Air Pollution Affecting Pollen Concentrations through Radiative Feedback in the Atmosphere

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022-2023, Using the Automatic Sensor Beenose

As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter-spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.

Pollen long-distance transport associated with symptoms in pollen allergics on the German Alps: An old story with a new ending?

Pollen grains are among the main causes of respiratory allergies worldwide and hence they are routinely monitored in urban environments. However, their sources can be located farther, outside cities' borders. So, the fundamental question remains as to how frequent longer-range pollen transport incidents are and if they may actually comprise high-risk allergy cases. The aim was to study the pollen exposure on a high-altitude location where only scarce vegetation exists, by biomonitoring airborne pollen and symptoms of grass pollen allergic individuals, locally. The research was carried out in 2016 in the alpine research station UFS, located at 2650 m height, on the Zugspitze Mountain in Bavaria, Germany. Airborne pollen was monitored by use of portable Hirst-type volumetric traps. As a case study, grass pollen-allergic human volunteers were registering their symptoms daily during the peak of the grass pollen season in 2016, during a 2-week stay on Zugspitze, 13-24 June. The possible origin of some pollen types was identified using back trajectory model HYSPLIT for 27 air mass backward trajectories up to 24 h. We found that episodes of high aeroallergen concentrations may occur even at such a high-altitude location. More than 1000 pollen grains m^-3 of air were measured on the UFS within only 4 days. It was confirmed that the locally detected bioaerosols originated from at least Switzerland, and up to northwest France, even eastern American Continent, because of frequent long-distance transport. Such far-transported pollen may explain the observed allergic symptoms in sensitized individuals at a remarkable rate of 87 % during the study period. Long-distance transport of aeroallergens can cause allergic symptoms in sensitized individuals, as evidenced in a sparse-vegetation, low-exposure, 'low-risk' alpine environment. We strongly suggest that we need cross-border pollen monitoring to investigate long-distance pollen transport, as its occurrence seems both frequent and clinically relevant.

Pollution of the Environment and Pollen: A Review

Bioindication of the environment is one of the actively developing directions of ecology. Information about pollutants and the level of environmental pollution can be obtained as a result of studying the biological reaction of plants to pollution. Ecological palynology is a new direction, when pollen of various woody and herbaceous species is used for bioindication of the level of environmental pollution and the presence of mutagens. The review considers the morphological variability of pollen, its fertility and viability under the influence of pollutants, the possibility of its use as a bioindicator of pollution of urban areas by emissions of vehicle transport and industry.

Towards an Automatic Pollen Detection System in Ambient Air Using Scattering Functions in the Visible Domain

Pollen grains strongly affect human health by inducing allergies. Although the monitoring of airborne pollens particles is of major importance, the current measurement methods are manually conducted and are expensive, limiting the number of monitoring stations. Thus, there is a need for relatively low-cost instruments that can work automatically. The possible detection of pollen in urban ambient air (Paris, France) has been reported using the LOAC optical aerosol counter. These measurements indicate that the pollen grains and their nature could be determined using their scattering properties. For this purpose, the scattering functions (intensity and linear polarization) of 21 different airborne pollens were established in the laboratory using a PROGRA2 instrument. The linear polarization curves were close together, with a maximum polarization lower than 10% in the red domain and 5% in the green domain. The variability from one sample to another was partly due to the different sizes of the grains. An instrument with an absolute accuracy of about ±1% for polarization measurements should then be needed, coupled with a counting instrument to take into account the effects of size. On the other hand, the scattering curves for intensity presented with different shapes and strong differences up to a factor of 20 at some scattering angles, due to the size, shape, surface texture, and composition of the grains. Thus, we propose a proof of concept for new automated sensors that can be used in dense networks to count and identify pollen grains by analyzing the light they scatter at some specific angles.