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.

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.

The Effect of Changes to the Method of Estimating the Pollen Count from Aerobiological Samples

Pollen data have been recorded at Novi Sad in Serbia since 2000. The adopted method of producing pollen counts has been the use of five longitudinal transects that examine 19.64% of total sample surface. However, counting five transects is time consuming and so the main objective of this study is to investigate whether reducing the number to three or even two transects would have a significant effect on daily average and bi-hourly pollen concentrations, as well as the main characteristics of the pollen season and long-term trends. This study has shown that there is a loss of accuracy in daily average and bi-hourly pollen concentrations (an increase in % ERROR) as the sub-sampling area is reduced from five to three or two longitudinal transects. However, this loss of accuracy does not impact on the main characteristics of the season or long-term trends. As a result, this study can be used to justify changing the sub-sampling method used at Novi Sad from five to three longitudinal transects. The use of two longitudinal transects has been ruled out because, although quicker, the counts produced: (a) had the greatest amount of % ERROR, (b) altered the amount of influence of the independent variable on the dependent variable (the slope in regression analysis) and (c) the total sampled surface (7.86%) was less than the minimum requirement recommended by the European Aerobiology Society working group on Quality Control (at least 10% of total slide area).

Job-demand for Learning and Job-related Learning: The Moderating Effect of Need For Achievement

Purpose – The purpose of the research is to examine whether need for achievement moderates the relationship between job-demand for learning and job-related learning. Design/methodology/approach – Data were obtained from 153 participants full-time. The scales for job-demand for learning and job-related learning were developed for this research, whilst the scale for need for achievement was obtained from an external source. Hierarchical regression analysis was used in testing the hypothesized moderating effects. Findings – It was found that need for achievement moderates the relationship between job-demand for learning and job-related learning. Specifically, although job-demand for learning is correlated positively to job-related learning for both the high and the low need for achievement groups, this correlation is stronger amongst the high group. Research limitations/implications – The use of a cross-sectional design in this study prohibits inferences being drawn regarding the causal relationships between job-demand for learning, need for achievement and job-related learning. Practical implications – Job-related learning can be facilitated through increasing the need for achievement by allowing employees to establish their own learning and performance goals rather than by imposing such goals on them. The redesign of jobs to create challenges for employees is an equally important practical consideration. Originality/value – This research provides a succinct view of the relationship between an external factor, job-demand for learning, and an internal motivator, need for achievement. It emphasizes the degree to which organizations can facilitate learning through design of challenging jobs, to the extent of the individuals’ motivation.

Validity and Test –retest Reliability of the TIVRE-Basket® Test for the Determination of Aerobic Power in Elite Male Basketball Players

The aims of this study were to 1) determine the relationship between performance on the court-based TIVRE-Basket® test and peak aerobic power determined from a criterion lab-based incremental treadmill test and 2) to examine the test-retest reliability of the TIVRE-Basket® test in elite male basketball players. To address aim 1, 36 elite male basketball players (age 25.2 + 4.7 years, weight 94.1 + 11.4 kg, height 195.83 + 9.6 cm) completed a graded treadmill exercise test and the TIVRE-Basket® within 72 hours. Mean distance recorded during the TIVRE-Basket® test was 4001.8 + 176.4m, and mean VO2 peak was 54.7 + 2.8 ml.kg.min-1, and the correlation between the two parameters was r=0.824 (P= <0.001). Linear regression analysis identified TIVRE-Basket® distance (m) as the only unique predictor of VO2 peak in a single variable plus constant model: VO2 peak = 2.595 + ((0.13* TIVRE-Basket® distance (m)). Performance on the TIVRE-Basket® test accounted for 67.8% of the variance in VO2 peak (t=8.466, P=<.001, 95% CI 0.01 - 0.016, SEE 1.61). To address aim 2, 20 male basketball players (age 26.7±4.2; height 1.94±0.92; weight 94.0±9.1) performed the TIVRE-Basket® test on two occasions. There was no significant difference in total distance covered between Trial 1 (4138.8 + 677.3m) and Trial 2 (4188.0 + 648.8m; t = 0.5798, P = 0.5688). Mean difference between trials was 49.2 + 399.5m, with an ICC of 0.85 suggesting a moderate level of reliability. Standardised TEM was 0.88%, representing a moderate degree of trial to trial error, and the CV was 6.3%. The TIVRE-Basket® test therefore represents a valid and moderately reliable court-based sport-specific test of aerobic power for use with individuals and teams of elite level male basketball players. Future research is required to ascertain its validity and reliability in other basketball populations e.g. across age groups, at different levels of competition, in females and in different forms of the game e.g. wheelchair basketball.