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

Abstract

<p><strong>Abstract.</strong> We combined a chemistry transport model (CTM), a multiple regression model and available ground observations, to derive top-down estimate of black carbon (BC) emissions and to reduce deviations between simulations and observations for southern Jiangsu city cluster, a typical developed region of eastern China. Scaled from a high-resolution inventory for 2012 based on changes in activity levels, the BC emissions in southern Jiangsu were calculated at 27.0 Gg/yr for 2015 (JS-prior). The annual mean concentration of BC at Xianlin Campus of Nanjing University (NJU, a suburban site) was simulated at 3.4 μg/m<sup>3</sup>, 11 % lower than the observed 3.8 μg/m<sup>3</sup>. In contrast, it was simulated at 3.4 μg/m<sup>3</sup> at Jiangsu Provincial Academy of Environmental Science (PAES, an urban site), 36 % higher than the observed 2.5 μg/m<sup>3</sup>. The discrepancies at the two sites implied the uncertainty of the bottom-up inventory of BC emissions. Assuming a near-linear response of BC concentrations to emission changes, we applied a multiple regression model to fit the hourly surface concentrations of BC at the two sites, based on the detailed source contributions to ambient BC levels from brute-force simulation. Constrained with this top-down method, BC emissions were estimated at 13.4 Gg/yr (JS-posterior), 50 % smaller than the bottom-up estimate, and stronger seasonal variations were found. Biases between simulations and observations were reduced for most months at the two sites when JS-posterior was applied. At PAES, in particular, the simulated annual mean was elevated to 2.6 μg/m<sup>3</sup> and the annual normalized mean error (NME) decreased from 72.0 % to 57.6 %. However, application of JS-posterior slightly enhanced NMEs in July and October at NJU where simulated concentrations with JS-prior were lower than observations, implying that reduction in total emissions could not correct CTM underestimation. The effects of numbers and spatial representativeness of observation sites on top-down estimate were further quantified. The best CTM performance was obtained when observations of both sites were used with their difference in spatial functions considered in emission constraining. Given the limited BC observation data in the area, therefore, more measurements with better spatiotemporal coverage were recommended for constraining BC emissions effectively. Top-down estimates derived from JS-prior and the Multi-resolution Emission Inventory for China (MEIC) were compared to test the sensitivity of the method to initial emission input. The differences in emission levels, spatial distributions and CTM performances were largely reduced after constraining, implying that the impact of initial inventory was limited on top-down estimate. Sensitivity analysis proved the rationality of near linearity assumption between emissions and concentrations, and the impact of wet deposition on the multiple regression model was demonstrated moderate through data screening based on simulated wet deposition and satellite-derived precipitation.</p>