Long-term trends and influence of climate and land-use changes on pollen profiles of a Mediterranean oak forest.

Climate change is disrupting phenology and interaction patterns of natural ecosystems, but also human activities that modify land-uses have a direct impact, especially on species distribution and loss of biodiversity. The objective of this study is to evaluate the impact of climate and land-use changes on phenology and airborne pollen spectrum in a Mediterranean natural area, dominated by Quercus Forest and 'dehesa', in the South of the Iberian Peninsula. 61 different pollen types were identified over a 23-year period (1998-2020), mainly from trees and shrubs, such as Quercus, Olea, Pinus or Pistacia, and from herbaceous plants, such as Poaceae, Plantago, Urticaceae or Rumex. A comparison of pollen data from the first years of the study (1998-2002) up recent years (2016-2020), showed a substantial decrease in the relative abundance of pollen from autochthonous species associated with natural areas, such as Quercus or Plantago. However, the relative abundance of the pollen from cultivated ones such as Olea and Pinus, which is used for reforestation has increased. Regarding flowering phenology trends, our analyses revealed variations between -1.5 and 1.5 days per year. Taxa showing an advance phenology were Olea, Poaceae and Urticaceae, whereas Quercus, Pinus, Plantago, Pistacia or Cyperaceae experienced delayed pollination. Meteorological trends in the area generally resulted in an increase in both minimum and maximum temperatures, along with a decrease in precipitations. Changes in pollen concentration and phenology were correlated with changes in air temperatures and precipitation, although the positive or negative influence varied for each pollen type. The results suggest that climate change together with those motivated by land cover changes lead by human activities are having an impact on the phenology and pollen concentration, with the related consequences on pollination and therefore biodiversity more concerning in threatened areas as the Mediterranean Basin.

Methods for interpolating missing data in aerobiological databases.

Missing data is a common problem in scientific research. The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.

Evidences of different meteorological patterns governing 7Be and 210Pb surface levels in the southern Iberian Peninsula.

7Be, 210Pb and PM10 levels in surface air have been simultaneously measured at two sampling sites in the southern Iberian Peninsula for a period of two years. Each site covers one different meteorological area of the Guadalquivir valley, from the mouth (Huelva) to the middle point (Cordoba). The objective of the present study is to analyse the spatial variability of both natural radionuclides, and to identify and characterise the meteorological patterns associated with similar and different surface concentration levels in this complex region. Concentrations are similar in both sampling sites. 7Be level is in the 0.6-15.5 mBq m-3 range in Huelva and 1.2-13.3 mBq m-3 in Córdoba, 210Pb activity concentrations are between 0.04 and 2.30 mBq m-3 in Huelva, and between 0.03 and 1.2 mBq m-3 in Cordoba, and PM10 concentrations are found to be in the 5.1-81.3 µg m-3 range in Huelva, and 8.2-76.3 µg m-3 in Cordoba, respectively. A linear regression analysis indicates more regional variability for 210Pb than for 7Be between simultaneous measurements. Principal components analysis (PCA) is applied to the datasets and the results reveal that aerosol behaviour is mainly represented by two components, which explain 82% of the total variance. The analysis of surface measurements and meteorological parameters revealed that component F1 groups sampling periods in which the influence of similar meteorological conditions over the region lead to similar 7Be, 210Pb and PM10 concentration levels in both sampling sites. On the other hand, component F2 detaches the 7Be, 210Pb and PM10 concentration levels between monitoring sites, and the meteorological analysis shows how surface concentrations within this component are associated with the development of different mesoscale circulations in each part of the valley. The identification of sampling periods characterised by differences in surface concentrations and wind patterns between stations suggests that the valley could not be considered as one single unit for certain meteorological scenarios. These results evidence how the understanding of wind characteristics within a complex terrain provide some essential knowledge in the regionalization and/or optimization of monitoring networks.

Wind dynamics' influence on south Spain airborne olive-pollen during African intrusions.

