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This study focused on phenological timing and spatial patterns in 30 Poaceae species flowering in spring in different types of plant cover (scrub, riverbank and pasture). Grass community composition was studied, and the influence of species and plant cover on the start date and duration of flowering was assessed from March to June in both 2014 and 2015. Twenty-nine sampling sites were selected for phenological monitoring using the BBCH scale. Data were subjected to GLMM analyses. Binary discriminant analysis revealed differences in grass community composition as a function of plant cover type; scrub cover comprised a considerably larger number of species than those in riverbank and pasture. Moreover, more species diversity was observed in 2014 than in 2015 with a drier and stressed pre-flowering period. Differences on phenology were also recorded between plant cover types and study years. Species in pasture and riverbank flowered before (113.4 days; 116.1 days) than species in scrub (120.9 days), being these species with shorter flowering length because they are more exposed to the characteristic of the Mediterranean region during the summer. In general, flowering onset occurred later in 2014 (118.2 days) than in 2015 (115.8 days), probably attributable to precipitation occurring during March. On the other hand, spatial autocorrelation within some cover types has been observed, showing spatial patterns exist at a smaller scale. The findings of this study contribute to a better understanding of grass phenology in different environments.

Olea europaea L. pollen is the second-largest cause of pollinosis in the southern Iberian Peninsula. Airborne-pollen monitoring networks provide essential data on pollen dynamics over a given study area. Recent research, however, has shown that airborne pollen levels alone do not always provide a clear indicator of actual exposure to aeroallergens. This study sought to evaluate correlations between airborne concentrations of olive pollen and Ole e 1 allergen levels in Córdoba (southern Spain), in order to determine whether atmospheric pollen concentrations alone are sufficient to chart changes in hay fever symptoms. The influence of major weather-related variables on local airborne pollen and allergen levels was also examined. Monitoring was carried out from 2012 to 2014. Pollen sampling was performed using a Hirst-type sampler, following the protocol recommended by the Spanish Aerobiology Network. A multi-vial cyclone sampler was used to collect aeroallergens, and allergenic particles were quantified by ELISA assay. Significant positive correlations were found between daily airborne allergen levels and atmospheric pollen concentrations, although there were occasions when allergen was detected before and after the pollen season and in the absence of airborne pollen. The correlation between the two was irregular, and pollen potency displayed year-on-year variations and did not necessarily match pollen-season-intensity.

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.

The aim of the present study was to develop pheno-meteorological models to explain and forecast the main olive flowering phenological phases within the Mediterranean basin, across a latitudinal and longitudinal gradient that includes Tunisia, Spain, and Italy. To analyze the aerobiological sampling points, study periods from 13 years (1999-2011) to 19 years (1993-2011) were used. The forecasting models were constructed using partial least-squares regression, considering both the flowering start and full-flowering dates as dependent variables. The percentages of variance explained by the full-flowering models (mean 84 %) were greater than those explained by the flowering start models (mean 77 %). Moreover, given the time lag from the North African areas to the central Mediterranean areas in the main olive flowering dates, the regional full-flowering predictive models are proposed as the most useful to improve the knowledge of the influence of climate on the olive tree floral phenology. The meteorological parameters related to the previous autumn and both the winter and the spring seasons, and above all the temperatures, regulate the reproductive phenology of olive trees in the Mediterranean area. The mean anticipation of flowering start and full flowering for the future period from 2081 to 2100 was estimated at 10 and 12 days, respectively. One question can be raised: Will the olive trees located in the warmest areas be northward displaced or will they be able to adapt their physiology in response to the higher temperatures? The present study can be considered as an approach to design more detailed future bioclimate research.

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.

The main characteristics of the heat accumulation period and the possible existence of different types of biological response to the environment in different populations of olive through the Mediterranean region have been evaluated. Chilling curves to determine the start date of the heat accumulation period were constructed and evaluated. The results allow us to conclude that the northern olive populations have the greatest heat requirements for the development of their floral buds, and they need a period of time longer than olives in others areas to completely satisfy their biothermic requirements. The olive trees located in the warmest winter areas have a faster transition from endogenous to exogenous inhibition once the peak of chilling is met, and they show more rapid floral development. The lower heat requirements are due to better adaptation to warmer regions. Both the threshold temperature and the peak of flowering date are closely related to latitude. Different types of biological responses of olives to the environment were found. The adaptive capacity shown by the olive tree should be considered as a useful tool with which to study the effects of global climatic change on agro-ecosystems.

