Research

Here is a list of specific research topics:

Feel free to contact me (dienwu@caltech.edu) if you’re interested in my work.


I. Anthropogenic CO2 emissions for cities 🌆

Q1: How much fossil fuel CO2 do cities produce?

To answer this question from a top-down perspective, I developed the Column version of the Stochastic Time-Inverted Lagrangian Transport, X-STILT (Wu et al., GMD, 2018) based on the STILT model version 2 (Lin et al., 2003, Fasoli et al., 2018). This X-STILT model can now work with other sensors besides OCO-2 (e.g., TROPOMI, TCCON, EM27/SUN) and has now been increasingly used by the remote sensing GHG community! =)

  • Initially designed for working with OCO-2 data by incorporating sounding-specific weighting profiles (e.g., AK and PWF), but has now been modified to adapt to other sensors including OCO-3 and TROPOMI (i.e., CO, NO2, CH4).

  • Provide the atmospheric transport and calculate the imprints of potential upwind sources and sinks onto downwind satellite column measurements (so-called “column footprint”)

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

Model code archived on GitHub; with a Cesium animation of X-STILT on YouTube.


Q2: Does an individual in the “denser city” emit more carbon?

Scientific significance:
We provide the first independent observational constraint on per capita emissions and their relationship with population density and per capita GDP, by using spaceborne CO2 measurements from OCO-2.

  • 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., ERL, 2020).

  • Previous urban-scaling studies relied on bottom-up inventory that has implicit assumption between emissions and population density. However, this method provides (1) an objective definition of urban extent and (2) observational constraints from satellite retrieved mixing ratios, which were lacking from previous work.

  • Identify cities’ roles in “net exporting” or “importing” carbon, which matters for proper carbon accounting.

  • This work was featured by NASA.


II. Biogenic CO2 fluxes over cities 🌳

Q1: How much CO2 does urban vegetation uptake?

A new approach to separate biogenic fluxes from anthropogenic emissions: A Model for Urban Biogenic CO2 Fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF, Wu et al., GMD, 2021). Check out model scripts and data release from here.

  • Quantify and evaluate biogenic fluxes against flux tower observations around the globe
  • Reveal differences between biogenic vs. anthropogenic fluxes over 40 cities
  • Reveal urban-rural contrast in NEE fluxes and their diurnal cycles
  • Estimate the resulting imprints on atmospheric column CO2 (using X-STILT).

SMUrF has been utilized and evaluated over Europe and Canada:

  1. against radiocarbon measurements in London (Zazzeri et al., GRL, 2023);
  2. against a few independent eddy-covariance towers in Canada (Madsen et al., in prep);
  3. as biogenic priors for testing the ability of MicroCarb over Paris and London (Wu et al., AMT, 2023).

Q2: How will land use change in cities affect the CO2 sequestration?

Check out this super cool study led by Sabrina Madsen from the University of Toronto!! Sabrina utilized and improved SMUrF over high-latitude regions using downscaled TROPOMI SIF and investigated the impact of urban land use change on carbon sequestration (Madsen et al., accepted).


III. Bridging climate-related GHGs and environment-related Air Pollutants 🚗

Q1: Do heavy industries in a city burn less efficiently?

Yes and No - depending on the specific types of industrial activities. By combining space-based CO and CO2 observations (Wu et al., ACP, 2022), we examine several KEY factors that can influence the interpretation of ERCO using TROPOMI and OCO-3, and identify distinct combustion efficiencies between cities and within city area (i.e., areas strongly affected by heavy industry in a city). The novelty comes from the integration of high-resolution urban land cover map and atmospheric transport for identifying the industry-affected observations.

For example, industries in Los Angeles are relatively more “efficient” compared to the city average, likely due to the population of diesel trucks and vessels over the Port of LA (left panel). In contrast, industries in Shanghai are mainly activities associated with steel production that generates much more CO over CO2 compared to the city average (right panel).


Q2: Can perturbation in (sectoral) emissions be detected from space?

This question becomes increasingly critical as many cities are committed to reducing emissions of GHGs and air pollutants!
  • I developed a simplified non-linear chemical transport model for NOx chemistry (Wu et al., GMD, 2023), which is less time-consuming than full chemical models. Still, STILT-NOx model preserves and reproduces the non-linear relationship between the NOx emissions and NOx chemistry.

  • The importance of addressing NOx chemistry is emphasized when jointly using satellite observations of GHGs and air pollutants. Key factors affecting how much NO is presented as NO2 and the chemical loss of NOx include time of the year & day and distance between emissions and satellite observations (see Fig. 9 for more clues).

  • This model is now being coupled to a non-linear chemical inversion to optimize the multiple species from space (Wu et al. in prep). Email me if you are interested in my multi-tracer modeling and inverse system.

IV. Probing agricultural water use 🌾

Q1: How much irrigation water do crop trees in the Central Valley consume?

Fun project! Stay tuned!