Importance of the quality management of aerobiological monitoring networks: The case study of Madrid Region in Spain

Interest in biological air quality monitoring is rising, and updated public information is increasingly demanded by stakeholders in the case of airborne pollen, which requires maintaining high standards of data quality. The number of aerobiological stations worldwide is continuously growing, and quality management is becoming more complex with the increase in the scale of aerobiological networks. Quality control exercises are crucial for maintaining the quality of the data used in the pollen monitoring routine over time. In this study we show the results of an intercomparison test among technicians in the Madrid Region Palynological Network in central Spain in order to identify potential sources of error during the pollen analysis. The findings of this intercomparison exercise indicated very high-quality pollen data based on two different proficiency tests: i) the technicians' analysis of common samples with the light microscopes used in the routine pollen analysis; and ii) the analysis of common samples with all technicians using the same light microscope. A few specific remarkable errors were detected (i.e., those whose Absolute Error > |10| and Relative Error > |20 %|), such as confusion by four participants between Quercus vs. Acer and Quercus vs. Platanus pollen. Since the pollen analysis routine requires very experienced professionals, we believe it is essential to provide adequate training programmes for pollen analysis and the specific identification of these problematic pollen types. High-quality environmental data is crucial for sharing with regional, national and continental networks to ensure applicability, reproducibility and integration in large-scale studies. [Display omitted] •High-quality environmental data is crucial for sharing between large-scale networks.•Proficiency of the staff of the Madrid Region Palynological Network has been assessed.•The proficiency of the personnel and the data quality in this study was very good.•A few isolated significant errors were detected (mistakes in tricolpate pollen).•Adequate training in pollen analysis is essential to ensure high quality standards.