Airborne Pollen Concentrations Research Articles

Influence of meteorological parameters and air pollutants on the airborne pollen of city Chandigarh, India

Pollen, climatic variables and air pollutants coexist in nature with the potential to interact with one another and play a crucial role in increasing allergic diseases. The current study evaluates the influence of meteorological parameters and air pollutants on the airborne pollen in an urban city, Chandigarh, situated in the Indo-Gangetic Plains. Airborne pollen monitoring was done following Spanish Aerobiological Network guidelines and dynamics of daily total pollen and six most abundant taxa were studied from June 2018 to June 2020. Among meteorological parameters, temperature and wind were the most correlated and influential parameters to airborne pollen concentration. Annual Pollen Integral (APIn) of Cannabis sativa (r = 0.52), Parthenium hysterophorus (r = 0.27), Poaceae (r = 0.32) and total pollen concentration (r = 0.30) showed a statistically significant positive correlation with temperature. In contrast, precipitation and relative humidity negatively correlated with APIn of total pollen concentration, Eucalyptus sp. and Poaceae except for Parthenium hysterophorus and Celtis occidentalis. Similar results were found with Seasonal Pollen Integral (SPIn) of total pollen concentration, six major taxa and meteorological variables. Spearman correlation performed for NOx showed a significant positive correlation among APIn and SPIn of Celtis occidentalis and insignificant among APIn and SPIn of Eucalyptus sp. and Morus alba. In contrast, except for Eucalyptus sp., PM10 and PM2.5 were negatively correlated among APIn and SPIn of total pollen concentration and other major taxa. Spearman's correlation of APIn and SPIn for each pollen taxon, meteorological parameters and air pollutants suggests that each taxon has a different pattern in response to all parameters. The study findings suggest that pollen response must be examined at the taxon level, not the assemblage level, having long time-series data. This will help to compute future scenarios of changing environmental factors and comprehend the relationships and trends among meteorology, air pollutants and aerobiology.

Biodiversity, abundance, seasonal and diurnal airborne pollen distribution patterns at two different heights in Augsburg, Germany

Airborne pollen are the most important aeroallergens worldwide. Because of climate change, pollen seasonality and abundance have been altering significantly, raising the fundamental question: when and how much is the pollen exposure increasing? To answer this, we applied a multi-resolution study design, from bi-hourly to yearly scale, investigating the diversity, abundance and temporal occurrence of airborne pollen.The whole spectrum of airborne pollen concentrations was registered during 2015–2017, using a 7-day recording Hirst-type volumetric trap. Monitoring took place at ground-level, where we mostly commute and reside, and at the ‘gold-standard’ rooftop-level (12 m above ground level), at resolutions: A) bi-hourly, B) daily. The biodiversity and the relative abundance of all taxa were assessed, and the first pollen season calendars, along with circadian calendars, for Augsburg, Germany, were developed.More than 40 pollen types were identified, of which 13 were the most abundant (>0.5% relative abundance each, accounting for a total of 91.8%). Biodiversity did not present any striking differences between heights, with pollen from Urticaceae, Betula and Poaceae representing consistently more than half of the regional atmospheric biodiversity. At rooftop-level, pollen abundances often appeared to be higher, particularly for Betula, Picea and Quercus. The main pollen season extended from March to October, with the highest peak occurring April–May. At rooftop-level, the pollen seasons of most taxa were observed earlier and the overall seasons were longer. Within the day, higher pollen concentrations were observed either at midday to early afternoon (Urticaceae, Poaceae, Plantago and mostly taxa at ground-level) or night to early morning, frequently with multi-modal diurnal patterns (Betula, Fraxinus and mostly taxa at rooftop-level).Our findings reveal that generalisation of abundance and temporal distribution patterns between ground-level and ‘gold-standard’ rooftop-level pollen measurements should be intensively reconsidered. While the pollen diversity and abundance may be well represented within this height range, the temporal occurrence is not, with pollen vertical variability being more important than originally anticipated. Hence, we need to reassess when and how much the relevant pollen exposure is increasing.

