Mapping soil trace metal distribution using remote sensing and multivariate analysis.

التفاصيل البيبلوغرافية
العنوان:	Mapping soil trace metal distribution using remote sensing and multivariate analysis.
المؤلفون:	Singh S; CSIR-National Botanical Research Institute, Lucknow, 226001, India. swati.sikarwar12@gmail.com.
المصدر:	Environmental monitoring and assessment [Environ Monit Assess] 2024 May 06; Vol. 196 (6), pp. 516. Date of Electronic Publication: 2024 May 06.
نوع المنشور:	Journal Article; Review
اللغة:	English
بيانات الدورية:	Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE
أسماء مطبوعة:	Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-
مواضيع طبية MeSH:	Environmental Monitoring/methods , Soil Pollutants/analysis , Remote Sensing Technology* , Soil/chemistry , Metals/analysis, Multivariate Analysis ; Geographic Information Systems ; Trace Elements/analysis
مستخلص:	Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies. (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
References:	Abuzaid, A. S., Mazrou, Y. S., El Baroudy, A. A., Ding, Z., & Shokr, M. S. (2022). Multi-indicator and geospatial based approaches for assessing variation of land quality in arid agroecosystems. Sustainability, 14(10), 5840. (PMID: 10.3390/su14105840) Acosta, J. A., Faz, A., Martínez-Martínez, S., & Arocena, J. M. (2011). Enrichment of metals in soils subjected to different land uses in a typical Mediterranean environment (Murcia City, southeast Spain). Applied Geochemistry, 26(3), 405–414. (PMID: 10.1016/j.apgeochem.2011.01.023) Adnan, M., Xiao, B., Xiao, P., Zhao, P., & Bibi, S. (2022). Heavy metal, waste, COVID-19, and rapid industrialization in this modern era—Fit for sustainable future. Sustainability, 14(8), 4746. (PMID: 10.3390/su14084746) Aggarwal, S. (2004). Principles of remote sensing. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 23(2), 23–28. Ahmad, W., Alharthy, R. D., Zubair, M., Ahmed, M., Hameed, A., & Rafique, S. (2021). Toxic and heavy metals contamination assessment in soil and water to evaluate human health risk. Scientific Reports, 11(1), 17006. (PMID: 10.1038/s41598-021-94616-4) Alemu, R., Gelaw, A. M., Gashu, D., Tafere, K., Mossa, A. W., Bailey, E. H., ..., & Lark, R. M. (2022). Sub-sampling a large physical soil archive for additional analyses to support spatial mapping; a pre-registered experiment in the Southern Nations, Nationalities, and Peoples Region (SNNPR) of Ethiopia. Geoderma, 424, 116013. Ali, H., Khan, E., & Sajad, M. A. (2013). Phytoremediation of heavy metals—Concepts and applications. Chemosphere, 91(7), 869–881. (PMID: 10.1016/j.chemosphere.2013.01.075) Allee, K. D., Do, C., & Raymundo, F. G. (2022). Principal component analysis and factor analysis in accounting research. Journal of Financial Reporting, 7(2), 1–39. Alloway, B. J. (Ed.). (2012). Heavy metals in soils: trace metals and metalloids in soils and their bioavailability (Vol. 22). Springer Science & Business Media. https://doi.org/10.1007/978-94-007-4470-7_24. Anaman, R., Peng, C., Jiang, Z., Liu, X., Zhou, Z., Guo, Z., & Xiao, X. (2022). Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF. Science of the Total Environment, 823, 153759. (PMID: 10.1016/j.scitotenv.2022.153759) Armenise, E., Redmile-Gordon, M. A., Stellacci, A. M., Ciccarese, A., & Rubino, P. (2013). Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. Soil and Tillage Research, 130, 91–98. (PMID: 10.1016/j.still.2013.02.013) Artiola, J. F., Walworth, J. L., Musil, S. A., & Crimmins, M. A. (2019). Soil and land pollution. In Environmental and pollution science (pp. 219–235). Academic Press. Arumugam, T., Kinattinkara, S., Nambron, D., Velusamy, S., Shanmugamoorthy, M., Pradeep, T., & Mageshkumar, P. (2022). An integration of soil characteristics by using GIS based geostatistics and multivariate statistics analysis sultan Batheri block, Wayanad District India. Urban Climate, 46, 101339. (PMID: 10.1016/j.uclim.2022.101339) Awais, M., Naqvi, S. M. Z. A., Zhang, H., Li, L., Zhang, W., Awwad, F. A., ... & Hu, J. (2023). AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresources and Bioprocessing, 10(1), 90. Awasthi, G., Nagar, V., Mandzhieva, S., Minkina, T., Sankhla, M. S., Pandit, P. P., ..., & Srivastava, S. (2022). Sustainable amelioration of heavy metals in soil ecosystem: Existing developments to emerging trends. Minerals, 12(1), 85. Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120. https://doi.org/10.1080/02757259509532298. (PMID: 10.1080/02757259509532298) Barra, I., Haefele, S. M., Sakrabani, R., & Kebede, F. (2021). Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review. TrAC Trends in Analytical Chemistry, 135, 116166. https://doi.org/10.1016/j.trac.2020.116166. (PMID: 10.1016/j.trac.2020.116166) Bhadra, B. K., Pathak, S., Karunakar, G., & Sharma, J. R. (2013). ASTER data analysis for mineral potential mapping around Sawar-Malpura area, Central Rajasthan. Journal of the Indian Society of Remote Sensing, 41(2), 391–404. https://doi.org/10.1007/s12524-012-0237-0. (PMID: 10.1007/s12524-012-0237-0) Bhat, S. A., Hassan, T., & Majid, S. (2019). Heavy metal toxicity and their harmful effects on living organisms–A review. International Journal of Medical Science and Diagnosis Research, 3(1), 106–122. Boente, C., Salgado, L., Romero-Macías, E., Colina, A., López-Sánchez, C. A., & Gallego, J. L. R. (2020). Correlation between geochemical and multispectral patterns in an area severely contaminated by former Hg-As mining. ISPRS International Journal of Geo-Information, 9(12), 739. https://doi.org/10.3390/ijgi9120739. (PMID: 10.3390/ijgi9120739) Chabrillat, S., Ben-Dor, E., Cierniewski, J., Gomez, C., Schmid, T., & van Wesemael, B. (2019). Imaging spectroscopy for soil mapping and monitoring. Surveys in Geophysics, 40, 361–399. (PMID: 10.1007/s10712-019-09524-0) Chen, Y., Guerschman, J. P., Cheng, Z., & Guo, L. (2019). Remote sensing for vegetation monitoring in carbon capture storage regions: A review. Applied Energy, 240, 312–326. (PMID: 10.1016/j.apenergy.2019.02.027) Choe, E., van der Meer, F., van Ruitenbeek, F., van der Werff, H., de Smeth, B., & Kim, K. W. (2008). Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the rodalquilar mining area, SE Spain. Remote Sensing of Environment, 112(7), 3222–3233. Collins, A. L., Blackwell, M., Boeckx, P., Chivers, C. A., Emelko, M., Evrard, O., ..., & Zhang, Y. (2020). Sediment source fingerprinting: Benchmarking recent outputs, remaining challenges and emerging themes. Journal of Soils and Sediments, 20, 4160–4193. Crowley, J., Brickey, D., & Rowan, L. (1989). Airborne imaging spectrometer data of the Ruby Mountains, Montana: Mineral discrimination using relative absorption band-depth images. Remote Sensing of Environment, 29, 121–134. https://doi.org/10.1016/j.ecss.2010.03.011. (PMID: 10.1016/j.ecss.2010.03.011) Davis, H. T., Aelion, C. M., McDermott, S., & Lawson, A. B. (2009). Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation. Environmental Pollution, 157(8–9), 2378–2385. (PMID: 10.1016/j.envpol.2009.03.021) Delgado, J., Nieto, J. M., & Boski, T. (2010). Analysis of the spatial variation of heavy metals in the Guadiana Estuary sediments (SW Iberian Peninsula) based on GIS-mapping techniques. Estuarine, Coastal and Shelf Science, 88(1), 71–83. (PMID: 10.1016/j.ecss.2010.03.011) Deng, W., Wang, F., & Liu, W. (2023). Identification of factors controlling heavy metals/metalloid distribution in agricultural soils using multi-source data. Ecotoxicology and Environmental Safety, 253, 114689. (PMID: 10.1016/j.ecoenv.2023.114689) Erdogan Erten, G., Yavuz, M., & Deutsch, C. V. (2022). Combination of machine learning and kriging for spatial estimation of geological attributes. Natural Resources Research, 31(1), 191–213. (PMID: 10.1007/s11053-021-10003-w) Escadafal, R., Girard, M. C., & Courault, D. (1989). Munsell soil color and soil reflectance in the visible spectral bands of Landsat MSS and TM data. Remote Sensing of Environment, 27(1), 37–46. https://doi.org/10.1016/0034-4257(89)90035-7. (PMID: 