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