50 citations as recorded by crossref.

1. Quantifying Global Power Plant Carbon Dioxide Emissions With Imaging Spectroscopy D. Cusworth et al. 10.1029/2020AV000350
2. A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS) E. McDuffie et al. 10.5194/essd-12-3413-2020
3. Impact Measurement of COVID-19 Lockdown on China’s Electricity-Carbon Nexus M. Zhao et al. 10.3390/ijerph18189736
4. Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects C. Hong et al. 10.5194/gmd-10-2447-2017
5. Electrodeposited Porous Mn1.5Co1.5O4/Ni Composite Electrodes for High-Voltage Asymmetric Supercapacitors G. Pan et al. 10.3390/ma10040370
6. Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. 10.1007/s11869-021-01041-7
7. Investigation and Control Technology on Excessive Ammonia-Slipping in Coal-Fired Plants X. Yao et al. 10.3390/en13164249
8. Spatiotemporal variation of domestic biomass burning emissions in rural China based on a new estimation of fuel consumption X. Xing et al. 10.1016/j.scitotenv.2018.01.048
9. A city-level comparison of fossil-fuel and industry processes-induced CO2 emissions over the Beijing-Tianjin-Hebei region from eight emission inventories P. Han et al. 10.1186/s13021-020-00163-2
10. Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology D. Chen et al. 10.5194/acp-19-7409-2019
11. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2 P. Han et al. 10.3390/s21010256
12. Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> over China M. Li et al. 10.5194/acp-18-3433-2018
13. Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios D. Tong et al. 10.5194/acp-20-5729-2020
14. Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing: Mechanisms and Implications Z. Sun et al. 10.1007/s13351-018-7064-3
15. A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. 10.5194/acp-21-13747-2021
16. Improved combustion air preheating design using multiple heat sources incorporating bypass flue in large-scale coal-fired power unit H. Chen et al. 10.1016/j.energy.2018.12.010
17. The control of anthropogenic emissions contributed to 80 % of the decrease in PM<sub>2.5</sub> concentrations in Beijing from 2013 to 2017 Z. Chen et al. 10.5194/acp-19-13519-2019
18. Improved statistical consistency: The effect of data revisions on the energy use gap between China and its provinces B. Ma & M. Liu 10.1016/j.chieco.2022.101781
19. The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
20. Control of both PM2.5 and O3 in Beijing-Tianjin-Hebei and the surrounding areas S. Xiang et al. 10.1016/j.atmosenv.2020.117259
21. Anthropogenic emission inventories in China: a review M. Li et al. 10.1093/nsr/nwx150
22. Challenges and opportunities for carbon neutrality in China Z. Liu et al. 10.1038/s43017-021-00244-x
23. Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS) R. Hoesly et al. 10.5194/gmd-11-369-2018
24. Underreported coal in statistics: A survey-based solid fuel consumption and emission inventory for the rural residential sector in China L. Peng et al. 10.1016/j.apenergy.2018.11.043
25. Dominant role of emission reduction in PM<sub>2.5</sub> air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis J. Cheng et al. 10.5194/acp-19-6125-2019
26. Pollutant emission reductions deliver decreased PM<sub>2.5</sub>-caused mortality across China during 2015–2017 B. Silver et al. 10.5194/acp-20-11683-2020
27. Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China R. Feng et al. 10.1016/j.envpol.2019.05.101
28. Quantifying the uncertainties of China's emission inventory for industrial sources: From national to provincial and city scales Y. Zhao et al. 10.1016/j.atmosenv.2017.06.045
29. Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign M. Biggart et al. 10.5194/acp-20-2755-2020
30. Province-level fossil fuel CO2 emission estimates for China based on seven inventories P. Han et al. 10.1016/j.jclepro.2020.123377
31. Multi-perspective comparisons and mitigation implications of SO2 and NO x discharges from the industrial sector of China: a decomposition analysis J. Jia et al. 10.1007/s11356-018-1306-x
32. Top-down estimate of black carbon emissions for city clusters using ground observations: a case study in southern Jiangsu, China X. Zhao et al. 10.5194/acp-19-2095-2019
33. A new method of energy-related carbon dioxide emissions estimation at the provincial-level: A case study of Shandong Province, China L. Li & J. Yang 10.1016/j.scitotenv.2019.134384
34. Multi-year application of WRF-CAM5 over East Asia-Part I: Comprehensive evaluation and formation regimes of O3 and PM2.5 J. He et al. 10.1016/j.atmosenv.2017.06.015
35. Finding Ocean States That Are Consistent with Observations from a Perturbed Physics Parameter Ensemble S. Sparrow et al. 10.1175/JCLI-D-17-0514.1
36. The consistency of China's energy statistics and its implications for climate policy H. Li et al. 10.1016/j.jclepro.2018.07.094
37. Impact of the ‘13th Five-Year Plan’ Policy on Air Quality in Pearl River Delta, China: A Case Study of Haizhu District in Guangzhou City Using WRF-Chem J. Zhan et al. 10.3390/app10155276
38. Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design I. Super et al. 10.5194/acp-20-1795-2020
39. Evaluating China's fossil-fuel CO<sub>2</sub> emissions from a comprehensive dataset of nine inventories P. Han et al. 10.5194/acp-20-11371-2020
40. Interannual and Decadal Changes in Tropospheric Ozone in China and the Associated Chemistry-Climate Interactions: A Review Y. Fu et al. 10.1007/s00376-019-8216-9
41. MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia L. Chen et al. 10.5194/acp-19-11911-2019
42. Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production Z. Liu et al. 10.1038/s41597-020-00708-7
43. Evaluating national and subnational CO2 mitigation goals in China’s thirteenth five-year plan from satellite observations G. Pan et al. 10.1016/j.envint.2021.106771
44. A new method for deriving trace gas emission inventories from satellite observations: The case of SO2 over China K. Kourtidis et al. 10.1016/j.scitotenv.2017.08.313
45. Characteristics and source apportionment of fine haze aerosol in Beijing during the winter of 2013 X. Shang et al. 10.5194/acp-18-2573-2018
46. How modifications of China's energy data affect carbon mitigation targets H. Zheng et al. 10.1016/j.enpol.2018.02.031
47. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP M. Li et al. 10.5194/acp-17-935-2017
48. Cleaning up the air: effectiveness of air quality policy for SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions in China R. van der A et al. 10.5194/acp-17-1775-2017
49. Identifying primary energy requirements in structural path analysis: A case study of China 2012 B. Zhang et al. 10.1016/j.apenergy.2017.01.066
50. Spatial Configuration of Energy Consumption and Carbon Emissions of Shanghai, and Our Policy Suggestions K. Pan et al. 10.3390/su9010104