Poaceae pollen in the air depending on the thermal conditions

Forecast models for start and peak dates of Poaceae pollen season in Tétouan (NW Morocco) using multiple regression analysis

Poaceae pollen is one of the most widespread sources of aeroallergens in the world. The aim of this study is to build predictive models for the pollen season start day (PSsd) and peak dates of the Poaceae pollen season and thus give an overview of the climatic parameters that have the greatest influence. In Tétouan, sampling was carried out using a volumetric spore trap of the Burkard Hirst type. The relationships between the PSsd, peak dates and meteorological parameters were determined using correlation analysis. The models were constructed using multiple regression analysis with data from 2008 to 2019 and tested on data from 2022. The PSsd was especially significantly influenced by minimum temperature during winter and precipitation in the autumn of the previous year. The peak dates were significantly correlated with precipitation in January, March and April, but not with temperature. Three models were obtained for each of the season's parameters; the most accurate model for the PSsd explained a variability of 61% and includes as main predictors rainfall from the autumn of the previous year and the mean daily average temperature from 23 February to 8 March. The two most efficient peak dates models included precipitation in January and April as the main predictor variables, and explained greater variability (87 and 88%). Precipitation in autumn and the mean daily and the sum of minimum temperature in winter, showed significant decreasing tendencies. However, the PSsd trend delay was not statistically significant. This study draws the importance of the weather during preseason for grass pollen production and emphasises the usefulness of the models for allergic patients to take preventive measures and for healthcare professionals in allergy therapy.

Phenological Analysis of Grasses (Poaceae) in Comparison with Aerobiological Data in Moscow (Russia)

Grasses (Poaceae) produce large amounts of pollen and are among the main causes of pollinosis worldwide. Despite their morphological similarity, pollen grains of different grass species may have different allergenicities. Therefore, quantification of the roles of individual species in airborne pollen is an important task. There are very few studies on this topic, and none of them have been conducted in a temperate continental climate. Our study was carried out for three years (2020-2022) in the urban territory of Moscow (Russia) and aimed to understand what grass species contribute the most to the total pollen load of the atmosphere. The comparison of aerobiological and phenological data was based on calculating the phenological index, which is a combination of phenological parameters, pollen productivity of individual species, and their abundance. Our data showed that the decomposition of pollination curves based on the phenological index was sometimes very efficient but not always possible in temperate continental climates. The main reasons for disagreement between aerobiological and phenological data were weather conditions and lawn mowing. Not all grasses were equally important as sources of allergenic pollen. The greatest contribution to the pollen load at the beginning of the season in Moscow was made by Dactylis glomerata, and to a lesser extent by Phleum pratense and Festuca pratensis. These are the most common species, which are widespread throughout Europe. The contribution of minor components is insignificant.

Atmospheric transport reveals grass pollen dispersion distances

Identifying the origin of bioaerosols is of central importance in many biological disciplines, such as human health, agriculture, forestry, aerobiology and conservation. Modelling sources, transportation pathways and sinks can reveal how bioaerosols vary in the atmosphere and their environmental impact. Grass pollen are particularly important due to their widely distributed source areas, relatively high abundance in the atmosphere and high allergenicity. Currently, studies are uncertain regarding sampler representability between distance and sources for grass pollen. Using generalized linear modelling, this study aimed to analyse this relationship further by answering the question of distance-to-source area contribution. Grass pollen concentrations were compared between urban and rural locations, located 6.4 km apart, during two years in Worcestershire, UK. We isolated and refined vegetation areas at 100 m × 100 m using the 2017 CEH Crop Map and conducted atmospheric modelling using HYSPLIT to identify which source areas could contribute pollen. Pollen concentrations were then modelled with source areas and meteorology using generalized linear mixed-models with three temporal variables as random variation. We found that the Seasonal Pollen Integral for grass pollen varied between both years and location, with the urban location having higher levels. Day of year showed higher temporal variation than the diurnal or annual variables. For the urban location, grass source areas within 30 km had positive significant effects in predicting grass pollen concentrations, while source areas within 2-10 km were important for the rural one. The source area differential was likely influenced by an urban-rural gradient that caused differences in the source area contribution. Temperature had positive highly significant effects on both locations while precipitation affected only the rural location. Combining atmospheric modelling, vegetation source maps and generalized linear modelling was found to be a highly accurate tool to identify transportation pathways of bioaerosols in landscape environments.

