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Atmosphere
Special Issues
GIS Applications for Airborne Pollen Monitoring and Prediction
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GIS Applications for Airborne Pollen Monitoring and Prediction

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biometeorology".

Deadline for manuscript submissions: closed (15 November 2019) | Viewed by 13663

Special Issue Editors

Dr. Jose Antonio Oteros Moreno

E-Mail Website
Guest Editor
Botany, Ecology and Vegetal physiology department, University of Córdoba, 14071 Córdoba, Spain
Interests: aerobiology; botany; pollen; GIS; phenological models; R programming
Special Issues, Collections and Topics in MDPI journals
Dr. José María Maya-Manzano

E-Mail Website
Guest Editor
School of Chemical and Pharmaceutical Sciences, Technological University Dublin, Dublin 2, Ireland
Interests: fungi spores; land uses; airborne pollen; climate change; phenology; sampling methods; GIS; R
Dr. Jesús Rojo

E-Mail Website
Guest Editor
Institute of Environmental Sciences, University of Castilla-La Mancha, E-45071 Toledo, Spain
Interests: allergy; grass; aerobiology; pollen sources; phenology; botany; GIS; R

Special Issue Information

Dear Colleagues,

Pollen is naturally emitted, but it is also considered as an anthropogenic pollutant. This kind of particles is very relevant in many fields, such as public health, urban planning, crop sciences or climate change monitoring. However, pollen monitoring is a difficult and time-consuming task and, therefore, very often does not sufficiently cover relevant geographical areas. Thus, modeling of pollen concentrations for unmonitored areas is necessary.

This call is for works using or developing GIS methods for understanding geographical distribution of pollen, identifying potential pollen sources but also helping to perform forecasting of pollen concentrations in space and time.

Dr. José Oteros
Dr. José María Maya-Manzano
Dr. Jesús Rojo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GIS
  • pollen
  • modeling
  • aerobiology
  • forecasting

Published Papers (3 papers)

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Research

17 pages, 1881 KiB  
Article
Impact of Climate Change on Olive Crop Production in Italy
by Fabio Orlandi, Jesús Rojo, Antonio Picornell, Jose Oteros, Rosa Pérez-Badia and Marco Fornaciari
Atmosphere 2020, 11(6), 595; https://doi.org/10.3390/atmos11060595 - 4 Jun 2020
Cited by 30 | Viewed by 6508
Abstract
The effects of climate change on agricultural systems raise important uncertainties about the future productivity and suitability of crops, especially in areas suffering from intense environmental changes. Olive groves occupy Mediterranean areas characterized by seasonal temporary droughts, which cause this cultivation to be [...] Read more.
The effects of climate change on agricultural systems raise important uncertainties about the future productivity and suitability of crops, especially in areas suffering from intense environmental changes. Olive groves occupy Mediterranean areas characterized by seasonal temporary droughts, which cause this cultivation to be highly dependent on local microclimatic conditions. Olive crop production can be reliably estimated using pollen intensity metrics together with post-pollination environmental conditions. In this study, we applied this kind of statistics-based models to identify the most relevant meteorological variables during the post-pollination periods for olive fruit production. Olive pollen time-series for the period of 1999–2012 was analyzed in 16 Italian provinces. Minimum and maximum temperature during spring and summer (March–August) showed a negative relationship with olive production, while precipitation always showed a positive correlation. The increase in aridity conditions observed in areas of Italy during the summer represents an important risk of decreasing olive crop production. The effect of climate change on the olive production trend is not clear because of the interactions between human and environmental factors, although some areas might show an increase in productivity in the near future under different climate change scenarios. However, as more drastic changes in temperature or precipitation take place, the risk to olive production will be considerably greater. Full article
(This article belongs to the Special Issue GIS Applications for Airborne Pollen Monitoring and Prediction)
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14 pages, 2201 KiB  
Article
Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany
by Jesús Rojo, Jose Oteros, Antonio Picornell, Franziska Ruëff, Barbora Werchan, Matthias Werchan, Karl-Christian Bergmann, Carsten B. Schmidt-Weber and Jeroen Buters
Atmosphere 2020, 11(2), 145; https://doi.org/10.3390/atmos11020145 - 29 Jan 2020
Cited by 27 | Viewed by 3803
Abstract
Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other [...] Read more.
Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other one at rooftop (35 m AGL), 4.2 km apart. In general, 1.4 ± 0.5 times higher pollen amounts were measured by the trap located at ground level, but this effect was less than expected considering the height difference between the traps. Pollen from woody trees such as Alnus, Betula, Corylus, Fraxinus, Picea, Pinus and Quercus showed a good agreement between the traps in terms of timing and intensity. Similar amounts of pollen were recorded in the two traps when pollen sources were more abundant outside of the city. In contrast, pollen concentrations from Cupressaceae/Taxaceae, Carpinus and Tilia were influenced by nearby pollen sources. The representativeness of both traps for herbaceous pollen depended on the dispersal capacity of the pollen grains, and in the case of Poaceae pollen, nearby pollen sources may influence the pollen content in the air. The timing of the pollen season was similar for both sites; however, the season for some pollen types ended later at ground level probably due to resuspension processes that would favor recirculation of pollen closer to ground level. We believe measurements from the higher station provides a picture of background pollen levels representative of a large area, to which local sources add additional and more variable pollen amounts. Full article
(This article belongs to the Special Issue GIS Applications for Airborne Pollen Monitoring and Prediction)
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10 pages, 423 KiB  
Article
Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks
by Ricardo Navares and José Luis Aznarte
Atmosphere 2019, 10(11), 717; https://doi.org/10.3390/atmos10110717 - 16 Nov 2019
Cited by 5 | Viewed by 2274
Abstract
Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption [...] Read more.
Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper. Full article
(This article belongs to the Special Issue GIS Applications for Airborne Pollen Monitoring and Prediction)
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