Standardised index for measuring atmospheric grass-pollen emission

Romero-Morte, Jorge; Rojo, Jesús; Rivero, Rosario; Fernández‐González, Federico; Pérez-Badía, Rosa

doi:10.1016/j.scitotenv.2017.08.139

Cited by 31 publications

(12 citation statements)

References 45 publications

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“…Poaceae showed important differences between pollen traps (slope < 1, the highest pollen concentrations were collected by the trap located at lower height), and very variable data (low R 2 , Table 1). This different behavior according to the pollen type can be related to the dispersal capacity of the pollen grain and the local distribution of the main pollen sources [52,53]. Peel et al [54] also found different daily patterns for grass pollen in several locations within a city, and even different intradiurnal emission patterns depending on the distribution of the pollen sources [40].…”

Section: Comparison Of the Daily Pollen Concentrationsmentioning

confidence: 99%

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

et al. 2020

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Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other one at rooftop (35 m AGL), 4.2 km apart. In general, 1.4 ± 0.5 times higher pollen amounts were measured by the trap located at ground level, but this effect was less than expected considering the height difference between the traps. Pollen from woody trees such as Alnus, Betula, Corylus, Fraxinus, Picea, Pinus and Quercus showed a good agreement between the traps in terms of timing and intensity. Similar amounts of pollen were recorded in the two traps when pollen sources were more abundant outside of the city. In contrast, pollen concentrations from Cupressaceae/Taxaceae, Carpinus and Tilia were influenced by nearby pollen sources. The representativeness of both traps for herbaceous pollen depended on the dispersal capacity of the pollen grains, and in the case of Poaceae pollen, nearby pollen sources may influence the pollen content in the air. The timing of the pollen season was similar for both sites; however, the season for some pollen types ended later at ground level probably due to resuspension processes that would favor recirculation of pollen closer to ground level. We believe measurements from the higher station provides a picture of background pollen levels representative of a large area, to which local sources add additional and more variable pollen amounts.

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Section: Comparison Of the Daily Pollen Concentrationsmentioning

confidence: 99%

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

et al. 2020

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“…To fully understand the ecology of grasses, a population-based approach is often needed (Bolnick et al 2003;Watkinson and Ormerod 2001). Spatial and temporal observations of flowering of multiple populations are often used to understand demographic elements in the study of grasses in agriculture (Rossignol et al 2014;Smith 1944), ecology (Eagles 1972;Lindner and Garcia 1997) and aerobiology (Rojo et al 2017;Romero-Morte et al 2018). Combining this approach with a stochastic Markov chain modelling approach (Balzter 2000) and a model grass species may provide deeper multifaceted understanding with respect to underlying flowering processes.…”

Section: Introductionmentioning

confidence: 99%

Stochastic flowering phenology in Dactylis glomerata populations described by Markov chain modelling

2021

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Understanding the relationship between flowering patterns and pollen dispersal is important in climate change modelling, pollen forecasting, forestry and agriculture. Enhanced understanding of this connection can be gained through detailed spatial and temporal flowering observations on a population level, combined with modelling simulating the dynamics. Species with large distribution ranges, long flowering seasons, high pollen production and naturally large populations can be used to illustrate these dynamics. Revealing and simulating species-specific demographic and stochastic elements in the flowering process will likely be important in determining when pollen release is likely to happen in flowering plants. Spatial and temporal dynamics of eight populations of Dactylis glomerata were collected over the course of two years to determine high-resolution demographic elements. Stochastic elements were accounted for using Markov chain approaches in order to evaluate tiller-specific contribution to overall population dynamics. Tiller-specific developmental dynamics were evaluated using three different RV matrix correlation coefficients. We found that the demographic patterns in population development were the same for all populations with key phenological events differing only by a few days over the course of the seasons. Many tillers transitioned very quickly from non-flowering to full flowering, a process that can be replicated with Markov chain modelling. Our novel approach demonstrates the identification and quantification of stochastic elements in the flowering process of D. glomerata, an element likely to be found in many flowering plants. The stochastic modelling approach can be used to develop detailed pollen release models for Dactylis, other grass species and probably other flowering plants.

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“…According to Romero-Morte et al [14], a small number of species were responsible for majority of the airborne grass pollen and critical factors affecting the contribution were flowering phenology, pollen size, abundance of the species and pollen production in Toledo, Spain. It has been suggested that the pollen dispersal capacity of several species of grasses is limited, because the size of pollen grain is relatively large and because pollen grains are released close to the ground [15].…”

Section: Introductionmentioning

confidence: 99%

The effect of sampling height on grass pollen concentrations in different urban environments in the Helsinki Metropolitan Area, Finland

Hugg¹,

Tuokila²,

Korkonen³

et al. 2020

PLoS ONE

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Introduction It is important to study potential differences in pollen concentrations between sampling heights because of diverse outdoor and indoor activity of humans (exposure) at different height levels in urban environments. Previous studies have investigated the effect of height on pollen concentrations based on just one or a few sampling points. We studied the effect of sampling height on grass pollen concentrations in several urban environments with different levels of urbanity. Methods This study was conducted in the Helsinki Metropolitan Area, Finland, in 2013 during the pollen season of grasses. Pollen grains were monitored in eight different points in the morning and afternoon. Rotorod-type samplers were attached on sampling poles at the heights of 1.5 meters and 4 meters. Results Grass pollen concentrations were on average higher at the height of 1.5 meters (Helsinki mean 5.24 grains / m 3 ; Espoo mean 75.71 grains / m 3) compared to the height of 4 meters (Helsinki mean 3.84 grains / m 3 ; Espoo mean 37.42 grains / m 3) with a difference of 1.40 grains / m 3 (95% CI-0.21 to 3.01) in Helsinki, and 38.29 grains / m 3 (7.52 to 69.07) in Espoo, although not always statistically significant. This was detected both in the morning and in the afternoon. However, in the most urban sites the levels were lower at 1.5 meters compared to 4 meters, whereas in the least urban sites the concentrations were higher at 1.5 meters.

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Standardised index for measuring atmospheric grass-pollen emission

Cited by 31 publications

References 45 publications

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Stochastic flowering phenology in Dactylis glomerata populations described by Markov chain modelling

The effect of sampling height on grass pollen concentrations in different urban environments in the Helsinki Metropolitan Area, Finland

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