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Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Particulate Matter (PM) has been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe threshold of exposure in place to-date. Ammonia (NH3) emissions are linked to the secondary production of PM through atmospheric reactions occurring with acidic atmospheric components such as sulfuric acid, nitric acid, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. More than 95% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. This study aims to advance knowledge and understanding of the role of arable agricultural practices and management in NH3 enrichment and aid in mapping of the sources of PM production. The nature and contribution of NH3 in the atmosphere to secondary PM in defined arable settings will be examined to provide greater insight into system dynamics facilitating emission control and mitigation measures to be implemented. This will be achieved through a review of existing literature and database assessment combined with the application of a localised field monitoring network in arable agricultural settings. As Ireland currently has no active atmospheric NH3 monitoring in place, reported emission levels can prove to be imprecise. And lead to over- and under-estimation of NH3 gas emissions to the atmosphere from sources such as agriculture. By establishing localized monitoring stations at emission sources, the precision of the estimated NH3 concentrations in the atmosphere can be improved. This can also lead to improved understanding of PM dynamics and formation. This will be achieved by using a combination of active and passive sampling instruments for in-field atmospheric sample collection, which will then be analysed in the laboratory using ion chromatography. Additionally, to gain a fuller understanding of the dynamics of an agricultural system, background monitoring of soil properties and water nutrient enrichment will also be carried out. The output of this project will build on existing theories of NH3, and PM dynamics established by previous research, and combine these with field data, including agricultural practices, NH3 source production and PM generation, soil and water enrichment and quality background monitoring to synthesise a new mechanistic paradigm. This new understanding will be operationalised through the development of a conceptual model of NH3 dynamics and PM generation, and agri-ecological interactions known as Conceptual Ammonia-aeroSol bIOspheric Simulation (CASIOS). The model builds on a Drivers, Pressures, State, Impacts, Responses framework, with an additional attribute introduced under the term ‘Concept’ which includes environmental conditions previously not considered under this paradigm.

LUWQ 2019: International Interdisciplinary Conference on Land Use and Water Quality. Agriculture and the Environment. Aarhus, Denmark, 3-6 June 2019 Policymakers, farm advisors and water agencies require up-to-date national maps of critical source areas (CSAs) of nitrogen (N) and phosphorus (P) losses from agricultural land to improve catchment management decisions. The DiffuseTools project aimed to achieve this in Ireland by updating the existing Catchment Characterisation Tool and sub-model NCYCLE_IRL, which predicts environmental losses of N and P from the farm via surface runoff, leaching, denitrification and volatilisation. Updates included (i) using improved national maps of farm-scale source loadings as inputs, (ii) sub-field scale modelling of surface transport risk using soil topographic indices derived from 1 m and 5 m NEXTMap digital elevation models (DEMs), (iii) modelling hydrological disconnectivity from microtopography (HSA Index) and reinfiltration (SCIMAP), (iv) improving the national ditch and stream channel network used by the model by DEM extraction, and (v) using SCIMAP to improve predictions of erosion risk. The improved national source loading maps included mean nationally weighted farm-gate N and P imports (fertilizer, feed and livestock) and balance surpluses (kg/ha) calculated for each stocking rate and soil group (land use potential) category within each sector type (dairy, mixed livestock, suckler cattle, non-suckler cattle, sheep and tillage), using annual Teagasc National Farm Survey data (2008-15). Furthermore, updated national maps of soil P and atmospheric N and P deposition inputs were also used within the national source loading maps to improve model performance. National CSA maps for N and P for each pathway were then produced and evaluated using water quality monitoring data and field observations from the Environmental Protection Agency and Teagasc Agricultural Catchments Programme. These maps will be able to support sustainable intensification by informing farm and catchment management decisions such as where to cost effectively target mitigation measures to reduce environmental losses, where to distribute nutrient surpluses (to non-CSAs in nutrient deficit), and improving functional land management. Environmental Protection Agency Check for published version during checkdate report - AC

The 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019 This paper provides a research plan focusing on the application of self-learning techniques for energy systems integration in the residential building sector. Demand response is becoming increasingly important in the evolution of the power grid since demand no longer necessarily determines system supply but is now more closely constrained by generation profiles. Demand response can offer energy flexibility services across wholesale and balancing markets. Different applications have focused on the Internet of Things in demand response to assist customers, aggregators and utility companies to manage the energy consumption and energy usage through the adjustment of consumer behaviour. Even though there is extensive work in the literature regarding the potential of the commercial and the residential building sectors to provide flexibility, to date there is no standardised framework to evaluate this flexibility in a customer-Tailored way. At the same time, demand response events may affect occupant comfort expectations hindering the utilisation of flexibility that building energy systems can provide. In this research, the integration of machine learning algorithms into building control systems is investigated, in order to unify the monitoring and control of the separate systems under a holistic approach. This will allow the operation of the systems to be optimised with respect to reducing their energy consumption and their environmental footprint in tandem with the maximisation of flexibility, while maintaining occupant comfort. Science Foundation Ireland

The Environmental and Sustainable Resource Management (ESRM) Post-graduate Research Day, University College Dublin, Ireland, 6 December 2019 The inherent factor of poor site productivity in western peatland forests combined with the reduction in management intensity from increased environmental considerations has brought some new challenges into forest management. Our study investigates new, alternative forest management models in the area chosen for this study, Cloosh forest, Co. Galway, to assess how these forests should be managed under future impacts of climate change and dynamic timber prices due to an expanding bioeconomy, and to quantify the impact this will have on forest ecosystem services (ES). Department of Agriculture, Food and the Marine European Commission Horizon 2020

Computer Aided Process Engineering - CAPE Forum 2013, 2013 Increases in fuel and feed prices are placing a significant burden on the poultry industry in Ireland and worldwide. For producers to meet their financial targets, increased performance and output is a key issue, now more than ever. To optimise performance in broiler production houses, the effect of environmental and air quality parameters on bird performance and energy consumption must be known to allow farmers make informed management decisions. This paper concentrates on the application precision livestock farming sensors to develop recommendations for improved bird performance and energy consumption in broiler production farms in Ireland. Air temperature, relative humidity, light, air speed and air quality (in particular CO2 and NH3 concentrations) are identified as important parameters for improving bird performance and energy consumption in broiler production houses. Several of these parameters (temperature, relative humidity, CO2 and NH3) were monitored on two farms during the study over the initial 2 weeks of the production cycle. Air quality was often overlooked during the production process, as farmers struggled to limit high heating and feed costs. However, elevated levels of CO2 (>3000 ppm) did not appear to affect broiler growth rates. Additionally, a strong correlation was observed between relative humidity and NH3 (R2 = 0.86 - 0.92). Producers tend to use relative humidity as an indication for NH3 levels and the research shown in this study confirms the close relationship between the two parameters. It is recommended that further data should be gathered from producing units and novel performance technologies should also be investigated. Author has checked copyright Papers may be published in a special issue of the SpringerOpen journal Energy, Society and Sustainability. Conference website: http://cape2013.tugraz.at/?show=index - OR 28/03/2013 RB 16/04/13

The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre