71 citations as recorded by crossref.

1. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. https://doi.org/10.5194/acp-23-6319-2023
2. Covariation of Airborne Biogenic Tracers (CO2, COS, and CO) Supports Stronger Than Expected Growing Season Photosynthetic Uptake in the Southeastern US N. Parazoo et al. https://doi.org/10.1029/2021GB006956
3. Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations K. Sun https://doi.org/10.1029/2022GL101102
4. Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ B. Gaubert et al. https://doi.org/10.5194/acp-20-14617-2020
5. A new methodology for inferring surface ozone from multispectral satellite measurements N. Colombi et al. https://doi.org/10.1088/1748-9326/ac243d
6. Large discrepancy between observed and modeled wintertime tropospheric NO2 variabilities due to COVID-19 controls in China J. Chen et al. https://doi.org/10.1088/1748-9326/ac4ec0
7. Improve Initial Field Estimation with Deep Learning in Data Assimilation for Climate Models J. Wang et al. https://doi.org/10.3390/jmse13122406
8. Assessment of regional and interannual variations in tropospheric ozone in chemical reanalyses D. Jones et al. https://doi.org/10.5194/acp-26-6655-2026
9. Improving ozone simulations in Asia via multisource data assimilation: results from an observing system simulation experiment with GEMS geostationary satellite observations L. Shu et al. https://doi.org/10.5194/acp-23-3731-2023
10. Beyond binary maps from HCHO∕NO2: a deep neural network approach to global daily mapping of net ozone production rates and sensitivities constrained by satellite observations (2005–2023) A. Souri et al. https://doi.org/10.5194/acp-26-809-2026
11. Carbon emissions from the 2023 Canadian wildfires B. Byrne et al. https://doi.org/10.1038/s41586-024-07878-z
12. Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme S. Ha https://doi.org/10.5194/gmd-15-1769-2022
13. The Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0): demonstration with simulated CO observations V. Khade et al. https://doi.org/10.5194/gmd-14-2525-2021
14. Description and Evaluation of the Emission and Atmospheric Processes Integrated and Coupled Community (EPICC) Model Version 1.0 https://doi.org/10.1007/s00376-025-4384-y
15. Opinion: Beyond global means – novel space-based approaches to indirectly constrain the concentrations of and trends and variations in the tropospheric hydroxyl radical (OH) B. Duncan et al. https://doi.org/10.5194/acp-24-13001-2024
16. Intercomparison of Magnitudes and Trends in Anthropogenic Surface Emissions From Bottom‐Up Inventories, Top‐Down Estimates, and Emission Scenarios N. Elguindi et al. https://doi.org/10.1029/2020EF001520
17. Impacts of global NOx inversions on NO2 and ozone simulations Z. Qu et al. https://doi.org/10.5194/acp-20-13109-2020
18. Arctic tropospheric ozone: assessment of current knowledge and model performance C. Whaley et al. https://doi.org/10.5194/acp-23-637-2023
19. Global tropospheric ozone responses to reduced NO x emissions linked to the COVID-19 worldwide lockdowns K. Miyazaki et al. https://doi.org/10.1126/sciadv.abf7460
20. Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints C. DiMaria et al. https://doi.org/10.1029/2022JD037822
21. Decadal Variabilities in Tropospheric Nitrogen Oxides Over United States, Europe, and China Z. Jiang et al. https://doi.org/10.1029/2021JD035872
22. Cause of a Lower‐Tropospheric High‐Ozone Layer in Spring Over Hanoi S. Ogino et al. https://doi.org/10.1029/2021JD035727
23. Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations A. Souri et al. https://doi.org/10.5194/acp-24-8677-2024
24. Constraints on the modeled vertical distribution of smoke during the 2020 western US wildfires from satellite data M. Arnold et al. https://doi.org/10.1038/s44407-025-00036-3
25. Neutral Tropical African CO2 Exchange Estimated From Aircraft and Satellite Observations B. Gaubert et al. https://doi.org/10.1029/2023GB007804
26. Intercomparison of global ground-level ozone datasets for health-relevant metrics H. Wang et al. https://doi.org/10.5194/acp-25-15969-2025
27. Some features of solving an inverse backward problem for a generalized Burgers’ equation D. Lukyanenko et al. https://doi.org/10.1515/jiip-2020-0078
28. Intercomparison of multiple chemical mechanisms in simulating severe haze event over the North China plain Z. Li et al. https://doi.org/10.1016/j.atmosenv.2025.121439
29. Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set X. Kou et al. https://doi.org/10.1029/2020JD034382
30. The technological advancements that enabled the age of big data in the environmental sciences: A history and future directions C. Butts-Wilmsmeyer et al. https://doi.org/10.1016/j.coesh.2020.07.006
31. Joint spectral retrievals of ozone with Suomi NPP CrIS augmented by S5P/TROPOMI E. Malina et al. https://doi.org/10.5194/amt-17-5341-2024
32. The potential for geostationary remote sensing of NO2 to improve weather prediction X. Liu et al. https://doi.org/10.5194/acp-21-9573-2021
33. Quantitative analysis of global air cleanliness using the Clean aIr Index (CII) X. Guan et al. https://doi.org/10.1016/j.atmosenv.2026.121930
34. New Adaptive PI Controller for Photovoltaic Systems N. Mensia et al. https://doi.org/10.1007/s40998-024-00734-w
