Thames Valley Berkshire has set out to improve the health and wellbeing of people through enhanced information related to air quality. Simon Beasley, from the Live Labs team, discusses their trials.
The importance of air quality
The general objective of the project is to help individuals understand both the impact of air quality (AQ) on themselves and also the impact of their own behaviour on the AQ impact on others. Through this we aim to enhance decision making through provision of information in an easy and understandable way. In addition, the enhanced data and insight generated around AQ will be used to improve air quality through better planning and network management.
There is a perception that negative impacts of road-based traffic on AQ, including particulates, NOx and CO2 will addressed through the increased use of electric vehicles (EVs) and hence the solution to AQ is already here. However, whilst EVs are becoming more common and their adoption is set to accelerate with government intervention and targets, they will not change the AQ situation overnight. NOx pollution will be addressed by EVs, however heavier vehicles (such as EVs), when compared to their petrol equivalents, generate increased particulates from road and tyre wear. Although, the regenerative braking of EV’s aids in offsetting this challenge as regenerative breaking converts the kinetic energy of a vehicle into storage energy in the battery during braking. Hence air quality, from a local pollution level as well as carbon impact at energy generation level, remains a real issue for local authorities to tackle.
Collaborating with our partners
We are working in collaboration with Siemens who are implementing Earthsense AQ sensors in three focused trials across local authority areas in Reading, Wokingham and West Berkshire. The gathered information will be combined with O2 prediction tools and public health data for evaluation by Stantec and the University of Reading (UoR).
Within the local authority trial sites, we are looking to determine how many AQ sensors are required to get a good understanding of the AQ at any given time. To do this we’re combining a high density of sensors with AQ monitoring down to a resolution of 10m. It’s expected that as the density of sensors is reduced the accuracy of modelling is also reduced. Our aim is to help establish the most cost-effective approach to monitoring air quality by balancing investment in AQ monitors (which require on-going maintenance) against the data required to maintain high quality data set for use in modelling.
Data from the AQ sensors and the modelling are integrated into the Siemens STRATOS traffic management system for the purpose to better optimise the traffic network through manipulation of traffic signals to reduce the impact of poor AQ in local hotspots.
The impacts of air quality on our communities
Data produced from these trials will be fed into ROADCAST, a Siemens prediction tool using several data sets, including anonymised and aggregated data from O2 Motion (created by the mobile phone network to offer insight into movement trends), to determine short-term future traffic congestion that will have a resulting impact on AQ.* O2 Motion can combine AQ data obtained from the sensors with its anonymised and aggregated movement data. This allows us to understand the AQ data in the context of exposure to people, using the trend data from O2 Motion to provide valuable insight into the number of people being affected, the duration they’re affected and the purpose for them being affected by poor AQ zones e.g. do they live, work or just pass through poor AQ zones? From this, O2 Motion is creating a dashboard for local authorities to bring AQ data together with health data for the purpose of improved information for decision making around urban (transport) planning.
We are also directly engaging with people through an O2 led collaboration which won’t just focus on AQ, but will include a wide range of health and transport initiatives targeted towards the individual that will focus on encouraging more sustainable and healthy patterns of travel. For instance, encouraging behavioural change through gamification. This is to be delivered within the next phase of the project over the following few months.
We will also investigate strategies to influence driver decisions through using existing road sign variable messaging systems and social media. For example, from weather forecasting we will be able to determine the likely AQ exposure expected the following day in specific trial areas. Delivered through social media with an enhanced information service, we have the ability to influence the individual user’s decision making on how they intend to travel the following day.
We have a strong process of evaluation through Stantec and University of Reading to determine the accuracy of the AQ monitors and modelling outputs allowing us to assess the strategies to either improve AQ and/or reduce peoples exposure to poor AQ.
As part of the wider ADEPT Live Labs programme, we are running a series of shared learning outputs with the Staffordshire Live labs who are also implementing Earthsense sensors and are piloting a series of innovative solutions to tackle poor AQ.
*Anonymous and aggregated data insights from O2 Motion never allow identification or mapping of individuals, and operate within strict privacy guidelines