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Anthropogenic CO2 emissions for cities around the world

Column version of the Stochastic Time-Inverted Lagrangian Transport, X-STILT model on GitHub.

  • X-STILT (Wu et al., 2018, GMD) is modified based upon STILT (Lin et al., 2003, Fasoli et al., 2018); One can check out the Cesium demo of X-STILT on

  • Initially build upon OCO-2 data and vertical sensivities (e.g., AK and PWF), but can be applied to other sensors;

  • Provide the atmospheric transport and calculate the imprints of potential upwind sources and sinks onto downwind satellite column measurements;

  • Provide comprehensive horizontal and vertical transport uncertainties of column CO2 (XCO2);

Combined with population density data, we were able to derive independent emission estimates of per capita CO2 emissions for 20 cities around the world and explore their relationship with population density using satellite data (Wu et al., 2020, ERL). This method removes the dependence of bottom-up emission inventories often adopted in atmospheric inversion.

This work has also been featured by NASA.

Biogenic CO2 fluxes for for cities around the world

  • Develop a simple model representation to separate out biogenic fluxes from anthropogenic emissions: A Model for Urban Biogenic CO2 Fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF)

  • Currently in review on GMDD with model code available on GitHub.

  • The SMUrF model leverages satellite-based Solar-Induced Fluorescence data along with a machine learning technique to estimate biogenic CO2 fluxes over urban areas around the globe.

  • We evaluate the biogenic fluxes against available flux observations and show differences between biogenic versus anthropogenic fluxes over 40 cities, revealing urban-rural flux gradients, diurnal cycles, and the resulting imprints on atmospheric column CO2 (using X-STILT).

Towards detecting combustion features for cities (postdoc work in progress)