On the public citation graph

Disentangling Meteorological Dynamics and Emission Footprints of Ground-Level NO2 in the Beijing-Tianjin-Hebei Region

0 signed · 27 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.atmosenv.2019.117214openalex-snapshot
Tropospheric SO2 and NO2 in 2012–2018: Contrasting views of two sensors (OMI and OMPS) from space
inferredcites →https://doi.org/10.1016/j.envpol.2021.117711openalex-snapshot
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia
inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.03.018openalex-snapshot
Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP
inferredcites →https://doi.org/10.1016/j.jenvman.2022.116822openalex-snapshot
Contributions of cleaner production and end-of-pipe treatment to NOx emissions and intensity reductions in China, 1997–2018
inferredcites →https://doi.org/10.1016/j.jenvman.2024.121851openalex-snapshot
A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO2): Evidence from remote sensing data in 2019–2022
inferredcites →https://doi.org/10.1016/j.jenvman.2025.127680openalex-snapshot
The impact of China's Clean Air Action and future strategies: Actions targeting regionally dominant sources
inferredcites →https://doi.org/10.1021/acs.est.5b05940openalex-snapshot
Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China
inferredcites →https://doi.org/10.1021/acs.est.5c07239openalex-snapshot
Synergy of Natural Sources Exacerbates Ozone Pollution in China during Drought–Heatwave Extremes
inferredcites →https://doi.org/10.1021/acs.est.7b05669openalex-snapshot
Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model
inferredcites →https://doi.org/10.1021/acs.est.9b03107openalex-snapshot
Predicting Fine-Scale Daily NO2 for 2005–2016 Incorporating OMI Satellite Data Across Switzerland
inferredcites →https://doi.org/10.1021/es302673eopenalex-snapshot
Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States
inferredcites →https://doi.org/10.1029/2000jd900762openalex-snapshot
Global tropospheric NO2 column distributions: Comparing three‐dimensional model calculations with GOME measurements
inferredcites →https://doi.org/10.1029/2020gl089269openalex-snapshot
Disentangling the Impact of the COVID‐19 Lockdowns on Urban NO 2 From Natural Variability
inferredcites →https://doi.org/10.1029/2020gl090080openalex-snapshot
NOx Emission Changes Over China During the COVID‐19 Epidemic Inferred From Surface NO2 Observations
inferredcites →https://doi.org/10.1038/s41586-019-0912-1openalex-snapshot
Deep learning and process understanding for data-driven Earth system science
inferredcites →https://doi.org/10.1038/s41586-021-04229-0openalex-snapshot
Global fine-scale changes in ambient NO2 during COVID-19 lockdowns
inferredcites →https://doi.org/10.1088/1748-9326/aae718openalex-snapshot
Substantial changes in air pollution across China during 2015–2017
inferredcites →https://doi.org/10.1093/bioinformatics/btq134openalex-snapshot
Permutation importance: a corrected feature importance measure
inferredcites →https://doi.org/10.1109/access.2020.2976199openalex-snapshot
Explainable Machine Learning for Scientific Insights and Discoveries
inferredcites →https://doi.org/10.1109/tnnls.2020.3017200openalex-snapshot
Encoder–Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation
inferredcites →https://doi.org/10.1175/1520-0493(1976)104<1623:svidas>2.0.co;2openalex-snapshot
SMS/GOES Visible Images Detect a Synoptic-Scale Air Pollution Episode
inferredcites →https://doi.org/10.1175/bams-d-18-0195.1openalex-snapshot
Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
inferredcites →https://doi.org/10.5194/acp-15-2969-2015openalex-snapshot
Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions
inferredcites →https://doi.org/10.5194/acp-20-1483-2020openalex-snapshot
Effect of changing NO x lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO 2 columns over China
inferredcites →https://doi.org/10.5194/acp-21-8863-2021openalex-snapshot
Boundary layer structure characteristics under objective classification of persistent pollution weather types in the Beijing area
inferredcites →https://doi.org/10.5194/acp-23-8325-2023openalex-snapshot
Superimposed effects of typical local circulations driven by mountainous topography and aerosol–radiation interaction on heavy haze in the Beijing–Tianjin–Hebei central and southern plains in winter
inferredcites →https://doi.org/10.5194/amt-13-1315-2020openalex-snapshot
S5P TROPOMI NO 2 slant column retrieval: method, stability, uncertainties and comparisons with OMI
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.