Innovative Approaches in Air Quality Analysis with Deep Air Learning

Adarsh Bajpai; Dr. Anita Pal

Innovative Approaches in Air Quality Analysis with Deep Air Learning

Authors: Adarsh Bajpai, Dr. Anita Pal

Unique Paper ID: 165862
Volume: 11
Issue: 1
PageNo: 1982-1990

Keywords: Air Quality Pollutant Detection Internet of Things (IoT) Deep Air Learning Environmental Risks.

Abstract:
Air quality analysis is critical for understanding environmental impacts on public health and ecosystems. Traditional methods often rely on sparse monitoring networks, limiting spatial coverage and real-time insights. In response, this paper proposes a novel approach leveraging Deep Air Learning (DAL), a fusion of deep learning techniques with atmospheric science. By harnessing vast datasets from various sources including satellite imagery, ground-based sensors, and meteorological data, DAL offers unprecedented accuracy and spatial resolution in air quality assessment. This abstract explores the principles behind DAL, its applications in pollutant detection, forecasting, and hotspot identification, and its potential to revolutionize air quality management. Through case studies and comparative analyses, we demonstrate the efficacy of DAL in addressing complex challenges such as urban pollution, industrial emissions, and wildfire smoke monitoring. Furthermore, we discuss future directions including integration with Internet of Things (IoT) devices and policy implications for sustainable environmental governance. Overall, this paper advocates for the adoption of innovative approaches like DAL to advance air quality analysis and mitigate environmental risks in an increasingly interconnected world.

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ISSN: 2349-6002
Volume: 11
Issue: 1
PageNo: 1982-1990

Innovative Approaches in Air Quality Analysis with Deep Air Learning

Available:https://ijirt.org/Article?manuscript=165862

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