On the public citation graph
Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations
0 signed · 103 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jamanetworkopen.2020.32064openalex-snapshot
Associations Between Simulated Future Changes in Climate, Air Quality, and Human Health
inferredcites →https://doi.org/10.1007/s10994-006-9449-2openalex-snapshot
An analysis of diversity measures
inferredcites →https://doi.org/10.1007/s13253-009-0004-zopenalex-snapshot
A Spatio-Temporal Downscaler for Output From Numerical Models
inferredcites →https://doi.org/10.1016/j.atmosenv.2019.117130openalex-snapshot
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration
inferredcites →https://doi.org/10.1016/j.atmosenv.2019.117242openalex-snapshot
Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model
inferredcites →https://doi.org/10.1016/j.envint.2019.104909openalex-snapshot
An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution
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.envint.2020.105827openalex-snapshot
Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States
inferredcites →https://doi.org/10.1016/j.envint.2020.106060openalex-snapshot
New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data
inferredcites →https://doi.org/10.1016/j.envint.2021.106576openalex-snapshot
A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation
inferredcites →https://doi.org/10.1016/j.envpol.2017.10.029openalex-snapshot
Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment
inferredcites →https://doi.org/10.1016/j.envpol.2019.06.088openalex-snapshot
Machine learning models accurately predict ozone exposure during wildfire events
inferredcites →https://doi.org/10.1016/j.envres.2017.07.041openalex-snapshot
Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure
inferredcites →https://doi.org/10.1016/j.envres.2018.01.046openalex-snapshot
Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia
inferredcites →https://doi.org/10.1016/j.envres.2020.110432openalex-snapshot
Examining PM2.5 concentrations and exposure using multiple models
inferredcites →https://doi.org/10.1016/j.envsoft.2011.06.004openalex-snapshot
Bayesian networks in environmental modelling
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.04.040openalex-snapshot
Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.10.243openalex-snapshot
Development of a semi-mechanistic allergenic pollen emission model
inferredcites →https://doi.org/10.1021/acs.est.0c01791openalex-snapshot
An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States
inferredcites →https://doi.org/10.1021/acs.est.0c02923openalex-snapshot
Deep Learning for Prediction of the Air Quality Response to Emission Changes
inferredcites →https://doi.org/10.1021/acs.est.0c05815openalex-snapshot
High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019
inferredcites →https://doi.org/10.1021/acs.est.1c04047openalex-snapshot
National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment
inferredcites →https://doi.org/10.1021/acs.est.5b01209openalex-snapshot
Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM 2.5 , Particle Size) Using Mobile Monitoring
inferredcites →https://doi.org/10.1021/acs.est.5b05134openalex-snapshot
Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution
inferredcites →https://doi.org/10.1021/acs.est.6b00096openalex-snapshot
Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application
inferredcites →https://doi.org/10.1021/acs.est.7b01864openalex-snapshot
Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution
inferredcites →https://doi.org/10.1021/acs.est.8b02917openalex-snapshot
An Ensemble Machine-Learning Model To Predict Historical PM 2.5 Concentrations in China from Satellite Data
inferredcites →https://doi.org/10.1021/acs.est.9b01117openalex-snapshot
Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017
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/acs.est.9b03950openalex-snapshot
Toward a Unified Terminology of Processing Levels for Low-Cost Air-Quality Sensors
inferredcites →https://doi.org/10.1021/acs.est.9b04279openalex-snapshot
Estimating Daily PM 2.5 and PM 10 over Italy Using an Ensemble Model
inferredcites →https://doi.org/10.1029/2019ef001195openalex-snapshot
Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1080/10962247.2016.1200159openalex-snapshot
A hybrid model for spatially and temporally resolved ozone exposures in the continental United States
inferredcites →https://doi.org/10.1080/10962247.2021.1970048openalex-snapshot
Regional temperature-ozone relationships across the U.S. under multiple climate and emissions scenarios
inferredcites →https://doi.org/10.1088/1748-9326/ab2dcbopenalex-snapshot
Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA
inferredcites →https://doi.org/10.1109/tii.2020.2972813openalex-snapshot
Probabilistic Weighted Copula Regression Model With Adaptive Sample Selection Strategy for Complex Industrial Processes
inferredcites →https://doi.org/10.1111/j.0006-341x.2005.030821.xopenalex-snapshot
Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
inferredcites →https://doi.org/10.1201/9780429027192-11openalex-snapshot
Local Interpretable Model-agnostic Explanations (LIME)
inferredcites →https://doi.org/10.1289/ehp5878openalex-snapshot
Into the Black Box: What Can Machine Learning Offer Environmental Health Research?
