Global warming contributes to reduction in the intensity of Artemisia pollen seasons in Lublin, central-eastern Poland.

INTRODUCTION AND OBJECTIVE: Species of the genus Artemisia (Asteraceae) are weeds and ruderal plants growing in northern temperate regions of the world. Many of them are used in medicine and the cosmetic industry and for culinary purposes. Pollen grains of plants of this genus contain the most important aeroallergens. MATERIAL AND METHODS: An aerobiological study conducted with the volumetric method in Lublin in 2001-2022. Trend lines for the season parameters were established. Spearman's correlation and stepwise regression analyses were carried out to determine relationships between various parameters of the pollen season and meteorological factors. PCA analysis was also carried out to visually compare the pollen seasons. RESULTS: In Lublin, central-eastern Poland, the Artemisia pollen season lasted on average from the second ten days of July to the end of August, with its beginning depending on the temperature in April and May. The highest pollen concentrations were mainly recorded in the first half of August and were largely dependent on the mean temperature in June and July. The second peak in the pollen season recorded in September was associated with the presence of Artemisia annua pollen. Intense sunshine in June and the higher temperatures in June and July resulted in significant reduction in the Artemisia annual pollen sum (by 65%) over 22 years. Artemisia vulgaris is abundant in the Lublin region and contributes substantially to the amount of Artemisia pollen in the aeroplankton. CONCLUSIONS: The downward trend in the amount of Artemisia pollen was a result of the increase in temperatures observed in the summer months, and the declining rainfall rates. The global warming effect is extremely unfavourable for plants of Artemisia vulgaris, as they require moist soil substrates for growth.

Effects of airborne pollen on allergic rhinitis and asthma across different age groups in Beijing, China.

In the context of global warming and rapid urbanization, pollen has become a significant public health concern for Chinese citizens. However, there is a paucity of epidemiological research on the impact of pollen on allergen-linked diseases, such as allergic rhinitis and asthma, in China. Using data from the Beijing Chaoyang Hospital between 2013 and 2019, which included allergic rhinitis and asthma incidence, meteorological records, and air pollution data, we employed a Generalized Additive Model (GAM) to examine the relationship between overall and type-specific pollen concentrations in relation to varying population exposures. We found that increased overall pollen concentrations significantly increased the risks of allergic rhinitis and asthma in diverse populations. Notably, the risk of allergic rhinitis was higher than that of asthma at equivalent pollen concentrations. Seasonal trends indicated that spring pollen peaks, primarily from trees, were associated with a lower risk of both allergic rhinitis and asthma than autumn peaks, predominantly from weeds. This study underscores the importance of identifying pollen species that pose heightened risks to different demographic groups across seasons, thereby providing targeted interventions for public health agencies.

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

We analyse the impact of ground-based data assimilation to the Weather 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 reduces model overestimation of air temperature, which is the main 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 season was 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 evaluation of pollen concentrations with statistical and computational methods on rooftop and on ground level in Vienna - How to include daily crowd-sourced symptom data.

BACKGROUND: It is recommended to position pollen monitoring stations on rooftop level to assure a large catchment area and to gain data that are representative for a regional scale. Herein, an investigation of the representativeness of pollen concentrations was performed for 20 pollen types in the pollen seasons 2015-2016 in Vienna for rooftop and ground level and was compared with weather data and for the first time with symptom data. METHODS: The complete data set was analyzed with various statistical methods including Spearmen correlation, ANOVA, Kolmogorov-Smirnov test and logistic regression calculation: Odds ratio and Yule's Q values. Computational intelligence methods, namely Self Organizing Maps (SOMs) were employed that are capable of describing similarities and interdependencies in an effective way taking into account the U-matrix as well. The Random Forest algorithm was selected for modeling symptom data. RESULTS: The investigation of the representativeness of pollen concentrations on rooftop and ground level concerns the progress of the season, the peak occurrences and absolute quantities. Most taxa examined showed similar patterns (e.g. Betula), while others showed differences in pollen concentrations exposure on different heights (e.g. the Poaceae family). Maximum temperature, mean temperature and humidity showed the highest influence among the weather parameters and daily pollen concentrations for the majority of taxa in both traps. CONCLUSION: The rooftop trap was identified as the more adequate one when compared with the local symptom data. Results show that symptom data correlate more with pollen concentrations measured on rooftop than with those measured on ground level.

