Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil

Saha, Arnab; Sengupta, Bhaskar; Patidar, Sandhya; Martínez-Villegas, Nadia

doi:10.1016/j.jsames.2022.103763

Cited by 29 publications

(18 citation statements)

References 70 publications

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“…This study was designed to ascertain the concentrations of heavy toxic metals (e.g., As, Sr, Mn, and Fe) and light metals (e.g., Ca, Mg, Na, and K) contamination in recreational and agricultural surface soil of Cerrito Blanco area in Matehuala municipality, San Luis Potosi, Mexico and to explore the factors that affect the spatial distribution and source identifications. The previous studies done in this particular region established Cd, Cu, Cr, Zn, or Pb as primary contamination risk factors (19,22,65,66). Therefore, these heavy metals were not repeated for this study.…”

Section: Introductionmentioning

confidence: 88%

“…These two study sites are near a mining area where metals have been extracted for more than 240 years (65,66). According to previous research, the distribution of mining slags and various waste materials also affected the ecosystem over 100 km 2 (19,65,(67)(68)(69). Overall, the climate in these regions is semi-arid and dry (20).…”

Section: Study Areamentioning

confidence: 99%

“…Some of these recorded incidents might be thought of as the beginning stages of As and other heavy metals-related environmental, geochemical, and medical investigations in Mexico (14). Heavy metal extraction from various mining waste materials, and exposure through smelting dust and fumes, including its distribution, were all explored at various mining and ore processing sites in the regions of Baja California, Guerrero, Guanajuato, Hidalgo, and San Luis Potosi (16)(17)(18)(19). In the San Luis Potosi area, soil contaminated by heavy metals from the dissolution of natural metal sources could be distinguished from various sources with metal contamination originating from tailings by chemometric and isotopic approaches (20)(21)(22).…”

Section: Introductionmentioning

confidence: 99%

“…To identify the source contributions, standard source apportionment procedures (e.g., PCA and PMF) and contamination monitoring techniques (e.g., contamination indices and ecological risk factors) are often implemented (32-36) and estimate the risk of metal contamination in soils, accordingly (37)(38)(39)(40). However, several contamination indices such as the geo-accumulation index (I geo ), contamination factor (C f ), pollution load index (PLI), degree of contamination (C d ), potential ecological risk index, Nemerow's Pollution Index (PI N ), and human health risk assessment analysis (carcinogenic and non-carcinogenic risks) were used to assess heavy metal contamination in soils systematically (19,(41)(42)(43)(44). In general metal contamination in soils is derived from two major sources: natural (e.g., soil mineral and organic components) and anthropogenic sources (e.g., agricultural activities, transportation, mining activities, industrial activities, etc.…”

Section: Introductionmentioning

confidence: 99%

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Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

et al. 2022

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The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.

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Section: Introductionmentioning

confidence: 88%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

et al. 2022

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“…Metals in this area mainly come from pesticides, atmospheric deposition, tra c discharge, and industrial sewage (Shi et al, 2022). Saha explored different types of GIS spatial interpolation methods, and the nal results show that the inverse distance weighting method (IDW) is the optimal interpolation model to study the spatial distribution pattern of toxic metal content (Saha et al, 2022). Sediment quality guidelines (SQG), pollution load index (PLI), and human health risk assessment have been widely applied to metal ion hazard assessment in various regions (Kolawole et al, 2022;Lim et al, 2022;Mor et al, 2022;Tnoumi et al, 2022;Zhang et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution, sources, and risk assessment of metal ions in sediments from industrial areas in the Kuye River Basin in Shenmu, China

Wang

Liu

Wei

et al. 2022

Preprint

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Metal pollution of river sediments in industrial areas has attracted widespread attention. In this study, 11 metal ions (including Fe, Al, Ag, Mn, Cd, Cr, As, Cu, Pb, Zn, Ni) were determined and analyzed in river sediments of typical industrial areas in the Kuye River Basin, a first-level tributary of the Yellow River. The geological accumulation index, enrichment factor, and pollution load index indicated that the metal ions in this area were enriched and polluted to different degrees. The spatial distribution of the 8 kinds of heavy metals showed that the content in the upstream and downstream was higher, and the middle reaches were lower. the heavy metals mainly came from local industrial activities, coal mining and transportation, and soil weathering, Human activities were the main reasons for the dramatic spatial changes and high levels of heavy metals. The results of the combined potential ecological risk index and sediment quality guidelines showed that the ecological risk downstream of the area was high, and Ni and Pb have caused adverse biological reactions. The human carcinogenic risk assessment result showed that the non-carcinogenic hazard quotient of metal ions in the study area was at an acceptable level, but the carcinogenic risk level of Ni and Cr was higher than 10-4, which was an unacceptable level and must be got enough recognition. The results of this study can provide theoretical support for the pollution control and environmental restoration of rivers in energy-enriched areas.

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Complexity of hydrology, sewage and industries in distribution and migration pathways of heavy metals at spatial scale of China's brownfields

Yu,

Wang,

Zhang

et al. 2023

Ecohydrology

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Hydrologic dynamics, sewage and industries determine the distribution and migration pathways of heavy metals in the natural environments including soils across the urbanized area. In this study, 323 stratified soil samples from a brownfield in Jiangsu Province, China, were collected to assess the heavy metals (Cu, Ni, Pb, Cd, As and Hg) contaminations. Contamination factor (Cfi), Nemerow pollution index (PIN) and enrichment factor (EFi) were evaluated to assess the heavy metal pollution, while sources of pollution were identified in combination with geo‐statistical, correlations and principal components analysis. Moreover, transport of Ni in soil profiles over the next 30 years was simulated using HYDRUS. The vertical distribution revealed that the soil surface (0–10 cm) had the highest concentration of heavy metal contamination. ICP‐MS measurements showed that the soil in the brownfield was enriched with Cu, Ni, Pb, Cd, As and Hg, where Ni was the most severe and prevalent contaminant. The results of source apportionment analysis showed that Ni, Cd, Pb and Cu were mainly derived from building materials and sewage discharge, while As and Hg may come from fossil fuel combustion and agricultural discharges from upstream river catchment. The migration of Ni was largely driven by the combination of hydrological variability including the flow and solute contaminant gradients in soils. Our work highlights the need for a comprehensive understanding of the complexity of hydrology, and sewage discharge in heavy metal dynamics and migration pathways in China's brownfield soil at regional and national scales.

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Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil

Cited by 29 publications

References 70 publications

Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

Spatial distribution, sources, and risk assessment of metal ions in sediments from industrial areas in the Kuye River Basin in Shenmu, China

Complexity of hydrology, sewage and industries in distribution and migration pathways of heavy metals at spatial scale of China's brownfields

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