Monitoring Network Design Research Articles

Regional Modelling to Inform the Design of Sub-Sea CO2 Storage Monitoring Networks

A 3D hydrodynamic model (FVCOM) coupled to a carbonate system (ERSEM) has been used to model a number of seabed CO2 release scenarios ranging from 3 to 3000 t d -1 for the Goldeneye complex in the northern North Sea. The results of the scenario runs were used to characterise the fate of CO2 in the water column in space and time. A new approach to designing monitoring networks has been implemented and compared with a simple approach. A weighted greedy set algorithm is used to identify the positions within the model domain which yield the greatest combined coverage for the smallest number of sampling stations, further limited by selecting only a feasible number of sample sites. The weighted greedy set algorithm incorporates the effect of the unstructured grid in FVCOM as well as the proximity of the candidate sample locations to the Goldeneye complex. For the range of release rates simulated, the design of the optimal sampling strategy changes depending on the magnitude of the release. The role of the tides discriminates the four release scenarios into two categories: for the lower release rates (3 and 30 t/d), the effect of the tide is relatively unimportant in the distribution; for the larger release rates (300 and 3000 t/d), the direction of the principal tidal axis controls the distribution of the sampling stations more strongly. Comparison of the weighted greedy set approach shows it is able to identify releases sooner and with a stronger signal than a simple regular sampling approach.

Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations

The optimized design of water quality monitoring networks can not only minimize the pollution detection time and maximize the detection probability for river systems but also reduce redundant monitoring locations. In addition, it can save investments and costs for building and operating monitoring systems as well as satisfy management requirements. This paper aims to use the beneficial features of multi-objective discrete particle swarm optimization (MODPSO) to optimize the design of water quality monitoring networks. Four optimization objectives: minimum pollution detection time, maximum pollution detection probability, maximum centrality of monitoring locations and reservation of particular monitoring locations, are proposed. To guide the convergence process and keep reserved monitoring locations in the Pareto frontier, we use a binary matrix to denote reserved monitoring locations and develop a new particle initialization procedure as well as discrete functions for updating particle’s velocity and position. The storm water management model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We define three pollution detection thresholds and simulate pollution events respectively to obtain all the pollution detection time for all the potential monitoring locations when a pollution event occurs randomly at any potential monitoring locations. Compared to the results of an enumeration search method, we confirm that our algorithm could obtain the Pareto frontier of optimized monitoring network design, and the reserved monitoring locations are included to satisfy the management requirements. This paper makes fundamental advancements of MODPSO and enables it to optimize the design of water quality monitoring networks with reserved monitoring locations.

Groundwater quality monitoring network design and optimisation based on measured contaminant concentration and taking solute transit time into account

Groundwater quality monitoring networks are designed to give the best possible overview of the quality status and trend of aquifers from a limited number of measurement stations. Ideally, the stations of the network should (i) allow the detection of significant changes in water quality, either improvements due to successful mitigation measures, or deteriorations caused for instance by land use change or other increasing pressure and (ii) approximate closely the true unknown mean concentration of the considered aquifer. If these two features are met, the monitoring network is representative of the entire aquifer. In this article, we present a method with which to assess and improve the representativeness of a regional groundwater monitoring network. The method is designed specifically to optimize the selection of monitoring stations from a larger choice of fixed locations such as springs or existing observation wells using one contaminant for the optimisation procedure. Firstly, the empirical distribution of selected solutes (contaminants and age tracers) is calculated from a large dataset consisting of as many sampling points as possible and compared to the distributions obtained solely from the stations of the existing or planned monitoring network. If differences are large, the network is modified by adding new stations or replacing some of them, using one contaminant for the optimisation. Lastly, the distribution of the optimised network is compared to the total distribution for other contaminants and one groundwater age tracer.The use of the method is illustrated for the main aquifer of the country of Luxembourg, where it is shown that the representativeness of the current monitoring network can be improved by replacing two stations. In the case study, the optimisation was based on desethyl atrazine, a ubiquitous pesticide transformation compound which was assumed to be an indicator of diffuse agricultural pollution. Although the largest improvement was obviously obtained for the optimised compound, improvement was also observed for other contaminants, including nitrate, probably showing that at the regional scale, the pattern of diffuse pollution of widely used agrochemicals is similar between compounds. The temporal evolution of the major contaminants could also be reproduced by the optimised network, even though the range of tritium values used as age tracer was narrower for the modified network than for the original network, thus indicating some bias towards faster reaction times.

