&This database systematically compiles nationwide air quality monitoring results at both city and specific monitoring site levels, comprising two core datasets: the "City Air Quality Table" and the "Site Air Quality Table." With hourly frequency, the data records the Air Quality Index (AQI) and real-time concentrations of six major pollutants (PM2.5, PM10, SO2, NO2, O3, CO), along with their 24-hour moving average concentrations. Key metrics such as 1-hour and 8-hour moving averages for ozone are also included. Characterized by extensive temporal coverage and high spatiotemporal resolution, this dataset supports both macro-level analyses of urban air quality trends and fine-grained research on site-specific variations and environmental exposure. It provides a comprehensive, continuous, and standardized empirical foundation for academic research, policy evaluation, and commercial analysis related to air quality.
Data Uniqueness
- Complete Spatiotemporal Structure Supporting Multi-level Analysis: The data offers hourly observations at both city and site spatial levels, enabling users to conduct cross-city/regional macro-trend studies or delve into pollution variations across sites within a single city. This facilitates research on spatial heterogeneity of air quality, pollution dispersion, and local influencing factors.
- Comprehensive Indicators Aligned with National Standards: Beyond AQI, the dataset covers real-time values and 24-hour moving averages of six conventional pollutants (PM2.5, PM10, SO2, NO2, O3, CO), with additional provision of 1-hour and 8-hour moving averages for ozone. It fully complies with the statistical and evaluation requirements of the "Ambient Air Quality Standards" (GB 3095-2012), ensuring authority and applicability in policy assessment and environmental compliance analysis.
- High Temporal Frequency Facilitating Dynamic and Causal Inference: Hourly continuous monitoring data captures diurnal variations, short-term fluctuations, and abrupt events in air quality. This is suitable for studying dynamic relationships between pollution and meteorological conditions, traffic flow, industrial activities, etc., while enabling causal identification and real-time policy impact evaluation based on high-frequency data.
- Dual-Dimensional Statistics Balancing Real-Time and Long-Term Exposure: Simultaneous provision of 1-hour means and 24-hour moving averages supports both real-time pollution assessment and long-term exposure-related health effect studies, meeting diverse needs across public health, labor economics, and other fields for pollution exposure metrics at different temporal scales.
Data Application Value
- Environmental Health and Public Health Research: Integrated with population and healthcare data, the dataset enables analysis of short-term or long-term pollution exposure impacts on health outcomes such as hospitalization rates, mortality, and respiratory diseases, providing evidence for environmental health risk assessment and public health policy formulation.
- Environmental Policy Performance Evaluation: Leveraging long-term, multi-city panel data, users can evaluate the effectiveness of policies like clean air initiatives, traffic restrictions, and industrial emission standards, conduct spatiotemporal heterogeneity analysis of policy impacts, and support precision governance in environmental management.
- Urban Management and Corporate Analysis: Applicable to urban environmental planning, traffic pollution control, and key pollution source monitoring, the data also facilitates analysis of air quality impacts on corporate productivity, labor attendance, outdoor consumption, and other industry activities, serving sustainable urban development and corporate operational decision-making.
&This database presents hourly air quality dynamics across both city and site dimensions nationwide, combining macro-trend representativeness with micro-scale spatial resolution. With standardized metrics, continuous temporal coverage, and clear structure, it provides a reliable analytical foundation for empirical research in environmental science, public administration, economics, public health, and other multidisciplinary fields. Whether evaluating policy intervention effects, exploring pollution-health relationships, or supporting urban environmental management and corporate strategy analysis, this dataset delivers robust, flexible, and academically/practically valuable data support.
Time Range
2014.05.13–2025.10.11
Field Display
City Air Quality Table
Site Air Quality Table
Sample Data
City Air Quality Table
Site Air Quality Table
Related Literature
- Dong, R., Fisman, R., Wang, Y., and Xu, N.H., 2019, “Air pollution, affect, and forecasting bias: Evidence from Chinese financial analysts”, Journal of Financial Economics, forthcoming.
- Huang, J. K., Xu, N. H., and Yu, H.H., 2019, “Pollution and performance: Do investors make worse trades on hazy days?”, Management Science, forthcoming.
- Huang Rongbing, Zhao Qian, and Wang Liyan, 2019, “Natural Resource Asset Departure Audit and Air Pollution Control: 'Harmonious Tournament' or 'Environmental Qualification Competition'”, China Industrial Economics, No. 10.
- Shen Yongjian, Yu Shuangli, and Jiang Dequan, 2019, “Can Air Quality Improvement Reduce Corporate Labor Costs?”, Management World, No. 6.
- Sun Chuanwang, Luo Yuan, and Yao Xin, 2019, “Transport Infrastructure and Urban Air Pollution: Empirical Evidence from China”, Economic Research Journal, No. 8.
- Chen Shuo and Chen Ting, 2014, “Air Quality and Public Health: Evidence from Sulfur Dioxide Emissions of Thermal Power Plants”, Economic Research Journal, No. 8.
- Guo Feng and Shi Qingling, 2017, “Officer Turnover, Collusion Deterrence, and Temporary Air Quality Improvement”, Economic Research Journal, No. 7.
- Luo Zhi and Li Haoran, 2018, “Impact of the 'Air Pollution Prevention and Control Action Plan' on Air Quality”, China Industrial Economics, No. 9.
Data Update Frequency
Irregular updates