A comprehensive aerobiological study of the airborne pollen in the Irish environment.

Respiratory allergies triggered by pollen allergens represent a significant health concern to the Irish public. Up to now, Ireland has largely refrained from participating in long-term aerobiological studies. Recently, pollen monitoring has commenced in several sampling locations around Ireland. The first results of the pollen monitoring campaigns for Dublin (urban) and Carlow (rural) concerning the period 2017-2019 and 2018-2019, respectively, are presented herein. Additional unpublished pollen data from 1978-1980 and, 2010-2011 were also incorporated in creating the first pollen calendar for Dublin. During the monitoring period over 60 pollen types were identified with an average Annual Pollen Integral (APIn) of 32,217 Pollen × day/m3 for Dublin and 78,411 Pollen × day/m3 for Carlow. The most prevalent pollen types in Dublin were: Poaceae (32%), Urticaceae (29%), Cupressaceae/Taxaceae (11%), Betula (10%), Quercus (4%), Pinus (3%), Fraxinus (2%), Alnus (2%) and Platanus (1%). The predominant pollen types in Carlow were identified as Poaceae (70%), Urticaceae (12%), Betula (10%), Quercus (2%), Fraxinus (1%) and Pinus (1%). These prevalent pollen types increased in annual pollen concentration in both locations from 2018 to 2019 except for Fraxinus. Although higher pollen concentrations were observed for the Carlow (rural) site a greater variety of pollen types were identified for the Dublin (urban) site. The general annual trend in the pollen season began with the release of tree pollen in early spring, followed by the release of grass and herbaceous pollen which dominated the summer months with the annual pollen season coming to an end in October. This behaviour was illustrated for 21 different pollen types in the Dublin pollen calendar. The correlation between ambient pollen concentration and meteorological parameters was also examined and differed greatly depending on the location and study year. A striking feature was a substantial fraction of the recorded pollen sampled in Dublin did not correlate with the prevailing wind directions. However, using non-parametric wind regression, specific source regions could be determined such as Alnus originating from the Southeast, Betula originating from the East and Poaceae originating from the Southwest. Supplementary Information: The online version contains supplementary material available at 10.1007/s10453-022-09751-w.

Local NO2 concentrations and asthma among over-50s in Ireland: A microdata analysis.

BACKGROUND: Links between air pollution and asthma are less well established for older adults than some younger groups. Nitrogen dioxide (NO2) concentrations are widely used as an indicator of transport-related air pollution, and some literature suggests NO2 may directly affect asthma. METHODS: This study used data on 8162 adults >50 years old in the Republic of Ireland to model associations between estimated annual outdoor concentration of NO2 and the probability of having asthma. Individual-level geo-coded survey data from The Irish Longitudinal Study on Ageing (TILDA) were linked to model-based estimates of annual average NO2 at 50 m resolution. Asthma was identified using two methods: self-reported diagnoses and respondents' use of medications related to obstructive airway diseases. Logistic regressions were used to model the relationships. RESULTS: NO2 concentrations were positively associated with the probability of asthma [marginal effect (ME) per 1 ppb of airborne NO2 = 0.24 percentage points asthma self-report, 95% confidence interval (CI) 0.06-0.42, mean asthma prevalence 0.09; for use of relevant medications ME = 0.21 percentage points, 95% CI 0.049-0.37, mean prevalence 0.069]. Results were robust to varying model specification and time period. Respondents in the top fifth percentile of NO2 exposure had a larger effect size but also greater standard error (ME = 2.4 percentage points asthma self-report, 95% CI -0. 49 to 5.3). CONCLUSIONS: Associations between local air pollution and asthma among older adults were found at relatively low concentrations. To illustrate this, the marginal effect of an increase in annual average NO2 concentration from sample minimum to median (2.5 ppb) represented about 7-8% of the sample average prevalence of asthma.

An Exploratory Study of Extreme Sport Athletes' Nature Interactions: From Well-Being to Pro-environmental Behavior.

