Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.     

基于多源大数据可视化的城市居民区绿地土壤环境监测点位布设及采样研究

为科学合理的监测城市居民区绿地土壤环境状况,对居民区绿地土壤监测点位布设和采样的方法进行研究,包括代表性居民区的选取、监测点采样位置的确定、点位数量的确定等。通过多源大数据可视化手段选取某城市代表性的居民区,对其居民区内绿地土壤重金属(镉、汞、砷、铅、铜、铬、锌和镍)进行监测。结果表明:多源大数据的应用对城市土壤监测布点具有积极作用和优势;不同建筑年代的居民区土壤中重金属含量存在一定差异,为全面监测居民区土壤环境状况应选取不同建筑年代的居民区作为监测对象;不同采样位置对居民区土壤样品中重金属含量无明显影响,优先于居民区内面积占比较大的绿地采样;同一居民区内不同监测点位样品存在差异性,每个居民区内至少布设3~4个监测点位可代表该居民区土壤环境状况。     

Spatial�Ctemporal assessment and redesign of groundwater quality monitoring network: a case study

Assessment of groundwater quality monitoring networks requires methods to determine the potential efficiency and cost-effectiveness of the current monitoring programs. To this end, the concept of entropy has been considered as a promising method in previous studies since it quantitatively measures the information produced by a network. In this study, the measure of transinformation in the discrete entropy theory and the transinformation?Cdistance (T?CD) curves, which are used frequently by other researchers, are used to quantify the efficiency of a monitoring network. This paper introduces a new approach to decrease dispersion in results by performing cluster analysis that uses fuzzy equivalence relations. As a result, the sampling (temporal) frequency determination method also recommends the future sampling frequencies for each location based on certain criteria such as direction, magnitude, correlation with neighboring stations, and uncertainty of the concentration trend derived from representative historical concentration data. The proposed methodology is applied to groundwater resources in the Tehran?CKaradj aquifer, Tehran, Iran.     

Network design factors for assessing temporal variability in ground-water quality

Benchmark major ions and nutrients data were collected biweekly for about two years at 12 wells at two sites in a shallow sand and gravel aquifer in west-central Illinois. The purpose of the study was to explore the time series properties of ground-water quality data collected at a relatively high sampling frequency. A secondary purpose was to determine the relative magnitudes of natural and sampling-related sources of variance in ground-water quality time series. The absence of this kind of information has severely hindered the design of ground-water sampling programs in the past.An autocorrelation analysis showed that the median sampling frequency for which the predicted ratio of effective independent sample size to total sample size was 0.5 (50% sampling redundancy) ranged from 6 to 14 samples per year. For a predicted ratio of effective independent sample size to total sample size of 0.9 (10% sampling redundancy) the sampling frequency ranged from 3 to 6 samples per year. This suggests that, for the wells sampled, sampling frequencies much higher than monthly can result in considerable loss of information, and may not be cost effective. Care was taken in the design of the field and laboratory sampling protocol to minimize the effects of measurement error. The data analysis confirmed that this goal was accomplished. In most cases considerably less than five percent of the total variability could be attributed to sampling and analytical error. Because of the relatively short duration of the study (42 biweekly sampling occasions at most wells) it was not possible to identify the magnitude of seasonal variations reliably.     

A geostatistical methodology for the optimal design of space–time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer

This paper presents a new methodology for the optimal design of space–time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space–time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space–time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space–time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space–time analysis corresponds to information of 273 wells located within the aquifer for the period 1970–2007. A total of 1,435 hydraulic head data were used to construct the experimental space–time variogram. The results show that from the existing monitoring program that consists of 418 space–time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.     

