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Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector

https://doi.org/10.1371/journal.pone.0247284
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/widm.53openalex-snapshot
Algorithms for hierarchical clustering: an overview
inferredcites →https://doi.org/10.1007/978-0-387-98141-3openalex-snapshot
ggplot2: Elegant Graphics for Data Analysis
inferredcites →https://doi.org/10.1007/978-3-319-71365-6openalex-snapshot
Illustrated Pollen Terminology
inferredcites →https://doi.org/10.1007/978-94-007-4881-1_4openalex-snapshot
Monitoring, Modelling and Forecasting of the Pollen Season
inferredcites →https://doi.org/10.1007/978-94-007-4881-1_7openalex-snapshot
Presentation and Dissemination of Pollen Information
inferredcites →https://doi.org/10.1007/s00425-017-2774-9openalex-snapshot
Chemical characterization and identification of Pinaceae pollen by infrared microspectroscopy
inferredcites →https://doi.org/10.1007/s00484-014-0910-9openalex-snapshot
Cluster analysis of intradiurnal holm oak pollen cycles at peri-urban and rural sampling sites in southwestern Spain
inferredcites →https://doi.org/10.1007/s10453-005-4178-8openalex-snapshot
Examining high magnitude grass pollen episodes at Worcester, United Kingdom, using back-trajectory analysis
inferredcites →https://doi.org/10.1007/s10453-012-9271-1openalex-snapshot
Analysis and forecasting of airborne pollen–induced symptoms with the aid of computational intelligence methods
inferredcites →https://doi.org/10.1007/s10453-014-9335-5openalex-snapshot
Pollen monitoring: minimum requirements and reproducibility of analysis
inferredcites →https://doi.org/10.1007/s10453-015-9387-1openalex-snapshot
The impact of pollen load on quality of life: a questionnaire-based study in Lithuania
inferredcites →https://doi.org/10.1007/s10453-016-9454-2openalex-snapshot
The long-range transport of Pinaceae pollen: an example in Kraków (southern Poland)
inferredcites →https://doi.org/10.1007/s10453-019-09558-2openalex-snapshot
Cluster analysis of variations in the diurnal pattern of grass pollen concentrations in Northern Europe (Copenhagen) and Southern Europe (Cordoba)
inferredcites →https://doi.org/10.1007/s10453-019-09583-1openalex-snapshot
On possibilities of assimilation of near-real-time pollen data by atmospheric composition models
inferredcites →https://doi.org/10.1007/s10453-019-09593-zopenalex-snapshot
Representativeness of pollen traps: a review of the national pollen network of Switzerland
inferredcites →https://doi.org/10.1007/s10453-019-09619-6openalex-snapshot
Automatic pollen monitoring: first insights from hourly data
inferredcites →https://doi.org/10.1007/s10453-020-09666-4openalex-snapshot
The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe
inferredcites →https://doi.org/10.1016/j.atmosenv.2007.09.019openalex-snapshot
An algorithm and a device for counting airborne pollen automatically using laser optics
inferredcites →https://doi.org/10.1016/j.atmosenv.2011.07.044openalex-snapshot
The intrinsic fluorescence spectra of selected pollen and fungal spores
inferredcites →https://doi.org/10.1016/j.atmosenv.2016.05.062openalex-snapshot
All-optical automatic pollen identification: Towards an operational system
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.06.131openalex-snapshot
Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.06.348openalex-snapshot
Detecting distant sources of airborne pollen for Poland: Integrating back-trajectory and dispersion modelling with a satellite-based phenology
inferredcites →https://doi.org/10.1029/2010jd014743openalex-snapshot
Airborne pollen in three European cities: Detection of atmospheric circulation pathways by applying three‐dimensional clustering of backward trajectories
inferredcites →https://doi.org/10.1080/02786826.2013.872768openalex-snapshot
Using the WIBS-4 (Waveband Integrated Bioaerosol Sensor) Technique for the On-Line Detection of Pollen Grains
inferredcites →https://doi.org/10.1080/02786826.2019.1664724openalex-snapshot
Real-time sensing of bioaerosols: Review and current perspectives
inferredcites →https://doi.org/10.1080/02786826.2019.1711357openalex-snapshot
Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer
inferredcites →https://doi.org/10.1080/17517575.2020.1793391openalex-snapshot
RealForAll: real-time system for automatic detection of airborne pollen
inferredcites →https://doi.org/10.1111/2041-210x.12697openalex-snapshot
Monitoring of plant–environment interactions by high‐throughput FTIR spectroscopy of pollen
