Anthropogenic and biogenic CO2 fluxes in the Boston urban region

Significance Cities are taking a leading role in US efforts to reduce greenhouse gas emissions, and require traceable methods to assess the efficacy of their efforts. In this study, we developed an inverse model framework that quantified emissions in the Boston urban region over 16 months and is capable of detecting changes in emissions of greater than 18%. We show that a detailed representation of urban biological fluxes and knowledge of the spatial and temporal distribution of emissions are essential for accurate modeling of annual CO2 emissions. Across the globe, it is possible to quantifiably assess the efficacy of mitigation efforts by developing frameworks similar to the one we present here for Boston. With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model−measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m−2·y−1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model−measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.