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.

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.

The main goal of the present study was to provide a detailed analysis of olive pollen transport dynamics in the province of Córdoba (south-western Spain) by applying back-trajectory analysis. Pollen data from 2006 and 2007 were analysed at four monitoring sites: Córdoba city in the centre of the province, Baena and Priego de Córdoba located in the south, and El Cabril reserve (Hornachuelos Natural Park) in the north. Particular attention was paid to nine episodes of high pollen counts. Synoptic surface maps were used, and kinematic back-trajectories (3D) were computed using the hybrid single particle Lagrangian integrated trajectory model (HYSPLIT) at 500 m above ground level, run with a time-step of 6 h over a period of 36 h. Findings were analysed in conjunction with daily and bi-hourly airborne pollen data, field phenological data and hourly surface meteorological data recorded at nearby stations: temperature, relative humidity, rainfall, wind direction and wind speed. The results identified two pollen source areas over the Córdoba province, the largest one located in the south, affecting Baena, Priego de Córdoba and Córdoba city, and one smaller located in the west, which determines mainly the pollen cycle over the north of the province, El Cabril. In addition, two air mass circulations were found, one coming from the south and crossing the main olive pollen sources very close to the surface and being frequently associated with higher pollen counts, and the other coming from the west and, in the episodes investigated, influencing mainly the north of the province.

The aim of this study was to investigate the main climatic and biological trends related to olive flowering in central-southern Italy compared to those in Andalusia, Spain. Results since 1982 were compared for the two long-series monitoring areas of Cordoba and Perugia, and since 1992-1999 for the short-series areas. The relationship between climatic trends and the biological response of the olive, a widespread culture in the Mediterranean basin, were investigated. An aerobiological method involving capturing pollen released into the atmosphere was utilised as a bioindicator of flowering phenology. The study results confirm the strong relationship between flowering periods and spring temperature trends for the olive. Temperature during March, April and May was the parameter most related to flowering date in the study areas, particularly in Italy. In some cases we found a significant correlation between flowering and past autumn temperatures, probably due to their effect on floral bud dormancy induction, but this phenomenon appeared to be of minor importance in the studied areas. The phenological trend results show the continuous advance of flowering dates to the late 1990s, followed by a relatively stationary time series related to a short-term temperature fluctuation in the Mediterranean area. This latter period probably represents a mesoscale event forced by a macroscale event-the North Atlantic Oscillation. The results reveal that the trend towards increased temperatures, and the consequent flowering advance of some species, indicated some years ago is nowadays not as clear as was expected and should be confirmed over the next few years in the Mediterranean areas under investigation.

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.

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 influence of meteorological parameters on the dispersion of airborne pollen has been studied by several authors. Olive pollen is the major cause of allergy in southern Spain, where a large part of the arable surface area is given over to olive cultivation. Daily pollen forecasts provide important information both for pollen-allergy sufferers and for agronomists trying to achieve a better biological understanding of variations in airborne olive pollen levels. The main purpose of this paper is to study, by means of short-term statistical analysis, the effect of meteorological parameters on airborne olive pollen concentrations in the city of Cordoba (south-western Spain). Twenty-one-year (1982-2002) aerobiological and meteorological databases were used. Correlation and multiple regression analyses were used to study the relationships between olive pollen levels and several meteorological parameters. Statistical analysis was applied both to the whole pollen season and to the pre-peak period. Daily meteorological parameters, such as accumulated mean temperature, accumulated sunlight hours, and accumulated rainfall were used as independent variables in both statistical analyses. Accumulated meteorological variables were of the greatest value in most regression analysis equations, heat-related variables being the most important.

