97 citations as recorded by crossref.

1. Anthropogenic emission inventories in China: a review M. Li et al. https://doi.org/10.1093/nsr/nwx150
2. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions B. Zheng et al. https://doi.org/10.5194/acp-18-14095-2018
3. Trade-Driven Environmental and Socioeconomic Impacts and Regional Disparities: Evidence from China’s Provincial Scale with Its Neighbors X. Wang et al. https://doi.org/10.1021/acs.est.6c01868
4. 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. https://doi.org/10.1186/s13021-020-00163-2
5. Temporal and spatial differences in carbon emissions in the Pearl River Delta based on multi-resolution emission inventory modeling Q. Xu et al. https://doi.org/10.1016/j.jclepro.2018.12.280
6. Atmospheric anthropogenic CO2 variations observed by tower in-situ measurements and simulated by the STILT model in the Beijing megacity region H. Yang et al. https://doi.org/10.1016/j.atmosres.2025.108258
7. Development of a unit-based industrial emission inventory in the Beijing–Tianjin–Hebei region and resulting improvement in air quality modeling H. Zheng et al. https://doi.org/10.5194/acp-19-3447-2019
8. Spatio-Temporal Variations of Multiple Primary Air Pollutants Emissions in Beijing of China, 2006–2015 Y. Xue et al. https://doi.org/10.3390/atmos10090494
9. 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. https://doi.org/10.3390/app10155276
10. Satellite-based estimates of decline and rebound in China’s CO 2 emissions during COVID-19 pandemic B. Zheng et al. https://doi.org/10.1126/sciadv.abd4998
11. Towards reducing inter-city economic inequality embedded in China’s environmental protection tax law J. Wang et al. https://doi.org/10.1088/1748-9326/ac34bd
12. Source Apportionment of Urban GHGs in Hong Kong from Regional Transportation Based on Diagnostic Ratio Method Y. Xu et al. https://doi.org/10.3390/su172210099
13. Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts Y. Wang et al. https://doi.org/10.5194/acp-20-6651-2020
14. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.167763
15. A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling N. Charkovska et al. https://doi.org/10.1007/s11027-018-9836-6
16. Revealing Heterogeneous Trade-Offs and Synergies of Food–Carbon–Water Nexus for Sustainable Agricultural Development in Northeast China Z. Hou et al. https://doi.org/10.3390/agronomy16040437
17. Spatiotemporal variation in the impact of meteorological conditions on PM2.5 pollution in China from 2000 to 2017 Y. Xu et al. https://doi.org/10.1016/j.atmosenv.2019.117215
18. City-scale analysis of annual ambient PM2.5 source contributions with the InMAP reduced-complexity air quality model: a case study of Madison, Wisconsin C. Jackson et al. https://doi.org/10.1088/2634-4505/acb0fa
19. A high temporal-spatial resolution air pollutant emission inventory for agricultural machinery in China J. Lang et al. https://doi.org/10.1016/j.jclepro.2018.02.120
20. Air quality and health benefits from ultra-low emission control policy indicated by continuous emission monitoring: a case study in the Yangtze River Delta region, China Y. Zhang et al. https://doi.org/10.5194/acp-21-6411-2021
21. The Importance of Spatial Resolution in the Modeling of Methane Emissions from Natural Wetlands Y. Albuhaisi et al. https://doi.org/10.3390/rs15112840
22. Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble L. Yuan et al. https://doi.org/10.5194/acp-22-8617-2022
23. Evaluating the methods and influencing factors of satellite-derived estimates of NOX emissions at regional scale: A case study for Yangtze River Delta, China Y. Yang et al. https://doi.org/10.1016/j.atmosenv.2019.117051
24. High-resolution regional emission inventory contributes to the evaluation of policy effectiveness: a case study in Jiangsu Province, China C. Gu et al. https://doi.org/10.5194/acp-23-4247-2023
25. High-resolution livestock spatial distribution mapping in China based on big data and applications in ammonia emission inventories W. Zhang et al. https://doi.org/10.1016/j.jclepro.2025.146097
26. Measurements of traffic-dominated pollutant emissions in a Chinese megacity F. Squires et al. https://doi.org/10.5194/acp-20-8737-2020
27. A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China J. Qi et al. https://doi.org/10.1016/j.atmosenv.2017.09.039
28. Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis G. Geng et al. https://doi.org/10.5194/acp-17-4131-2017
29. NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015 F. Liu et al. https://doi.org/10.5194/acp-17-9261-2017
30. Exploring sources of uncertainty in premature mortality estimates from fine particulate matter: the case of China P. Giani et al. https://doi.org/10.1088/1748-9326/ab7f0f
31. Identifying hotspots based on high-resolution emission inventory of volatile organic compounds: A case study in China X. Liu et al. https://doi.org/10.1016/j.jenvman.2021.112419
