Open Access
| Issue |
E3S Web Conf.
Volume 702, 2026
Second International Conference on Innovations in Sustainable and Digital Construction Practices (ISDCP 2026)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 17 | |
| Section | Environmental Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202670202006 | |
| Published online | 01 April 2026 | |
- K. Kumar and B.P. Pande, Air pollution prediction with machine learning: A case study of Indian cities, Int. J. Environ. Sci. Technol. 20, 5333–5348, (2023). https://doi.org/10.1007/s13762-022-04241-5. [Google Scholar]
- G. Suthar and S. Singh, Application of machine learning models for PM2.5 prediction in Bengaluru using precursor air pollutants and meteorological data, Theor. Appl. Climatol. 156, 3 (2025). https://doi.org/10.1007/s00704-025-05413-2. [Google Scholar]
- E. Wigzell, M. Kendall, and M.J. Nieuwenhuijsen, The spatial and temporal variation of particulate matter within the home, J. Expo. Anal. Environ. Epidemiol. 10, 75–83 (2000). doi: 10.1038/sj.jea.7500091. [Google Scholar]
- F. Chai, J. Gao, Z. Chen, S. Wang, Y. Zhang, J. Zhang, H. Zhang, Y. Yun, C. Ren, Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China, J. Environ. Sci. 26, 75–82 (2014). doi: 10.1016/S1001-0742(13)60383-6. [Google Scholar]
- M. Hu, F. Zhou, K. Shao, Y. Zhang, and X. Tang, Diurnal variations of aerosol chemical compositions and related gaseous pollutants in Beijing and Guangzhou, J. Environ. Sci. Health A. 41, 679–690 (2006), doi: 10.1081/ESE-120003229. [Google Scholar]
- Farrell, W.J., Weichenthal, S., Goldberg, M., & Hatzopoulou, M. Evaluating air pollution exposures across cycling infrastructure types: Implications for facility design. Journal of Transport and Land Use, 8, 3, 131–149. (2015). https://doi.org/10.5198/jtlu.2015.702 [Google Scholar]
- W. Yuchi et al., Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city, Environ. Pollut. 245, 746–753 (2019). https://doi.org/10.1016/j.envpol.2018.11.034 [Google Scholar]
- C.A. Pio, T.V. Nunes, C.S. Borrego, and J. Martins, Assessment of air pollution sources in an industrial atmosphere using principal component and multilinear regression analysis, Sci. Total Environ. 80, 279–292 (1989). https://doi.org/10.1016/0048-9697(89)90082-X [Google Scholar]
- Y. Igaga and E. Sabah, Statistical analysis of air pollutants and meteorological parameters in Afyon, Turkey, Environ. Model. Assess, 14, 2 259–266 (2009). https://doi.org/10.1007/s10666-008-9139-5 [Google Scholar]
- R. Li et al., Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014-2016, Atmos. Environ. 161, 235–246 (2017). https://doi.org/10.1016/j.atmosenv.2017.05.008 [Google Scholar]
- T.A. Mukta, M.M.M. Hoque, M.E. Sarker, M.N. Hossain, and G.K. Biswas, Seasonal variations of gaseous air pollutants (SO2, NO2, O3, CO) and particulates (PM2.5, PM10) in Gazipur: An industrial city in Bangladesh, Adv. Environ. Technol. 6, 195–209 (2020). https://doi.org/10.22104/aet.2021.4890.1320 [Google Scholar]
- S. Kanno, S. Fukui, H. Ikeda, and Y. Ono, Atmospheric SO2 concentrations observed in the Keihin industrial center and their relation to meteorological elements. Air Water Pollut. Annu. Rep. 1, 234–240 (1958). [Google Scholar]
- P.S. Mahapatra et al., Seasonal trends, meteorological impacts, and associated health risks with atmospheric concentrations of gaseous pollutants at an Indian coastal city, Environ. Sci. Pollut. Res. 21, 11418–11432 (2014). https://doi.org/10.1007/s11356-014-3078-2 [Google Scholar]
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