Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman’s correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March–April, while the peak day was predicted using the temperature during the last 10 days of March.     

Long-term and short-term forecast models for Poaceae (grass) pollen in Poznań, Poland,constructed using regression analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than 10 years and the length of the dataset is now considered sufficient for statistical analysis. The objective of this paper is to produce long-range forecasts that predict certain characteristics of the grass pollen season (such as the start, peak and end dates of the grass pollen season) as well as short-term forecasts that predict daily variations in grass pollen counts for the next day or next few days throughout the main grass pollen season. The method of forecasting was regression analysis. Correlation analysis was used to examine the relationship between grass pollen counts and the factors that affect its production, release and dispersal. The models were constructed with data from 1994 to 2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within two days and achieved 61% and 70% accuracy on a scale of 1–4 when forecasting variations in daily average grass pollen counts in 2005 and 2006, respectively. This study has emphasised how important the weather during the few weeks or months preceding pollination is to grass pollen production, and draws attention to the importance of considering large-scale patterns of climate variability (indices of the North Atlantic Oscillation) when constructing forecast models for allergenic pollen.     

Poaceae pollen in the atmosphere of Tetouan (NW Morocco): effect of meteorological parameters and forecast of daily pollen concentration

The Poaceae pollen season has been characterized in Tetouan during a 7-year period, and the effect of weather conditions on daily concentrations was examined. The forecast models were produced using a stepwise multiple regression analyses. Firstly, three models were constructed to predict daily Poaceae pollen concentrations during the main pollen season, as well as the pre-peak and post-peak periods with data from 2008 to 2012 and tested on data from 2013 and 2014. Secondly, the regression models using leave-one-out cross-validation were produced with data obtained during 2008–2014 taking into account meteorological parameters and mean pollen concentrations of the same day in other years. The duration of the season ranged from 70 days in 2009 to 158 days in 2012. The highest amount of Poaceae pollen was detected in spring and the first fortnight of July. The annual sum of airborne Poaceae pollen concentrations varied between 2100 and 6251. The peak of anthesis was recorded in May in six of the other years studied. The regression models accounted for 36.3–85.7% of variance in daily Poaceae pollen concentrations. The models fitted best when the mean pollen concentration of the same day in other years was added to meteorological variables, and explained 78.4–85.7% of variance of the daily pollen changes. When the year 2014 was used for validating the models, the lowest root-mean-square errors values were found between the observed and estimated data (around 13). The reasonable predictor variables were the mean pollen concentration of the same day in other years, mean temperature, precipitations, and maximum relative humidity.     

Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco)

Poaceae pollen is one of the most prevalent aeroallergens causing allergenic reactions. The aim of this study was to characterise the grass pollen season in Tetouan during the years 2008–2010, to analyse the effect of some meteorological parameters on the incidence of the airborne Poaceae pollen, and to establish forecasting variables for daily pollen concentrations. Aerobiological sampling was undertaken over three seasons using the volumetric method. The pollen season started in April and showed the highest pollen index in May and June, when the maximum temperature ranged from 23 to 27 °C, respectively. The annual pollen score recorded varied from year to year between 2,588 and 5,404. The main pollen season lasted 114–173 days, with peak days occurring mainly in May; the highest concentration reached 308 pollen grains/m³. Air temperature was the most important meteorological parameter and correlated positively to daily pollen concentration increase. An increase in relative humidity and precipitation was usually related to a decrease in airborne pollen content. External validation of the models performed using data from 2011 showed that Poaceae pollen concentration can be highly predicted (64.2–78.6 %) from the maximum temperature, its mean concentration for the same day in other years, and its concentration recorded on the previous day. Sensitive patients suffering allergy to Poaceae pollen are at moderate to highest risk of manifesting allergic symptoms to grass pollen over 33–42 days. The results obtained provide new information on the quantitative contribution of the Poaceae pollen to the airborne pollen of Tetouan and on its temporal distribution. Airborne pollen can be surveyed and forecast in order to warn the atopic population.     