Objective: To develop a quick and sensitive method for identification of children with presumed meningococcal septic shock at risk of death at admission to the pediatric intensive care unit (PICU) and to compare its performance with three other prognostic systems: Glasgow Meningococcal Septicaemia Prognostic Score (GMSPS), Malley score and the Paediatric Index of Mortality (PIM)., Design: Multicenter retrospective cohort study., Setting: PICUs of 14 tertiary hospitals., Patients: The developmental sample included 192 children consecutively admitted to the PICUs with presumed or confirmed meningococcal septic shock from 1983 to 1995. The validation sample included 158 children consecutively admitted from 1996 to 1998., Interventions: Clinical and laboratory data gathered during the first 2 h after admission were used to develop the new score and to compute the other scoring systems. Logistic regression was applied to identify the independent predictors of death., Measurements and Results: Overall mortality was 31.5%. The new score included seven variables: cyanosis (2 points), Glasgow coma scale less than 8 (2 points), refractory hypotension (2 points), oliguria (1 point), leukocytes less than 4000/mm(3) (1 point), partial thromboplastin time more than 150% of control value (1 point) and base deficit more than 10 mmol/l (1 point). The new score provided the best discriminative capability, as measured by the area under the ROC curve (SEM) in the validation sample =0.88 (0.03), PIM =0.82 (0.04), Malley I =0.80 (0.04), GMSPS =0.79 (0.04) and Malley II =0.76 (0.04)., Conclusions: A new prognostic score is proposed for therapeutic stratification of children with presumed meningococcal septic shock.

Data on predicted average and maximum airborne pollen concentrations and the dates on which these maximum values are expected are of undoubted value to allergists and allergy sufferers, as well as to agronomists. This paper reports on the development of predictive models for calculating total annual pollen output, on the basis of pollen and weather data compiled over the last 19 years (1982-2000) for Córdoba (Spain). Models were tested in order to predict the 2000 pollen season; in addition, and in view of the heavy rainfall recorded in spring 2000, the 1982-1998 data set was used to test the model for 1999. The results of the multiple regression analysis show that the variables exerting the greatest influence on the pollen index were rainfall in March and temperatures over the months prior to the flowering period. For prediction of maximum values and dates on which these values might be expected, the start of the pollen season was used as an additional independent variable. Temperature proved the best variable for this prediction. Results improved when the 5-day moving average was taken into account. Testing of the predictive model for 1999 and 2000 yielded fairly similar results. In both cases, the difference between expected and observed pollen data was no greater than 10%. However, significant differences were recorded between forecast and expected maximum and minimum values, owing to the influence of rainfall during the flowering period.

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.

Winter is not traditionally considered to be a risky season for people who suffer from pollen allergies. However, increasing numbers of people are showing symptoms in winter. This prompted our investigation into the levels of solid material in the air, and some of the meteorological phenomena that allow their accumulation. This study showed a possible relationship between the phenomenon of thermal inversion, which occurs when very low temperatures, cloudless skies and atmospheric calms coincide, and an increase in the concentration of solid material in the atmosphere. Frequently, this situation is associated with other predictable phenomena such as fog, dew and frost. This may allow a warning system to be derived for urban pollution episodes. The effect caused by parameters such as wind and rainfall was also analysed. Solid material was differentiated into non-biological material from natural and non-natural sources (e.g. soot, dust, sand, diesel exhaust particles, partially burnt residues) and biological material. The latter mainly comprises pollen grains and fungal spores. Owing to its abundance and importance as a causal agent of winter allergies, Cupressaceae pollen was considered separately.

The influence of meteorological factors on daily Urticaceae pollen counts were studied in Córdoba (southwest Spain) in 1996 and 1997. The daily Urticaceae pollen concentrations were obtained by using a Hirst-type volumetric sampler, and meteorological data were obtained from the Córdoba airport, located near the sampling site. The highest correlation between pollen concentration and meteorological parameters was obtained during non-rainy seasons. Temperature was found to be the most important meteorological parameter influencing pollen counts in spring, as temperature is the main reason for the increase of pollen concentration in the atmosphere. In autumn, humidity was another important parameter influencing pollen counts. Rain, however, did not appear to be significant. The influence of the pollen concentration of the 2 previous days and the pollen concentration of the previous day has been studied. During periods with low precipitation, the pollen concentration of the previous day was a useful predictor of Urticaceae pollen concentrations for the following day.