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The Pannonian Plain is one of the centers of ragweed distribution in Europe. The province of Vojvodina (Serbia) is located on the southern part of the Pannonian Plain, representing a highly infested region. In this study, we have used the SILAM atmospheric dispersion model to simulate ragweed pollen concentrations during the season 2016 in the Vojvodina region. SILAM was tested with three different source maps of ragweed distribution in Vojvodina only: (1) map used in operational SILAM, which was calibrated with the SILAM model and observations, (2) map derived using “top-down” approach with land cover data inventory, and (3) map obtained with “top-down” approach using crop classification from the satellite data. Additionally, the sensitivity studies were done using two modified maps to study the effect of the source strength and long-range transport. Results of simulations were validated with the bi-hourly, daily, and seasonal pollen concentrations measured at five stations in Vojvodina. Overall Pearson correlation coefficients were 0.51 (Map 1), 0.50 (Map 2), and 0.42 (Map 3), while debiased scores were 232.95 pollen m−3 (Map 1), 245.59 pollen m−3 (Map 2), and 258.24 pollen m−3 (Map 3). Even though Vojvodina is in the area of a major European source, regional transport of ragweed pollen from a few hundred kilometers of the surrounding area was important in explaining the presence of pollen in the afternoon hours, although it could not completely explain total pollen quantity. The results confirmed that it is vital to calibrate source maps using atmospheric dispersion model with the observed pollen data.

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.

Cave sediments commonly contain crucial sedimentary evidence of past environmental change and human activity. Fossil pollen sequences within these deposits hold great potential for reconstructing paleoenvironmental changes and human–environment relationships; however, the extent to which cave pollen faithfully reflects the external environment remains controversial, especially in complex cave systems. This study conducted pollen analysis in surface sediment samples from two karst caves with complex geometry on the Guizhou Plateau. The pollen assemblages in surface sediment within 5–15 m of the entrance of complex caves with multiple entrances and passages or a single entrance and multiple chambers were highly similar to those of external surface soil/fresh moss samples and they exhibited strong correlations, indicating a good representation of external vegetation. Additionally, the high pollen concentration in the area made it an ideal sampling area for pollen analysis. In the middle-to-rear parts of the caves, although improved ventilation enhanced the representation of pollen assemblages for external vegetation, pollen concentrations were significantly lower than those near the entrance. This necessitates careful selection of samples for pollen analysis from cave sediments. Moreover, humid cave environments, animal transportation, and plant growth within the cave may lead to spatial heterogeneity in pollen assemblages, thereby affecting their representativeness of the external environment. The present study provides theoretical references for understanding the relationship between cave pollen assemblages and the external environment and offers important evidence for future archaeological and paleoenvironmental reconstruction studies in this region using cave pollen.

Exposure to pollen can contribute to increased hospital admissions for asthma exacerbation. This study applied an ecological time series analysis to examine associations between atmospheric concentrations of different pollen types and the risk of hospitalization for asthma in London from 2005 to 2011. The analysis examined short-term associations between daily pollen counts and hospital admissions in the presence of seasonal and long-term patterns, and allowed for time lags between exposure and admission. Models were adjusted for temperature, precipitation, humidity, day of week, and air pollutants. Analyses revealed an association between daily counts (continuous) of grass pollen and adult hospital admissions for asthma in London, with a 4–5-day lag. When grass pollen concentrations were categorized into Met Office pollen ‘alert’ levels, ‘very high’ days (vs. ‘low’) were associated with increased admissions 2–5 days later, peaking at an incidence rate ratio of 1.46 (95%, CI 1.20–1.78) at 3 days. Increased admissions were also associated with ‘high’ versus ‘low’ pollen days at a 3-day lag. Results from tree pollen models were inconclusive and likely to have been affected by the shorter pollen seasons and consequent limited number of observation days with higher tree pollen concentrations. Future reductions in asthma hospitalizations may be achieved by better understanding of environmental risks, informing improved alert systems and supporting patients to take preventive measures.

