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Interpretable Stepwise Multiple Linear Regression Model for Ozone Concentration Prediction

https://doi.org/10.2139/ssrn.4756670
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00521-019-04282-xopenalex-extracted
A real-time hourly ozone prediction system using deep convolutional neural network
inferredcites →https://doi.org/10.1016/j.atmosenv.2013.04.002openalex-extracted
Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis
inferredcites →https://doi.org/10.1016/j.atmosenv.2018.03.027openalex-extracted
Artificial neural network model for ozone concentration estimation and Monte Carlo analysis
inferredcites →https://doi.org/10.1016/j.atmosenv.2021.118599openalex-extracted
Simulation of surface ozone over Hebei province, China using Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) combined model
inferredcites →https://doi.org/10.1016/j.atmosres.2015.05.005openalex-extracted
Ground-level ozone in urban Beijing over a 1-year period: Temporal variations and relationship to atmospheric oxidation
inferredcites →https://doi.org/10.1016/j.chemosphere.2022.134817openalex-extracted
Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model
inferredcites →https://doi.org/10.1016/j.envpol.2018.10.078openalex-extracted
Characteristics of peroxyacetyl nitrate pollution during a 2015 winter haze episode in Beijing
inferredcites →https://doi.org/10.1016/j.envpol.2022.120389openalex-extracted
Quantification of enhanced VOC emissions from fireworks
inferredcites →https://doi.org/10.1016/j.jag.2022.102955openalex-extracted
Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data
inferredcites →https://doi.org/10.1016/j.jclepro.2022.130607openalex-extracted
Regional division and influencing mechanisms for the collaborative control of PM2.5 and O3 in China: A joint application of multiple mathematic models and data mining technologies
inferredcites →https://doi.org/10.1016/j.jclepro.2023.136871openalex-extracted
Heterogeneity and typology of the city-level synergy between CO2 emission, PM2.5, and ozone pollution in China
inferredcites →https://doi.org/10.1016/j.jenvman.2021.114210openalex-extracted
Improvements of response surface modeling with self-adaptive machine learning method for PM2.5 and O3 predictions
inferredcites →https://doi.org/10.1016/j.jes.2018.05.015openalex-extracted
Understanding unusually high levels of peroxyacetyl nitrate (PAN) in winter in Urban Jinan, China
inferredcites →https://doi.org/10.1016/j.jes.2020.02.019openalex-extracted
Meteorological mechanism for a large-scale persistent severe ozone pollution event over eastern China in 2017
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.146867openalex-extracted
A comparison investigation of atmospheric NMHCs at two sampling sites of Beijing city and a rural area during summertime
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.147325openalex-extracted
Mapping the daily nitrous acid (HONO) concentrations across China during 2006–2017 through ensemble machine-learning algorithm
inferredcites →https://doi.org/10.1016/s1352-2310(99)00462-8openalex-extracted
Heterogeneous chemistry and tropospheric ozone
inferredcites →https://doi.org/10.1021/acs.est.1c05929openalex-extracted
Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches
inferredcites →https://doi.org/10.1021/acs.est.8b06038openalex-extracted
Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces
inferredcites →https://doi.org/10.1021/acs.est.9b07248openalex-extracted
Persistent Heavy Winter Nitrate Pollution Driven by Increased Photochemical Oxidants in Northern China
inferredcites →https://doi.org/10.1021/acs.estlett.0c00511openalex-extracted
Driving Forces of Changes in Air Quality during the COVID-19 Lockdown Period in the Yangtze River Delta Region, China
inferredcites →https://doi.org/10.1021/acs.estlett.2c00246openalex-extracted
Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning
inferredcites →https://doi.org/10.1021/acs.estlett.3c00105openalex-extracted
Narrowing Differences in Urban and Nonurban Surface Ozone in the Northern Hemisphere Over 1990–2020
inferredcites →https://doi.org/10.1021/acs.estlett.8b00366openalex-extracted
Severe Surface Ozone Pollution in China: A Global Perspective
inferredcites →https://doi.org/10.1029/2018ms001457openalex-extracted
Quantifying Uncertainties of Ground‐Level Ozone Within WRF‐Chem Simulations in the Mid‐Atlantic Region of the United States as a Response to Variability
inferredcites →https://doi.org/10.1038/nature13767openalex-extracted
High winter ozone pollution from carbonyl photolysis in an oil and gas basin
inferredcites →https://doi.org/10.1038/ngeo415openalex-extracted
Rapid photochemical production of ozone at high concentrations in a rural site during winter
inferredcites →https://doi.org/10.1134/s1024856019050038openalex-extracted
The Role of Air Humidity in Variations in Near-Surface Ozone Concentration
inferredcites →https://doi.org/10.5194/acp-19-13367-2019openalex-extracted
The mechanisms and meteorological drivers of the summertime ozone–temperature relationship
inferredcites →https://doi.org/10.5194/egusphere-egu22-4170openalex-extracted
Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity
inferredcites →https://doi.org/10.5194/gmd-9-1201-2016openalex-extracted
Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality
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