Improving simulations of sulfate aerosols during winter haze over Northern China： the impacts of heterogeneous oxidation by NO2

We implemented the online coupled WRF-Chem model to reproduce the 2013 January haze event in North China, and evaluated simulated meteorological and chemical fields using multiple observations. The comparisons suggest that temperature and relative humidity （RH） were simulated well （mean biases are 0.2K and 2.7%, respectively）, but wind speeds were overestimated （mean bias is 0.5 m.s -1）. At the Beijing station, sulfur dioxide （SO2） concentrations were overpredicted and sulfhte concentrations were largely underpredicted, which may result from uncertainties in SO2 emissions and missing heterogeneous oxidation in current model. We conducted three parallel experiments to examine the impacts of doubling SO2 emissions and incorporating heterogeneous oxidation of dissolved SO2 by nitrogen dioxide （NO2） on sulfate formation during winter haze. The results suggest that doubling SO2 emissions do not significantly affect sulthte concentrations, but adding heterogeneous oxidation of dissolved SO2 by NO, substantially improve simulations of sulfate and other inorganic aerosols. Although the enhanced SO2 to sulfate conversion in the HetS （heterogeneous oxidation by NO2） case reduces SO2 concentrations, it is still largely overestimated by the model, indicating the overestimations of SO2 concentrations in the North China Plain （NCP） are mostly due to errors in SO2 emission inventory.

Direct Retrieval of NO_(2)Vertical Columns from UV-Vis(390-495nm)Spectral Radiances Using a Neural Network

Satellite retrievals of columnar nitrogen dioxide(NO_(2))are essential for the characterization of nitrogen oxides(NO_(x))processes and impacts.The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO_(2)retrievals.In this work,we instead use neural network(NN)models trained from over 360,000 radiative transfer(RT)simulations to translate satellite radiances across 390-495nm to total NO_(2)vertical column(NO_(2)C).Despite the wide variability of the many input parameters in the RT simulations,only a small number of key variables were found essential to the accurate prediction of NO_(2)C,including observing angles,surface reflectivity and altitude,and several key principal component scores of the radiances.In addition to the NO_(2)C,the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved(e.g.,extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%)under high-NO_(2)C conditions.Applying to four months of TROPOMI data,the trained NN model shows strong ability to reproduce the NO_(2)C observed by the ground-based Pandonia Global Network.The coefficient of determination(R2,0.75)and normalized mean bias(NMB,-33%)are competitive with the level 2 operational TROPOMI product(R^(2)=0:77,NMB=−29%)over clear(geometric cloud fraction<0:2)and polluted(NO_(2)C≥7:5×10^(15)molecules/cm2)regions.The NN retrieval approach is~12 times faster than predictions using high spatial resolution(~3 km)a priori profiles from chemical transport modeling,which is especially attractive to the handling of large volume satellite data.