Air Sampling and Analysis of Aeroallergens: Current and Future Approaches.

PURPOSE OF REVIEW: To review current air sampling instruments and analysis methods and to describe new approaches being developed. RECENT FINDINGS: Spore trap sampling with analysis by microscopy remains the most widely used methods for aeroallergen determination even though there are often long delays from sample acquisition to data availability, as well as a need for specially-trained staff for sample analysis. The use of immunoassays and molecular biology to analyze outdoor or indoor samples has expanded in recent years and has provided valuable data on allergen exposure. New automated sampling devices capture pollen, analyze, and identify pollen grains by light scattering, laser-induced fluorescence, microscopy, or holography using signal or image processing to classify the pollen in real time or near real time. Air sampling data from current methods provide valuable information on aeroallergen exposure. The automated devices in use and under development show great potential but are not ready to replace existing aeroallergen networks.

Aeroallergen Monitoring by the National Allergy Bureau: A Review of the Past and a Look Into the Future.

Monitoring aeroallergens has a long history within the American Academy of Allergy, Asthma & Immunology. The Aeroallergen Network of the National Allergy Bureau is composed mainly of members of the American Academy of Allergy, Asthma & Immunology, whose objectives are to enhance the knowledge of aerobiology and its relationship to allergy, increase the number of certified stations, maintain the standardization and quality of aerobiology data, improve the alert and forecast reporting system, and increase ties with other scientific entities inside and outside the United States. The public has a keen interest in pollen counts and pollen forecasts, as do many health professionals in the allergy community. In this review, we explore the past, present, and future of allergen monitoring with a focus on methods used for sampling, the training of those performing the analysis, and emerging technologies in the field. Although the development of automated samplers with machine intelligence offers great promise for meeting the goal of a fully automated system, there is still progress to be made regarding reliability and affordability.

The Importance of Binomial Nomenclature for the Identification of Pollen Aeroallergens.

The diagnosis and treatment of atopic disorders associated with specific aerobiological triggers require basic botanical training. However, the identification of specific pollen can often be confounded by broad naming conventions that range from categorized colloquial to scientific names based on either higher taxonomic levels or, in some cases, binomial nomenclature. Physicians specializing in allergy often lack a comprehensive understanding with respect to plant taxonomy and botanical nomenclature that are critical skills required for clinical practice and research programs evaluating pollen and airborne fungal spores. In addition, binomial and current family designation and synonyms, including author citation are often misused, causing a misinterpretation of existing plants species or pollen types. It is critical that the correct botanical name is linked to a validated specimen and scientific naming conventions are used where possible by the clinician and researcher. In relation to pollen identification, we propose that clinicians and researchers should provide the currently accepted binomial nomenclature, offer relevant synonyms, and use the Angiosperm Phylogeny Group names.

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America. Hence, estimating and predicting the daily atmospheric concentration of pollen (ragweed pollen in particular) is useful for both people with allergies and for the health professionals who care for them. In this study, we show that a suite of variables including meteorological and land surface parameters, as well as next-generation radar (NEXRAD) measurements together with machine learning can be used to estimate successfully the daily pollen concentration. The supervised machine learning approaches we used included random forests, neural networks, and support vector machines. The performance of the training is independently validated using 10% of the data partitioned using the holdout cross-validation method from the original dataset. The random forests (R= 0.61, R2= 0.37), support vector machines (R= 0.51, R2= 0.26), and neural networks (R= 0.46, R2= 0.21) effectively predicted the daily Ambrosia pollen, where the correlation coefficient (R) and R-squared (R2) values are given in brackets. Three independent approaches-the random forests, correlation coefficients, and interaction information-were employed to rank the relative importance of the available predictors.

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen.

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind are known to affect the amount of pollen in the atmosphere. In the past, various regression techniques have been applied to estimate and forecast the daily pollen concentration in the atmosphere based on the weather conditions. In this research, machine learning methods were applied to the Next Generation Weather Radar (NEXRAD) data to estimate the daily Ambrosia pollen over a 300 km × 300 km region centered on a NEXRAD weather radar. The Neural Network and Random Forest machine learning methods have been employed to develop separate models to estimate Ambrosia pollen over the region. A feasible way of estimating the daily pollen concentration using only the NEXRAD radar data and machine learning methods would lay the foundation to forecast daily pollen at a fine spatial resolution nationally.

