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.     

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.     

Predicting tree pollen season start dates using thermal conditions

Thermal conditions at the beginning of the year determine the timing of pollen seasons of early flowering trees. The aims of this study were to quantify the relationship between the tree pollen season start dates and the thermal conditions just before the beginning of the season and to construct models predicting the start of the pollen season in a given year. The study was performed in Krakow (Southern Poland); the pollen data of Alnus, Corylus and Betula were obtained in 1991–2012 using a volumetric method. The relationship between the tree pollen season start, calculated by the cumulated pollen grain sum method, and a 5-day running means of maximum (for Alnus and Corylus) and mean (for Betula) daily temperature was found and used in the logistic regression models. The estimation of model parameters indicated their statistically significance for all studied taxa; the odds ratio was higher in models for Betula, comparing to Alnus and Corylus. The proposed model makes the accuracy of prediction in 83.58 % of cases for Alnus, in 84.29 % of cases for Corylus and in 90.41 % of cases for Betula. In years of model verification (2011 and 2012), the season start of Alnus and Corylus was predicted more precisely in 2011, while in case of Betula, the model predictions achieved 100 % of accuracy in both years. The correctness of prediction indicated that the data used for the model arrangement fitted the models well and stressed the high efficacy of model prediction estimated using the pollen data in 1991–2010.     

Climate change effect on Betula (birch) and Quercus (oak) pollen seasons in the United States

Climatic change is expected to affect the spatiotemporal patterns of airborne allergenic pollen, which has been found to act synergistically with common air pollutants, such as ozone, to cause allergic airway disease (AAD). Observed airborne pollen data from six stations from 1994 to 2011 at Fargo (North Dakota), College Station (Texas), Omaha (Nebraska), Pleasanton (California), Cherry Hill and Newark (New Jersey) in the US were studied to examine climate change effects on trends of annual mean and peak value of daily concentrations, annual production, season start, and season length of Betula (birch) and Quercus (oak) pollen. The growing degree hour (GDH) model was used to establish a relationship between start/end dates and differential temperature sums using observed hourly temperatures from surrounding meteorology stations. Optimum GDH models were then combined with meteorological information from the Weather Research and Forecasting (WRF) model, and land use land coverage data from the Biogenic Emissions Land use Database, version 3.1 (BELD3.1), to simulate start dates and season lengths of birch and oak pollen for both past and future years across the contiguous US (CONUS). For most of the studied stations, comparison of mean pollen indices between the periods of 1994–2000 and 2001–2011 showed that birch and oak trees were observed to flower 1–2 weeks earlier; annual mean and peak value of daily pollen concentrations tended to increase by 13.6 %–248 %. The observed pollen season lengths varied for birch and for oak across the different monitoring stations. Optimum initial date, base temperature, and threshold GDH for start date was found to be 1 March, 8 °C, and 1,879 h, respectively, for birch; 1 March, 5 °C, and 4,760 h, respectively, for oak. Simulation results indicated that responses of birch and oak pollen seasons to climate change are expected to vary for different regions.     

Variations in Quercus sp. pollen seasons (1996–2011) in Poznań, Poland,in relation to meteorological parameters

The aim of this study is to supply detailed information about oak (Quercus sp.) pollen seasons in Poznań, Poland, based on a 16-year aerobiological data series (1996–2011). The pollen data were collected using a volumetric spore trap of the Hirst design located in Poznań city center. The limits of the pollen seasons were calculated using the 95 % method. The influence of meteorological parameters on temporal variations in airborne pollen was examined using correlation analysis. Start and end dates of oak pollen seasons in Poznań varied markedly from year-to-year (14 and 17 days, respectively). Most of the pollen grains (around 75 % of the seasonal pollen index) were recorded within the first 2 weeks of the pollen season. The tenfold variation was observed between the least and the most intensive pollen seasons. These fluctuations were significantly related to the variation in the sum of rain during the period second fortnight of March to first fortnight of April the year before pollination (r = 0.799; p < 0.001). During the analyzing period, a significant advance in oak pollen season start dates was observed (?0.55 day/year; p = 0.021), which was linked with an increase in the mean temperature during the second half of March and first half of April (+0.2 °C; p = 0.014). Daily average oak pollen counts correlated positively with mean and maximum daily temperatures, and negatively with daily rainfall and daily mean relative humidity.     

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.     

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.     

