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

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]09/10/2025 at 10:49 AM[/if 2224] | [if 1553 equals=””] Volume : 13 [else] Volume : 13[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 06 | Page : 332 347

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    Harish Reddy Gantla, Kasthuri Rajendra Prasad, Harish Chandra Mohanta, G. Anil Kumar, V S S P L N. Balaji Lanka, Reshma V K.,

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  1. Associate Professor, Assistant Professor, Professor, Assistant Professor, Assistant Professor, Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Department of Electronics and Communication Engineering, Centurion University of Technology and Management, Department of Physics, Sreenidhi Institute of Science and Technology, JNTU, Hyderabad, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, Department of Computer Science and Engineering, Sri Krishna college of Engineering and Technology, Coimbatore, Telangana, Telangana, Odisha, Telangana, Telangana, Tamil Nadu, India, India, India, India, India, India

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nReal-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.nn

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Keywords: Polymer-composite sensors, air quality prediction, IOT monitoring, LSTM neural networks, real-time environmental sensing.

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Polymer and Composites ]

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How to cite this article:
nHarish Reddy Gantla, Kasthuri Rajendra Prasad, Harish Chandra Mohanta, G. Anil Kumar, V S S P L N. Balaji Lanka, Reshma V K.. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks[/if 2584]. Journal of Polymer and Composites. 13/09/2025; 13(06):332-347.

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How to cite this URL:
nHarish Reddy Gantla, Kasthuri Rajendra Prasad, Harish Chandra Mohanta, G. Anil Kumar, V S S P L N. Balaji Lanka, Reshma V K.. [if 2584 equals=”][226 striphtml=1][else]Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks[/if 2584]. Journal of Polymer and Composites. 13/09/2025; 13(06):332-347. Available from: https://journals.stmjournals.com/jopc/article=13/09/2025/view=0

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Volume 13
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 06
Received 21/07/2025
Accepted 03/09/2025
Published 13/09/2025
Retracted
Publication Time 54 Days

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