Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations

• Published in: The Science of the total environment Vol. 688; pp. 1263 - 1274
• Main Authors: Oteros, Jose, Sofiev, Mikhail, Smith, Matt, Clot, Bernard, Damialis, Athanasios, Prank, Marje, Werchan, Matthias, Wachter, Reinhard, Weber, Alisa, Kutzora, Susanne, Heinze, Stefanie, Herr, Caroline E.W., Menzel, Annette, Bergmann, Karl-Christian, Traidl-Hoffmann, Claudia, Schmidt-Weber, Carsten B., Buters, Jeroen T.M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20-10-2019
• Subjects: Aerobiology; Air Pollutants - analysis; Air Pollution; Air quality; Allergens - analysis; Automatic pollen monitoring; BAA500; Biomonitoring network; Environmental Monitoring; Germany; Pollen; Germany; BAA500; Aerobiology; Air quality; Biomonitoring network; Pollen; Automatic pollen monitoring
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.06.131

Airborne pollen is a recognized biological indicator and its monitoring has multiple uses such as providing a tool for allergy diagnosis and prevention. There is a knowledge gap related to the distribution of pollen traps needed to achieve representative biomonitoring in a region. The aim of this manuscript is to suggest a method for setting up a pollen network (monitoring method, monitoring conditions, number and location of samplers etc.). As a case study, we describe the distribution of pollen across Bavaria and the design of the Bavarian pollen monitoring network (ePIN), the first operational automatic pollen network worldwide.
We established and ran a dense pollen monitoring network of 27 manual Hirst-type pollen traps across Bavaria, Germany, during 2015. Hierarchical cluster analysis of the data was then performed to select the locations for the sites of the final pollen monitoring network. According to our method, Bavaria can be clustered into three large pollen regions with eight zones. Within each zone, pollen diversity and distribution among different locations does not vary significantly. Based on the pollen zones, we opted to place one automatic monitoring station per zone resulting in the ePIN network, serving 13 million inhabitants. The described method defines stations representative for a homogeneous aeropalynologically region, which reduces redundancy within the network and subsequent costs (in the study case from 27 to 8 locations). Following this method, resources in pollen monitoring networks can be optimized and allergic citizens can then be informed in a timely and effective way, even in larger geographical areas.
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•The first automatic pollen monitoring network in the world was built in Bavaria, based on the pollen robot BAA500.•Collapsing a dense network by clustering analysis determined the number and position of monitoring stations.•Bavaria (Germany) can be clustered in 3 pollen zones and 8 sub-zones, based on airborne pollen concentrations.•In the studied network, the most abundant pollen types in Bavaria are: Pinus, Betula, Urticaceae and Poaceae.•In the studied network, the main pollination period in Bavaria ranges from February (Alnus) to October (Ambrosia).