Urban-scale variation in pollen concentrations: A single station is insufficient to characterize daily exposure

Comparison of Olea pollen sampling between gravimetric and volumetric traps (NW of Morocco)

Olea pollen is considered one of the most critical causes of respiratory allergic disease in the Mediterranean region, contributing to different symptoms in allergic sufferers. Due to the high increase in the prevalence of olive pollen caused by the expansion of cultivated areas especially in the Mediterranean region, the aim of this study was to examine the Olea pollen sampling efficiency between gravimetric and volumetric pollen traps over the period 2018-2022. Our data demonstrated that Olea pollen season start-date was generally recorded earlier with volumetric trap than shown with gravimetric method. Moreover, the peak dates occurred mainly in May for the years studied. Across the entire period, the greatest pollen concentrations were reached in the week 11th and >60 % of the total Olea pollen were already reached after the ninth week of sampling in 2020. In general, the evolution of pollen sampling for both methods demonstrates similar temporal development. Variations in the seasonal pollen integral could be attributed to the elevation above ground level at which the traps were placed, exposure to regional winds, the influence of long-range transported pollen and the mode of pollen transport. The findings indicate that the gravimetric method was equally practical as the volumetric method in estimating airborne pollen.

Pollen and viruses contribute to spatio-temporal variation in asthma-related emergency department visits

BACKGROUND

Asthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions.

OBJECTIVE

Here we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas.

METHODS

We used age stratified Poisson regression analyses to quantify the effects of allergenic pollen and viruses on asthma-related emergency department visits.

RESULTS

Young children (<5 years of age) had high asthma-related emergency department rates (24.1 visits/1,000,000 person-days), which were mainly attributed to viruses (51.2%). School-aged children also had high rates (20.7 visits/1,000,000 person-days), which were attributed to viruses (57.0%), Cupressaceae pollen (0.7%), and tree pollen (2.8%). Adults had lower rates (8.1 visits/1,000,000 person-days) which were attributed to viruses (25.4%), Cupressaceae pollen (0.8%), and tree pollen (2.3%). This risk was spread unevenly across space and time; for example, during peak Cuppressaceae season, this pollen accounted for 8.2% of adult emergency department visits near Austin where these plants are abundant, but 0.4% in cities like Houston where they are not; results for other age groups were similar.

CONCLUSIONS

Although viruses are a major contributor to asthma-related emergency department visits, airborne pollen can explain a meaningful portion of visits during peak pollen season and this risk varies over both time and space because of differences in plant composition.

Outdoor aeroallergen impacts on asthma exacerbation among sensitized and nonsensitized Philadelphia children

Background

Outdoor aeroallergens, such as pollens and molds, are known triggers of asthma exacerbation; however, few studies have examined children's aeroallergen response based on sensitization.

Objective

Our aim was to compare the relative impact of aeroallergen levels on asthma exacerbation between pediatric patients with asthma who tested positive or negative for sensitization to particular allergens.

Methods

A case-crossover design study was conducted to examine associations between outdoor aeroallergen levels and asthma exacerbation events among children living in Philadelphia, Pennsylvania, who were treated within a large pediatric care network. Sensitization to common allergens was characterized in a subset of patients with asthma exacerbation who had undergone skin prick testing (5.5%). Odds ratios (ORs) and 95% CIs were estimated in all patients with asthma exacerbation and in those sensitized or not sensitized to aeroallergens.

Results

Children who were sensitized to a particular allergen had higher odds of asthma exacerbation with exposure to the allergen (ie, early-season tree pollen, oak tree pollen, early-season weed pollen, and late-season molds) than did all patients with asthma or nonsensitized patients. For example, the association between early-season tree pollen and asthma exacerbation among sensitized children (>90th percentile vs ≤25th, OR = 2.28 [95% CI = 1.23-4.22]) was considerably stronger than that estimated among all patients (OR = 1.34 [95% CI = 1.19-1.50]), and it was also substantially different from the lack of association seen among nonsensitized children (OR = 0.89 [95% CI = 0.51-1.55] [P value for heterogeneity = .03]).

Conclusion

More prevalent allergy testing may be useful for prevention of asthma exacerbation by informing interventions targeted to sensitized children and tailored for particular aeroallergens.

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022-2023, Using the Automatic Sensor Beenose

As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter-spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.

