Models for forecasting airborne Cupressaceae pollen levels in central Spain

Increased rupture of cypress pollen type due to atmospheric water in central and southeastern Spain

This study aims to investigate the meteorological variables determining Cupressaceae pollen grain disruption in the environment. A parallel sampling of pollen grains and disrupted Cupressaceae pollen grains was performed in six cities using two Spanish aerobiological networks. The pollen concentrations, disrupted pollen concentrations, percentage of disrupted pollen and number of days when the percentage of disrupted pollen was above or equal to 50 % were quantified during two pollen seasons. The concentrations were determined following the standardised method EN 16868. Results show that the concentrations of pollen grains and disrupted pollen grains were not determined by geographical features and rarely by bioclimatic variables or indexes but by the ornamental use of the specimens in the vicinity of the pollen sampler, highlighting the possibility of using management practices to reduce exposure to allergens in the cities. African dust outbreaks coincided with higher concentrations of pollen grains and disrupted pollen grains, but the reduced percentage of disrupted pollen grains pointed to a non-causal relationship with long-distance transport. The effect of wind and maximum gusts remained negligible. The triggering factor for pollen disruption was the amount of water in the atmosphere, mainly reported as relative humidity. Rainfall increased the effect of disruption due to pollen grain swelling caused by its wash-out effect. The higher the relative humidity, the higher the disrupted pollen concentrations. This aligns with the mechanism of Cupressaceae reproduction since the family needs a water medium in the form of pollination droplets for the pollination tube to develop and the pollen grain to perform its biological function. Therefore, people that develop allergic symptoms to Cupresaceae pollen should avoid exposure during days with high relative humidity in the main pollen season.

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.

Forecast for Pollen Allergy: A Review from Field Observation to Modeling and Services in Korea

Pollen, a major causal agent of respiratory allergy, is mainly affected by weather conditions. In Korea, pollen and weather data are collected by the national observation network. Forecast models and operational services are developed and provided based on the national pollen data base. Using the pollen risk forecast information will help patients with respiratory allergy to improve their lives. Changes in temperature and CO₂ concentration by climate change affect the growth of plants and their capacity of producing more allergenic pollens, which should be considered in making the future strategy on treating allergy patients.

Allergenic Pollen Calendar in Korea Based on Probability Distribution Models and Up-to-Date Observations

PURPOSE

The pollen calendar is the simplest forecasting method for pollen concentrations. As pollen concentrations are liable to seasonal variations due to alterations in climate and land-use, it is necessary to update the pollen calendar using recent data. To attenuate the impact of considerable temporal and spatial variability in pollen concentrations on the pollen calendar, it is essential to employ a new methodology for its creation.

METHODS

A pollen calendar was produced in Korea using data from recent observations, and a new method for creating the calendar was proposed, considering both risk levels and temporal resolution of pollen concentrations. A probability distribution was used for smoothing concentrations and determining risk levels. Airborne pollen grains were collected between 2007 and 2017 at 8 stations; 13 allergenic pollens, including those of alder, Japanese cedar, birch, hazelnut, oak, elm, pine, ginkgo, chestnut, grasses, ragweed, mugwort and Japanese hop, were identified from the collected grains.

RESULTS

The concentrations of each pollen depend on locations and seasons due to large variability in species distribution and their environmental condition. In the descending order of concentration, pine, oak and Japanese hop pollens were found to be the most common in Korea. The pollen concentrations were high in spring and autumn, and those of oak and Japanese hop were probably the most common cause of allergy symptoms in spring and autumn, respectively. High Japanese cedar pollen counts were observed in Jeju, while moderate concentrations were in Jeonju, Gwangju and Busan.

CONCLUSIONS

A new methodology for the creation of a pollen calendar was developed to attenuate the impact of large temporal and spatial variability in pollen concentrations. This revised calendar should be available to the public and allergic patients to prevent aggravation of pollen allergy.

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

Increasing Juniperus virginiana L. pollen in the Tulsa atmosphere: long-term trends, variability, and influence of meteorological conditions

In the Tulsa area, the Cupressaceae is largely represented by eastern red cedar (Juniperus virginiana L.). The encroachment of this species into the grasslands of Oklahoma has been well documented, and it is believed this trend will continue. The pollen is known to be allergenic and is a major component of the Tulsa atmosphere in February and March. This study examined airborne Cupressaceae pollen data from 1987 to 2016 to determine long-term trends, pollen seasonal variability, and influence of meteorological variables on airborne pollen concentrations. Pollen was collected through means of a Burkard sampler and analyzed with microscopy. Daily pollen concentrations and yearly pollen metrics showed a high degree of variability. In addition, there were significant increases over time in the seasonal pollen index and in peak concentrations. These increases parallel the increasing population of J. virginiana in the region. Pollen data were split into pre- and post-peak categories for statistical analyses, which revealed significant differences in correlations of the two datasets when analyzed with meteorological conditions. While temperature and dew point, among others were significant in both datasets, other factors, like relative humidity, were significant only in one dataset. Analyses using wind direction showed that southerly and southwestern winds contributed to increased pollen concentrations. This study confirms that J. virginiana pollen has become an increasing risk for individuals sensitive to this pollen and emphasizes the need for long-term aerobiological monitoring in other areas.

