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

Identifying key environmental factors to model Alt a 1 airborne allergen presence and variation

Fungal spores, commonly found in the atmosphere, can trigger important respiratory disorders. The glycoprotein Alt a 1 is the major allergen present in conidia of the genus Alternaria and has a high clinical relevance for people sensitized to fungi. Exposure to this allergen has been traditionally assessed by aerobiological spore counts, although this does not always offer an accurate estimate of airborne allergen load. This study aims to pinpoint the key factors that explain the presence and variation of Alt a 1 concentration in the atmosphere in order to establish exposure risk periods and improve forecasting models. Alternaria spores were sampled using a Hirst-type volumetric sampler over a five-year period. The allergenic fraction from the bioaerosol was collected using a low-volume cyclone sampler and Alt a 1 quantified by Enzyme-Linked ImmunoSorbent Assay. A cluster analysis was executed in order to group days with similar environmental features and then analyze days with the presence of the allergen in each of them. Subsequently, a quadratic discriminant analysis was performed to evaluate if the selected variables can predict days with high Alt a 1 load. The results indicate that higher temperatures and absolute humidity favor the presence of Alt a 1 in the atmosphere, while time of precipitation is related to days without allergen. Moreover, using the selected parameters, the quadratic discriminant analysis to predict days with allergen showed an accuracy rate between 67 % and 85 %. The mismatch between daily airborne concentration of Alternaria spores and allergen load can be explained by the greater contribution of medium-to-long distance transport of the allergen from the major emission sources as compared with spores. Results highlight the importance of conducting aeroallergen quantification studies together with spore counts to improve the forecasting models of allergy risk, especially for fungal spores.

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.

Influence of synoptic meteorology on airborne allergenic pollen and spores in an urban environment in Northeastern Iberian Peninsula

The influence of the most frequent patterns of synoptic circulation on the dynamics of airborne pollen/spores recorded at the Barcelona Aerobiological Station (BCN) was analysed. Six pollen types (Platanus, Cupressaceae, Olea, Poaceae, Urticaceae and Amaranthaceae), and one fungal spore (Alternaria) were selected for their high allergenic effect in sensitive people. Six synoptic meteorological patterns were identified through cluster analysis of sea level pressure fields as the main responsible of the weather conditions in the Iberian Peninsula. The local meteorological conditions in Barcelona associated with each one of the synoptic types were also stablished. Different statistical methods were applied to analyse possible relationships between concentrations and timing of the recorded aerobiological particles and specific synoptic types. The study, focused in the 19-year period 2001-2019, shows that one of the scenarios, frequent in winter and linked to high stability and air-mass blockage, registered the highest mean and median values for Platanus and Cupressaceae, but it was not very relevant for the other taxa. It was also this scenario that turned out to be the most influent on the pollination timing showing a significant influence on the start occurrence of Urticaceae flowering and on the peak date of Platanus. On the other hand, the most frequent synoptic type in the period, relevant in spring and summer, was linked to sporadic episodes of levels considered to be of high risk of allergy to Platanus, Poaceae, and Urticaceae pollen, and Alternaria fungal spore. This synoptic pattern, characterized by the presence of the Azores anticyclone and the Atlantic low located in the north of the United Kingdom, was associated with high temperatures, low relative humidity and moderate winds from the NW in Barcelona. The identification of an interaction between synoptic meteorology and pollen/spore dynamics will allow better abatement measures, reducing adverse health effects on sensitive population.

Effect of prevailing winds and land use on Alternaria airborne spore load

Alternaria spores are a common component of the bioaerosol. Many Alternaria species are plant pathogens, and their conidia are catalogued as important aeroallergens. Several aerobiological studies showing a strong relationship between concentrations of airborne spore and meteorological parameters have consequently been developed. However, the Alternaria airborne load variation has not been thoroughly investigated because it is difficult to assess their sources, as they are a very common and widely established phytopathogen. The objective of this study is to estimate the impact of vegetation and land uses as potential sources on airborne spore load and to know their influence, particularly, in cases of long-medium distance transport. The daily airborne spore concentration was studied over a 5-year period in León and Valladolid, two localities of Castilla y León (Spain), with differences in their bioclimatic and land use aspects. Moreover, the land use analysis carried out within a 30 km radius of each monitoring station was combined with air mass data in order to search for potential emission sources. The results showed a great spatial variation between the two areas, which are relatively close to each other. The fact that the spore concentrations recorded in Valladolid were higher than those in León was owing to prevailing winds originating from large areas covered by cereal crops, especially during the harvest period. However, the prevailing winds in León came from areas dominated by forest and shrubland, which explains the low airborne spore load, since the main Alternaria sources were the grasslands located next to the trap. Furthermore, the risk days in this location presented an unusual wind direction. This study reveals the importance of land cover and wind speed and direction data for establishing potential airborne routes of spore transport in order to improve the Alternaria forecasting models. The importance of conducting Alternaria aerobiological studies at a local level is also highlighted.

Alternaria spore exposure in Bavaria, Germany, measured using artificial intelligence algorithms in a network of BAA500 automatic pollen monitors

Although Alternaria spores are well-known allergenic fungal spores, automatic bioaerosol recognition systems have not been trained to recognize these particles until now. Here we report the development of a new algorithm able to classify Alternaria spores with BAA500 automatic bioaerosol monitors. The best validation score was obtained when the model was trained on both data from the original dataset and artificially generated images, with a validation unweighted mean Intersection over Union (IoU), also called Jaccard Index, of 0.95. Data augmentation techniques were applied to the training set. While some particles were not recognized (false negatives), false positives were few. The results correlated well with manual counts (mean of four Hirst-type traps), with R² = 0.78. Counts from BAA500 were 1.92 times lower than with Hirst-type traps. The algorithm was then used to re-analyze the historical automatic pollen monitoring network (ePIN) dataset (2018-2022), which lacked Alternaria spore counts. Re-analysis of past data showed that Alternaria spore exposure in Bavaria was very variable, with the highest counts in the North (Marktheidenfeld, 154 m a.s.l.), and the lowest values close to the mountains in the South (Garmisch-Partenkirchen, 735 m a.s.l.). This approach shows that in our network future algorithms can be run on past datasets. Over time, the use of different algorithms could lead to misinterpretations as stemming from climate change or other phenological causes. Our approach enables consistent, homogeneous treatment of long-term series, thus preventing variability in particle counts owing to changes in the algorithms.