Leveraging active neighborhood schemes for more sustainable transportation
Cities around the world are starting to experiment with alternative infrastructure models that favor human-powered transport over vehicles. Reducing air pollution, particularly related to vehicle emissions, can vastly improve community well-being for overall healthier cities.
The City of Greater Manchester's vision for transportation is that by 2040, 50% of all journeys across the city will be made by walking, cycling, or via public transportation. Low-traffic neighborhood schemes are one such method of implementing the vision.
Popularized in the UK in response to the COVID-19 pandemic, low-traffic neighborhoods aim to mitigate reliance on cars with alternative, active forms of transportation. Modal filters, which are barriers placed to restrict certain vehicle access, allow for pedestrian and bike-friendly infrastructure to take root.
At the same time, some community members have concerns that restricting traffic through low-traffic neighborhood schemes may result in higher congestion, and therefore air pollution, on nearby roads. They also worry that the lack of vehicular traffic on filtered roads will damage profits for local businesses.
Regulatory bodies need reliable, actionable data to ensure their sustainable transportation initiatives will not result in negative impacts for their local communities.
Establishing a robust evaluation
When the Manchester City Council completed the first phase of an Active Neighborhood Scheme in the Levenshulme neighborhood, they wanted to ensure the scheme didn't have any unintended ill effects for the community members such as traffic or pollution displacement to nearby roads.
Urban 360, a provider of integrated urban monitoring solutions, was recruited to conduct a review of the scheme's impacts. The Urban 360 team needed to design and implement a comprehensive evaluation to determine how the initiative's first phase affected local air quality and traffic, informing further development of low-traffic neighborhoods. They aimed to show the measures increased active methods of travel and reduced pollution on the filtered streets without negatively impacting the surrounding areas.
In Phase 1 of the scheme, modal filters were placed on residential streets to prevent through-traffic while retaining access to local destinations. In order to track changes in local traffic and their associated emissions, the Urban 360 team strategically placed QuantAQ air quality sensors, traffic monitoring sensors, and cameras within the scheme and along some boundary roads.
Setting up and managing air quality sensors easily was an important aspect of efficiently conducting the analysis. The QuantAQ sensors arrived pre-calibrated and ready-to-go for simple setup with reliable results. The devices were also IoT-enabled to remotely stream data from the devices to the data management platform within minutes, allowing the Urban 360 team to quickly get back to big-picture analysis.
"We want to use the best available sensors to answer policy and related questions that local governments and charities have."
For 7 months, concentrations of nitrogen oxide (NO), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM1, PM2.5, and PM10) were measured at the selected key sites using six QuantAQ MODULAIR™ sensors. They examined the 24-hour pattern, or diel profile, of each pollutant concentration for insights into the sources of pollution. Concentration levels for the key pollutants (NO2 and PM2.5) were also compared to the regional air quality in Manchester and other comparable urban sites across the UK to determine whether concentrations were influenced by regional or local trends.
Supporting the growth of low-traffic communities
Analyses from the sensor measurements helped Urban 360 affirm the efficiency of the Levenshulme Active Neighborhood Scheme. Traffic measurements showed that walking and cycling increased not only on the filtered roads, but also in surrounding areas, suggesting the scheme had a positive impact on active transport across the whole area.
The surrounding unfiltered roads saw only a 3% increase in vehicle traffic, a modest increase aligned with broader regional trends in traffic levels during the year of data collection. This indicates the scheme - contrary to what neighbors had feared - didn't displace large amounts of traffic onto adjacent roads.
"I was pleased that we didn't see displacement of traffic onto the boundary roads or an uptick in emissions of NO or NO2 as a result. Having the data to verify some of the things we've seen from an air quality perspective was really quite important."
Robust data is critical in ensuring cities' sustainable transportation initiatives have the intended impacts. To fully understand the impacts of interventions like low-traffic neighborhoods and apply them effectively, city officials need more access to transportation and air quality data for their jurisdictions over longer periods of time.
With the help of QuantAQ's sensors, the Urban 360 team brought trusted, easy, and affordable data to the City of Greater Manchester to inform their sustainable city transition.
