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An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

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What this work says

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

inferredcites →https://doi.org/10.1002/2017gl073859openalex-snapshot
Long‐term (2005–2014) trends in formaldehyde (HCHO) columns across North America as seen by the OMI satellite instrument: Evidence of changing emissions of volatile organic compounds
inferredcites →https://doi.org/10.1007/s00190-012-0578-zopenalex-snapshot
Algorithms for geodesics
inferredcites →https://doi.org/10.1016/j.adapen.2021.100017openalex-snapshot
Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means
inferredcites →https://doi.org/10.1016/j.atmosenv.2010.10.050openalex-snapshot
Primary NO2 emissions and their role in the development of NO2 concentrations in a traffic environment
inferredcites →https://doi.org/10.1016/j.atmosenv.2012.07.080openalex-snapshot
Variation of NO2 and NO concentrations between and within 36 European study areas: Results from the ESCAPE study
inferredcites →https://doi.org/10.1016/j.atmosenv.2020.117322openalex-snapshot
Satellite-based estimation of surface NO2 concentrations over east-central China: A comparison of POMINO and OMNO2d data
inferredcites →https://doi.org/10.1016/j.atmosres.2009.05.001openalex-snapshot
Influences of characteristic meteorological conditions on atmospheric carbonyls in Beijing, China
inferredcites →https://doi.org/10.1016/j.envint.2012.04.008openalex-snapshot
Associations between short-term exposure to nitrogen dioxide and mortality in 17 Chinese cities: The China Air Pollution and Health Effects Study (CAPES)
inferredcites →https://doi.org/10.1016/j.envint.2014.08.016openalex-snapshot
Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013–2014
inferredcites →https://doi.org/10.1016/j.envint.2019.104934openalex-snapshot
A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide
inferredcites →https://doi.org/10.1016/j.envint.2020.105578openalex-snapshot
Bayesian geostatistical modelling of high-resolution NO 2 exposure in Europe combining data from monitors, satellites and chemical transport models
inferredcites →https://doi.org/10.1016/j.envint.2020.105823openalex-snapshot
Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach
inferredcites →https://doi.org/10.1016/j.envpol.2018.01.053openalex-snapshot
Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model
inferredcites →https://doi.org/10.1016/j.envpol.2021.116932openalex-snapshot
Mapping high resolution national daily NO2 exposure across mainland China using an ensemble algorithm
inferredcites →https://doi.org/10.1016/j.envpol.2021.118285openalex-snapshot
Estimating 2013–2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model
inferredcites →https://doi.org/10.1016/j.jag.2011.11.001openalex-snapshot
Increasing the accuracy of nitrogen dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics
inferredcites →https://doi.org/10.1016/j.rse.2017.12.018openalex-snapshot
Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.11.125openalex-snapshot
National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.145145openalex-snapshot
Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations
inferredcites →https://doi.org/10.1021/acs.est.7b01148openalex-snapshot
Global Land Use Regression Model for Nitrogen Dioxide Air Pollution
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.9b03358openalex-snapshot
Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging
inferredcites →https://doi.org/10.1021/es202856zopenalex-snapshot
Correction to National Satellite-Based Land-Use Regression: NO2 in the United States
inferredcites →https://doi.org/10.1021/es505846ropenalex-snapshot
Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning
inferredcites →https://doi.org/10.1029/2002jd002153openalex-snapshot
Mapping isoprene emissions over North America using formaldehyde column observations from space
inferredcites →https://doi.org/10.1029/2004jd004869openalex-snapshot
Evaluation of GOME satellite measurements of tropospheric NO2 and HCHO using regional data from aircraft campaigns in the southeastern United States
inferredcites →https://doi.org/10.1029/96jd00336openalex-snapshot
Origin of ozone and NOx in the tropical troposphere: A photochemical analysis of aircraft observations over the South Atlantic basin
inferredcites →https://doi.org/10.1029/eo068i046p01595-02openalex-snapshot
Atmospheric Chemistry and Physics of Air Pollution
inferredcites →https://doi.org/10.1038/274855a0openalex-snapshot
The origin of ozone in the troposphere
inferredcites →https://doi.org/10.1038/s41598-017-03929-wopenalex-snapshot
Assessing Spatial and Temporal Patterns of Observed Ground-level Ozone in China
inferredcites →https://doi.org/10.1109/tgrs.2006.872333openalex-snapshot
The ozone monitoring instrument
inferredcites →https://doi.org/10.1111/ejss.12687openalex-snapshot
Spatial modelling with Euclidean distance fields and machine learning
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1164/ajrccm.162.4.9909096openalex-snapshot
Association between Air Pollution and Lung Function Growth in Southern California Children
inferredcites →https://doi.org/10.1164/rccm.201303-0609ocopenalex-snapshot
Spatial Analysis of Air Pollution and Mortality in California
inferredcites →https://doi.org/10.1183/09031936.00114713openalex-snapshot
Nitrogen dioxide and mortality: review and meta-analysis of long-term studies
inferredcites →https://doi.org/10.1641/0006-3568(2003)053[0391:nedami]2.0.co;2openalex-snapshot
Nitrogen Emissions, Deposition, and Monitoring in the Western United States
inferredcites →https://doi.org/10.3390/rs10071001openalex-snapshot
A Conservative Downscaling of Satellite-Detected Chemical Compositions: NO2 Column Densities of OMI, GOME-2, and CMAQ
inferredcites →https://doi.org/10.3390/rs12142212openalex-snapshot
TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations
inferredcites →https://doi.org/10.3390/rs13030397openalex-snapshot
A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City
inferredcites →https://doi.org/10.3390/rs13040758openalex-snapshot
Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model
inferredcites →https://doi.org/10.3390/rs14071725openalex-snapshot
Spatiotemporal Analysis of NO2 Production Using TROPOMI Time-Series Images and Google Earth Engine in a Middle Eastern Country
inferredcites →https://doi.org/10.3390/rs14071726openalex-snapshot
Ground-Based MAX-DOAS Measurements of Tropospheric Aerosols, NO2, and HCHO Distributions in the Urban Environment of Shanghai, China
inferredcites →https://doi.org/10.3390/rs9090950openalex-snapshot
Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model
inferredcites →https://doi.org/10.5194/acp-17-11403-2017openalex-snapshot
A high-resolution and observationally constrained OMI NO 2 satellite retrieval
inferredcites →https://doi.org/10.5194/acp-17-1775-2017openalex-snapshot
Cleaning up the air: effectiveness of air quality policy for SO 2 and NO x emissions in China
inferredcites →https://doi.org/10.5194/gmd-9-1111-2016openalex-snapshot
OMI NO 2 column densities over North American urban cities: the effect of satellite footprint resolution

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Spatial and Spatiotemporal Modeling of Intra-Urban Ultrafine Particles: A Comparison of Linear, Nonlinear, Regularized, and Machine Learning Methods
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2026.121904openalex-snapshot
Estimating surface-level NO2 concentrations in the Madrid region using Sentinel-5P observations and ground-based meteorological data with machine learning approaches
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2025.103097openalex-snapshot
Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network
inferred← citeshttps://doi.org/10.1016/j.ecolind.2024.112203openalex-snapshot
Seamless terrestrial evapotranspiration estimation by machine learning models across the Contiguous United States
inferred← citeshttps://doi.org/10.1016/j.envint.2025.109672openalex-snapshot
Quantitative reconstruction of long-term spatiotemporal patterns of high-resolution ground-level NO2 concentrations in mainland China using fusion techniques and a machine learning framework
inferred← citeshttps://doi.org/10.1016/j.envres.2024.119751openalex-snapshot
Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review
inferred← citeshttps://doi.org/10.1016/j.jag.2025.104470openalex-snapshot
Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model
inferred← citeshttps://doi.org/10.1016/j.jenvman.2024.121198openalex-snapshot
Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model
inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101819openalex-snapshot
High-resolution ground NO2 estimation at hyperlocal level using deep learning with Sentinel-2 and Sentinel-5P data
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2023.165061openalex-snapshot
Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet
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FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning
inferred← citeshttps://doi.org/10.1029/2023gh000798openalex-snapshot
Satellite‐Based Long‐Term Spatiotemporal Trends in Ambient NO 2 Concentrations and Attributable Health Burdens in China From 2005 to 2020
inferred← citeshttps://doi.org/10.1029/2023jd040676openalex-snapshot
Inferring Surface NO 2 Over Western Europe: A Machine Learning Approach With Uncertainty Quantification
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Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model
inferred← citeshttps://doi.org/10.1109/mgrs.2024.3398434openalex-snapshot
Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review
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High-Resolution Ground No2 Estimation Using Deep Learning with Sentinel-2 and Sentinel-5p Data
inferred← citeshttps://doi.org/10.3390/atmos15010034openalex-snapshot
High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning
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Estimation of High Spatial Resolution CO2 Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data
inferred← citeshttps://doi.org/10.3390/ijgi12030107openalex-snapshot
Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements
inferred← citeshttps://doi.org/10.3390/su18189349openalex-snapshot
TROPOMI-Referenced Reconstruction and Model Interpretation of Long-Term City-Scale NO2 Column Density in East Asia Using Machine Learning
inferred← citeshttps://doi.org/10.5194/acp-23-10267-2023openalex-snapshot
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning
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