Research on Environmental and Climate Change Monitoring in Edge Computing focuses on leveraging distributed edge devices and intelligent computing frameworks to collect, process, and analyze environmental data in real-time for climate monitoring, pollution tracking, and natural disaster management. This area addresses challenges such as heterogeneous sensor networks, large-scale data streams, energy and computational constraints, and the need for low-latency processing to support timely decision-making. Key research directions include edge-based data aggregation and filtering, real-time analytics for environmental and climate indicators, and AI- and machine learning-driven anomaly detection in sensor data. Other emerging topics involve predictive modeling of climate patterns, adaptive resource allocation for energy-efficient monitoring, and integration of IoT, edge, and cloud systems for scalable and reliable environmental intelligence. Additionally, research on privacy-preserving data collection, fault-tolerant edge deployments, and edge-enabled early-warning systems represents significant avenues for advancing intelligent, responsive, and sustainable environmental and climate monitoring solutions.