Prediction of the beginning of the olive full pollen season in south-west Spain

Given the clinical and agricultural importance of the olive in SW Spain, we have carried out a study to predict the starting date of its full pollen season. The study covers 6 years of meteorological and palynological observations — the latter using a Cour sampler installed in Huelva (SW Spain). The results obtained show that olive full pollination begins when the plant has accumulated 731°C of daily temperature above 5°C from the end of its dormant period. The mean duration of this accumulation was 83 days. A positive relationship has been found between mean temperature of the months before the pollen season (February and March) and the date when the season starts (April). From the data available, rainfall registered between 1 September and 31 March (both before pollination), does not affect the starting date of the full pollen season, but can affect total pollen production, particularly in years with prolonged drought.     

Spatial variations in the dynamics of the Alnus and Corylus pollen seasons in Poland

The objective of this study was to analyse the dynamics of the Alnus and Corylus pollen seasons in Poland with reference to spatial and seasonal differentiation. Aerobiological monitoring was performed in 10 cities, in 1994–2007. Five characteristics defining the pollen season were considered: 1. beginning and end dates of the season phases (5, 25, 50, 75, 95% of annual totals), 2. pollen season duration (90% method), 3. skewness and 4. kurtosis of airborne pollen curves, and 5. annual pollen totals. The beginning of the Corylus pollen season in Warsaw started on the 53rd day of a year. The Alnus pollen season started 9.5 days (SE = 1.4) later. The start of the season for both taxa was delayed by 3.3 (SE = 0.5) days for each 100 km towards the east. The Corylus pollen season lasted about 15 days longer than the Alnus season. Season duration for both taxa decreased towards the east by 3.5 days (SE = 0.7) and towards the north by 1.3 days (SE = 0.6) for each 100 km. Seasonal dynamics of both taxa are skewed to the right. In cities located west of Warsaw the dynamics are more skewed (except at Szczecin, Wroclaw). Asymmetry decreases towards the east by 0.16/100 km. Almost all kurtosis values of pollen-season dynamics were positive and higher for Alnus. Kurtosis values for both taxa increase together with delay of the pollen season beginning by 4% per day (p < 0.0001). Mean pollen total increases: for Corylus mainly towards the north (by 64%/100 km), for Alnus mainly towards the west (by 15%/100 km). Geographical location (longitude and latitude) determines: the start and duration of the pollen season, skewness of the pollen curve, and annual totals.     

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.     

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France)

The aim of the present study was to forecast the start and duration of the pollen season of Ambrosia from meteorological data, in order to provide early information to allergists and allergic people. We used the airborne pollen data from Lyon (France), sampled using a Hirst trap from 1987 to 1999, and the meteorological data for the same period: air temperature (minimal, maximal, and average), rainfall, relative humidity, sunshine duration and soil temperature. Two forecasting models were used, one summing the temperatures and the other making use of a multiple regression on 10-day or monthly meteorological parameters. The start of the pollen season was predicted with both methods, results being more accurate with the regression (the errors between the predicted and the observed SDP ranging from 0 to 3 days). The duration of the pollen season was predicted by a regression model, errors ranging from 0 to 7 days. The models were later tested with satisfactory results from 2 additional years (2000 and 2001). Such forecasting models are helpful for allergic people, who have to begin their anti-allergic treatment before the start of the pollen season and not when the symptoms have appeared, since a preventive treatment is more efficient than a curative one. The regression allows predictions to be made 3-5 weeks in advance and so it is of particular interest. The forecasts will be broadcast on the Internet.     

Forecasting airborne Platanus pollen in the Madrid region

This study analyses the atmospheric concentration of Platanus pollen in four stations in the Madrid region over a period of 10 years (1994–2003). Various statistical analyses (regression analysis and decision tree) were used to prepare a forecasting model for possible application as a preventive measure in pollinosis. The data comes from the PALINOCAM network and the samplers used were Hirst type (Burkard pollen trap). Platanus pollen is present in the atmosphere during a short period of time in spring, and the maximum concentrations are detected during the last two weeks of March and the first week of April. Regression analysis shows that the pollen concentration of the two previous days is the best predictive variable. The models obtained for the four stations analysed account for between 37 and 61% of the variation in pollen levels in the air. The decision trees show how the introduction of meteorological variables improves prediction for this pollen type.     

