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Tao H, Luo L, Li Y, Zhao D, Cao H, Liao X. A risk-based approach for accurately delineating the extent of soil contamination: The role of additional sampling in transition zones. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168231. [PMID: 37923268 DOI: 10.1016/j.scitotenv.2023.168231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 10/11/2023] [Accepted: 10/28/2023] [Indexed: 11/07/2023]
Abstract
Accurate soil contamination delineation is crucial for deciding where remediation efforts are required. However, misjudgments, either in underestimating or overestimating contamination extents could incur different risks: underestimation may result in environmental risks, while overestimation may lead to financial risks. This study proposed an approach based on environmental and financial risks (loss risk) to improve the performance of contamination delineation. Additionally, the impact of additional sampling in the transition zones on the contamination delineation was evaluated. This approach was demonstrated in Hechi, southwest China, where the soil was polluted by arsenic and cadmium. Initially, geostatistical simulation and 512 initial soil sampling were utilized to generate two maps: the conditional coefficient of variation (CCV) and the conditional probability of exceeding a critical threshold (CPT). These two maps were integrated to quantify the uncertainty in identifying the transition zones, guiding additional sampling. Out of 189 candidate sampling sites, we selected 100 additional sites to address high uncertainty. Subsequently, the minimization risk principle was employed to delineate contamination boundaries. The results showed that contaminated areas in the initial phase were significantly underestimated. Additional sampling in the transition zones improved the performance of soil contamination delineation. The performance metrics of Recall and F1 score for arsenic exhibited a notable enhancement of 6 % and 7 %, respectively. As for cadmium, there was an enhancement with Recall and F1 scores increasing by 4 % and 7 %, respectively. Adding 100 extra samples reduced the financial risks of arsenic and cadmium by 13 % and 11 %, respectively. In comparison, the 100 additional samples reduced the environmental risks of arsenic and cadmium by 55 % and 72 %, respectively. The study demonstrates that combining CCV and CPT for additional sampling efficiently mitigates the risks of delineating contaminated areas, which could help better understand the boundaries and gradient of contamination.
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Affiliation(s)
- Huan Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Beijing 100101, China.
| | - Lingzhi Luo
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - You Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Beijing 100101, China.
| | - Dan Zhao
- Center for Environmental Risk and Damage Assessment, Chinese Academy for Environmental Planning, Beijing 100012, China.
| | - Hongying Cao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xiaoyong Liao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Beijing Key Laboratory of Environmental Damage Assessment and Remediation, Beijing 100101, China.
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Liu X, Zheng L, Li Z, Liu F, Obin N. Optimization of spatial prediction and sampling strategy of site contamination based on Thiessen polygon coupling interpolation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27943-w. [PMID: 37278892 DOI: 10.1007/s11356-023-27943-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/23/2023] [Indexed: 06/07/2023]
Abstract
Contaminated sites pose a serious threat to the ecological environment and human health. Because of the presence of multiple peaks in the pollution data of some contaminated sites, as well as strong spatial heterogeneity and skewness in their distribution, the accuracy of spatial interpolation prediction is low. This study proposes a method for investigating highly skewed contaminated sites, which uses Thiessen polygons coupled with geostatistics and deterministic interpolation to optimize the spatial prediction and sampling strategy of sites. An industrial site in Luohe is used as an example to validate the proposed method. The results indicate that using 40 × 40 m as the minimum initial sampling unit can obtain data that is representative of the regional pollution situation. Evaluation indexes reveal that the ordinary kriging (OK) method for interpolation prediction accuracy and the radial basis function_inverse distance weighted (RBF_IMQ) method for pollution scope prediction provides the best results, which can effectively improve the spatial prediction accuracy of pollution in the study area. Each accuracy indicator is enhanced by 20-70% after supplementing 11 sampling points in the suspect region, and the identification of the pollution scope approaches 95%. This method offers a novel approach for investigating highly biased contaminated sites, which can optimize the spatial prediction accuracy of pollution and reduce economic costs.
