Pollen concentration of invasive tree of heaven (Ailanthus altissima) on the Northern Great Plain, Hungary

In areas where the tree of heaven (Ailanthus altissima) appears and multiplies, the original vegetation degrades and transforms. The invasive tree of heaven is also of great importance in urban environments, where it causes building damage, static problems and endangers utilities. Ailanthus pollen concentration was measured during the 3-year period (2016-2018) at three county capitals (Szolnok, Debrecen, Nyíregyháza) of the Northern Great Plain, Hungary (JászNagykun-Szolnok county, Hajdú-Bihar county and Szabolcs-Szatmár-Bereg county), with a 7-day Hirst-type (Burkard) pollen trap. The highest total pollen count of A. altissima was measured in all three years in Nyíregyháza (1114 pollen m-3 in 2016; 788 pollen m-3 in 2017; 635 pollen m-3 in 2018), while the lowest values were measured in Szolnok in all three years (99 pollen m-3 in 2016; 78 pollen m-3 in 2017; 93 pollen m-3 in 2018). In Debrecen, the annual total pollen concentration varied between 109-127 pollen grains m-3 in the studied period. The extent of the prevalence of A. altissima can be deduced from its pollen concentrations. For this purpose, multi-year pollen data is displayed on a map in which areas characterized by different pollen concentrations are represented by colour codes. Pollen monitoring provides information on the size of A. altissima stands and provides a basis for proposals and plans for measures to control this invasive tree species and mitigate the damage caused by it

A növények potenciális allergenitása - Áttekintés és módszertani javaslat = Potential Allergenicity of Plants - Review and Methodological Proposal

A növényi pollen allergizáló képessége számos tényezőtől függ. E feltételek: a pollen allergiás immunválasz kiváltására alkalmas anyagot tartalmazzon, a pollent nagy mennyiségben termelje a növény, a pollenszem kis méretű legyen és könnyen, nagy mennyiségben szálljon a levegőben, a növény gyakori, tömegesen előforduló fajba tartozzon. E tényezőket külön súlyozva kiszámítható az egyes növénytaxonok potenciális allergenitása. Dolgozatunkban a szakirodalomban fellelhető módszereket hasonlítjuk össze, és keressük a megfelelő eljárást, amely a növények ültetésére, kereskedelmére irányuló jogi szabályozás alapja lehetne. Az irodalmi áttekintés alapján nem találtunk olyan jelenleg létező módszert, amely a jogi szabályozás követelményeit kielégítheti (megfelelő bizonyítékok felsorolása és reprodukálhatóság), ezért kidolgoztunk egy saját eljárást a növények potenciális allergenitásának kiszámítására

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

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites

Performance of multi-city land use regression models for nitrogen dioxide and fine particles

BACKGROUND: Land use regression (LUR) models have mostly been developed to explain intra-urban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES: To develop European and regional LUR models and to examine their transferability to areas not used for model development. METHODS: We evaluated LUR models for nitrogen dioxide (NO2) and Particulate Matter (PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE study areas across 14 European countries for PM and NO2. Models were evaluated with cross validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. RESULTS: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5 and 70% for PM2.5 absorbance. The HV R(2)s were only slightly lower than the model R(2) (NO2: 54%, PM2.5: 80%, absorbance: 70%). The European NO2, PM2.5 and PM2.5 absorbance models explained a median of 59%, 48% and 70% of within-area variability in individual areas. The transferred models predicted a modest to large fraction of variability in areas which were excluded from model building (median R(2): 59% NO2; 42% PM2.5; 67% PM2.5 absorbance). CONCLUSIONS: Using a large dataset from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained similar to 60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies. (C) 2016 Elsevier Inc. All rights reserved