Air pollution is the main environmental risk to public health in the UK as it increases the likelihood of the population developing respiratory and cardiovascular diseases. The UK monitors air quality through 171 monitoring sites part of the Automatic Urban and Rural Network known as AURN. However, those monitoring stations are stationary and expensive, which limits coverage and the ability to detect event-based pollution.
Viapontica AI, in collaboration with researchers and students at Strathclyde University in Glasgow, developed a prototype of a compact, modular, and cloud-connected air quality sensor that offers similar levels of accuracy to large traditional measurement stations, but at a lower cost and with the added flexibility to deploy easily at scale including at remote locations.
Both short and long-term exposure to air pollution can lead to a wide range of diseases, including stroke, chronic obstructive pulmonary disease, trachea, bronchus and lung cancers, aggravated asthma and lower respiratory infections. Air pollution is the biggest environmental threat to health in the UK, with between 28,000 and 36,000 deaths a year attributed to long-term exposure, and a healthcare cost of £8 to 20 billion. An enhanced network of air quality monitoring stations can provide data to inform public policy, improve urban planning and benchmarking of the most effective interventions to limit pollution.
Different chemicals, dust, or allergens such as such nitrogen oxide and sulphur dioxide contribute to pollution but the current project focused on a pollutant known as PM 2.5 which has been reported to kill the most people worldwide. The name PM 2.5 refers to the size of the tiny particles in the air that are two and one half microns or less in width.
While there are quality monitoring sensors available on the open market, they are heavy, mostly stationary and costly. The challenge was to develop a sufficiently accurate, portable, lightweight device which can be adapted for use in different environments and allow integration with data providers and consumers via standard Rest APIs.
We designed and validated the concept of a cloud-connected air quality sensor. The aim of the project was to assess the existing methods used for monitoring particulate matter (PM) levels and to report the avenues to commercial and opperational improvement. Market research and prototyping showed that there is an opportunity to make PM measurements cheaper, smarter and more widely available.
The prototype which we developed offers the potential for large scale deployments which collect sufficient data which can be used to analyse the sources of PM 2.5 and understand through mathematical and statistical models how the pollutant travels across the country.
The tested solution consisted of a battery-powered device containing a smart particulate matter sensor, GPS module and a 4G-connected microcontroller. There was a focus on minimising the form factor and the mass of the device in order to allow it to be used on a small quadcopter drone for vertical measurements.
The developed prototype device used threaded camera mounts to open possibilities for many other use cases. An innovative swivel mount allows the angle of the PM sensor to be adjusted to optimise measurement accuracy, and a sliding battery drawer allows easy battery changes, if required.
In its lightweight configuration, the device has a battery life of around 7 hours – ideal for dynamic surveying using a quadcopter drone. An additional battery pack and a solar panel offer prolonged monitoring without the need for recharging.
An array of devices can be situated around a city block to map air quality over time, with the data being made accessible to pedestrians via apps and smart street signs. Clusters of devices can further be deployed at highway maintenance sites to measure the effects of maintenance projects on air quality for environmental research purposes.
The project demonstrated the feasibility to reduce the unit cost of each measurement device paving the path to larger deployments to collect data and improve understanding how to reduce the harmful effects of pollution.