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Lookup NU author(s): Dr Sneha Verma
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 by the authors.This review explores advanced sensing technologies and deep learning (DL) methodologies for monitoring airborne particulate matter (PM), which is critical for environmental health assessments. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. This review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. This review highlights the data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. This review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications.
Author(s): Chauhan BVS, Verma S, Rahman BMA, Wyche KP
Publication type: Review
Publication status: Published
Journal: Atmosphere
Year: 2025
Volume: 16
Issue: 4
Online publication date: 22/03/2025
Acceptance date: 18/03/2025
ISSN (electronic): 2073-4433
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: https://doi.org/10.3390/atmos16040359
DOI: 10.3390/atmos16040359
Data Access Statement: The literature works considered for the purposes of this study are indexed in the Scopus database
