On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers

Adamov, Simon; Lemonis, Natalie; Clot, Bernard; Crouzy, Benoît; Gehrig, Regula; Graber, Marie-José; Sallin, Christine; Tummon, Fiona

doi:10.1007/s10453-021-09724-5

Cited by 37 publications

(28 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies (e.g., [ 42 ]) have shown that the number of sequence reads associated with each identified family is generally closely correlated with the number of pollen grains identified by optical microscopy, even if many factors associated with handling samples, technical processes, or the biological material itself can affect the accuracy of metabarcoding quantification. The Hirst technique, which may have a significant uncertainty at counts below 20 pollen grains/m 3 , according to Gottardini et al [ 43 ], Šikoparija et al [ 44 ], and Adamov et al [ 45 ], may have also contributed to the factors accounting for the differences between Brindisi and Lecce results. Campbell et al [ 46 ] also found that the number of pollen grains counted microscopically emulated DNA read counts, even if there were some observable differences.…”

Section: Resultsmentioning

confidence: 99%

“…The fitting regression line equation is also provided in addition to the Pearson correlation coefficient ( r = 0.87), which indicates that data points are linearly correlated at a p -level ≤ 0.01. The analysis has been restricted to the spring samples to limit the uncertainties associated with low grain numbers, according to the above comments [ 43 , 44 , 45 ]. In fact, higher grain numbers have on average been found only in the spring samples ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Fragola

Arsieni

Carelli

et al. 2022

IJERPH

View full text Add to dashboard Cite

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Fragola

Arsieni

Carelli

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…• The relative uncertainty decreases with the sampled area of the slide. An error of more than 30% is reported (Adamov et al, 2021;Smith et al, 2018), mainly when the sampled area is below the minimum recommendation of 10% (Comtois et al, 1999;Galán et al, 2014). • During very high pollen peaks there may be several layers of pollen grains, some of which may not get to touch the adhesive surface and therefore get lost.…”

Section: Manual Techniquesmentioning

confidence: 99%

“…Typically, automatic measurements are evaluated using manual observations from Hirst-type traps. While this is currently the only way to validate realtime instruments, at least in terms of the pollen identification, it is important to keep in mind the large uncertainty associated with the manual observations (Adamov et al, 2021). Further methods will need to be developed to assess the precision and accuracy of identification algorithms since this is a source of error unique to automatic measurements.…”

Section: Automatic Measurementsmentioning

confidence: 99%

Automatic detection of airborne pollen: an overview

et al. 2022

Self Cite

View full text Add to dashboard Cite

Pollen monitoring has traditionally been carried out using manual methods first developed in the early 1950s. Although this technique has been recently standardised, it suffers from several drawbacks, notably data usually only being available with a delay of 3–9 days and usually delivered at a daily resolution. Several automatic instruments have come on to the market over the past few years, with more new devices also under development. This paper provides a comprehensive overview of all available and developing automatic instruments, how they measure, how they identify airborne pollen, what impacts measurement quality, as well as what potential there is for further advancement in the field of bioaerosol monitoring.

show abstract

“…The issue in developing forecast models is that pollen counting so far has been mainly a human-centric activity, with a time intensive procedure where technicians or PhD students are used to count and classify the particles captures by Hirst traps [2] or similar devices [3]. These methods of getting pollen information have some inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

et al. 2022

View full text Add to dashboard Cite

Pollen allergies have become one of the most wide-spread afflictions that impact quality of life. This has made the need for automatic pollen detection, classification and monitoring a very important topic. This paper introduces a new public annotated image data-set of pollen with almost 45 thousand samples obtained from an automatic instrument. In this work we apply some of the best performing convolutional neural networks architectures on the task of pollen classification as well as some fully convolutional networks optimized for image segmentation on complex microscope images. We obtain an F1 scores of 0.95 on the new data-set when the best trained model is used as a fully convolutional classifier and a class mean Intersection over Union (IoU) of 0.88 when used as an object detector.

show abstract

On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers

Cited by 37 publications

References 49 publications

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Automatic detection of airborne pollen: an overview

Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

Contact Info

Product

Resources

About