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The study evaluated a new model of a Plair SA airflow cytometer, Rapid-E+, and assessed its suitability for airborne pollen monitoring within operational networks. Key features of the new model are compared with the previous one, Rapid-E. A machine learning algorithm is constructed and evaluated for (i) classification of reference pollen types in laboratory conditions and (ii) monitoring in real-life field campaigns. The second goal of the study was to evaluate the device usability in forthcoming monitoring networks, which would require similarity and reproducibility of the measurement signal across devices. We employed three devices and analysed (dis-)similarities of their measurements in laboratory conditions. The lab evaluation showed similar recognition performance to that of Rapid-E, but field measurements in conditions when several pollen types were present in the air simultaneously showed notably lower agreement of Rapid-E+ with manual Hirst-type observations than those of the older model. An exception was the total-pollen measurements. Comparison across the Rapid-E+ devices revealed noticeable differences in fluorescence measurements between the three devices tested. As a result, application of the recognition algorithm trained on the data from one device to another led to large errors. The study confirmed the potential of the fluorescence measurements for discrimination between different pollen classes, but each instrument needed to be trained individually to achieve acceptable skills. The large uncertainty of fluorescence measurements and their variability between different devices need to be addressed to improve the device usability.

Crop maps are highly desired information in modern agriculture as they enable possessors to manage their business in the most optimal way. Usually in remote sensing, crop mapping is performed using satellite images and within-season ground truth samples that are collected in extensive survey campaigns every year, neglecting information and knowledge that past seasons’ classification models provided. This paper assessed different temporal transferring approaches, including transfer learning, together with traditional crop mapping approach to provide an exhaustive comparison. Transferring approaches differed in portion of knowledge utilized from a historical model and that coming from a target season dataset. Three distinct algorithms, Random Forest, Convolutional Neural Network and Transformer, were employed and evaluated using highly dense time series of Sentinel-1 data. Source and target domain were respectively represented by two sets, 2017–2020 and 2021 season data, and 9 different crop types were classified. Results showcased that transferring a model has a great potential in crop mapping when little to no ground truth data is available for the target season. However, traditional approach catches up rather quickly and even surpasses transfer learning approach in terms of performance after a certain portion of target domain data is collected. Without target season ground truth data, model transferring can yield modest crop mapping performance of 78% for F1 score, between 84% and 86% F1 score with transfer learning employed in conjunction with limited target season ground truth (i.e. between 120 and 720 parcels), and 88% F1 score at best with traditional approach (ca. 720 parcels). Even though a good discriminatory is found between different crop types, there is still a room for improvement regarding the least represented classes in the dataset. The study significantly contributes to the area of agricultural monitoring and management by demonstrating the effectiveness of transfer learning while lessening the necessity for extensive and labor-intensive data collection, thereby fostering cost and time efficiency. Utilizing Sentinel-1 data, it provides a practical and efficient solution for agricultural analysis worldwide regardless of cloudiness.

Allergenic pollen affects the quality of life for over 30% of the European population. Since the treatment efficacy is highly related to the actual exposure to pollen, information about the type and number of airborne pollen grains in real-time is essential for reducing their impact. Therefore, the automation of pollen monitoring has become an important research topic. Our study is focused on the Rapid-E real-time bioaerosol detector. So far, vanilla convolutional neural networks (CNNs) are the only deep architectures evaluated for pollen classification on multi-modal Rapid-E data obtained by exposing collected pollen samples of known classes to the device in a controlled environment. This study contributes to the further development of pollen classification models on Rapid-E data by experimenting with more advanced concepts of CNNs, residual, and inception networks. Our experiments included a comprehensive comparison of different CNN architectures, and obtained results provided valuable insights into which convolutional blocks improve pollen classification. We propose a new model which, coupled with a specific training strategy, improves the current state-of-the-art by reducing its relative error rate by 9%.

Abstract Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induced fluorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fluorescence spectrum and fluorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish different pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the first results of applied explainable artificial intelligence (xAI) methodology on the pollen classification model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofluorometer measurements.