An exploratory analysis of seasonal and intraseasonal variations of the main airborne pollen types in Sunchales city, Argentina

Background and aims: The study of the seasonal and intra-seasonal variability of the airborne pollen concentration is of paramount importance to understand the relationships with the emitting vegetation and the atmospheric parameters that modulate pollen transport. This research aims to study these variabilities in Sunchales, a city located in the center-east of Argentina. M&M: Atmospheric monitoring was carried out with a Burkard trap during two seasons in 2012 and 2013 on the outskirts of the city. Results & Conclusions: The pollination periods of the studied pollen types show a delay in 2013 compared to the previous year, presumably related to a greater amount of cumulative heat units in 2012. However, the integral pollen for the period 2013 was 1.4 times higher than 2012, a fact that is not explained by accumulated precipitation but by the time of day when the hydrometeors occur. Binned pollen concentrations show that the highest concentrations coincide with the urban location of the tree sources while the herbaceous ones show an association with a rural origin. Regarding the intra-seasonal variability, the highest proportion of the airborne pollen variance accumulates on the synoptic-scale (80 - 60%) with periods between 3 and 10 days. During 2012 long waves predominated (> 5.5 days) while in 2013 medium waves prevailed (3.9 - 5.5 days).

A machine-learning Strategy for Generating Pollen Estimates and Forecasts in North America

BACKGROUND AND AIM: Pollen information is important for risk communication, diagnosis of allergic disease, medical management, and surveillance. Pollen observations are sparse, however, and existing models have relatively low skill in estimating pollen concentrations for certain taxa and locations. The aim of this work is to leverage recently developed supervised Random Forest machine learning models to estimate daily pollen concentrations for multiple allergenic pollen taxa in the continental US. METHODS: We built upon a previously developed statistical supervised Random Forest machine learning model with demonstrated skill in estimating and forecasting four pollen types (Quercus, Cupressaceae, Ambrosia, and Poaceae) to make retrospective forecasts of seven other allergenic pollen taxa for the contiguous US. Meteorological, vegetation, and web search information were input to the models at city and regional scales and geographic coverage was expanded using data augmentation techniques. The models were further developed to estimate pollen concentrations in locations where there are no observations. Daily pollen concentration estimates were made for each of the 11 taxa. RESULTS:Daily estimates of pollen concentrations for 11 allergenic taxa for the period 2000 to 2020 were developed. Model forecast skill for the seven newly modeled taxa was assessed and compared with skill for the four previously modeled pollen types and available diagnostics for other pollen forecasting models. CONCLUSIONS:Weather, vegetation, and web search data can be used to estimate airborne pollen concentrations for prevalent allergenic taxa in the contiguous US. Reanalysis data products may be of use in diagnostic and management activities for people with allergic disease and for epidemiological analyses. Forecast models can be used in risk communication to facilitate exposure avoidance. Model forecasting skill is limited by availability of pollen observations and could be augmented by development of additional observation sites. KEYWORDS: pollen, random forest machine learning, allergic rhinitis, allergic asthma, forecasting, climate

Effects of future climate change on birch abundance and their pollen load.

Climate change impacts on the structure and function of ecosystems will worsen public health issues like allergic diseases. Birch trees (Betula spp.) are important sources of aeroallergens in Central and Northern Europe. Birches are vulnerable to climate change as these trees are sensitive to increased temperatures and summer droughts. This study aims to examine the effect of climate change on airborne birch pollen concentrations in Central Europe using Bavaria in Southern Germany as a case study. Pollen data from 28 monitoring stations in Bavaria were used in this study, with time series of up 30years long. An integrative approach was used to model airborne birch pollen concentrations taking into account drivers influencing birch tree abundance and birch pollen production and projections made according to different climate change and socioeconomic scenarios. Birch tree abundance is projected to decrease in parts of Bavaria at different rates, depending on the climate scenario, particularly in current centres of the species distribution. Climate change is expected to result in initial increases in pollen load but, due to the reduction in birch trees, the amount of airborne birch pollen will decrease at lower altitudes. Conversely, higher altitude areas will experience expansions in birch tree distribution and subsequent increases in airborne birch pollen in the future. Even considering restrictions for migration rates, increases in pollen load are likely in Southwestern areas, where positive trends have already been detected during the last three decades. Integrating models for the distribution and abundance of pollen sources and the drivers that control birch pollen production allowed us to model airborne birch pollen concentrations in the future. The magnitude of changes depends on location and climate change scenario.

The Relationship between Metrological Parameters and Total Airborne Pollen Concentration in Atmosphere of Khartoum City During 2018

This study is aimed to find out the effect and relationship between metrological parameters and total airborne pollen concentration in atmosphere in Khartoum city (Sudan). For that purpose, one-year data during 2018 of Total Airborne Pollen Concentration (TAPC) and meteorological parameters is utilized. Pollen concentration in the atmosphere was measured using Non-volumetric air sampler (Vertical cylinder spore trap). The descriptive statistic of TAPC and metrological parameters were presented. The result showed TAPC in 2018 reached to their maximum in August (37.5/m³), and the minimum in June (14/ m³). Analysis of the relationship between TAPC and meteorological parameters showed that variations in the pollen concentrations were mainly affected by maximum temperature. In addition, there were substantial differences in these relationships in different seasons. In Autumn, they were affected by the rain falls, maximum temperature and relative humidity; in summer, they were affected by maximum temperature; and in winter, they were affected by maximum temperature and relative humidity. Temperature, rain falls and relative humidity promote plant growth and blooming. As well as the results show wind direction was significant on seasonally TAPC especially in Autumn. The results found significant effects between the meteorological on TAPC, were it is showed 72 % are affecting on TAPC refer to meteorological parameters and 28 % refer to another factors.

Long-term pollen trends and associations between pollen phenology and seasonal climate in Atlanta, Georgia (1992-2018)

BackgroundPrevious research has revealed that airborne pollen concentrations and phenology in allergenic plants are changing. In addition, variations in seasonal climate are known to affect pollen phenology in trees, weeds, and grasses. ObjectiveTo investigate localized trends in pollen concentrations and pollen phenology over time and the effect of seasonal climate variations. MethodsWe used daily pollen count concentrations from a National Allergy Bureau pollen counting station located in metropolitan Atlanta, Georgia, for 13 allergenic taxa. To evaluate long-term trends over time, we developed linear regression models for 6 pollen measures. To evaluate the effect of seasonal climate on phenology, we developed regression models using seasonal climate measures as independent variables and pollen measures as dependent variables. ResultsFor several tree pollen taxa, pollen concentrations increased over time, including oak and juniper pollen. In multiple species, pollen seasons trended toward an earlier release throughout the 27-year period. Variations in seasonal climate did have an effect on pollen counts and the timing of pollen release but varied by taxa. Generally, warmer spring temperatures were associated with an earlier pollen release. In addition, increased precipitation from the preceding fall was associated with increased pollen concentration in the spring months. ConclusionAllergenic pollen concentrations for several types of pollen are increasing and trending toward an earlier pollen release in Atlanta, Georgia. Warmer temperatures preceding the pollen season were associated with the earlier pollen release.

Long-Term Pollen Monitoring in the Benelux: Evaluation of Allergenic Pollen Levels and Temporal Variations of Pollen Seasons.

Airborne pollen is a major cause of allergic rhinitis, affecting between 10 and 30% of the population in Belgium, the Netherlands, and Luxembourg (Benelux). Allergenic pollen is produced by wind pollinating plants and released in relatively low to massive amounts. Current climate changes, in combination with increasing urbanization, are likely to affect the presence of airborne allergenic pollen with respect to exposure intensity, timing as well as duration. Detailed analysis of long-term temporal trends at supranational scale may provide more comprehensive insight into these phenomena. To this end, the Spearman correlation was used to statistically compare the temporal trends in airborne pollen concentration monitored at the aerobiological stations which gathered the longest time-series (30–44 years) in the Benelux with a focus on the allergenic pollen taxa: Alnus, Corylus, Betula, Fraxinus, Quercus, Platanus, Poaceae, and Artemisia. Most arboreal species showed an overall trend toward an increase in the annual pollen integral and peak values and an overall trend toward an earlier start and end of the pollen season, which for Betula resulted in a significant decrease in season length. For the herbaceous species (Poaceae and Artemisia), the annual pollen integral and peak values showed a decreasing trend. The season timing of Poaceae showed a trend toward earlier starts and longer seasons in all locations. In all, these results show that temporal variations in pollen levels almost always follow a common trend in the Benelux, suggesting a similar force of climate change-driven factors, especially for Betula where a clear positive correlation was found between changes in temperature and pollen release over time. However, some trends were more local-specific indicating the influence of other environmental factors, e.g., the increasing urbanization in the surroundings of these monitoring locations. The dynamics in the observed trends can impact allergic patients by increasing the severity of symptoms, upsetting the habit of timing of the season, complicating diagnosis due to overlapping pollen seasons and the emergence of new symptoms due allergens that were weak at first.