Land use regression study in Lanzhou, China: A pilot sampling and spatial characteristics of pilot sampling sites

BackgroundLand use regression (LUR) has been widely used to estimate air pollution exposure in recent epidemiology studies. However, few LUR studies were conducted in China, and even fewer used purposefully designed monitoring networks. The objectives of this study are to obtain preliminary understanding of fine-scale air pollution distributions, and to provide a foundation for a future extended study in Lanzhou, China, a major industrial city. MethodsA pilot monitoring network was designed using stratified-random sampling, and purposeful selection in gaps of spatial predictor distributions. Based on this network, NO2 were measured using Palmes tubes for 2 weeks in summer 2015, which were used to develop a pilot LUR model considering spatial information of traffic and population densities, elevation, land cover, and land use. We developed linear regression, kriging models, including ordinary kriging, universal kriging, and compared them using AIC. ResultsThe sampling sites of the pilot monitoring network represented wide ranges of spatial predictors (N = 47). The pilot LUR model explained 71% of the variance in the measured NO2 at the sampling sites. The spatial predictors in the model included road densities, elevation, and district indicator. Predicted NO2 concentrations were higher in the east of the city, which is more developed and has dense road networks. Linear regression model performed better than the kriging models due to the lowest AIC. ConclusionsThis study developed a pilot monitoring network that can effectively capture variability in spatial characteristics and developed a robust LUR model capturing small-scale spatial variations of air pollution in an understudied area. The predicted and measured NO2 showed substantial spatial heterogeneity that was not captured by the limited government monitors. A future study with extended monitoring network and measurements from more seasons is needed to fully understand the distribution of air pollution in Lanzhou, China.

Search Space Representation and Reduction Methods to Enhance Multiobjective Water Supply Monitoring Design

AbstractOptimal design of groundwater monitoring networks is challenging due to (1) conflicting objectives for assessing the performance of candidate monitoring networks, (2) uncertainty in system dynamics and hydrogeological context, and (3) the large decision space of possible monitoring‐well positions (also termed the search space). The immensity of the search space poses a significant challenge for modern multiobjective optimization tools. This study introduces two approaches that improve the efficiency and effectiveness of evolutionary multiobjective optimization tools when solving monitoring design problems. We show how a careful mathematical representation of the monitoring design search space and reductions of possible monitoring‐well positions enhance the solution and attainment of decision‐relevant multiobjective trade‐offs in monitoring quality. We demonstrate the value of our improved representation and reduction techniques on a three‐objective monitoring network design problem focused on urban source water protection (termed the U_Protect benchmarking problem). U_Protect abstracts a real‐world case study within an urban drinking‐water well catchment, including inaccessible and restricted areas for monitoring‐well installation, and random heterogeneities in the conductivity field. Our representation and reduction methods significantly enhance the effectiveness, efficiency, and reliability of the optimization. Our proposed framework shifts focus to the most impactful monitoring design decisions while also enhancing decision makers understanding of key performance trade‐offs. In combination, our proposed representation and reduction techniques have significant promise for enhancing the size and the scope of combinatorial monitoring problems that can be explored.