inferredcites →https://doi.org/10.1111/all.13210openalex-snapshot
Poaceae pollen as the leading aeroallergen worldwide: A review
inferredcites →https://doi.org/10.1111/all.14111openalex-snapshot
Pollen season is reflected on symptom load for grass and birch pollen‐induced allergic rhinitis in different geographic areas—An EAACI Task Force Report
inferredcites →https://doi.org/10.1111/j.1467-985x.2010.00676_9.xopenalex-snapshot
ggplot2: Elegant Graphics for Data Analysis
inferredcites →https://doi.org/10.1159/000436968openalex-snapshot
Automatic and Online Pollen Monitoring
inferredcites →https://doi.org/10.1186/s13601-018-0197-8openalex-snapshot
Pollen and spore monitoring in the world
inferredcites →https://doi.org/10.1364/oe.19.024516openalex-snapshot
Individual bioaerosol particle discrimination by multi-photon excited fluorescence
inferredcites →https://doi.org/10.1371/journal.pone.0166887openalex-snapshot
Pollen Season Trends (1973-2013) in Stockholm Area, Sweden
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.3390/f10110959openalex-snapshot
Detection and Microscopy of Alnus glutinosa Pollen Fluorescence Peculiarities
inferredcites →https://doi.org/10.3846/jeelm.2010.16openalex-snapshot
ANALYSIS OF CHANGES IN FLOWERING PHASES AND AIRBORNE POLLEN DISPERSION OF THE GENUS BETULA (BIRCH)
inferredcites →https://doi.org/10.5124/jkma.2009.52.6.579openalex-snapshot
Development of Pollen Concentration Prediction Models
inferredcites →https://doi.org/10.5194/amt-12-3435-2019openalex-snapshot
Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps
inferredcites →https://doi.org/10.5194/amt-13-1539-2020openalex-snapshot
Real-time pollen monitoring using digital holography
inferredcites →https://doi.org/10.5586/aa.2015.036openalex-snapshot
The comparison of pollen abundance in air and honey samples

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.5281/zenodo.10602113zenodo-depositor-declared
Assessment Of Fog Influence On Augmentation Of Fluorescent Particles In Real-Time Bioaerosol Measurement
inferred← citeshttps://doi.org/10.1007/s10453-022-09750-xopenalex-snapshot
Automatic detection of airborne pollen: an overview
inferred← citeshttps://doi.org/10.1007/s10453-022-09755-6openalex-snapshot
Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines
inferred← citeshttps://doi.org/10.1007/s10453-022-09769-0openalex-snapshot
Comparison of computer vision models in application to pollen classification using light scattering
inferred← citeshttps://doi.org/10.1007/s10453-026-09902-3openalex-snapshot
The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2026.122220openalex-snapshot
A systematic review of two LIF-based instruments for bioaerosol monitoring: Data treatment, performance evaluation, applications and comparison of WIBS and Rapid-E
inferred← citeshttps://doi.org/10.1016/j.fmre.2023.05.012openalex-snapshot
Recent progress in online detection methods of bioaerosols
inferred← citeshttps://doi.org/10.1016/j.rineng.2024.102867openalex-snapshot
Pollen detection through integrated microfluidics and smartphone-driven deep learning systems
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2021.148932openalex-snapshot
Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2022.161220openalex-snapshot
Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps
inferred← citeshttps://doi.org/10.1038/s41598-023-30064-6openalex-snapshot
Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy
inferred← citeshttps://doi.org/10.1080/02786826.2022.2142510openalex-snapshot
Pollen classification using a single particle fluorescence spectroscopy technique
inferred← citeshttps://doi.org/10.1088/2399-6528/aca45eopenalex-snapshot
Deep learning in airborne particulate matter sensing: a review
inferred← citeshttps://doi.org/10.15835/nbha49312444openalex-snapshot
Automatic particle detectors lead to a new generation in plant diversity investigation
inferred← citeshttps://doi.org/10.24057/2071-9388-2026-4053openalex-snapshot
Identification of Biological Aerosols By its Fluorescence Spectra and Comparison With Volumetric Data
inferred← citeshttps://doi.org/10.3390/aerobiology2030004openalex-snapshot
Assessing Characteristics and Variability of Fluorescent Aerosol Particles: Comparison of Two Case Studies in Southeastern Italy Using a Wideband Integrated Bioaerosol Sensor
inferred← citeshttps://doi.org/10.3390/atmos14010118openalex-snapshot
Aerobiological Monitoring in an Indoor Occupational Setting Using a Real-Time Bioaerosol Sampler
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