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Affiliation(s)
- Xingwang Liu
- College of Environment and Resources, Xiangtan University, Xiangtan, 411105, China
| | - Lanting Zheng
- College of Environment and Resources, Xiangtan University, Xiangtan, 411105, China
| | - Zhuang Li
- Ecological Environment Affairs Center of Hunan Province, Changsha, 410014, China
| | - Fan Liu
- Ecological Environment Affairs Center of Hunan Province, Changsha, 410014, China.
| | - Nicolas Obin
- College of Environment and Resources, Xiangtan University, Xiangtan, 411105, China
- Department of Geology Engineering, Polytechnic School of Antananarivo, University of Antananarivo, 101, Antananarivo, Madagascar
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Molla A, Ren Y, Zuo S, Qiu Y, Li L, Zhang Q, Ju J, Zhu J, Zhou Y. Evaluating sample sizes and design for monitoring and characterizing the spatial variations of potentially toxic elements in the soil. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 847:157489. [PMID: 35882327 DOI: 10.1016/j.scitotenv.2022.157489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/04/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79-86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42-57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil.
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Affiliation(s)
- Abiot Molla
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Agriculture and Natural Resources, Debre Markos University, Debre Markos +251269, Ethiopia
| | - Yin Ren
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Shudi Zuo
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Yue Qiu
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Liangbin Li
- Wuyishan National Park Scientific Research and Monitoring Center, Wuyishan 354300, China
| | - Qijiong Zhang
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Jiaheng Ju
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Jianqin Zhu
- Wuyishan National Park Scientific Research and Monitoring Center, Wuyishan 354300, China
| | - Yan Zhou
- Wuyishan National Park Scientific Research and Monitoring Center, Wuyishan 354300, China
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Molla A, Zuo S, Zhang W, Qiu Y, Ren Y, Han J. Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 802:149728. [PMID: 34454139 DOI: 10.1016/j.scitotenv.2021.149728] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/27/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.
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Affiliation(s)
- Abiot Molla
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Agriculture and Natural Resources, Debre Markos University, Debre Markos +251269, Ethiopia
| | - Shudi Zuo
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Weiwei Zhang
- Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China; Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai 200232, China
| | - Yue Qiu
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Yin Ren
- Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Jigang Han
- Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China; Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai 200232, China.
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5
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Huang Y, Li J, Ma Y, Li F, Chen D. A simple method to determine the sampling numbers in decision-making units with unknown variations of soil cadmium. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:552. [PMID: 34355292 DOI: 10.1007/s10661-021-09332-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/22/2021] [Indexed: 06/13/2023]
Abstract
Sampling number is one critical issue to achieve credible results when surveying soil contamination and making remediation decisions. Traditional methods based on a normal distribution for determining numbers of samples are not always optimal because most distributions of soil heavy metal concentrations followed a log-normal distribution. Moreover, the variation of soil heavy metal concentrations is a prerequisite for previous methods to determine sampling numbers. Unfortunately, the variation is often unknown before soil sampling. Therefore, a simple method under the log-normal distribution without relying on variation to determine quickly the sampling number (QSN) was developed for soil cadmium and compared with other methods based on classical statistics and Chebyshev inequality. Results showed that an equation as a function of sampling areas could be used to determine QSN (QSN = 18.44 × A0.54 + 8.69, A is sampling areas, km2), with acceptable errors ranging from 13 to 33% at the sampling areas of 0.03-10 km2. The developed simple method for QSN was easy to use and cost-effective without prerequisite on the estimation of variation. Moreover, when the sampling cost was enough and the improved accuracy was requested, the increased sampling numbers were recommended as 1.53 times as the number calculated by the simple method. Therefore, the proposed method is believed as a simple and cost-effective method to determine the sampling numbers of soil Cd in decision-making units with unknown variations.
