Background Ongoing climate change might, through rising temperatures, alter allergenic pollen biology across the northern hemisphere. We aimed to analyse trends in pollen seasonality and pollen load and to establish whether there are specific climate-related links to any observed changes.Methods For this retrospective data analysis, we did an extensive search for global datasets with 20 years or more of airborne pollen data that consistently recorded pollen season indices (eg, duration and intensity). 17 locations across three continents with long-term (approximately 26 years on average) quantitative records of seasonal concentrations of multiple pollen (aeroallergen) taxa met the selection criteria. These datasets were analysed in the context of recent annual changes in maximum temperature (T max ) and minimum temperature (T min ) associated with anthropogenic climate change. Seasonal regressions (slopes) of variation in pollen load and pollen season duration over time were compared to T max , cumulative degree day T max , T min , cumulative degree day T min , and frost-free days among all 17 locations to ascertain significant correlations.Findings 12 (71%) of the 17 locations showed significant increases in seasonal cumulative pollen or annual pollen load. Similarly, 11 (65%) of the 17 locations showed a significant increase in pollen season duration over time, increasing, on average, 0•9 days per year. Across the northern hemisphere locations analysed, annual cumulative increases in T max over time were significantly associated with percentage increases in seasonal pollen load (r=0•52, p=0•034) as were annual cumulative increases in T min (r=0•61, p=0•010). Similar results were observed for pollen season duration, but only for cumulative degree days (higher than the freezing point [0°C or 32°F]) for T max (r=0•53, p=0•030) and T min (r=0•48, p=0•05). Additionally, temporal increases in frost-free days per year were significantly correlated with increases in both pollen load (r=0•62, p=0•008) and pollen season duration (r=0•68, p=0•003) when averaged for all 17 locations.Interpretation Our findings reveal that the ongoing increase in temperature extremes (T min and T max ) might already be contributing to extended seasonal duration and increased pollen load for multiple aeroallergenic pollen taxa in diverse locations across the northern hemisphere. This study, done across multiple continents, highlights an important link between ongoing global warming and public health-one that could be exacerbated as temperatures continue to increase.

; Šikoparija, Branko; Weryszko-Chmielewska, Elżbieta; Bullock, James M. 2016. Modelling the introduction and spread of non-native species: international trade and climate change drive ragweed invasion. Global Change Biology, 22 (9). 3067-3079. 10.1111/gcb.13220 Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.13220 This article is protected by copyright. All rights reserved. Running-title:Integrating introduction and spread in the modelling of invasion AbstractBiological invasions are a major driver of global change, for which models can attribute causes, assess impacts and guide management. However, invasion models typically focus on spread from known introduction points or non-native distributions and ignore the transport processes by which species

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About 30% of the Hungarian population has some type of allergy, 65% of them have pollen sensitivity, and at least 60% of this pollen sensitivity is caused by ragweed. The short (or common) ragweed (Ambrosia artemisiifolia = Ambrosia elatior) has the most aggressive pollen of all. Clinical investigations prove that its allergenic pollen is the main reason for the most massive, most serious and most long-lasting pollinosis. The air in the Carpathian Basin is the most polluted with ragweed pollen in Europe. The aim of the study is to analyse how ragweed pollen concentration is influenced by meteorological elements in a medium-sized city, Szeged, Southern Hungary. The data basis consists of daily ragweed pollen counts and averages of 11 meteorological parameters for the 5-year daily data set, between 1997 and 2001. The study considers some of the ragweed pollen characteristics for Szeged. Application of the Makra test indicates the same period for the highest pollen concentration as that established by the main pollination period. After performing factor analysis for the daily ragweed pollen counts and the 11 meteorological variables examined, four factors were retained that explain 84.4% of the total variance of the original 12 variables. Assessment of the daily pollen number was performed by multiple regression analysis and results based on deseasonalised and original data were compared.

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.

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[1] The long-range transport of particulates can substantially contribute to local air pollution. The importance of airborne pollen has grown due to the recent climate change; the lengthening of the pollen season and rising mean airborne pollen concentrations have increased health risks. Our aim is to identify atmospheric circulation pathways influencing pollen levels in three European cities, namely Thessaloniki, Szeged, and Hamburg. Trajectories were computed using the HYSPLIT model. The 4 day, 6 hourly three-dimensional (3-D) backward trajectories arriving at these locations at 1200 UT are produced for each day over a 5 year period. A k-means clustering algorithm using the Mahalanobis metric was applied in order to develop trajectory types. The delimitation of the clusters performed by the 3-D function "convhull" is a novel approach. The results of the cluster analysis reveal that the main pathways for Thessaloniki contributing substantially to the high mean Urticaceae pollen levels cover western Europe and the Mediterranean. The key pathway patterns for Ambrosia for Szeged are associated with backward trajectories coming from northwestern Europe, northeastern Europe, and northern Europe. A major pollen source identified is a cluster over central Europe, namely the Carpathian basin with peak values in Hungary. The principal patterns for Poaceae for Hamburg include western Europe and the mid-Atlantic region. Locations of the source areas coincide with the main habitat regions of the species in question. Critical daily pollen number exceedances conditioned on the clusters were also evaluated using two statistical indices. An attempt was made to separate medium-and long-range airborne pollen transport.

• For Szeged, MLP and tree-based models, while for Lyon only MLP performs well for predicting pollen concentration.• When predicting alarm levels, the performance of MLP is the best for both cities.• When forecasting high pollen episodes, the more complex CI methods prove better for both cities.• The selection of the optimal method depends on climate, as a function of geographical location and relief. a b s t r a c t a r t i c l e i n f o Forecasting ragweed pollen concentration is a useful tool for sensitive people in order to prepare in time for high pollen episodes. The aim of the study is to use methods of Computational Intelligence (CI) (Multi-Layer Perceptron, M5P, REPTree, DecisionStump and MLPRegressor) for predicting daily values of Ambrosia pollen concentrations and alarm levels for 1-7 days ahead for Szeged (Hungary) and Lyon (France), respectively. Ten-year daily mean ragweed pollen data (within 1997-2006) are considered for both cities. 10 input variables are used in the models including pollen level or alarm level on the given day, furthermore the serial number of the given day of the year within the pollen season and altogether 8 meteorological variables. The study has novelties as (1) daily alarm thresholds are firstly predicted in the aerobiological literature; (2) data-driven modelling methods including neural networks have never been used in forecasting daily Ambrosia pollen concentration; (3) algorithm J48 has never been used in palynological forecasts; (4) we apply a rarely used technique, namely factor analysis with special transformation, to detect the importance of the influencing variables in defining the pollen levels for 1-7 days ahead. When predicting pollen concentrations, for Szeged Multi-Layer Perceptron models deliver similar results with tree-based models 1 and 2 days ahead; while for Lyon only Multi-Layer Perceptron provides acceptable result. When predicting alarm levels, the performance of Multi-Layer Perceptron is the best for both cities. It is presented that the selection of the optimal method depends on climate, as a function of geographical location and relief. The results show that the more complex CI methods perform well, and their performance is case-specific for ≥2 days forecasting horizon. A determination coefficient of 0.98 (Ambrosia, Szeged, one day and two days ahead) using Multi-Layer Perceptron ranks this model the best one in the literature.