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4665616openalex-extracted
A Comprehensive Prediction and Interpretation of Concrete Corrosion Induced by Carbon Dioxide Using Machine Learning
inferred← citeshttps://doi.org/10.1007/s11269-026-04858-yopenalex-snapshot
Integrating Long Short-Term Memory and Gaussian Process Regression for Enhanced Meteorological Drought Forecasting
inferred← citeshttps://doi.org/10.1007/s11783-025-2092-6openalex-snapshot
Deep learning for air pollutant forecasting: opportunities, challenges, and future directions
inferred← citeshttps://doi.org/10.1007/s13762-024-06036-2openalex-snapshot
Modelling climatic variable impacts on ground-level ozone in Malaysia using backward trajectory and Generative Additive Models
inferred← citeshttps://doi.org/10.1007/s40572-025-00510-wopenalex-snapshot
Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges
inferred← citeshttps://doi.org/10.1007/s41062-025-01931-0openalex-snapshot
Prediction and in-depth analysis of precast concrete strength by machine learning
inferred← citeshttps://doi.org/10.1007/s41748-026-01074-xopenalex-snapshot
Machine and Deep Learning of Dust Storm Pm10 in Inner Mongolia: Model Comparison and Spatial Pollution Risk
inferred← citeshttps://doi.org/10.1016/b978-0-44-343852-3.00009-7openalex-snapshot
A review on the applications of game theory in environmental health
inferred← citeshttps://doi.org/10.1016/j.apr.2026.103149openalex-snapshot
Estimation method of hourly near-surface ozone based on a residual correction framework
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2023.120111openalex-snapshot
Estimation of daily NO2 with explainable machine learning model in China, 2007–2020
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2024.120916openalex-snapshot
Machine learning integrated PMF model reveals influencing factors of ozone pollution in a coal chemical industry city at the Jiangsu-Shandong-Henan-Anhui boundary
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2024.120952openalex-snapshot
Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution
inferred← citeshttps://doi.org/10.1016/j.atmosenv.2025.121164openalex-snapshot
Hourly estimation of black carbon in China based on sparse observation data and stacking model
inferred← citeshttps://doi.org/10.1016/j.atmosres.2025.108396openalex-snapshot
VOCs-driven ozone extremes during dry and wet heatwaves in the Jiangsu–Shandong–Henan–Anhui Boundary: Integrating meteorological forcings and SHAP interpretation
inferred← citeshttps://doi.org/10.1016/j.atmosres.2026.108869openalex-snapshot
Interpretable machine learning for ambient air pollution modeling: A systemic review
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2026.147450openalex-snapshot
Machine learning prediction and interpretable analysis of steel corrosion in concrete subjected to chloride and sulfate
inferred← citeshttps://doi.org/10.1016/j.corsci.2024.112100openalex-snapshot
Prediction and interpretation of concrete corrosion induced by carbon dioxide using machine learning
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2023.114911openalex-snapshot
The application of machine learning to air pollution research: A bibliometric analysis
inferred← citeshttps://doi.org/10.1016/j.envpol.2023.122291openalex-snapshot
Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts
inferred← citeshttps://doi.org/10.1016/j.envpol.2024.124991openalex-snapshot
Key factors in epidemiological exposure and insights for environmental management: Evidence from meta-analysis
inferred← citeshttps://doi.org/10.1016/j.envres.2026.123741openalex-snapshot
Seasonally adaptive data-driven ozone prediction in megacity environments
inferred← citeshttps://doi.org/10.1016/j.fmre.2023.02.016openalex-snapshot
Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale
inferred← citeshttps://doi.org/10.1016/j.fmre.2023.06.010openalex-snapshot
An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience
inferred← citeshttps://doi.org/10.1016/j.heliyon.2022.e11670openalex-snapshot
Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model
inferred← citeshttps://doi.org/10.1016/j.jenvman.2024.120335openalex-snapshot
Tree-Structured Parzen Estimator Optimized-Automated Machine Learning Assisted by Meta–Analysis for Predicting Biochar–Driven N2o Mitigation Effect in Constructed Wetlands
inferred← citeshttps://doi.org/10.1016/j.resconrec.2024.107467openalex-snapshot
