A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence

Year : 2025 | Volume : 02 | Issue : 02 | Page : 50 62
    By

    Milind shivaji kadam,

  • Vaishnavi Gopal Shirsikar,

  • N. N. Shaikh,

  • Aditi Dinanath Shahane,

  • Dr. Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India
  2. Assistant Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India
  3. Assistant Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India
  4. Assistant Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India
  5. Professor and Head, Brahmdevdada Mane Institute of Technology, Solapur, Maharastra, India

Abstract

The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI for robust data standardization and enhancement. Deep Neural Networks (DNNs) are employed for predictive calibration, sophisticated noise reduction, and the fusion of heterogeneous data sources (sensor data, meteorological inputs, traffic patterns, satellite imagery). This approach moves atmospheric monitoring from static, generalized reporting to dynamic, hyper-local spatial mapping. The resultant AI-driven atmosphere monitoring system provides unprecedented spatiotemporal resolution, enabling real-time anomaly detection, accurate short-term pollutant forecasting (e.g., ozone and PM2.5), and the identification of previously invisible pollution hotspots. This framework represents a crucial step toward creating reliable, intelligent environmental early warning systems necessary for proactive public health interventions and targeted regulatory efforts

Keywords: Air Quality Monitoring (AQM), Internet of Things (IoT) Sensors, Artificial Intelligence (AI), Atmosphere

[This article belongs to International Journal of Atmosphere ]

How to cite this article:
Milind shivaji kadam, Vaishnavi Gopal Shirsikar, N. N. Shaikh, Aditi Dinanath Shahane, Dr. Kazi Kutubuddin Sayyad Liyakat. A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence. International Journal of Atmosphere. 2025; 02(02):50-62.
How to cite this URL:
Milind shivaji kadam, Vaishnavi Gopal Shirsikar, N. N. Shaikh, Aditi Dinanath Shahane, Dr. Kazi Kutubuddin Sayyad Liyakat. A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence. International Journal of Atmosphere. 2025; 02(02):50-62. Available from: https://journals.stmjournals.com/ijat/article=2025/view=234909


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Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 29/11/2025
Accepted 09/12/2025
Published 27/12/2025
Publication Time 28 Days
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