Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Ingrida Šaulienė,Bernard Clot,Lukas Vaitkevičius,Marko Panic,Predrag Matavulj,Benoît Crouzy,Gediminas Valiulis,Gintautas Daunys,Sanja Brdar,Branko Sikoparija,Mikhail Sofiev,Laura Šukienė

doi:10.5194/amt-12-3435-2019

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.

Highlights

Pollen of many wind-pollinated plants has specific proteins that cause human allergies (Valenta et al, 1992; Bousquet et al, 2006, 2015; Radauer and Breiteneder, 2006; Choual et al, 2018), affecting children (Skoner, 2001; Høst et al, 2003; Douladiris et al, 2018)
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
Timely data about pollen concentration in the air are needed for improving the accuracy of tools for personalized medicine (Bousquet et al, 2017; Horgan and Pazzagli, 2017; Pereira et al, 2018; Tabatabaian and Casale, 2018)

Summary

Introduction

Pollen of many wind-pollinated plants has specific proteins that cause human allergies (Valenta et al, 1992; Bousquet et al, 2006, 2015; Radauer and Breiteneder, 2006; Choual et al, 2018), affecting children (Skoner, 2001; Høst et al, 2003; Douladiris et al, 2018). Timely data about pollen concentration in the air are needed for improving the accuracy of tools for personalized medicine (for example, PASYFO app, http://www.pasyfo.lt, last access: 14 November 2018; POLLEN app, http://www.polleninfo.org, last access: 14 November 2018; NORKKO forecast and app http:// www.norkko.fi, last access: 14 November 2018; etc.) (Bousquet et al, 2017; Horgan and Pazzagli, 2017; Pereira et al, 2018; Tabatabaian and Casale, 2018). It can be used for informing people about the current pollen concentration in the air. Real-time data are needed for short-term pollen forecasts with statistical and atmospheric dispersion models (Sofiev et al, 2013, 2015, 2017; Prank et al, 2016; Ritenberga et al, 2016; Zink et al, 2017)

Objectives

Methods

Results

Conclusion