## 11 Development Trends and Summary
Integration of these models with sensory and instrumental data has led to more accurate and comprehensive assessments of odour impacts, as demonstrated in projects such as predicting odour impact with modelling and assessment (PRIMA), which combines dynamic olfactometry, chemical analysis and dispersion simulations. The rise in artificial intelligence and machine learning has opened new horizons for odour management. Techniques such as random forests, support vector machines (SVMs) and neural networks have been employed to predict odour episode occurrence, classify emission sources, and analyse large, complex environmental datasets. Citizen science initiatives, exemplified by the OdourCollect project in Spain and Smell Pittsburgh in the United States, demonstrate the potential of integrating public odour observations via mobile applications with machine learning models to dynamically monitor and manage odour nuisances. Such initiatives not only enhance the spatial and temporal resolution of odour data but also foster greater community engagement and responsiveness in environmental governance. The practical implementations of integrated odour management systems further highlight the feasibility and benefits of multidisciplinary approaches. In Rotterdam, a comprehensive odour monitoring network combining instrumental measurements, dynamic olfactometry and meteorological data has been deployed in the port area to manage industrial odour emissions effectively. In Victoria, Australia, stringent regulations require the incorporation of odour dispersion modelling and community impact assessments for new industrial developments. In California's San Joaquin Valley, machine learning models fed with citizen-reported data have been integrated into environmental complaint management systems, allowing the real-time identification of odour events and proactive mitigation measures. Overall, the future of odour nuisance management lies in the convergence of technological innovation, regulatory harmonisation and community-driven data collection. The integration of real-time monitoring technologies, advanced analytical models and participatory approaches offers a promising pathway toward more effective odour impact mitigation. Achieving sustainable solutions will require not only technical and scientific advancements but also interdisciplinary collaboration across environmental engineering, public health, urban planning and policy-making domains. As awareness of the health and quality-of-life impacts of odour nuisance continues to grow, the development of robust, harmonised and transparent odour management frameworks will become increasingly critical for ensuring environmental justice and public well-being. Conceptualization, I.W. and M.D. ; formal analysis, M.D. ; investigation, I.W. and M.D. ; resources, I.W. and M.D. ; data curation, I.W. and M.D. ; supervision, M.D. ; writing--original draft preparation, I.W. and M.D. ; writing--review and editing, I.W. and M.D. ; visualization, I.W. and M.D. ; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript. This research was financially supported by works no. 29.610.023-110 of the University of Warmia and Mazury in Olsztyn, funded by the Minister of Science and Higher Education. Data are contained within the article. The authors declare no conflicts of interest.