Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps

Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters

Conventional techniques for monitoring pollen currently have significant limitations in terms of labour, cost and the spatiotemporal resolution that can be achieved. Pollen monitoring networks across the world are generally sparse and are not able to fully represent the detailed characteristics of airborne pollen. There are few studies that observe concentrations on a local scale, and even fewer that do so in ecologically rich rural areas and close to emitting sources. Better understanding of these would be relevant to occupational risk assessments for public health, as well as ecology, biodiversity, and climate. We present a study using low-cost optical particle counters (OPCs) and the application of machine learning models to monitor particulate matter and pollen within a mature oak forest in the UK. We characterise the observed oak pollen concentrations, first during an OPC colocation period (6 days) for calibration purposes, then for a period (36 days) when the OPCs were distributed on an observational tower at different heights through the canopy. We assess the efficacy and usefulness of this method and discuss directions for future development, including the requirements for training data. The results show promise, with the derived pollen concentrations following the expected diurnal trends and interactions with meteorological variables. Quercus pollen concentrations appeared greatest when measured at the canopy height of the forest (20-30 m). Quercus pollen concentrations were lowest at the greatest measurement height that is above the canopy (40 m), which is congruent with previous studies of background pollen in urban environments. The attenuation of pollen concentrations as sources are depleted is also observed across the season and at different heights, with some evidence that the pollen concentrations persist later at the lowest level beneath the canopy (10 m) where catkins mature latest in the season compared to higher catkins.

Comparison of Olea pollen sampling between gravimetric and volumetric traps (NW of Morocco)

Olea pollen is considered one of the most critical causes of respiratory allergic disease in the Mediterranean region, contributing to different symptoms in allergic sufferers. Due to the high increase in the prevalence of olive pollen caused by the expansion of cultivated areas especially in the Mediterranean region, the aim of this study was to examine the Olea pollen sampling efficiency between gravimetric and volumetric pollen traps over the period 2018-2022. Our data demonstrated that Olea pollen season start-date was generally recorded earlier with volumetric trap than shown with gravimetric method. Moreover, the peak dates occurred mainly in May for the years studied. Across the entire period, the greatest pollen concentrations were reached in the week 11th and >60 % of the total Olea pollen were already reached after the ninth week of sampling in 2020. In general, the evolution of pollen sampling for both methods demonstrates similar temporal development. Variations in the seasonal pollen integral could be attributed to the elevation above ground level at which the traps were placed, exposure to regional winds, the influence of long-range transported pollen and the mode of pollen transport. The findings indicate that the gravimetric method was equally practical as the volumetric method in estimating airborne pollen.

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

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.

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. This study uses the historical aerobiological database of the Madrid Region Palynological Network (central Spain) from ten pollen stations from 1994 to 2022, and the land-use information from the Corine Land Cover. Multiple clustering approaches were followed to group the sampling stations and subsequently all the 1 × 1km pixels for the Madrid Autonomous Region. The clustering dendrogram for land-use distribution was compared to the dendrogram for historical airborne pollen data. The two dendrograms showed a good alignment with a very high correlation (0.95) and very low entanglement (0.15), which indicates a close correspondence between the distribution of the potential pollen sources and the airborne pollen dynamics. Based on this knowledge, the Madrid Autonomous Region was divided into six aerobiological risk areas following a clear anthropogenic gradient in terms of the potential pollen sources that determine pollen exposure in the Madrid Region. Spatial regionalisation is a common practice in environmental risk assessment to improve the application of management plans and optimise the air quality monitoring networks. The risk areas proposed by scientific criteria in the Madrid Autonomous Region can be adjusted to other operational criteria following a framework equivalent to other air quality networks.

A Review of Pollen Counting Networks: From the Nineteenth Century into the Twenty-first Century

PURPOSE OF REVIEW

Pollen is enormously important to the human experience. As pollen became germane to human health in the late nineteenth century, methods for pollen collection and measurement were developed. Techniques were standardized and pollen counting networks were established in many parts of the world during the middle to late part of the twentieth century. With some notable exceptions, the technology of the 1950s and 1960s is presently employed to create the current pollen counting networks. Pollen counting networks in the past faced substantial challenges. Pollen counting networks using the same technology as the past face the same challenges.