Critical Sampling Points Methodology: Case Studies of Geographically Diverse Watersheds

Only with a properly designed water quality monitoring network can data be collected that can lead to accurate information extraction. One of the main components of water quality monitoring network design is the allocation of sampling locations. For this purpose, a design methodology, called critical sampling points (CSP), has been developed for the determination of the critical sampling locations in small, rural watersheds with regard to total phosphorus (TP) load pollution. It considers hydrologic, topographic, soil, vegetative, and land use factors. The objective of the monitoring network design in this methodology is to identify the stream locations which receive the greatest TP loads from the upstream portions of a watershed. The CSP methodology has been translated into a model, called water quality monitoring station analysis (WQMSA), which integrates a geographic information system (GIS) for the handling of the spatial aspect of the data, a hydrologic/water quality simulation model for TP load estimation, and fuzzy logic for improved input data representation. In addition, the methodology was purposely designed to be useful in diverse rural watersheds, independent of geographic location. Three watershed case studies in Pennsylvania, Amazonian Ecuador, and central Chile were examined. Each case study offered a different degree of data availability. It was demonstrated that the developed methodology could be successfully used in all three case studies. The case studies suggest that the CSP methodology, in form of the WQMSA model, has potential in applications world-wide.     

Optimizing water quality monitoring networks using continuous longitudinal monitoring data: a case study of Wen-Rui Tang River, Wenzhou, China

Identification of representative sampling sites is a critical issue in establishing an effective water quality monitoring program. This is especially important at the urban-agriculture interface where water quality conditions can change rapidly over short distances. The objective of this research was to optimize the spatial allocation of discrete monitoring sites for synoptic water quality monitoring through analysis of continuous longitudinal monitoring data collected by attaching a water quality sonde and GPS to a boat. Sampling was conducted six times from March to October 2009 along a 6.5 km segment of the Wen-Rui Tang River in eastern China that represented an urban-agricultural interface. When travelling at a velocity of ～2.4 km h(-1), this resulted in water quality measurements at ～20 m interval. Ammonia nitrogen (NH(4)(+)-N), electrical conductivity (EC), dissolved oxygen (DO), and turbidity data were collected and analyzed using Cluster Analysis (CA) to identify optimal locations for establishment of long-term monitoring sites. The analysis identified two distinct water quality segments for NH(4)(+)-N and EC and three distinct segments for DO and turbidity. According to our research results, the current fixed-location sampling sites should be adjusted to more effectively capture the distinct differences in the spatial distribution of water quality conditions. In addition, this methodology identified river reaches that require more comprehensive study of the factors leading to the changes in water quality within the identified river segment. The study demonstrates that continuous longitudinal monitoring can be a highly effective method for optimizing monitoring site locations for water quality studies.     

Sampling The Soil In Long-Term Forest Plots: The Implications Of Spatial Variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.     

Multiple-point variance analysis for optimal adjustment of a monitoring network

The selection of sampling sites is one of the major tasks in thedesign of a monitoring network. Many environmental networkssuffer from either insufficient information or redundantinformation. This study presents a new, effective algorithm thataddresses the issues of insufficient and reduction information.The new algorithm is denoted as Multiple-Point Variance Analysis(MPV). MPV includes both Multiple-Point Variance ReductionAnalysis (MPVR) for adding information-effectives sites, andMultiple-Point Variance Increase Analysis (MPVI) for deletinginformation-redundant sites. The MPVR and MPVI equations areverified under two hypothetical cases. The optimal procedures ofthis new algorithm include determination of simultaneousadditions or deletions of groups of sampling points. Theseproposed optimization procedures eliminate the need for anyspatial discretizations or sequential selections. The efficiencyof these optimal procedures is tested under actual fieldconditions. The results show that the optimal MPV is aneffective tool for adjustment of existing sampling networks.     

Post-remediation evaluation of a LNAPL site using electrical resistivity imaging

Present understanding of the earth's subsurface is most often derived from samples at discrete points (wells) and interpolations or models that interpret the space between these points. Electrical resistivity imaging techniques have produced an improved capability to map contaminants (especially NAPLs--NonAqueous Phase Liquids) away from traditional wells using actual field data. Electrical resistivity image data, confirmed by drilling, have demonstrated that LNAPL (Light NAPL--less dense than water, such as gasoline) contaminants exist outside of a delineated and remediated area in Golden, Oklahoma. The data also demonstrate that LNAPL exists between monitoring and remediation wells which indicate low contaminant levels when sampled. Additionally, the electrical images provided the drilling location with the highest concentration of hydrocarbon ever found on the site, even after two phases of remediation work had been performed, although the sampling protocols varied. The results indicate that current methods of post-remediation site characterization are inadequate for complete site characterization.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

