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 at 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 21st May to 8th 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 1961-1999 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 one to four; and 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 one to four, 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.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Examining High Magnitude Grass Pollen Episodes at Worcester, United Kingdom, Using Back-Trajectory Analysis

Trajectory analysis is a valuable tool that has been used before in aerobiological studies, to investigate the movement of airborne pollen. This study has employed back-trajectories to examine the four highest grass pollen episodes at Worcester, during the 2001 grass pollen season. The results have shown that the highest grass pollen counts of the 2001 season were reached when air masses arrived from a westerly direction. Back-trajectory analysis has a limited value to forecasters because the method is retrospective and cannot be employed directly for forecasting. However, when used in conjunction with meteorological data this technique can be used to examine high magnitude events in order to identify conditions that lead to high pollen counts.

Characteristics of Grass Pollen Seasons in Areas of Southern Spain and the United Kingdom

Spatial and temporal variations in daily grass pollen counts and weather variables are described for two regions with different bio-geographical and climatic regimes, southern Spain and the United Kingdom. Daily average grass pollen counts are considered from six pollen-monitoring sites, three in southern Spain (Ciudad Real, Córdoba and Priego) and three in the United Kingdom (Edinburgh, Worcester and Cambridge). Analysis shows that rainfall and maximum temperatures are important factors controlling the magnitude of the grass pollen season in both southern Spain and the United Kingdom, and that the strength and direction of the influence exerted by these variables varies with geographical location and time.

Temporal and Georaphical Variations in Grass Pollen Seasons in Areas of Western Europe: an Analysis of Season dates at Sites of the European Pollen Information System.

Geographical and temporal variations in the start dates of grass pollen seasons are described for selected sites of the European Pollen Information Service. Daily average grass pollen counts are derived from Network sites in Finland, the Netherlands, Denmark, United Kingdom, Austria, Italy and Spain, giving a broad longitudinal transect over Western Europe. The study is part of a larger project that also examines annual and regional variations in the severity, timing of the peak and duration of the grass pollen seasons. For several sites, data are available for over twenty years enabling long term trends to be discerned. The analyses show notable contrasts in the progression of the seasons annually with differing lag times occurring between southern and northern sites in various years depending on the weather conditions. The patterns identified provide some insight into geographical differences and temporal trends in the incidence of pollinosis. The paper discusses the main difficulties involved in this type of analysis and notes possibilities for using data from the European Pollen Information service to construct pan European predictive models for pollen seasons.

Influence of the North Atlantic Oscillation on Grass Pollen Counts in Europe

Relationships between temporal variations in the North Atlantic Oscillation (NAO) and grass pollen counts at 13 sites in Europe, ranging from Córdoba in the South-West and Turku in the North-East, were studied in order to determine spatial differences in the amount of influence exerted by the NAO on the timing and magnitude of grass pollen seasons. There were a number of significant (p<0.05) relationships between the NAO and start dates of the grass pollen season at the 13 pollen-monitoring sites. The strongest associations were generally recorded near to the Atlantic coast. Several significant correlations also existed between winter averages of the NAO and grass pollen season severity. Traditional methods for predicting the start or magnitude of grass pollen seasons have centred on the use of local meteorological observations, but this study has shown the importance of considering large-scale patterns of climate variability like the NAO.

Long-term and Short-term Forecast Models for Poaceae (Grass) Pollen in Poznan, Poland, Constructed Using Regression Analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than ten 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 1994-2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within 2 days and achieved 61% and 70% accuracy on a scale of 1-4 when forecasting variations in daily 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.

Do Urban Canyons Influence Street Level Grass Pollen Concentrations

In epidemiological studies, outdoor exposure to pollen is typically estimated using rooftop monitoring station data, whilst exposure overwhelmingly occurs at street level. In this study the relationship between street level and roof level grass pollen concentrations was investigated for city centre street canyon environments in Aarhus, Denmark, and London, UK, during the grass pollen seasons of 2010 and 2011 respectively. For the period mid-day to late evening, street level concentrations in both cities tended to be lower than roof-level concentrations, though this difference was found to be statistically significant only in London. The ratio of street/roof level concentrations was compared with temperature, relative humidity, wind speed and direction, and solar radiation. Results indicated that the concentration ratio responds to wind direction with respect to relative canyon orientation and local source distribution. In the London study, an increase in relative humidity was linked to a significant decrease in street/roof level concentration ratio, and a possible causative mechanism involving moisture mediated pollen grain buoyancy is proposed. Relationships with the other weather variables were not found to be significant in either location. These results suggest a tendency for monitoring station data to overestimate exposure in the canyon environment.

