Relationships between airborne pollen grains, wind direction and land cover using GIS and circular statistics

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning

The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen allergy risks requires identifying key factors and their thresholds for aerosol pollen. To address this, we developed a technical framework combining advanced machine learning and SHapley Additive exPlanations (SHAP) technology, focusing on Beijing. By analyzing meteorological data and vegetation phenology, we identified the factors influencing next-day's pollen concentration (NDP) in Beijing and their thresholds. Our results highlight vegetation phenology data from Synthetic Aperture Radar (SAR), temperature, wind speed, and atmospheric pressure as crucial factors in spring. In contrast, the Normalized Difference Vegetation Index (NDVI), air temperature, and wind speed are significant in autumn. Leveraging SHAP technology, we established season-specific thresholds for these factors. Our study not only confirms previous research but also unveils seasonal variations in the relationship between radar-derived vegetation phenology data and NDP. Additionally, we observe seasonal fluctuations in the influence patterns and threshold values of daily air temperatures on NDP. These insights are pivotal for improving pollen concentration prediction accuracy and managing allergic risks effectively.

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.

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.

Influence of meteorological parameters and air pollutants on the airborne pollen of city Chandigarh, India

Pollen, climatic variables and air pollutants coexist in nature with the potential to interact with one another and play a crucial role in increasing allergic diseases. The current study evaluates the influence of meteorological parameters and air pollutants on the airborne pollen in an urban city, Chandigarh, situated in the Indo-Gangetic Plains. Airborne pollen monitoring was done following Spanish Aerobiological Network guidelines and dynamics of daily total pollen and six most abundant taxa were studied from June 2018 to June 2020. Among meteorological parameters, temperature and wind were the most correlated and influential parameters to airborne pollen concentration. Annual Pollen Integral (APIn) of Cannabis sativa (r = 0.52), Parthenium hysterophorus (r = 0.27), Poaceae (r = 0.32) and total pollen concentration (r = 0.30) showed a statistically significant positive correlation with temperature. In contrast, precipitation and relative humidity negatively correlated with APIn of total pollen concentration, Eucalyptus sp. and Poaceae except for Parthenium hysterophorus and Celtis occidentalis. Similar results were found with Seasonal Pollen Integral (SPIn) of total pollen concentration, six major taxa and meteorological variables. Spearman correlation performed for NOx showed a significant positive correlation among APIn and SPIn of Celtis occidentalis and insignificant among APIn and SPIn of Eucalyptus sp. and Morus alba. In contrast, except for Eucalyptus sp., PM₁₀ and PM_2.5 were negatively correlated among APIn and SPIn of total pollen concentration and other major taxa. Spearman's correlation of APIn and SPIn for each pollen taxon, meteorological parameters and air pollutants suggests that each taxon has a different pattern in response to all parameters. The study findings suggest that pollen response must be examined at the taxon level, not the assemblage level, having long time-series data. This will help to compute future scenarios of changing environmental factors and comprehend the relationships and trends among meteorology, air pollutants and aerobiology.

Atmospheric transport reveals grass pollen dispersion distances

Identifying the origin of bioaerosols is of central importance in many biological disciplines, such as human health, agriculture, forestry, aerobiology and conservation. Modelling sources, transportation pathways and sinks can reveal how bioaerosols vary in the atmosphere and their environmental impact. Grass pollen are particularly important due to their widely distributed source areas, relatively high abundance in the atmosphere and high allergenicity. Currently, studies are uncertain regarding sampler representability between distance and sources for grass pollen. Using generalized linear modelling, this study aimed to analyse this relationship further by answering the question of distance-to-source area contribution. Grass pollen concentrations were compared between urban and rural locations, located 6.4 km apart, during two years in Worcestershire, UK. We isolated and refined vegetation areas at 100 m × 100 m using the 2017 CEH Crop Map and conducted atmospheric modelling using HYSPLIT to identify which source areas could contribute pollen. Pollen concentrations were then modelled with source areas and meteorology using generalized linear mixed-models with three temporal variables as random variation. We found that the Seasonal Pollen Integral for grass pollen varied between both years and location, with the urban location having higher levels. Day of year showed higher temporal variation than the diurnal or annual variables. For the urban location, grass source areas within 30 km had positive significant effects in predicting grass pollen concentrations, while source areas within 2-10 km were important for the rural one. The source area differential was likely influenced by an urban-rural gradient that caused differences in the source area contribution. Temperature had positive highly significant effects on both locations while precipitation affected only the rural location. Combining atmospheric modelling, vegetation source maps and generalized linear modelling was found to be a highly accurate tool to identify transportation pathways of bioaerosols in landscape environments.

