Advertisement

EarthSense launches ‘hi-res’ UK air pollution map

Air quality data specialist EarthSense Systems has published what it claims is the first ever high resolution nationwide map of pollution hotspots.

MappAir, which was launched this week, combines data from satellites and air quality data from EarthSense’s ‘Zephyr’ network of fixed and static air pollution sensors to provide nitrogen dioxide projections for the whole of the UK up to a 100 metre resolution.

EarthSense, which is a joint venture between aerial mapping company Bluesky and the University of Leicester, will also be releasing a map of PM2.5 (ultrafine pollution particles smaller than 2.5 micrometres) later in 2017.

As additional sensors come online and more historical data is made available, the company says it plans to produce a range of MappAir products, including an ultra-high resolution 1m dataset for detailed study areas, a 10m map for urban areas, an historic time series of maps showing how air pollution changes over the course of a day and on different days, and forecast maps giving an indication of fluctuations up to three days ahead.

Data

James Eddy, managing director of EarthSense Systems, said: “Air pollution is making headlines across the world for all the wrong reasons. However, there simply isn’t enough data available for those charged with tackling the issue to make informed decisions. MappAir can provide a street-view to city-wide visualisation of air pollution, and is the first in a series of nationwide products that are coming to market in the next year.

“Air pollution is not a constant threat. Not only does it differ from location to location, as MappAir clearly shows, but it also changes from morning rush hour to afternoon school run, and from week day commutes to weekend leisure pursuits. This is why we are already working on the next products in the MappAir range, including near real time altering maps and forecast maps.”

Comments

Comments are closed.

Help us break the news – share your information, opinion or analysis
Back to top