Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Pollen production in olive cultivars and its interannual variability.

BACKGROUND AND AIMS: Olive (Olea europaea subsp. europaea var. europaea) is the most extensively cultivated fruit crop worldwide. It is considered a wind-pollinated and strictly outcrossing crop. Thus, elevated pollen production is crucial to guarantee optimum fruit set and yield. Despite these facts, the variability of pollen production within the cultivated olive has been scarcely studied. This study aimed to characterize this feature by analysing a representative set of worldwide olive cultivars. METHODS: We evaluated the average number of pollen grains per anther in 57 principal cultivars over three consecutive years. We applied a standard generalized linear model (GLM) approach to study the influence of cultivar, year and the previous year's fruit load on the amount of pollen per anther. Additionally, the K-means method was used for cluster analysis to group cultivars based on their pollen production capacity. KEY RESULTS: Pollen production per anther was highly variable among olive cultivars. The cultivar significantly accounted for 51.3 % of the variance in pollen production and the year for 0.3 %. The interaction between the two factors explained 8.4 % of the variance, indicating that not all cultivars were equally stable in producing pollen across the years. The previous year's fruit load and its interaction with the year were significant, but barely accounted for 1.5 % of the variance. Olive cultivars were classified into four clusters according to their capacity to produce pollen. Interestingly, the fourth cluster was composed of male-sterile cultivars, which presumably share this character by inheritance. CONCLUSIONS: Pollen production per anther varied extensively within the cultivated olive. This variation was mainly driven by the cultivar and its interaction with the year. The differential capacity of olive cultivars to produce pollen should be considered not only for designing new orchards but also gardens where this species is used as an ornamental.

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.

Perceptions of change in the environment caused by the COVID-19 pandemic: Implications for environmental policy.

COVID-19 lockdown measures have impacted the environment with both positive and negative effects. However, how human populations have perceived such changes in the natural environment and how they may have changed their daily habits have not been yet thoroughly evaluated. The objectives of this work were to investigate (1) the social perception of the environmental changes produced by the COVID-19 pandemic lockdown and the derived change in habits in relation to i) waste management, energy saving, and sustainable consumption, ii) mobility, iii) social inequalities, iv) generation of noise, v) utilization of natural spaces, and, vi) human population perception towards the future, and (2) the associations of these potential new habits with various socio-demographic variables. First, a SWOT analysis identified strengths (S), weaknesses (W), opportunities (O), and threats (T) generated by the pandemic lockdown measures. Second, a survey based on the aspects of the SWOT was administered among 2370 adults from 37 countries during the period from February to September 2021. We found that the short-term positive impacts on the natural environment were generally well recognized. In contrast, longer-term negative effects arise, but they were often not reported by the survey participants, such as greater production of plastic waste derived from health safety measures, and the increase in e-commerce use, which can displace small storefront businesses. We were able to capture a mismatch between perceptions and the reported data related to visits to natural areas, and generation of waste. We found that age and country of residence were major contributors in shaping the survey participants ´answers, which highlights the importance of government management strategies to address current and future environmental problems. Enhanced positive perceptions of the environment and ecosystems, combined with the understanding that livelihood sustainability, needs to be prioritized and would reinforce environmental protection policies to create greener cities. Moreover, new sustainable jobs in combination with more sustainable human habits represent an opportunity to reinforce environmental policy.

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.

Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.

Betula pollen is frequently found in the atmosphere of central and northern Europe. Betula pollen are health relevant as they cause severe allergic reactions in the population. We developed models of thermal requirements to predict start, peak and end dates of the Betula main pollen season for Bavaria (Germany). Betula pollen data of one season from 19 locations were used to train the models. Estimated dates were compared with observed dates, and the errors were spatially represented. External validation was carried out with time series datasets of 3 different locations (36years in total). RESULTS: The temperature requirements to detonate the main pollen season proved non-linear. For the start date model (error of 8,75days during external validation), daily mean temperatures above a threshold of 10°C from 28th of February onwards were the most relevant. The peak model (error of 3.58days) takes into account mean daily temperatures accumulated since the first date of the main pollen season in which the daily average temperature exceeded 11°C. The end model (error of 3.75days) takes into account all temperatures accumulated since the start of the main pollen season. CONCLUSION: These models perform predictions that enable the allergic population to better manage their disease. With the established relationship between temperatures and pollen season dates, changes in the phenological behaviour of Betula species due to climate change can be also estimated in future studies by taking into account the different climate scenarios proposed by previous climate change studies.