The effect of the meteorological factors on the Alnus pollen season in Lublin (Poland)

Alder pollen seasons and the effect of meteorological conditions on daily average pollen counts in the air of Lublin (Poland) were analysed. Alnus pollen grains reach very high concentrations in the atmosphere of this city during the early spring period and the parameters of pollen seasons were very different in the particular years studied. The pollen season lasted on average one month. The highest variation was observed for the peak value and the Seasonal Pollen Index (SPI). The pollen seasons, which started later, had shorter duration. Peak daily average pollen counts and SPI value were higher during the shorter seasons. Similarities in the stages of pollen seasons designated by the percentage method depended on the start date of the pollen season. Season parameters were mainly correlated with thermal conditions at the beginning of the year. Regression analysis was used to predict certain characteristics of the alder pollen season. The highest level of explanation of the variation in Alnus pollen season start and peak dates was obtained in the model using mean temperature in February. The obtained regression models may predict 82% of the variation in the pollen season start date, 73% of the variation in the duration, and 62% in the peak date.     

Statistical modeling,forecasting and time series analysis of birch phenology in Montreal,Canada

The aim of this study was to analyse birch pollen time series observed in Montreal (Canada) in order to understand the link between inter-annual variability of phenology and environmental factors and to build predictive models for the upcoming pollen season. Modeling phenology is challenging, especially in Canada, where phenological observations are rare. Nevertheless, understanding phenology is required for scientific applications (e.g. inputs to numerical models of pollen dispersion) but also to help allergy sufferers to better prepare their medication and avoidance strategies before the start of the pollen season. We used multivariate statistical regression to analyse and predict phenology. The predictors were drawn from a large basin (over 60) of potential environmental predictors including meteorological data and global climatic indices such NAO (North Atlantic Oscillation index) and ENSO/MEI (Multivariate Enso Index). Results of this paper are summarized as follows: (1) an accurate forecast for the upcoming season starting date of the birch pollen season was obtained (showing low bias and total forecast error of about 4 days in Montreal), (2) NAO and ENSO/MEI indices were found to be well correlated (i.e. 44% of the variance explained) with birch phenology, (3) a long-term trend of 2.6 days per decade (p < 0.1) towards longer season duration was found for the length of the birch pollen season in Montreal. Finally, perturbations of the quasi-biennial cycle of birch were observed in the pollen data during the pollen season following the Great Ice Storm of 1998 which affected south-eastern Canada.     

Poaceae pollen in the air depending on the thermal conditions

The relationship between the meteorological elements, especially the thermal conditions and the Poaceae pollen appearance in the air, were analysed as a basis to construct a useful model predicting the grass season start. Poaceae pollen concentrations were monitored in 1991–2012 in Kraków using the volumetric method. Cumulative temperature and effective cumulative temperature significantly influenced the season start in this period. The strongest correlation was seen as the sum of mean daily temperature amplitudes from April 1 to April 14, with mean daily temperature >15 °C and effective cumulative temperature >3 °C during that period. The proposed model, based on multiple regression, explained 57 % of variation of the Poaceae season starts in 1991–2010. When cumulative mean daily temperature increased by 10 °C, the season start was accelerated by 1 day. The input of the interaction between these two independent variables into the factor regression model caused the increase in goodness of model fitting. In 2011 the season started 5 days earlier in comparison with the predicted value, while in 2012 the season start was observed 2 days later compared to the predicted day. Depending on the value of mean daily temperature from March 18th to the 31st and the sum of mean daily temperature amplitudes from April 1st to the 14th, the grass pollen seasons were divided into five groups referring to the time of season start occurrence, whereby the early and moderate season starts were the most frequent in the studied period and they were especially related to mean daily temperature in the second half of March.     

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.     

