Pollen Monitoring Research Articles

CYBER-PHYSICAL SYSTEM FOR MONITORING THE ENVIRONMENT FOR ALLERGENS USING GEOLOCATION DATA

The task of creating a cyber-physical system for monitoring the environment for allergens is relevant. Therefore, our study is devoted to the development of a method and a cyber-physical system for monitoring the environment for allergens using geolocation data. The developed cyber-physical system for monitoring the environment for allergens not only uses the available information on the activity of pollen sources in a given region, but also measures the concentration of pollen in the air in real time, which helps to monitor the level of pollen in the region where the user is located, and, taking into account the user's geolocation data, provides him with useful information for route planning to avoid staying in high-risk areas. Collecting and analyzing geolocation data allows you to track changes in the distribution of allergens over time and in space. This can be useful for understanding trends in allergenic reactions and for developing effective allergy management strategies. The cyber-physical system for monitoring the environment for allergens is useful for people with allergies by providing them with accessible and up-to-date information about the environment and the risks of allergic reactions, allowing them to better plan their activities and avoid areas with high levels of plant pollen concentration. Such a cyber-physical system enables an individualized approach to each user, taking into account their unique problems by performing allergen filtering. This allows for effective and personalized allergy management solutions. The proposed cyber-physical system can create a valuable database with allergic reactions and predictions that will be used to improve the methods of diagnosis, treatment and management of allergies. The proposed cyber-physical system can be used for global monitoring of pollen and allergen levels in different parts of the world. This will allow us to identify global trends in the distribution of allergens and develop effective strategies for managing allergies at the global level.

Allergenic Pollen Monitoring at Sapienza University Campus (Rome, Italy): Patterns of Pollen Dispersal and Implications for Human Health

The Campus of the Sapienza University of Rome, frequented daily by several thousands of students and workers, collects allergenic airborne pollen from many sources. Here, we report the results of detailed pollen monitoring of 49 pollen types within the University Campus, allowing us to trace the main local and regional sources of airborne pollen throughout the year. The amount of allergenic pollen has been calculated for each daily record to evaluate the risk of exposure for students and workers on Campus in relation to academic activities and to suggest possible mitigation measures. Our results show that the maximum pollen concentrations are recorded from March to May, and the highest floristic richness occurs in April–June. We distinguish massive local pollen producers from pollen of regional origin and local ornamental and invasive taxa. Pollen with extreme allergenicity is dominant from mid-January to mid-March and in May with Cupressaceae/Taxaceae, Corylus and Olea; high allergenicity from late March to late April with Platanus, Ostrya, Ginkgo and Moraceae; and medium allergenicity from late April to the beginning of May with Quercus and Pinus. In August–December, pollen concentration is relatively low. Diversified mitigation actions are suggested in relation to the provenance, allergenicity and emission period of pollen.

The role of automatic pollen and fungal spore monitoring across major end-user domains

The advent of automatic pollen and fungal spore monitoring over the past few years has brought about a paradigm change. The provision of real-time information at high temporal resolution opens the door to a wide range of improvements in terms of the products and services made available to a widening range of end-users and stakeholders. As technology and methods mature, it is essential to properly quantify the impact automatic monitoring has on the different end-user domains to better understand the real long-term benefits to society. In this paper, we focus the main domains where such impacts are expected, using Europe as a basis to provide qualitative estimates and to describe research needs to better quantify impacts in future. This will, in part, also serve to justify further investment and help to expand monitoring networks.

Current State of Japanese Cedar (Cryptomeria japonica D. Don) Pollen Information and Future Directions for Its Airborne Allergen Determination and Improved Pollen Monitoring

About 40% of cedar pollinosis patients living in the Yamagata Prefecture showed pollinosis symptoms before the first day of the pollen season, which was determined by Durham samplers, the standard sampler for pollen information in Japan. The amount of Cry j 1 (major cedar pollen allergen) per cedar pollen is reported to be six pg. This amount is difficult to measure using the ELISA method, so we applied the highly sensitive ESR radical immunoassay method to measure the allergen; now we can provide information for sensitive patients. It revealed that Cry j 1 exists in orbicles and tapetum. It is presumed that it is smaller than pollen, so it comes from a place where cedar are already in bloom. It is desirable to obtain real-time information on an hourly basis. Currently, information from automatic cedar pollen monitors is becoming main-stream. However, this monitor may count during snowfalls, Asian dust flying, etc., even when there was no apparent pollen examined with a microscope. This paper describes the current status of automatic cedar pollen monitors, their usefulness, and their advantages and disadvantages in comparison with results obtained by other methods of measurement. Lastly, the paper describes expectations for cedar pollen information in the future.