Increasing resolution of airborne pollen forecasting at a discrete sampled area in the southwest Mediterranean Basin.

Daily fluctuations of the airborne pollen concentrations produce variations on symptomatology in allergic population. Such fluctuations are influenced by local vegetal coverage, flowering phenology, geography and climatology. Since 1991, airborne pollen of Malaga province (southern Spain) has been monitored in 7 different locations. Malaga station has been kept operational uninterruptedly throughout the studied period, while the rest of the stations only worked in periods of 2-4 years. Weekly, its pollen information is updated online to inform the population in order to prevent allergic diseases. Increasing the spatial resolution of pollen information would be very useful for allergic population living at unsampled locations. Due to the impossibility of keeping operational a high number of pollen stations covering the whole province of Malaga, the aim of this study is to create spatial models to extrapolate and forecast the pollen concentrations to Malaga province by using the concentrations registered at the capital as unique input. To do so, the relationships obtained between the airborne pollen concentrations detected at Malaga city and those detected at the other stations have been used to elaborate models for the main pollen types registered at the province. These models were spatially interpolated all over the province by using co-kriging techniques and the Compensated Thermicity Index as covariable. As result of this work, pollen distribution of the 8 most prevalent taxa has been depicted all over the whole Malaga province and an allergy alert system has been set up to extrapolate pollen information from Malaga to the whole province.

Pollen and spore monitoring in the world.

BACKGROUND: Ambient air quality monitoring is a governmental duty that is widely carried out in order to detect non-biological ("chemical") components in ambient air, such as particles of < 10 µm (PM10, PM2.5), ozone, sulphur dioxide, and nitrogen oxides. These monitoring networks are publicly funded and air quality data are open to the public. The situation for biological particles that have detrimental effects on health, as is the case of pollen and fungal spores, is however very different. Most pollen and spore monitoring networks are not publicly funded and data are not freely available. The information regarding which biological particle is being monitored, where and by whom, is consequently often not known, even by aerobiologists themselves. This is a considerable problem, as local pollen data are an important tool for the prevention of allergic symptoms. OBJECTIVE: The aim of this study was to review pollen monitoring stations throughout the world and to create an interactive visualization of their distribution. METHODS: The method employed to collect information was based on: (a) a review of the recent and historical bibliography related to pollen and fungal spore monitoring, and (b) personal surveys of the managers of national and regional monitoring networks. The interactive application was developed using the R programming language. RESULTS: We have created an inventory of the active pollen and spore monitoring stations in the world. There are at least 879 active pollen monitoring stations in the world, most of which are in Europe (> 500). The prevalent monitoring method is based on the Hirst principle (> 600 stations). The inventory is visualised as an interactive and on-line map. It can be searched, its appearance can be adjusted to the users' needs and it is updated regularly, as new stations or changes to those that already exist can be submitted online. CONCLUSIONS: The map shows the current situation of pollen and spore monitoring and facilitates collaboration among those individuals who are interested in pollen and spore counts. It might also help to improve the monitoring of biological particles up to the current level employed for non-biological components.

Relationships between airborne pollen grains, wind direction and land cover using GIS and circular statistics.

Airborne bio-aerosol content (mainly pollen and spores) depends on the surrounding vegetation and weather conditions, particularly wind direction. In order to understand this issue, maps of the main land cover in influence areas of 10km in radius surrounding pollen traps were created. Atmospheric content of the most abundant 14 pollen types was analysed in relation to the predominant wind directions measured in three localities of SW of Iberian Peninsula, from March 2011 to March 2014. Three Hirst type traps were used for aerobiological monitoring. The surface area for each land cover category was calculated and wind direction analysis was approached by using circular statistics. This method could be helpful for estimating the potential risk of exposure to various pollen types. Thus, the main land cover was different for each monitoring location, being irrigated crops, pastures and hardwood forests the main categories among 11 types described. Comparison of the pollen content with the predominant winds and land cover shows that the atmospheric pollen concentration is related to some source areas identified in the inventory. The study found that some pollen types (e.g. Plantago, Fraxinus-Phillyrea, Alnus) come from local sources but other pollen types (e.g. Quercus) are mostly coming from longer distances. As main conclusions, airborne particle concentrations can be effectively split by addressing wind with circular statistics. By combining circular statistics and GIS method with aerobiological data, we have created a useful tool for understanding pollen origin. Some pollen loads can be explained by immediate surrounding landscape and observed wind patterns for most of the time. However, other factors like medium or long-distance transport or even pollen trap location within a city, may occasionally affect the pollen load recorded using an air sampler.

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.

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.

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.