Effect of intra-urban temperature variation on tree flowering phenology, airborne pollen, and measurement error in epidemiological studies of allergenic pollen.

Temperature gradients in cities can cause inter-neighborhood differences in the timing of pollen release. However, most epidemiological studies examining allergenic pollen utilize daily measurements from a single pollen monitoring station with the implicit assumption that the measured time series of airborne pollen concentrations applies across the study areas, and that the temporal mismatch between concentrations at the counting station and elsewhere in the study area is negligible. This assumption is tested by quantifying temperature using satellite imagery, observing flowering times of oak (Quercus) and mulberry (Morus) trees at multiple sites, and collecting airborne pollen. Epidemiological studies of allergenic pollen are reviewed and temperatures within their study areas are quantified. In this one-year study, peak oak flowering time was well explained by average February nighttime temperature (R2 = 0.94), which varied by 6 °C across Detroit. This relationship was used to predict flowering phenology across the study region. Peak flowering ranged from April 20-May 13 and predicted a substantial portion of relative airborne oak pollen concentrations in Detroit (R2 = 0.46) and at the regional pollen monitoring station (R2 = 0.61). The regional pollen monitoring station was located in a cooler outlying area where peak flowering occurred around May 12 and peak pollen concentrations were measured on May 15. This provides evidence that the timing of pollen release varies substantially within a metropolitan area and challenges the assumption that pollen measurements at a single location are representative of an entire city. Across the epidemiological studies, 50% of study areas were not within 1 °C (equal to a lag or lead of 4 days in flowering time) of temperatures at the pollen measurement location. Epidemiological studies using a single pollen station as a proxy for pollen concentrations are prone to significant measurement error if the study area is climatically variable.

The connection of pollen concentrations and crowd-sourced symptom data: new insights from daily and seasonal symptom load index data from 2013 to 2017 in Vienna.

BACKGROUND: Online pollen diaries and mobile applications nowadays allow easy and fast documentation of pollen allergy symptoms. Such crowd-sourced symptom data provides insights into the development and the onset of a pollen allergy. Hitherto studies of the symptom load index (SLI) showed a discrepancy between the SLI and the total pollen amount of a season, but did not analyze the daily data. METHODS: The Patient's Hayfever Diary (PHD) was used as data pool for symptom data. Symptom data of Vienna (Austria) was chosen as a large and local sample size within the study period of 2013 until 2017. The city was divided into three different areas based on equal population densities and different environmental factors. Correlation factors, regression lines, locally weighted smoothing (LOESS) curves and line plots were calculated to examine the data. RESULTS: Daily SLI and pollen concentration data correlates well and the progress of the SLI within a pollen season is mirrored by the pollen concentrations. The LOESS curves do not deviate much from the regression line and support the linearity of the symptom-pollen correlation on a daily basis. Seasonal SLI data does not follow the same pattern as the respective seasonal pollen indices. Results did not vary in the three areas within Vienna or when compared with the Eastern region of Austria showing no significant spatial variation of the SLI. DISCUSSION: Results indicate a linear relationship of the SLI and pollen concentrations/seasonal polllen index (SPIn) on a daily basis for both in general and throughout the season, but not on a seasonal basis. These findings clarify the frequent misinterpretation of the SLI as index that is tightly connected to pollen concentrations, but reflects as well the seasonal variation of the burden of pollen allergy sufferers. CONCLUSION: More than just the seasonal pollen index has to be considered when the SLI of a selected pollen season has to be explained. Cross-reactivity to other pollen types, allergen content and air pollution could play a considerable role. The similar behavior of the SLI in Vienna and a whole region indicate the feasibility of a possible symptom forecast in future and justifies the use of a single pollen monitoring station within a city of the size of Vienna.