10.1016/0034-4257(89)90035-7) Huete, A. R., & Escadafal, R. (1991). Assessment of biophysical soil properties through spectral decomposition techniques. Remote Sensing of Environment, 35(2-3), 149–159. Esmaeili, A., Moore, F., Keshavarzi, B., Jaafarzadeh, N., & Kermani, M. (2014). A geochemical survey of heavy metals in agricultural and background soils of the Isfahan industrial zone Iran. Catena, 121, 88–98. (PMID: 10.1016/j.catena.2014.05.003) Fang, Y., Xu, L., Peng, J., Wang, H., Wong, A., & Clausi, D. A. (2018). Retrieval and mapping of heavy metal concentration in soil using time series landsat 8 imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 335–340. Fang, Y., Hu, Z., Xu, L., Wong, A., & Clausi, D. A. (2019). Estimation of iron concentration in soil of a mining area from UAV-based hyperspectral imagery. In 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1–5). IEEE. Gall, J. E., Boyd, R. S., & Rajakaruna, N. (2015). Transfer of heavy metals through terrestrial food webs: A review. Environmental Monitoring and Assessment, 187, 1–21. (PMID: 10.1007/s10661-015-4436-3) Gholizadeh, A., & Kopačková, V. (2019). Detecting vegetation stress as a soil contamination proxy: A review of optical proximal and remote sensing techniques. International Journal of Environmental Science and Technology, 16, 2511–2524. (PMID: 10.1007/s13762-019-02310-w) Ghrefat, H., Awawdeh, M., Howari, F., & Al-Rawabdeh, A. (2023). Mineral exploration using multispectral and hyperspectral remote sensing data. In Geoinformatics for Geosciences (pp. 197–222). Elsevier. Gokhberg, K., Kolorenč, P., Kuleff, A. I., & Cederbaum, L. S. (2014). Site-and energy-selective slow-electron production through intermolecular Coulombic decay. Nature, 505(7485), 661–663. (PMID: 10.1038/nature12936) Goodarzi, R., Mokhtarzade, M., & Valadan Zoej, M. J. (2015). A robust fuzzy neural network model for soil lead estimation from spectral features. Remote Sensing, 7(7), 8416–8435. (PMID: 10.3390/rs70708416) Grunwald, S., Thompson, J. A., & Boettinger, J. L. (2011). Digital soil mapping and modeling at continental scales: Finding solutions for global issues. Soil Science Society of America Journal, 75(4), 1201–1213. (PMID: 10.2136/sssaj2011.0025) Guan, Z., Wang, Y., & Stuedlein, A. W. (2022). Efficient three-dimensional soil liquefaction potential and reconsolidation settlement assessment from limited CPTs considering spatial variability. Soil Dynamics and Earthquake Engineering, 163, 107518. (PMID: 10.1016/j.soildyn.2022.107518) Hou, D., O’Connor, D., Nathanail, P., Tian, L., & Ma, Y. (2017). Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review. Environmental Pollution, 231, 1188–1200. (PMID: 10.1016/j.envpol.2017.07.021) Hou, F., Zhang, Y., Zhou, Y., Zhang, M., Lv, B., & Wu, J. (2022). Review on Infrared Imaging Technology. Sustainability, 14(18), 11161. (PMID: 10.3390/su141811161) Huang, Y., Li, T., Wu, C., He, Z., Japenga, J., Deng, M., & Yang, X. (2015). An integrated approach to assess heavy metal source apportionment in peri-urban agricultural soils. Journal of Hazardous Materials, 299, 540–549. (PMID: 10.1016/j.jhazmat.2015.07.041) Huang, S., Xiao, L., Zhang, Y., Wang, L., & Tang, L. (2021). Interactive effects of natural and anthropogenic factors on heterogenetic accumulations of heavy metals in surface soils through geodetector analysis. Science of the Total Environment, 789, 147937. (PMID: 10.1016/j.scitotenv.2021.147937) Huguet, A., Vacher, L., Relexans, S., Saubusse, S., Froidefond, J. M., & Parlanti, E. (2009). Properties of fluorescent dissolved organic matter in the Gironde Estuary. Organic Geochemistry, 40(6), 706–719. (PMID: 10.1016/j.orggeochem.2009.03.002) Hussain, M., Liu, S., Ashraf, U., Ali, M., Hussain, W., Ali, N., & Anees, A. (2022). Application of machine learning for lithofacies prediction and cluster analysis approach to identify rock type. Energies, 15(12), 4501. (PMID: 10.3390/en15124501) Inoue, Y. (2020). Satellite-and drone-based remote sensing of crops and soils for smart farming–A review. Soil Science and Plant Nutrition, 