A Six-year long (2013–2018) High-resolution Air Quality Reanalysis Dataset over China base on the assimilation of surface observations from CNEMC
- 1LAPC & ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- 2College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- 3Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- 4China National Environmental Monitoring Centre, Beijing, 100012, China
- 5Guanghua School of Management, Peking University, Beijing 100871, China
- 6College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
- 7Shanghai Environmental Monitoring Center, Shanghai, 200030, China
- 8State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring Center, Guangzhou, 510308, China
- 9Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
- 10School of Environment and Energy, South China University of Technology, University Town, Guangzhou 510006, China
- 11State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
- 12Center for Global and Regional Environmental Research, University of Iowa, Iowa City, IA, 52242, USA
Abstract. Air pollution in China has changed substantially since 2013, and the effects such changes bring to the human health and environment has been an increasingly hot topic in many scientific fields. Such studies, however, are often hindered by a lack of long-term air quality dataset in China of high accuracy and spatiotemporal resolutions. In this study, a six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). Surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3 for period 2013–2018, are provided at high spatial (15 km ×15 km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis dataset. A five-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the surface air pollutants in China (CV R2 = 0.52–0.81, CV RMSE = 0.54 mg/m3 for CO and 16.4–39.3 μg/m3 for other pollutants at the hourly scale). The interannual changes of the air quality in China were also well represented by CAQRA. Through the comparisons with the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) produced by the European Centre for Medium-Range Weather Forecasts (ECWMF) based on assimilating satellite products, we show that the CAQRA has higher accuracy in representing the surface gaseous air pollutants in China due to the assimilation of surface observations. The finer horizontal resolution of CAQRA also makes it more suitable for the air quality studies in the regional scale. We further validate the PM2.5 reanalysis dataset against the independent datasets from the U.S. Department of State Air Quality Monitoring Program over China, and the accuracy of PM2.5 reanalysis was also compared to that of the satellite estimated PM2.5 concentrations. The results indicate that the PM2.5 reanalysis shows good agreement with the independent observations (R2 = 0.74–0.86, RMSE = 16.8–33.6 μg/m3 in different cities) and its accuracy is higher than most satellite estimates. This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution, investigation of the changes of air quality in China and providing training data for the statistical or AI (Artificial Intelligence) based forecast. The whole datasets are freely available at: https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a teaser product which contains the monthly and annual mean of the CAQRA has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the potential users to download and to evaluate the improvement of CAQRA.
Lei Kong et al.
Lei Kong et al.
A Six-year long High-resolution Air Quality Reanalysis Dataset over China from 2013 to 2018 https://doi.org/10.11922/sciencedb.00053
A Six-year long High-resolution Air Quality Reanalysis Dataset over China from 2013 to 2018 (monthly and annual version) https://doi.org/10.11922/sciencedb.00092
Lei Kong et al.
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