An aeropalynological study of metropolitan Toronto

This study uses 6 years of atmospheric pollen data to examine temporal variability of airborne pollen concentrations at various scales. Airborne pollen was collected from 1985 to 1990 with a Burkard trap, located 18 m above ground at Scarborough College, Toronto, Canada. Pollen season parameters are defined and summarized for all taxa in preparation for developing forecasting models. Annual totals of pollen concentration show great interannual variability. The highest coefficient of variation occurs inTsuga, Fraxinus, Betula andFagus, while the lowest inQuercus andAmbrosia. Some taxa show periodic cycles consistent with mast reproductive behaviour. In many studies, the start of the pollen season is defined as an arbitrary percentage of the annual sum. As a result, the start of the season cannot be identified until the season has passed. As well, due to large fluctuations in annual sum, start dates are more variable. This is not practical for the purposes of forecasting. In this study, the start of the pollen season is defined by a critical concentration threshold which signals the onset of the main pollen season in all years. These critical levels ranged from 2 to 60 grains/m³ for the abundant taxa. Interannual variation in the start of the season is approximately 20 days for tree taxa, 5 days for Poaceae, and 2 days forAmbrosia. For many plants, dehiscence is triggered at a critical level of accumulated degree-days. Since annual rates of temperature increase show great variation, there is also interannual variability in the onset of pollen release. Multi-year average pollen curves incorporate these differences in onset and may give an inaccurate representation of the pollen season in a typical year. This paper presents a method of aligning yearly pollen curves to reduce seasonal variation and more accurately represent both the average timing and magnitude of the pollen season. For some types, such asBetula and Poaceae, the resulting curves are positively skewed. Tree taxa, in general, exhibit a more symmetric pollen concentration curve. Aligned average pollen concentration curves are presented for Toronto in the form of a pollen calendar. In addition, phenological data for all common taxa are summarized.     

A predictive study of cupressaceae pollen season onset, severity, maximum value and maximum value date

In this paper Cupressaceae pollen season onset, severity, maximum value and maximum value date, have been studied for 15 consecutive years (1982–1996). The data set was obtained using a Hirst spore-trap (Burkard Manufacturing). In order to determine the influence of the previous months’ meteorological variables on Cupressaceae season’s parameters, the sums of maximum, average and minimum temperatures, and total rainfall for the months of October, November and December were used as independent variables in predictive formulae built by multiple regression analyses. The variance explained percentage by regression analyses varied between 60 and 87%. Total rainfall in the months prior to anthesis and temperature (particularly minimum temperature) are important factors to consider in forecasting models of Cupressaceae pollen season parameters, but meteorological conditions at the time of pollen production are also important and can modify the pre-established potential of pollination.     

The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate

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.     

Forecasting ragweed pollen characteristics with nonparametric regression methods over the most polluted areas in Europe

Nonparametric time-varying regression methods were developed to forecast daily ragweed pollen concentration, and the probability of the exceedance of a given concentration threshold 1 day ahead. Five-day and 10-day predictions of the start and end of the pollen season were also addressed with a nonparametric regression technique combining regression analysis with the method of temperature sum. Our methods were applied to three of the most polluted regions in Europe, namely Lyon (Rhône Valley, France), Legnano (Po River Plain, Italy) and Szeged (Great Plain, Hungary). For a 1-day prediction of both the daily pollen concentration and daily threshold exceedance, the order of these cities from the smallest to largest prediction errors was Legnano, Lyon, Szeged and Legnano, Szeged, Lyon, respectively. The most important predictor for each location was the pollen concentration of previous days. The second main predictor was precipitation for Lyon, and temperature for Legnano and Szeged. Wind speed should be considered for daily concentration at Legnano, and for daily pollen threshold exceedances at Lyon and Szeged. Prediction capabilities compared to the annual cycles for the start and end of the pollen season decreased from west to east. The order of the cities from the lowest to largest errors for the end of the pollen season was Lyon, Legnano, Szeged for both the 5- and 10-day predictions, while for the start of the pollen season the order was Legnano, Lyon, Szeged for 5-day predictions, and Legnano, Szeged, Lyon for 10-day predictions.     

Exploring the spatio-temporal relationship between two key aeroallergens and meteorological variables in the United Kingdom

