CarboSense4D

Four-dimensional mapping of carbon dioxide using low-cost sensors, atmospheric transport simulations and machine learning

Started
April 1, 2018
Status
Completed
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Abstract

How to determine real-time CO2 emissions of the city of Zurich and track their year-to-year evolution, enhance the understanding of CO2 exchange between biosphere and atmosphere over Switzerland, and improve the data quality of low-cost sensor networks.

People

Collaborators

SDSC Team:
No items found.

PI | Partners:

EMPA:

  • Dominik Brunner

More info

description

Problem

  • Determine real-time CO2 emissions of the city of Zurich and track their year-to-year evolution
  • Enhance understanding of CO2 exchange between biosphere and atmosphere over Switzerland
  • Improve data quality of low-cost sensor networks

Solution

Integrate complimentary information from

  • Dense network of CO2 sensors across Switzerland
  • Atmospheric transport simulations * Data analysis and machine learning

Impact

  • CarboSense4D improves the operation of dense trace gas sensor networks and the understanding of CO2 fluxes at urban and regional scales to support the assessment of CO2 emission reduction measures.

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Additional resources

Bibliography

Publications

Mueller, M.; Jaehn, M.; Pentina, A.; Brunner, D.; Perez-Cruz, F.; Hueglin, C.; Emmenegger, L. "Carbosense - CO2 sensor network and simulations" 1st Swiss Workshop on Machine Learning for Environmental and Geosciences (MLEG) 2019 View publication
Brunner, D.; Mueller, M.; Jaehn, M.; Graf, P.; Meyer, J.; Hueglin, C.; Pentina, A.; Perez Cruz, F.; Emmenegger, L. "A low-cost sensor network to monitor the CO2 emissions of the city of Zurich" The 3rd ICOS Science Conference on Greenhouse Gases and Biogeochemical Cycles 2018 View publication

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