Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×10 5 Mg in 2016) and a minimum 61% decrease in residential sector emissions (