32. How to distinguish dwellings taking into account the information about supply from the district heating system? CO2 emission inventory in the Silesia, Poland D. Zasina & J. Zawadzki https://doi.org/10.1080/17583004.2018.1537515
33. The effect of recent controls on emissions and aerosol pollution at city scale: A case study for Nanjing, China Y. Zhao et al. https://doi.org/10.1016/j.atmosenv.2020.118080
34. First High-Resolution Emission Inventory of Levoglucosan for Biomass Burning and Non-Biomass Burning Sources in China J. Wu et al. https://doi.org/10.1021/acs.est.0c06675
35. High-resolution (0.05°  ×  0.05°) NOx emissions in the Yangtze River Delta inferred from OMI H. Kong et al. https://doi.org/10.5194/acp-19-12835-2019
36. Spatial distribution of residential wood combustion emissions in the Nordic countries: How well national inventories represent local emissions? V. Paunu et al. https://doi.org/10.1016/j.atmosenv.2021.118712
37. Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign M. Biggart et al. https://doi.org/10.5194/acp-20-2755-2020
38. A Multi-Agglomeration Assessment of Air Quality Responses to Top-Down NOx Emission Changes: Insights from Trends in Surface NO2 and O3 Across Urban China (2014–2021) Y. Shen et al. https://doi.org/10.3390/atmos17030313
39. Evaluation of modeling NO2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China F. Liu et al. https://doi.org/10.5194/acp-18-4171-2018
40. Nitrate-driven extreme winter PM2.5 pollution in Shanghai, China G. Chen et al. https://doi.org/10.1038/s44407-025-00028-3
41. Considerable Unaccounted Local Sources of NOx Emissions in China Revealed from Satellite H. Kong et al. https://doi.org/10.1021/acs.est.1c07723
42. Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios J. Cheng et al. https://doi.org/10.1029/2021GL093197
43. A two-way coupled regional urban–street network air quality model system for Beijing, China T. Wang et al. https://doi.org/10.5194/gmd-16-5585-2023
44. Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling B. Zheng et al. https://doi.org/10.1016/j.scib.2020.12.008
45. More effective synergies to reduce carbon and air pollutants in economically dynamic regions X. Zhang et al. https://doi.org/10.1016/j.resconrec.2025.108566
46. Downscaling global anthropogenic emissions for high-resolution urban air quality studies V. Valencia et al. https://doi.org/10.1016/j.apr.2022.101516
47. An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region B. Zhou et al. https://doi.org/10.1029/2022JD038122
48. Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States X. Chen et al. https://doi.org/10.1029/2022JD038228
49. Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing L. Yuan et al. https://doi.org/10.1007/s11869-021-01041-7
50. Modeling particle number concentrations and sources in the Beijing and Shanghai metropolitan areas with the UCD/CIT model Y. Zhu et al. https://doi.org/10.1016/j.jes.2026.01.005
51. Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020 B. Zheng et al. https://doi.org/10.5194/essd-13-2895-2021
52. New Method for Improving Spatial Allocation Accuracy of Industrial Energy Consumption and Implications for Polycyclic Aromatic Hydrocarbon Emissions in China B. Li et al. https://doi.org/10.1021/acs.est.8b06915
53. Comparison and evaluation of anthropogenic emissions of SO2 and NOx over China M. Li et al. https://doi.org/10.5194/acp-18-3433-2018
54. How does upgrading an emissions inventory affect air quality simulations? M. Russo et al. https://doi.org/10.1007/s11869-019-00692-x
55. Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. https://doi.org/10.1007/s11783-025-1922-x
56. Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method Z. Yang et al. https://doi.org/10.1088/2515-7620/ade7d8
57. Emission of primary microplastics in mainland China: Invisible but not negligible T. Wang et al. https://doi.org/10.1016/j.watres.2019.06.042
58. Dynamic harmonization of source-oriented and receptor models for source apportionment X. Zhang et al. https://doi.org/10.1016/j.scitotenv.2022.160312
59. Emission inventory of air pollutants and chemical speciation for specific anthropogenic sources based on local measurements in the Yangtze River Delta region, China J. An et al. https://doi.org/10.5194/acp-21-2003-2021
60. Spatial inter-comparison of Top-down emission inventories in European urban areas M. Trombetti et al. https://doi.org/10.1016/j.atmosenv.2017.10.032
61. Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis J. Cheng et al. https://doi.org/10.5194/acp-19-6125-2019
62. Urban blue-green spaces for wintertime microclimate regulation: Interactive and marginal effects W. Ding et al. https://doi.org/10.1016/j.scs.2025.107095
63. Province-level fossil fuel CO2 emission estimates for China based on seven inventories P. Han et al. https://doi.org/10.1016/j.jclepro.2020.123377
64. Space-based inversion reveals underestimated carbon monoxide emissions over Shanxi X. Li et al. https://doi.org/10.1038/s43247-025-02301-5
65. Nitrogen isotopes suggest agricultural and non-agricultural sources contribute equally to NH3 and NH4+ in urban Beijing during December 2018 N. Bhattarai et al. https://doi.org/10.1016/j.envpol.2023.121455