Adding the variable of environmental complexity into the COVID-19 pandemic equation.

In a recently published data-orientedstudyin March 2021, a relationship between SARS-CoV-2 infection rates and airborne pollen concentrationswas detected [1]. The studyincluded a data compilation of airborne pollen concentrations and SARS-CoV-2 infection numbers across 31 countries from both hemispheres and all inhabited continents from an inter-disciplinary team consisting of 154 scientists. The salient finding wasa significant and positive correlation between SARS-CoV-2 infection rates and airborne pollen concentrations in the spring of 2020 for the majority of examined countries, in synergy with air temperature and/or relative humidity (Fig. 1).

Neural networks for increased accuracy of allergenic pollen monitoring

Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

Causes of increased pollen exposure during Saharan-Sahel dust intrusions

Airborne particulate matter such as mineral dust comes mainly from natural sources, and the African regions of Sahara and Sahel originate large amounts of the aerosols dispersed worldwide. There is little knowledge about the influence of dust episodes on airborne pollen concentrations, and although the centre and southeast of the Iberian Peninsula are frequently affected by dust intrusions, until now, no specific works have analysed the effect of these episodes on airborne pollen concentrations in these areas. The aims of this study were to analyse the simultaneous occurrence of airborne pollen peaks and Saharan-Sahel dust intrusions in the central and south-eastern Iberian Peninsula, and to study the weather conditions – air mass pathways and conditions of air temperature, relative humidity and atmospheric pressure – that influence the airborne pollen concentrations during dust episodes. The results showed that the rise in airborne pollen concentrations during dust episodes is apparent in inland Iberian areas, although not in coastal areas in the southeast where pollen concentrations are even observed to decrease, coinciding with prevailing easterly winds from the sea. Total pollen concentrations and specific pollen types such as Olea, Poaceae and Quercus showed an increase in the central Iberian Peninsula during dust episodes when two meteorological phenomena concur: 1) prevailing winds from extensive areas of major wind-pollinated pollen sources over a medium or short distance (mainly from western and southwestern areas); and 2) optimal meteorological conditions that favour pollen release and dispersal into the atmosphere (mainly high temperatures and subsequently low humidity in central areas). Both conditions often occur during the Saharan-Sahel dust intrusions in the centre. Maximum pollen peaks are therefore most likely to occur during dust episodes in the central Iberian Peninsula, thus dramatically increasing the risk of outbreaks of pollinosis and other respiratory diseases in the population.

The need for Pan-European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper.

BackgroundInformation about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe.Materials & MethodsTo promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed.ResultsThe provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & ConclusionA European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management.

A systematic review of the effects of temperature and precipitation on pollen concentrations and season timing, and implications for human health.

Climate and weather directly impact plant phenology, affecting airborne pollen. The objective of this systematic review is to examine the impacts of meteorological variables on airborne pollen concentrations and pollen season timing. Using PRISMA methodology, we reviewed literature that assessed whether there was a relationship between local temperature and precipitation and measured airborne pollen. The search strategy included terms related to pollen, trends or measurements, and season timing. For inclusion, studies must have conducted a correlation analysis of at least 5 years of airborne pollen data to local meteorological data and report quantitative results. Data from peer-reviewed articles were extracted on the correlations between seven pollen indicators (main pollen season start date, end date, peak date, and length, annual pollen integral, average daily pollen concentration, and peak pollen concentration), and two meteorological variables (temperature and precipitation). Ninety-three articles were included in the analysis out of 9,679 articles screened. Overall, warmer temperatures correlated with earlier and longer pollen seasons and higher pollen concentrations. Precipitation had varying effects on pollen concentration and pollen season timing indicators. Increased precipitation may have a short-term effect causing low pollen concentrations potentially due to "wash out" effect. Long-term effects of precipitation varied for trees and weeds and had a positive correlation with grass pollen levels. With increases in temperature due to climate change, pollen seasons for some taxa in some regions may start earlier, last longer, and be more intense, which may be associated with adverse health impacts, as pollen exposure has well-known health effects in sensitized individuals.

Airborne pollen concentrations and daily mortality from respiratory and cardiovascular causes.

