# A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

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. PMinline-formula2.5, PMinline-formula10, SOinline-formula2, NOinline-formula2, CO, and Oinline-formula3) for the period 2013–2018 at high spatial (inline-formula15 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 inline-formulaR2=0.52–0.81, CV root mean square error (RMSE) inline-formula=0.54inline-formula $M9inlinescrollmathmlunit\mathrm{normal mg}/{\mathrm{normal m}}^{normal 3}$ 37pt15ptsvg-formulamathimga726523f946f0c195dfd0fa50355ab2d essd-13-529-2021-ie00001.svg37pt15ptessd-13-529-2021-ie00001.png for CO, and CV RMSE inline-formula=16.4–39.3 inline-formula $M11inlinescrollmathmlunit\mathrm{normal µ}\mathrm{normal g}/{\mathrm{normal m}}^{normal 3}$ 34pt15ptsvg-formulamathimg972f061ce0aa8e38360e444fff9cc757 essd-13-529-2021-ie00002.svg34pt15ptessd-13-529-2021-ie00002.png 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 PMinline-formula2.5 reanalysis dataset is further validated againstpage530 the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (inline-formulaR2=0.74–0.86 and RMSE inline-formula=16.8–33.6 inline-formula $M15inlinescrollmathmlunit\mathrm{normal µ}\mathrm{normal g}/{\mathrm{normal m}}^{normal 3}$ 34pt15ptsvg-formulamathimg5ba525e2a1855b8d1cba48c2e4884b21 essd-13-529-2021-ie00003.svg34pt15ptessd-13-529-2021-ie00003.png in different cities). Furthermore, through the comparison to satellite-estimated PMinline-formula2.5 concentrations, we show that the accuracy of the PMinline-formula2.5 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.00053https://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.00092https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

### Zitieren

Zitierform:

Kong, Lei / Tang, Xiao / Zhu, Jiang / et al: A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC. 2021. Copernicus Publications.

### Zugriffsstatistik

Gesamt:
Volltextzugriffe: