We assimilate multiple trace gas species within a single high-resolution Bayesian inversion system to optimize CO 2 ff emissions for individual source sectors. Starting with carbon monoxide (CO), an atmospheric trace gas with fairly well-known emissions, we use emission factors of CO and CO 2 ff (called R CO ) defined for each source sector to enable us to jointly use CO and CO 2 atmospheric mole fractions to constrain CO 2 ff sectoral emissions. We first show that our combined CO-CO 2 inversion is theoretically capable of estimating the relative magnitude of sectoral emissions for two, specially defined sectors over Indianapolis, while CO 2 -only inversions failed at quantifying sectoral emissions. When assimilating hourly mole fractions collected over 4 months, inverse sectoral emissions converge toward high-resolution CO 2 ff bottom-up emissions from Hestia. The emission ratios between the two sectors agree within 15% with Hestia across various inversion configurations. The assimilation of CO mole fractions preferentially improves flux estimates from traffic emissions, because the CO levels originating from the combustion engine sector are large relative to those from other economic sectors. In a further investigation, we find that including an additional third tracer sensitive to the other sectors only slightly improves the accuracy of the inversion compared to our current two-sector inversions with CO and CO 2 mole fractions. We finally examined the impact of errors in trace gas emission factors and quantify their relative impact on sector-based inverse emissions. We conclude that multispecies inversions can constrain sectoral emissions at policy-level uncertainties if trace gas emission factors are sufficiently well known at the city level. Plain Language SummaryAs global urbanization grows rapidly, policymakers need to collect information about the relative changes in anthropogenic emissions from the different sectors of the economy in order to make informed policy decisions. However, bottom-up approaches, relying on activity data, remain the only methods able to produce emissions at the sector level. In our study, we investigate whether atmospheric inversions can help inform about CO 2 emissions from specific sectors of the economy through the inclusion of atmospheric measurements of non-CO 2 trace gases. Our results demonstrate that the joint optimization of atmospheric trace gases, here CO 2 and carbon monoxide (CO), within a single inversion framework can help characterize sector-level emissions of CO 2 over the city of Indianapolis, IN. Emissions from traffic are constrained in parallel with the other sectors of activities. City mitigation policies targeting specific sectors of the local economy can now be better evaluated by atmospheric measurements, with a nearly independent approach to provide more confidence in the effectiveness of emission reduction measures. These results will have important implications for policy makers and the carbon cycle community, reinforcing the link between policy decisi...

Current bottom up estimates ofCO2emission fluxes are based on a mixture of direct and indirect flux estimates relying to varying degrees on regulatory or self-reported data. Hence, it is important to use additional, independent information to assess biases and lower the flux uncertainty. We explore the use of a self-organizing map (SOM) as a tool to use multi-species observations to partition fossil fuelCO2(CO2ff) emissions by economic source sector. We use the Indianapolis Flux experiment (INFLUX) multi-species observations to provide constraints on the types of relationships we can expect to see, and show from the observations and existing knowledge of likely sources for these species that relationships do exist but can be complex. An Observing System Simulation Experiment (OSSE) is then created to test, in a pseudodata framework, the abilities and limitations of using an SOM to accurately attribute atmospheric tracers to their source sector. These tests are conducted for a variety of emission scenarios, and make use of the corresponding high-resolution footprints for the pseudo-measurements. We show here that the attribution of sector-specific emissions to measured trace gases cannot be addressed by investigating the atmospheric trace gas measurements alone. We conclude that additional a priori information such as inventories of sector-specific trace gases are required to evaluate sector-level emissions using atmospheric methods, to overcome the challenge of the spatial overlap of nearly every predefined source sector. Our OSSE additionally allows us to demonstrate that increasing the (already high) data density cannot solve the co-localization problem.

Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

Abstract. MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps. The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.