We conducted a time-series analysis of the relations between daily levels of allergenic pollen and mortality in the Helsinki Metropolitan Area with 153 378 deaths; 9742 from respiratory and 57 402 from cardiovascular causes. Daily (average) pollen counts of alder, birch, mugwort and grass were measured. In quasi-Poisson regression analysis, abundant alder pollen increased the risk of non-accidental deaths with an adjusted cumulative mortality rate ratio (acMRR) of 1.10 (95% CI 1.01–1.19) and of deaths from respiratory-diseases with acMRR of 1.78 (95% CI 1.19–2.65). Abundant mugwort pollen increased cardiovascular mortality (1.41, 1.02–1.95). These findings identify an important global public health problem.

Forecasting Betula and Poaceae airborne pollen concentrations on a 3-hourly resolution in Augsburg, Germany: toward automatically generated, real-time predictions

Airborne allergenic pollen impact the health of a great part of the global population. Under climate change conditions, the abundance of airborne pollen has been rising dramatically and so is the effect on sensitized individuals. The first line of allergy management is allergen avoidance, which, to date, is by rule achieved via forecasting of daily pollen concentrations. The aim of this study was to elaborate on 3-hourly predictive models, one of the very few to the best of our knowledge, attempting to forecast pollen concentration based on near-real-time automatic pollen measurements. The study was conducted in Augsburg, Germany, during four years (2016–2019) focusing on Betula and Poaceae pollen, the most abundant and allergenic in temperate climates. ARIMA and dynamic regression models were employed, as well as machine learning techniques, viz. artificial neural networks and neural network autoregression models. Air temperature, relative humidity, precipitation, air pressure, sunshine duration, diffuse radiation, and wind speed were additionally considered for the development of the models. It was found that air temperature and precipitation were the most significant variables for the prediction of airborne pollen concentrations. At such fine temporal resolution, our forecasting models performed well showing their ability to explain most of the variability of pollen concentrations for both taxa. However, predictive power of Betula forecasting model was higher achieving R2 up to 0.62, whereas Poaceae up to 0.55. Neural autoregression was superior in forecasting Betula pollen concentrations, whereas, for Poaceae, seasonal ARIMA performed best. The good performance of seasonal ARIMA in describing variability of pollen concentrations of both examined taxa suggests an important role of plants’ phenology in observed pollen abundance. The present study provides novel insight on per-hour forecasts to be used in real-time mobile apps by pollen allergic patients. Despite the huge need for real-time, short-term predictions for everyday clinical practice, extreme weather events, like in the year 2019 in our case, still comprise an obstacle toward highly performing forecasts at such fine timescales, highlighting that there is still a way to go to this direction.

Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe

Pollen exposure weakens the immunity against certain seasonal respiratory viruses by diminishing the antiviral interferon response. Here we investigate whether the same applies to the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is sensitive to antiviral interferons, if infection waves coincide with high airborne pollen concentrations. Our original hypothesis was that more airborne pollen would lead to increases in infection rates. To examine this, we performed a cross-sectional and longitudinal data analysis on SARS-CoV-2 infection, airborne pollen, and meteorological factors. Our dataset is the most comprehensive, largest possible worldwide from 130 stations, across 31 countries and five continents. To explicitly investigate the effects of social contact, we additionally considered population density of each study area, as well as lockdown effects, in all possible combinations: without any lockdown, with mixed lockdown-no lockdown regime, and under complete lockdown. We found that airborne pollen, sometimes in synergy with humidity and temperature, explained, on average, 44% of the infection rate variability. Infection rates increased after higher pollen concentrations most frequently during the four previous days. Without lockdown, an increase of pollen abundance by 100 pollen/m3 resulted in a 4% average increase of infection rates. Lockdown halved infection rates under similar pollen concentrations. As there can be no preventive measures against airborne pollen exposure, we suggest wide dissemination of pollen-virus coexposure dire effect information to encourage high-risk individuals to wear particle filter masks during high springtime pollen concentrations.

Development of a Random Forest model for forecasting allergenic pollen in North America

Pollen allergies have negative impacts on health. Information about airborne pollen concentration can improve symptom management by guiding choices affecting timing of medicines and pollen exposure. Observations provide accurate pollen concentrations at point locations. However, in the contiguous United States and southern Canada (CUSSC), observations are sparse, and sampling is often seasonal, intermittent or both. Modeling pollen concentration can fill in the gaps with estimates where direct observations are unavailable and also provide much-needed forecasts. The goal of this study is to develop and evaluate statistical models that predict daily pollen concentrations using a machine learning Random Forest algorithm. To evaluate our methods, we made retrospective forecasts of four pollen types (Quercus, Cupressaceae, Ambrosia and Poaceae), each in one of four CUSSC locations. Meteorological and vegetation conditions were input to the models at city and regional scales. A data augmentation technique was investigated and found to improve model skill. Models were also developed to forecast pollen in locations where there are no observations. Forecast skill in these models were found to be greater than in previous models. Nevertheless, the skill is limited by the spatiotemporal resolution of the pollen observations.

