Forecasting daily total pollen concentrations on a global scale.

BACKGROUND: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. METHODS: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. RESULTS: The best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. CONCLUSIONS: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.

Fungal Sensitization and Human Allergic Disease.

PURPOSE OF THE REVIEW: Fungal sensitizations have been associated with hypersensitivity reactions with variable levels of evidence available to link types of fungi with human disease. We conducted systematic reviews of the literature to identify the strength of evidence linking lesser-studied fungi for which there are commercially available extracts to identify populations in which they were useful in clinical practice. RECENT FINDINGS: Excluding five fungi for which hundreds of articles were identified, there are 54 articles on the remaining fungi with clinical data. For 12 of the fungi, the prevalence of fungal sensitization varies in different hypersensitivity disorders due to factors related to geographic areas, age, and other underlying medical conditions. There were no studies linking seven genera to human disease. Most of the commercially available fungal extracts are uncommonly associated with hypersensitivity reactions in humans. Specific extracts may be useful in particular disease states such as allergic fungal sinusitis or allergic bronchopulmonary mycosis, or when routine testing fails to identify a cause of uncontrolled disease, such as in asthma.

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.

Impact of weather and climate change with indoor and outdoor air quality in asthma: A Work Group Report of the AAAAI Environmental Exposure and Respiratory Health Committee.

Weather and climate change are constant and ever-changing processes that affect allergy and asthma. The purpose of this report is to provide information since the last climate change review with a focus on asthmatic disease. PubMed and Internet searches for topics included climate and weather change, air pollution, particulates, greenhouse gasses, traffic, insect habitat, and mitigation in addition to references contributed by the individual authors. Changes in patterns of outdoor aeroallergens caused by increasing temperatures and amounts of carbon dioxide in the atmosphere are major factors linked to increased duration of pollen seasons, increased pollen production, and possibly increased allergenicity of pollen. Indoor air pollution threats anticipated from climate changes include microbial and mold growth secondary to flooding, resulting in displacement of persons and need for respiratory protection of exposed workers. Air pollution from indoor burning of mosquito repellants is a potential anticipatory result of an increase in habitat regions. Air pollution from fossil fuel burning and traffic-related emissions can alter respiratory defense mechanisms and work synergistically with specific allergens to enhance immunogenicity to worsen asthma in susceptible subjects. Community efforts can significantly reduce air pollution, thereby reducing greenhouse gas emission and improving air quality. The allergist's approach to weather pattern changes should be integrated and anticipatory to protect at-risk patients.

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.

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.

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 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 %.

Comparison of the interleukin-1ß-inducing potency of allergenic spores from higher fungi (basidiomycetes) in a cryopreserved human whole blood system.

BACKGROUND: Spores from basidiomycete fungi (basidiospores) are highly prevalent in the atmosphere of urban and rural settings. Studies have confirmed their potential to affect human health as allergens. Less is known about their potential to serve as stimuli of the innate immune system and induce proinflammatory reactions. METHODS: In this study, we evaluated the proinflammatory potential of spores from 11 allergenic basidiomycete species (gilled: Pleurotus ostreatus,Oudemansiella radicata,Armillaria tabescens,Coprinus micaceus,Pluteus cervinus, and Chlorophyllum molybdites, and nongilled: Pisolithus arhizus,Merulius tremellosus,Calvatia cyathiformis,Lycoperdon pyriforme, andBoletus bicolor) based on their potency to induce the release of the proinflammatory cytokine interleukin (IL)-1ß in a cryopreserved human whole blood system. In addition, the roles of morphological features of the spores (surface area, shape, and pigmentation) were examined for their role in the IL-1ß-including potency of spores. Peripheral blood from healthy volunteers was collected, pooled, and cryopreserved. After stimulating the cryopreserved pooled blood with 10(6) to 10(3) basidiospores/ml, the concentration of IL-1ß in culture supernatants was determined with ELISA. RESULTS: Basidiospores manifested concentration-dependent IL-1ß-inducing potency, which was more marked among basidiospores from gilled basidiomycetes. At higher concentrations of basidiospores, the IL-1ß-inducing potency could be differentiated in the cryopreserved human whole blood system. Morphological features did not correlate with the IL-1ß-inducing potency of the basidiospores, suggesting that nonmorphological properties modulate the IL-1ß-inducing potency. CONCLUSION: Our data provide evidence of the proinflammatory potential of basidiospores, and the utility of cryopreserved human whole blood as a human-based in vitro system to study the immune reactivity of allergenic basidiospores.

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.

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.

Aeroallergens and Climate Change in Tulsa, Oklahoma: Long-Term Trends in the South Central United States.

Climate change is having a significant effect on many allergenic plants resulting in increased pollen production and shifts in plant phenology. Although these effects have been well-studied in some areas of the world, few studies have focused on long-term changes in allergenic pollen in the South Central United States. This study examined airborne pollen, temperature, and precipitation in Tulsa, Oklahoma over 25 to 34 years. Pollen was monitored with a Hirst-type spore trap on the roof of a building at the University of Tulsa and meteorology data were obtained from the National Weather Service. Changes in total pollen intensity were examined along with detailed analyses of the eight most abundant pollen types in the Tulsa atmosphere. In addition to pollen intensity, changes in pollen season start date, end date, peak date and season duration were also analyzed. Results show a trend to increasing temperatures with a significant increase in annual maximum temperature. There was a non-significant trend toward increasing total pollen and a significant increase in tree pollen over time. Several individual taxa showed significant increases in pollen intensity over the study period including spring Cupressaceae and Quercus pollen, while Ambrosia pollen showed a significant decrease. Data from the current study also indicated that the pollen season started earlier for spring pollinating trees and Poaceae. Significant correlations with preseason temperature may explain the earlier pollen season start dates along with a trend toward increasing March temperatures. More research is needed to understand the global impact of climate change on allergenic species, especially from other regions that have not been studied.

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.

Guide for interpreting reports from inspections/investigations of indoor mold.

Inspections and testing of indoor environments for mold growth increased dramatically in the past decade. Allergists can now be presented copies of reports and laboratory data and asked to provide an interpretation, although allergists are seldom trained to review environmental data. There is no single sampling method that is both specific for mold growth and robust enough to reliably detect mold growth. There is no standard method for these inspections or testing and no widely recognized credential for investigators, and therefore reports also vary in quality, objectives, and thoroughness. Despite these issues, observations from informed inspections coupled with results from qualified analyses of samples that are collected with a useful strategy can usually indicate whether mold growth is present in a building, but the nature of the report should be assessed before any interpretation of the results and data are attempted. This rostrum discusses objectives of inspections, describes qualifications for investigators, outlines the limitations of various sampling methods applicable to mold and to some degree endotoxin, and provides guidance for data interpretation.

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.

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.

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.