One expected effect of climate change on human health is increasing allergic and asthmatic symptoms through changes in pollen biology. Allergic diseases have a large impact on human health globally, with 10-30% of the population affected by allergic rhinitis and more than 300 million affected by asthma. Pollen from grass species, which are highly allergenic and occur worldwide, elicits allergic responses in 20% of the general population and 40% of atopic individuals. Here we examine the effects of elevated levels of two greenhouse gases, carbon dioxide (CO2), a growth and reproductive stimulator of plants, and ozone (O3), a repressor, on pollen and allergen production in Timothy grass (Phleum pratense L.). We conducted a fully factorial experiment in which plants were grown at ambient and/or elevated levels of O3 and CO2, to simulate present and projected levels of both gases and their potential interactive effects. We captured and counted pollen from flowers in each treatment and assayed for concentrations of the allergen protein, Phl p 5. We found that elevated levels of CO2 increased the amount of grass pollen produced by ∼50% per flower, regardless of O3 levels. Elevated O3 significantly reduced the Phl p 5 content of the pollen but the net effect of rising pollen numbers with elevated CO2 indicate increased allergen exposure under elevated levels of both greenhouse gases. Using quantitative estimates of increased pollen production and number of flowering plants per treatment, we estimated that airborne grass pollen concentrations will increase in the future up to ∼200%. Due to the widespread existence of grasses and the particular importance of P. pratense in eliciting allergic responses, our findings provide evidence for significant impacts on human health worldwide as a result of future climate change.

The effects of tree pollen on precipitation chemistry are not fully understood and this can lead to misinterpretations of element deposition in European forests. We investigated the relationship between forest throughfall (TF) element fluxes and the Seasonal Pollen Integral (SPIn) using linear mixed-effects modelling (LME). TF was measured in 1990–2018 during the main pollen season (MPS, arbitrary two months) in 61 managed, mostly pure, even-aged Fagus, Quercus, Pinus, and Picea stands which are part of the ICP Forests Level II network. The SPIn for the dominant tree genus was observed at 56 aerobiological monitoring stations in nearby cities. The net contribution of pollen was estimated as the TF flux in the MPS minus the fluxes in the preceding and succeeding months. In stands of Fagus and Picea, two genera that do not form large amounts of flowers every year, TF fluxes of potassium (K+), ammonium-nitrogen (NH4+-N), dissolved organic carbon (DOC), and dissolved organic nitrogen (DON) showed a positive relationship with SPIn. However- for Fagus- a negative relationship was found between TF nitrate-nitrogen (NO3−-N) fluxes and SPIn. For Quercus and Pinus, two genera producing many flowers each year, SPIn displayed limited variability and no clear association with TF element fluxes. Overall, pollen contributed on average 4.1–10.6% of the annual TF fluxes of K+ > DOC > DON > NH4+-N with the highest contribution in Quercus > Fagus > Pinus > Picea stands. Tree pollen appears to affect TF inorganic nitrogen fluxes both qualitatively and quantitatively, acting as a source of NH4+-N and a sink of NO3−-N. Pollen appears to play a more complex role in nutrient cycling than previously thought.

Air pollution in large cities produces numerous diseases and even millions of deaths annually according to the World Health Organization. Pollen exposure is related to allergic diseases, which makes its prediction a valuable tool to assess the risk level to aeroallergens. However, airborne pollen concentrations are difficult to predict due to the inherent complexity of the relationships among both biotic and environmental variables. In this work, a stochastic approach based on supervised machine learning algorithms was performed to forecast the daily Olea pollen concentrations in the Community of Madrid, central Spain, from 1993 to 2018. Firstly, individual Light Gradient Boosting Machine (LightGBM) and artificial neural network (ANN) models were applied to predict the day of the year (DOY) when the peak of the pollen season occurs, resulting the estimated average peak date 149.1 ± 9.3 and 150.1 ± 10.8 DOY for LightGBM and ANN, respectively, close to the observed value (148.8 ± 9.8). Secondly, the daily pollen concentrations during the entire pollen season have been calculated using an ensemble of two-step GAM followed by LightGBM and ANN. The results of the prediction of daily pollen concentrations showed a coefficient of determination (r2) above 0.75 (goodness of the model following cross-validation). The predictors included in the ensemble models were meteorological variables, phenological metrics, specific site-characteristics, and preceding pollen concentrations. The models are state-of-the-art in machine learning and their potential has been shown to be used and deployed to understand and to predict the pollen risk levels during the main olive pollen season.

