Manuscript Topics

The escalating concern over air pollution underscores the need for sophisticated tools and applications utilizing advanced Artificial Intelligence (AI) techniques to monitor and assess associated health risks effectively. Worldwide, the pervasive nature of air pollution, its environmental impact, and the potential health risks it poses have raised alarm. Numerous studies have been conducted to identify the origins and severity of air pollution-related issues. Continuous monitoring of air quality and pollutant levels over an extended period helps establish the extent of pollution and reveals trends in air quality. This evaluation provides valuable insights for environmental agencies, policymakers, and the general public. Artificial intelligence-powered air pollution monitoring tools offer actionable insights that can benefit various stakeholders. Additionally, Ai-based health risk assessment models aim to enhance our understanding of the intricate relationship between air quality and public health. These models support proactive measures to mitigate the adverse effects of air pollution on individual well-being. By leveraging advanced Machine Learning(ML) and Deep Learning(DL) technologies, we can foster a more informed and responsive approach to address the challenges posed by air pollution worldwide.

The primary objective of this Special Issue is to offer an overview of recent developments in air pollution monitoring systems. These advancements encompass the design of artificial intelligence (AI), machine learning (ML), and deep learning (DL) models for the development of advanced monitoring systems

Topics of interest for this special issue include, but are not limited to:
• AI-enabled pollution monitoring techniques.
• Machine Learning models for air quality assessment .
• Deep Learning for air quality assessment.
• ML-based air pollution and health risk detection.
• Health risk assessment.
• Investigation of indoor and outdoor air pollution.
• Advanced AI techniques for health impact assessment.
• Sensor applications for air pollution monitoring.
• IoT based smart system for air pollution assessment.
• Development of air quality prediction models.
• Predictive models and forecasting models.

We invite submissions for this special issue, which aims to explore AI-based air pollution monitoring and health risk assessment. We welcome contributions from researchers, academicians, and policymakers from diverse disciplines and backgrounds.

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