66(6), 798–810. (PMID: 10.1080/00380768.2020.1738899) Janssen, R. P., Peijnenburg, W. J., Posthuma, L., & Van Den Hoop, M. A. (1997). Equilibrium partitioning of heavy metals in Dutch field soils. I. Relationship between metal partition coefficients and soil characteristics. Environmental Toxicology and Chemistry: An International Journal, 16(12), 2470–2478. (PMID: 10.1002/etc.5620161206) Jeliazkov, A., Gavish, Y., Marsh, C. J., Geschke, J., Brummitt, N., Rocchini, D., ..., & Henle, K. (2022). Sampling and modelling rare species: Conceptual guidelines for the neglected majority. Global change biology, 28(12), 3754–3777. Jose, S., Joshy, D., Narendranath, S. B., & Periyat, P. (2019). Recent advances in infrared reflective inorganic pigments. Solar Energy Materials and Solar Cells, 194, 7–27. (PMID: 10.1016/j.solmat.2019.01.037) Kattenborn, T., Schiefer, F., Frey, J., Feilhauer, H., Mahecha, M. D., & Dormann, C. F. (2022). Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks. ISPRS Open Journal of Photogrammetry and Remote Sensing, 5, 100018. (PMID: 10.1016/j.ophoto.2022.100018) Kazemi, F., & Hosseinpour, N. (2022). GIS-based land-use suitability analysis for urban agriculture development based on pollution distributions. Land Use Policy, 123, 106426. (PMID: 10.1016/j.landusepol.2022.106426) Keshavarzi, A., Kumar, V., Ertunç, G., & Brevik, E. C. (2021). Ecological risk assessment and source apportionment of heavy metals contamination: An appraisal based on the Tellus soil survey. Environmental Geochemistry and Health, 43(5), 2121–2142. (PMID: 10.1007/s10653-020-00787-w) Khan, S., Naushad, M., Lima, E. C., Zhang, S., Shaheen, S. M., & Rinklebe, J. (2021). Global soil pollution by toxic elements: Current status and future perspectives on the risk assessment and remediation strategies–A review. Journal of Hazardous Materials, 417, 126039. (PMID: 10.1016/j.jhazmat.2021.126039) Khanlari, Z. V., & Jalali, M. (2008). Concentrations and chemical speciation of five heavy metals (Zn, Cd, Ni, Cu, and Pb) in selected agricultural calcareous soils of Hamadan Province, western Iran. Archives of Agronomy and Soil Science, 54(1), 19–32. (PMID: 10.1080/03650340701697317) Kowalska, J. B., Mazurek, R., Gąsiorek, M., & Zaleski, T. (2018). Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination–A review. Environmental Geochemistry and Health, 40, 2395–2420. (PMID: 10.1007/s10653-018-0106-z) Krami, L. K., Amiri, F., Sefiyanian, A., Shariff, A. R. B. M., Tabatabaie, T., & Pradhan, B. (2013). Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments. Environmental Monitoring and Assessment, 185, 9871–9888. (PMID: 10.1007/s10661-013-3298-9) Kumar, S. (2022). Effective hedging strategy for us treasury bond portfolio using principal component analysis. Academy of Accounting and Financial Studies, 26(1). Lamine, S., Petropoulos, G. P., Brewer, P. A., Bachari, N. E. I., Srivastava, P. K., Manevski, K., ..., & Macklin, M. G. (2019). Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom. Sensors, 19(4), 762. https://doi.org/10.3390/s19040762. Lasalvia, M., Capozzi, V., & Perna, G. (2022). A comparison of PCA-LDA and PLS-DA techniques for classification of vibrational spectra. Applied Sciences, 12(11), 5345. (PMID: 10.3390/app12115345) Levi, N., Karnieli, A., & Paz-Kagan, T. (2022). Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands. ISPRS Journal of Photogrammetry and Remote Sensing, 186, 34–54. (PMID: 10.1016/j.isprsjprs.2022.01.018) Lin, Y. P., Teng, T. P., & Chang, T. K. (2002). Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua County in Taiwan. Landscape and Urban Planning, 62(1), 19–35. (PMID: 10.1016/S0169-2046(02)00094-4) Liu, K., Zhao, D., Fang, J. Y., Zhang, X., Zhang, Q. Y., & Li, X. K. (2017). Estimation of heavy-metal contamination in soil using remote sensing spectroscopy and a statistical approach. Journal of the Indian Society of Remote Sensing, 45(5), 805–813. https://doi.org/10.1007/s12524-016-0648-4. (PMID: 10.1007/s12524-016-0648-4) Liu, L., Li, W., Song, W., & Guo, M. (2018). Remediation techniques for heavy metal-contaminated soils: Principles and applicability. Science of the Total Environment, 633, 206–219. (PMID: 10.1016/j.scitotenv.2018.03.161) Liu, Z., Lu, Y., Peng, Y., Zhao, L., Wang, G., & Hu, Y. (2019). Estimation of soil heavy metal content using hyperspectral data. Remote Sensing, 11(12), 1464. https://doi.org/10.3390/rs11121464. (PMID: 10.3390/rs11121464) Lü, G., Batty, M., Strobl, J., Lin, H., Zhu, A. X., & Chen, M. (2019). Reflections and speculations on the progress in geographic information systems (GIS): A geographic perspective. International Journal of Geographical Information Science, 33(2), 346–367. (PMID: 10.1080/13658816.2018.1533136) Luo, X., Wu, C., Lin, Y., Li, W., Deng, M., Tan, J., & Xue, S. (2023). Soil heavy metal pollution from Pb/Zn smelting regions in China and the remediation potential of biomineralization. Journal of Environmental Sciences, 125, 662–677. (PMID: 10.1016/j.jes.2022.01.029) Machiwal, D., & Jha, M. K. (2015). Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. Journal of Hydrology: Regional Studies, 4, 80–110. Madeira, J., Bédidi, A., Cervelle, B., Pouget, M., & Flay, N. (1997). Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: The application of a thematic mapper (TM) image for soil-mapping in Brasilia, Brazil. International Journal of Remote Sensing, 18, 2835–2852. https://doi.org/10.1080/014311697217369. (PMID: 10.1080/014311697217369) Malinconico, S., Paglietti, F., Serranti, S., Bonifazi, G., & Lonigro, I. (2022). Asbestos in soil and water: A review of analytical techniques and methods. Journal of Hazardous Materials, 436, 129083. (PMID: 10.1016/j.jhazmat.2022.129083) Mathieu, R., Pouget, M., Cervelle, B., & Escadafal, R. (1998). Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil color of an arid environment. Remote Sensing of Environment, 66(1), 17–28. https://doi.org/10.1016/S0034-4257(98)00030-3. (PMID: 10.1016/S0034-4257(98)00030-3) Mauderly, J. L., Burnett, R. T., Castillejos, M., Özkaynak, H., Samet, J. M., Stieb, D. M., ..., & Wyzga, R. E. (2010). Is the air pollution health research community prepared to support a multipollutant air quality management framework?. Inhalation toxicology, 22(sup1), 1–19. Maurya, K., Mahajan, S., & Chaube, N. (2021). Remote sensing techniques: Mapping and monitoring of mangrove ecosystem—A review. Complex & Intelligent Systems, 7(6), 2797–2818. https://doi.org/10.1007/s40747-021-00457-z. (PMID: 10.1007/s40747-021-00457-z) Mittal, V., Sasetty, S., Choudhary, R., & Agarwal, A. (2022). Deep-learning spatiotemporal prediction framework for particulate matter under dynamic monitoring. Transportation Research Record, 2676(8), 56–73. (PMID: 10.1177/03611981221082589) Mitzia, A., Vítková, M., & Komárek, M. (2020). Assessment of biochar and/or nano zero-valent iron for the stabilisation of Zn, Pb and Cd: A temporal study of solid phase geochemistry under changing soil conditions. Chemosphere, 242, 125248. (PMID: 10.1016/j.chemosphere.2019.125248) Mohan, A., Singh, A. K., Kumar, B., & Dwivedi, R. (2021). Review on remote sensing methods for landslide detection using machine and deep learning. Transactions on Emerging Telecommunications Technologies, 32(7), e3998. (PMID: 10.1002/ett.3998) Molla, A., Zuo, S., Zhang, W., Qiu, Y., Ren, Y., & Han, J. (2022). Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Science of the Total Environment, 802, 149728. (PMID: 10.1016/j.scitotenv.2021.149728) Mustapha, A., & Aris, A. Z. (2012). Multivariate statistical analysis and environmental modeling of heavy metals pollution by industries. Polish Journal of Environmental Studies, 21(5). Naidu, R., Kookana, R. S., Sumner, M. E., Harter, R. D., & Tiller, K. G. (1997). Cadmium sorption and transport in variable charge soils: A review. Journal of Environmental Quality, 26(3), 602–617. (PMID: 10.2134/jeq1997.00472425002600030004x) Naidu, R., & Bolan, N. S. (2008). Contaminant chemistry in soils: key concepts and bioavailability. Developments in Soil Science, 32, 9–37. Natarajan, S. (2023). Prediction