Cardiovascular diseases are the leading cause of death and the main cause of disability. In the last decade, homocysteine has been found to be a risk factor or a marker for cardiovascular diseases, including myocardial infarction (MI) and heart failure (HF). There are indications that vitamin B6 plays a significant role in the process of transsulfuration in homocysteine metabolism, specifically, in a part of the reaction in which homocysteine transfers a sulfhydryl group to serine to form α-ketobutyrate and cysteine. Therefore, an elevated homocysteine concentration (hyperhomocysteinemia) could be a consequence of vitamin B6 and/or folate deficiency. Hyperhomocysteinemia in turn could damage the endothelium and the blood vessel wall and induce worsening of atherosclerotic process, having a negative impact on the mechanisms underlying MI and HF, such as oxidative stress, inflammation, and altered function of gasotransmitters. Given the importance of the vitamin B6 in homocysteine metabolism, in this paper, we review its role in reducing oxidative stress and inflammation, influencing the functions of gasotransmitters, and improving vasodilatation and coronary flow in animal models of MI and HF.

To benefit allergy patients and the medical practitioners, pollen information should be available in both a reliable and timely manner; the latter is only recently possible due to automatic monitoring. To evaluate the performance of all currently available automatic instruments, an international intercomparison campaign was jointly organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich, Germany (March-July 2021). The automatic systems (hardware plus identification algorithms) were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. We report results for total pollen as well as for Betula, Fraxinus, Poaceae, and Quercus, for all instruments that provided these data. The results for daily averages compared better with Hirst observations than the 3-hourly values. For total pollen, there was a considerable spread among systems, with some reaching R 2 > 0.6 (3 h) and R 2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R 2 < 0.3). For individual pollen types, results similar to the Hirst were frequently shown by a small group of systems. For Betula, almost all systems performed well (R 2 > 0.75 for 9 systems for 3-hourly data). Results for Fraxinus and Quercus were not as good for most systems, while for Poaceae (with some exceptions), the performance was weakest. For all pollen types and for most measurement systems, false positive classifications were observed outside of the main pollen season. Different algorithms applied to the same device also showed different results, highlighting the importance of this aspect of the measurement system. Overall, given the 30 % error on daily concentrations that is currently accepted for Hirst-type traps, several automatic systems are currently capable of being used operationally to provide real-time observations at high temporal resolutions. They provide distinct advantages compared to the manual Hirst-type measurements., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jeroen Buters, Jose M. Maya Manzano, Carsten B. Schmidt-Weber and Marina Triviño report financial support, administrative support, equipment, drugs, or supplies, and travel were provided by Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit (LGL) and EUMETNET. Carsten Skjoth reports financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by COST Action CA18226 ADOPT – New approaches in detection of pathogens and aeroallergens. Bernard Clot and Fiona Tummon report financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by European Meteorological Society and the EUMETNET AutoPollen Programme. Branko Sikoparija and Predrag Matavulj report financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by BREATHE project from the Science Fund of the Republic of Serbia PROMIS program, under grant agreement no. 6039613 and by the Ministry of Education, Science and Technological Development of the Republic of Serbia (grant agreement number 451–03-68/2022–14/200358). Evgeny Kadantsev and Julia Palamarchuk report financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by Academy of Finland PS4A (grant 318,194). Mikhail Sofiev reports financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by Academy of Finland project ALL-Impress (grant 329,215). Mikhail Sofiev reports financial support, administrative support, article publishing charges, equipment, drugs, or supplies, and travel were provided by European Social Fund (project no. 09.3.3-LMT-K-712-01-0066) and Research Council of Lithuania (LMTLT). Nathan Allan, Landon Bunderson, Richard W. Lucas (Pollen science TM), Jorg Haus, Stefan Schaefer, Martin Schnitzler and Tom Stemmler (Helmut Hund Wetzlar), Reto Abt, Elias Graf, Erny Niederberger and Yanick Zeder (Swisens AG) report a relationship with Pollen science TM, Helmut Hund Wetzlar and Swisens AG respectively, that includes: board membership, employment, and travel reimbursement. The investigations were carried out in compliance with good scientific practices and the support provided by these companies in terms of instrumentation had no effect on the results presented., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility and transferability of implemented models are recommended since a reference dataset for local pollen of interest needs to be collected for each device to classify pollen, which is complex and time-consuming. Therefore, it would be beneficial to incorporate the reference dataset collected from other devices in different locations. However, it must be considered that laser-induced data are prone to device-specific noise due to laser and detector sensibility. This study collected data from two Rapid-E bioaerosol identifiers in Serbia and Italy and implemented a multi-modal convolutional neural network for pollen classification. We showed that models lost their performance when trained on data from one and tested on another device, not only in terms of the recognition ability but also in comparison with the manual measurements from Hirst-type traps. To enable pollen classification with just one model in both study locations, we first included the missing pollen classes in the dataset from the other study location, but it showed poor results, implying that data of one pollen class from different devices are more different than data of different pollen classes from one device. Combining all available reference data in a single model enabled the classification of a higher number of pollen classes in both study locations. Finally, we implemented a domain adaptation method, which improved the recognition ability and the correlations of transferred models only for several pollen classes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Considerable amounts of starch granules can be present in the atmosphere from both natural and anthropogenic sources. The aim of this study is to investigate the variability and potential origin of starch granules in ambient air recorded at six cities situated in a region with dominantly agricultural land use. This is achieved by using a combination of laser spectroscopy bioaerosol measurements with 1 min temporal resolution, traditional volumetric Hirst type bioaerosol sampling and atmospheric modelling. The analysis of wind roses identified potential sources of airborne starch (i.e., cereal grain storage facilities) in the vicinity of all aerobiological stations analysed in this study. The analysis of the CALPUFF dispersion model confirmed that emission of dust from the location of storage towers situated about 2.5 km north of the aerobiological station in Novi Sad is a plausible source of high airborne concentrations of starch granules. This study is important for environmental health since it contributes body of knowledge about sources, emission, and dispersion of airborne starch, known to be involved in phenomena such as thunderstorm-triggered asthma. The presented approach integrates monitoring and modelling, and provides a roadmap for examining a variety of bioaerosols previously considered to be outside the scope of traditional aerobiological measurements., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

