Architectural Design of a Deep Learning Framework for Real-Time Urban Air Quality Anomaly Detection

The background of this research addresses the escalating environmental crisis caused by rapid urbanization and the urgent need for advanced technological interventions to monitor atmospheric conditions. Rapid industrial growth has led to severe air pollution which directly threatens public health and sustainable development across the globe. Therefore the primary object of this study is the architectural design of a highly efficient predictive framework capable of identifying sudden spikes in air pollution levels across smart city infrastructures. To achieve this the proposed method utilizes a novel deep learning architecture that synergizes one dimensional Convolutional Neural Networks and Long Short Term Memory networks. This combination is specifically designed to extract spatial features from environmental sensors and analyze temporal sequences without relying on extensive empirical datasets or physical surveys. Through the formulation of the algorithm and the presentation of the core source code the theoretical result demonstrates a lightweight yet robust processing pipeline. This architectural blueprint significantly reduces computational overhead while maintaining high precision in anomaly identification during continuous data streaming. Ultimately the conclusion emphasizes that implementing this conceptual design will profoundly impact modern environmental technology. By providing a scalable software foundation for environmental agencies this framework directly supports the global sustainability agenda specifically aligning with the United Nations Sustainable Development Goals by empowering policymakers with proactive tools to mitigate atmospheric degradation and safeguard communities in rapidly growing urban centers worldwide.