Digital twins are virtual models that replicate physical systems in real time. They integrate data from various sources, enabling the simulation and analysis of complex scenarios such as DIDYMOS-XR’s industrial, tourist, or city maintenance and planning use cases, for example. For local planners, digital twins offer a transformative tool for addressing challenges like air pollution, providing actionable insights to create healthier and more sustainable urban environments.
By simulating urban dynamics, such as traffic patterns, hazards and emissions, digital twins can empower stakeholders to predict pollution levels, evaluate intervention strategies, and enhance public health outcomes.
At Trilateral Research, we take a sociotechnical approach to understanding the complex interplay between air pollution, traffic, public health and socio-economic impacts. Our recent work in County Meath (IE) demonstrated how Trim residents and those from further afield can access hyperlocal air pollution information from two sensors in the town (on Market Street and Patrick Street), along with insights on traffic levels and their health consequences, and the best and worst times of day for air quality. Such an initiative relies on a real-time sensor network, but a digital twin could offer wider benefits for scenario planning and climate action investment considerations.
Potential Benefits of Digital Twins for Addressing Air Pollution
- Real-Time Monitoring: Digital twins collect and process live data from sensors, providing up-to-date views of pollution hotspots.
- Scenario Modelling: They simulate “what-if” scenarios, helping planners understand the impact of measures like traffic rerouting, low emissions zones or adding green spaces.
- Predictive Analysis: AI-driven models can forecast pollution trends and their health implications, enabling early interventions.
- Enhanced Public Engagement: Visual representations of data foster community understanding and involvement in decision-making.
- Integrated Planning: Digital twins consolidate data from various sectors, improving coordination between transport, health, and environmental planning.
Key Considerations for Implementing Digital Twins
- Data Quality: Accurate and comprehensive data is essential for reliable simulations and predictions.
- Data Protection: Handling sensitive data requires robust mechanisms to ensure data privacy mechanisms are aligned with the General Data Protection Regulation (GDPR).
- Scalability: Solutions must adapt to varying urban sizes and complexities in socio-economic variables without significant loss of functionality.
- Responsible AI: AI algorithms should be transparent, fair, and accountable to avoid biased outcomes in decision-making. This domain is a key expertise of Trilateral Research across its research and commercial activities.
- Cost and Accessibility: Developing and maintaining digital twins can be resource-intensive, requiring careful planning to ensure equitable access. Innovative compression and rendering techniques such as those driven by DIDYMOS-XR offer a path to ameliorate these issues.
Conclusion
At Trilateral Research, our solution, STRIAD:AIR, exemplifies how data-driven tools can predict the impacts of air pollution on health and socio-economic welfare. By integrating such capabilities into digital twins like the DIDYMOS-XR application, planners can explore new avenues for scenario planning, informed decision-making, and community dialogue and behaviour change as it relates to climate action initiatives. Digital twins are not just a technology; they represent a collaborative route toward sustainable, healthier cities.
Authors: Dr Hayley Watson, Director of Innovation & Research, and Dr Alex Murphy, Research Communications Officer, Trilateral Research