Feasibility study to optimize a near-surface sensor network design for improving detectability of CO2 leakage at a geologic storage site

We explored the feasibility of an improved design of a CO2 leakage monitoring network at a geologic storage site. To effectively represent CO2 pathways, a rule-based percolation model was adopted rather than the rigorous multiphase flow model based on Darcy’s equation. Five different monitoring network designs were compared using a few scenarios and multiple correlated random field realizations. The ensemble results showed that given the detection uncertainty across the entire sensor network, regular spacing deployment of sensors is the most effective method when a sufficient number of sensors is available. The results also showed that an aggressive monitoring design based on information of the spatial permeability distribution near the surface can be a viable method when a limited number of sensors is employed. However, such an aggressive design can lead to elevated uncertainty in leakage detectability.

Optimising water quality monitoring network design for bidirectional river systems

Optimising water quality monitoring network design for bidirectional river systems

Influence of available data on the geostatistical-based design of optimal spatiotemporal groundwater-level-monitoring networks

Several methodologies exist for the design of optimal groundwater monitoring networks. Those that employ geostatistical techniques are usually dependent on a variogram. In practice, different variograms for an environmental variable can be fitted depending on the number of available data, their spatial distribution, and their variability. For this reason, in most cases, it is difficult to get an adequate variogram. This report evaluates the influence of the groundwater level data available in the construction of a product-sum spatiotemporal variogram model and its consequences on a geostatistical-based methodology that uses the Kalman filter for the design of optimal spatiotemporal monitoring networks. Additionally, a sensitivity analysis is carried out to determine the variations in the optimal number of wells and monitoring frequency for the monitoring network, depending on the selected spatiotemporal variogram model. The results show the importance of getting a good quality spatiotemporal variogram when designing optimal groundwater-level monitoring networks using geostatistical-based methodologies.

Adaptive Management of Coastal Aquifers Using Entropy-Set Pair Analysis–Based Three-Dimensional Sequential Monitoring Network Design

A three-dimensional compliance monitoring network design methodology is presented to develop an adaptive and sequentially modified management policy that intends to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble metamodel-based multiobjective prescriptive model is developed using a coupled simulation-optimization approach to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of metamodels is addressed by utilizing a weighted average ensemble using set pair analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed three-dimensional sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy is possible by using saltwater concentrations measured at the proposed optimal compliance monitoring locations. Therefore, the salinity concentration data collected at the designed compliance monitoring wells can be used to collect feedback information in terms of salinity concentrations. This feedback information can be applied to improve the initially prescribed optimal groundwater extraction patterns while keeping the original management goal intact.

Protecting and restoring Europe's waters: An analysis of the future development needs of the Water Framework Directive

The Water Framework Directive (WFD) is a pioneering piece of legislation that aims to protect and enhance aquatic ecosystems and promote sustainable water use across Europe. There is growing concern that the objective of good status, or higher, in all EU waters by 2027 is a long way from being achieved in many countries. Through questionnaire analysis of almost 100 experts, we provide recommendations to enhance WFD monitoring and assessment systems, improve programmes of measures and further integrate with other sectoral policies. Our analysis highlights that there is great potential to enhance assessment schemes through strategic design of monitoring networks and innovation, such as earth observation. New diagnostic tools that use existing WFD monitoring data, but incorporate novel statistical and trait-based approaches could be used more widely to diagnose the cause of deterioration under conditions of multiple pressures and deliver a hierarchy of solutions for more evidence-driven decisions in river basin management. There is also a growing recognition that measures undertaken in river basin management should deliver multiple benefits across sectors, such as reduced flood risk, and there needs to be robust demonstration studies that evaluate these. Continued efforts in ‘mainstreaming’ water policy into other policy sectors is clearly needed to deliver wider success with WFD goals, particularly with agricultural policy. Other key policy areas where a need for stronger integration with water policy was recognised included urban planning (waste water treatment), flooding, climate and energy (hydropower). Having a deadline for attaining the policy objective of good status is important, but even more essential is to have a permanent framework for river basin management that addresses the delays in implementation of measures. This requires a long-term perspective, far beyond the current deadline of 2027.

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Rain gauge networks are crucial for enhancing the spatio‐temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16‐year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi‐objective monitoring networks.