Aerobiological modeling I: A review of predictive models

The present work is the first of two reviews on applied modeling in the field of aerobiology. The aerobiological predictive models for pollen and fungal spores, usually defined as predictive statistical models, will, amongst other objectives, forecast airborne particles' concentration or dynamical behavior of the particles. These models can be classified into Observation Based Models (OBM), Phenological Based Models (PHM), or OTher Models (OTM). The aim of this review is to show, analyze and discuss the different predictive models used in pollen and spore aerobiological studies. The analysis was performed on published electronic scientific articles from 1998 to 2016 related to the type of model, the taxa and the modelled parameters. From a total of 503 studies, 55.5% used OBM (44.8% on pollen and 10.7% on fungal spores), 38.5% PHM (all on pollen) and 6% OTM (5.4% on pollen and 0.6% on fungal spores). OBM have been used with high frequency to forecast concentration. The most frequent model of OBM was linear regression (18.5% out of 503) on pollen and artificial neural networks (4.6%) on fungal spores. In the PHM, the principal use was to characterize the main pollen season (flowering season) based on the model of growth degree days. Finally, OTM have been used to estimate concentrations at unmonitored areas. Olea (14,5%) on pollen and Alternaria (4,8%) on fungal spores were the taxa most frequently modelled. Daily concentration was the most modelled parameter by OBM (25.2%) and season start day by PHM (35.6%). The PHM approaches include greater model diversity and use fewer independent variables than OBM. In addition, PHM show to be easier to apply than OBM; however, the wide range of criteria to define the parameters to use in PHM (e.g.: pollination start day) makes that each model is used with a lesser frequency than other models.

Tracking Montane Mediterranean grasslands: Analysis of the effects of snow with other related hydro-meteorological variables and land-use change on pollen emissions

This paper explores the dynamics of temporal evolution of the high mountain Mediterranean grasslands, (Sierra Nevada, Spain SE). The indicator used is the emission of pollen (Pollen Index, PI) with respect to two important aspects: the incidence of the snow dynamic together with other hydro-meteorological parameters, and the changes in land use, which can Influence the evolution of the grasslands throughout time. The results reveal that pollen emissions in the last 25 years have shown a slight downward trend, with large interannual fluctuations, which are a consequence of diverse environmental factors, both general and specific to the area. One of the most influential parameters on pollen concentrations is snow cover, which reinforces the importance of the presence of snow-packs as water resource outside the winter season in the High Mediterranean Mountain environments. The changes in land use experienced in the area are a driver of change, especially due to the losses experienced in the last decades in the preferred habitats for many species of grasses. It can be concluded that the vulnerability of these ecosystems will be affected by an increase in winter temperatures and/or a decrease in rainfall (climate change) and an increase in the intensity of anthropogenic activities on land use. In this context, the PI is shown as a useful indicator of global change given its sensitivity to both anthropic and hydro-meteorological changes. In addition, it has a wide range of spatial detection and discrimination capacity by altitudinal dimensions.

Shifts in pollen release envelope differ between genera with non-uniform climate change

PREMISE OF THE STUDY

Plant phenological responses to climate change now constitute one of the best studied areas of the ecological impacts of climate change. Flowering time responses to climate change of wind-pollinated species have, however, been less well studied. A novel source of flowering time data for wind-pollinated species is allergen monitoring records.

METHODS

We studied the male flowering time response to climatic variables of two wind-pollinated genera, Betula (Betulaceae) and Populus (Salicaceae), using pollen count records over a 17-year period.

KEY RESULTS

We found that changes in the pollen release envelope differed between the two genera. Over the study period, the only month with a significant rise in temperature was April, resulting in the duration of pollen release of the April-flowering Populus to shorten and the start and peak of the May-flowering Betula to advance. The quantity of pollen released by Betula has increased and was related to increases in the previous year's August precipitation, while the quantity of pollen released by Populus has not changed and was related to the previous year's summer and autumn temperatures.

CONCLUSIONS

Our findings suggest that taxa differ in the reproductive consequences of environmental change. Differing shifts in phenology among species may be related to different rates of change in climatic variables in different months of the year. While our study only considers two genera, the results underscore the importance of understanding non-uniform intra-annual variation in climate when studying the ecological implications of climate change.