Quantifying CO2 Emissions from World Megacities with Emerging Dense Urban CO2 Satellite Data: Using Lagrangian Particle Dispersion Modeling in a Los Angeles Case Study

Abstract:

To understand the dynamics of urban CO₂ sources, several cities are now equipped with ground-based observation networks, monitoring CO₂ concentrations at strategic locations. Although effective for measurements at fine temporal scales, maintenance and installation costs are obstacles to comprehensive spatial coverage. This lack of coverage is being overcome by the launch of several space-based carbon observing systems, most notably JAXA’s GOSAT, GOSAT-II, the Chinese TanSat, and NASA’s OCO-2 instrument. These space-based platforms provide increased spatial resolution with the ability to resolve rural-urban gradients. Using atmospheric modeling techniques, column-averaged CO₂ (XCO₂) enhancements can be linked to urban sources. With the addition of NASA’s latest instrument (OCO-3) and future missions (GeoCarb, Japan’s GOSAT-GW and ESA’s CO2M), the spatial resolution of observed XCO₂ is set to increase such that intra-city characteristics can be investigated. These high-resolution observations will present challenges to current modeling techniques. The amount of data available for analyses will increase, requiring additional computational time for analyses. In anticipation of these high-resolution observations, this work presents an interpolation scheme that may be used in tandem with the X-STILT atmospheric model to determine upwind sources of XCO₂ enhancements. This method generates backwards-in-time influence footprints for a fraction of relevant XCO₂ observations and then interpolates influence footprints for the remainder, reducing the computational time required for analyses by 50% or more. Here we apply the modeling approach to the Los Angeles megacity. The contributions to observed XCO₂ enhancements are disaggregated into emissions from large point sources, the urban core, regional background, and the biosphere, providing a methodology to enable space-based dense XCO₂ observations to constrain urban emissions at finer spatiotemporal scales. The methodology is applied to initial OCO-3 snapshot area mapping (SAM) data collected over the megacity.