STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks

  1. Harish Reddy Gantla Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad
  2. Kasthuri Rajendra Prasad Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad
  3. Harish Chandra Mohanta Department of Electronics and Communication Engineering, Centurion University of Technology and Management
  4. G. Anil Kumar Department of Physics, Sreenidhi Institute of Science and Technology, JNTU, Hyderabad
  5. V S S P L N. Balaji Lanka Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad
  6. Reshma V K. Department of Computer Science and Engineering, Sri Krishna college of Engineering and Technology, Coimbatore

Abstract

Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to capture nonlinear and temporal dynamics inherent in gas dispersion patterns. Moreover, few efforts effectively integrate polymer-based sensing materials with real-time intelligent inference pipelines. To address this, we propose an IoT-integrated system leveraging polymer-composite gas sensors in conjunction with a Long Short-Term Memory (LSTM) neural architecture for dynamic NO₂ prediction. The polymer matrix, synthesized with conductive fillers and tailored for gas sensitivity, provides enhanced selectivity and faster response rates. These sensors are embedded within a low-latency wireless acquisition framework, enabling real-time data streaming to a cloud-based LSTM engine for time-series prediction. Experimental results demonstrate that the proposed model outperforms conventional baselines including ARIMA, SVR, and Random Forest in terms of prediction accuracy (MAE: 2.14 ppb, R²: 0.93), while maintaining sub-second latency in edge-to-cloud inference cycles. Sensitivity analysis confirms superior sensor response across varying NO₂ concentrations under controlled and outdoor conditions. This fusion of polymer-based sensing and deep sequence learning presents a scalable and adaptive architecture for smart environmental monitoring. The approach holds potential for deployment in edge-intelligent air quality systems, supporting public health policy and sustainable urban planning.

Keywords

References (39)

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