Design of River Water Quality Monitoring Networks: A Case Study

Karoon River, from Gotvand Dam to Persian Gulf with more than 450 km in length and an annual discharge of 11,891 million cubic meters, is the largest river in Iran. Increasing water withdrawal from and wastewater discharge to the river has endangered the aquatic life of this important ecosystem. Furthermore, the drinking and in-stream water quality standards have been violated in many instances. In this paper, a river water quality monitoring network is designed, including determination of sampling frequencies as well as location of water quality monitoring stations. In this regard, two models are developed. The first model is a Genetic Algorithm-based optimization model and the second one is a combination of Kriging method and Analytical Hierarchy Process. The temporal variation of the concentration of water quality variables along Karoon and Dez Rivers are evaluated and the main water quality indicators are selected. Then, thirty five stations are selected and the application of Entropy Theory in calculating the sampling frequency is demonstrated. The results show the significant value of the proposed methodology in the design of monitoring network.     

A method for monitoring ground water quality near waste storage facilities

A practical optimization approach developed in this paper derives effective monitoring configurations for detecting contaminants in ground water. The approach integrates numerical simulation of contaminant transport and mathematical programming. Well sites identified by the methodology can be monitored to establish the occurrence of a contaminant release before a plume migrates to a regulatory compliance boundary. Monitoring sites are established along several horizons located between the downgradient margin of a contaminant source and a compliance boundary. A horizon can form an effective line of defense against contaminant migration to the compliance boundary if it is spanned (covered) by a sufficient number of sites to yield a well spacing that is equal to or less than a maximum value established by numerical modeling. The objective function of the integer programming model formulation expresses the goals of: (1) covering a maximum number of siting horizons, and (2) allocating wells to the single most effective horizon. The latter is determined from well spacing requirements and the width of the zone of potential contaminant migration traversed by the horizon. The methodology employs a highly tractable linear programming model formulation, and the user is not required to predefine a set of potential well sites. These attributes can facilitate its implementation in practice.     

Determination of arsenic content of some Romanian natural mineral groundwaters

Romania is one of the countries that have natural arsenic groundwater problems. This paper presents the results of a study of arsenic concentration monitoring in natural mineral waters collected from 23 sampling sites located in the northern, central, and western regions of Romania. The sampling sites are both natural springs and drilled wells. The graphite furnace atomic absorption spectrometry was used for arsenic content determination. The Piper??s classification principle was applied in order to find out the hydrochemical type of the analyzed waters. Depending on the concentration of arsenic, the water analyzed can be classified into three main categories: (1) mineral natural waters containing less than 10???g/L arsenic, (2) mineral natural waters containing arsenic at concentrations several times higher than the limit of 10???g/L but less than 100???g/L, and (3) mineral natural waters containing arsenic at concentrations of ten to a hundred times higher than the allowed limit of 10???g/L. The last-mentioned waters are of bicarbonatate sodium type and were sampled from seven sources only, being prohibited for human and animal use.     

Estimating sampling frequency in pollen exposure assessment over time

A time series model was fitted to the pollen concentration data collected in the Greater Cincinnati area for the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). A traditional time series analysis and temporal variogram approach were applied to the regularly spaced databases (collected in 2003) and irregularly spaced ones (collected in 2002), respectively. The aim was to evaluate the effect of the sampling frequency on the sampling precision in terms of inverse of standard error of the overall level of mean value across time. The presence of high autocorrelation in the data was confirmed and indicated some degree of temporal redundancy in the pollen concentration data. Therefore, it was suggested that sampling frequency could be reduced from once a day to once every several days without a major loss of sampling precision of the overall mean over time. Considering the trade-offs between sampling frequency and the possibility of sampling bias increasing with larger sampling interval, we recommend that the sampling interval should take values from 3 to 5 days for the pollen monitoring program, if the goal is to track the long-term average.     