Variations in airborne pollen and spores in urban Guangzhou and their relationships with meteorological variables

Airborne pollen causes various types of allergies in humans, and the extent of allergic infection is related to the presence of different types of sporo-pollen and existing meteorological conditions in a certain area. Therefore, an aeropalynological study of 72 airborne samples with a hydrofluoric acid (HF) treatment was conducted in the Haizhu district of Guangzhou, China, in 2016, to identify the temporal variations in airborne sporo-pollen and the relationship between airborne sporo-pollen concentrations and different meteorological variables in Guangzhou, China. Forty-five types of airborne pollen, seven types of airborne spores, and some undetermined sporo-pollen taxa were identified with two separate plant habitats occurring during this period (from January to December 2016): arboreal pollen (tree-based) and non-arboreal pollen (herb, shrub, aquatic, liane, etc.). Furthermore, the daily records of four key meteorological variables (temperature, precipitation, relative humidity, and wind speed) were acquired to distinguish the pollen seasons and correlated with Spearman's rho test to establish a pollen-weather data book with the seasonal variations. The two leading seasons were identified based on pollen abundance: spring and autumn. Among them, the primary dominant sporo-pollen families during the spring season were Poaceae, Pinaceae, Euphorbiaceae, Moraceae, Microlepia sp., and Polypodiaceae. Conversely, Artemisia sp., Asteraceae, Cyperaceae, Poaceae, Alnus sp., Corylus sp., Myrtaceae, and Rosaceae were the dominant pollen species during autumn. However, few pollen grains were identified in January, May-July, and December. The statistical analysis revealed that temperature had both positive and negative correlations with sporo-pollen concentrations. However, precipitation and relative humidity had a strong impact on the sporo-pollen dispersion and exhibited a negative correlation with the sporo-pollen concentrations. The wind speed had a positive but strong correlation with the sporo-pollen concentration during the study period. Some inconsistent results were found due to environmental variations, vegetation type, and climate change around the study area. This study will facilitate the identification of pollen seasons to prevent the occurrence of pollen-related allergies in the Guangzhou city area.

Understanding hourly patterns of Olea pollen concentrations as tool for the environmental impact assessment

Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011-2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Q1: 0%-25%), in-season (Q2 and Q3: 25%-75%) and postseason (Q4: 75%-100%). Days with pollen concentrations above 100 grains/m³ were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in cluster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves.

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

Epidemiological analyses of airborne allergenic pollen often use concentration measurements from a single station to represent exposure across a city, but this approach does not account for the spatial variation of concentrations within the city. Because there are few descriptions of urban-scale variation, the resulting exposure measurement error is unknown but potentially important for epidemiological studies. This study examines urban scale variation in pollen concentrations by measuring pollen concentrations of 13 taxa over 24-hr periods twice weekly at 25 sites in two seasons in Detroit, Michigan. Spatio-temporal variation is described using cumulative distribution functions and regression models. Daily pollen concentrations across the 25 stations varied considerably, and the average quartile coefficient of dispersion was 0.63. Measurements at a single site explained 3-85% of the variation at other sites, depending on the taxon, and 95% prediction intervals of pollen concentrations generally spanned one to two orders of magnitude. These results demonstrate considerable heterogeneity of pollen levels at the urban scale, and suggest that the use of a single monitoring site will not reflect pollen exposure over an urban area and can lead to sizable measurement error in epidemiological studies, particularly when a daily time-step is used. These errors might be reduced by using predictive daily pollen levels in models that combine vegetation maps, pollen production estimates, phenology models and dispersion processes, or by using coarser time-steps in the epidemiological analysis.

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other one at rooftop (35 m AGL), 4.2 km apart. In general, 1.4 ± 0.5 times higher pollen amounts were measured by the trap located at ground level, but this effect was less than expected considering the height difference between the traps. Pollen from woody trees such as Alnus, Betula, Corylus, Fraxinus, Picea, Pinus and Quercus showed a good agreement between the traps in terms of timing and intensity. Similar amounts of pollen were recorded in the two traps when pollen sources were more abundant outside of the city. In contrast, pollen concentrations from Cupressaceae/Taxaceae, Carpinus and Tilia were influenced by nearby pollen sources. The representativeness of both traps for herbaceous pollen depended on the dispersal capacity of the pollen grains, and in the case of Poaceae pollen, nearby pollen sources may influence the pollen content in the air. The timing of the pollen season was similar for both sites; however, the season for some pollen types ended later at ground level probably due to resuspension processes that would favor recirculation of pollen closer to ground level. We believe measurements from the higher station provides a picture of background pollen levels representative of a large area, to which local sources add additional and more variable pollen amounts.

Lidar-Derived Tree Crown Parameters: Are They New Variables Explaining Local Birch (Betula sp.) Pollen Concentrations?

Birch trees are abundant in central and northern Europe and are dominant trees in broadleaved forests. Birches are pioneer trees that produce large quantities of allergenic pollen efficiently dispersed by wind. The pollen load level depends on the sizes and locations of pollen sources, which are important for pollen forecasting models; however, very limited work has been done on this topic in comparison to research on anthropogenic air pollutants. Therefore, we used highly accurate aerial laser scanning (Light Detection and Ranging—LiDAR) data to estimate the size and location of birch pollen sources in 3-dimensional space and to determine their influence on the pollen concentration in Poznań, Poland. LiDAR data were acquired in May 2012. LiDAR point clouds were clipped to birch individuals (mapped in 2012–2014 and in 2019), normalised, filtered, and individual tree crowns higher than 5 m were delineated. Then, the crown surface and volume were calculated and aggregated according to wind direction up to 2 km from the pollen trap. Consistent with LIDAR data, hourly airborne pollen measurements (performed using a Hirst-type, 7-day volumetric trap), wind speed and direction data were obtained in April 2012. We delineated 18,740 birch trees, with an average density of 14.9/0.01 km2, in the study area. The total birch crown surface in the 500–1500 m buffer from the pollen trap was significantly correlated with the pollen concentration aggregated by the wind direction (r = 0.728, p = 0.04). The individual tree crown delineation performed well (r2 ≥ 0.89), but overestimations were observed at high birch densities (> 30 trees/plot). We showed that trees outside forests substantially contribute to the total pollen pool. We suggest that including the vertical dimension and the trees outside the forest in pollen source maps have the potential to improve the quality of pollen forecasting models.