Main Airborne Pollen Species and Characteristics of Allergic Rhinitis Patients with Pollen-Related Allergies in 13 Northern Chinese Cities.

Pollen allergies have a high prevalence in northern China, whereas, the types of pollen allergens and population characteristics among different regions remain unclear. To study the species and temporal distribution of the main allergenic pollen, as well as the characteristics of patients with pollen-related allergic rhinitis (AR) in different cities in northern China. Pollen data were obtained from pollen-monitoring stations in 13 cities of northern China between 2020 and 2021. Questionnaire surveys and allergen testing were conducted in 494 patients with pollen-related allergies from Beijing in Central, Shenyang in Northeast, and Xi'an in Northwest China. In 13 cities of northern China, the main sources of pollen were cypress, poplar, elm, pine, birch and ash in spring, and mugwort, goosefoot, hop and ragweed in autumn. In Northwest China, the spring and autumn pollen periods started earlier and lasted longer than that in Central and Northeast China, and the pollen counts in autumn in was significantly higher than that in Central and Northeast China. Furthermore, the nasal, ocular and respiratory symptom and quality of life scores of AR patients in Northwest China were significantly higher than that in Central and Northeast China. 69.32-73.28% of patients had annual cost of anti-allergic medication between 500-5000 yuan. However, 40.93-48.86% of patients reported minor control of symptoms. Our results can be used as a basis for developing effective prevention and management measures for patients with pollen-related allergy in these regions, including timely pollen monitoring, patient guidance on protective measures, early intervention, and specific immunotherapy, to improve pollen-related allergy management.

Environmental exposure and sensitization patterns in a Swiss alpine pediatric cohort

BackgroundThe level of environmental exposure throughout life may contribute to the prevalence of allergic sensitization and allergic disease. The alpine climate has been considered a healthy climate with little allergen exposure and pollution. We conducted a cross-sectional study to investigate local environmental exposure and concomitant prevalence of allergic sensitization among local school children born and raised in an alpine environment. MethodsClinical and demographic data were collected with a questionnaire. Allergen content was assessed in residential settled dust samples, lifetime exposure to pollen and air pollution was calculated using data from national pollen and air pollution monitoring stations, and the allergic sensitization profile was determined with component resolved diagnostics (ISAC®). Univariate and multivariate regression models were used to estimate the relation between exposure and sensitization. ResultsIn a cohort of children born and raised in an alpine environment, sensitization to aeroallergens is quite common (38%), especially to grass (33%) and cat (16%). House dust mite allergen was detected in up to 38% of residential dust samples, but sensitization to HDM was low (2.5%). Pollutant levels were low, but an increasing trend was observed in the amount of ozone and PM10. Living close to a busy road was associated with increased odds OR (95% CI) for being sensitized to any allergen 2.7 (1.0–7.2), to outdoor allergens 2.8 (1.1–7.1) and being sensitized plus reporting symptoms of rhinoconjunctivitis 4.4 (1.3–14.8) and asthma 5.5 (1.4–21). Indoor living conditions, including the presence of visible mold, increased the odds of being sensitized to indoor allergens (1.9 (1.1–3.2) and being sensitized plus reporting symptoms of rhinoconjunctivitis 1.9 (1.0–3.6) and asthma 2.1 (1.0–4.1). ConclusionIn a healthy alpine environment, pollution might still be an important factor contributing to allergic sensitization.

