Articles | Volume 11, issue 11
https://doi.org/10.5194/amt-11-6203-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-6203-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2018
Research article |
| 19 Nov 2018
| 19 Nov 2018
Machine learning for improved data analysis of biological aerosol using the WIBS
Simon Ruske
CORRESPONDING AUTHOR
Centre of Atmospheric Science, SEES, University of Manchester, Manchester, UK
David O. Topping
Centre of Atmospheric Science, SEES, University of Manchester, Manchester, UK
Virginia E. Foot
Defence, Science and Technology Laboratory, Porton Down, Salisbury, UK
Andrew P. Morse
Department of Geography and Planning, University of Liverpool, Liverpool, UK
Martin W. Gallagher
Centre of Atmospheric Science, SEES, University of Manchester, Manchester, UK
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Cited
23 citations as recorded by crossref.
- Assessment of real-time bioaerosol particle counters using reference chamber experiments G. Lieberherr et al. 10.5194/amt-14-7693-2021
- Identification of fluorescent aerosol observed by a spectroscopic lidar over northwest China Y. Wang et al. 10.1364/OE.493557
- On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen J. Gómez Martín et al. 10.1016/j.jqsrt.2021.107761
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
- Real time detection and characterisation of bioaerosol emissions from wastewater treatment plants J. Tian et al. 10.1016/j.scitotenv.2020.137629
- Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al. 10.1007/s10453-020-09669-1
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- Assessment of indoor bioaerosol exposure using direct-reading versus traditional methods—potential application to home health care Y. Addor et al. 10.1080/15459624.2023.2212007
- Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
- A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I E. Markey et al. 10.3390/s22228747
- Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
- Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area J. Huang et al. 10.3390/rs11080980
- A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber F. Mahrt et al. 10.5194/amt-12-3183-2019
- Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification I. Crawford et al. 10.3390/atmos14081214
- Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
- Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration M. Moss et al. 10.3390/atmos14091416
- Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
- 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
- Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques N. Savage & J. Huffman 10.5194/amt-11-4929-2018
22 citations as recorded by crossref.
- Assessment of real-time bioaerosol particle counters using reference chamber experiments G. Lieberherr et al. 10.5194/amt-14-7693-2021
- Identification of fluorescent aerosol observed by a spectroscopic lidar over northwest China Y. Wang et al. 10.1364/OE.493557
- On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen J. Gómez Martín et al. 10.1016/j.jqsrt.2021.107761
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Monitoring techniques for pollen allergy risk assessment C. Suanno et al. 10.1016/j.envres.2021.111109
- Real time detection and characterisation of bioaerosol emissions from wastewater treatment plants J. Tian et al. 10.1016/j.scitotenv.2020.137629
- Testing the Raman parameters of pollen spectra in automatic identification S. Pereira et al. 10.1007/s10453-020-09669-1
- Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests S. Mills et al. 10.1016/j.scitotenv.2023.161969
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- Assessment of indoor bioaerosol exposure using direct-reading versus traditional methods—potential application to home health care Y. Addor et al. 10.1080/15459624.2023.2212007
- Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
- A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I E. Markey et al. 10.3390/s22228747
- Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
- Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area J. Huang et al. 10.3390/rs11080980
- A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber F. Mahrt et al. 10.5194/amt-12-3183-2019
- Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification I. Crawford et al. 10.3390/atmos14081214
- Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
- Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration M. Moss et al. 10.3390/atmos14091416
- Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
- 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
Latest update: 23 Nov 2024
Short summary
Pollen, bacteria and fungal spores are common in the environment, can have very important implications for public health and may influence the weather. Biological sensors potentially could be used to monitor quantities of these types of particles. However, it is important to transform the measurements from these instruments into counts of these biological particles. The paper tests a variety of approaches for achieving this aim on data collected in a laboratory.
Pollen, bacteria and fungal spores are common in the environment, can have very important...
