Transport and air quality: supporting resilient communities through collaboration
Measuring and mitigating the impact of air quality on public health has become a priority for network operators and funding bodies. To that end, transport models traditionally used for appraising proposed infrastructure schemes are increasingly being used to assess the environmental impacts of existing transport networks. But this ‘input-output’ approach is too simplistic – there’s a much better way, say transport and air quality experts David O’Brien and Duncan Urquhart.
Public awareness of the effects of pollution has never been greater. The widespread lockdown and resultant drop in economic activity have led to dramatic improvements in air quality. While we are keen for things to return to ‘normal’, no-one wants to see pollution rise to pre-pandemic levels.
The emerging significance of air quality management is forcing network operators and local authorities to reassess existing transport networks to investigate ways of minimising pollution as economic activity resumes. Not only that, transport networks need to embrace the adoption of active travel modes that have increased in popularity as a result of the lockdown such as walking and cycling, as well as ‘new’ modes such as e-scooters. An improved interface between transport and air quality models is needed to meet these challenges.
Transport models traditionally used for appraising proposed infrastructure schemes are increasingly being used to assess the environmental impacts of such interventions. As well as addressing energy consumption, traffic noise impacts and vehicle emissions, there is an increasing need for environmental assessments to address air quality outcomes for public health and wellbeing. In this article, we explain why a joined-up approach to transport and air quality modelling addresses these challenges by providing powerful new insights, empowering local authorities and network operators to make decisions regarding policy and infrastructure with greater confidence.
Improving current practice through a joined-up approach
At AECOM, our air quality and transport modelling teams are working collaboratively to enhance existing modelling and appraisal tools for transport with the specific needs of prioritised air quality outcomes. Our focus is not only on improving the way we address standard quantifiable outputs but also the wider impacts on communities, society and the economy.
There are numerous practical considerations and complexities involved in aligning model inputs and outputs between the air quality and transport disciplines, while ensuring that the context, confidence and resolution of each model is proportionate – so a collaborative approach is hugely advantageous. Regular routine communications between the two disciplines overlap to create a deeper integration of data at both the technical and, importantly, the contextual levels. Our approach includes early engagement between the teams to understand not only the required data inputs and outputs but also the wider context and purpose of such data.
For example, highways-orientated transport models have historically placed less focus on goods vehicles and buses, which are often managed and operated separately from other parts of the network fleet. However, these vehicles can contribute a disproportionate amount of roadside emissions. Understanding the distribution and extent of emissions across the network from discrete groups of vehicles like these can enhance the quality of model outputs in order to identify the best way to tackle pollution without compromising the ability to move people and goods. For instance, measures to tackle these vehicles must not unfairly disadvantage deprived demographic groups who tend to own older, less efficient and higher-emission vehicles. If identified at an early enough stage, a shared understanding of the importance of these different vehicles can help our transport and air quality teams to address these potentially conflicting priorities.
Here are three further features of our joined-up approach:
1/Iterative feedback loops: The closer working of transport and air quality models also enables us to create an iterative feedback loop to test options. Rather than treating information transfer between transport and air quality as a one-way process, the impacts from assumptions and changes in each model are tested in parallel. This requires a collaborative and structured approach between air quality and transport teams in order to carefully review the interfaces and sensitivities between models. We are moving away from a simplistic “input-output” approach towards a reciprocal relationship between the two models to ensure that they are fully integrated.
2/Improved data validation: A commonality between transport and air quality models is that both require validation to demonstrate results are in line with observed data, such as roadside NO2 concentrations or traffic journey times. However, instead of validating each model in isolation, the process is enhanced by identifying common metrics across both models. For example, this might involve focusing on an existing air quality management area or a particular vehicle type. By considering such outputs at an early stage and allowing for adjustment between models, we can establish iterative enhancements to quality and consistency. Where parameters are altered, we can also ensure that the context of the outcome is consistent across the whole study.
3/Refined confidence limits: Fixing our attention beyond transport outcomes to an intervention’s wider impacts also helps decision makers understand the sources and impacts of uncertainty. It is currently commonplace in transport model forecasts to address uncertainty in future-year assumptions. However, these assumptions are generally at a high level, such as variable growth scenarios. When assessing air quality improvement schemes, uncertainty can also be found in fleet projection forecasts. By considering alternative scenarios around these assumptions and adopting an approach based on tangible identified risks, we can help decision makers by providing them with a more comprehensive picture of the confidence limits associated with forecasts. An iterative feedback of results between the models can, therefore, be used to refine confidence limits and better inform outcomes.
A direct focus on real-world outcomes
AECOM’s multi-disciplinary capabilities have been the catalyst for this fresh approach, which ensures that the context of a study infuses every stage of a project to increase value and reduce risk. Highly-integrated, well-tested modelling procedures are applied to maintain consistent confidence throughout a study, while maintaining essential context for interpreting the outcomes.
The use of a joined-up approach helps operators to refine their priorities by moving away from conventional traffic flow and journey time indicators towards wider considerations of health and wellbeing. The approach also provides a direct focus on the real-world outcomes needed to inform potential policy measures and scheme options.