A 15-year survey of pollen aeroallergens in North Texas.

An aeropalynological study during the years 2014–2015 was performed in Hatay, which is a unique sociocultural and phytogeographical area located on the border of Turkey and Syria on the eastern coast of the Mediterranean. The sampling was performed by a Hirst-type volumetric sampler (Lanzoni VPPS 2000), and pollen grains of 54 taxa were identified, of which 83.21% of the annual sum belonged to woody taxa. The highest pollen concentration was recorded in February, of which a large amount came from the Cupressaceae/Taxaceae families. The diversity of the pollen reflected the vegetation of the area and plantations of the city center, but pollen grains from Euro-Siberian elements specific to Mount Amanos could not be recorded. Pollen types found at more than 3% of the annual pollen index and considered dominant pollen types were as follows: Cupressaceae/Taxaceae (50.86%), Olea europaea (12.67%), Moraceae (7.20%), Poaceae (5.99%), Quercus (5.35%), Urticaceae (3.79%) and Pinus (3.70%); almost all dominant pollen types in the city atmosphere were previously stated to be allergic. The main pollen season starting dates of common pollen types found were one or two weeks earlier than those of the surroundings. Many statistically significant correlations were found between daily pollen concentrations and daily meteorological parameters, e.g., Cupressaceae/Taxaceae Poaceae and Urticaceae pollen correlated negatively with mean temperature in both years, and in the hindermost two families daily pollen amounts significantly correlated with wind speed in the second year. Daily Olea europaea pollen concentration showed a significant negative correlation with the amount of total daily rainfall in the second year.

Climate and weather directly impact plant phenology, affecting airborne pollen. The objective of this systematic review is to examine the impacts of meteorological variables on airborne pollen concentrations and pollen season timing. Using PRISMA methodology, we reviewed literature that assessed whether there was a relationship between local temperature and precipitation and measured airborne pollen. The search strategy included terms related to pollen, trends or measurements, and season timing. For inclusion, studies must have conducted a correlation analysis of at least 5 years of airborne pollen data to local meteorological data and report quantitative results. Data from peer-reviewed articles were extracted on the correlations between seven pollen indicators (main pollen season start date, end date, peak date, and length, annual pollen integral, average daily pollen concentration, and peak pollen concentration), and two meteorological variables (temperature and precipitation). Ninety-three articles were included in the analysis out of 9,679 articles screened. Overall, warmer temperatures correlated with earlier and longer pollen seasons and higher pollen concentrations. Precipitation had varying effects on pollen concentration and pollen season timing indicators. Increased precipitation may have a short-term effect causing low pollen concentrations potentially due to "wash out" effect. Long-term effects of precipitation varied for trees and weeds and had a positive correlation with grass pollen levels. With increases in temperature due to climate change, pollen seasons for some taxa in some regions may start earlier, last longer, and be more intense, which may be associated with adverse health impacts, as pollen exposure has well-known health effects in sensitized individuals.

Forecasting daily airborne pollen concentrations is of great importance for management of seasonal allergies. This paper explores the performance of the pollen calendar as the most basic observation-oriented model for predicting daily concentrations of airborne Ambrosia, Betula and Poaceae pollen. Pollen calendars were calculated as the mean or median value of pollen concentrations on the same date in previous years of the available historic dataset, as well as the mean or median value of pollen concentrations of the smoothed dataset, pre-processed using moving mean and moving median. The performance of the models was evaluated by comparing forecasted to measured pollen concentrations at both daily and 10-day-average resolutions. This research demonstrates that the interpolation of missing data and pre-processing of the calibration dataset yields lower prediction errors. The increase in the number of calibration years corresponds to an improvement in the performance of the calendars in predicting daily pollen concentrations. However, the most significant improvement was obtained using four calibration years. The calendar models correspond well to the shape of the pollen curve. It was also found that daily resolution instead of 10-day averages adds to their value by emphasising variability in pollen exposure, which is important for personal assessment of dose-response for pollen-sensitive individuals.

