Articles | Volume 8, issue 11
https://doi.org/10.5194/amt-8-4979-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-4979-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Nov 2015
Research article |
| 27 Nov 2015
Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol
Centre for Atmospheric Science, SEAES, University of Manchester, Manchester, UK
Centre for Atmospheric Science, SEAES, University of Manchester, Manchester, UK
D. O. Topping
Centre for Atmospheric Science, SEAES, University of Manchester, Manchester, UK
NCAS, National Centre for Atmospheric Science, University of Manchester, Manchester, UK
M. W. Gallagher
Centre for Atmospheric Science, SEAES, University of Manchester, Manchester, UK
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Cited
41 citations as recorded by crossref.
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- Scoping studies to establish the capability and utility of a real-time bioaerosol sensor to characterise emissions from environmental sources Z. Nasir et al. 10.1016/j.scitotenv.2018.08.120
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40 citations as recorded by crossref.
- Effect of Stove Technology and Combustion Conditions on Gas and Particulate Emissions from Residential Biomass Combustion D. Bhattu et al. 10.1021/acs.est.8b05020
- Ambient measurement of fluorescent aerosol particles with a WIBS in the Yangtze River Delta of China: potential impacts of combustion-related aerosol particles X. Yu et al. 10.5194/acp-16-11337-2016
- Fluorescence calibration method for single-particle aerosol fluorescence instruments E. Robinson et al. 10.5194/amt-10-1755-2017
- Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer S. Ruske et al. 10.5194/amt-10-695-2017
- Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
- Real-time detection of airborne fluorescent bioparticles in Antarctica I. Crawford et al. 10.5194/acp-17-14291-2017
- 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
- Summertime fluorescent bioaerosol particles in the coastal megacity Tianjin, North China B. Cheng et al. 10.1016/j.scitotenv.2020.137966
- Measurements and modeling of surface–atmosphere exchange of microorganisms in Mediterranean grassland F. Carotenuto et al. 10.5194/acp-17-14919-2017
- Biogenic cloud nuclei in the central Amazon during the transition from wet to dry season J. Whitehead et al. 10.5194/acp-16-9727-2016
- Phase transition observations and discrimination of small cloud particles by light polarization in expansion chamber experiments L. Nichman et al. 10.5194/acp-16-3651-2016
- Fluorescent bioaerosol particle, molecular tracer, and fungal spore concentrations during dry and rainy periods in a semi-arid forest M. Gosselin et al. 10.5194/acp-16-15165-2016
- Drivers of the fungal spore bioaerosol budget: observational analysis and global modeling R. Janssen et al. 10.5194/acp-21-4381-2021
- Biological and dust aerosols as sources of ice-nucleating particles in the eastern Mediterranean: source apportionment, atmospheric processing and parameterization K. Gao et al. 10.5194/acp-24-9939-2024
- Aerosol measurements during COPE: composition, size, and sources of CCN and INPs at the interface between marine and terrestrial influences J. Taylor et al. 10.5194/acp-16-11687-2016
- Characterisation and source identification of biofluorescent aerosol emissions over winter and summer periods in the United Kingdom E. Forde et al. 10.5194/acp-19-1665-2019
- Quantifying bioaerosol concentrations in dust clouds through online UV-LIF and mass spectrometry measurements at the Cape Verde Atmospheric Observatory D. Morrison et al. 10.5194/acp-20-14473-2020
- Improved real-time bio-aerosol classification using artificial neural networks M. Leśkiewicz et al. 10.5194/amt-11-6259-2018
- Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
- 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
- High Abundance of Fluorescent Biological Aerosol Particles in Winter in Beijing, China S. Yue et al. 10.1021/acsearthspacechem.7b00062
- Assessing Characteristics and Variability of Fluorescent Aerosol Particles: Comparison of Two Case Studies in Southeastern Italy Using a Wideband Integrated Bioaerosol Sensor M. Fragola et al. 10.3390/aerobiology2030004
- Chamber catalogues of optical and fluorescent signatures distinguish bioaerosol classes M. Hernandez et al. 10.5194/amt-9-3283-2016
- Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles N. Savage et al. 10.5194/amt-10-4279-2017
- Using flow cytometry and light-induced fluorescence to characterize the variability and characteristics of bioaerosols in springtime in Metro Atlanta, Georgia A. Negron et al. 10.5194/acp-20-1817-2020
- Overview of primary biological aerosol particles from a Chinese boreal forest: Insight into morphology, size, and mixing state at microscopic scale W. Li et al. 10.1016/j.scitotenv.2020.137520
- Laboratory validation of rapid discrimination of single microbial cells via SPAMS with machine learning C. Liu et al. 10.1016/j.ijms.2020.116340
- Observation of bioaerosol transport using wideband integrated bioaerosol sensor and coherent Doppler lidar D. Tang et al. 10.5194/amt-15-2819-2022
- Analysis of single‐cell microbial mass spectra profiles from single‐particle aerosol mass spectrometry C. Liu et al. 10.1002/rcm.9069
- Machine learning for improved data analysis of biological aerosol using the WIBS S. Ruske et al. 10.5194/amt-11-6203-2018
- Development and characterization of an inexpensive single-particle fluorescence spectrometer for bioaerosol monitoring B. Swanson & J. Huffman 10.1364/OE.26.003646
- 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
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
- Spectral Intensity Bioaerosol Sensor (SIBS): an instrument for spectrally resolved fluorescence detection of single particles in real time T. Könemann et al. 10.5194/amt-12-1337-2019
- Influence of moisturizer and relative humidity on human emissions of fluorescent biological aerosol particles J. Zhou et al. 10.1111/ina.12349
- 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
- Real time detection and characterisation of bioaerosol emissions from wastewater treatment plants J. Tian et al. 10.1016/j.scitotenv.2020.137629
- Ice Nucleating Particle Measurements at 241 K during Winter Months at 3580 m MSL in the Swiss Alps Y. Boose et al. 10.1175/JAS-D-15-0236.1
- Scoping studies to establish the capability and utility of a real-time bioaerosol sensor to characterise emissions from environmental sources Z. Nasir et al. 10.1016/j.scitotenv.2018.08.120
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Latest update: 23 Nov 2024
Short summary
HCA analysis methods were evaluated for the purpose of identifying primary biological aerosol sampled with a WIBS. The ward linkage with z-score normalisation could discriminate between five test particles with 98% accuracy. We applied these methods to a previously studied ambient data set, where both methods produced similar results with some minor differences in cluster partitioning. Finally we compared to previous approaches and found our new method offered improved quantification of PBA.
HCA analysis methods were evaluated for the purpose of identifying primary biological aerosol...
