In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, ar...In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error;2) locate the plume boundary;3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells.展开更多
Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application o...Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.展开更多
Background Effective monitoring of infectious diseases is crucial for safeguarding public health.Compared to comprehensive nationwide surveillance,selecting representative sample cities to constitute the monitoring ne...Background Effective monitoring of infectious diseases is crucial for safeguarding public health.Compared to comprehensive nationwide surveillance,selecting representative sample cities to constitute the monitoring network for surveillance provides similar effectiveness at a lower cost.We developed Spatial Cluster Stratified Sampling(SCSS)to select sample cities for infectious diseases exhibiting spatial autocorrelation.Methods To improve monitoring efficiency for hand,foot,and mouth disease(HFMD),we used SCSS to design a monitoring network,which involved four main steps.First,we used Spatial Kluster Analysis by Tree Edge Removal(SKATER)to stratify the data.Second,we applied the cost-benefit balance to determine the optimal sample size.Third,we performed simple random sampling within each stratum to establish an initial monitoring network.Fourth,we used cyclic optimization to finalize the monitoring network.We evaluated the spatiotemporal representativeness using root mean square error(RMSE),Spearman’s rank correlation,global Moran’s I,local Getis-Ord G*,and Joinpoint Regression.We also compared the effectiveness of SCSS with K-means,traditional stratified sampling,and simple random sampling using RMSE.Results The optimal sample size was determined to be 103.Overall,the predicted values for each city significantly correlated with the true values(r=0.81,P<0.001).Both the predicted and true values showed positive spatial autocorrelation(Moran’s I>0,P<0.05),and the sensitivity,specificity,and accuracy of the predicted local Getis-Ord G*values,evaluated against the true values as the gold standard,were 0.76,0.91,and 0.87,respectively.The weekly predicted values for each city showed significant correlation with the true values(P<0.05).The 95%confidence intervals(CI)for the predicted values of joinpoint locations,annual percent change(APC),and average annual percent change(AAPC)encompassed the true values,and the number of joinpoints matched the true values.Among the four methods compared,SCSS exhibited the lowest and most centralized RMSE.Conclusions SCSS proved to be more accurate and stable than traditional methods,which overlook spatial information.This method offers a valuable reference for future design of monitoring networks for infectious diseases exhibiting spatial autocorrelation,enabling more efficient and cost-effective surveillance.展开更多
文摘In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error;2) locate the plume boundary;3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells.
文摘Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.
基金supported by the Natural Science Foundation of Shanghai(24ZR1414700)the National Natural Science Foundation of China(82473736)+1 种基金the National Key Research and Development Program of China(2022YFC2602900)the Emergency Response Mechanism Operation Program.
文摘Background Effective monitoring of infectious diseases is crucial for safeguarding public health.Compared to comprehensive nationwide surveillance,selecting representative sample cities to constitute the monitoring network for surveillance provides similar effectiveness at a lower cost.We developed Spatial Cluster Stratified Sampling(SCSS)to select sample cities for infectious diseases exhibiting spatial autocorrelation.Methods To improve monitoring efficiency for hand,foot,and mouth disease(HFMD),we used SCSS to design a monitoring network,which involved four main steps.First,we used Spatial Kluster Analysis by Tree Edge Removal(SKATER)to stratify the data.Second,we applied the cost-benefit balance to determine the optimal sample size.Third,we performed simple random sampling within each stratum to establish an initial monitoring network.Fourth,we used cyclic optimization to finalize the monitoring network.We evaluated the spatiotemporal representativeness using root mean square error(RMSE),Spearman’s rank correlation,global Moran’s I,local Getis-Ord G*,and Joinpoint Regression.We also compared the effectiveness of SCSS with K-means,traditional stratified sampling,and simple random sampling using RMSE.Results The optimal sample size was determined to be 103.Overall,the predicted values for each city significantly correlated with the true values(r=0.81,P<0.001).Both the predicted and true values showed positive spatial autocorrelation(Moran’s I>0,P<0.05),and the sensitivity,specificity,and accuracy of the predicted local Getis-Ord G*values,evaluated against the true values as the gold standard,were 0.76,0.91,and 0.87,respectively.The weekly predicted values for each city showed significant correlation with the true values(P<0.05).The 95%confidence intervals(CI)for the predicted values of joinpoint locations,annual percent change(APC),and average annual percent change(AAPC)encompassed the true values,and the number of joinpoints matched the true values.Among the four methods compared,SCSS exhibited the lowest and most centralized RMSE.Conclusions SCSS proved to be more accurate and stable than traditional methods,which overlook spatial information.This method offers a valuable reference for future design of monitoring networks for infectious diseases exhibiting spatial autocorrelation,enabling more efficient and cost-effective surveillance.
