Abstract
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.
Highlights
Air pollution is a critical environmental issue that adversely affects human health and is closely connected to climate change (von Schneidemesser et al, 2015)
Surface observations of the hourly ambient PM2.5, PM10, SO2, NO2, carbon monoxide (CO), and O3 concentrations retrieved from the China National Environmental Monitoring Centre (CNEMC) were used in this study
The mean bias error (MBE) and root mean square error (RMSE) values are smaller in the Chinese air quality reanalysis (CAQRA) dataset than those in the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset, especially for the SO2 and O3 concentrations. This is attributed to the assimilation of surface observations in CAQRA, while CAMSRA only assimilates satellite retrievals. These results suggest that the CAQRA dataset provides surface air quality datasets in China of a higher quality than the air quality datasets provided by the CAMSRA dataset, which is especially valuable for relevant future studies with high demands on spatiotemporal resolution and accuracy
Summary
Air pollution is a critical environmental issue that adversely affects human health and is closely connected to climate change (von Schneidemesser et al, 2015). As the largest developing country, has achieved great economic development since the 1980s This large-scale economic expansion, is accompanied by a dramatic increase in air pollutant emissions, leading to severe air pollution in China (Kan et al, 2012). The air quality in China has changed dramatically over the past 6 years (Silver et al, 2018; Zheng et al, 2017). Such large changes in Chinese air quality and their effects on human health and the environment have become an increasingly hot topic in many scientific fields Such large changes in Chinese air quality and their effects on human health and the environment have become an increasingly hot topic in many scientific fields (e.g. Xue et al, 2019; Zheng et al, 2017), necessitating a long-term air quality dataset in China with high accuracy and spatiotemporal resolutions
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