Review on Monitoring and Measurement Strategies for Ammonia Combustion-Related N2O Emissions

While global decarbonization and low-temperature engines curb nitrogen oxides, they risk significant N2O emissions, which is also one of the greenhouse gases. This review evaluates N2O monitoring strategies across maritime and industrial combustion environments. Furthermore, this paper provides a rigorous comparative assessment of analytical monitoring methodologies. We evaluate the adaptability of established agricultural techniques. In parallel, advanced spectroscopic and non-spectroscopic sensor technologies (DAS, NDIR, TDLAS, PAS, CRDS, CEAS, FTIR, GC, and electrochemical/chemiresistive sensors) are critically analyzed regarding their sensitivity, response time, cross-interference vulnerabilities, and suitability for harsh industrial stacks and maritime settings. Finally, the levels of N2O above background from individual sources are estimated using wind-tunnel modeling studies. Emissions from ships, truck lines, and industrial turbines typically cause urban ambient concentrations ranging from hundreds down to tens of ppb, whereas individual trucks and rural ship levels are significantly lower, from below 1 ppb up to about 70 ppb depending on engine optimization. For industrial and combustion sources, real-time N2O analysis can be performed using FTIR, which allows for multi-gas mixture measurement, or TDLAS for targeted N2O detection. For distributed screening and monitoring around industrial plants, NDIR is a technically viable option under specific conditions. For mobile sources, TDLAS, FTIR, and highly sensitive cavity-enhanced methods (CRDS, CEAS) are suitable, provided they are optimized for harsh conditions. NDIR is applicable here only in dense urban areas. In all scenarios, gas chromatography can be used, though it requires physical sample collection and does not offer real-time analysis. As we will show, electrochemical and chemiresistive sensors cannot currently be used. Similarly, PAS is unsuitable for ambient enhancement detection, despite its applicability to stack sampling.