Standard pollen monitoring programs evaluate outdoor pollen concentrations; however, information on indoor pollen is crucial for human wellbeing as people spend most of the day in indoor environments. In this study, we investigated the differences in indoor mountain cedar pollen loads between rooms of different uses and with different ventilation at The University of Texas in Austin and focused on the effect of rainy episodes on indoor/outdoor ratios of pollen concentrations. Pollen were sampled outdoors and indoors, specifically in seven rooms and in two thermal labs with controlled ventilation, during the daytime on 6 days in 2015. We calculated daily pollen concentrations, campaign pollen integrals (CPIn, the sum of all daily pollen concentrations) and ratios between indoor and outdoor concentrations (I/O ratio). Pollen concentrations differed substantially based on features related to room use and ventilation: Whereas the highest CPIn was observed in a room characterized by a frequently opened window and door, the smallest CPIn was related to a storeroom without any windows and no forced ventilation. Our results showed that rainy episodes were linked to a higher mean I/O ratio (0.98; non-rainy episodes: 0.05). This suggests that pollen accumulated indoors and reached higher levels than outdoors. Low ratios seem to signal a low level of risk for allergic people when staying inside. However, under very high outdoor pollen concentrations, small ratios can still be associated with high indoor pollen levels. In turn, high I/O ratios are not necessarily related to a (very) high indoor exposure. Therefore, I/O ratios should be considered along with pollen concentration values for a proper risk assessment. Exposure may be higher in indoor environments during prevailing precipitation events and at the end of the pollen season of a specific species. Standardized indoor environments (e.g., thermal labs) should be included in pollen monitoring programs.

Air pollution in large cities produces numerous diseases and even millions of deaths annually according to the World Health Organization. Pollen exposure is related to allergic diseases, which makes its prediction a valuable tool to assess the risk level to aeroallergens. However, airborne pollen concentrations are difficult to predict due to the inherent complexity of the relationships among both biotic and environmental variables. In this work, a stochastic approach based on supervised machine learning algorithms was performed to forecast the daily Olea pollen concentrations in the Community of Madrid, central Spain, from 1993 to 2018. Firstly, individual Light Gradient Boosting Machine (LightGBM) and artificial neural network (ANN) models were applied to predict the day of the year (DOY) when the peak of the pollen season occurs, resulting the estimated average peak date 149.1 ± 9.3 and 150.1 ± 10.8 DOY for LightGBM and ANN, respectively, close to the observed value (148.8 ± 9.8). Secondly, the daily pollen concentrations during the entire pollen season have been calculated using an ensemble of two-step GAM followed by LightGBM and ANN. The results of the prediction of daily pollen concentrations showed a coefficient of determination (r2) above 0.75 (goodness of the model following cross-validation). The predictors included in the ensemble models were meteorological variables, phenological metrics, specific site-characteristics, and preceding pollen concentrations. The models are state-of-the-art in machine learning and their potential has been shown to be used and deployed to understand and to predict the pollen risk levels during the main olive pollen season.

This study has been focused on airborne pollen concentration in Northern Tunis. Pollen has been detected by a volumetric Hirst-type spore trap. This suction sampler was placed for two hydrologic years in the area of Mornag, northeastof Tunisia (36°40N; 10°17E). Fifty-two taxa were identified with heterogeneous daily pollen concentrations and a dominance of anemophilous plants. The main pollen types detected in the atmosphere were Olea europaea (38.7 and 20.75%), Cupressus (33.57 and 55.4%), Urticaceae (9.22 and 12.24%), Poaceae (3.55 and 3.32%), Mercurialis annua (2.96 and 1.6%) and Amaranthaceae (2.49 and 1.55%). The monthly pollen spectrum indicated a seasonal periodicity of airborne pollen with the main pollen season during spring. Two pollen seasons have been observed during these hydrologic years, due to both Cupressus and Amaranthaceae airborne pollen is represented during winter or spring, and also during autumn and late summer, respectively. Other pollen types represent a long pollen season, i.e., Urticaceae, starting in autumn and following until late spring. Daily pollen concentration showed a different behavior during the flowering season between both years, observing differences related to pollen index. Correlation between daily pollen concentrations of the dominant taxa showed a positive and significant correlation between airborne pollen concentrations of spring-pollinated taxa and mean temperature, but negative with maximum temperature, humidity and rainfall. In the case of minimum temperature, a different response, positive for trees and negative for herbaceous plants, has been observed.

