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.

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

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 R2 > 0.6 (3 h) and R2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R2 < 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 (R2 > 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.

Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe.

Pollen exposure weakens the immunity against certain seasonal respiratory viruses by diminishing the antiviral interferon response. Here we investigate whether the same applies to the pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is sensitive to antiviral interferons, if infection waves coincide with high airborne pollen concentrations. Our original hypothesis was that more airborne pollen would lead to increases in infection rates. To examine this, we performed a cross-sectional and longitudinal data analysis on SARS-CoV-2 infection, airborne pollen, and meteorological factors. Our dataset is the most comprehensive, largest possible worldwide from 130 stations, across 31 countries and five continents. To explicitly investigate the effects of social contact, we additionally considered population density of each study area, as well as lockdown effects, in all possible combinations: without any lockdown, with mixed lockdown-no lockdown regime, and under complete lockdown. We found that airborne pollen, sometimes in synergy with humidity and temperature, explained, on average, 44% of the infection rate variability. Infection rates increased after higher pollen concentrations most frequently during the four previous days. Without lockdown, an increase of pollen abundance by 100 pollen/m3 resulted in a 4% average increase of infection rates. Lockdown halved infection rates under similar pollen concentrations. As there can be no preventive measures against airborne pollen exposure, we suggest wide dissemination of pollen-virus coexposure dire effect information to encourage high-risk individuals to wear particle filter masks during high springtime pollen concentrations.

Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector.

Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.

Modelling grass pollen levels in Belgium.

Biogenic aerosols such as airborne grass pollen affect the public health badly by putting additional distress on people already suffering from cardiovascular and respiratory diseases. In Belgium, daily airborne pollen concentrations are monitored offline at a few sites only, hampering the timely coverage of the country and short-term forecasts. Here we apply the Chemistry Transport Model SILAM to the Belgian territory to model the spatio-temporal airborne grass pollen levels near the surface based on bottom-up inventories of grass pollen emissions updated with the Copernicus land monitoring Service grassland map of 2015. Transport of aerosols in SILAM is driven by ECMWF ERA5 meteorological data. The emitted grass pollen amounts in SILAM are computed by the multiplication of the grass pollen source map with the release rate determined by the seasonal shape production curve during the grass flowering period. The onset and offset of this period follow a location-dependent prescribed calendar days. Here we optimize the grass pollen seasonal start and end in SILAM by comparing a 2008-2018 time series of daily airborne grass pollen concentrations from the Belgian aerobiological surveillance network with the simulations. The effect of the spatial distribution of grass pollen sources is quantified by constructing pollen source-receptor relations using model simulations with varying grass pollen emissions in five areas of the model domain as input. Up to 33% of the airborne grass pollen in one area was transport from others areas inside Belgium. Adjusting the start and end of the grass pollen season improved the model performance substantially by almost doubling the correlation with local observations. By introducing the temporal scaling of the inter-seasonal pollen amounts in the model, an additional R2 increase up to 22% was obtained. Further improvements can be made by including more detailed grass pollen sources and more dynamic start and end dates of the pollen season.

Pollen season is reflected on symptom load for grass and birch pollen-induced allergic rhinitis in different geographic areas-An EAACI Task Force Report.

BACKGROUND: The effectiveness of allergen immunotherapy (AIT) in seasonal and perennial allergic rhinitis (AR) depends on the definition of pollen exposure intensity or time period. We recently evaluated pollen and symptom data from Germany to examine the new definitions of the European Academy of Allergy and Clinical Immunology (EAACI) on pollen season and peak pollen period start and end. Now, we aim to confirm the feasibility of these definitions to properly mirror symptom loads for grass and birch pollen-induced allergic rhinitis in other European geographical areas such as Austria, Finland and France, and therefore their suitability for AIT and clinical practice support. METHODS: Data from twenty-three pollen monitoring stations from three countries in Europe and for 3 years (2014-2016) were used to investigate the correlation between birch and grass pollen concentrations during the birch and grass pollen season defined via the EAACI criteria, and total nasal symptom and medication scores as reported with the aid of the patient's hay-fever diary (PHD). In addition, we conducted a statistical analysis, together with a graphical investigation, to reveal correlations and dependencies between the studied parameters. RESULTS: The analysis demonstrated that the definitions of pollen season as well as peak pollen period start and end as proposed by the EAACI are correlated to pollen-induced symptom loads reported by PHD users during birch and grass pollen season. A statistically significant correlation (slightly higher for birch) has been found between the Total Nasal Symptom and Medication Score (TNSMS) and the pollen concentration levels. Moreover, the maximum symptom levels occurred mostly within the peak pollen periods (PPP) following the EAACI criteria. CONCLUSIONS: Based on our analyses, we confirm the validity of the EAACI definitions on pollen season for both birch and grass and for a variety of geographical locations for the four European countries (including Germany from a previous publication) analyzed so far. On this basis, the use of the EAACI definitions is supported in future clinical trials on AIT as well as in daily routine for optimal patient care. Further evaluation of the EAACI criteria in other European regions is recommended.