This is the first time that atmospheric concentrations of individual pollen types have been recorded by an automatic sampler with 1-hour and sub-hourly resolution (i.e. 1-minute and 1-second data). The data were collected by traditional Hirst type methods and state-of the art Rapid-E real-time bioaerosol detector. Airborne pollen data from 7 taxa, i.e. Acer negundo, Ambrosia, Broussonetia papyrifera, Cupressales (Taxaceae and Cupressaceae families), Platanus, Salix and Ulmus, were collected during the 2019 pollen season in Novi Sad, Serbia. Pollen data with daily, hourly and sub-hourly temporal resolution were analysed in terms of their temporal variability. The impact of turbulence kinetic energy (TKE) on pollen cloud homogeneity was investigated. Variations in Seasonal Pollen Integrals produced by Hirst and Rapid-E show that scaling factors are required to make data comparable. Daily average and hourly measurements recorded by the Rapid-E and Hirst were highly correlated and so examining Rapid-E measurements with sub-hourly resolution is assumed meaningful from the perspective of identification accuracy. Sub-hourly data provided an insight into the heterogenous nature of pollen in the air, with distinct peaks lasting ~5-10 min, and mostly single pollen grains recorded per second. Short term variations in 1-minute pollen concentrations could not be wholly explained by TKE. The new generation of automatic devices has the potential to increase our understanding of the distribution of bioaerosols in the air, provide insights into biological processes such as pollen release and dispersal mechanisms, and have the potential for us to conduct investigations into dose-response relationships and personal exposure to aeroallergens., Competing Interests: Declaration of competing interest The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)