Background and Aims: The ongoing spread of Ambrosia artemisiifolia in Europe is an increasing problem for human health and as an agricultural and non-agricultural weed. Hungarian Aerobiological Network (HAN) has monitored the airborne pollen of ragweed for 18 years, these data are a sound basis to create indicators to monitor the changes of the ragweed pollen season in time and space and the population exposure. Methods: HAN has 18 monitoring sites out of which 8 sites were selected being representative for the characteristic macroclimate types of the country. The relevant population was defined as the population living around the monitoring site in a circle of 17.5 km. The start and end of the pollen season (1% resp. 99% of cumulative daily pollen count) was defined, daily pollen concentration were categorised into 7 groups (0-9, 10-29, 30-99, 100-299, 300-499, ≥500 pollen grains/m3 resp. missing value). Two sub-indices were defined and computed by year. Rate of time of population exposure to pollen concentration categories (TR) and rate of population (PR) exposed to different categories of daily pollen concentration at selected monitoring sites during the pollen season TR(x) and PR(x) indicate, what percentage of the total population is exposed to a given pollen concentration category (x) in what percentage of days of the total pollen season. Results: Based on selected pollen data for 2010, 52.2% of the population was exposed to daily pollen concentration over 30 grains/m3 (evoking allergic symptoms in every patient) during 40.8% of days of the season (32 days). 3.7% of the population was exposed to ≥500 grains/m3 (extremely high category) for 4.8% of days. Conclusions: In the future the indices can be used to examine spatial differences and time trends. This project is supported by the New Hungary Development Plan (Project ID:TÁMOP-4.2.1/B-09/1/KMR-2010-0005).

Do the threats of alder and birch allergenic pollen differ within an urban area?

We examined the atmospheric conditions favourable to the occurrence of maximum concentrations of ragweed pollen with an extremely high risk of producing allergy. Over the 2002-2009 period, daily pollen data collected in Zagreb were used to identify two periods of high pollen concentration (> 600 grains/m(3)) for our analysis: period A (3-4 September 2002) and period B (6-7 September 2003). Synoptic conditions in both periods were very similar: Croatia was under the influence of a lower sector high pressure system moving slowly eastward over Eastern Europe. During the 2002-2009 period, this type of weather pattern (on ~ 70% of days), in conjunction with almost non-gradient surface pressure conditions in the area (on ~ 30% of days) characterised days when the daily pollen concentrations were higher than 400 grains/m(3). Numerical experiments using a mesoscale model at fine resolution showed successful multi-day simulations reproducing the local topographic influence on wind flow and in reasonable agreement with available observations. According to the model, the relatively weak synoptic flow (predominantly from the eastern direction) allowed local thermal circulations to develop over Zagreb during both high pollen episodes. Two-hour pollen concentrations and 48-h back-trajectories indicated that regional-range transport of pollen grains from the central Pannonian Plain was the cause of the high pollen concentrations during period A. During period B, the north-westward regional-range transport in Zagreb was supplemented significantly by pronounced horizontal recirculation of pollen grains. This recirculation happened within the diurnal local circulation over the city, causing a late-evening increase in pollen concentration.

