Modelos de distribución espacial de hidrocarburos en suelos contaminados y su implementación en software sig. Una revisión
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Esta revisión analiza y compara los principales modelos empleados para representar la distribución espacial de hidrocarburos en suelos contaminados, con énfasis en su implementación en entornos de Sistemas de Información Geográfica (SIG). Se identificaron dos enfoques principales: métodos geoestadísticos como el Kriging ordinario y modelos de aprendizaje automático como Random Forest y redes neuronales 3D. El Kriging ordinario destaca por su equilibrio entre precisión, manejo de incertidumbre y compatibilidad con SIG, frente a la mayor complejidad de modelos avanzados. Se concluye que, el Kriging ordinario es el modelo más adecuado para representar este tipo de contaminación, y se recomienda su implementación con herramientas en Python, para lograr un análisis automatizado, preciso y accesible.
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Atteia, O., Dubois, J., & Webster, R. (1994). Geostatistical analysis of soil contamination in the Swiss Jura. Environmental Pollution, 86(3), 315–327. https://doi.org/10.1016/0269-7491(94)90172-4.
Bengtsson, G., & Törneman, N. (2009). A spatial approach to environmental risk assessment of PAH contamination. Risk Analysis, 29(1), 48–61. https://doi.org/10.1111/j.1539-6924.2008.01128.x.
Gao, A., Cheng, L., Wei, X. (2024). Study on the Spatial Distribution and Migration Characteristics of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.J. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2023. Lecture Notes in Electrical Engineering, vol 1190. Springer.
Gao, A., Chen, L., Wei, X., Liu, C., & Cheng, L. (2024). Spatial Distribution Prediction and Migration Characteristic of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks. Journal of Information Processing Systems, 20(6). https://doi.org/10.3745/JIPS.04.0330.
Gu, Y., Zuo, Z., Shi, C., & Hu, X. (2020). Feasibility study for spatial distribution of diesel oil in contaminated soils by laser induced fluorescence. Applied Sciences, 10(3), 1103. https://doi.org/10.3390/app10031103.
Hyde, K., Ma, W., Obal, T., Bradshaw, K., Carlson, T., Mamet, S., & Siciliano, S. (2019). Incremental sampling methodology for petroleum hydrocarbon contaminated soils: volume estimates and remediation strategies. Soil and Sediment Contamination: An International Journal, 28(1), 51-64. https://doi.org/10.1080/15320383.2018.1529736.
Karaca, G. (2016). Spatial distribution of polycyclic aromatic hydrocarbon (PAH) concentrations in soils from Bursa, Turkey. Archives of Environmental Contamination and Toxicology, 70, 406-417. https://doi.org/10.1007/s00244-015-0248-2.
Liang, Y., Zhang, X., Wang, J., & Li, G. (2012). Spatial variations of hydrocarbon contamination and soil properties in oil exploring fields across China. Journal of Hazardous Materials, 241, 371-378. https://doi.org/10.1016/j.jhazmat.2012.09.055.
Liu, G., Niu, J., Guo, W., Zhao, L., Zhang, C., Wang, M., Zhang, Z., & Guo, G. (2017). Assessment of terrain factors on the pattern and extent of soil contamination surrounding a chemical industry in Chongqing, Southwest China. Catena, 156, 237–243. https://doi.org/10.1016/j.catena.2017.04.005.
Liu, G., Zhou, X., Li, Q., Shi, Y., Guo, G., Zhao, L., Wang, J., Su, Y., & Zhang, C. (2020). Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data. Environmental Pollution, 267, 115631. https://doi.org/10.1016/j.envpol.2020.115631.
Luca, C., Si, B., & Farrell, R. (2007). Assessing spatial distribution and joint uncertainty of TPH-fractions: Indicator kriging and sequential indicator simulation. Canadian Journal of Soil Science, 87(4), 551–563. DOI:10.4141/CJSS07003.
Mahanty, B., Behera, S., Godio, A., & Chiampo, F. (2025). Hybrid Modeling with Artificial Neural Networks for Predicting In-Situ Bioremediation Dynamics of Diesel Fuel-Spiked Soil. Water Air & Soil Pollution, 236, 285 (2025). https://doi.org/10.1007/s11270-025-07940-0.
Metahni, S., Coudert, L., Gloaguen, E., Guemiza, K., Mercier, G., & Blais, J. (2019). Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans. Environmental Pollution, 252, 409–419. https://doi.org/10.1016/j.envpol.2019.05.122.
Miao, S., Ni, G., Kong, G., Yuan, X., Liu, C., Shen, X., & Gao, W. (2025). A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. PLOS ONE, 20(1), e0316940. https://doi.org/10.1371/journal.pone.0316940.
Ouabo, R., Sangodoyin, A., & Ogundiran, M. (2020). Assessment of ordinary kriging and inverse distance weighting methods for modeling chromium and cadmium soil pollution in e-waste sites in Douala, Cameroon. Journal of Health & Pollution, 10(26), 1–20. https://doi.org/10.5696/2156-9614-10.26.200605.
Parvizi, Y., & Fatehi, S. (2025). Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses. Scientific Reports, 15, 4449. https://doi.org/10.1038/s41598-025-88062-9.
Rakotonirina, H., Honeine, P., Atteia, O., & Van Exem, A. (2025). Estimating Contaminated Soil Volumes Using a Generative Neural Network: A Hydrocarbon Case in France. Mathematical Geosciences, https://doi.org/10.1007/s11004-025-10193-6.
Stewart, R., & Purucker, S. (2011). An environmental decision support system for spatial assessment and selective remediation. Environmental Modelling & Software, 26(6), 751-760. https://doi.org/10.1016/j.envsoft.2010.12.010.
Yu, H., Li, T., Liu, Y., & Ma, L. (2019). Spatial distribution of polycyclic aromatic hydrocarbon contamination in urban soil of China. Chemosphere, 230, 498-509. https://doi.org/10.1016/j.chemosphere.2019.05.006.
Zhang, G., Lan, T., Yang, G., Li, J., & Zhang, K. (2022). Contamination, spatial distribution, and source contribution of persistent organic pollutants in the soil of Guiyang city, China: a case study. Environmental Geochemistry and Health, 44, 3265–3278. https://doi.org/10.1007/s10653-021-01089-5.