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Use case · Cities

Expand urban monitoring coverage

Reference networks are sparse. Fill the spatial gaps with hyperlocal sensors and know where and when pollution is worst, street by street.

Eliminate the blind spots

Air quality varies significantly from district to district and street to street, but reference networks are too sparse to show it. Without that resolution, cities lack the data to say where and when pollution is worst.

Indicative sensors integrated with the existing regulatory network eliminate blind spots within a defined budget: assess pollution levels, identify sources and hotspots, monitor policy effectiveness and measure progress.

  • Dense placement in traffic corridors shows when vehicle emissions reach dangerous levels.
  • Reference stations anchor the network's accuracy; sensors extend its reach (the Hybrid Network).
  • Street-level mapping becomes a foundation for urban planning and clean air zones.
How the Hybrid Network works
City skyline, street by street
How we handle it

The solution we provide

We establish dense, city-wide networks of typically 5 to 30+ units mounted on existing lamp posts, traffic lights and municipal buildings, installed in minutes and solar-powered. Municipal technical staff use Lens to identify hotspots, compare districts, and track whether traffic and heating policies are actually working.

Automatic monthly reports produce stakeholder-ready evidence for council, ministry and EU reporting, and deployments of this kind are eligible for EU funding, World Bank projects and national smart-city grants.

In this deployment
  • Unit L network, 5 to 30+ units for meaningful spatial coverage
  • Unit N noise sensors for traffic, nightlife and construction zones
  • Anemometer add-ons at key reference points
  • Airqoon Lens with regional comparison and automatic reports
  • Public Map for citizen-facing transparency
Example references

Mudanya

5 coastal units giving a district that had no reference station its first continuous air-quality picture.

İnegöl

District network whose PM data underpins a scientific assessment presented at ASIC 2026.

Avcılar

İstanbul district running its own neighbourhood-resolution network on the same platform.

Map your city's air, district by district.

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