Olea europaea L. pollen is one of the main causes of pollinosis and respiratory diseases in the Iberian Peninsula (IP). The aim of this study was to provide a pollen calendar in different regions of the IP, which could help allergists and allergic patients in the management of Olea europaea allergic diseases, and to update/complement what has already been reported on olive trees’ aeropalynology in this region. Airborne Olea pollen dynamics were analyzed over a period of 8 years in a total of 21 localities, 7 in Portugal and 14 in Spain. Airborne pollen monitoring was carried out using the Hirst-type spore trap method and following the recommendations of the Quality Control Working Group of the European Aerobiology Society. The daily pollen count, the annual pollen profile, the Annual Pollen Integral (APIn), the Seasonal Pollen Integral (SPIn) and the Pollen Peak, all expressed in number of pollen grains per cubic metre of air, together with the main pollen season and its characteristics, the Start Day, the End Day and the length of the pollen season, were calculated for each sampling station. Differences in mean Olea pollen concentration between odd and even years were also analyzed. On average, the main pollen season (MPS) started in April/May and ended in June, with Pollen Peaks recorded in May, except in Burgos, where it was recorded in June. The longest MPS occurred in Lisbon, Oviedo and Valencia (53 days) and the shortest in Vitoria (25 days). A high daily pollen concentration (i.e., >200 grains/m3) was recorded between 1 and 38 days along the year in all sampling stations of the southwest quadrant of the IP and in Jaén. A biannual pattern, characterized by alternating years of high and low pollen production, was found in the southwest of the IP. In conclusion, the study provided a deeper understanding of the pollination behaviour of olive trees in the IP and allowed the establishment of a representative Olea pollen calendar for this region. In addition, our results suggest the usefulness of investigating more detailed relationships between annual Olea pollen, allergen sensitization and symptoms, both for allergists involved in the study and management of allergic respiratory diseases caused by this species and for the self-management of disease in allergic subjects.

Influence of ENSO, droughts, and temperature rise on pollen and pollen seasons in Australia.

In order to study allergic people responding to daily changes in pollen concentrations, we compared personal diary data on allergic symptoms and the use of allergy medicines to daily pollen counts during the two unequal alder and birch pollen seasons of 2009 and 2010. Almost 90% of the 61 subjects with physician-diagnosed birch pollinosis developed conjunctival, nasal or other symptoms during the peak birch pollination. Most subjects (95%) also reported symptoms during the alder pollination. Despite a delay between the most severe symptoms and the pollen peaks and the increased risk of allergy symptoms between the alder and birch pollen peaks at much lower pollen concentrations, the number of subjects with allergy symptoms correlated with the daily pollen concentrations in both years (r 09 = 0.35, r 10 = 0.36, p < 0.01). The positive correlation was even stronger (r 09 = 0.69, r 10 = 0.74, p < 0.001) in relation to the cumulative sum of daily concentrations. The use of allergy medicines precisely followed the abundance of allergy symptoms in both years (r 09 = 0.96, r 10 = 0.70, p < 0.001). We conclude that there is a fair correlation between the daily allergy symptoms and the particular pollen concentrations, but the risk of developing symptoms at low, moderate and high concentrations is affected by the progression of the pollen season.

Keywords: Pollen, Machine Learning, Allergic Disease Abstract Background and aim High concentrations of airborne pollen trigger seasonal allergies and possibly severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies to determine the effects on cardiovascular, respiratory, and cognitive health. We aimed to develop a spatiotemporal land-use regression model for pollen, predicting daily concentrations at a fine spatial resolution of 1x1km across Switzerland between 2003 and 2020. Methods Daily pollen concentrations for hazel, alder, ash, birch, and grasses were available from 14 sites. We considered a range of spatial (elevation, tree type), temporal (date, season, month, week and day of the year, national daily pollen concentration) and spatiotemporal predictors (wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index (NDVI), and land-use (CLC, Landsat satellite) to explain variation in total pollen concentration for five specific pollen species. We applied a range of feature engineering techniques to encode categorical variables (land-use) and fill in missing values (Landsat). We applied a random forest model with 5-fold cross-validation. Results The median grass pollen concentration was 24 pollen/m3 (P5-P95 range 0-187 pollen/m3) during the main grass pollen season (May-July for all years). Preliminary results of a model predicting grass pollen concentration achieved an overall R2 of 0.74 and a root mean squared error (RMSE) of 24.12 pollen/m3 (cross-validation). Temperature, humidity, wind speed, NDVI, Landsat, average national daily pollen concentration and date features were the most important predictors for grass pollen concentration. Conclusions Building upon national observed pollen concentrations and using random forest machine learning, these spatiotemporal pollen models will serve to estimate individual residential pollen exposure. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions using historical data from the Swiss National Cohort and the Swiss Federal Office of Statistics.