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Affiliation(s)
- Yajie Huang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Environmental Development Center of the Ministry of Ecology and Environment, Beijing, 100029, China
| | - Jumei Li
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yibing Ma
- Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality, Macao Environmental Research Institute, Macau University of Science and Technology, Macao, 999078, China.
| | - Fangbai Li
- Guangdong Institute of Eco-Environmental and Soil Sciences, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Guangzhou, 510650, China
| | - Deli Chen
- School of Agriculture and Food, The University of Melbourne, Melbourne, VIC, 3010, Australia
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6
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Brand JH, Spencer KL. Potential contamination of the coastal zone by eroding historic landfills. MARINE POLLUTION BULLETIN 2019; 146:282-291. [PMID: 31426158 DOI: 10.1016/j.marpolbul.2019.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 04/19/2019] [Accepted: 06/08/2019] [Indexed: 05/12/2023]
Abstract
Historically solid waste was commonly landfilled in the coastal zone in sites with limited engineering to isolate waste from adjacent coastal environments. Climate change is increasing the likelihood that these historic coastal landfills will erode releasing solid waste to the coastal zone. Historic coastal landfills are frequently located near designated ecological sites; yet, there is little understanding of the environmental risk posed by released waste. This research investigated inorganic and organic contaminant concentrations in a range of solid waste materials excavated from two historic coastal landfills, and the potential ecological impact should eroded waste be released to the coastal environment. Contaminant concentrations in the analysed waste materials exceeded sediment quality guidelines, indicating erosion of historic coastal landfills may pose a significant environmental threat. Paper and textile wastes were found to make a significant contribution to the total contaminant load, suggesting risk assessments should consider a wide range of solid waste materials.
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Affiliation(s)
- James H Brand
- School of Geography, Queen Mary University of London, Mile End Road, London, E1 4NS, England, United Kingdom of Great Britain and Northern Ireland.
| | - Kate L Spencer
- School of Geography, Queen Mary University of London, Mile End Road, London, E1 4NS, England, United Kingdom of Great Britain and Northern Ireland.
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Hu B, Zhao R, Chen S, Zhou Y, Jin B, Li Y, Shi Z. Heavy Metal Pollution Delineation Based on Uncertainty in a Coastal Industrial City in the Yangtze River Delta, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15040710. [PMID: 29642623 PMCID: PMC5923752 DOI: 10.3390/ijerph15040710] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 03/31/2018] [Accepted: 04/03/2018] [Indexed: 11/29/2022]
Abstract
Assessing heavy metal pollution and delineating pollution are the bases for evaluating pollution and determining a cost-effective remediation plan. Most existing studies are based on the spatial distribution of pollutants but ignore related uncertainty. In this study, eight heavy-metal concentrations (Cr, Pb, Cd, Hg, Zn, Cu, Ni, and Zn) were collected at 1040 sampling sites in a coastal industrial city in the Yangtze River Delta, China. The single pollution index (PI) and Nemerow integrated pollution index (NIPI) were calculated for every surface sample (0–20 cm) to assess the degree of heavy metal pollution. Ordinary kriging (OK) was used to map the spatial distribution of heavy metals content and NIPI. Then, we delineated composite heavy metal contamination based on the uncertainty produced by indicator kriging (IK). The results showed that mean values of all PIs and NIPIs were at safe levels. Heavy metals were most accumulated in the central portion of the study area. Based on IK, the spatial probability of composite heavy metal pollution was computed. The probability of composite contamination in the central core urban area was highest. A probability of 0.6 was found as the optimum probability threshold to delineate polluted areas from unpolluted areas for integrative heavy metal contamination. Results of pollution delineation based on uncertainty showed the proportion of false negative error areas was 6.34%, while the proportion of false positive error areas was 0.86%. The accuracy of the classification was 92.80%. This indicated the method we developed is a valuable tool for delineating heavy metal pollution.
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Affiliation(s)
- Bifeng Hu
- Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China.
- Unité de Recherche en Science du Sol, INRA, Orléans 45075, France.
- InfoSol, INRA, US 1106, Orléans F-4075, France.
- Sciences de la Terre et de l'Univers, Orléans University, Orleans 45067, France.
| | - Ruiying Zhao
- Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China.
| | - Songchao Chen
- InfoSol, INRA, US 1106, Orléans F-4075, France.
- Unité Mixte de Rercherche (UMR) Sol Agro et hydrosystème Spatialisation (SAS), INRA, Agrocampus Ouest, Rennes 35042, France.
| | - Yue Zhou
- Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China.
| | - Bin Jin
- Ningbo Agricultural Food Safety Management Station, Ningbo 315000, China.
| | - Yan Li
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China.
| | - Zhou Shi
- Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China.
