Abstract
This paper presents a GIS-based integration of a multidisciplinary dataset concerning a heavy metal contaminated soil. The case study is an area extending for about half a hectare where nearly 600 young trees have been planted to carry out a phytostabilization treatment. Three different plant species have been used for the experiment, lentisk, poplar and oleander, with different characteristics in terms of foliage and mean heights. The area has been monitored by exploiting geomatics, geophysical and chemical techniques, to acquire a comprehensive dataset able to describe the vegetation health status and the soil pollution levels. Low-cost UAV flights, GNSS surveys, and geophysical and chemical investigations have been performed in the area at four different epochs. The different acquisitions were elaborated resulting in specific products (point clouds, DSMs, specific maps) and the related quantitative and qualitative parameters. All the available data have been managed in a GIS project to provide a broad description of the site and to ease the understanding of complex dynamics. The presented analysis belongs to a wider project, aiming at defining monitoring procedures for soils contaminated by heavy metals, using a combination of geomatics, geophysical, and chemical derived parameters. The final purpose will be the definition of algorithms implemented in the GIS environment, specific for different soils, plant species and microbial populations allowing the identification of the optimal conditions for the contaminated site’s restoration.
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The authors have no competing interests to declare that are relevant to the content of this article.
References
SardegnaArpa. https://www.sardegnaambiente.it/arpas. Accessed 01 Feb 2024
SardegnaAmbiente. https://portal.sardegnasira.it/-/aggiornamento-del-piano-regionale-bonifica-siti-inquinati. Accessed 01 Feb 2024
Ashraf, S., Ali, Q., Zahir, Z.A., Ashraf, S., Asghar, H.N.: Phytoremediation: environmentally sustainable way for reclamation of heavy metal polluted soils. In: Ecotoxicology and Environmental Safety, vol. 174, pp. 714–727. Elsevier, Amsterdam (2019)
Pivetz, B.E.: Phytoremediation of contaminated soil and ground water at hazardous waste sites. US Environmental Protection Agency, Office of Research and Development, Office of Solid Waste and Emergency Response (2001)
Mahar, A., et al.: Challenges and opportunities in the phytoremediation of heavy metals contaminated soils: a review. In: Ecotoxicology and Environmental Safety, vol. 126, pp.111–121. Elsevier, Amsterdam (2016)
Saleem, M.H., et al.: Jute: a potential candidate for phytoremediation of metals—a review. In: Plants, vol. 9, no. 2, p. 258. MDPI, Basel (2020)
Kafle, A., Timilsina, A., Gautam, A., Adhikari, K., Bhattarai, A., Aryal, N.: Phytoremediation: mechanisms, plant selection and enhancement by natural and synthetic agents. In: Environmental Advances, vol. 8, pp. 100203. Elsevier, Amsterdam (2022)
Raihan, A.: A systematic review of geographic information systems (GIS) in agriculture for evidence-based decision making and sustainability. In: Global Sustainability Research, vol. 3, no. 1, pp. 1–24 (2024). https://doi.org/10.56556/gssr.v3i1.636
Kross, A., Kaur, G., Jaeger, J.A.: A geospatial framework for the assessment and monitoring of environmental impacts of agriculture. In: Environmental Impact Assessment Review, vol. 97, 106851. Elsevier, Amsterdam (2022)
Avanidou, K., Alexandridis, T., Kavroudakis, D., Kizos, T.: Development of a multi scale interactive web-GIS system to monitor farming practices: a case study in Lemnos Island, Greece. In: Smart Agricultural Technology, vol. 5, pp. 100313. Elsevier, Amsterdam (2023)
Deidda, M., Musa, C., Vacca, G.: A GIS of Sardinia’s coastal defense system (XVI – XVIII century). In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40(4/W7), pp. 17–22 (2015)
Trivedi, A., Rao, K.V.R., Rajwade, Y., Yadav, D., Verma, N.S.: Remote sensing and geographic information system applications for precision farming and natural resource management. In: Indian Journal of Ecology, vol. 49, no. 5, pp. 1624–1633 (2022)
Vacca, G., Vecchi, E.: UAV photogrammetric surveys for tree height estimation. In: Drones, vol. 8, no. 3, pp. 106. MDPI, Basel (2024)
Bai, P., Vignoli, G., Viezzoli, A., Nevalainen, J., Vacca, G.: Quasi-real-time inversion of airborne time-domain electromagnetic data via artificial neural network. In: Remote Sensing, vol. 12, no. 20, pp. 3440. MDPI, Basel (2020)
