Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment.

Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011-2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Q1: 0%-25%), in-season (Q2 and Q3: 25%-75%) and postseason (Q4: 75%-100%). Days with pollen concentrations above 100 grains/m3 were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in cluster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves.

Urban aerobiological risk mapping of ornamental trees using a new index based on LiDAR and Kriging: A case study of plane trees.

Ornamental trees bring benefits for human health, including reducing urban pollution. However, some species, such as plane trees (Platanus sp.), produce allergenic pollen. Consequently, urban maps are a valuable tool for allergic patients and allergists, but they often fail to include variables that contribute to the "building downwash effect", such as the width and shape of streets and the height of buildings. Other factors that directly influence pollen dispersion (slopes and other geographical features) also have not traditionally been discussed. The LiDAR (Laser Imaging Detection and Ranging) technique enables one to consider these variables with high accuracy. This work proposes an Aerobiological Index to create Risk maps for Ornamental Trees (AIROT) and the establishment of potential areas of risk of exposure to Platanus pollen. LiDAR data from five urban areas were used to create the DEM and DSM (Digital Elevation and Surface Models) needed to perform further analysis. GIS software was used to map the points for each city and to create risk maps by Kriging, with stable (3 cases) and exponential function (2 cases) as the optimal models. In short, the AIROT index was a useful tool to map possible biological risks in cities. Since AIROT allows each city to consider its own characteristics, including geographical specifications, by using remote sensing and geostatistics techniques, the establishment of risk maps and healthy itineraries is valuable for allergic patients, allergists, architects and urban planners. This new aerobiological index provides a new decision-making tool related to urban planning and allergenicity assessment.

Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region.

Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI) related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus) daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx) of Spain and the Fifth National Forest Inventory (IFN5) from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN) was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77) between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.