Interactions Between Air Pollution and Pollen Season for Rhinitis Using Mobile Technology: A MASK-POLLAR Study.

BACKGROUND: Several studies have suggested an interaction between air pollution and pollen exposure with an impact on allergy symptoms. However, large studies with real-life data are not available. OBJECTIVE: To investigate associations between major air pollutants (ozone and particulate matter with a diameter of <2.5 µm) and allergic rhinitis (AR) control during grass and birch pollen seasons as well as outside the pollen season. METHODS: The daily impact of allergic symptoms was recorded by the Allergy Diary (Mobile Airways Sentinel NetworK [MASK-air]) app (a validated mHealth tool for rhinitis management) using visual analog scales (VASs) in Northern and Central Europe users in 2017 and 2018. Uncontrolled AR was defined using symptoms and medications. Pollutant levels were assessed using the System for Integrated modeLing of Atmospheric coMposition database. Pollen seasons were assessed by regions using Google Trends. Generalized estimating equation models were used to account for repeated measures per user, adjusting for sex, age, treatment, and country. Analyses were stratified by pollen seasons to investigate interactions between air pollutants and pollen exposure. RESULTS: A total of 3323 geolocated individuals (36,440 VAS-days) were studied. Associations between uncontrolled rhinitis and pollutants were stronger during the grass pollen season. Days with uncontrolled AR increased by 25% for an interquartile range increase in ozone levels during the grass pollen season (odds ratio of 1.25 [95% CI, 1.11-1.41] in 2017 and of 1.14 [95% CI, 1.04-1.25] in 2018). A similar trend was found for particulate matter with a diameter of less than 2.5 µm, especially in 2017. CONCLUSIONS: These results suggest that the relationship between uncontrolled AR and air pollution is modified by the presence of grass pollens. This study confirms the impact of pollutants in the grass pollen season but not in the birch pollen season.

Building an automatic pollen monitoring network (ePIN): Selection of optimal sites by clustering pollen stations.

Airborne pollen is a recognized biological indicator and its monitoring has multiple uses such as providing a tool for allergy diagnosis and prevention. There is a knowledge gap related to the distribution of pollen traps needed to achieve representative biomonitoring in a region. The aim of this manuscript is to suggest a method for setting up a pollen network (monitoring method, monitoring conditions, number and location of samplers etc.). As a case study, we describe the distribution of pollen across Bavaria and the design of the Bavarian pollen monitoring network (ePIN), the first operational automatic pollen network worldwide. We established and ran a dense pollen monitoring network of 27 manual Hirst-type pollen traps across Bavaria, Germany, during 2015. Hierarchical cluster analysis of the data was then performed to select the locations for the sites of the final pollen monitoring network. According to our method, Bavaria can be clustered into three large pollen regions with eight zones. Within each zone, pollen diversity and distribution among different locations does not vary significantly. Based on the pollen zones, we opted to place one automatic monitoring station per zone resulting in the ePIN network, serving 13 million inhabitants. The described method defines stations representative for a homogeneous aeropalynologically region, which reduces redundancy within the network and subsequent costs (in the study case from 27 to 8 locations). Following this method, resources in pollen monitoring networks can be optimized and allergic citizens can then be informed in a timely and effective way, even in larger geographical areas.

A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe.

The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical model is constructed using meteorological, geophysical and biological characteristics of the previous year). A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and Norway, where the lack of data did not allow for conclusive analysis. The constructed model was capable of predicting the SPI with correlation coefficient reaching up to 0.9 for some stations, odds ratio is infinitely high for 50% of sites inside the region and the fraction of prediction falling within factor of 2 from observations, stays within 40-70%. In particular, model successfully reproduced both the bi-annual cycle of the SPI and years when this cycle breaks down.

Costs and benefits of low-sulphur fuel standard for Baltic Sea shipping.

The maximum allowable fuel sulphur content for shipping in the Baltic Sea dropped from 1%S to 0.1%S in 1 January 2015. We provide a cost-benefit analysis of the sulphur reduction policy in the Baltic Sea Sulphur Emission Control Area (SECA). We calculated the abatement costs based on shipowners' optimal decision-making in choosing between low-sulphur fuel and a sulphur scrubber, and the benefits were modelled through a high-resolution impact pathway analysis, which took into account the formation and dispersion of the emissions, and considered the positive health impacts resulting from lowered ambient PM2.5 concentrations. Our basic result indicates that for the Baltic Sea only, the latest sulphur regulation is not cost-effective. The expected annual cost is roughly €465 M and benefit 2200 saved Disability Adjusted Life-Years (DALYs) or monetized €105 M. Based on our sensitivity analysis, the benefits yet have a potential to exceed the costs. The analysis neither takes into account the acidifying impact of sulphur nor the impact North Sea shipping has on the cost-benefit ratio. Lastly, a similar approach is found highly recommendable to study the implications of the upcoming Tier III NOx standard for shipping.

