Preprints
https://doi.org/10.5194/amt-2021-120
https://doi.org/10.5194/amt-2021-120

  11 May 2021

11 May 2021

Review status: this preprint is currently under review for the journal AMT.

The Berkeley Environmental Air-quality and CO2 Network: field calibrations of sensor temperature dependence and assessment of network scale CO2 accuracy

Erin Rose Delaria1, Jinsol Kim2, Helen L. Fitzmaurice2, Catherine Newman1, Paul J. Wooldridge1, Kevin Worthington1, and Ronald C. Cohen1,2 Erin Rose Delaria et al.
  • 1Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA
  • 2Department of Earth and Planetary Science, University of California Berkeley, Berkeley, CA 94720, USA

Abstract. The majority of global CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼2 km measurement spacing and ∼1 ppm node-to-node precision would be effective at constraining point, line and area sources within cities. Here we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference.

Erin Rose Delaria et al.

Status: open (until 06 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-120', Anonymous Referee #1, 14 Jun 2021 reply

Erin Rose Delaria et al.

Erin Rose Delaria et al.

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Short summary
The use of a dense network of low-cost CO2 sensors is an attractive option for measuring CO2 emissions in cities. However, these low-cost sensors are also subject to uncertainties. Here we describe a novel method of field calibration for correcting temperature-related errors in the CO2 sensors deployed in the BEACO2N network. We show that with this temperature correction we can achieve a sufficiently low network error to allow for evaluation of CO2 emissions at a neighborhood scale.