Ammonia (NH3) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH3 emissions still have large uncertainties. In this study, an improved inversion estimation of NH3 emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH3 observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH3 emissions. More enhanced NH3 emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH3 emissions were more than twice the a priori emissions. Monthly variations of NH3 emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH3 and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH3 emissions in China and their environmental impacts.

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.