Detection and Quantification of Urban CO2 Emissions over Several Megacities using In-situ and Remote-sensing Measurements: Inversion Framework and Methodology

Carbon dioxide (CO2) remains the largest single contributor to the increase in anthropogenic radiative forcing with 80% of the emissions originating from fossil fuel combustion and industrial processes. Currently, urban emissions represent about 70% of the global emissions and will very likely increase rapidly as large metropolitan areas are projected to grow twice as fast as the world population in the coming 15 years. Monitoring changes in urban emissions using independent approaches will give an additional assurance to emission reduction efforts and is therefore a critical need for current and future regulation policies. Atmospheric inversion systems are expected to be a promising tool and currently being developed and examined for quantifying/verifying emissions from several cities and metropolitan areas. We propose here an atmospheric inversion-based urban CO2 emission monitoring framework that can be applicable to any locations on the globe. We define the different components of the system including the a priori emission products based on various remote-sensing products globally available and a flexible inverse modeling framework. We developed a nested high resolution configuration on an optimal grid resolution, adapted to specific cities, and present some initial experiments on several megacities with various urban landscapes, both in terms of economic activities, spatial distribution, and local atmospheric dynamics. The system is tested with in-situ measurements from the Indianapolis Flux Experiment (INFLUX) and compared to existing inverse CO2 emissions estimates. Fine-grained emission inventories that can be used for prior information are likely to be unavailable at many locations of interest. Prior CO2 emissions are thus given using disaggregation of the reported country emissions. We examined several remotely-sensed data such as nighttime light data and impervious surface data to prescribe fine spatial emission structures in urban domains. Finally, we simulate column CO2 observed from carbon observing satellites such as GOSAT and OCO2 and discuss how to utilize those space-based information in our monitoring framework.