35. A comparison of the impact of TROPOMI and OMI tropospheric NO2 on global chemical data assimilation T. Sekiya et al. https://doi.org/10.5194/amt-15-1703-2022
36. Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead A. Sorooshian et al. https://doi.org/10.1021/acsestair.3c00033
37. Predictions of failed satellite retrieval of air quality using machine learning E. Malina et al. https://doi.org/10.5194/amt-18-1689-2025
38. Improved ozone simulation in East Asia via assimilating observations from the first geostationary air-quality monitoring satellite: Insights from an Observing System Simulation Experiment L. Shu et al. https://doi.org/10.1016/j.atmosenv.2022.119003
39. FootNet v1.0: development of a machine learning emulator of atmospheric transport T. He et al. https://doi.org/10.5194/gmd-18-1661-2025
40. Two-decade spatiotemporal variations in ground-level ozone over Ontario, Canada Z. Zang et al. https://doi.org/10.3389/fenve.2025.1601213
41. Predictability of fossil fuel CO2 from air quality emissions K. Miyazaki & K. Bowman https://doi.org/10.1038/s41467-023-37264-8
42. The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget T. Sekiya et al. https://doi.org/10.1126/sciadv.adh2688
43. Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018 K. Miyazaki et al. https://doi.org/10.5194/essd-12-2223-2020
44. [Retracted] Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players S. Liang et al. https://doi.org/10.1155/2021/4728814
45. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. https://doi.org/10.1029/2020MS002180
46. The improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications Z. Zang et al. https://doi.org/10.5194/acp-24-13889-2024
47. Evaluating the effects of COVID-19 lockdowns on air quality across some African countries Z. Han et al. https://doi.org/10.1039/D5EA00111K
48. NMVOC emission optimization in China through assimilating formaldehyde retrievals from multiple satellite products C. Xu et al. https://doi.org/10.5194/acp-26-33-2026
49. Comparing rural and urban NO2 column densities from GEMS across East Asia: Diurnal and seasonal differences L. Gagnon et al. https://doi.org/10.1016/j.atmosenv.2026.122176
50. Deep Learning to Evaluate US NOx Emissions Using Surface Ozone Predictions T. He et al. https://doi.org/10.1029/2021JD035597
51. Photochemical environment over Southeast Asia primed for hazardous ozone levels with influx of nitrogen oxides from seasonal biomass burning M. Marvin et al. https://doi.org/10.5194/acp-21-1917-2021
52. Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems T. Sekiya et al. https://doi.org/10.5194/acp-25-2243-2025
53. Tropospheric ozone as an atmospheric pollutant and short-lived climate forcer in the Third Pole B. Sharma et al. https://doi.org/10.1016/j.chemosphere.2025.144474
54. Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI) M. Kelp et al. https://doi.org/10.1088/1748-9326/acf0b7
55. Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation P. Rijsdijk et al. https://doi.org/10.5194/gmd-18-483-2025
56. Elevated carbon monoxide observations over the North Pacific upper troposphere (July 2012–February 2013) K. Wang et al. https://doi.org/10.1371/journal.pone.0323460
57. A comprehensive assessment of yield loss in rice due to surface ozone pollution in India during 2005–2020: A great concern for food security K. Anagha et al. https://doi.org/10.1016/j.agsy.2023.103849
58. High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM) Q. Ye et al. https://doi.org/10.5194/gmd-14-7573-2021
59. Observation-driven dynamic correction of urban CO2 simulations in WRF-Chem for high-resolution carbon monitoring S. Wu et al. https://doi.org/10.3389/fenvs.2026.1826851
60. Air Quality Response in China Linked to the 2019 Novel Coronavirus (COVID‐19) Lockdown K. Miyazaki et al. https://doi.org/10.1029/2020GL089252
61. An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. https://doi.org/10.5194/gmd-15-3555-2022
62. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
63. Is the efficacy of satellite-based inversion of SO2 emission model dependent? N. Li et al. https://doi.org/10.1088/1748-9326/abe829
64. Changes in US background ozone associated with the 2011 turnaround in Chinese NOx emissions K. Miyazaki et al. https://doi.org/10.1088/2515-7620/ac619b
65. An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2 V. Huijnen et al. https://doi.org/10.5194/gmd-13-1513-2020
66. Constraining NOx emissions with satellite NO2 data to improve modeling of 2023 wildfire air quality J. Kumm & Z. Qu https://doi.org/10.1016/j.atmosenv.2026.121985
67. Ensemble-Based Source Attribution of Fine Particulate Matter over South Korea during the ASIA-AQ/SIJAQ Campaign H. Kim et al. https://doi.org/10.1021/acs.est.5c15003
68. Preindustrial-to-present-day changes in atmospheric carbon monoxide: agreement and gaps between ice archives and global model reconstructions X. Faïn et al. https://doi.org/10.5194/acp-25-1105-2025
69. Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) L. Kong et al. https://doi.org/10.5194/essd-16-4351-2024
70. Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al. https://doi.org/10.5194/acp-25-8507-2025
71. Quantifying biases in TROPESS AIRS, CrIS, and joint AIRS+OMI tropospheric ozone products using ozonesondes E. Pennington et al. https://doi.org/10.5194/acp-25-8533-2025