Agricultural emissions are one of the most significant contributors of greenhouse gas (GHG) emissions (agricultural emissions contribute around 40% towards Ireland’s national GHG emissions). One potential solution could be the use of chemical inhibitors in agriculture to reduce GHG emissions. Agricultural inhibitors, however, have a major drawback in their utility due to the lack of safety information and human health risks associated with them. There is an urgent requirement to understand the potential risks to human health (when inhibitors are used in agriculture) as there is potential for these chemicals to transfer and accumulate in the food chain. The primary aim of this study was to investigate the food safety concerns of inhibitors (NBPT, DCD and 3-NOP) used in grass systems in Ireland, and examine the potential human exposure via various animal products (predominantly milk and milk products) using quantitative modelling techniques. The major knowledge gap identified in the study was the absence of human health risk predictions when inhibitors were used in agriculture. Therefore, to address such data gaps and food safety concerns, a number of risk factors governing the potential transfer of agriculture inhibitors in the food chain were identified. These risk factors include inhibitors’ chemical properties, soil conditions, and bio-transfer factors (BTF). Additionally, the study also proposed a risk assessment framework for evaluating human exposure to inhibitors like NBPT, DCD, or 3-NOP, via foods of animal origin (e.g., milk) to reduce the emerging food safety concerns for the agricultural inhibitors. Overall, the results highlight that the human exposure to agriculture inhibitors, through various animal produce like milk and its products, constitute negligible risks to human health (under the assumptions and conditions studied). The important information generated by this thesis can aid stakeholders in minimising the human exposure to inhibitors through the food chain. It can serve as a guide, and can assist local authorities, farmers, policy makers, as well as risk managers in creating regulations or policies regarding maximum levels (Maximum Residue Levels, for example) for inhibitors in grass/milk, for the production of milk with minimal inhibitor residues. Additionally, the current exposure model can also be used to predict inhibitor residues in dairy products before product manufacturing/production to minimise recall losses as well as food wastage. The methods described in this study have the potential to be applied as a food safety tool to predict the concentration of inhibitors in multiple dairy products, hence assisting in risk management (controlling residues) at the farm, as well as at the end-product level. Furthermore, the quantitative methods described in this study encompass promising future applications in the area of ‘Human Health Risk Assessment’ research, and can be particularly useful to quantify unknown chemicals (characterise risk from chemicals with set safety information) which humans might be exposed to in the food chain. The model developed in this study have the potential to be altered accordingly to accept newer data/knowledge and understanding of the inhibitors as it emerges in order to better characterise risk. Results from this study should be acknowledged as part of a cumulative effort to curb national GHG emissions and achieve carbon neutrality by 2030. Consequently, the findings from this study should be applied in conjunction with other solutions to abate global GHG emissions.