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BackgroundAmbient air quality monitoring is a governmental duty that is widely carried out in order to detect non-biological (“chemical”) components in ambient air, such as particles of < 10 µm (PM10, PM2.5), ozone, sulphur dioxide, and nitrogen oxides. These monitoring networks are publicly funded and air quality data are open to the public. The situation for biological particles that have detrimental effects on health, as is the case of pollen and fungal spores, is however very different. Most pollen and spore monitoring networks are not publicly funded and data are not freely available. The information regarding which biological particle is being monitored, where and by whom, is consequently often not known, even by aerobiologists themselves. This is a considerable problem, as local pollen data are an important tool for the prevention of allergic symptoms.ObjectiveThe aim of this study was to review pollen monitoring stations throughout the world and to create an interactive visualization of their distribution.MethodsThe method employed to collect information was based on: (a) a review of the recent and historical bibliography related to pollen and fungal spore monitoring, and (b) personal surveys of the managers of national and regional monitoring networks. The interactive application was developed using the R programming language.ResultsWe have created an inventory of the active pollen and spore monitoring stations in the world. There are at least 879 active pollen monitoring stations in the world, most of which are in Europe (> 500). The prevalent monitoring method is based on the Hirst principle (> 600 stations). The inventory is visualised as an interactive and on-line map. It can be searched, its appearance can be adjusted to the users’ needs and it is updated regularly, as new stations or changes to those that already exist can be submitted online.ConclusionsThe map shows the current situation of pollen and spore monitoring and facilitates collaboration among those individuals who are interested in pollen and spore counts. It might also help to improve the monitoring of biological particles up to the current level employed for non-biological components.

Mobile health (mHealth) uses mobile communication devices such as smartphones and tablet computers to support and improve health‐related services, data and information flow, patient self‐management, surveillance, and disease management from the moment of first diagnosis to an optimized treatment. The European Academy of Allergy and Clinical Immunology created a task force to assess the state of the art and future potential of mHealth in allergology. The task force endorsed the “Be He@lthy, Be Mobile” WHO initiative and debated the quality, usability, efficiency, advantages, limitations, and risks of mobile solutions for allergic diseases. The results are summarized in this position paper, analyzing also the regulatory background with regard to the “General Data Protection Regulation” and Medical Directives of the European Community. The task force assessed the design, user engagement, content, potential of inducing behavioral change, credibility/accountability, and privacy policies of mHealth products. The perspectives of healthcare professionals and allergic patients are discussed, underlining the need of thorough investigation for an effective design of mHealth technologies as auxiliary tools to improve quality of care. Within the context of precision medicine, these could facilitate the change in perspective from clinician‐ to patient‐centered care. The current and future potential of mHealth is then examined for specific areas of allergology, including allergic rhinitis, aerobiology, allergen immunotherapy, asthma, dermatological diseases, food allergies, anaphylaxis, insect venom, and drug allergy. The impact of mobile technologies and associated big data sets are outlined. Facts and recommendations for future mHealth initiatives within EAACI are listed.

Background: Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500. Methods: The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m³/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously. Results: Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98). Conclusions: The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting.

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Olive oil is a major economic resource of the Mediterranean region. Olive crop management can be improved by models that forecast the variable reproductive biology of olive tree. However, the processes controlling olive harvest are complex on large scales. Here, we study the parameters that influence olive fruit production for developing accurate forecasting models. Seventeen aerobiological sampling points have monitored olive pollen grains in Spain, Italy and Tunisia from 1993 to 2012. Six crop models have been developed at two provinces and country scales. The modelling has been done in two steps: (1) typification and (2) modelling by partial least square regression. Results show that higher pollen indexes and water availability during spring are related to an increase of final fruit production in all the studied area. Higher pollen indexes are also positively correlated with air temperature during early spring and autumn. Furthermore, a decrease of fruit production is related with increasing air temperature during winter and summer. To conclude, we have designed accurate models that allow accurate predictions of olive production.

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