Prediction model for cabbage stem weevil Ceutorhynchus pallidactylus Mrsh. occurrence on winter rape based on an artificial neural network

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Early spring warming as one of the factors responsible for expansion of aquatic fern Salvinia natans (L.) All. in the Vistula delta (south Baltic Sea coast)

From the mid-19th century to the end of the 20th century, Salvinia natans (L.) All. occurred very rarely in the Vistula Delta (northern Poland), but from the beginning of the 21st century it was present in almost every watercourse and had formed very abundant populations. We examined the influence of temperature on the abundance of this plant and the efficiency of macrospore germination. Field work was carried out in 10 permanent plots every 14 days for 5 years. Macrospores germinate at water temperature of 12.4 ± 0.2°C or higher; at 20°C they develop more effectively than at 15°C. Usually, ice cover on the rivers melts in the second half of March. At this time, macro- and microspores emerge on the water surface and germinate in April. They occur in the water surface film at 15.1 ± 2.4°C and massively die during spring frost. After 1989, March and April mean temperature in the Vistula Delta rose by 1.6°C versus 1901–1988, and by 1.9°C versus 1851–1988. In 1951–1988, the mean temperature for March and April was +4.6°C and was characterized by considerable interannual variation (SD = 1.64), whereas in 1989–2009, it rose to +5.7°C and the variation range narrowed (SD = 1.24). We found that macrospores are active earlier during the warm and mild summers, germination is more effective, survival of young stages is higher, the growing season is longer, and the number of vegetative offsprings in a year is larger.     

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.     

Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis

One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R ²?=?0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R ²?=?0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.     

Airborne pine (Pinus spp.) pollen in the atmosphere of Perugia (Central Italy): Behaviour of pollination in the two last decades

The phenology of many species, which grow intemperate climate, is principally regulated bythe temperature and the plants respond withvariations in the beginning, in the durationand in the intensity of the various phenophasestowards every climate change. We have analysedthe data of Pinus pollination in Perugia,Central Italy, during last 2 decades(1982–2001), in a period during which theannual mean temperature significantly increasedby about 0.8 °C.The pine pollination started, on average,between the end of March and mid-April andended in the last days of June, with a meanduration of 65 days. The start dates showed asignificant negative correlation with theaverage air temperature in March andsignificant trends towards an earlier beginningof pollination by 18 days (–0.9 day/year) and ashorter duration of the pollen season by 10days (–0.6 day/year) were found over thestudied period. Moreover, the trend of thedaily pollen counts showed, on average, analmost normal distribution, but the analysis ofeach yearly trend revealed significantdifferences correlated with the meantemperature during the pollen season. Theseobserved trends in pine pollination suggest theuse of aerobiological monitoring of thisairborne pollen as indicator of temperaturechange in Central Italy over a relatively longperiod.     

The influence of air temperature on the starting dates of the pollen season of alnus and populus

In this work we have studied the influence of air temperature on the starting dates of Alnus and Populus pollination in two different climatic regions in Europe: central Italy and The Netherlands. The start of the Alnus pollen season varied between 27th January and 16th February in the Italian stations while in The Netherlands it showed an average delay of about one month. For Populus the beginning of the pollen season was delayed on an average 15 days at Dutch places compared to central Italy. In the former it varied between 14th March and 21st April while in the latter between 28th February and 24th March. Significant correlations exist between the beginning of pollination for these taxa and temperature conditions in the preceding periods. The highest correlations found were with daily mean decade temperature for three decades before the average starting dates of the pollen season. These correlations were better for The Netherlands than for central Italy perhaps because the temperature in Holland is the more prominent meteorological factor (relative to precipitation) compared with central Italy, where precipitation has much influence in winter. This study indicated correlations between the pollination and temperature also during the dormant period in the preceding season.     

Model for forecasting Olea europaea L. airborne pollen in South-West Andalusia, Spain

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. Received: 25 October 2000 / Revised: 26 February 2001 / Accepted: 28 February 2001     

An aerobiological study of Urticaceae pollen in the city of Granada (S. Spain): Correlation with meteorological parameters

An aerobiological study was made of Urticaceae pollen in the city of Granada, relating the mean values of the daily counts to meteorological parameters. Sampling was carried out with a Burkard seven‐day‐recording spore trap from October 1992 to September 1997. This pollen type has an extremely long main pollen season (MPS), with maximum counts in (January) February, March and April, causing numerous cases of human pollinosis throughout the entire Mediterranean region, including Granada. A highly constant intradiurnal variation pattern was obtained showing that the maximum peaks usually occur between 12.00 and 20.00. According to Spearman's correlation coefficient, during the pre‐peak period the parameters which have the greatest effect on the levels of this type of pollen are daily and accumulated temperature and sunshine, accumulated rainfall, and wind direction from the third quadrant; during the post‐peak period these same variables presented significantly negative coefficients. Daily rainfall and relative humidity presented negative coefficients during the entire MPS. The maximum daily temperature was the variable which provided the closest match with the theoretical predictive pattern presented here.