First comparison of symptom data with allergen content (Bet v 1 and Phl p 5 measurements) and pollen data from four European regions during 2009-2011.

BACKGROUND: The level of symptoms in pollen allergy sufferers and users of the Patient's Hayfever Diary (PHD), does not directly reflect the total amount of pollen in the air. It is necessary to explain the symptom load and thus the development of allergic symptoms and to determine which environmental factors, besides the pollen load, influence variables. It seems reasonable to suspect allergen content because the amount of allergen varies throughout seasons and regions and is not always correlated with the total pollen amount. METHODS: Data on the allergen content of ambient air (Bet v 1 and Phl p 5) from 2009 until 2011 was used to compare the respective pollen and symptom loads for study regions in Austria, Germany, France and Finland. RESULTS: Our findings suggest that allergen amount (Bet v 1/Phl p 5) has a strong but regionally dependent impact on the symptom load of pollen allergy sufferers. Peak symptom loads can be traced with peak allergen loads. The influence of other important aeroallergens should also be assessed during the pollen season. CONCLUSION: Allergen concentrations have an impact on pollen allergy sufferers although not as clear as assumed previously. The pattern of pollen load and major allergen content distribution does not directly explain the symptom load pattern, although significant positive correlations were found. Thus, monitoring of symptoms via voluntary crowdsourcing should be considered for future pollen and symptom forecasts in order to support pollen allergy sufferers.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

BACKGROUND: Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. OBJECTIVES: We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. METHODS: We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). RESULTS: GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. CONCLUSIONS: The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. CITATION: Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

Variation of the group 5 grass pollen allergen content of airborne pollen in relation to geographic location and time in season.

BACKGROUND: Allergies to grass pollen are the number one cause of outdoor hay fever. The human immune system reacts with symptoms to allergen from pollen. OBJECTIVE: We investigated the natural variability in release of the major group 5 allergen from grass pollen across Europe. METHODS: Airborne pollen and allergens were simultaneously collected daily with a volumetric spore trap and a high-volume cascade impactor at 10 sites across Europe for 3 consecutive years. Group 5 allergen levels were determined with a Phl p 5-specific ELISA in 2 fractions of ambient air: particulate matter of greater than 10 µm in diameter and particulate matter greater than 2.5 µm and less than 10 µm in diameter. Mediator release by ambient air was determined in FcεRI-humanized basophils. The origin of pollen was modeled and condensed to pollen potency maps. RESULTS: On average, grass pollen released 2.3 pg of Phl p 5 per pollen. Allergen release per pollen (potency) varied substantially, ranging from less than 1 to 9 pg of Phl p 5 per pollen (5% to 95% percentile). The main variation was locally day to day. Average potency maps across Europe varied between years. Mediator release from basophilic granulocytes correlated better with allergen levels per cubic meter (r(2) = 0.80, P < .001) than with pollen grains per cubic meter (r(2) = 0.61, P < .001). In addition, pollen released different amounts of allergen in the non-pollen-bearing fraction of ambient air, depending on humidity. CONCLUSION: Across Europe, the same amount of pollen released substantially different amounts of group 5 grass pollen allergen. This variation in allergen release is in addition to variations in pollen counts. Molecular aerobiology (ie, determining allergen in ambient air) might be a valuable addition to pollen counting.

A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis.

An evaluation of performance of the System for Integrated modeLling of Atmospheric coMposition (SILAM) in application to birch pollen dispersion is presented. The system is described in a companion paper whereas the current study evaluates the model sensitivity to details of the pollen emission module parameterisation and to the meteorological input data. The most important parameters are highlighted. The reference year considered for the analysis is 2006. It is shown that the model is capable of predicting about two-thirds of allergenic alerts, with the odds ratio exceeding 12 for the best setup. Several other statistics corroborate with these estimations. Low-pollen concentration days are also predicted correctly in more than two-thirds of cases. The model experiences certain difficulties only with intermediate pollen concentrations. It is demonstrated that the most important input parameter is the near-surface temperature, the bias of which can easily jeopardise the results. The model sensitivity to random fluctuations of temperature is much lower. Other parameters important at various stages of pollen development, release, and dispersion are precipitation and ambient humidity, as well as wind direction.