E-Science workflow: A semantic approach for airborne pollen prediction

Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks’ high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Málaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period). The predictions have been conducted using machine and deep learning algorithms like SARIMA or CNN-LSTM that intend to optimise the pollen prediction procedure depending on its stational and seasonal profile.

The South African Pollen Monitoring Network: Insights from 2years of national aerospora sampling (2019-2021).

Pollen monitoring has been discontinuously undertaken in South Africa, a country with high biodiversity, a seasonal rainfall gradient, and nine biomes from arid to subtropical. The South African Pollen Monitoring Network was set up in 2019 to conduct the first long-term national aerospora monitoring across multiple biomes, providing weekly reports to allergy sufferers and healthcare providers. Daily airborne pollen concentrations were measured from August 2019 to August 2021 in seven cities across South Africa. Updated pollen calendars were created for the major pollen types (>3%), the average Annual Pollen Index over 12 months was calculated, and the results were compared to available historical data. The main pollen types were from exotic vegetation. The most abundant taxa were Poaceae, Cupressaceae, Moraceae and Buddleja. The pollen season start, peak and end varied widely according to the biome and suite of pollen taxa. The main tree season started in the last week of August, peaked in September and ended in early December. Grass seasons followed rainfall patterns: September-January and January-April for summer and winter rainfall areas, respectively. Major urban centres, for example, Johannesburg and Pretoria in the same biome with similar rainfall, showed substantive differences in pollen taxa and abundance. Some major differences in pollen spectra were detected compared with historical data. However, we are cognisant that we are describing only 2years of data that may be skewed by short-term weather patterns. Differences in pollen spectra and concentrations were noted across biomes and between geographically close urban centres. Comparison with historical data suggests pollen spectra and seasons may be changing due to anthropogenic climate change and landscaping. These data stress the importance of regional and continuous pollen monitoring for informed care of pollinosis.

Estimation of historical daily airborne pollen concentrations across Switzerland using a spatio temporal random forest model

High concentrations of airborne pollen trigger seasonal allergies and possibly more severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies, in order to study the effects of pollen exposure. We aimed to develop a spatiotemporal machine learning model predicting daily pollen concentrations at a spatial resolution of 1 × 1 km across Switzerland between 2000 and 2019. Daily pollen concentrations for five common, highly allergenic pollen types (hazel, alder, birch ash, and grasses) were available from fourteen measurement sites across Switzerland. We considered several predictors such as elevation, species distribution, wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index, and land-use (CORINE, Landsat satellite) to explain variation in pollen concentration. We employed feature engineering techniques to encode categorical variables and fill in missing values. We applied a random forest machine learning model with 5-fold cross-validation. The 5th–99th percentiles for concentrations of hazel, alder, birch, ash, and grass pollen at the pollen monitoring stations were 0–298, 0–306, 0–1153, 0–800, and 0–290 pollen grains/m3, respectively. The results of a predictive model for these concentrations yielded overall R2 values of 0.87, 0.84, 0.89, 0.88, and 0.91, and temporal root mean squared errors (RMSEs) of 16.07, 16.72, 69.04, 41.50, and 22.45 pollen grains/m3. An analysis of predictor variable importance indicates that the average national daily pollen concentration is the most important predictor of pollen concentrations for all pollen types. Furthermore, meteorological variables including temperature, total precipitation, humidity, boundary layer height, wind speed, and wind direction, as well as date and satellite features, are important factors in pollen concentration prediction. These spatiotemporal pollen models will serve to estimate individual residential pollen exposure for epidemiological studies. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions in Switzerland.

Impact of climate change on allergic diseases in Germany.

Allergic diseases, especially inhalation allergies, have reached epidemic levels and environmental factors play an important role in their development. Climate change influences the occurrence, frequency, and severity of allergic diseases. The contents of this article were selected by the authors and developed section by section according to their expertise and the current state of knowledge. The sections were then discussed and agreed upon amongst all authors. The article highlights direct and indirect effects of climate change on allergies. It goes into detail about the connections between climate change and (new) pollen allergens as well as (new) occupational inhalation allergens, explains the effects of climate change on the clinical picture of atopic dermatitis, discusses the connections between air pollutants and allergies, and provides information about the phenomenon of thunderstorm asthma. There is a need for action in the field of pollen and fungal spore monitoring, allergy and sensitisation monitoring, urban planning from an allergological perspective, and changes in the working environment, among others.

Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields

We consider several approaches to a design of a regional-to-continent-scale automatic pollen monitoring network in Europe. Practical challenges related to the arrangement of such a network limit the range of possible solutions. A hierarchical network is discussed, highlighting the necessity of a few reference sites that follow an extended observations protocol and have corresponding capabilities.Several theoretically rigorous approaches to a network design have been developed so far. However, before starting the process, a network purpose, a criterion of its performance, and a concept of the data usage should be formalized. For atmospheric composition monitoring, developments follow one of the two concepts: a network for direct representation of concentration fields and a network for model-based data assimilation, inverse problem solution, and forecasting. The current paper demonstrates the first approach, whereas the inverse problems are considered in a follow-up paper.We discuss the approaches for the network design from theoretical and practical standpoints, formulate criteria for the network optimality, and consider practical constraints for an automatic pollen network. An application of the methodology is demonstrated for a prominent example of Germany's pollen monitoring network. The multi-step method includes (i) the network representativeness and (ii) redundancy evaluation followed by (iii) fidelity evaluation and improvement using synthetic data.

Air Pollen Monitoring in a Specific Region as a Part of the Pollinosis Prevention

Background: The prevalence and severity of allergic diseases, including pollinosis, are increasing worldwide. Hay fever develops due to the complex interaction of genes and manifests itself due to exogenous factors. The main environmental etiology of allergy is plant pollen. Continuous study and analysis of the air pollen spectrum constitute the basis of air monitoring and are important in developing preventive measures for allergic diseases. Objective: To perform an air pollen monitoring in Kr snodar and to assess the treatment of patients with pollinosis in various cities and districts of the Krasnodar Region according to the Krasnodar Regional Allergy Center data. Materials and methods: We retrospectively analyzed the patients seeking medical care due to allergy in the Krasnodar Region using the statistical data obtained in 2022 from the Krasnodar Regional Allergy Center. We assessed the effects of air pollen pollution according to the data of air pollen monitoring in Krasnodar during the main activity of herbaceous plants’ taxa (from April 1, 2022 to October 31, 2022). We used AeRobiology and Microsoft Excel 2010 tools for data processing and development of the main parameters of the pollen season. Results: According to the Krasnodar Regional Allergy Center data, there were 37,212 people diagnosed with J45.0, J46 and 20,012 people diagnosed with J30.1-30.4 in 2022. We identified 8 allergenic taxa of herbaceous plants in the Krasnodar air. We found that the total seasonal pollen concentration increased by an average of 1.8 in 2022 compared to previous monitoring periods in 2018-2021. Conclusions: Comparative assessment of the prevalence of pollinosis and the air pollen monitoring are important for effective medical care.

AMFF-Net: An attention-based multi-scale feature fusion network for allergic pollen detection

Automatic pollen detection based on light microscope (LM) images is helpful for pollinosis symptoms prevention. Recently, many deep learning methods have been proposed to identify pollen grains based on multi-scale feature fusion mechanism. However, in real scenarios, there are two main challenges that need to be considered: (1) Complex pollen characteristics; (2) Irrelevant objects interference. It means that the pollen detection requires not only learning the relationship among multi-scale features but also refining the feature representation. To this end, this paper proposes an attention-based multi-scale feature fusion network (AMFF-Net) for automatic pollen detection on real-world LM images. The proposed AMFF-Net includes three modules: The feature extraction module utilizes the series-connection attention to capture the spatial and channel dependencies of different level feature maps (for solving the challenge 1). In the feature fusion module, a parallel connection attention is able to learn more discriminative feature representation based on bidirectional pathway guidance (for solving the challenge 2). Both are jointly adopted to enhance the representational capacity of the final results in the pollen prediction module. Extensive experiments are conducted on the real-world RPD dataset, and our AMFF-Net achieves the best performance (83.9% of mean average precision) comparing with other state-of-the-art methods. We believe that this work can serve as an important reference for the development of pollen monitoring system in a real scenario.

