23 citations as recorded by crossref.

1. A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
2. A Novel Air Quality Labelling and Classification Approach Using Supervised Learning . Towqir Ahmed Shaem et al. 10.51583/IJLTEMAS.2025.140300074
3. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al. 10.1016/j.envint.2023.108361
4. Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. 10.1016/j.atmosenv.2024.120793
5. Regarding the data inconsistency from different data sources on emissions and ground-level pollutants’ concentrations in the atmospheric air over Ukraine M. Savenets et al. 10.15407/Meteorology2024.06.017
6. Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US M. Childs et al. 10.1021/acs.est.2c02934
7. A Novel Air Quality Labelling and Classification Approach Using Supervised Learning . Towqir Ahmed Shaem et al. 10.51583/IJLTEMAS.2025.140300061
8. An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al. 10.1038/s41612-023-00536-7
9. Constructing transferable and interpretable machine learning models for black carbon concentrations P. Fung et al. 10.1016/j.envint.2024.108449
10. Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China J. Li et al. 10.1016/j.scitotenv.2024.173011
11. Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach M. Hassan et al. 10.1007/s11356-023-29665-5
12. Quantifying role of source variations on PM2.5-bound toxic components under climate change: Measurement at multiple sites during 2018–2022 in a Chinese megacity X. Feng et al. 10.1016/j.jhazmat.2025.138584
13. Improving the Air Pollution Control Measures More Efficiently and Cost-Effectively: View from the Practice in the 7th Military World Games in Wuhan S. Kong et al. 10.1007/s41810-024-00245-5
14. Assessing soybean yield in Madhya Pradesh by using a multi-model approach R. Nargund et al. 10.1016/j.fcr.2024.109716
15. Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends T. Lin et al. 10.1021/acs.est.4c07460
16. Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
17. Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India S. Wang et al. 10.1016/j.atmosenv.2024.120834
18. Characterization of Synanthropic Habitats on Shallow Seabeds Using Map Clustering Techniques: A Case Study in Taranto, Apulia, Italy C. Massarelli et al. 10.3390/ecologies5040037
19. Impact of meteorological conditions and reductions in anthropogenic emissions on PM2.5 concentrations in China from 2016 to 2020 Z. Xu et al. 10.1016/j.atmosenv.2023.120265
20. Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States M. El-Sayed et al. 10.1021/acs.est.3c01259
21. Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools Y. Lin et al. 10.5194/acp-22-16073-2022
22. Improved daily PM 2.5 estimates in India reveal inequalities in recent enhancement of air quality A. Kawano et al. 10.1126/sciadv.adq1071
23. Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches M. Schlüter et al. 10.1073/pnas.2215676120