoor safety monitoring. Traditional IoT systems operate with siloed components and one-way information flow, primarily providing reactive monitoring capabilities. In contrast, the IoPVT creates a seamless integration between physical and virtual environments through digital twins and immersive analytics, enabling proactive monitoring and multidimensional context awareness with continuous feedback loops. In addition, despite technological advancements, monitoring the safety, health, and environmental conditions in outdoor environments remains challenging [17; 18]. Traditional systems often focus on indoor settings or controlled environments, leaving a gap in monitoring capabilities in outdoor, dynamic, and complex settings. On the one hand, outdoor environments are subject to changing weather conditions, natural disasters, and varying terrain, affecting the reliability of sensors and monitoring equipment. However, in rural and coastal areas, there may be limited infrastructure for data transmission and sensor deployment. In addition, the unpredictability of human behavior in public spaces adds complexity to monitoring efforts. Furthermore, collecting and integrating data from various sources, such as environmental sensors, biometric devices, and infrastructure monitoring systems, requires sophisticated data fusion techniques.