Methods and standards of pollen monitoring—significance of pollen measurements at different altitudes

Abstract Background The measurement of pollen concentrations has been performed according to various protocols in recent decades. In all approaches the following questions were of importance: the positioning of the measuring device (whether at roof or nose level), the reliability as well as the comparability of measurements. Methods Current methods for pollen measurements and previous studies on measurement heights are presented and compared. Results The most widely used device is the Hirst pollen and spore trap. Its data is widely used for pollen information, pollen prediction models, and clinical studies. The position of the trap at roof level remains the current standard as the pollen concentrations correlate best with “real world” situation and regional symptom data. Conclusion The guideline for the detection of pollen and spores for allergy networks (EN 16868:2019-09) in Europe was an important step towards standardization. Modern, automated measurement methods are still in experimental stage in terms of validation and comparability. Harmonization of data from different measurement methods is a future challenge.

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.

Airborne grass pollen and thunderstorms influence emergency department asthma presentations in a subtropical climate

BackgroundGrass pollen is considered a major outdoor aeroallergen source worldwide. It is proposed as a mechanism for thunderstorm asthma that lightning during thunderstorms promotes electrical rupture of pollen grains that leads to allergic airway inflammation. However, most evidence of associations between grass pollen and asthma comes from temperate regions. The objective of this study was to investigate short-term associations between airborne grass pollen exposure and asthma emergency department presentations in a subtropical population. MethodsEpisode level public hospital presentations for asthma (2016–2020) were extracted for greater Brisbane, Australia, from Queensland Health's Emergency Data Collection. Concentrations of airborne pollen were determined prospectively using a continuous flow volumetric impaction sampler. Daily time series analysis using a generalised additive mixed model were applied to determine associations between airborne grass pollen concentrations, and lightning count data, with asthma presentations. ResultsAirborne grass pollen showed an association with asthma presentations in Brisbane; a significant association was detected from same day exposure to three days lag. Grass pollen exposure increased daily asthma presentations up to 48.5% (95% CI: 12%, 85.9%) in female children. Lightning did not modify the effect of grass pollen on asthma presentations, however a positive association was detected between cloud-to-cloud lightning strikes and asthma presentations (P = 0.048). ConclusionAirborne grass pollen exposure may exacerbate symptoms of asthma requiring urgent medical care of children and adults in a subtropical climate. This knowledge indicates an opportunity for targeted management of respiratory allergic disease to reduce patient and health system burden. For the first time, an influence of lightning on asthma was detected in this context. The outcomes support a need for continued pollen monitoring and surveillance of thunderstorm asthma risk in subtropical regions.

Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3-day moving predictive model.

Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide early warning for the protection and treatment of patients as well as provide a scientific basis for allergen specific clinical immunotherapy. To develop a model of Artemisia pollen grading to predict development in patients with pollen allergy. Artemisia pollen data from four pollen monitoring stations in Beijing as well as the number of Artemisia pollen allergen serum specific immunoglobulin E positive cases in Beijing Tongren Hospital from 2014 to 2016 were used to develop a statistical model of pollen deposition and provide optimised threshold values. A logarithmic correlation existed between the number of patients with Artemisia pollen allergy and Artemisia pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of Artemisia pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy. Graded prediction of pollen deposition may help pollen allergic populations benefit from preventive interventions before onset.

Volunteer-contributed observations of flowering often correlate with airborne pollen concentrations.