RECENT FINDINGS

As we move into the twenty-first century, automated pollen counting technology enables pollen counting networks to be robust, available, scalable, self-perpetuating, and able to meet modern demands. Automated pollen measurement networks present a promising path towards a more informed, data-driven, and effective approach to managing allergens, improving crop yields, and minimizing human suffering caused by pollen. By empowering individuals with comprehensive pollen data, a feat not possible with manual counting, we can help people make informed decisions and take proactive measures to minimize exposure to allergens and improve their well-being.

Machine learning methods for low-cost pollen monitoring - Model optimisation and interpretability

Pollen is a major issue globally, causing as much as 40 % of the population to suffer from hay fever and other allergic conditions. Current techniques for monitoring pollen are either laborious and slow, or expensive, thus alternative methods are needed to provide timely and more localised information on airborne pollen concentrations. We have demonstrated previously that low-cost Optical Particle Counter (OPC) sensors can be used to estimate pollen concentrations when machine learning methods are used to process the data and learn the relationships between OPC output data and conventionally measured pollen concentrations. This study demonstrates how methodical hyperparameter tuning can be employed to significantly improve model performance. We present the results of a range of models based on tuned hyperparameter configurations trained to predict Poaceae (Barnhart), Quercus (L.), Betula (L.), Pinus (L.) and total pollen concentrations. The results achieved here are a significant improvement on results we previously reported: the average R2 scores for the total pollen models have at least doubled compared to using previous parameter settings. Furthermore, we employ the explainable Artificial Intelligence (XAI) technique, SHAP, to interpret the models and understand how each of the input features (i.e. particle sizes) affect the estimated output concentration for each pollen type. In particular, we found that Quercus pollen has a strong positive correlation with particles of optical diameter 1.7-2.3 μm, which distinguishes it from other pollen types such as Poaceae and may suggest that type-specific subpollen particles are present in this size range. There is much further work to be done, especially in training and testing models on data obtained across different environments to evaluate the extent of generalisability. Nevertheless, this work demonstrates the potential this method can offer for low-cost monitoring of pollen and the valuable insight we can gain from what the model has learned.

Isolating the species element in grass pollen allergy: A review

Grass pollen is a leading cause of allergy in many countries, particularly Europe. Although many elements of grass pollen production and dispersal are quite well researched, gaps still remain around the grass species that are predominant in the air and which of those are most likely to trigger allergy. In this comprehensive review we isolate the species aspect in grass pollen allergy by exploring the interdisciplinary interdependencies between plant ecology, public health, aerobiology, reproductive phenology and molecular ecology. We further identify current research gaps and provide open ended questions and recommendations for future research in an effort to focus the research community to develop novel strategies to combat grass pollen allergy. We emphasise the role of separating temperate and subtropical grasses, identified through divergence in evolutionary history, climate adaptations and flowering times. However, allergen cross-reactivity and the degree of IgE connectivity in sufferers between the two groups remains an area of active research. The importance of future research to identify allergen homology through biomolecular similarity and the connection to species taxonomy and practical implications of this to allergenicity is further emphasised. We also discuss the relevance of eDNA and molecular ecological techniques (DNA metabarcoding, qPCR and ELISA) as important tools in quantifying the connection between the biosphere with the atmosphere. By gaining more understanding of the connection between species-specific atmospheric eDNA and flowering phenology we will further elucidate the importance of species in releasing grass pollen and allergens to the atmosphere and their individual role in grass pollen allergy.

Climate change, airborne allergens, and three translational mitigation approaches

One of the important adverse impacts of climate change on human health is increases in allergic respiratory diseases such as allergic rhinitis and asthma. This impact is via the effects of increases in atmospheric carbon dioxide concentration and air temperature on sources of airborne allergens such as pollen and fungal spores. This review describes these effects and then explores three translational mitigation approaches that may lead to improved health outcomes, with recent examples and developments highlighted. Impacts have already been observed on the seasonality, production and atmospheric concentration, allergenicity, and geographic distribution of airborne allergens, and these are projected to continue into the future. A technological revolution is underway that has the potential to advance patient management by better avoiding associated increased exposures, including automated real-time airborne allergen monitoring, airborne allergen forecasting and modelling, and smartphone apps for mitigating the health impacts of airborne allergens.