Constructing accurate predictive models for grass and birch pollen in the air, the two most important aeroallergens, for areas with variable climate conditions such as the United Kingdom, require better understanding of the relationships between pollen count in the air and meteorological variables. Variations in daily birch and grass pollen counts and their relationship with daily meteorological variables were investigated for nine pollen monitoring sites for the period 2000–2010 in the United Kingdom. An active pollen count sampling method was employed at each of the monitoring stations to sample pollen from the atmosphere. The mechanism of this method is based on the volumetric spore traps of Hirst design (Hirst in Ann Appl Biol 39(2):257–265, 1952). The pollen season (start date, finish date) for grass and birch were determined using a first derivative method. Meteorological variables such as daily rainfall; maximum, minimum and average temperatures; cumulative sum of Sunshine duration; wind speed; and relative humidity were related to the grass and birch pollen counts for the pre-peak, post peak and the entire pollen season. The meteorological variables were correlated with the pollen count data for the following temporal supports: same-day, 1-day prior, 1-day mean prior, 3-day mean prior, 7-day mean prior. The direction of influence (positive/negative) of meteorological variables on pollen count varied for birch and grass, and also varied when the pollen season was treated as a whole season, or was segmented into the pre-peak and post-peak seasons. Maximum temperature, sunshine duration and rainfall were the most important variables influencing the count of grass pollen in the atmosphere. Both maximum temperature (pre-peak) and sunshine produced a strong positive correlation, and rain produced a strong negative correlation with grass pollen count in the air. Similarly, average temperature, wind speed and rainfall were the most important variables influencing the count of birch pollen in the air. Both wind speed and rain produced a negative correlation with birch pollen count in the air and average temperature produced a positive correlation.     

Forecasting the start of <Emphasis Type="Italic">Quercus</Emphasis> pollen season using several methods – the evaluation of their efficiency

Forecasting the time when the atmospheric pollen season of allergenic plants begins is particularly important for doctors and their patients. The aim of this paper is to determine whether it is possible to forecast the start of the oak (Quercus) pollen season in Rzeszów, Poland. In the elaboration of the most effective model, various forecasting techniques were tested: growth degree days (GDD°C); meteorological factors; bioclimatic factors; and indicator taxon. The aerobiological monitoring was carried out in 1997–2005 and 2007 in Rzeszów (SE Poland). In the presented investigation, three methods defining the start of the Quercus pollen season were selected on the basis of accumulated sums of pollen or the constant occurrence of pollen grains in air. Despite the application of different combinations of GDD°C methods and threshold temperatures, the correlation coefficients between the expected and obtained values were low. In some cases, however, they proved highly effective for the test years (2005, 2007) with the accuracy of a few days. For GDD°C methods, the best threshold temperatures range between 5 and 6°C. Models based on bioclimatic indices and meteorological variables were not satisfactory. On the basic of the 10 years of results, the method of indicator species were good for forecast the start of oak pollen season. Birch was the best indicator taxa.     

Airborne pollen of Betula,Corylus and Alnus in Zagreb,Croatia. A three‐year record

The aim of this study was to construct a picture of the influence of meteorological conditions on the start and duration of the airborne Betulaceae pollen season and the pollen concentrations in the atmosphere of Zagreb, Croatia. The study during three seasons (2002–2004) used a 7‐day Hirst‐type volumetric pollen and spore trap. Total annual airborne pollen of Alnus, Corylus and Betula greatly varied from year to year. The differences in the dates of onset of airborne pollen presence of Alnus, Corylus and Betula noted in Zagreb in 2002–2004 were controlled by weather conditions, particularly temperature and precipitation. In all years studied, airborne pollen peaks were recorded on days with temperature above 0°C and without or minimal precipitation. The mean number of days with airborne pollen concentrations exceeding levels which provoke symptoms of an allergic reaction was 15, 16 and 29 days for alder, hazel and birch, respectively. The results of the present study may provide useful data for allergologists to reach accurate diagnoses, and timely information on concentrations of airborne pollen types and concentrations for individuals with pollen hypersensitivity.     

Types of Artemisia pollen season depending on the weather conditions in Wrocław (Poland), 2002–2011

The aim of the study was to characterise Artemisia pollen season types according to weather conditions in Wroc?aw (south-western Poland) in the years 2002–2011. Over the period analysed, the start date of the pollen season (determined by the 95 % method) ranged from 10 July 2002 to 28 July 2010. The start date of the pollen season can be determined by using Crop Heat Units (CHUs). During the period 2002–2011, the Artemisia pollen season started after the cumulative value of CHUs had reached 2,000–2,100 °C. The three distinguished types of Artemisia pollen season are best described by the frequency of weather types defined by the type of circulation, mean daily air temperature, and the occurrence of rain. The variation in these factors affected the dynamics of the pollen season. The noteworthy frequency of days with rain and high seasonal sum of precipitation totals as well as the dominance of cyclonic weather from the westerly direction had an impact on the extension of the pollen season. The meteorological factors that directly affect pollen release and transport primarily include air humidity, expressed as vapour pressure (r > 0.3, p < 0.01), temperature(r from 0.2 to 0.4, p < 0.01). The relationships between averaged meteorological data and daily pollen concentration were stronger (r > 0.5, p < 0.01). Based on the correlation analysis, the meteorological variables were selected and regression equations were established using stepwise backward regression analysis.     