Characterizing airborne pollen concentrations is crucial for supporting allergy and asthma management; however, pollen monitoring is labor intensive and, in the USA, geographically limited. The USA National Phenology Network (USA-NPN) engages thousands of volunteer observers in regularly documenting the developmental and reproductive status of plants. The reports of flower and pollen cone status contributed to the USA-NPN's platform, Nature's Notebook, have the potential to help address gaps in pollen monitoring by providing real-time, spatially explicit information from across the country. In this study, we assessed whether observations of flower and pollen cone status contributed to Nature's Notebook can serve as effective proxies for airborne pollen concentrations. We compared daily pollen concentrations from 36 National Allergy Bureau (NAB) stations in the USA with flowering and pollen cone status observations collected within 200 km of each NAB station in each year, 2009-2021, for 15 common tree taxa using Spearman's correlations. Of 350 comparisons, 58% of correlations were significant (p < 0.05). Comparisons could be made at the largest numbers of sites for Acer and Quercus. Quercus demonstrated a comparatively high proportion of tests with significant agreement (median ρ = 0.49). Juglans demonstrated the strongest overall coherence between the two datasets (median ρ = 0.79), though comparisons were made at only a small number of sites. For particular taxa, volunteer-contributed flowering status observations demonstrate promise to indicate seasonal patterns in airborne pollen concentrations. The quantity of observations, and therefore, their utility for supporting pollen alerts, could be substantially increased through a formal observation campaign.

The "allergic nose as a pollen detector" concept: e-Diaries to predict pollen trends.

Hirst pollen traps and operator pollen recognition are worldwide used by aerobiologists, providing essential services for the diagnosis and monitoring of allergic patients. More recently, semiautomated or fully automated detector systems have been developed, which facilitate prediction of pollen exposure and risk for the individual patient. In parallel, smartphone apps consisting of short questionnaires filled in daily by the patient/user provide daily scores, time trajectories, and descriptive reports of the severity of respiratory allergies in patients with pollen allergy. The usual scientific and clinical approach to this matter is to monitor the environment (pollen concentration) in order to predict the risk of symptoms (allergic rhinitis) in a population. We discuss here the opposite, contraintuitive possibility, that is, the use of e-diaries to collect daily information of mono-sensitized pollen-allergic patients in order to predict the clinically efficient airborne exposure to a given pollen, area, and time period. In line with the "Patient as Sensor" concept, proposed in 2013 by Bernd Resch, the "allergic nose" may be used as a pollen detector in addition to existing calibrated hardware sensors, namely the pollen stations, thus contributing with individual measurements, sensations, and symptoms' perception. The target of this review is to present a novel concept of pollen monitoring based on "pollen-detector" patients to inspire future cooperative studies aimed at investigating and hopefully validating our hypothesis.

A study of airborne pollen monitoring and its connection with allergic rhinitis visits in Taiyuan over the summer and autumn seasons

Objective:To explore the distribution of airborne pollen in summer and autumn in Taiyuan, analyze the correlation between pollen characteristics, meteorological factors and allergic sensitization, and provide for the prevention and treatment of allergic diseases in this. Methods:The gravity sedimentation method was used to investigate the types, quantities and dispersion patterns of airborne pollen in Taiyuan City from July 21, 2022 to October 20, 2022. he meteorological and patient information was collected during the same period SPSS 26.0 software. Results:①A total of 17 118 pollen grains were collected, and identified as 14 families, 10 genera, and 4 species. The peak period for pollen dispersal in summer and autumn in Taiyuan City from late August to early September. airborne pollen Artemisia（66.62%）, Cannabis/Humulus（17.79%）, Sophora japonica（8.18%）, Chenopodiaceae/Amaranthaceae（2.83%）, Gramineae（2.11%）. ②The concentration of airborne pollen in Taiyuan City positively correlated with the average temperature（5-20℃） and maximum temperature（11-30℃） within a certain range（r=0.547, 0.315, P<0.05）. ③The content of airborne pollen in Taiyuan City positively correlated with the number of visits and allergen positive rate of patients with allergic rhinitis（AR） in our hospital（r=0.702, 0.747, P<0.05）. Conclusion:The peak period for airborne pollen dispersal during the summer and autumn seasons in Taiyuan City from late August to early September. The dominant pollen is Artemisia, Cannabis/Humulus, Sophora japonica, Chenopodiaceae/Amaranthaceae, Gramineae, and the absolute advantage pollen is Artemisia. Meteorological factors pollen content. Within a certain range, temperature the diffusion and transportation of pollen. The number of pollen grains the number of visits, which can serve as an environmental warning indicator for AR patients to take preventive, thereby reducing the risk of allergies.