Comparison of Ambrosia L. Pollen Seasons in Lublin (Poland) and Ivano-Frankivsk (Ukraine) and Presentation of the Morphotypes of Trichomes on A. artemisiifolia L. Shoots in Terms of the Allergenic Properties of the Plant

Ambrosia pollen contains strong allergens. Allergic reactions can also be caused by direct contact with the plant. The investigations of the dynamics of Ambrosia pollen seasons were conducted in Lublin (Poland) and Ivano-Frankivsk (Ukraine) in 2013–2015. The onset and end of the seasons, maximum concentrations, annual sums, and the number of days with an allergy risk were determined. Additionally, the types of trichomes present on different parts of Ambrosia artemisiifolia L. shoots were determined using light microscopy. Morphometric studies were carried out on trichomes sampled from staminate inflorescences. The maximum concentrations and annual sums of Ambrosia pollen were shown to be substantially higher in Ivano-Frankivsk than in Lublin. Similarly, the risk of allergies is higher in the study site in Ukraine. The study results indicate that the presence of Ambrosia pollen grains in Lublin may be associated with long-distance transport. The presence of non-glandular and glandular trichomes was found on the examined organs. The staminate inflorescences were covered by two types of non-glandular trichomes (short and long) and two types of glandular trichomes (linear and biseriate), whose secretory product can cause dermatitis in sensitive subjects upon contact with the plant.

The influence of meteorological factors on the dynamic of Ambrosia artemisiifolia pollen in an invaded area

The aim of the present study was to analyse the effect of weather conditions on Ambrosia artemisiifolia airpollen concentrations in the highly invaded area of western Romania. The investigation of Ambrosia pollen concentrations was carried out from 2000 to 2010 by means of the volumetric method. Ambrosia pollen concentrations had increasing trend over study period. The results of cluster analysis show that two main groups were identified: group A, with lower SPI (2000, 2001, 2002, 2003, 2004, 2007) and group B, with much higher SPI (2006, 2008, 2009, 2010). The statistical correlation between pollen concentrations and meteorological factors was determined by Pearson’s test. The relationships between Ambrosia pollen concentrations and meteorological parameters, were further assessed using multiple linear regression techniques. The pollen emissions are affected by meteorological factors in the main pollen season. Our results suggest that the abundance of Ambrosia artemisiifolia in western Romania is massive. The Ambrosia pollen load of Timisoara is most important between 15 August – 15 September. Consequently, this is the most dangerous period of the year for allergic reactions. The investigation of Ambrosia pollen behavior in the atmosphere is a compulsory step for measures to stop the spread and establishing control. Ambrosia pollen represents a major health problem and can be considered the main aeroallergenic plant pollen in our region.

Allergenic Ambrosia pollen grains in the air of some Polish cities in 2019

Ambrosia is regarded as the most dangerous allergy-related plant posing a considerable threat to human health with its highly allergenic pollen. In Europe, there are 4 Ambrosia species originating from North America; they most often colonize ruderal habitats and agricultural fields. The aim of the study was to compare Ambrosia pollen seasons in 9 cities located in different parts of Poland in 2019. Aerobiological tests were conducted in Bialystok, Bydgoszcz, Lublin, Olsztyn, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, and Wroclaw. The investigations were carried out with the volumetric method using Burkard or Lanzoni pollen samplers. The 98% method was used to determine the duration of the pollen season. The earliest onset of the pollen season was recorded in Szczecin (August 7th) and Sosnowiec (August 9th), whereas the latest beginning was noted in Wroclaw (August 22nd) and Bydgoszcz (August 21st). The longest pollen season was recorded in Sosnowiec (52 days) as well as Lublin and Szczecin (50 days), while the shortest pollen season was noted in Wroclaw (10 days). The highest mean daily concentrations of Ambrosia pollen grains were recorded in Sosnowiec (104 P/m3) and Wroclaw (77 P/m3), whereas the lowest value was obtained in Szczecin (18 P/m3). Peak days were noted mostly during the last 10 days of August. The highest value of Ambrosia annual pollen sum was reported from Sosnowiec (326) and Lublin (310), while the lowest sum was noted in Szczecin (69). The multimodal course of the graph presenting the pollen seasons in the analysed cities and literature data indicate that the pollen originated not only from local sources but also from long-distance transport. The highest risk of Ambrosia pollen-induced allergy in sensitive subjects was demonstrated in Lublin, Piotrkow Trybunalski and Warsaw. The concentration of Ambrosia pollen in the air of the analysed Polish cities was substantially lower than the values indicated by measurement stations located in other parts of Europe.

A 1200-year history of environmental changes in Bay Jimmy area, coastal Louisiana, USA

Paleoecological studies from the northern Gulf of Mexico (GOM) have mostly aimed at understanding long-term paleoenvironmental changes. Only a few studies have been performed in the southern United States focusing on paleoenvironmental changes during the common era. This study investigated paleoenvironmental changes in coastal Louisiana over the past 1200 years by utilizing proxy data, including loss-on-ignition (LOI) and pollen from a sediment core collected from a wetland in Bay Jimmy. The results indicate that the marsh in the study area was formed at ca. AD 1090 and has been primarily shaped by prevailing climatic conditions, including rare extreme events. At least four major hurricanes impacted the site over the 1200 years, including two that made landfall in recent times. The findings show that coastal Louisiana was warm and dry during the ‘Medieval Warm Period’ (ca. AD 950–1250). The environment after AD 1090 contained scattered Pinus and Juglans vegetation communities that were later succeeded by a closed forest that included Quercus and Morus. Red mangrove ( Rhizophora) was established in the vicinity of Bay Jimmy until shortly after cal AD 1450 and 1640. The pollen record indicates that the ‘Little Ice Age’ period (AD 1550–1850) was cold and dry, characterized by a more open vegetation community. There is evidence of forest disturbance that is marked by a rise in Ambrosia pollen in the 1700s, coinciding with the time of European settlement in North America. The presence of Ambrosia and Cheno/Am pollen throughout the record suggests that anthropogenic influence has been part of the fabric of the southern Louisiana landscapes throughout the 1200 years.

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.