35 citations as recorded by crossref.

1. Geo-STO3Net: A Deep Neural Network Integrating Geographical Spatiotemporal Information for Surface Ozone Estimation B. Chen et al. 10.1109/TGRS.2024.3358397
2. A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China W. Han et al. 10.5194/gmd-15-4225-2022
3. Formation Mechanisms and Source Apportionments of Airborne Nitrate Aerosols at a Himalayan-Tibetan Plateau Site: Insights from Nitrogen and Oxygen Isotopic Compositions Y. Lin et al. 10.1021/acs.est.1c03957
4. Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model X. Hu et al. 10.3390/atmos13040632
5. Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model X. Chen et al. 10.1016/j.atmosenv.2023.119819
6. Ground-based vertical profile observations of atmospheric composition on the Tibetan Plateau (2017–2019) C. Xing et al. 10.5194/essd-13-4897-2021
7. Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM) X. Zhang et al. 10.3390/rs13071374
8. Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model Y. Wang et al. 10.1016/j.gsf.2021.101286
9. Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. 10.1117/1.JRS.18.012004
10. Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model Z. Li et al. 10.1109/JSTARS.2023.3298996
11. Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China W. Sun et al. 10.3390/atmos13071068
12. MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning J. Ma et al. 10.1016/j.scitotenv.2022.154363
13. Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. 10.5194/acp-22-14401-2022
14. Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data B. Chen et al. 10.1016/j.scitotenv.2022.160928
15. Substantial Changes of Gaseous Pollutants and Health Effects During the COVID‐19 Lockdown Period Across China C. Ling & Y. Li 10.1029/2021GH000408
16. Recent advances in studies of ozone pollution and impacts in China: A short review X. Xu 10.1016/j.coesh.2020.100225
17. Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data J. Chen et al. 10.1016/j.jag.2022.102955
18. Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China M. Ma et al. 10.3390/su16010123
19. Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method Z. Li et al. 10.1016/j.apr.2024.102114
20. Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach T. Li & X. Cheng 10.1016/j.jag.2021.102356
21. Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. 10.1016/j.scitotenv.2023.163189
22. Metals, PAHs and oxidative potential of size-segregated particulate matter and inhalational carcinogenic risk of cooking at a typical university canteen in Shanghai, China W. Zhou et al. 10.1016/j.atmosenv.2022.119250
23. 臭氧卫星遥感反演进展及挑战 迟. Chi Yulei & 赵. Zhao Chuanfeng 10.3788/AOS230583
24. Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review A. Yafouz et al. 10.1007/s11270-021-04989-5
25. Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks L. Wang et al. 10.1016/j.atmosres.2023.106889
26. Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign B. Guo et al. 10.1016/j.envint.2022.107606
27. Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method Y. Qiu et al. 10.1016/j.scitotenv.2022.153849
28. Spatiotemporal variation, source and secondary transformation potential of volatile organic compounds (VOCs) during the winter days in Shanghai, China S. Wang et al. 10.1016/j.atmosenv.2022.119203
29. Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. 10.5194/gmd-16-6337-2023
30. Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. 10.1016/j.atmosenv.2024.120347
31. Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data G. Chen et al. 10.1016/j.scs.2021.102807
32. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China J. Wei et al. 10.1016/j.rse.2021.112775
33. An Enhanced Geographically and Temporally Weighted Neural Network for Remote Sensing Estimation of Surface Ozone T. Li et al. 10.1109/TGRS.2022.3187095
34. Tree-based ensemble deep learning model for spatiotemporal surface ozone (O3) prediction and interpretation Z. Zang et al. 10.1016/j.jag.2021.102516
35. Comparisons of Combined Oxidant Capacity and Redox-Weighted Oxidant Capacity in Their Association with Increasing Levels of COVID-19 Infection H. Guo et al. 10.3390/atmos13040569