In November 2016, an unprecedented epidemic thunderstorm asthma event in Victoria, Australia, resulted in many thousands of people developing breathing difficulties in a very short period of time, including 10 deaths, and created extreme demand across the Victorian health services. To better prepare for future events, a pilot forecasting system for epidemic thunderstorm asthma (ETSA) risk has been developed for Victoria. The system uses a categorical risk-based approach, combining operational forecasting of gusty winds in severe thunderstorms with statistical forecasts of high ambient grass pollen concentrations, which together generate the risk of epidemic thunderstorm asthma. This pilot system provides the first routine daily epidemic thunderstorm asthma risk forecasting service in the world that covers a wide area, and integrates into the health, ambulance, and emergency management sector. Epidemic thunderstorm asthma events have historically occurred infrequently, and no event of similar magnitude has impacted the Victorian health system since. However, during the first three years of the pilot, 2017–19, two high asthma presentation events and four moderate asthma presentation events were identified from public hospital emergency department records. The ETSA risk forecasts showed skill in discriminating between days with and without health impacts. However, even with hindsight of the actual weather and airborne grass pollen conditions, some high asthma presentation events occurred in districts that were assessed as low risk for ETSA, reflecting the challenge of predicting this unusual phenomenon.

Lidar observations during the pollen season 2019 at the European Aerosol Research Lidar Network (EARLINET) station in Kuopio, Finland, were analyzed in order to optically characterize atmospheric pollen. Pollen concentration and type information were obtained by a Hirst-type volumetric air sampler. Previous studies showed the detectability of non-spherical pollen using depolarization ratio measurements. We present lidar depolarization ratio measurements at three wavelengths of atmospheric pollen in ambient conditions. In addition to the depolarization ratio detected with the multiwavelength Raman polarization lidar PollyXT at 355 and 532 nm, depolarization measurements of a co-located Halo Doppler lidar at 1565 nm were utilized. During a 4 d period of high birch (Betula) and spruce (Picea abies) pollen concentrations, unusually high depolarization ratios were observed within the boundary layer. Detected layers were investigated regarding the share of spruce pollen to the total pollen number concentration. Daily mean linear particle depolarization ratios of the pollen layers on the day with the highest spruce pollen share are 0.10 ± 0.02, 0.38 ± 0.23 and 0.29 ± 0.10 at 355, 532 and 1565 nm, respectively, whereas on days with lower spruce pollen share, depolarization ratios are lower with less wavelength dependence. This spectral dependence of the depolarization ratios could be indicative of big, non-spherical spruce pollen. The depolarization ratio of pollen particles was investigated by applying a newly developed method and assuming a backscatter-related Ångström exponent of zero. Depolarization ratios of 0.44 and 0.16 at 532 and 355 nm for the birch and spruce pollen mixture were determined.

Large primary bioparticles such as pollen can be abundant in the atmosphere; for example near-surface pollen concentrations above 10 000 particles per cubic metre can occur during intense pollination periods. On one hand, due to their large size (10–100 µm), pollen can act as giant cloud condensation nuclei and enhance the collision–coalescence process in clouds that leads to drizzle formation. On the other hand, in humid conditions pollen is known to rupture and release many fine particles that can increase the cloud stability by reducing the droplet size. Additionally, both whole pollen grains and the sub-pollen particles released by pollen rupture are known to act as ice-nucleating particles (INPs). Due to these complex interactions, the role of pollen in modulating the cloud cover and precipitation remains uncertain. We used the UCLALES-SALSA (UCLA Large-Eddy Simulation Code and Sectional Aerosol module for Large-Scale Applications) large eddy simulator for simulating birch pollen effects on liquid- and mixed-phase clouds. Our simulations show that the pollen concentrations observed during the most intense pollination seasons can locally enhance precipitation from both liquid- and mixed-phase clouds, while more commonly encountered pollen concentrations are unlikely to cause a noticeable change. The liquid precipitation enhancement depended linearly on the emitted pollen flux in both liquid- and mixed-phase clouds; however, the slope of this relationship was case-dependent. Ice nucleation happened at a relevant degree only if the process of rupturing pollen producing large number of fine ice-nucleating particles was included in the simulations. The resulting precipitation saturated for the highest INP concentrations. Secondary ice formation by rime splintering had only a minor effect in the considered 1 d timescale.