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.     

Evaluation of thermal discomfort in Athens territory and its effect on the daily number of recorded patients at hospitals’ emergency rooms

Previous research has shown that temperature and humidity affect human health. However, only a few studies have examined the association of a biometeorological index, which combines several meteorological parameters and human physiology, with health outcomes. The aim of the present study is to assess the thermal discomfort in Athens city by using the Predicted Mean Vote (PMV) as well as to examine its association with the number of patients recorded at the emergency rooms of four main hospitals. Patients were selected based on their diagnosis during the summer season (June–August) from 1998 to 2004. Data included hourly values of meteorological parameters and daily numbers of patients who visited the emergency units of cardiology departments. Poisson regression models were applied using generalized estimating equations. A strong negative correlation between mean and maximum daily values of PMV and the number of emergency department visits was identified. More studies are needed to explore the association of this biometeorological index with health outcomes in other regions.     

Influence of meteorological parameters on olea pollen concentrations in Córdoba (South-western Spain)

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

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.     

Instrumental test to estimate a model of forecast yield: a non-parametric application to the pollen index in South Italy

A statistical test is described to verify the characteristics of the biological information contained in the dynamics of the flowering process. The test focuses on interactions between the pollen index and climatic variables to investigate if the biological indicator can synthesise the information of the pre-flowering phases. The multiple-regression model is built upon two pre-flowering climate macro-indicators extracted by Principal Component Analysis (PCA) and the optimised pollen index is obtained by non-parametric estimation. The empirical analysis is applied to 15 stations located in southern Italy in regions that have a longstanding tradition of olive production. Using the variance explained, we find that an optimised pollen index is fairly well predicted by the pre-flowering climatic data. We conclude that the optimised pollen index makes more parsimonious the modelling for predicting olive production.     

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.     

湿地翅碱蓬生物量遥感估算模型

以黄河三角洲HJ-1A CCD遥感数据和滨海湿地翅碱蓬生物量实测数据为数据源,通过对比分析参数回归模型（单变量线性和非线性回归模型,多元线性逐步回归模型）和人工神经网络模型（BP网络、RBF网络、GRNN网络）,构建黄河三角洲湿地翅碱蓬生长初期的生物量湿重遥感估算最优模型。研究表明：基于遥感信息变量能够建立生长初期翅碱蓬生物量湿重估算模型。尽管基于RDVI、MSAVI和PC2的3个变量的多元线性回归模型的拟合效果较优,但是以SAVI、MSAVI、RVI、DVI、RDVI和PC2等7个遥感信息变量构建的BP神经网络模型的精度更高,平均相对误差为12.73%,估算效果最优,能够满足较高精度的生物量湿重估算需求。翅碱蓬生长初期生物量湿重最优估算模型的建立,为滨海地区植被生物量监测、区域翅碱蓬生物量季节动态模拟以及黄河三角洲生态系统功能评价提供技术支持与基础。     

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.     

Altitudinal fluctuations in the olive pollen emission: an approximation from the olive groves of the south-east Iberian Peninsula

The possible existence of altitudinal fluctuations in the seasonal behaviour of the olive pollen emission was studied. Three pollen volumetric samplers distributed in olive groves all over the altitudinal cliseries of the province of Jaén (south-east Spain) were used. Pollen emission data were recorded during a 3-year period (2007–2009). This research has revealed the effect of altitude on consecutive olive pollen season in the province of Jaén. The first pollen grains were detected in the olive growing areas located within the area of the Guadalquivir River, where are found the lowest levels of altitude into the province. A notable delay in the pollination season of the olive groves located at higher altitudes was observed. Geographical fluctuations on both daily pollen concentrations and number of critical days were also detected. Accumulated variables of temperature and precipitation since the start of the pre-flowering period have been shown to be two of the main factors affecting olive pollen levels. The fluctuations observed in the olive pollen season may similarly occur in the case of other allergenic plant species such as cypress (Cupressaceae), plane tree (Platanaceae) or grasses (Poaceae). Furthermore, and for the clinical consequences of the findings presented in this study, we believe that it would be advisable to install a micro-aerobiological network permanently in the province of Jaén.     

Analysis of atmospheric dispersion of olive pollen in southern Spain using SILAM and HYSPLIT models

SILAM atmospheric dispersion model and the HYSPLIT trajectory model were used to detect the source areas and calculate transport dynamics for airborne olive pollen observed in the city of Córdoba, southwest of Iberian Peninsula. The ECMWF weather data with 3-h time interval and spatial resolution of 25 × 25 km² and 75 hybrid vertical levels were used as meteorological inputs in both models to produce a coherent set of results in order to compare these two different approaches. Seven episodes recorded before and after the local flowering season in 2006 were analyzed using both models. The results provided an indication of the origins of olive pollen recorded in the city of Córdoba, revealing the influence of three main source areas at specific periods. One area was located nearby, to the southwest of the city (early May), another in the south of the province (mid-May) and the third to the east (late May/early June). The SILAM model yielded more detailed and quantitative results when identifying olive pollen sources and charting transport dynamics. The results from the HYSPLIT trajectory approach and SILAM footprints were qualitatively similar. However, a weak point of back trajectories was their lower sensitivity to details of the transport, as well as the necessity of subjective analysis of the trajectory plots, which were subject for possible misinterpretations. Information on both pollen source locations and local tree flowering phenology was required in order to ensure consistent analysis of the influence of olive sources for both models. Further than this, due to the fact that both models are widely used in other research areas, the results of this work could have a widespread range of application, such as to simulate the transport of radionuclides, e.g., in emergency preparedness exercises.     