Personal Exposure to Grass Pollen: Relating Inhaled Dose to Background Concentration

Background Very few studies on human exposure to allergenic pollen have been conducted using direct methods, with background concentrations measured at city center monitoring stations typically taken as a proxy for exposure despite the inhomogeneous nature of atmospheric pollen concentrations. A 2003 World Health Organization report highlighted the need for an improved understanding of the relation between monitoring station data and actual exposure. Objective To investigate the relation between grass pollen dose and background concentrations measured at a monitoring station, to assess the fidelity of monitoring station data as a qualitative proxy for dose, and to evaluate the ratio of dose rate to background concentration. Methods Grass pollen dose data were collected in Aarhus, Denmark, in an area where grass pollen sources were prevalent, using Nasal Air Samplers. Sample collection lasted for approximately 25 to 30 minutes and was performed at 2-hour intervals from noon to midevening under moderate exercise by 2 individuals. Results A median ratio of dose rate to background concentration of 0.018 was recorded, with higher ratio values frequently occurring at 12 to 2 pm, the time of day when grass species likely to be present in the area are expected to flower. From 4 to 8 pm, dose rate and background concentration data were found to be strongly and significantly correlated (rs = 0.81). Averaged dose rate and background concentration data showed opposing temporal trends. Conclusion Where local emissions are not a factor, background concentration data constitute a good quantitative proxy for inhaled dose. The present ratio of dose rate to background concentration may aid the study of dose–response relations.

Seasonal Variation in Diurnal Atmospheric Grass Pollen Concentration Profiles

In this study, the diurnal atmospheric grass pollen concentration profile within the Danish city of Aarhus was shown to change in a systematic manner as the pollen season progressed. Although diurnal grass pollen profiles can differ greatly from day-to-day, it is common practice to establish the time of day when peak concentrations are most likely to occur using seasonally averaged diurnal profiles. Atmospheric pollen loads are highly dependent upon emissions, and different species of grass are known to flower and emit pollen at different times of the day and during different periods of the pollen season. Pollen concentrations are also influenced by meteorological factors – directly through those parameters that govern pollen dispersion and transport, and indirectly through the weather-driven flowering process. We found that three different profiles dominated the grass pollen season in Aarhus – a twin peak profile during the early season, a single evening profile during the middle of the season, and a single midday peak during the late season. Whilst this variation could not be explained by meteorological factors, no inconsistencies were found with the theory that it was driven by a succession of different grass species with different diurnal flowering patterns dominating atmospheric pollen loads as the season progressed. The potential for exposure was found to be significantly greater during the late-season period than during either the early- or mid-season periods.

Variation of the Group 5 Grass Pollen Allergen Content of Airborne Pollen in Relation to Geographical Location and Time in Season

Allergies to grass pollen are the number one cause of outdoor hay fever. The human immune system reacts with symptoms to allergens from pollen. Objective: We investigated the natural variability in release of the major group 5 allergen from grass pollen across Europe. Methods: Airborne pollen and allergens were simultaneously collected daily with a volumetric spore trap and a high-volume cascade impactor at 10 sites across Europe for 3 consecutive years. Group 5 allergen was determined with a Phl p 5 specific ELISA in two fractions of ambient air: Particulate Matter (PM) >10μm and 10μm>PM>2.5μm. Mediator release by ambient air was determined in FcεR1-humanized basophils. Origin of pollen was modeled and condensed to pollen potency maps. Results: On average grass pollen released 2.3 pg Phl p 5/pollen. Allergen release per pollen (potency) varied substantially, ranging from 0 to 9 pg Phl p 5/pollen (5 to 95% percentile). The main variation was locally day-to-day. Average potency maps across Europe varied between years. Mediator release from basophilic granulocytes correlated better with allergen/m3 (r2=0.80, p<0.001) than with pollen/m3 (r2=0.61, p<0.001). In addition, pollen released different amounts of allergen in the nonpollen bearing fraction of ambient air depending on humidity. Conclusion: Across Europe, the same amount of pollen released substantially different amounts of group 5 grass pollen allergen. This variation in allergen release is on top of variations in pollen counts. Molecular aerobiology, i.e. determining allergen in ambient air, may be a valuable addition to pollen counting.

Voucher #22 to James McCoppen (copy) which includes a letter from James McCoppen regarding a claim for having water backed up into his grain and grass by the embankment of the railroad. The letter is dated June 24, 1857 and the voucher is dated Jul. 1, 1857

Voucher #22 to James McCoppen (copy) which includes a letter from James McCoppen regarding a claim for having water backed up into his grain and grass by the embankment of the railroad. The letter is dated June 24, 1857 and the voucher is dated Jul. 1, 1857.

Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

Digital image analysis as a tool to estimate legume contributions in legume-grass swards

Summary: Productivity and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. For these objectives the botanical composition of the swards is of particular importance, especially, the content of legumes due to their ability to fix airborne nitrogen. As it can vary considerably within a field, a non-destructive detection method while doing other tasks would facilitate a more targeted sward management and could predict the nitrogen supply of the soil for the subsequent crop. This study was undertaken to explore the potential of digital image analysis (DIA) for a non destructive prediction of legume dry matter (DM) contribution of legume-grass mixtures. For this purpose an experiment was conducted in a greenhouse, comprising a sample size of 64 experimental swards such as pure swards of red clover (Trifolium pratense L.), white clover (Trifolium repens L.) and lucerne (Medicago sativa L.) as well as binary mixtures of each legume with perennial ryegrass (Lolium perenne L.). Growth stages ranged from tillering to heading and the proportion of legumes from 0 to 80 %. Based on digital sward images three steps were considered in order to estimate the legume contribution (% of DM): i) The development of a digital image analysis (DIA) procedure in order to estimate legume coverage (% of area). ii) The description of the relationship between legume coverage (% area) and legume contribution (% of DM) derived from digital analysis of legume coverage related to the green area in a digital image. iii) The estimation of the legume DM contribution with the findings of i) and ii). i) In order to evaluate the most suitable approach for the estimation of legume coverage by means of DIA different tools were tested. Morphological operators such as erode and dilate support the differentiation of objects of different shape by shrinking and dilating objects (Soille, 1999). When applied to digital images of legume-grass mixtures thin grass leaves were removed whereas rounder clover leaves were left. After this process legume leaves were identified by threshold segmentation. The segmentation of greyscale images turned out to be not applicable since the segmentation between legumes and bare soil failed. The advanced procedure comprising morphological operators and HSL colour information could determine bare soil areas in young and open swards very accurately. Also legume specific HSL thresholds allowed for precise estimations of legume coverage across a wide range from 11.8 - 72.4 %. Based on this legume specific DIA procedure estimated legume coverage showed good correlations with the measured values across the whole range of sward ages (R2 0.96, SE 4.7 %). A wide range of form parameters (i.e. size, breadth, rectangularity, and circularity of areas) was tested across all sward types, but none did improve prediction accuracy of legume coverage significantly. ii) Using measured reference data of legume coverage and contribution, in a first approach a common relationship based on all three legumes and sward ages of 35, 49 and 63 days was found with R2 0.90. This relationship was improved by a legume-specific approach of only 49- and 63-d old swards (R2 0.94, 0.96 and 0.97 for red clover, white clover, and lucerne, respectively) since differing structural attributes of the legume species influence the relationship between these two parameters. In a second approach biomass was included in the model in order to allow for different structures of swards of different ages. Hence, a model was developed, providing a close look on the relationship between legume coverage in binary legume-ryegrass communities and the legume contribution: At the same level of legume coverage, legume contribution decreased with increased total biomass. This phenomenon may be caused by more non-leguminous biomass covered by legume leaves at high levels of total biomass. Additionally, values of legume contribution and coverage were transformed to the logit-scale in order to avoid problems with heteroscedasticity and negative predictions. The resulting relationships between the measured legume contribution and the calculated legume contribution indicated a high model accuracy for all legume species (R2 0.93, 0.97, 0.98 with SE 4.81, 3.22, 3.07 % of DM for red clover, white clover, and lucerne swards, respectively). The validation of the model by using digital images collected over field grown swards with biomass ranges considering the scope of the model shows, that the model is able to predict legume contribution for most common legume-grass swards (Frame, 1992; Ledgard and Steele, 1992; Loges, 1998). iii) An advanced procedure for the determination of legume DM contribution by DIA is suggested, which comprises the inclusion of morphological operators and HSL colour information in the analysis of images and which applies an advanced function to predict legume DM contribution from legume coverage by considering total sward biomass. Low residuals between measured and calculated values of legume dry matter contribution were found for the separate legume species (R2 0.90, 0.94, 0.93 with SE 5.89, 4.31, 5.52 % of DM for red clover, white clover, and lucerne swards, respectively). The introduced DIA procedure provides a rapid and precise estimation of legume DM contribution for different legume species across a wide range of sward ages. Further research is needed in order to adapt the procedure to field scale, dealing with differing light effects and potentially higher swards. The integration of total biomass into the model for determining legume contribution does not necessarily reduce its applicability in practice as a combined estimation of total biomass and legume coverage by field spectroscopy (Biewer et al. 2009) and DIA, respectively, may allow for an accurate prediction of the legume contribution in legume-grass mixtures.