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.

Assessment of environmental risk areas based on airborne pollen patterns as a response to land use and land cover distribution.

Allergic respiratory diseases are considered to be among the most important public health concerns, and pollen is the main cause of allergic respiratory diseases worldwide. However, the biological component of air quality is largely underestimated, and there is an important gap in the legislation in this area. The aims of this study were to characterise the occurrence and incidence of pollen exposure in relation to potential pollen sources and to delineate the main areas of aerobiological risk in the Madrid Autonomous Region based on homogeneous patterns of pollen exposure.

Predicting the Olea pollen concentration with a machine learning algorithm ensemble.

Air pollution in large cities produces numerous diseases and even millions of deaths annually according to the World Health Organization. Pollen exposure is related to allergic diseases, which makes its prediction a valuable tool to assess the risk level to aeroallergens. However, airborne pollen concentrations are difficult to predict due to the inherent complexity of the relationships among both biotic and environmental variables.

Near-ground effect of height on pollen exposure.

The effect of height on pollen concentration is not well documented and little is known about the near-ground vertical profile of airborne pollen. This is important as most measuring stations are on roofs, but patient exposure is at ground level. Our study used a big data approach to estimate the near-ground vertical profile of pollen concentrations based on a global study of paired stations located at different heights.