Given its proximity to northern Africa, southern Spain is regularly affected by high-altitude African intrusions. This determines a well-defined wind dynamics at surface levels. Although this weather event-mainly recorded in spring and summer-coincides with the flowering season of many wind pollinated species, its potential influence on long term airborne pollen transport has been not investigated in detail. We analyse their influence on olive pollen transport at surface level in south Spain. Daily and bi-hourly olive pollen data from 2010 to 2015, recorded at two sites 150km apart, Málaga (coast) and Córdoba (inland), were analysed together with 1) air masses at 300m above ground level (m.a.g.l.), 2) surface wind direction and 3) surface wind speed over the same period. Air masses at 3000m.a.g.l. were used to identify the periods under the influence of African intrusions. The combined analysis has enabled the identification of different pollen patterns and source contributions. In Málaga, hourly pollen peaks were recorded during the early morning coinciding with the arrival of north-westerly winds (developing sea-land breezes), with a minimal impact of local pollen sources; in Córdoba, by contrast, pollen concentrations reflected the joint contribution of local and long term sources, being the maximum concentrations associated with the arrival of southerly air masses in the afternoon. These results help to understand the potential distant sources and back-trajectories of olive pollen detected. In our case pollen from sources located at the west-northwest areas in the case of Malaga, and from the south in Cordoba. These results reinforce the idea that combined studies between synoptic meteorological and aerobiological data together with different atmospheric height air masses data, offer us a better explanation and understanding of the behaviour and the potential sources of recorded airborne data in a given place.

Poaceae pollen as the leading aeroallergen worldwide: A review.

The Poaceae family comprises over 12 000 wind-pollinated species, which release large amounts of pollen into the atmosphere. Poaceae pollen is currently regarded as the leading airborne biological pollutant and the chief cause of pollen allergy worldwide. Sensitization rates vary by country, and those variations are reviewed here. Grass pollen allergens are grouped according to their protein structure and function. In Poaceae, although species belonging to different subfamilies are characterized by distinct allergen subsets, there is a considerable degree of cross-reactivity between many species. Cross-reactivity between grass pollen protein and fresh fruit pan-allergens is associated with the appearance of food allergies. The additional influence of urban pollution may prompt a more severe immunological response. The timing and the intensity of the pollen season are governed by species genetics, but plant phenology is also influenced by climate; as a result, climate changes may affect airborne pollen concentrations. This article reviews the findings of worldwide research which has highlighted the major impact of climate change on plant phenology and also on the prevalence and severity of allergic disease.

Airborne pollen trends in the Iberian Peninsula.

Airborne pollen monitoring is an effective tool for studying the reproductive phenology of anemophilous plants, an important bioindicator of plant behavior. Recent decades have revealed a trend towards rising airborne pollen concentrations in Europe, attributing these trends to an increase in anthropogenic CO2 emissions and temperature. However, the lack of water availability in southern Europe may prompt a trend towards lower flowering intensity, especially in herbaceous plants. Here we show variations in flowering intensity by analyzing the Annual Pollen Index (API) of 12 anemophilous taxa across 12 locations in the Iberian Peninsula, over the last two decades, and detecting the influence of the North Atlantic Oscillation (NAO). Results revealed differences in the distribution and flowering intensity of anemophilous species. A negative correlation was observed between airborne pollen concentrations and winter averages of the NAO index. This study confirms that changes in rainfall in the Mediterranean region, attributed to climate change, have an important impact on the phenology of plants.

A new method for determining the sources of airborne particles.

Air quality is a major issue for humans owing to the fact that the content of particles in the atmosphere has multiple implications for life quality, ecosystem dynamics and environment. Scientists are therefore particularly interested in discovering the origin of airborne particles. A new method has been developed to model the relationship between the emission surface and the total amount of airborne particles at a given distance, employing olive pollen and olive groves as examples. A third-degree polynomial relationship between the air particles at a particular point and the distance from the source was observed, signifying that the nearest area to a point is not that which is most correlated with its air features. This work allows the origin of airborne particles to be discovered and could be implemented in different disciplines related to atmospheric aerosol, thus providing a new approach with which to discover the dynamics of airborne particles.

Cluster analysis of intradiurnal holm oak pollen cycles at peri-urban and rural sampling sites in southwestern Spain.