of recently occurred soil erosion by integrating revised universal soil loss equation (RUSLE) with geo-spatial techniques-A case study on Pettimudi Hills, Kerala-India. Oliver, M. A., & Webster, R. (2015). Basic steps in geostatistics: the variogram and kriging (No. 11599). Springer International Publishing. Pascal, S., David, S., Andraud, C., & Maury, O. (2021). Near-infrared dyes for two-photon absorption in the short-wavelength infrared: Strategies towards optical power limiting. Chemical Society Reviews, 50(11), 6613–6658. (PMID: 10.1039/D0CS01221A) Pasquel, D., Roux, S., Richetti, J., Cammarano, D., Tisseyre, B., & Taylor, J. A. (2022). A review of methods to evaluate crop model performance at multiple and changing spatial scales. Precision Agriculture, 23(4), 1489–1513. (PMID: 10.1007/s11119-022-09885-4) Peng, Y., Kheir, R. B., Adhikari, K., Malinowski, R., Greve, M. B., Knadel, M., & Greve, M. H. (2016). Digital mapping of toxic metals in Qatari soils using remote sensing and ancillary data. Remote Sensing, 8(12), 1003. https://doi.org/10.3390/rs8121003. (PMID: 10.3390/rs8121003) Pour, A. B., Hashim, M., & Hong, J. K. (2016). Application of multispectral satellite data for geological mapping in Antarctic environments. International Archives of Photogrammetry, Remote Sensing & Spatial Information Sciences, 42. https://doi.org/10.1080/10106049.2018.1434684. Pouyat, R. V., Yesilonis, I. D., & Golubiewski, N. E. (2009). A comparison of soil organic carbon stocks between residential turf grass and native soil. Urban Ecosystems, 12, 45–62. (PMID: 10.1007/s11252-008-0059-6) Radočaj, D., Jurišić, M., & Gašparović, M. (2022). The role of remote sensing data and methods in a modern approach to fertilization in precision agriculture. Remote Sensing, 14(3), 778. (PMID: 10.3390/rs14030778) Raheem, A. M., Naser, I. J., Ibrahim, M. O., & Omar, N. Q. (2023). Inverse distance weighted (IDW) and kriging approaches integrated with linear single and multi-regression models to assess particular physico-consolidation soil properties for Kirkuk city. Modeling Earth Systems and Environment, 9(4), 3999–4021. Razas, M. A., Hassan, A., Khan, M. U., Emach, M. Z., & Saki, S. A. (2023). A critical comparison of interpolation techniques for digital terrain modelling in mining. Journal of the Southern African Institute of Mining and Metallurgy, 123(2), 53–62. (PMID: 10.17159/2411-9717/2271/2023) Reddy, G. O. (2018). Satellite remote sensing sensors: Principles and applications. Geospatial Technologies in Land Resources Mapping, Monitoring and Management, 21–43. Ren, S., Song, C., Ye, S., Cheng, C., & Gao, P. (2022). The spatiotemporal variation in heavy metals in China’s farmland soil over the past 20 years: A meta-analysis. Science of the Total Environment, 806, 150322. (PMID: 10.1016/j.scitotenv.2021.150322) Rey, M., Nikitin, A. V., Babikov, Y. L., & Tyuterev, V. G. (2016). TheoReTS–An information system for theoretical spectra based on variational predictions from molecular potential energy and dipole moment surfaces. Journal of Molecular Spectroscopy, 327, 138–158. (PMID: 10.1016/j.jms.2016.04.006) Rowan, L., Hook, S., Abrams, M., & Mars, J. (2003). Mapping hydrothermally altered rocks at Cuprite, Nevada, using the advanced spaceborne thermal emission and reflection radiometer (ASTER), a new satellite-imaging system. Economic Geology, 98, 1019–1027. https://doi.org/10.2113/gsecongeo.98.5.1019. (PMID: 10.2113/gsecongeo.98.5.1019) Saha, A., Gupta, B. S., Patidar, S., & Martínez-Villegas, N. (2022). Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil. Journal of South American Earth Sciences, 115, 103763. (PMID: 10.1016/j.jsames.2022.103763) Sankaran, S., & Ehsani, R. (2014). Introduction to the electromagnetic spectrum. Imaging with electromagnetic spectrum: Applications in food and agriculture (pp. 1–15). Springer, Berlin Heidelberg: Berlin, Heidelberg. Sawut, R., Kasim, N., Abliz, A., Hu, L., Yalkun, A., Maihemuti, B., & Qingdong, S. (2018). Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area. International Journal of Applied Earth Observation and Geoinformation, 73, 14–25. (PMID: 10.1016/j.jag.2018.05.018) Senathirajah, K., Attwood, S., Bhagwat, G., Carbery, M., Wilson, S., & Palanisami, T. (2021). Estimation of the mass of microplastics ingested–A pivotal first step towards human health risk assessment. Journal of Hazardous Materials, 404, 124004. (PMID: 10.1016/j.jhazmat.2020.124004) Shokr, M. S., El Baroudy, A. A., Fullen, M. A., El-Beshbeshy, T. R., Ali, R. R., Elhalim, A., ..., & Jorge, M. C. (2016). Mapping of heavy metal contamination in alluvial soils of the Middle Nile Delta of Egypt. Journal of Environmental Engineering and Landscape Management, 24(3), 218–231. https://doi.org/10.3846/16486897.2016.1184152. Shoshany, M., Goldshleger, N., & Chudnovsky, A. (2013). Monitoring of agricultural soil degradation by remote-sensing methods: A review. International Journal of Remote Sensing, 34(17), 6152–6181. (PMID: 10.1080/01431161.2013.793872) Shravanraj, K., Rejith, R. G., & Sundararajan, M. (2021). Evaluation of heavy metals in coastal aquifers and seawater: An appraisal of geochemistry using ICPMS and remote sensing. In Remote Sensing of Ocean and Coastal Environments (pp. 155–176). Elsevier. Shukla, A. K., Shukla, S., Surampalli, R. Y., Zhang, T. C., Yu, Y. L., & Kao, C. M. (2023). Modeling microconstituents based on remote sensing and GIS techniques. Microconstituents in the Environment: Occurrence, Fate, Removal and Management, 227–246. Sikakwe, G. U. (2023). Mineral exploration employing drones, contemporary geological satellite remote sensing and geographical information system (GIS) procedures: A review (p. 100988). Society and Environment. Singh, S. (2016). Remote sensing applications in soil survey and mapping: A review. International Journal of Geomatics and Geosciences, 7(2), 192–203. Singh, B. M., Singh, D., & Dhal, N. K. (2022). Enhanced phytoremediation strategy for sustainable management of heavy metals and radionuclides. Case Studies in Chemical and Environmental Engineering, 5, 100176. (PMID: 10.1016/j.cscee.2021.100176) Singh, S. (2022). Forest fire emissions: A contribution to global climate change. Frontiers in Forests and Global Change, 5, 925480. Singh, S., & KV, S. B. (2022). Role of hyperspectral imaging for precision agriculture monitoring. ADBU Journal of Engineering Technology, 11(1). Song, P., Xu, D., Yue, J., Ma, Y., Dong, S., & Feng, J. (2022). Recent advances in soil remediation technology for heavy metal contaminated sites: A critical review. Science of the Total Environment, 838, 156417. (PMID: 10.1016/j.scitotenv.2022.156417) Srinivasan, R., Lalitha, M., Chandrakala, M., Dharumarajan, S., & Hegde, R. (2022). Application of remote sensing and GIS techniques in assessment of salt affected soils for management in large scale soil survey. Soil Health and Environmental Sustainability: Application of Geospatial Technology (pp. 131–161). Springer International Publishing. (PMID: 10.1007/978-3-031-09270-1_7) Suh, J., Lee, H., & Choi, Y. (2016). A rapid, accurate, and efficient method to map heavy metal-contaminated soils of abandoned mine sites using converted portable XRF data and GIS. International Journal of Environmental Research and Public Health, 13(12), 1191. (PMID: 10.3390/ijerph13121191) Tao, H., Liao, X., Cao, H., Zhao, D., & Hou, Y. (2022). Three-dimensional delineation of soil pollutants at contaminated sites: Progress and prospects. Journal of Geographical Sciences, 32(8), 1615–1634. (PMID: 10.1007/s11442-022-2013-6) Thakare, M., Sarma, H., Datar, S., Roy, A., Pawar, P., Gupta, K., ..., & Prasad, R. (2021). Understanding the holistic approach to plant-microbe remediation technologies for removing heavy metals and radionuclides from soil. Current Research in Biotechnology, 3, 84–98. Thompson, J. B., & Ferris, F. G. (1990). Cyanobacterial precipitation of gypsum, calcite, and magnesite from natural alkaline lake water. Geology, 18(10), 995–998. https://doi.org/10.1130/0091-7613(1990)018%3c0995:CPOGCA%3e2.3.CO;2. (PMID: 10.1130/0091-7613(1990)018<0995:CPOGCA>2.3.CO;2) Vilas, D. (2022). Spatiotemporal ecosystem dynamics on the west Florida shelf: Prediction, validation, and application to red tides and stock assessment (Doctoral dissertation, University of Florida). Wang, J., Hu, X., Shi, T., He, L., Hu, W., & Wu, G. (2022). Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development. Geoderma, 405, 115399. (PMID: 10.1016/j.geoderma.2021.115399) Wang, C., Wang, J., Zhou, S., Tang, J., Jia, Z., Ge, L., ..., & Wu, S. (2020). Polycyclic aromatic hydrocarbons and heavy metals in urban environments: Concentrations and joint risks in surface soils with diverse land uses. Land Degradation & Development, 31(3), 383–391. Wei, L., Yuan, Z., Zhong, Y., Yang, L., Hu, X., & Zhang, Y. (2019). An improved gradient boosting regression tree estimation model for soil heavy metal (Arsenic) pollution monitoring using hyperspectral remote sensing. Applied Sciences, 9(9), 1943. https://doi.org/10.3390/app9091943. (PMID: 10.3390/app9091943) Wen, L., Zhang, L., Bai, J., Wang, Y., Wei, Z., & Liu, H. (2022). Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China. Chemosphere, 309, 136789. (PMID: 10.1016/j.chemosphere.2022.136789) Wuana, R. A., & Okieimen, F. E. (2011). Heavy metals in contaminated soils: A review of sources, chemistry, risks and best available strategies for remediation. International Scholarly Research Notices, 2011. Yan, A., Wang, Y., Tan, S. N., Mohd Yusof, M. L., Ghosh, S., & Chen, Z. (2020). Phytoremediation: A promising approach for revegetation of heavy metal-polluted land. Frontiers in Plant Science, 11, 359. (PMID: 10.3389/fpls.2020.00359) Yan, J., Chen, J., & Zhang, W. (2022). Impact of land use and cover on shallow groundwater quality in Songyuan city, China: A multivariate statistical analysis. Environmental Pollution, 307, 119532. (PMID: 10.1016/j.envpol.2022.119532) Yan, G., Mao, L., Liu, S., Mao, Y., Ye, H., Huang, T., ..., & Chen, L. (2018). Enrichment and sources of trace metals in roadside soils in Shanghai, China: A case study of two urban/rural roads. Science of the Total Environment, 631, 942–950. Yang, S., Taylor, D., Yang, D., He, M., Liu, X., & Xu, J. (2021). A synthesis framework using machine learning and spatial bivariate analysis to identify drivers and hotspots of heavy metal pollution of agricultural soils. Environmental Pollution, 287, 117611. (PMID: 10.1016/j.envpol.2021.117611) Zahedifar, M. (2023). Assessing alteration of soil quality, degradation, and resistance indices under different land uses through network and factor analysis. Catena, 222, 106807. (PMID: 10.1016/j.catena.2022.106807) Zeng, F., Ali, S., Zhang, H., Ouyang, Y., Qiu, B., Wu, F., & Zhang, G. (2011). The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants. Environmental Pollution, 159(1), 84–91. (PMID: 10.1016/j.envpol.2010.09.019) Zhang, X., Wei, S., Sun, Q., Wadood, S. A., & Guo, B. (2018). Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principle components analysis and geo statistical analysis. Ecotoxicology and Environmental Safety, 159, 354–362. (PMID: 10.1016/j.ecoenv.2018.04.072) Zhu, Y., Li, W., Wang, D., Wu, Z., & Shang, P. (2022). Spatial pattern of soil erosion in relation to land use change in a Rolling Hilly Region of Northeast China. Land, 11(8), 1253. (PMID: 10.3390/land11081253) Žížala, D., Minařík, R., Skála, J., Beitlerová, H., Juřicová, A., Rojas, J. R., ..., & Zádorová, T. (2022). High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic. Catena, 212, 106024.
فهرسة مساهمة:	Keywords: Environmental contamination; Geospatial imaging spectroscopy; Multivariate analysis; Remote sensing; Soil trace metal distribution
المشرفين على المادة:	0 (Soil Pollutants) 0 (Soil) 0 (Metals) 0 (Trace Elements)
تواريخ الأحداث:	Date Created: 20240506 Date Completed: 20240507 Latest Revision: 20240620
رمز التحديث:	20240620
DOI:	10.1007/s10661-024-12682-3
PMID:	38710964
قاعدة البيانات:	MEDLINE

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تدمد:	1573-2959
DOI:	10.1007/s10661-024-12682-3