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO₂ pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO₂ annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NO_x emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO₂ concentration is above the annual limit value.     

Multiscale Spatial and Small-Scale Temporal Variation in the Composition of Riverine Fish Communities

We studied the multiscale (sites, river reaches and rivers) and short-term temporal (monthly) variability in a freshwater fish assemblage. We found that small-scale spatial variation and short-term temporal variability significantly influenced fish community structure in the Macquarie and Namoi Rivers. However, larger scale spatial differences between rivers were the largest source of variation in the data. The interaction between temporal change and spatial variation in fish community structure, whilst statistically significant, was smaller than the variation between rivers. This suggests that although the fish communities within each river changed between sampling occasions, the underlying differences between rivers were maintained. In contrast, the strongest interaction between temporal and spatial effects occurred at the smallest spatial scale, at the level of individual sites. This means whilst the composition of the fish assemblage at a given site may fluctuate, the magnitude of these changes is unlikely to affect larger scale differences between reaches within rivers or between rivers. These results suggest that sampling at any time within a single season will be sufficient to show spatial differences that occur over large spatial scales, such as comparisons between rivers or between biogeographical regions.     

Fitting Data to Linear Combinations of Covariograms

We pursue a regression model for spatially-indexed data whose spatial correlation is determined by a linear combination of simple covariograms. The main interest lies in the estimation of the spatial parameters. As several common techniques appear ineffective for this setting, an algorithm is proposed to obtain parameter estimates and is assessed through simulation. It is found to provide greater stability than other methods of estimation. We discuss the influence of parametrization and site location on the efficacy of the estimation algorithms, and develop some guidelines as to the placement of sampling sites to improve the algorithm's performance. This revised version was published online in July 2006 with corrections to the Cover Date.     

污染场地土壤初步调查布点及采样方法探讨

结合我国污染场地的普遍特征以及场地初步调查的基本目标,从布点前期准备、布点原则、布点程序和布点方法等方面详细探讨了污染场地土壤初步调查的点位布设方法;并从采样准备、采集方法、采集程序以及现场记录等环节探讨了现场样品采集的注意事项和要求,以期为污染场地环境调查的土壤监测和采样提供借鉴。     

Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection

The goal of this paper is to provide a methodology for assessing the optimal localization of new monitoring stations within an existing rain gauge monitoring network. The methodology presented, which uses geostatistics and probabilistic techniques (simulated annealing) combined with GIS instruments, could be extremely useful in any area where an extension of whatever existing environmental monitoring network is planned. The methodology has been applied to the design of an extension to a rainfall monitoring network in the Apulia region (South Italy). The considered monitoring network is managed by the Apulian Regional Consortium for Crop Protection (ARCCP), and, currently consists of 45 gauging stations distributed over the regional territory, mainly located on the basis of administrative needs. Fifty new stations have been added to the existing monitoring network, split in two groups: 15 fixed and 35 mobile stations. Two different methods were applied and tested: the Minimization of the Mean of Shortest Distances method (MMSD) and Ordinary Kriging (OK) whose related objective function is estimation variance. The MMSD, being a purely geometric method, produced a spatially uniform configuration of the gauging stations. On the contrary, the approach based on the minimization of the average of the kriging estimation variances, produced a less regular configuration, though a more reliable one from a spatial standpoint. Nevertheless, the MMSD approach was chosen, since the ARCCP's intention was to create a new monitoring network characterized by uniform spatial distribution throughout the regional territory. This was the most important constraint given to the project by the ARCCP, whose main objective was to accomplish a territorial network capable of detecting hazardous events quickly. A seasonal aggregation of the available rainfall data was considered. The choice of the temporal aggregation in quarterly averages allowed four different optimal configurations to be determined per season. The overlapping of the four configurations allowed a number of new station locations, which tended to remain fixed season after season, to be identified. Other stations, instead, changed their coordinates considerably over the four seasons. Constraints were defined in order to avoid placing new monitoring locations either near existing stations, belonging to other Agencies, or near the coast line.