New air pollution monitor provides real-time pollution data
Siemens Mobility has launched a new real-time air quality monitoring system that can be used by local authorities to make timely interventions to improve air pollution.
Poor air quality in towns and cities is an ever-increasing problem, and with traffic levels beginning to increase as more people return to work, continuing to mitigate air pollution and traffic congestion is more important than ever.
Siemens has partnered with air quality experts EarthSense to provide the Zephyr air quality measurement system for traffic management applications.
The Zephyr sensor is easy to install and can be integrated into any traffic signal pole or lighting column, either as part of a new scheme or retrofitted to existing infrastructure.
The sensor has a replaceable cartridge system and an active sampling process to measure real-time pollutants such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5).
Siemens has said they hope the sensors will allow local authorities to make meaningful interventions, by using the pollution data to implement timely traffic mitigation strategies.
Wilke Reints, managing director for Siemens Mobility said: ‘Air quality is a real priority for us all, but to fully understand the impact of traffic and any strategic interventions, it must be constantly monitored and measured and that can only be done using intelligent road infrastructure.
‘We have a complete portfolio of solutions focused on helping cities achieve clean air targets. Take London for example, nitrogen dioxide levels have been reduced by 36% as a result of our proven technology — the most efficient clean air zone in the world, invented in the UK.
‘The introduction of Zephyr demonstrates our commitment to further reduce air pollution. It has real-time monitoring capabilities to help cities meet their air quality targets without putting too much pressure on local economies. That’s what I call a true environmental traffic management solution.’
Photo Credit – Pixabay
