Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans

Metahni, Sabrine; Coudert, Lucie; Gloaguen, Erwan; Guemiza, Karima; Mercier, Guy; Blais, Jean‐François

doi:10.1016/j.envpol.2019.05.122

Cited by 22 publications

(13 citation statements)

References 42 publications

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“…The map produced by the OK method smoothed out most of the local details. These findings were consistent with those of previous studies [82][83][84]. In the PTE estimation results produced by the EBK algorithm, we detected a more dramatic PTE variation in the study area.…”

Section: Spatial Prediction Accuracy Using Different Interpolation Mesupporting

confidence: 93%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

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Section: Spatial Prediction Accuracy Using Different Interpolation Mesupporting

confidence: 93%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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“…For runoff simulation of the entire basin, satellite‐based dataset TRMM 3B42V7 reflects the characteristics of clouds over the region and it is not restricted by topographic fluctuations (Zhao, & Yatagai, 2014). In regions like the Hengduan Mountain, the highly heterogeneous topography limits the performance of meteorological station measurements in valleys and the excessive smoothing of the interpolation method itself will also result in generally low or high estimates (Metahni et al, 2019). These inaccuracies will lead to errors in runoff simulation, since runoff is greatly affected by surface precipitation.…”

Section: Discussionmentioning

confidence: 99%

Evaluation ofmulti‐sourceprecipitation data in a watershed with complex topography based on distributed hydrological modeling

Wang

Sun

Cai

et al. 2020

River Research & Apps

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Hydrological simulation in areas with complex topography and pronounced climatic heterogeneity has a certain degree of uncertainty, and how precipitation data from multiple types of sources influence hydrological simulation remains unclear. Performances of precipitation datasets based on multiple interpolation methods (i.e., inverse distance weighted [IDW], cokriging, and ANUSPLIN) and satellite remote sensing datasets (i.e., tropical rainfall measuring mission multi‐satellite precipitation analysis 3B42 version7 [TRMM 3B42V7], precipitation estimation from remote sensing information using artificial neural networks–climate data record [PERSIANN‐CDR], climate predication centre morphing technique, and sian precipitation–highly resolved observational data integration towards evaluation of water resources [APHRODITE]) were obtained for comparison. Hydrological responses from these precipitation datasets were evaluated through distributed hydrological model variable infiltration capacity. The Yalong river basin (YRB), located in the Hengduan Mountains region and characterized by the significant heterogeneity of precipitation in China, was focused. Four sub‐regions (i.e., sub‐region I to IV) were divided. The results indicated that: (a) TRMM 3B42V7 and APHRODITE data were superior in detection capability from daily to annual measurement than other satellite precipitation data sets generally; (b) under the scale of gauges, TRMM 3B42V7, APHRODITE, and cokriging performed well in sub‐regions I, II, and IV, respectively. In sub‐region III, performance of the interpolation methods was better than that of TRMM 3B42V7; (c) the interpolation datasets exhibited a poor capability to simulate peak runoff. The combination dataset COM could improve the overall runoff simulation in flood periods; and (d) the interpolation method has the problem of excessive smoothness. Whatever the interpolation dataset is, hydrological simulation tends to result in smaller runoff, which was related to that the measurements from meteorological stations in mountainous valleys are not necessarily representative of the entire region. In this paper, the applicability of multi‐source precipitation data in the hydrological simulation process of YRB was comprehensively analysed, which could provide effective references for improving the accuracy of precipitation data and the applicability of hydrological simulation.

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“…For confidentiality reasons, soil samples are only identified as coming from an industrial area and are refered to as S3. The industrial activity generated a spatially heterogeneous soil contamination by As, Cr, Cu, PCP and PCDD/F (Metahni et al, 2019). Sampling was conducted on the first 0 to 15 cm depth interval over an area of 16 m 2 (4 m x 4 m).…”

Section: Soil Sampling and Characterizationmentioning

confidence: 99%

“…Sampling was conducted on the first 0 to 15 cm depth interval over an area of 16 m 2 (4 m x 4 m). This area was chosen because of its high PCDD/F contamination, based on a previous sampling campaign completed in November of 2014 (Metahni et al, 2019). This sampling campaign, revealed initial PCDD/F levels of 6,678 -11,322 and 12,625 ng dioxin toxicity equivalence (ng TEQ/kg), for exploration holes of 1 m 2 each (1 m x 1 m) located in the area of interest.…”

Section: Soil Sampling and Characterizationmentioning

confidence: 99%

Techno-economic assessment of an hydrometallurgical process to simultaneously remove As, Cr, Cu, PCP and PCDD/F from contaminated soil

Metahni

Coudert

Blais

et al. 2020

Journal of Environmental Management

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Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans

Abstract: Comparison of different interpolation methods and sequential Gaussian simulation to estimate volumes of soil contaminated by As, Cr, Cu, PCP and dioxins/furans

Cited by 22 publications

References 42 publications

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Evaluation ofmulti‐sourceprecipitation data in a watershed with complex topography based on distributed hydrological modeling

Techno-economic assessment of an hydrometallurgical process to simultaneously remove As, Cr, Cu, PCP and PCDD/F from contaminated soil

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