Abstract
Air pollution poses an urgent challenge to public health and ecosystems, particularly in rapidly urbanizing regions. Despite the severity of this issue, there is a lack of robust analytical frameworks capable of identifying key variables and their spatial effects across landscapes. Our study directly addresses this void by applying an innovative supervised clustering approach to air quality data in Ho Chi Minh City (HCMC), Vietnam—a rapidly growing urban area grappling with escalating pollution levels. The analytical model employs Shapley Additive exPlanations (SHAP) to interpret feature importance within tree-based machine learning models, supplemented by the Unified Manifold Approximation and Projection (UMAP) technique to explore intersections between affected areas. We utilize a feature set from Rakholia et al. (2023) as input variables for each target time series, with a focus on answering key questions: What pollutants exert the most influence at different times of day? Which areas of the city are most affected? And can this method effectively pinpoint intersections of pollutant effects? Our results reveal morning traffic congestion predominantly elevates levels of Nitrogen Dioxide (NO2), Humidity, and Carbon Monoxide (CO), while afternoon emissions are significantly impacted by Sulfur Dioxide (SO2), CO, and Ozone (O3) due to solar radiation and industrial activities. Through this research, we expect to contribute to the ongoing discourse on urban air pollution management, highlighting the potential of artificial intelligence-driven tools in environmental research and policy-making.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mills, N.L., Donaldson, K., Hadoke, P.W., Boon, N.A., MacNee, W., Cassee, F.R., et al.: Adverse cardiovascular effects of air pollution. Nat. Clin. Pract. Cardiovasc. Med. 6(1), 36–44 (2009)
Perez Velasco, R., Jarosinska, D.: Update of the WHO global air quality guidelines: systematic reviews - An introduction. Environ. Int. 170, 107556 (2022)
Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525(7569), 367–371 (2015)
Amann, M., Klimont, Z., An Ha, T., Rafaj, P., Kiesewetter, G., Gomez Sanabria, A., et al.: Future air quality in Ha Noi and Northern Vietnam. IIASA Research Report. Laxenburg, Austria (2019)
Fan, P., Ouyang, Z., Nguyen, D.D., Nguyen, T.T.H., Park, H., Chen, J.: Urbanization, economic development, environmental and social changes in transitional economies: Vietnam after Doimoi. Landsc. Urban Plan. 187, 145–155 (2019)
Xu, Z., Dun, M., Wu, L.: Prediction of air quality based on hybrid grey double exponential smoothing model. Complexity 2020, 1–13 (2020)
Rakholia, R., Le, Q., Vu, K., Ho, B.Q., Carbajo, R.S.: AI-based air quality PM2.5 forecasting models for developing countries: a case study of Ho Chi Minh City, Vietnam. Urban Climate 46, 101315 (2022)
Rakholia, R., Le, Q., Ho, B.Q., Vu, K., Carbajo, R.S.: Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam. Environ. Int. 173, 107848 (2023)
Joharestani, M.Z., Cao, C., Ni, X., Bashir, B., Talebiesfandarani, S.: PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere 10(7), 373 (2019)
Zhong, J., Zhang, X., Gui, K., Wang, Y., Che, H., Shen, X., et al.: Robust prediction of hourly PM2.5 from meteorological data using LightGBM. Nation. Sci. Rev. 8(10), nwaa307 (2021)
Li, T., Hua, M., Wu, X.: A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5). IEEE Access 8, 26933–26940 (2020)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30 (2017)
Wu, Y., Lin, S., Shi, K., Ye, Z., Fang, Y.: Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: a case study of Beijing, China. Environ. Sci. Pollut. Res. 29(30), 45821–45836 (2022)
Gu, Y., Li, B., Meng, Q.: Hybrid interpretable predictive machine learning model for air pollution prediction. Neurocomputing 468, 123–136 (2022)
García, M.V., Aznarte, J.L.: Shapley additive explanations for NO2 forecasting. Eco. Inform. 56, 101039 (2020)
Rakholia, R., Le, Q., Vu, K.H.N., Ho, B.Q., Carbajo, R.S.: Outdoor air quality data for spatiotemporal analysis and air quality modelling in Ho Chi Minh City, Vietnam: a part of HealthyAir Project. Data Brief 46, 108774 (2023)
Acknowledgements
This work is part of the MSCA Career-FIT PLUS fellowship, funded by the Enterprise Ireland and the European Commission (Fellowship Ref. Number: MF20200157).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Goktas, P., Rakholia, R., Carbajo, R.S. (2024). Investigating Air Pollution Dynamics in Ho Chi Minh City: A Spatiotemporal Study Leveraging XAI-SHAP Clustering Methodology. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1948. Springer, Cham. https://doi.org/10.1007/978-3-031-50485-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-50485-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50484-6
Online ISBN: 978-3-031-50485-3
eBook Packages: Computer ScienceComputer Science (R0)