Medium- and long-range transport events of Alnus pollen in western Mediterranean.

Alnus pollen has been frequently detected in the atmosphere of different airborne sampling sites of Southern Spain. However, Alnus sp. populations are very scarce and fragmented in the area, being restricted to a few river valleys in the southwest, and other further away regions of the Iberian Peninsula. This leads to think that the airborne pollen detected could be mainly the result of a medium- or long-distant transport. So, the aim of this study was to characterize the annual patterns of airborne Alnus pollen detected at three different locations of Malaga province, as well as to determine its possible origin, the pollen dispersion potential of these Alnus isolated populations, and their possible reproductive connectivity. Pollen sampling was conducted by means of three Hirst-type volumetric pollen traps. Samples were mounted and counted following the recommendations of the Spanish Aerobiology Network and the European Aeroallergen Society. The possible pollen sources were detected by means of a combination of meteorological information and backward air trajectories analysis. A high inter-annual variability in the annual pollen integrals was found in all the stations, favouring certain meteorological conditions a long-range transport and, therefore, causing the high concentrations detected in some specific days. Alnus pollen seems to have a heterogeneous origin with prevalence of the long-distant transport, which would suggest a possible reproductive connection among distant populations.

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public health. The current forecast models are generally useful, but they falter when long time series of data are managed. The emergence of new computational techniques such as the LSTM algorithms could constitute a significant improvement for the pollen risk assessment. In this study, several LSTM variants were applied to forecast monthly pollen integrals in Málaga (southern Spain) using meteorological variables as predictors. Olea and Urticaceae pollen types were modelled as proxies of different annual pollen curves, using data from the period 1992-2022. The aims of this study were to determine the LSTM variants with the highest accuracy when forecasting monthly pollen integrals as well as to compare their performance with the traditional pollen forecast methods. The results showed that the CNN-LSTM were the most accurate when forecasting the monthly pollen integrals for both pollen types. Moreover, the traditional forecast methods were outperformed by all the LSTM variants. These findings highlight the importance of implementing LSTM models in pollen forecasting for public health and research applications.

Environmental drivers of the seasonal exposure to airborne Alternaria spores in Spain.

Alternaria conidia have high allergenic potential and they can trigger important respiratory diseases. Due to that and to their extensive detection period, airborne Alternaria spores are considered as a relevant airborne allergenic particle. Several studies have been developed in order to predict the human exposure to this aeroallergen and to prevent their negative effects on sensitive population. These studies revealed that some sampling locations usually have just one single Alternaria spore season while other locations generally have two seasons within the same year. However, the reasons of these two different seasonal patterns remain unclear. To understand them better, the present study was carried out in order to determine if there are any weather conditions that influence these different behaviours at different sampling locations. With this purpose, the airborne Alternaria spore concentrations of 18 sampling locations in a wide range of latitudinal, altitudinal and climate ranges of Spain were studied. The aerobiological samples were obtained by means of Hirst-Type volumetric pollen traps, and the seasonality of the airborne Alternaria spores were analysed. The optimal weather conditions for spore production were studied, and the main weather factor affecting Alternaria spore seasonality were analysed by means of random forests and regression trees. The results showed that the temperature was the most relevant variable for the Alternaria spore dispersion and it influenced both the spore integrals and their seasonality. The water availability was also a very significant variable. Warmer sampling locations generally have a longer period of Alternaria spore detection. However, the spore production declines during the summer when the temperatures are extremely warm, what splits the favourable period for Alternaria spore production and dispersion into two separate ones, detected as two Alternaria spore seasons within the same year.