Influence of meteorological parameters on Olea flowering date and airborne pollen concentration in four regions of Portugal

For calculating the total annual Olea pollen concentration, the onset of the main pollen season and the peak pollen concentration dates, using data from 1998 to 2004, predictive models were developed using multiple regression analysis. Four Portuguese regions were studied: Reguengos de Monsaraz, Valença do Douro, Braga and Elvas. The effect of some meteorological parameters such as temperature and precipitation on Olea spatial and temporal airborne pollen distribution was studied. The best correlations were found when only the pre‐peak period was used, with thermal parameters (maximum temperature) showing the highest correlation with airborne pollen distribution. Independent variables, selected by regression analysis for the predictive models, with the greatest influence on the Olea main pollen season features were accumulated number of days with rain and rainfall in the previous autumn, and temperatures (average and minimum) from January through March. The models predict 59 to 99% of the total airborne pollen concentration recorded and the initial and peak concentration dates of the main Olea pollen season.     

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°C and 12.5°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 models 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 olives 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 effects of large-scale atmospheric fields on the cypress pollen season in Tuscany (Central Italy)

This research was performed for the purpose of analysing the relationships between large-scale meteorological information, in particular the North Atlantic Oscillation (NAO) index and the Sea Surface Temperature (SST), and the timing and magnitude of the Cupressaceae pollen season in the Pistoia district of Central Italy. The results demonstrated that in specific periods of the year, the NAO index, by partially determining the distribution of the main meteorological variables over the study area, is negatively correlated with the start and the end, as well as the peak day of pollen concentration. Pollen data were also correlated with the SST of the North Atlantic Ocean east of the Azores for the September–December period of the previous year, which is significant for exploring possibilities in terms of predicting the timing and magnitude of the cypress pollen season. The analysis of such meteorological variables and indices could be used to improve the existing forecasting systems of the phenology of the cypress pollen season. Moreover, the possibility of using meteorological information freely available on internet could cut costs and reduce spatial and temporal representativeness limitations relating to weather monitoring in loco.     

天山雪岭云杉大气花粉含量对气温变化的响应

对中国东天山天池自2001年7月至2006年7月连续5a收集的雪岭云杉大气花粉含量进行统计分析,结果表明:1)一年四季大气中都有雪岭云杉花粉,但花粉数量变化比较大,超过全年90％的大气花粉集中在5、6月份的花粉高峰期,之后花粉浓度逐渐下降,至翌年1月份浓度降至最低,2月开始花粉浓度有升高的趋势;2)5a平均花粉浓度是42.66粒/m3,最高年是2005年,花粉浓度可达99.54粒/m3,最低年2003年,仅为2.13粒/m3;3)雪岭云杉大气花粉高峰期出现在5月22至6月2日,高峰日出现在5月28至6月6日,结束日是在6月18至6月25日,平均持续时间为27 d.观测时段雪岭云杉大气花粉高峰期出现日、高峰日逐年提前,2006年出现日期比2002年提前了7d、高峰日提前9d,结束日期滞后,2006年比2002年滞后6d,花粉高峰期持续时间逐年延长,2006年比2002年延长了12d.分析显示,影响雪岭云杉大气花粉高峰期变化的主要因素是春季气温的升高;4)粗略估算每年新疆的雪岭云杉林带内由大气中降落到表土的花粉量达61 kg/hm2,新疆现有雪岭云杉52.84×104hm2,全年由大气降落到林带内表土的花粉多达3223 t,一部分降落到戈壁、荒漠以及沙漠等一些极端气候区的花粉为一些先锋种植物提供必要的营养物质,具有重要的生态意义.