Traditionally, perceptions about extreme sport athletes being disconnected from nature and a risk-taking population have permeated the research literature. Drawing upon theoretical perspectives from environmental, sport, organizational and positive psychology, this qualitative study attempts to explore the lived experiences of four male and four female extreme sport athletes. The purpose of this study was to gain insight and understanding into the individuals' attitudes toward the benefits of extreme sport activities for well-being, resilience and pro-environmental behavior. Eight participants (Mean age = 40.5 years; SD = ± 12.9) provided written informed consent to partake in semi-structured interviews. Each athlete provided written consented to allow the publication of their identifiable data and in order to facilitate sharing of their autobiographical account of their experiences. After conducting thematic analysis, meta-themes that emerged from the analyses were as follows: (a) early childhood experiences, (b) the challenge of the outdoors, (c) their emotional response to nature, (d) nature for coping, (e) restorative spaces, and (f) environmental concern. The findings convey great commonalities across the participants with regard to their mindset, their emotional well-being as well as their connectivity with nature and attitudes toward the natural environment. The cognitive-affective-social-behavioral linkage of the benefits of extreme sport participation for well-being, psychological recovery and pro-environmental behavior are highlighted. This study examining the lived experiences of extreme sportspeople provides a novel contribution to our contemporary understanding of extreme athletes' relationship to nature and its commensurate impact upon well-being and pro-environmental attitudes. The findings suggest that extreme sport participation, while inherently risky has psychological benefits ranging from evoking positive emotions, developing resilience and life coping skills to cultivating strong affinity to and connection with nature and the natural environment.

Environmental Influences on Elite Sport Athletes Well Being: From Gold, Silver, and Bronze to Blue Green and Gold.

This paper considers the environmental impact on well-being and performance in elite athletes during Olympic competition. The benefits of exercising in natural environments are recognized, but less is known about the effects on performance and health in elite athletes. Although some Olympic events take place in natural environments, the majority occur in the host city, usually a large densely populated area where low exposure to natural environments is compounded by exposure to high levels of air, water, and noise pollution in the ambient environment. By combining methods and expertise from diverse but inter-related disciplines including environmental psychology, exercise physiology, biomechanics, environmental science, and epidemiology, a transdisciplinary approach will facilitate a greater understanding of the effects of the environment on Olympic athletes.

Maximizing the spatial representativeness of NO2 monitoring data using a combination of local wind-based sectoral division and seasonal and diurnal correction factors.

This article describes a new methodology for increasing the spatial representativeness of individual monitoring sites. Air pollution levels at a given point are influenced by emission sources in the immediate vicinity. Since emission sources are rarely uniformly distributed around a site, concentration levels will inevitably be most affected by the sources in the prevailing upwind direction. The methodology provides a means of capturing this effect and providing additional information regarding source/pollution relationships. The methodology allows for the division of the air quality data from a given monitoring site into a number of sectors or wedges based on wind direction and estimation of annual mean values for each sector, thus optimising the information that can be obtained from a single monitoring station. The method corrects for short-term data, diurnal and seasonal variations in concentrations (which can produce uneven weighting of data within each sector) and uneven frequency of wind directions. Significant improvements in correlations between the air quality data and the spatial air quality indicators were obtained after application of the correction factors. This suggests the application of these techniques would be of significant benefit in land-use regression modelling studies. Furthermore, the method was found to be very useful for estimating long-term mean values and wind direction sector values using only short-term monitoring data. The methods presented in this article can result in cost savings through minimising the number of monitoring sites required for air quality studies while also capturing a greater degree of variability in spatial characteristics. In this way, more reliable, but also more expensive monitoring techniques can be used in preference to a higher number of low-cost but less reliable techniques. The methods described in this article have applications in local air quality management, source receptor analysis, land-use regression mapping and modelling and population exposure studies.

The effect of long-range air mass transport pathways on PM10 and NO2 concentrations at urban and rural background sites in Ireland: Quantification using clustering techniques.

The specific aims of this paper are to: (i) quantify the effects of various long range transport pathways nitrogen dioxide (NO2) and particulate matter with diameter less than 10µm (PM10) concentrations in Ireland and identify air mass movement corridors which may lead to incidences poor air quality for application in forecasting; (ii) compare the effects of such pathways at various sites; (iii) assess pathways associated with a period of decreased air quality in Ireland. The origin of and the regions traversed by an air mass 96h prior to reaching a receptor is modelled and k-means clustering is applied to create air-mass groups. Significant differences in air pollution levels were found between air mass cluster types at urban and rural sites. It was found that easterly or recirculated air masses lead to higher NO2 and PM10 levels with average NO2 levels varying between 124% and 239% of the seasonal mean and average PM10 levels varying between 103% and 199% of the seasonal mean at urban and rural sites. Easterly air masses are more frequent during winter months leading to higher overall concentrations. The span in relative concentrations between air mass clusters is highest at the rural site indicating that regional factors are controlling concentration levels. The methods used in this paper could be applied to assist in modelling and forecasting air quality based on long range transport pathways and forecast meteorology without the requirement for detailed emissions data over a large regional domain or the use of computationally demanding modelling techniques.

Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites.

Background concentrations of nitrogen dioxide (NO(2)) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO(2) concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO(2) concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO(2) in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO(2) at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling studies.