Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring
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)...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/4/359 |
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| author | Balendra V. S. Chauhan Sneha Verma B. M. Azizur Rahman Kevin P. Wyche |
| author_facet | Balendra V. S. Chauhan Sneha Verma B. M. Azizur Rahman Kevin P. Wyche |
| author_sort | Balendra V. S. Chauhan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36c0a43f58a04442a4a0698e40b86c0d |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-36c0a43f58a04442a4a0698e40b86c0d2025-08-20T02:24:43ZengMDPI AGAtmosphere2073-44332025-03-0116435910.3390/atmos16040359Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental MonitoringBalendra V. S. Chauhan0Sneha Verma1B. M. Azizur Rahman2Kevin P. Wyche3Centre for Earth Observation Science, School of Applied Sciences, University of Brighton, Brighton BN2 4GJ, UKTech GPT Ltd., Newcastle upon Tyne NE6 2SR, UKSchool of Science and Technology, City University of London, London EC1V 0HB, UKCentre for Earth Observation Science, School of Applied Sciences, University of Brighton, Brighton BN2 4GJ, UKThis 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.https://www.mdpi.com/2073-4433/16/4/359surface plasmon resonance (SPR)particulate matter (PM)sensorsmachine learning (ML)deep learning (DL)artificial intelligence (AI) |
| spellingShingle | Balendra V. S. Chauhan Sneha Verma B. M. Azizur Rahman Kevin P. Wyche Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring Atmosphere surface plasmon resonance (SPR) particulate matter (PM) sensors machine learning (ML) deep learning (DL) artificial intelligence (AI) |
| title | Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring |
| title_full | Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring |
| title_fullStr | Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring |
| title_full_unstemmed | Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring |
| title_short | Deep Learning in Airborne Particulate Matter Sensing and Surface Plasmon Resonance for Environmental Monitoring |
| title_sort | deep learning in airborne particulate matter sensing and surface plasmon resonance for environmental monitoring |
| topic | surface plasmon resonance (SPR) particulate matter (PM) sensors machine learning (ML) deep learning (DL) artificial intelligence (AI) |
| url | https://www.mdpi.com/2073-4433/16/4/359 |
| work_keys_str_mv | AT balendravschauhan deeplearninginairborneparticulatemattersensingandsurfaceplasmonresonanceforenvironmentalmonitoring AT snehaverma deeplearninginairborneparticulatemattersensingandsurfaceplasmonresonanceforenvironmentalmonitoring AT bmazizurrahman deeplearninginairborneparticulatemattersensingandsurfaceplasmonresonanceforenvironmentalmonitoring AT kevinpwyche deeplearninginairborneparticulatemattersensingandsurfaceplasmonresonanceforenvironmentalmonitoring |