本研究利用集合卡尔曼滤波器（EnKF）和嵌套空气质量预测模拟系统（NAQPMS），同化了中国国家环境监测中心（CNEMC）的 1000 多个地表空气质量监测点，建立了长达六年的高分辨率中国空气质量再分析数据集（CAQRA）。提供了 2013-2018 年期间中国六种常规空气污染物（即 PM2.5、PM10、SO2、NO2、CO 和 O3）的高空间分辨率（15 千米 ×15 千米）和高时间分辨率的表面场。该数据集将是中国首个可同时提供六种常规空气污染物地表浓度的高分辨率空气质量再分析数据集，对空气污染健康影响评估、中国空气质量变化研究以及为基于统计或人工智能（AI）的预报提供训练数据等多项研究具有重要价值。", "ds_source": "
从中国环境监测总站获取的 PM2.5、PM10、二氧化硫、二氧化氮、一氧化碳和臭氧浓度的地面观测数据.", "ds_process_way": "
数据集由ChemDAS生成，ChemDAS采用NAQPMS模式作为预报模式，LETKF采用后处理模式同化观测数据。背景误差协方差由集合模拟计算得出，其中考虑了主要空气污染物排放的不确定性。此外，还采用了一种膨胀技术来动态膨胀背景误差，以防止低估真实的背景误差协方差。", "ds_quality": "
采用了五倍交叉验证（CV）方法来评估 CAQRA 的质量。交叉验证结果表明，CAQRA 在再现中国地面空气污染物的大小和变率方面表现出色， 也很好地反映了中国空气质量的年际变化。
\n通过与欧洲中期天气预报中心（ECWMF）基于卫星产品同化制作的哥白尼大气监测服务再分析（CAMSRA）的比较，我们发现由于同化了地面观测资料，CAQRA在表现中国地面气态空气污染物方面具有更高的精度。CAQRA 更精细的水平分辨率也使其更适合区域尺度的空气质量研究。
\n进一步将PM2.5再分析数据集与美国国务院中国空气质量监测项目的独立数据集进行了验证，并将PM2.5再分析的精度与卫星估算的PM2.5浓度进行了比较。结果表明，PM2.5 再分析数据与独立观测数据具有良好的一致性（R2=0.74-0.86，RMSE=16.8-33.6 μg/m3），其精度高于大多数卫星估算值。", "ds_acq_start_time": "2013-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.01666666666668, "ds_acq_lat_south": 3.8666666666666667, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 587935682, "ds_files_count": 3, "ds_format": "nc", "ds_space_res": "15000", "ds_time_res": "年、月", "ds_coordinate": "WGS84", "ds_projection": "WGS-1984_48N", "ds_thumbnail": "9e82406a-ad95-437b-8607-928540c7959a.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2024-01-26 10:22:36", "last_updated": "2026-01-14 10:07:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.00092", "i18n": { "en": { "title": "A Six-year long High-resolution Air Quality Reanalysis Dataset over China-monthly and annual version（2013-2018）", "ds_format": "nc", "ds_source": "
Ground observation data on PM2.5, PM10, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone concentrations obtained from the China Environmental Monitoring Station", "ds_quality": "
Five fold cross validation (CV) method was used to evaluate the quality of CAQRA. The cross validation results indicate that CAQRA performs well in reproducing the size and variability of surface air pollutants in China, and also reflects the interannual changes in air quality in China\n
By comparing with the Copernican Atmospheric Monitoring Service Reanalysis (CAMSRA) developed by the European Centre for Medium Range Weather Forecasts (ECWMF) based on satellite product assimilation, we found that due to the assimilation of ground observation data, CAQRA has higher accuracy in representing surface gaseous air pollutants in China. The finer horizontal resolution of CAQRA also makes it more suitable for regional scale air quality research\n
Further validation was conducted between the PM2.5 reanalysis dataset and the independent dataset of the US State Department's China Air Quality Monitoring Program, and the accuracy of PM2.5 reanalysis was compared with satellite estimated PM2.5 concentrations. The results indicate that there is good consistency between the reanalysis data of PM2.5 and independent observation data (R2=0.74-0.86, RMSE=16.8-33.6) μ G/m3), its accuracy is higher than most satellite estimates.", "ds_ref_way": "", "ds_abstract": "
This study utilized Ensemble Kalman Filter (EnKF) and Nested Air Quality Prediction Simulation System (NAQPMS) to assimilate over 1000 surface air quality monitoring points from China National Environmental Monitoring Center (CNEMC), and established a high-resolution China Air Quality Reanalysis Dataset (CAQRA) for six years. Provided high spatial resolution (15 kilometers) of six conventional air pollutants (i.e. PM2.5, PM10, SO2, NO2, CO and O3) in China from 2013 to 2018 × A surface field with a resolution of 15 kilometers and high temporal resolution. This dataset will be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide surface concentrations of six conventional air pollutants. It is of great value for multiple studies such as air pollution health impact assessment, research on changes in air quality in China, and providing training data for statistical or artificial intelligence (AI) based forecasting.
", "ds_time_res": "年、月", "ds_acq_place": "China", "ds_space_res": "15000", "ds_projection": "WGS-1984_48N", "ds_process_way": "The dataset is generated by ChemDAS, which uses the NAQPMS model as the prediction model, and LETKF uses a post-processing model to assimilate observation data. The background error covariance is calculated by set simulation, taking into account the uncertainty of major air pollutant emissions. In addition, an inflation technique was employed to dynamically inflate the background error to prevent underestimation of the true background error covariance.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "空气质量数据", "PM2.5", "SO2", "NO2", "CO" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "唐潇", "email": "tangxiao@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" }, { "true_name": "朱江", "email": "jzhu@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "唐潇", "email": "tangxiao@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" }, { "true_name": "朱江", "email": "jzhu@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "唐潇", "email": "tangxiao@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" }, { "true_name": "朱江", "email": "jzhu@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "category": "气象" }