Landscape Plant Selection Criteria for the Allergic Patient.

Patients with pollen-related allergies are concerned about the species within their landscape that provoke their symptoms. Allergists are often asked for guidance but few information sources are available to aid patients in the recognition of allergenic plants and strategies to avoid personal exposure to them. Landscaping and horticultural workers also have few reliable guidance references, and what is available usually extols the virtues of the plants rather than their negative features. The aim of this article was to provide the results of the Landscape Allergen Working Group that was formed by the AAAAI Aerobiology Committee, which aimed to fill these existing knowledge gaps and develop guidance on producing a low-allergenic landscape. Within the context that complete pollen avoidance is unrealistic, the workgroup introduces selection criteria, avoidance strategies, and guidance on low-allergenic plants that could be selected by patients to reduce the overall pollen burden in their landscape environment. Specific focus is placed on entomophilous plants, which require insects as dispersal vectors and generally produce lower quantities of pollen, compared with anemophilous (wind-pollinated) species. Other biological hazards that can be encountered while performing landscaping activities are additionally reviewed and avoidance methods presented with the aim of protecting gardeners, and workers in the landscape and horticulture industries. The guidance presented in this article will ultimately be a helpful resource for the allergist and assist in engaging patients who are seeking to reduce the burden of allergen in their landscape environment.

Increasing Juniperus virginiana L. pollen in the Tulsa atmosphere: long-term trends, variability, and influence of meteorological conditions.

In the Tulsa area, the Cupressaceae is largely represented by eastern red cedar (Juniperus virginiana L.). The encroachment of this species into the grasslands of Oklahoma has been well documented, and it is believed this trend will continue. The pollen is known to be allergenic and is a major component of the Tulsa atmosphere in February and March. This study examined airborne Cupressaceae pollen data from 1987 to 2016 to determine long-term trends, pollen seasonal variability, and influence of meteorological variables on airborne pollen concentrations. Pollen was collected through means of a Burkard sampler and analyzed with microscopy. Daily pollen concentrations and yearly pollen metrics showed a high degree of variability. In addition, there were significant increases over time in the seasonal pollen index and in peak concentrations. These increases parallel the increasing population of J. virginiana in the region. Pollen data were split into pre- and post-peak categories for statistical analyses, which revealed significant differences in correlations of the two datasets when analyzed with meteorological conditions. While temperature and dew point, among others were significant in both datasets, other factors, like relative humidity, were significant only in one dataset. Analyses using wind direction showed that southerly and southwestern winds contributed to increased pollen concentrations. This study confirms that J. virginiana pollen has become an increasing risk for individuals sensitive to this pollen and emphasizes the need for long-term aerobiological monitoring in other areas.

Molecular analysis confirms the long-distance transport of Juniperus ashei pollen.

Although considered rare, airborne pollen can be deposited far from its place of origin under a confluence of favorable conditions. Temporally anomalous records of Cupressacean pollen collected from January air samples in London, Ontario, Canada have been cited as a new case of long-distance transport. Data on pollination season implicated Juniperus ashei (mountain cedar), with populations in central Texas and south central Oklahoma, as the nearest source of the Cupressacean pollen in the Canadian air samples. This finding is of special significance given the allergenicity of mountain cedar pollen. While microscopy is used extensively to identify particles in the air spora, pollen from all members of the Cupressaceae, including Juniperus, are morphologically indistinguishable. Consequently, we implemented a molecular approach to characterize Juniperus pollen using PCR in order to test the long-distance transport hypothesis. Our PCR results using species-specific primers confirmed that the anomalous Cupressacean pollen collected in Canada was from J. ashei. Forward trajectory analysis from source areas in Texas and the Arbuckle Mountains in Oklahoma and backward trajectory analysis from the destination area near London, Ontario were completed using models implemented in HYSPLIT4 (Hybrid Single-Particle Lagrangian Integrated Trajectory). Results from these trajectory analyses strongly supported the conclusion that the J. ashei pollen detected in Canada had its origins in Texas or Oklahoma. The results from the molecular findings are significant as they provide a new method to confirm the long-distance transport of pollen that bears allergenic importance.

Molecular approaches for the analysis of airborne pollen: A case study of Juniperus pollen.

BACKGROUND: Pollen monitoring is a common and vital tool in the field of allergy, creating awareness in pollen sensitive individuals. Traditionally, pollen monitoring has been based on conventional microscopic counting techniques that are labor intensive and limited in the identification to the genus or family level. Molecular techniques provide an alternative approach that is less labor intensive and enable identification of any species by its genetic fingerprint. OBJECTIVE: To use quantitative polymerase chain reaction (qPCR) to evaluate pollen concentrations in air samples. METHODS: Juniperus pollen was selected as our model because of the importance of this pollen in the southcentral United States. We analyzed 105 air samples collected with a Burkard spore trap from 2013 to 2015 using species-specific primers and probes. To evaluate the feasibility of a molecular approach, we used duplicate air samples that allowed us to compare results from classical identification based on light microscopy with our qPCR results. RESULTS: Pollen concentrations from the qPCR data were significantly correlated with concentrations determined through light microscopy (R = 0.902, P < .001). We also confirmed an overlap in the pollination seasons between Juniperus ashei and Juniperus pinchotii and between J ashei and Juniperus virginiana. CONCLUSION: We found that this method correctly identified different Juniperus species present in mixed air samples in the southcentral United States, an accomplishment that cannot be achieved using microscopic identification. We conclude that the qPCR method is more accurate and sensitive than current pollen monitoring techniques and, therefore, has the potential to be used in various pollen monitoring stations.

Hygroscopic weight gain of pollen grains from Juniperus species.

Juniperus pollen is highly allergenic and is produced in large quantities across Texas, Oklahoma, and New Mexico. The pollen negatively affects human populations adjacent to the trees, and since it can be transported hundreds of kilometers by the wind, it also affects people who are far from the source. Predicting and tracking long-distance transport of pollen is difficult and complex. One parameter that has been understudied is the hygroscopic weight gain of pollen. It is believed that juniper pollen gains weight as humidity increases which could affect settling rate of pollen and thus affect pollen transport. This study was undertaken to examine how changes in relative humidity affect pollen weight, diameter, and settling rate. Juniperus ashei, Juniperus monosperma, and Juniperus pinchotii pollen were applied to greased microscope slides and placed in incubation chambers under a range of temperature and humidity levels. Pollen on slides were weighed using an analytical balance at 2- and 6-h intervals. The size of the pollen was also measured in order to calculate settling rate using Stokes' Law. All pollen types gained weight as humidity increased. The greatest settling rate increase was exhibited by J. pinchotii which increased by 24 %.

Ambrosia pollen in Tulsa, Oklahoma: aerobiology, trends, and forecasting model development.

BACKGROUND: Ambrosia pollen is an important aeroallergen in North America; the ability to predict daily pollen levels may provide an important benefit for sensitive individuals. OBJECTIVE: To analyze the long-term Ambrosia pollen counts and develop a forecasting model to predict the next day's pollen concentration. METHODS: Airborne pollen has been collected since December 1986 with a Burkard spore trap at the University of Tulsa. Summary statistics and season metrics were calculated for the 27 years of data. Concentration and previous-day meteorologic data from 1987 to 2011 were used to develop a multiple regression model to predict pollen levels for the following day. Model output was compared to 2012 and 2013 ragweed pollen data. RESULTS: The Tulsa ragweed season extends from the middle of August to late October. The mean start date is August 22, the mean peak date is September 10, and the mean end date is October 20. The mean cumulative season total is 11,599 pollen/m(3), and the mean daily concentration is 197 pollen/m(3). Previous-day meteorologic and phenologic data were positively related to pollen concentration (P < .001). Precipitation was modeled as a dichotomous variable. The final model included minimum temperature, dichotomous precipitation, dew point, and phenology variable (R = 0.7146, P < .001). Analysis of the model's accuracy revealed that the model was highly representative of the 2012 and 2013 seasons (R = 0.680, P < .001). CONCLUSION: Multiple regression models may be useful in explaining the variability of Ambrosia pollen levels. Further testing of the modeling parameters in different geographical areas is needed.

Changing pollen types/concentrations/distribution in the United States: fact or fiction?