Exploring sludge yield patterns through interpretable machine learning models in China's municipal wastewater treatment plants
inferred← citeshttps://doi.org/10.1016/j.scib.2024.05.018openalex-snapshot
Heatwave exacerbates air pollution in China through intertwined climate-energy-environment interactions
inferred← citeshttps://doi.org/10.1021/acs.est.2c04400openalex-snapshot
Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation
inferred← citeshttps://doi.org/10.1021/acs.est.2c06133openalex-snapshot
Interpretation and Prediction of the CO2 Sequestration of Steel Slag by Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.est.3c05424openalex-snapshot
Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019
inferred← citeshttps://doi.org/10.1021/acs.est.4c00783openalex-snapshot
Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants
inferred← citeshttps://doi.org/10.1021/acs.est.4c02926openalex-snapshot
Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.est.5c16412openalex-snapshot
Four-Decade (1980–2023) Surface Ozone Concentrations across the Contiguous United States: Fine-Resolution Estimates and Health Implications
inferred← citeshttps://doi.org/10.1021/acsestwater.3c00330openalex-snapshot
Reducing Arsenic, Cadmium, and Lead Exposure in Urban Areas via Limiting Nutrient Discharges into Rivers
inferred← citeshttps://doi.org/10.1029/2025gl115654openalex-snapshot
Co‐Benefits Between PM 2.5 Control and Carbon Reduction Revealed by Interpretable Machine Learning Methods
inferred← citeshttps://doi.org/10.1038/s41370-023-00518-0openalex-snapshot
Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches
inferred← citeshttps://doi.org/10.1038/s41598-025-87702-4openalex-snapshot
Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application
inferred← citeshttps://doi.org/10.1038/s43247-023-00932-0openalex-snapshot
Machine learning coupled structure mining method visualizes the impact of multiple drivers on ambient ozone
inferred← citeshttps://doi.org/10.1073/pnas.2215688121openalex-snapshot
Equity and modeling in sustainability science: Examples and opportunities throughout the process
inferred← citeshttps://doi.org/10.1109/access.2024.3425602openalex-snapshot
Ladder Curriculum Learning for Domain Generalization in Cross-Domain Classification
inferred← citeshttps://doi.org/10.1109/access.2026.3670712openalex-snapshot
BREATHE: Interpretable Stacked Hybrid Models for Multi-Disease Prediction of SO 2 -Linked Respiratory Illnesses in Kankoyo, a Mining Community in Zambia
inferred← citeshttps://doi.org/10.3390/agriculture14091453openalex-snapshot
Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data
inferred← citeshttps://doi.org/10.3390/rs16111948openalex-snapshot
A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China
inferred← citeshttps://doi.org/10.3390/rs16183374openalex-snapshot
Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review
inferred← citeshttps://doi.org/10.4209/aaqr.230151openalex-snapshot
Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review
inferred← citeshttps://doi.org/10.5194/acp-24-3163-2024openalex-snapshot
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
inferred← citeshttps://doi.org/10.5194/acp-24-4177-2024openalex-snapshot
Diagnosing ozone–NO x –VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
inferred← citeshttps://doi.org/10.5194/egusphere-2023-632-ac1openalex-snapshot
Response to reviewer comments
inferred← citeshttps://doi.org/10.5194/egusphere-2023-632-rc1openalex-snapshot
Comment on egusphere-2023-632
inferred← citeshttps://doi.org/10.5194/egusphere-2023-632-rc2openalex-snapshot
Comment on egusphere-2023-632
inferred← citeshttps://doi.org/10.5194/essd-16-3565-2024openalex-snapshot
Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)
inferred← citeshttps://doi.org/10.5194/essd-17-5113-2025openalex-snapshot
Tracking county-level cooking emissions and their drivers in China from 1990 to 2021 with ensemble machine learning
inferred← citeshttps://doi.org/10.5194/gmd-15-8913-2022openalex-snapshot
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
inferred← citeshttps://doi.org/10.5194/gmd-2022-122-ac1openalex-snapshot
Authors' response to referee comments (gmd-2022-122)
inferred← citeshttps://doi.org/10.55730/1300-0632.4071openalex-snapshot
Uncovering and mitigating spurious features in domain generalization
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.