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.
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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40 citations as recorded by crossref.
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- 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
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- Improved real-time bio-aerosol classification using artificial neural networks M. Leśkiewicz et al. 10.5194/amt-11-6259-2018
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- 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
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- 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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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...