Integrated screening techniques reveal insight into hyperlocal non-traffic emission sources

Area of Science:

• Environmental Science
• Sensor Technology
• Urban Planning

Background:

• Low-cost sensors (LCS) offer potential for enhanced air quality management.
• Existing methods struggle to identify non-traffic emission sources using LCS networks.
• Nitrogen dioxide (NO2) and particulate matter (PM2.5) data often lack specificity for non-traffic sources.

Purpose of the Study:

• To develop a scalable screening method for hyperlocal non-traffic emission source identification.
• To overcome limitations of NO2 and PM2.5 data in pinpointing diverse pollution origins.
• To leverage citywide LCS networks for detailed air quality insights.

Main Methods:

• Integration of network analysis and peak analysis techniques.
• Network analysis compares data across sensors to identify local hotspots.
• Peak analysis clusters concentration spikes to emphasize local emission impacts.

Main Results:

• Successfully identified hyperlocal non-traffic emission sources in the Greater London area.
• Demonstrated capability in detecting influences from construction activities.
• Showcased ability to pinpoint sources like nighttime cooking without prior site knowledge.

Conclusions:

• The proposed method offers a scalable solution for identifying non-traffic emission sources.
• Network and peak analysis effectively extracts hyperlocal pollution insights from LCS data.
• This approach empowers targeted air quality interventions by revealing localized pollution drivers.