Previous research indicated that airborne ragweed pollen concentrations may be influenced by weather-related factors. Therefore, the object of this work was to examine the variation in daily pollen concentrations during four ragweed pollen seasons (2006–2009) in the highly urban area of Zagreb. Ragweed pollen grains were collected using a Burkard volumetric sampler (N45°49′55″, E15°58′54″). Meteorological data (maximum, minimum and mean temperatures, relative humidity, wind speed, precipitation, atmospheric pressure and irradiance) were related to daily pollen counts during the ragweed pollen season. The ragweed pollen season started around late July in 2007 and 2009, while it started on 15 August in 2006, the year characterized by a cold spring. However, the start dates of the pollen seasons were not related to the accumulation of thermal units. Maximum daily concentration of 363 grains m−3 was detected on 27 August 2008. Total airborne pollen concentrations ranged from 1188 grains m−3 in 2007 to 4384 grains m−3 in the following year, whereas the duration of ragweed pollen season varied from 50 days in 2008 to 72 days in 2007. The peak of the ragweed pollen season varied from 21 days in 2007 to 36 days in 2009 for airborne pollen concentrations ≥20 grains m−3 and from 1 day in 2007 to 20 days in 2008 for airborne pollen concentrations >80 grains m−3. Airborne pollen levels were affected by weather parameters such as temperature, sunshine, relative humidity, precipitation and wind speed in some ragweed pollen seasons in Zagreb, but these responses were inconsistent over the entire investigated period. Our study showed that large year-to-year variations in atmospheric pollen concentrations in Zagreb could not be consistently related to any of the analysed weather parameters.

High concentrations of airborne pollen trigger seasonal allergies and possibly more severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies, in order to study the effects of pollen exposure. We aimed to develop a spatiotemporal machine learning model predicting daily pollen concentrations at a spatial resolution of 1 × 1 km across Switzerland between 2000 and 2019. Daily pollen concentrations for five common, highly allergenic pollen types (hazel, alder, birch ash, and grasses) were available from fourteen measurement sites across Switzerland. We considered several predictors such as elevation, species distribution, wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index, and land-use (CORINE, Landsat satellite) to explain variation in pollen concentration. We employed feature engineering techniques to encode categorical variables and fill in missing values. We applied a random forest machine learning model with 5-fold cross-validation. The 5th–99th percentiles for concentrations of hazel, alder, birch, ash, and grass pollen at the pollen monitoring stations were 0–298, 0–306, 0–1153, 0–800, and 0–290 pollen grains/m3, respectively. The results of a predictive model for these concentrations yielded overall R2 values of 0.87, 0.84, 0.89, 0.88, and 0.91, and temporal root mean squared errors (RMSEs) of 16.07, 16.72, 69.04, 41.50, and 22.45 pollen grains/m3. An analysis of predictor variable importance indicates that the average national daily pollen concentration is the most important predictor of pollen concentrations for all pollen types. Furthermore, meteorological variables including temperature, total precipitation, humidity, boundary layer height, wind speed, and wind direction, as well as date and satellite features, are important factors in pollen concentration prediction. These spatiotemporal pollen models will serve to estimate individual residential pollen exposure for epidemiological studies. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions in Switzerland.