The buildup of greenhouse gases in the atmosphere has resulted in global climate change that is having a significant effect on many allergenic plants through increases in plant productivity and pollen allergenicity and shifts in plant phenology. Based on experimental studies, increased atmospheric levels of carbon dioxide have directly increased plant productivity. This has affected the total amount of pollen produced in some species. Research has also shown increased levels of birch allergen at warmer temperatures. Warmer temperatures have resulted in earlier flowering for many spring-flowering species in many countries, recorded through visual observations of flowering and by airborne pollen. Increases in the cumulative season totals of various pollen types also have been recorded; some of these increases may be explained by changes in plant distribution.

Correlation of environmental factors with asthma and rhinitis symptoms in Tulsa, OK.

BACKGROUND: Airborne allergens, pollutants, and climatic changes are known to influence the symptoms of asthma patients. OBJECTIVE: To correlate airborne fungal spore and pollen concentrations, meteorological data, and airborne pollutants with asthma and rhinitis symptoms to develop predictive models for asthma severity. METHODS: Patients from the Tulsa community participated in this study from September 1 to October 31, 2000, by filling out daily symptom diaries and measuring morning and evening peak expiratory flow rates. Air samples were collected using a volumetric spore trap. Meteorological variables and maximum and average pollutants were also included in the analysis. Linear regression analyses were performed for all environmental variables and symptom scores. Forward stepwise multiple regression analyses were performed to determine sets of variables that could be used to predict the conditions of increased symptom severity. RESULTS: Twenty-four patients participated in this study. The predominant spore types included Cladosporium, ascospores, and basidiospores. The predominant pollen type was Ambrosia. September was unusually hot and dry in Tulsa, but 161 mm of precipitation fell in October, primarily during the last 11 days. Two periods of peak symptoms occurred during the study, the first during the peak week of Ambrosia and the second after a 22 degrees C drop in temperature over 6 days. Numerous environmental variables showed significant correlations with symptom scores; however, there was no single predictive model for all symptoms. CONCLUSIONS: Ambrosia pollen and other environmental variables, including ozone levels, were significantly correlated with asthma and rhinitis symptoms.

Pollen count forecasting.

Pollen forecasting is becoming increasingly important to allergists as an adjunct to effective patient care. Forecasts allow patients to avoid exposure to high pollen levels and prompt them to take prophylactic medication and to plan outdoor activities for periods of low pollen levels. Investigators are making progress in developing effective models for daily and seasonal forecasts for important pollen allergens; however, current models are limited to specific geographic areas. Models for the onset of the season for spring tree pollen are based on the chilling and heat units that are required before flowering can occur. Models for pollen season severity are based on regression analysis of preseason meteorologic conditions, and models for daily forecasts are based on the normal pollen curve and responses to day-to-day meteorologic conditions. When winds are favorable, long-distance transport can introduce allergenic pollen types into a local area. The Mountain Cedar Pollen Forecasting model, which combines day-to-day release forecasts at source areas and dispersion forecasts to downwind areas, has been reasonablly successful over the past 4 years. All pollen forecasting models are dependent on accurate meteorologic forecasts, and pollen forecasting will become more accurate as meteorologic forecasts improve.

An assessment of predictive forecasting of Juniperus ashei pollen movement in the Southern Great Plains, USA.

Juniperus ashei pollen, a significant aeroallergen, has been recorded during December and January in Tulsa, Oklahoma, over the past 20 years. The nearest upwind source for this pollen is populations growing in southern Oklahoma and central Texas, at distances of 200 km and 600 km respectively. Long-distance dispersal of J. ashei pollen into the Tulsa area shows a strong correlation with the trajectories of wind blowing across southern populations before traveling north towards eastern Oklahoma. The strong tie between climatic conditions and the occurrence of this aeroallergen within the Tulsa, Oklahoma, atmosphere provided a unique opportunity to forecast the dispersal, entrainment, and downwind deposition of this significant aeroallergen. Forecasts of long-distance J. ashei pollen dispersal began during the winter of 1998/1999. Each forecast uses defined climatic parameters to signal pollination at each source site. Coupled to these estimates of pollen release, forecast weather conditions and modeled wind trajectories are used to determine the threat of dispersal to downwind communities. The accuracy of these forecasts was determined by comparing the forecast "threat" to aerobiological records for the same period collected in the "Tulsa region". Analysis of the two seasons revealed only a single occurrence of "high" or "very high" pollen concentrations in Tulsa not directly linked to "moderate" or "severe" forecast threats from the southern source areas.