Alder pollen season in poland in 2018

Alder pollen grains constitute the important allergen sources in this respect in the Northern Hemisphere. The aim of the study was to investigate the concentration of alder (Alnus spp.) in Bialystok, Bydgoszcz, Cracow, Drawsko Pomorskie, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora in 2018. Measurements were performed by the volumetric method (Burkard and Lanzoni pollen samplers). Seasonal Pollen Index (SPI) was estimated as the annual sum of daily average pollen concentrations. The pollen season of alder in all Polish stations began on the 11th and 13th March and the high concentration persisted until the first days of April. The highest, record airborne concentration of 1068 pollen grains/m3 was noted in Lublin on the 13th March. The peak values of seasonal pollen count occurred between of 11th March and 4th April in all cities. In 2018 pollen concentration of alder was one of the lowest in all analysed cities

Analysis of the plantain pollen season in selected Polish cities in 2018

The paper presents the course of pollen season of plantain in Bialystok, Bydgoszcz, Drawsko Pomorskie, Cracow, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora in 2018. Measurements were performed by the volumetric method (Burkard and Lanzoni pollen samplers). Pollen season was defined as the period in which 98% of the annual total catch occurred. The plantain pollen season started in the first decade of May and lasted until the end of September. Despite such a long pollen season in Poland, only in some cities there are days with an average concentration exceeding 10 P/m3. Significant differences were observed in annual sum values. The highest values were recorded in Lublin (400 grains) and Zielona Gora (308 grains), and the lowest in Drawsko Pomorskie (160 grains) and Olsztyn (184 grains). The value of annual average in 2018 was usually lower than in the previous years

The oak pollen concentration in the air of selected cities in Poland in 2018

This paper contains an analysis of oak pollen seasons in selected cities of Poland in 2018. Sampling sites were located in the following cities: Bialystok, Bydgoszcz, Cracow, Drawsko Pomorskie, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora. The volumetric method was applied using the Burkard or Lanzoni trap. The pollen season was determined by the 98% method. The season started earliest in Sosnowiec (April 14th). The mean duration of the pollen season was 33 days. The highest pollen concentration (713 P/m3) was observed in Wroclaw (April 19th). The peak values were recorded between April 19th and May 1st in the different cities

Concentration of pollen of Chenopodiaceae/Amaranthaceae plants in the air of selected Polish cities in 2020

Various concentrations of Chenopodiaceae/Amaranthaceae pollen are detected in the air of many regions of Europe. The highest content of pollen produced by the taxon is reported in southern Europe and in other countries with a warm climate and low precipitation sums. The study was focused on characterization of the Chenopodiaceae/Amaranthaceae pollen season in 11 Polish cities: Bialystok, Bydgoszcz, Cracow, Lublin, Olsztyn, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw, and Zielona Gora in 2020. The volumetric method based on the use of the Lanzoni or Burkard pollen sampler was employed in the study. In 2020, the pollen season in the analyzed plant family began in the second half of June and ended during the first ten days of October. The earliest pollen season onset was recorded in Lublin (June 13th) and Szczecin (June 14th), whereas the latest beginning was noted in Wroclaw (July 5th). The earliest and latest end of the pollen season was recorded in Bialystok (September 6th) and in Olszyn (October 5th), respectively. In terms of length, the season was characterized by the shortest duration in Wroclaw (70 days) and the longest duration in Olsztyn (106 days). In most of the analyzed cities, maximum pollen concentrations were detected in the second half of August, and the highest values were recorded in Zielona Gora and Sosnowiec. Compared to 2019 and 2018, relatively low sums of the annual concentrations of Chenopodiaceae/Amaranthaceae pollen grains, i.e. in the range of 35–231, were recorded in Poland in 2020. The highest values of this parameter were reported in Olsztyn (231) and Lublin (230), whereas the lowest value was noted in Bialystok (35). The relatively low maximum concentrations of Chenopodiaceae/Amaranthaceae pollen recorded during the study year indicate a low risk of development of allergy symptoms induced by the presence of pollen of this taxon in the air

Goosefoot - a plant that likes drought. The goosefoot family pollen season in 2019 in Poland, Hungary and Slovakia