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8
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Liu G, Niu J, Zhang C, Guo G. Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2015; 22:20121-30. [PMID: 26300353 DOI: 10.1007/s11356-015-5122-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Accepted: 07/23/2015] [Indexed: 05/27/2023]
Abstract
Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.
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Affiliation(s)
- Geng Liu
- Research Center for Scientific Development in Fenhe River Valley, Taiyuan Normal University, Taiyuan, 030012, China
| | - Junjie Niu
- Research Center for Scientific Development in Fenhe River Valley, Taiyuan Normal University, Taiyuan, 030012, China
| | - Chao Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Anwai Dayangfang 8, Beijing, 100012, China
| | - Guanlin Guo
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Anwai Dayangfang 8, Beijing, 100012, China.
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Milillo TM, Sinha G, Gardella JA. Use of geostatistics for remediation planning to transcend urban political boundaries. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2012; 170:52-62. [PMID: 22771352 DOI: 10.1016/j.envpol.2012.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Revised: 05/24/2012] [Accepted: 06/02/2012] [Indexed: 06/01/2023]
Abstract
Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible.
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Affiliation(s)
- Tammy M Milillo
- Department of Chemistry, University at Buffalo, State University of New York, Buffalo, NY 14260-3000, USA
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Lin YP, Cheng BY, Shyu GS, Chang TK. Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2010; 158:235-44. [PMID: 19665827 DOI: 10.1016/j.envpol.2009.07.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2008] [Revised: 07/14/2009] [Accepted: 07/17/2009] [Indexed: 05/23/2023]
Abstract
This study identifies the natural background, anthropogenic background and distribution of contamination caused by heavy metal pollutants in soil in Chunghua County of central Taiwan by using a finite mixture distribution model (FMDM). The probabilities of contaminated area distribution are mapped using single-variable indicator kriging and multiple-variable indicator kriging (MVIK) with the FMDM cut-off values and regulation thresholds for heavy metals. FMDM results indicate that Cr, Cu, Ni and Zn can be individually fitted by a mixture model representing the background and contamination distributions of the four metals in soil. The FMDM cut-off values for contamination caused by the metals are close to the regulation thresholds, except for the cut-off value of Zn. The receiver operating characteristic (ROC) curve validates that indicator kriging and MVIK with FMDM cut-off values can reliably delineate heavy metals contamination, particularly for areas lacking background information and high heavy metal concentrations in soil.
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Affiliation(s)
- Yu-Pin Lin
- Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Section 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan, ROC.
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Zhang H, Huang GH, Zeng GM. Health risks from arsenic-contaminated soil in Flin Flon-Creighton, Canada: integrating geostatistical simulation and dose-response model. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2009; 157:2413-2420. [PMID: 19359076 DOI: 10.1016/j.envpol.2009.03.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2009] [Revised: 03/09/2009] [Accepted: 03/10/2009] [Indexed: 05/27/2023]
Abstract
Elevated concentrations of arsenic were detected in surface soils adjacent to a smelting complex in northern Canada. We evaluated the cancer risks caused by exposure to arsenic in two communities through combining geostatistical simulation with demographic data and dose-response models in a framework. Distribution of arsenic was first estimated using geostatistical circulant-embedding simulation method. We then evaluated the exposures from inadvertent ingestion, inhalation and dermal contact. Risks of skin cancer and three internal cancers were estimated at both grid scale and census-unit scale using parametric dose-response models. Results indicated that local residents could face non-negligible cancer risks (skin cancer and liver cancer mainly). Uncertainties of risk estimates were discussed from the aspects of arsenic concentrations, exposed population and dose-response model. Reducing uncertainties would require additional soil sampling, epidemic records as well as complementary studies on land use, demographic variation, outdoor activities and bioavailability of arsenic.
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Affiliation(s)
- Hua Zhang
- Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada
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12
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Affiliation(s)
- Soledad Rubio
- Department of Analytical Chemistry, Facultad de Ciencias, Edificio Anexo Marie Curie, Campus de Rabanales, 14071 Córdoba, Spain
| | - Dolores Pérez-Bendito
- Department of Analytical Chemistry, Facultad de Ciencias, Edificio Anexo Marie Curie, Campus de Rabanales, 14071 Córdoba, Spain
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