Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps
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Published:2019-06-28
Issue:6
Volume:12
Page:3435-3452
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Šaulienė Ingrida,Šukienė Laura,Daunys Gintautas,Valiulis Gediminas,Vaitkevičius Lukas,Matavulj Predrag,Brdar Sanja,Panic Marko,Sikoparija Branko,Clot Bernard,Crouzy Benoît,Sofiev Mikhail
Abstract
Abstract. Pollen-induced allergies are among the most prevalent non-contagious
diseases, with about a quarter of the European population being sensitive to various
atmospheric bioaerosols. In most European countries, pollen information is
based on a weekly-cycle Hirst-type pollen trap method. This method is
labour-intensive and requires narrow specialized abilities and substantial
time, so that the pollen data are always delayed and subject to sampling- and
counting-related uncertainties. Emerging new approaches to automatic pollen
monitoring can, in principle, allow for real-time availability of the data
with no human involvement. The goal of the current paper is to evaluate the capabilities of the new
Plair Rapid-E pollen monitor and to construct a first-level pollen
recognition algorithm. The evaluation was performed for three devices
located in Lithuania, Serbia and Switzerland, with independent calibration
data and classification algorithms. The Rapid-E output data include
multi-angle scattering images and the fluorescence spectra recorded at
several times for each particle reaching the device. Both modalities of the
Rapid-E output were treated with artificial neural networks (ANNs) and the
results were combined to obtain the pollen type. For the first
classification experiment, the monitor was challenged with a large variety
of pollen types and the quality of many-to-many classification was
evaluated. It was shown that in this case, both scattering- and
fluorescence-based recognition algorithms fall short of acceptable quality.
The combinations of these algorithms performed better, exceeding 80 %
accuracy for 5 out of 11 species. Fluorescence spectra showed similarities
among different species, ending up with three well-resolved groups: (Alnus,
Corylus, Betula and Quercus), (Salix and Populus) and (Festuca, Artemisia and Juniperus). Within these groups, pollen is practically
indistinguishable for the first-level recognition procedure. Construction
of multistep algorithms with sequential discrimination of pollen inside
each group seems to be one of the possible ways forward. In order to connect
the classification experiment to existing technology, a short comparison
with the Hirst measurements is presented and the issue of false positive
pollen detections by Rapid-E is discussed.
Funder
Academy of Finland
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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