Almost all the species of the Chenopodiaceae family present in our flora flower from July–August to the autumn. Unfortunately, allergies do not take a vacation. Warm, dry July and August weather should limit pollen emissions. However, similarly to most plants in dry habitats, goosefoot are well adapted to such conditions and does not provide even a short reprieve to pollen allergic patients. However, goosefoot pollen does not have a very large allergenic significance; despite the long pollen season lasting about 3 months, pollen concentrations in the air are low and very rarely exceed the concentration of 30 grains/m3. This study compares Chenopodiaceae pollen seasons in Poland, Hungary and Slovakia in 2019. The investigations were carried out using the volumetric method (Hirst type pollen sampler). Seasonal pollen index was estimated as the sum of daily average pollen concentrations in the given season. The pollen season ranges from June to September, depending on the geographical latitude. In Hungary and Slovakia there are much longer pollen seasons than in Poland. Pollen of goosefoot family contains the panallergen profilins, which are responsible for cross-reactivity among pollen-sensitized patients. In 2019 the pollen season of goosefoot started first in Hungary, in Kaposvar on June 7th and in Slovakia, in Žilina, on June 8th; in Poland pollen season started much later, on June 14th in Szczecin and Opole. At the latest, a pollen season ended in Nitria (Slovakia) on October 16th; in Kecskemet (Hungary) on October 3rd. In Poland the season ended much earlier than in Hungary and Slovakia already on August 25th. The differences of pollen season durations are considerable, the number of days ranged from 72 to 128. The dynamics of the pollen seasons of goosefoot family show similarities within a given country and considerable differences between these countries. However, the differences of the highest airborne concentration between the countries are not considerable (25 pollen grains/m3 in Poland, 49 pollen grains/m3 in Hungary, and 30 pollen grains/m3 in Slovakia. The maximum values of seasonal pollen count in Polish cities occurred between July 26th and August 29th, in Hungarian cities between August 27th and 30th, and in Slovakian cities between August 7th and 28th. Pollen season was characterized by extremely different total annual pollen SPI, in Poland from 116 to 360; in Hungary and Slovakia within the limits 290 to 980. Droughts that occur more frequently during the summer facilitate the spread of species of the goosefoot family due to the possibility of these plants gaining new habitats

The impact of data assimilation into the meteorological WRF model on birch pollen modelling

We analyse the impact of ground-based data assimilation to theWeather Research and Forecasting (WRF) meteorological model on parameters relevant for birch pollen emission calculations. Then, we use two different emission databases (BASE – no data assimilation, OBSNUD – data assimilation for the meteorological model) in the chemical transport model and evaluate birch pollen concentrations. Finally, we apply a scaling factor for the emissions (BASE and OBSNUD), based on the ratio between simulated and observed seasonal pollen integral (SPIn) to analyse its impact on birch concentrations over Central Europe. Assimilation of observational data significantly reducesmodel overestimation of air temperature,which is themain parameter responsible for the start of pollen emission and amount of released pollen. The results also show that a relatively small bias in air temperature from the model can lead to significant differences in heating degree days (HDD) value. This may cause the HDD threshold to be attained several days earlier/later than indicated from observational data which has further impact on the start of pollen emission. Even though the bias for air temperature was reduced for OBSNUD, the model indicates a start for the birch pollen season that is too early compared to observations. The start date of the seasonwas improved at two of the 11 stations in Poland. Data assimilation does not have a significant impact on the season's end or SPIn value. The application of the SPIn factor for the emissions results in a much closer birch pollen concentration level to observations even though the factor does not improve the start or end of the pollen season. The post-processing of modelled meteorological fields, such as the application of bias correction, can be considered as a way to further improve the pollen emission modelling

The Consequences of the Inflow of Capital from the Area of the European Union

The main purpose of the article is to investigate changes in property structure which took place in the Polish banking sector from the beginning of the nineties to the present time. The direction of changes of banking in Poland was determined by the policy of demonopolization of the sector with an increasing share of foreign investors. The endeavours of Poland towards membership in the European Union are accompanied by strengthening of economic-financial relationships with the states of the Union, which as a consequence also causes that most of the investments to the banking sector in Poland came actually from this area. The presence of such a numerous group of foreign banks, especially from the second half of the nineties, has decisive meaning in the forming of a new quality of financial services in Poland. Moreover an additional motor accelerating the transformation is the obligation of Poland in the scope of achieving conformity in the general directions and goals of regulations defining the framework for functioning of the banking system. The forthcoming time of the entry of Poland to the European Union and the predominant share of financial institutions controlled by subjects from the member countries cause that the direction of changes in the banking sector in Poland for the nearest years will be determined by events occurring on the Uniform Financial Market.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests