Presented by: Lekan Popoola, University of Cambridge

Summary: Low-cost sensors are now increasingly being used in air quality research including emission source identification, ambient air pollution monitoring for assessment and human exposure studies. Although the benefits of low-cost sensors are recognised, one of the main challenges has been their ease and accuracy of calibration, particularly when deployed as dense networks. These devices are typically calibrated in the field by either collocation with reference instruments or by using transfer of calibrated standards approach (collocations with pre-calibrated low-cost sensors). While these methods are reliable, they present logistic challenges, often taking a long time to complete. In this paper we present a cloud-based calibration method developed at University of Cambridge as part of the Breathe-London (BL) project.

The Breathe-London project (https://www.breathelondon.org) combines state-of-the-art measurement technology with over 100 low-cost sensor nodes (each measuring NO, NO2, O3, and CO2, PM2.5 and PM10) along with two Google Street View cars instrumented with reference grade air quality instruments. 
In this presentation we will focus on the static network, showing how by making measurements more rapidly (at 1 minute intervals), pollutants emitted locally can be distinguished from those due to longer range transport, leading to an innovative cloud-based method for remote calibration of the entire network for both gases and particulates. 

In this presentation we will assess the performance of this method for PM, NO2 and NO against traditional approaches.

Click here for presentation video!