66. Aerosol vertical mass flux measurements during heavy aerosol pollution episodes at a rural site and an urban site in the Beijing area of the North China Plain R. Yuan et al. https://doi.org/10.5194/acp-19-12857-2019
67. Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020 R. Zhu et al. https://doi.org/10.5194/acp-23-9745-2023
68. Quantifying the impact of the uncertainty arising from spatial allocation on public health using CMAQ F. Cingiroglu et al. https://doi.org/10.1016/j.aeaoa.2025.100338
69. Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements K. Che et al. https://doi.org/10.1088/1748-9326/ac7477
70. A ten-year meteorological simulation and optimization in China based on traditional data assimilation and machine learning methods M. Wu & Q. Shu https://doi.org/10.1016/j.envsoft.2025.106707
71. Coupling population dynamics with earth system models: the POPEM model A. Navarro et al. https://doi.org/10.1007/s11356-017-0127-7
72. Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements N. Wu et al. https://doi.org/10.1088/1748-9326/ac175f
73. A big data approach to improving the vehicle emission inventory in China F. Deng et al. https://doi.org/10.1038/s41467-020-16579-w
74. Air pollution emission inventory using national high-resolution spatial parameters for the Nordic countries and analysis of PM2.5 spatial distribution for road transport and machinery and off-road sectors V. Paunu et al. https://doi.org/10.5194/essd-16-1453-2024
75. Improving spatial surrogates for area source emissions inventories in California Y. Li et al. https://doi.org/10.1016/j.atmosenv.2020.117665
76. Regional Policies Targeting Residential Solid Fuel and Agricultural Emissions Can Improve Air Quality and Public Health in the Greater Bay Area and Across China L. Conibear et al. https://doi.org/10.1029/2020GH000341
77. Fast optimization of the emission reduction pathways of major air pollutants in China: From the perspective of different decision preferences Y. Wang et al. https://doi.org/10.1016/j.envint.2025.109282
78. Direct measurements of black carbon fluxes in central Beijing using the eddy covariance method R. Joshi et al. https://doi.org/10.5194/acp-21-147-2021
79. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. https://doi.org/10.1029/2021GH000570
80. Co-effect assessment on regional air quality: A perspective of policies and measures with greenhouse gas reduction potential W. Chen et al. https://doi.org/10.1016/j.scitotenv.2022.158119
81. Embodied black carbon emission transfer within and across the Jing-Jin-Ji urban agglomeration S. Guo et al. https://doi.org/10.1016/j.eiar.2024.107678
82. Updating emission inventories for vehicular organic gases: Indications from cold-start and temperature effects on advanced technology cars Z. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.163544
83. 基于多源数据的城市CO2源汇高空间分辨网格的构建方法研究 孙. Sun Erchang et al. https://doi.org/10.3788/AOS241741
84. Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2 B. Zheng et al. https://doi.org/10.5194/acp-20-8501-2020
85. Update of SO2 emission inventory in the Megacity of Chongqing, China by inverse modeling X. Feng et al. https://doi.org/10.1016/j.atmosenv.2022.119519
86. Spatiotemporal comparative analysis of three carbon emission inventories in mainland China L. Zheng et al. https://doi.org/10.1016/j.apr.2025.102417
87. A novel method for spatial allocation of volatile chemical products emissions: A case study of the Pearl River Delta Z. Cai et al. https://doi.org/10.1016/j.atmosenv.2023.120119
88. Hyperspectral remote sensing for air pollutants: Stereoscopic monitoring, source localization & warning, and a dynamic emission inventory concept C. Xing et al. https://doi.org/10.1016/j.envint.2025.109375
89. Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories P. Han et al. https://doi.org/10.5194/acp-20-11371-2020
90. Development of a high-resolution integrated emission inventory of air pollutants for China N. Wu et al. https://doi.org/10.5194/essd-16-2893-2024
91. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. https://doi.org/10.5194/gmd-16-5949-2023
92. Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. https://doi.org/10.1029/2021GH000391
93. Development of water basin pollution emission inventory: a preliminary literature review and Its implication for China S. Zhao et al. https://doi.org/10.2166/aqua.2020.071
94. Optimized environmental justice calculations for air pollution disparities in Southern California Y. Li et al. https://doi.org/10.1016/j.heliyon.2022.e10732
95. High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects C. Zeng et al. https://doi.org/10.1038/s41598-024-71836-y
96. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification T. Ju et al. https://doi.org/10.1007/s11783-023-1608-1
97. Surface–atmosphere fluxes of volatile organic compounds in Beijing W. Acton et al. https://doi.org/10.5194/acp-20-15101-2020