Microscale pollen release and dispersal patterns in flowering grass populations

Characterizing pollen release and dispersion processes is fundamental for knowledge advancement in ecological, agricultural and public health disciplines. Understanding pollen dispersion from grass communities is especially relevant due to their high species-specific allergenicity and heterogeneously distributed source areas. Here, we aimed to address questions concerning fine level heterogeneity in grass pollen release and dispersion processes, with a focus on characterizing the taxonomic composition of airborne grass pollen over the grass flowering season using eDNA and molecular ecology methods. High resolution grass pollen concentrations were compared between three microscale sites (<300 m apart) in a rural area in Worcestershire, UK. The grass pollen was modelled with local meteorology in a MANOVA (Multivariate ANOVA) approach to investigate factors relevant to pollen release and dispersion. Simultaneously, airborne pollen was sequenced using Illumina MySeq for metabarcoding, analysed against a reference database with all UK grasses using the R packages DADA2 and phyloseq to calculate Shannon's Diversity Index (α-diversity). The flowering phenology of a local Festuca rubra population was observed. We found that grass pollen concentrations varied on a microscale level, likely attributed to local topography and the dispersion distance of pollen from flowering grasses in local source areas. Six genera (Agrostis, Alopecurus, Arrhenatherum, Holcus, Lolium and Poa) dominated the pollen season, comprising on average 77 % of the relative abundance of grass species reads. Temperature, solar radiation, relative humidity, turbulence and wind speeds were found to be relevant for grass pollen release and dispersion processes. An isolated flowering Festuca rubra population contributed almost 40 % of the relative pollen abundance adjacent to the nearby sampler, but only contributed 1 % to samplers situated 300 m away. This suggests that most emitted grass pollen has limited dispersion distance and our results show substantial variation in airborne grass species composition over short geographical scales.

A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.

Aerobiological monitoring in a desert type ecosystem: Two sampling stations of two cities (2017–2020) in Qatar

Background

The increasing number of aerobiological stations empower comparative studies to determine the relationship between pollen concentrations in different localities and the appropriate distance, which should be established between sampling stations. In Qatar, this is basically the first aerobiological study for a continuous monitoring interval.

Objectives

The study aimed to assess the abundance and seasonality of the most prevalent pollen types, plus identify potential differences between two sites within the country.

Methods

Airborne pollen data were collected during 2017–2020 by using Hirst-type volumetric samplers in Doha capital city and Al Khor city in Qatar, placed 50 km apart.

Results

Higher total pollen indexes were recorded in the Al Khor station (2931 pollen * day/m3) compared to the Doha station (1618 pollen * day/m3). Comparing the pollen spectrum between the sampling stations revealed that ten pollen types were found in common. Amaranthaceae and Poaceae airborne pollen constituted 73.5% and 70.9% of the total amount of pollen detected at the samplers of Al Khor station and Doha station. In both sampling sites, a very pronounced seasonality was shown; August–October appeared as the period with the most intense incidence of atmospheric herbaceous pollen, with 71% and 51% of the annual total counts in Al Khor and Doha stations, respectively. August (Al Khor, 21%; Doha, 9%), September (Al Khor, 33%; Doha, 26%), October (Al Khor, 17%; Doha, 16%) were the months in which the herbs pollen concentrations were highest.

Significant statistical differences between the two stations were observed in specific pollen types with local distribution in each trap’s vicinity.

Conclusions

Comparison of data obtained by the two samplers running at a distance of 50 Km indicated that potential inter-site differences could be attributed to the vegetation surrounding the city having a decisive influence on data collected.

Spatiotemporal assessment of aeromycoflora under differing urban green space, sampling height, and meteorological regimes: the atmospheric fungiscape of Thessaloniki, Greece

We studied the diversity and abundance of the airborne fungal spores in the city of Thessaloniki, Greece, for two consecutive years. Air samples were collected at one rooftop station (at 30 m) and six near-ground stations (at 1.5 m) that differed in the size and composition of adjacent green spaces. The effects of meteorological factors on airborne fungal spore concentrations were also explored. Cladosporium spores were dominant everywhere in the air of the city. The total concentration of the airborne fungal spores at 30 m was 10 times lower than near the ground. Differences in concentration and composition were far less pronounced among near-ground stations. The attributes of the fungal spore season did not change in a consistent way among stations and years. Concentrations at the near-ground stations matched the grouping of the latter into stations of high, intermediate, and low urban green space. Minimum air temperature was the primary meteorological factor affecting spore abundance, followed by relative humidity. Airborne fungal spores are more homogeneously distributed in the air of the city, but their concentrations decrease more rapidly with height than pollen.

Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada

BACKGROUND

The exacerbation of asthma and respiratory allergies has been associated with exposure to aeroallergens such as pollen. Within an urban area, tree cover, level of urbanization, atmospheric conditions, and the number of source plants can influence spatiotemporal variations in outdoor pollen concentrations.

OBJECTIVE

We analyze weekly pollen measurements made between March and October 2018 over 17 sites in Toronto, Canada. The main goals are: to estimate the concentration of different types of pollen across the season; estimate the association, if any, between pollen concentration and environmental variables, and provide a spatiotemporal surface of concentration of different types of pollen across the weeks in the studied period.

METHODS

We propose an extension of the land-use regression model to account for the temporal variation of pollen levels and the high number of measurements equal to zero. Inference is performed under the Bayesian framework, and uncertainty of predicted values is naturally obtained through the posterior predictive distribution.

RESULTS

Tree pollen was positively associated with commercial areas and tree cover, and negatively associated with grass cover. Both grass and weed pollen were positively associated with industrial areas and TC brightness and negatively associated with the northing coordinate. The total pollen was associated with a combination of these environmental factors. Predicted surfaces of pollen concentration are shown at some sampled weeks for all pollen types.

SIGNIFICANCE

The predicted surfaces obtained here can help future epidemiological studies to find possible associations between pollen levels and some health outcome like respiratory allergies at different locations within the study area.

Strong variations in urban allergenicity riskscapes due to poor knowledge of tree pollen allergenic potential

Exposure to allergenic tree pollen is an increasing environmental health issue in urban areas. However, reliable, well-documented, peer-reviewed data on the allergenicity of pollen from common tree species in urban environments are lacking. Using the concept of 'riskscape', we present and discuss evidence on how different tree pollen allergenicity datasets shape the risk for pollen-allergy sufferers in five cities with different urban forests and population densities: Barcelona, Montreal, New York City, Paris, and Vancouver. We also evaluate how tree diversity can modify the allergenic risk of urban forests. We show that estimates of pollen exposure risk range from 1 to 74% for trees considered to be highly allergenic in the same city. This variation results from differences in the pollen allergenicity datasets, which become more pronounced when a city's canopy is dominated by only a few species and genera. In an increasingly urbanized world, diverse urban forests offer a potentially safer strategy aimed at diluting sources of allergenic pollen until better allergenicity data is developed. Our findings highlight an urgent need for a science-based approach to guide public health and urban forest planning.

Pollen production for 13 urban North American tree species: Allometric equations for tree trunk diameter and crown area

Estimates of airborne pollen concentrations at the urban scale would be useful for epidemiologists, land managers, and allergy sufferers. Mechanistic models could be well suited for this task, but their development will require data on pollen production across cities, including estimates of pollen production by individual trees. In this study, we developed predictive models for pollen production as a function of trunk size, canopy area, and height, which are commonly recorded in tree surveys or readily extracted from remote sensing data. Pollen production was estimated by measuring the number of flowers per tree, the number of anthers per flower, and the number of pollen grains per anther. Variability at each morphological scale was assessed using bootstrapping. Pollen production was estimated for the following species: Acer negundo, Acer platanoides, Acer rubrum, Acer saccharinum, Betula papyrifera, Gleditsia triacanthos, Juglans nigra, Morus alba, Platanus x acerfolia, Populus deltoides, Quercus palustris, Quercus rubra, and Ulmus americana. Basal area predicted pollen production with a mean R2 of 0.72 (range: 0.41 - 0.99), whereas canopy area predicted pollen production with a mean R2 of 0.76 (range: 0.50 - 0.99). These equations are applied to two tree datasets to estimate total municipal pollen production and the spatial distribution of street tree pollen production for the focal species. We present some of the first individual-tree based estimates of pollen production at the municipal scale; the observed spatial heterogeneity in pollen production is substantial and can feasibly be included in mechanistic models of airborne pollen at fine spatial scales.

Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.