Zaru, N., Rossi, M., Vacca, G., Vignoli, G.: Spreading of localized information across an entire 3D electrical resistivity volume via constrained EMI inversion based on a realistic prior distribution. In: Remote Sensing, vol. 15, no. 16, pp. 3993. MDPI, Basel (2023)
Zaru, N., Silvestri, S., Assiri, M, Bai, P., Hansen, T.M., Vignoli, G.: Probabilistic petrophysical reconstruction of danta’s alpine peatland via electromagnetic induction data. In: Earth and Space Science, vol. 11, no. 3, pp. e2023EA003457. Wiley, Hoboken (2024)
Mambwe, M., Kalebaila, K.K., Johnson, T.: Photochemical oxidation and landfarming as remediation techniques for oil-contaminated soil. In: Global Journal of Environmental Science and Management, vol. 10.2, pp. 517–536 (2024)
Vacca, G., Quaquero, E., Pili, D., Brandolini, M.: Integrating BIM and GIS data to support the management of large building stocks. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 647–653 (2018)
Panda, S., et al.: Decision support system for lespedeza cuneata production and quality evaluation: a WebGIS dashboard approach to precision agriculture. In: Preprints (2024)
Lanki, A.D., Onwu, C.A.: Geographic Information System (GIS) application in soil fertility management: a review. In: Journal of Global Agriculture and Ecology, vol. 16, no. 2, pp. 29–40 (2024)
Adão, T., Soares, A., Pádua, L., Guimãrdes, N., Pinho, T., Sousa, J.J., Morais, R., Peres, E.: Mysense-Webgis: a graphical map layering-based decision support tool for agriculture. In: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 4195–4198 (2020)
Vacca, G.: 3D Survey with Apple LiDAR sensor - test and assessment for architectural and cultural heritage. In: Heritage, vol. 6, no. 2, pp. 1476–1501. MDPI, Basel (2023)
Sanz-Ablanedo, E., Chandler, J.H., Rodríguez-Pérez, J.R., Ordóñez, C.: Accuracy of Unmanned Aerial Vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. In: Remote Sensing, vol. 10, no. 10, pp. 1606 (2018)
Dzikunoo, E.A., Vignoli, G., Jørgensen, F., Yidana, S.M., Banoeng-Yakubo, B.: New regional stratigraphic insights from a 3D geological model of the Nasia sub-basin, Ghana, developed for hydrogeological purposes and based on reprocessed B-field data originally collected for mineral exploration. In: Solid Earth, vol. 11, no. 2, pp. 349–361 (2020)
Christiansen, A.V., Auken, E., Kirkegaard, C., Schamper, C., Vignoli, G.: An efficient hybrid scheme for fast and accurate inversion of airborne transient electromagnetic data. In: Exploration Geophysics, vol 47, no. 4, pp. 323–330. Taylor&Francis (2016)
Klose, T., Guillemoteau, J., Vignoli, G., Tronicke, J.: Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness. In: Journal of Applied Geophysics, vol. 196, pp. 104519: Elsevier, Amsterdam (2022)
Bai, P., Vignoli, G., Hansen, T.M.: 1D stochastic inversion of airborne time-domain electromagnetic data with realistic prior and accounting for the forward modeling error. In: Remote Sensing, vol. 13, no. 19, pp. 3881. MDPI, Basel (2021)
Klose, T., Guillemoteau, J., Vignoli, G., Walter, J., Herrmann, A., Tronicke, J.: Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data. In: Geophysical Journal International, vol. 233, no. 3, pp. 1938–1949 (2023)
Wang, Y., et al.: Remediation of Cd (II), Zn (II) and Pb (II) in contaminated soil by KMnO4 modified biochar: stabilization efficiency and effects of freeze–thaw ageing. In: Chemical Engineering Journal, vol. 487, pp. 150619. Elsevier, Amsterdam (2024)
Ecoserdiana. Progetto di Ricerca su Tecnologie di CARatterizzazione Monitoraggio e Analisi per il ripristino e la bonifica (CARMA) - Fondo Europeo di Sviluppo Regionale - Por Fesr Sardegna 2014–2020. https://www.ecoserdiana.com/servizi/progetti-di-ricerca.html. Accessed 01 Apr 2024
Sarnet. Web Server della Rete di Stazioni Permanenti Della Sardegna. www.sarnet.it/servizi.html. Accessed 01 Jan 2024
AgiSoft PhotoScan Standard (Version 1.2.6) (Software). (2016*). http://www.agisoft.com/downloads/installer/. Accessed 03 Mar 2024
CloudCompare. https://www.danielgm.net/cc/. Accessed 17 Jan 2024
Qgis Documentation. https://docs.qgis.org/2.8/en/. Accessed 01 Feb 2024
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This research was funded by Sardegna Ricerche CUP: G28C17000250006, https://www.ecoserdiana.com/servizi/progetti-di-ricerca.html.
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De Montis, S. et al. (2024). Integration of Geomatic, Geophysical and Chemical Data in a GIS Environment for Monitoring Contaminated Soils. In: Gervasi, O., Murgante, B., Garau, C., Taniar, D., C. Rocha, A.M.A., Faginas Lago, M.N. (eds) Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14824. Springer, Cham. https://doi.org/10.1007/978-3-031-65332-2_23
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