Aerobiology in the International Journal of Biometeorology, 1957-2017

Aerobiology and biometeorology are related fields. Here we provide a broad review of aerobiology articles published in the International Journal of Biometeorology (IJB) over the past 60 years. We consider how the quantity of such work has varied over this period as well as which regions and countries have been the focus of such work, and where there is a relative paucity. We then focus on a number of highlights and themes in this research, including aerobiology and climate change and aerobiological modelling and forecasting. While much of the article focusses on airborne pollen research, we also discuss the extent to which other airborne organic particles such as fungal spores and bacteria have been the focus of research published in IJB. Also considered are knowledge gaps and research needs and priorities with respect to the field of aerobiology. While the IJB has been one of the main platforms for presenting aerobiological research over recent decades, the article highlights the need for the field of aerobiology to embrace new sampling technologies such as spectral analysis and next-generation sequencing to identify and quantify airborne biological particles.

A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

Climate change: consequences on the pollination of grasses in Perugia (Central Italy). A 33-year-long study

Many works carried out in the last decades have shown that the pollen season for taxa flowering in winter and spring, in temperate regions, has tended to be earlier, probably due to the continuous rise in temperature. The mean annual temperature in Perugia, Central Italy, was about 0.5 °C higher in the last three decades compared with that registered from 1952 to 1981. The increase of temperature took place mainly in winter and spring, while no significant variation was recorded during the summer and autumn. This scenario shows variations in the timing and behavior of flowering of many spontaneous plants such as grasses, whose phenology is strongly influenced by air temperature. This work reports fluctuations in the airborne grass pollen presence in Perugia over a 33-year period (1982-2014), in order to study the influence of the warming registered in recent years on the behavior of pollen release of this taxon. The grass pollen season in Perugia typically lasts from the beginning of May to late July. The start dates showed a marked trend to an earlier beginning of the season (-0.4 day/year), as well as a strong correlation with the average temperatures of March and April. The peak is reached around 30th May, but the annual pollen index (API) is following a decreasing trend. The correlation between starting dates and spring temperatures could be interesting for the constitution of a forecasting model capable of predicting the presence of airborne grass pollen, helping to plan therapies for allergic people.

Temporal modelling and forecasting of the airborne pollen of Cupressaceae on the southwestern Iberian Peninsula

Cupressaceae includes species cultivated as ornamentals in the urban environment. This study aims to investigate airborne pollen data for Cupressaceae on the southwestern Iberian Peninsula over a 21-year period and to analyse the trends in these data and their relationship with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1993 to 2013 in Badajoz (SW Spain). The main pollen season for Cupressaceae lasted, on average, 58 days, ranging from 55 to 112 days, from 24 January to 22 March. Furthermore, a short-term forecasting model has been developed for daily pollen concentrations. The model proposed to forecast the airborne pollen concentration is described by one equation. This expression is composed of two terms: the first term represents the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term is obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological parameters multiplied by a fitting coefficient. Temperature was the main meteorological factor by its influence over daily pollen forecast, being the rain the second most important factor. This model represents a good approach to a continuous balance model of Cupressaceae pollen concentration and is supported by a close agreement between the observed and predicted mean concentrations. The novelty of the proposed model is the analysis of meteorological parameters that are not frequently used in Aerobiology.

Effect of land uses and wind direction on the contribution of local sources to airborne pollen

The interpretation of airborne pollen levels in cities based on the contribution of the surrounding flora and vegetation is a useful tool to estimate airborne allergen concentrations and, consequently, to determine the allergy risk for local residents. This study examined the pollen spectrum in a city in central Spain (Guadalajara) and analysed the vegetation landscape and land uses within a radius of 20km in an attempt to identify and locate the origin of airborne pollen and to determine the effect of meteorological variables on pollen emission and dispersal. The results showed that local wind direction was largely responsible for changes in the concentrations of different airborne pollen types. The land uses contributing most to airborne pollen counts were urban green spaces, though only 0.1% of the total surface area studied, and broadleaved forest which covered 5% of the study area. These two types of land use together accounted for 70% of the airborne pollen. Crops, scrubland and pastureland, though covering 80% of the total surface area, contributed only 18.6% to the total pollen count, and this contribution mainly consisted of pollen from Olea and herbaceous plants, including Poaceae, Urticaceae and Chenopodiaceae-Amaranthaceae. Pollen from ornamental species were mainly associated with easterly (Platanus), southerly (Cupressaceae) and westerly (Cupressaceae and Platanus) winds from the areas where the city's largest parks and gardens are located. Quercus pollen was mostly transported by winds blowing in from holm-oak stands on the eastern edge of the city. The highest Populus pollen counts were associated with easterly and westerly winds blowing in from areas containing rivers and streams. The airborne pollen counts generally rose with increasing temperature, solar radiation and hours of sunlight, all of which favour pollen release. In contrast, pollen counts declined with increased relative humidity and rainfall, which hinder airborne pollen transport.

A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995-2006

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995-2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.