The impact of regional and local weather and of local topography on intradiurnal variations in airborne pollen levels was assessed by analysing bi-hourly holm oak (Quercus ilex subsp. ballota (Desf.) Samp.) pollen counts at two sampling stations located 40 km apart, in southwestern Spain (Cordoba city and El Cabril nature reserve) over the period 2010-2011. Pollen grains were captured using Hirst-type volumetric spore traps. Analysis of regional weather conditions was based on the computation of backward trajectories using the HYSPLIT model. Sampling days were selected on the basis of phenological data; rainy days were eliminated, as were days lying outside a given range of percentiles (P95-P5). Analysis of cycles for the study period, as a whole, revealed differences between sampling sites, with peak bi-hourly pollen counts at night in Cordoba and at midday in El Cabril. Differences were also noted in the influence of surface weather conditions (temperature, relative humidity and wind). Cluster analysis of diurnal holm oak pollen cycles revealed the existence of five clusters at each sampling site. Analysis of backward trajectories highlighted specific regional air-flow patterns associated with each site. Findings indicated the contribution of both nearby and distant pollen sources to diurnal cycles. The combined use of cluster analysis and meteorological analysis proved highly suitable for charting the impact of local weather conditions on airborne pollen-count patterns. This method, and the specific tools used here, could be used not only to study diurnal variations in counts for other pollen types and in other biogeographical settings, but also in a number of other research fields involving airborne particle transport modelling, e.g. radionuclide transport in emergency preparedness exercises.

Improvement in the accuracy of back trajectories using WRF to identify pollen sources in southern Iberian Peninsula.

Airborne pollen transport at micro-, meso-gamma and meso-beta scales must be studied by atmospheric models, having special relevance in complex terrain. In these cases, the accuracy of these models is mainly determined by the spatial resolution of the underlying meteorological dataset. This work examines how meteorological datasets determine the results obtained from atmospheric transport models used to describe pollen transport in the atmosphere. We investigate the effect of the spatial resolution when computing backward trajectories with the HYSPLIT model. We have used meteorological datasets from the WRF model with 27, 9 and 3 km resolutions and from the GDAS files with 1° resolution. This work allows characterizing atmospheric transport of Olea pollen in a region with complex flows. The results show that the complex terrain affects the trajectories and this effect varies with the different meteorological datasets. Overall, the change from GDAS to WRF-ARW inputs improves the analyses with the HYSPLIT model, thereby increasing the understanding the pollen episode. The results indicate that a spatial resolution of at least 9 km is needed to simulate atmospheric flows that are considerable affected by the relief of the landscape. The results suggest that the appropriate meteorological files should be considered when atmospheric models are used to characterize the atmospheric transport of pollen on micro-, meso-gamma and meso-beta scales. Furthermore, at these scales, the results are believed to be generally applicable for related areas such as the description of atmospheric transport of radionuclides or in the definition of nuclear-radioactivity emergency preparedness.

Statistical approach to the analysis of olive long-term pollen season trends in southern Spain.

Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen-allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982-2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series.

Airborne olive pollen counts are not representative of exposure to the major olive allergen Ole e 1.

Pollen is routinely monitored, but it is unknown whether pollen counts represent allergen exposure. We therefore simultaneously determined olive pollen and Ole e 1 in ambient air in Córdoba, Spain, and Évora, Portugal, using Hirst-type traps for pollen and high-volume cascade impactors for allergen. Pollen from different days released 12-fold different amounts of Ole e 1 per pollen (both locations P < 0.001). Average allergen release from pollen (pollen potency) was much higher in Córdoba (3.9 pg Ole e 1/pollen) than in Évora (0.8 pg Ole e 1/pollen, P = 0.004). Indeed, yearly olive pollen counts in Córdoba were 2.4 times higher than in Évora, but Ole e 1 concentrations were 7.6 times higher. When modeling the origin of the pollen, >40% of Ole e 1 exposure in Évora was explained by high-potency pollen originating from the south of Spain. Thus, olive pollen can vary substantially in allergen release, even though they are morphologically identical.

Biometeorological and autoregressive indices for predicting olive pollen intensity.

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982-2010), a multivariate regression model based on five indices-the index-based model-was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.

Year clustering analysis for modelling olive flowering phenology.

It is now widely accepted that weather conditions occurring several months prior to the onset of flowering have a major influence on various aspects of olive reproductive phenology, including flowering intensity. Given the variable characteristics of the Mediterranean climate, we analyse its influence on the registered variations in olive flowering intensity in southern Spain, and relate them to previous climatic parameters using a year-clustering approach, as a first step towards an olive flowering phenology model adapted to different year categories. Phenological data from Cordoba province (Southern Spain) for a 30-year period (1982-2011) were analysed. Meteorological and phenological data were first subjected to both hierarchical and "K-means" clustering analysis, which yielded four year-categories. For this classification purpose, three different models were tested: (1) discriminant analysis; (2) decision-tree analysis; and (3) neural network analysis. Comparison of the results showed that the neural-networks model was the most effective, classifying four different year categories with clearly distinct weather features. Flowering-intensity models were constructed for each year category using the partial least squares regression method. These category-specific models proved to be more effective than general models. They are better suited to the variability of the Mediterranean climate, due to the different response of plants to the same environmental stimuli depending on the previous weather conditions in any given year. The present detailed analysis of the influence of weather patterns of different years on olive phenology will help us to understand the short-term effects of climate change on olive crop in the Mediterranean area that is highly affected by it.