Urban aerobiological risk mapping of ornamental trees using a new index based on LiDAR and Kriging: A case study of plane trees

Ornamental trees bring benefits for human health, including reducing urban pollution. However, some species, such as plane trees (Platanus sp.), produce allergenic pollen. Consequently, urban maps are a valuable tool for allergic patients and allergists, but they often fail to include variables that contribute to the "building downwash effect", such as the width and shape of streets and the height of buildings. Other factors that directly influence pollen dispersion (slopes and other geographical features) also have not traditionally been discussed. The LiDAR (Laser Imaging Detection and Ranging) technique enables one to consider these variables with high accuracy. This work proposes an Aerobiological Index to create Risk maps for Ornamental Trees (AIROT) and the establishment of potential areas of risk of exposure to Platanus pollen. LiDAR data from five urban areas were used to create the DEM and DSM (Digital Elevation and Surface Models) needed to perform further analysis. GIS software was used to map the points for each city and to create risk maps by Kriging, with stable (3 cases) and exponential function (2 cases) as the optimal models. In short, the AIROT index was a useful tool to map possible biological risks in cities. Since AIROT allows each city to consider its own characteristics, including geographical specifications, by using remote sensing and geostatistics techniques, the establishment of risk maps and healthy itineraries is valuable for allergic patients, allergists, architects and urban planners. This new aerobiological index provides a new decision-making tool related to urban planning and allergenicity assessment.

Allergenic pollen production across a large city for common ragweed (Ambrosia artemisiifolia)

Predictions of airborne allergenic pollen concentrations at fine spatial scales require information on source plant location and pollen production. Such data are lacking at the urban scale, largely because manually mapping allergenic pollen producing plants across large areas is infeasible. However, modest-sized field surveys paired with allometric equations, remote sensing, and habitat distribution models can predict where these plants occur and how much pollen they produce. In this study, common ragweed (Ambrosia artemisiifolia) was mapped in a field survey in Detroit, MI, USA. The relationship between ragweed presence and habitat-related variables derived from aerial imagery, LiDAR, and municipal data were used to create a habitat distribution model, which was then used to predict ragweed presence across the study area (392 km²). The relationship between inflorescence length and pollen production was used to predict pollen production in the city. Ragweed occurs in 1.7% of Detroit and total pollen production is 312 × 10¹² pollen grains annually, but ragweed presence was highly heterogeneous across the city. Ragweed was predominantly found in in vacant lots (75%) and near demolished structures (48%), and had varying associations with land cover types (e.g., sparse vegetation, trees, pavement) detected by remote sensing. These findings also suggest several management strategies that could help reduce levels of allergenic pollen, including appropriate post-demolition management practices. Spatially-resolved predictions for pollen production will allow mechanistic modeling of airborne allergenic pollen and improved exposure estimates for use in epidemiological and other applications.

Relationship of NDVI and oak (Quercus) pollen including a predictive model in the SW Mediterranean region

Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI) related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus) daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx) of Spain and the Fifth National Forest Inventory (IFN5) from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN) was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77) between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.

Tracking Montane Mediterranean grasslands: Analysis of the effects of snow with other related hydro-meteorological variables and land-use change on pollen emissions

This paper explores the dynamics of temporal evolution of the high mountain Mediterranean grasslands, (Sierra Nevada, Spain SE). The indicator used is the emission of pollen (Pollen Index, PI) with respect to two important aspects: the incidence of the snow dynamic together with other hydro-meteorological parameters, and the changes in land use, which can Influence the evolution of the grasslands throughout time. The results reveal that pollen emissions in the last 25 years have shown a slight downward trend, with large interannual fluctuations, which are a consequence of diverse environmental factors, both general and specific to the area. One of the most influential parameters on pollen concentrations is snow cover, which reinforces the importance of the presence of snow-packs as water resource outside the winter season in the High Mediterranean Mountain environments. The changes in land use experienced in the area are a driver of change, especially due to the losses experienced in the last decades in the preferred habitats for many species of grasses. It can be concluded that the vulnerability of these ecosystems will be affected by an increase in winter temperatures and/or a decrease in rainfall (climate change) and an increase in the intensity of anthropogenic activities on land use. In this context, the PI is shown as a useful indicator of global change given its sensitivity to both anthropic and hydro-meteorological changes. In addition, it has a wide range of spatial detection and discrimination capacity by altitudinal dimensions.