Forecasting Pollen Concentration using ML and Pollen Taxa Classification using CNN

Natural fluctuations within the climate and increase in emission of greenhouse gases are now considered to be a heavy issue for inflicting environmental conditions. Effect of which can also be seen as an increase in the distribution of plants releasing allergic pollen. Thus, predicting pollen count and classification of the pollen taxa can be very useful for those who are prone to seasonal allergies like hay fever, allergic rhinitis, etc. The proposed work in this paper focuses on the development of a web-based application using Django which will be forecasting the concentration of airborne pollen for the next two days using Random Forest Regressor, a machine learning algorithm, by considering various meteorological variables and performing classification of pollen taxa using CNN based architecture Inception_V3, a deep learning approach.

Comparison of Artemisia L. pollen concentrations and risk of development of allergy symptoms in different regions of Poland in 2020

In Central Europe, mugwort pollen is a frequent cause of pollen allergy. Poland is one of the countries with the highest airborne concentrations of pollen of this taxon. Due to its high allergenic potential, Artemisia pollen may pose a significant threat to sensitive subjects during summer months. Plants from this genus often grow in urban and suburban areas.
 The aim of the study was to compare mugwort pollen seasons and concentrations of airborne pollen of these plants in 12 cities located in different regions of Poland: Bialystok, Bydgoszcz, Cracow, Lublin, Olsztyn, Opole, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw, and Zielona Gora. The investigations were carried out with the volumetric method using a Hirst-type pollen sampler (Lanzoni or Burkard) operating on a 24-hour basis. The duration of the pollen season was determined with the 98% method. The earliest onset of the mugwort pollen season was noted in Opole (July 12th), and the latest beginning was recorded in Cracow and Sosnowiec (July 23rd). The maximum pollen concentrations were reported on August 7th and 8th in Lublin (177 grains/m3) and Wroclaw (100 grains/m3). In all the cities, peak days were recorded on the first 10 days of August. The maximum pollen concentrations in the other cities were in the range of 18–89 grains/m3. The highest annual pollen sum was recorded in Lublin (1423) and Wroclaw (1050). These values coincided with the highest pollen concentrations determined in these cities.
 The annual Artemisia pollen sums in 2020 did not have the highest values in comparison with other years in these cities. The average annual pollen sum in the five-year period of 2001–2005 was estimated at 2065 in Lublin and 1662 in Wroclaw. Therefore, it can be concluded that the risk of mugwort pollen allergy in the pollen season 2020 was lower than in some previous years.

One-year study of airborne sugar compounds: Cross-interpretation with other chemical species and meteorological conditions

The daily evolution of seventeen sugar compounds (seven saccharides, seven alcohol-saccharides and three anhydrosaccharides) in atmospheric aerosol samples collected between 9 March 2016 and 14 March 2017 was studied in León (Spain). The main links between the concentration of sugar compounds and various chemical species, pollen, fungal spores and meteorological conditions were investigated. The results showed that, in spring, when high levels of metabolic activity of the plants occur and temperatures increase, glucose, sucrose, 2-methyl-erithritol, mannitol, arabitol and inositol, are significantly correlated with airborne pollen concentrations. Between spring and autumn, Alternaria air concentrations are significantly correlated with temperatures, arabitol and sorbitol + adonitol concentrations. Furthermore, during rainy days, Alternaria is also correlated with mannitol. In autumn, lower temperatures cause an increase in the concentrations of levoglucosan, mannosan and galactosan, probably due to the increased use of domestic heating devices. These anhydrosugars and arabinose, fructose and glucose, are significantly correlated with K, NO3−, EC, OC, Cu, Zn, Se, Pb, V and Ni, while mannosan also correlates with As, showing that these anhydrosaccharides can be emitted from different anthropogenic sources. Precipitation causes an increase in glucose and sucrose concentrations, due to the break of pollen particles that produce hundreds of fine size particles. Besides, precipitation causes an increase in arabitol concentrations, due to the release and growth of fungi.