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. We combined a chemistry transport model(the Weather Research and Forecasting and the Models-3 Community Multi-scaleAir Quality Model, WRF/CMAQ), a multiple regression model, and availableground observations to optimize black carbon (BC) emissions at monthly,emission sector, and city cluster level. We derived top-down emissions andreduced deviations between simulations and observations for the southernJiangsu city cluster, a typical developed region of eastern China. Scaledfrom a high-resolution inventory for 2012 based on changes in activitylevels, the BC emissions in southern Jiangsu were calculated at27.0 Gg yr −1 for 2015 (JS-prior). Theannual mean concentration of BC at Xianlin Campus of Nanjing University (NJU,a suburban site) was simulated at 3.4  µ g m −3 , 11 % lowerthan the observed 3.8  µ g m −3 . In contrast, it was simulatedat 3.4  µ g m −3 at Jiangsu Provincial Academy of EnvironmentalScience (PAES, an urban site), 36 % higher than the observed2.5  µ g m −3 . The discrepancies at the two sites implied theuncertainty of the bottom-up inventory of BC emissions. Assuming anear-linear response of BC concentrations to emission changes, we applied amultiple regression model to fit the hourly surface concentrations of BC atthe two sites, based on the detailed source contributions to ambient BClevels from brute-force simulation. Constrained with this top-down method, BCemissions were estimated at 13.4 Gg yr −1 (JS-posterior), 50 %smaller than the bottom-up estimate, and stronger seasonal variations werefound. Biases between simulations and observations were reduced for mostmonths at the two sites when JS-posterior was applied. At PAES, inparticular, the simulated annual mean declined to 2.6  µ g m −3 and the annual normalized mean error (NME) decreased from 72.0 % to57.6 %. However, application of JS-posterior slightly enhanced NMEs inJuly and October at NJU where simulated concentrations with JS-prior werelower than observations, implying that reduction in total emissions could notcorrect modeling underestimation. The effects of the observation site,including numbers and spatial representativeness on the top-down estimate,were further quantified. The best modeling performance was obtained whenobservations of both sites were used with their difference in spatialfunctions considered in emission constraining. Given the limited BCobservation data in the area, therefore, more measurements with betterspatiotemporal coverage were recommended for constraining BC emissionseffectively. Top-down estimates derived from JS-prior and theMulti-resolution Emission Inventory for China (MEIC) were compared to testthe sensitivity of the method to the a priori emission input. The differencesin emission levels, spatial distributions, and modeling performances werelargely reduced after constraining, implying that the impact of the a prioriinventory was limited on the top-down estimate. Sensitivity analysis provedthe rationality of the near-linearity assumption between emissions andconcentrations, and the impact of wet deposition on the multiple regressionmodel was demonstrated to be moderate through data screening based onsimulated wet deposition and satellite-derived precipitation.

Top-down estimate of black carbon emissions for city clusters using ground observations: a case study in southern Jiangsu, China