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 > $15 ^{\circ}C$ and effective cumulative temperature >$3 ^{\circ}C$ 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 $10 ^{\circ}C$, 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

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

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

Bilans bilansowi nierówny, czyli MSSF a US GAAP na przykładzie sektora paliwowego

Sponsorami konferencji byli: Stowarzyszenie Księgowych w Polsce Oddział Okręgowy w Łodzi, Krajowa Izba Biegłych Rewidentów

Pollen grains as allergenic environmental factors – new approach to the forecasting of the pollen concentration during the season

[b]introduction and objectives[/b]. It is important to monitor the threat of allergenic pollen during the whole season, because of practical application in allergic rhinitis treatment, especially in the specific allergen immunotherapy. The aim of the study was to propose the forecast models predicting the pollen occurrence in the defined pollen concentration categories related to the patient exposure and symptom intensity. [b]material and methods[/b]. The study was performed in Cracow (southern Poland), pollen data were collected using the volumetric method in 1991–2012. For all independent variables (meteorological elements) and the daily pollen concentrations the running mean for periods: 2-, 3-, 4-, 5-, 6- and 7 days before the predicted day were calculated. The multinomial logistic regression was used to find the relation between the probability of the pollen concentration occurrence in the selected categories and meteorological elements and pollen concentration in days preceding the predicted daily concentration. The models were constructed for each taxon using data in 1991–2011 (without 1992 and 1996 due to missing data in these years) and 1998–2011 pollen seasons. [b]results.[/b] The days classified among the lowest category (0–10 PG/m[sup] 3[/sup] ) (pollen grains/m 3 of air) dominated for all the studied taxa. The percentage of the obtained predictions of the pollen occurrence fluctuated between 35–78% which is a sufficient value of model predictions. Considering the studied taxon, the best model accuracy was obtained for models forecasting Betula pollen concentration (both data series), and Poaceae (both data series). [b]conclusions[/b]. The application of the recommended threshold values during the predictive models construction seems to be really useful to estimate the real threat of allergen exposure. It was indicated that the polynomial logistic regression models could be a practical tool for effective forecasting in biological monitoring of pollen exposure

Pollen grains as allergenic environmental factors : new approach to the forecasting of the pollen concentration during the season

Introduction and objectives. It is important to monitor the threat of allergenic pollen during the whole season, because of practical application in allergic rhinitis treatment, especially in the specific allergen immunotherapy. The aim of the study was to propose the forecast models predicting the pollen occurrence in the defined pollen concentration categories related to the patient exposure and symptom intensity. Material and methods. The study was performed in Cracow (southern Poland), pollen data were collected using the volumetric method in 1991–2012. For all independent variables (meteorological elements) and the daily pollen concentrations the running mean for periods: 2-, 3-, 4-, 5-, 6- and 7 days before the predicted day were calculated. The multinomial logistic regression was used to find the relation between the probability of the pollen concentration occurrence in the selected categories and meteorological elements and pollen concentration in days preceding the predicted daily concentration. The models were constructed for each taxon using data in 1991–2011 (without 1992 and 1996 due to missing data in these years) and 1998–2011 pollen seasons. Results. The days classified among the lowest category (0–10 PG/m3 ) (pollen grains/m3 of air) dominated for all the studied taxa. The percentage of the obtained predictions of the pollen occurrence fluctuated between 35–78% which is a sufficient value of model predictions. Considering the studied taxon, the best model accuracy was obtained for models forecasting Betula pollen concentration (both data series), and Poaceae (both data series). Conclusions. The application of the recommended threshold values during the predictive models construction seems to be really useful to estimate the real threat of allergen exposure. It was indicated that the polynomial logistic regression models could be a practical tool for effective forecasting in biological monitoring of pollen exposure

Charakterystyka badań aeromykologicznych i wstępny opis badań prowadzonych w Worcester w Wielkiej Brytanii w ramach współpracy wieloośrodkowej

The aim of this article is to inform about aerobiological research which has been commenced in cooperation between the University of Worcester, Jagiellonian University, Adam Mickiewicz University and the University of Szczecin. Occurrence of four allergenic fungal spores in the air will be studied, i.e. Alternaria spp., Cladosporium spp., Ganoderma spp. and Didymella spp. in relation to meteorological factors. Diurnal, daily and spatial variations of the fungal spore concentration will be compared between spore monitoring sites to produce forecast models for selected fungi. Forecasts play an important role in the timing of prophylactic medication and in maintaining compliance in treatments. They also help allergic people to plan their activities in order to avoid exposure to high atmospheric concentrations of fungal spores

The pollen season dynamics and the relationship among some season parameters (start, end, annual total, season phases) in Kraków, Poland, 1991–2008

The dynamics of 15 taxa pollen seasons in Kraków, in 1991–2008 was monitored using a Burkard volumetric spore trap of the Hirst design. The highest daily pollen concentrations were achieved in the first half of May, and they were caused mainly by Betula and Pinus pollen. The second period of the high concentrations took place from the middle of July to the end of August (mainly Urtica pollen). Tree pollen seasons were shorter (18–24 days) in comparison with the most herbaceous pollen seasons (73–89 days), except at Artemisia and Ambrosia seasons (30 and 24 days, respectively). The season phases (percentyles) of the spring and late-summer taxa were the most variable in the consecutive years. The highest annual sums were noted for Urtica, Poaceae (herbaceous pollen seasons) and for Betula, Pinus, Alnus (tree pollen seasons), and the highest variability of annual totals was stated for Urtica, Populus, Fraxinus and the lowest for Ambrosia, Corylus, Poaceae. For the plants that pollinate in the middle of the pollen season (Quercus, Pinus and Rumex), the date of the season start seems not to be related to the season end, while for late pollen seasons, especially for Ambrosia and Artemisia, the statistically negative correlation between the start and the end season dates was found. Additionally, for the most studied taxa, the increase in annual pollen totals was observed. The presented results could be useful for the allergological practice and general botanical knowledge