Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines

Tummon, Fiona; Bruffaerts, Nicolas; Çelenk, Sevcan; Choël, M.; Clot, Bernard; Crouzy, Benoît; Galán, Carmen Rueda; Gilge, Stefan; Hájková, Lenka; Mokin, V. B.; O’Connor, David J.; Rodinkova, Victoria; Šaulienė, Ingrida; Šikoparija, Branko; Sofiev, Mikhail; Sozinova, Olga; Tešendić, Danijela; Vasilatou, Konstantina

doi:10.1007/s10453-022-09755-6

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…The correlation was evaluated for daily pollen concentrations to limit the shot-noise uncertainty resulting from substantial detection limits due to the limited flow rate of the devices (Tummon et al, 2022). The correlations were calculated both for the entire measurement period (to account for the effect of false positives outside the main flowering season) and for days when average pollen concentrations measured by the manual method exceeded 10 pollen m −3 , a suggested threshold for calculating the uncertainty by the standard EN 16868:2019.…”

Section: Discussionmentioning

confidence: 99%

“…This observation emphasizes the advantage of measuring with a high temporal resolution simultaneously resolving particle size distribution, for exploring the behaviour of 195 aerosols in changing meteorological conditions. However, quite low flow rate (5 l min -1 ) limited the temporal resolution of the observations (Tummon et al, 2022).…”

Section: Aerosol Quantificationmentioning

confidence: 99%

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Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Sikoparija,

Matavulj,

Simovic

et al. 2024

Preprint

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Abstract. The study evaluated a new model of a Plair SA air flow cytometer, Rapid-E+, and assessed its suitability for airborne pollen monitoring within operational networks. Key features of the new model are compared with the previous one, Rapid-E. A machine learning algorithm is constructed and evaluated for (i) classification of reference pollen types in laboratory conditions and (ii) monitoring in real-life field campaigns. The second goal of the study was to evaluate the device usability in forthcoming monitoring networks, which would require similarity and reproducibility of the measurement signal across devices. We employed three devices and analysed (dis-)similarities of their measurements in laboratory conditions. The lab evaluation showed similar recognition performance as that of Rapid-E, but field measurements in conditions when several pollen types are present in the air simultaneously, showed a notably lower agreement of Rapid-E+ with manual Hirst-type observations than those of the older model. An exception was the total-pollen measurements. Comparison across the Rapid-E+ devices revealed noticeable differences in fluorescence measurements between the three devices tested. As a result, application of the recognition algorithm trained on the data of one device to another one led to large errors. The study confirmed the potential of the fluorescence measurements for discrimination between different pollen classes, but each monitor needed to be trained individually to achieve acceptable skills. A large uncertainty of fluorescence measurements and their variability between different devices need to be addressed to improve the device usability.

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Section: Discussionmentioning

confidence: 99%

Section: Aerosol Quantificationmentioning

confidence: 99%

Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Sikoparija,

Matavulj,

Simovic

et al. 2024

Preprint

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“…Parallel measurement campaigns ensure that manual and automatic data are comparable for long-term studies. 48 …”

Section: Automated Monitoringmentioning

confidence: 99%

“…At a very early stage of development of these new technologies, the EUMETNET AutoPollen programme was set up to favour collaboration, as well as to validate the methods and recommend standards 48 , 49 , 56 in close collaboration with the metrology community (e.g., Lieberherr et al. 57 ).…”

Section: Automated Monitoringmentioning

confidence: 99%

Climate change, airborne allergens, and three translational mitigation approaches

Beggs¹,

Clot²,

Sofiev³

et al. 2023

eBioMedicine

Self Cite

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“…To tentatively automate the process of pollen monitoring and to provide end-users with an actionable and a higher quality information, various real-time pollen sensors have recently been developed. They involve physical concepts such as infrared spectroscopy, fluorescence spectroscopy, holography, image recognition, and the use of machine learning models [ 17 , 18 , 19 , 20 , 21 ]. Despite promising results when intercompared with Hirst-type traps [ 22 , 23 ], almost all these techniques still cannot meet the challenge of providing a local pollinic information with a high spatial resolution due to their significant cost, and the sensors are still requiring heavy calibration or maintenance operations.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose

Renard,

El Azari,

Lauthier

et al. 2024

Sensors

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As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter–spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.

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Towards standardisation of automatic pollen and fungal spore monitoring: best practises and guidelines

Cited by 16 publications

References 31 publications

Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer

Climate change, airborne allergens, and three translational mitigation approaches

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose

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