Case study of skin temperature and thermal perception in a hot outdoor environment

Focusing on the understanding and the estimation of the biometeorological conditions during summer in outdoor places, a field study was conducted in July 2010 in Athens, Greece over 6 days at three different sites: Syntagma Square, Ermou Street and Flisvos coast. Thermo-physiological measurements of five subjects were carried out from morning to evening for each site, simultaneously with meteorological measurements and subjective assessments of thermal sensation reported by questionnaires. The thermo-physiological variables measured were skin temperature, heat flux and metabolic heat production, while meteorological measurements included air temperature, relative humidity, wind speed, globe temperature, ground surface temperature and global radiation. The possible relation of skin temperature with the meteorological parameters was examined. Theoretical values of mean skin temperature and mean radiant temperature were estimated applying the MENEX model and were compared with the measured values. Two biometeorological indices, thermal sensation (TS) and heat load (HL)—were calculated in order to compare the predicted thermal sensation with the actual thermal vote. The theoretically estimated values of skin temperature were underestimated in relation to the measured values, while the theoretical model of mean radiant temperature was more sensitive to variations of solar radiation compared to the experimental values. TS index underestimated the thermal sensation of the five subjects when their thermal vote was ‘hot’ or ‘very hot’ and overestimated thermal sensation in the case of ‘neutral’. The HL index predicted with greater accuracy thermal sensation tending to overestimate the thermal sensation of the subjects.     

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 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.     

Modern and ancient olive stands near Sagalassos (south-west Turkey) and reconstruction of the ancient agricultural landscape in two valleys

Aim To study the present‐day olive stands and their ecology in the eastern part of the territory of the ancient city of Sagalassos, to study the variation of olive pollen production and dispersal near the olive stands, to establish a modern pollen reference model, and to compare Hellenistic–Roman pollen data from two wetlands with this modern reference model. Location Eastern part of the territory of the ancient city of Sagalassos, western Taurus mountain range in south‐west Turkey. Methods The study is based on field survey, pollen analysis of surface samples, multivariate statistics of modern pollen data and the use of ‘modern analogues’ in comparison with Hellenistic–Roman pollen samples. Results A field survey revealed the presence of 35 olive stands in the study area. These are mainly small‐scale stands. The olive pollen representation in the surface samples is highly variable. Two groups of modern ‘olive’ pollen spectra could be distinguished: (1) a group representing mainly olive stands from lush and moist mixed orchards; and (2) a group representing mainly olive stands from open small‐scale olive stands in combination with annual crop agriculture. Although no ‘perfect’ modern analogue was found for the Hellenistic–Roman pollen data, the fossil pollen data show similarities with modern spectra from the second group, due to the presence of relatively high pollen values for secondary anthropogenic indicators. Main conclusion A well‐organized and diverse, but time‐ and energy‐consuming, agricultural system was maintained nearby the wetlands of Çanakl? soils, presumably to maximize the yields in both valleys.     

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.     

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.     

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.     

A 30-day-ahead forecast model for grass pollen in north London, United Kingdom

A 30-day-ahead forecast method has been developed for grass pollen in north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21 May to 8 August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961 to 1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961–1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of 1 to 4; the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of 1 to 4, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002, respectively, when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.     

Quantitative forecasting of olive yield in Northern Portugal using a bioclimatic model

In this work the objective was to develop a bioclimatic model to forecast olive yield based on airborne pollen, soil water content, and favourable conditions for phytopathological attacks. Olive airborne pollen was sampled from 1998 to 2006 using Cour traps installed in the Trás-os-Montes e Alto Douro region, in the provinces of Valença do Douro and Vila Nova de Foz-Côa. Meteorological data from a meteorological station located in Pinhão, near the pollen samplers, was used to calculate other independent variables. According to the bioclimatic model, at the flowering stage 63% of regional olive production can be predicted from the regional pollen index, with an average deviation between observed and predicted production of 10%. The variable soil water content enabled an increase in forecasting accuracy of about 30%, and a reduction in the average deviation between observed and predicted production of 6%. The final regression with all three variables tested showed that the bioclimatic model was able to predict the annual variability of regional olive fruit production with an accuracy of 97%, the average deviation between observed and predicted production being 3% for internal validation and 6% for external validation.     

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