Abstract. We developed a top-down methodology combining the inversed chemistry transport modeling and satellite-derived tropospheric vertical column of NO2 and estimated the NOx emissions of the Yangtze River Delta (YRD) region at a horizontal resolution of 9 km for January, April, July, and October 2016. The effect of the top-down emission estimation on air quality modeling and the response of ambient ozone (O3) and inorganic aerosols (SO42-, NO3-, and NH4+, SNA) to the changed precursor emissions were evaluated with the Community Multi-scale Air Quality (CMAQ) system. The top-down estimates of NOx emissions were smaller than those (i.e., the bottom-up estimates) in a national emission inventory, Multi-resolution Emission Inventory for China (MEIC), for all the 4 months, and the monthly mean was calculated to be 260.0 Gg/month, 24 % less than the bottom-up one. The NO2 concentrations simulated with the bottom-up estimate of NOx emissions were clearly higher than the ground observations, indicating the possible overestimation in the current emission inventory, attributed to its insufficient consideration of recent emission control in the region. The model performance based on top-down estimate was much better, and the biggest change was found for July, with the normalized mean bias (NMB) and normalized mean error (NME) reduced from 111 % to −0.4 % and from 111 % to 33 %, respectively. The results demonstrate the improvement of NOx emission estimation with the nonlinear inversed modeling and satellite observation constraint. With the smaller NOx emissions in the top-down estimate than the bottom-up one, the elevated concentrations of ambient O3 were simulated for most of the YRD, and they were closer to observations except for July, implying the VOC (volatile organic compound)-limited regime of O3 formation. With available ground observations of SNA in the YRD, moreover, better model performance of NO3- and NH4+ was achieved for most seasons, implying the effectiveness of precursor emission estimation on the simulation of secondary inorganic aerosols. Through the sensitivity analysis of O3 formation for April 2016, the decreased O3 concentrations were found for most of the YRD region when only VOC emissions were reduced or the reduced rate of VOC emissions was 2 times of that of NOx, implying the crucial role of VOC control in O3 pollution abatement. The SNA level for January 2016 was simulated to decline 12 % when 30 % of NH3 emissions were reduced, while the change was much smaller with the same reduced rate for SO2 or NOx. The result suggests that reducing NH3 emissions was the most effective way to alleviate SNA pollution of the YRD in winter.

Abstract. We combined a chemistry transport model (the Weather Research and Forecasting and the Models-3 Community Multi-scale Air Quality Model, WRF/CMAQ), a multiple regression model, and available ground observations to optimize black carbon (BC) emissions at monthly, emission sector, and city cluster level. We derived top-down emissions and reduced deviations between simulations and observations for the 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−1 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−3, 11 % lower than the observed 3.8 µg m−3. In contrast, it was simulated at 3.4 µg m−3 at Jiangsu Provincial Academy of Environmental Science (PAES, an urban site), 36 % higher than the observed 2.5 µg m−3. 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−1 (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 declined to 2.6 µg m−3 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 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 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 the a priori emission input. The differences in emission levels, spatial distributions, and modeling performances were largely reduced after constraining, implying that the impact of the a priori inventory was limited on the top-down estimate. Sensitivity analysis proved the rationality of the near-linearity assumption between emissions and concentrations, and the impact of wet deposition on the multiple regression model was demonstrated to be moderate through data screening based on simulated wet deposition and satellite-derived precipitation.

<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>