Prevalence of Artemisia species pollinosis in western Poland: impact of climate change on aerobiological trends, 1995-2004.

BACKGROUND: Artemisia species pollen represents a major cause of allergy in Central Europe. Variations in the pollen season, the influence of climate variables and the prevalence of pollinosis to it were analyzed in Poznan, in western Poland between 1995 and 2004. METHODS: A Hirst volumetric spore trap was used for atmospheric sampling. Pollination date trend analysis and Spearman correlation tests were performed. Skin prick tests (SPT) and allergen specific immunoglobulin (lg)E antibody measurements were performed in 676 and 524 patients, respectively. RESULTS: The Artemisia species pollen season grew longer due to a clear advance in the starting day and only a slightly earlier end point; the peak day also came slightly earlier. Rainfall in the first fortnight of July highly influenced pollen season severity. Temperature was directly correlated with daily Artemisia species pollen levels; relative humidity was inversely correlated. Twelve percent of patients had a positive SPT reaction to Artemisia species. Their symptoms were rhinitis and conjunctivitis (15%), atopic dermatitis (15%), chronic urticaria (14.3%), bronchial asthma (2.4%), and facial and disseminated dermatitis (1.3%). Elevated specific IgE concentrations were detected in the sera of 10.1% of patients. CONCLUSIONS: Artemisia species pollen is an important cause of pollinosis in western Poland. Pollen season intensity is highly influenced by rainfall in the previous weeks. Trends towards earlier season starts and longer duration, possibly caused by climate change, may have an impact on the allergic population.

Heat requirement for the onset of the Olea europaea L. pollen season in several sites in Andalusia and the effect of the expected future climate change.

Olives are one of the largest crops in the Mediterranean region, especially in Andalusia, in southern Spain. A thermal model has been developed for forecasting the start of the olive tree pollen season at five localities in Andalusia: Cordoba, Priego, Jaen, Granada and Malaga using airborne pollen and meteorological data from 1982 to 2001. Threshold temperatures varied between 5 degrees C and 12.5 degrees C depending on bio-geographical characteristics. The external validity of the results was tested using the data for the year 2002 as an independent variable and it confirmed the model's accuracy with only a few days difference from predicted values. All the localities had increasingly earlier start dates during the study period. This could confirm that olive flower phenology can be considered as a sensitive indicator of the effects of climate fluctuations in the Mediterranean area. The theoretical impact of the predicted climatic warming on the olive's flowering phenology at the end of the century is also proposed by applying Regional Climate Model data. A general advance, from 1 to 3 weeks could be expected, although this advance will be more pronounced in mid-altitude inland areas.

The role of temperature in the onset of the Olea europaea L. pollen season in southwestern Spain.

Temperature is one of the main factors affecting the flowering of Mediterranean trees. In the case of Olea europaea L., a low-temperature period prior to bud development is essential to interrupt dormancy. After that, and once a base temperature is reached, the plant accumulates heat until flowering starts. Different methods of obtaining the best-forecast model for the onset date of the O. europaea pollen season, using temperature as the predictive parameter, are proposed in this paper. An 18-year pollen and climatic data series (1982-1999) from Cordoba (Spain) was used to perform the study. First a multiple-regression analysis using 15-day average temperatures from the period prior to flowering time was tested. Second, three heat-summation methods were used, determining the the quantities heat units (HU): accumulated daily mean temperature after deducting a threshold, growing degree-days (GDD): proposed by Snyder [J Agric Meteorol 35:353-358 (1985)] as a measure of physiological time, and accumulated maximum temperature. In the first two, the optimum base temperature selected for heat accumulation was 12.5 degrees C. The multiple-regression equation for 1999 gives a 7-day delay from the observed date. The most accurate results were obtained with the GDD method, with a difference of only 4.7 days between predicted and observed dates. The average heat accumulation expressed as GDD was 209.9 degrees C days. The HU method also gives good results, with no significant statistical differences between predictions and observations.