Fungal spores make up a significant portion of Primary Biological Aerosol Particles (PBAPs) with large quantities of such particles noted in the air. Fungal particles are of interest because of their potential to affect the health of both plants and humans. They are omnipresent in the atmosphere year-round, with concentrations varying due to meteorological parameters and location. Equally, differences between indoor and outdoor fungal spore concentrations and dispersal play an important role in occupational health. This review attempts to summarise the different spore sampling methods, identify the most important spore types in terms of negative effects on crops and the public, the factors affecting their growth/dispersal, and different methods of predicting fungal spore concentrations currently in use.

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Ambient fungal spores within the atmosphere can contribute to a range of negative human, animal and plant health conditions and diseases. However, trends in fungal spore seasonality, species prevalence, and geographical origin have been significantly understudied in Ireland. Previously unpublished data from the late 1970s have recently been collected and analysed to establish historical fungal spore trends/characteristics for Dublin. Historical spore concentrations were largely dominated by Alternaria, Ascospores, Basidiospores, Botrytis, Cladosporium, Erysiphe and Rusts. The main fungal spore season for Dublin commenced in April with the fructification of Scopulariopsis and Ganoderma. However, the vast majority of other spore types did not reach peak spore release until late summer. The correlation between ambient spore concentration, and meteorological parameters was examined using Multivariable Regression Tree (MRT) analysis. The notable correlations found for fungal spore concentrations tended to involve temperature-based parameters. The use of a non-parametric wind regression was also employed to determine the potential geographical origin of ambient fungal spores. The impact of wind direction, and high windspeed on fungal spores was established, ultimately highlighting the importance of studying and monitoring fungal spores within Ireland, rather than attempting to rely on data from other regions, as most fungal spores collected in Dublin appeared to originate from within the island.

Non-Road Mobile Machinery (NRMM) incorporate a wide range of machinery, with or without bodywork and wheels, and are installed with a combustion engine and not intended for carrying passengers or goods on the road. These are used in many different sectors including construction, agriculture, forestry, mining, local authorities, airport and port ground operations, railways, inland waterways and within the household and gardening sector. This article presents a review of the state of knowledge with regard to non-road mobile machinery, particularly focusing on their regulation and the atmospheric emissions associated with them. This was undertaken as there is currently a lack of this information available in the literature, which is an oversight due to the potential for Non-Road Mobile Machinery to form a greater part of atmospheric emissions in the future, as other areas of emissions are tackled by regulations, as is outlined in the article. Emissions such as particulate matter (PM), carbon oxide (CO), carbon dioxide (CO2), hydrocarbons (HC), nitrogen oxides (NOx) and sulphur oxides (SOx) from NRMM contribute considerably to total emissions released into the air. NRMM are diverse in application, engine type and fuel use, and are therefore difficult to categorise. This leads to numerous issues when it comes to the control and regulation of their emissions. The most recent European and international regulations are outlined in this article. Due to the divergent nature of NRMM, their emissions profiles are highly varied, and in-use emissions monitoring is challenging. This has led to a lack of data and inaccuracies in the estimation of total emissions and emission inventories. It was assumed in the past that emissions from non-road sources did not contribute as significantly to total emissions as those from on-road sources. This assumption was partly due to the difficulty in gathering relevant data, and it was disproven in the 1990s by studies in The Netherlands, Finland and Sweden. It is now understood that NRMM will eventually surpass on-road vehicles as the leading source of mobile pollution due to the continuing efforts to reduce emissions from other sources. Many states worldwide gather emissions data from NRMM, and EU member states are required to report their emissions. As of January 2017, a new European regulation establishing limits for gaseous and particulate pollutants from NRMM applies, and this regulation also defines administrative and technical requirements for EU approval. The exact number of NRMM and the total amount of fuel they use is currently not known. In Ireland, for example, their fuel use has been reported under stationary boilers and engines. However, this results in the underestimation of emissions of some pollutants (NOx in particular) because emissions of air pollutants tend to be higher in mobile than in stationary machinery.

A range of commercially-available automatic pollen monitors were run in parallel and evaluated for the first time during the 2019 spring season; this includes the Droplet Measurement Technologies WIBS-NEO, Helmut-Hund BAA-500, the Plair Rapid-E, two Swisens Poleno, and two Yamatronics KH-3000 devices. The instruments were run from 19 April to 31 May 2019 and located in Payerne, Switzerland, representative of a semi-rural site on the Swiss plateau. The devices were validated against Hirst-type traps in terms of total pollen counts for daily and sub-daily averages. While the manual measurements cannot be considered a "gold standard" in terms of absolute values, they provide an established reference against which the automatic instruments can be evaluated. Overall, there was considerable spread between instruments compared to the manual observations. The devices showed better performance when daily averages were considered, with three of the seven showing non-significantly different values from the manual measurements. However, when six-hourly averages were considered, only one of the instruments was not significantly different from the Hirst trap average. The largest differences between instruments were evident at low pollen concentrations (< 20 pollen grains/m 3 ), no matter the temporal resolution considered. This is in part, however, to be expected since it is at such low concentrations that the Hirst measurements are most uncertain. It is also important to note that in 2019 many of the instruments tested had only recently been developed. Differences may also have arisen due to their varying abilities to identify specific pollen taxa or because the classification algorithms applied were developed for different pollen taxa and not total pollen, the variable considered in this study Keywords pollen • automatic monitoring • validation • real-time • Hirst 1. Introduction Airborne pollen form just a small fraction of the atmospheric bioaerosol loading ([36]; [18]; [39]), but despite their low number concentrations these particles are of significant importance because of their impact on human health ([38]; [47]; [14]), agriculture and sylviculture ([6]; [20]), as well as on climate through their role in the hydrological cycle ([35]; [10]). Pollen monitoring networks exist in most countries, providing information to a range of end-users from allergy sufferers and their doctors through to researchers. The current standard that is used across these networks is based on manual technology developed in the early 1950s ([17]; [13]; [2]) that is both time-consuming and laborious. Furthermore, this measurement technique suffers from several shortcomings, including the fact that data are delivered at low temporal resolution (usually daily averages) with a delay of between 3-9 days from the time of observation. New technologies developed over the past few years have made it possible to automatically measure pollen at high temporal resolutions and in real-time ([41],[33], [40]). The provision of such observations is dramatically ...