Size-segregated Allergenic Particles Released from Airborne Cryptomeria japonica Pollen Grains during the Yellow Sand Events within the Pollen Scattering Seasons

Wang, Qingyue; Gong, Xiumin; Suzuki, Miho; Lu, Shengyong; Sekiguchi, Kazuhiko; Nakajima, Daisuke; Miwa, Makoto

doi:10.5572/ajae.2013.7.4.191

Cited by 16 publications

(17 citation statements)

References 12 publications

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“…This makes it particularly difficult for the monitors to satisfy the needs of allergic people and allergists -unlike in many other regions. For instance, Cryptomeria japonica identified more than 10 years ago by Kawashima et al (2007) is still the main pollen type recognized by that system (Kawashima et al, 2007;Wang et al, 2013Wang et al, , 2014 I. Šaulienė et al: Automatic pollen recognition with the Rapid-E particle counter Figure 10. Novi Sad ANN overall cost as a function of the training epoch.…”

Section: Comparison With Other Studies On Pollen Recognitionmentioning

confidence: 99%

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

Šaulienė¹,

Šukienė²,

Daunys³

et al. 2019

Atmos. Meas. Tech.

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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.

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Section: Comparison With Other Studies On Pollen Recognitionmentioning

confidence: 99%

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

Šaulienė¹,

Šukienė²,

Daunys³

et al. 2019

Atmos. Meas. Tech.

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“…They can thus fly as depots on diesel exhaust particles (DEP) generated from transportation [ 62 ]. They can also be associated with the presence of alkaline rainwater in polluted zones (experiments on atmospheric Cry j 1 from Cryptomeria japonica pollen) [ 63 ]. The technical device used for such detection combined, for instance, (i) Andersen high-volume sampler allowing collecting, on quartz fiber filters, size-segregated particles from less than 1.1 μ m to 7 μ m and (ii) surface plasmon resonance (SPR) technology enabling through monoclonal antibodies binding to ensure atmospheric allergen detection.…”

Section: Effects Of Pollutants On Pollen Grains and Pollen Contentmentioning

confidence: 99%

A Review of the Effects of Major Atmospheric Pollutants on Pollen Grains, Pollen Content, and Allergenicity

Sénéchal

Visez

Charpin

et al. 2015

The Scientific World Journal

171

111

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This review summarizes the available data related to the effects of air pollution on pollen grains from different plant species. Several studies carried out either on in situ harvested pollen or on pollen exposed in different places more or less polluted are presented and discussed. The different experimental procedures used to monitor the impact of pollution on pollen grains and on various produced external or internal subparticles are listed. Physicochemical and biological effects of artificial pollution (gaseous and particulate) on pollen from different plants, in different laboratory conditions, are considered. The effects of polluted pollen grains, subparticles, and derived aeroallergens in animal models, in in vitro cell culture, on healthy human and allergic patients are described. Combined effects of atmospheric pollutants and pollen grains-derived biological material on allergic population are specifically discussed. Within the notion of “polluen,” some methodological biases are underlined and research tracks in this field are proposed.

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“…However, our synthesis found no consistent patterns. For example, three studies found a positive relationship between rainfall and atmospheric allergen concentration (Schäppi et al, 1997a(Schäppi et al, , 1997bWang et al, 2013a), while four studies showed the inverse (Beck et al, 2013;De Linares et al, 2010;Schäppi et al, 1997b;Wang et al, 2013b). Two studies demonstrated a negative correlation between rainfall and atmospheric pollen concentration (Jochner et al, 2015;Schäppi et al, 1997b), while one study did not find any correlation (Rodríguez-Rajo et al, 2011).…”

Section: Factors Influencing Pollen Potencymentioning

confidence: 98%

‘Pollen potency’: the relationship between atmospheric pollen counts and allergen exposure

et al. 2021

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Pollen allergies are responsible for a considerable global public health burden, and understanding exposure is critical to addressing the health impacts. Atmospheric pollen counts are routinely used as a predictor of risk; however, immune responses are triggered by specific proteins known as allergens, which occur both within and on the surface of the pollen grain. The ratio between atmospheric pollen counts and allergen concentrations (‘pollen potency’) has been shown to be inconsistent, with potentially important implications for pollen monitoring practice. Despite this, there has been no previous synthesis of the literature and our understanding of the factors that influence pollen potency remains poor. We conducted a scoping review with the aim of deriving a current understanding of: (a) the factors that influence pollen potency; (b) its variation through time, between taxa and by location; and (c) the implications for pollen monitoring practice. Our synthesis found that pollen potency is highly variable within and between seasons, and between locations; however, much of this variability remains unexplained and has not been deeply investigated. We found no predictable pollen potency patterns relating to taxon, geography or time, and inconclusive evidence regarding possible driving factors. With respect to human health, the studies in our synthesis generally reported larger associations between atmospheric allergen loads and allergy symptoms than whole pollen counts. This suggests that pollen potency influences public health risk; however, the evidence base remains limited. Further research is needed to better understand both pollen potency variability and its implications for health.

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Size-segregated Allergenic Particles Released from Airborne Cryptomeria japonica Pollen Grains during the Yellow Sand Events within the Pollen Scattering Seasons

Cited by 16 publications

References 12 publications

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

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

A Review of the Effects of Major Atmospheric Pollutants on Pollen Grains, Pollen Content, and Allergenicity

‘Pollen potency’: the relationship between atmospheric pollen counts and allergen exposure

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