Development of an Entropy Method for Groundwater Quality Monitoring Network Design

A new approach is presented based on the entropy theory to design an optimal groundwater quality monitoring network. First, the unrecorded values of Total Dissolved Solids (TDS) are estimated in the study area using Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) methods, and then, using Differential Evolution (DE) optimization algorithm aimed at maximizing the pure information gained by stations. The number and location of monitoring stations are selected optimally from the active wells located in the study area. The spatial distribution of chosen wells provides adequate coverage of the whole aquifer and, at the same time, gives the maximum useful information about the water quality status. It also avoids selecting any redundant station considering the operational and temporal costs. The proposed entropy method is compared with “error minimization” and “K-Means clustering” methods and is shown to be superior. The proposed approach is used for optimizing Eshtehard aquifer monitoring network in central part of Iran. As a result of its implementation, 20 wells are selected among 79 active monitoring wells in the study area, so that by sampling the wells, the maximum qualitative information can be obtained without needing to sample all the wells.

Sensitivity of contaminant travel time to the combined effect of porosity and hydraulic conductivity

An approach is developed to analyze sensitivity of advective contaminant travel time to porosity and hydraulic conductivity and their dependence. The approach is applicable in situations when groundwater specific discharge can be calculated from an available groundwater flow model that has already been developed. The groundwater specific discharge results allow estimate of advective contaminant travel time together with porosity values for all grid cells in the groundwater flow model. This is particularly useful when a full contaminant transport model is not available. In this study, a sensitivity index is developed based on the covariance between the advective contaminant travel time and grid cell porosity and its relationship with hydraulic conductivity. The spatially variable sensitivity index provides a quantitative measure of the combined effect of porosity and hydraulic conductivity on the sensitivity of advective contaminant travel time. The index can be used to identify the most influential region to reduce uncertainty of predicting advective contaminant travel time, which in turn helps to facilitate field characterization to reduce uncertainty and to aid the design of monitoring networks. Three theoretical examples are presented and discussed to illustrate the approach. However, the approach can be easily applied to more complex aquifer domains where advective contaminant travel time can be calculated based on simulated flow results from available groundwater flow models.

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

Use of Low-Cost Air Pollution Sensors in the Adult Changes in Thought Air Pollution Study

Incorporating modern air pollution sensor technology into epidemiologic cohort studies is appealing because of its low cost, allowing much better spatial representation of pollution exposure. Exposure sampling design can also leverage emerging understanding from measurement error correction methods that the monitoring network design should be spatially compatible with the locations where the health study participants live. However, research has not yet been conducted to determine whether these low-cost sensor data are reliable, accurate, and consistent enough to use for epidemiological study exposure assessment. Our scientific goal is to estimate individual-level long-term average exposures to PM2.5 and oxides of nitrogen for inference about the effects of air pollution on brain health. To accomplish this we are developing new spatio-temporal pollution predictions based on recently collected low-cost sensor data combined with existing ambient monitoring data. I will discuss the necessary quality control criteria for using the low-cost sensor data in a spatio-temporal prediction model, including calibration of these measurements to federal reference measurements. I will present the improvement in the performance of the spatio-temporal predictions from adding the low-cost sensor data and discuss the implications of these results for improving inference from epidemiologic cohort studies such as the Adult Changes in Thought Air Pollution study.

Kriging for NSRDB PSM version 3 satellite-derived solar irradiance

The National Solar Radiation Database (NSRDB) has provided solar resource data for over 25 years. Its most recent update, namely, physical solar model (PSM) version 3, generates half-hourly, gridded, satellite-derived irradiance data with a spatial resolution of 4 km × 4 km, covering most of America. The total volume of the data is over 40 TB. Since the main method to access the data is using API with a daily limit of 2000 requests, it would require 1000 days to obtain one year of PSM data from approximately 2 million pixels. Furthermore, such a big dataset is difficult to store and manipulate. In this regard, this paper empirically investigates the accuracies of various kriging methods, so that a suitable, dimension-reduced (in space) dataset can be opted during various spatio-temporal analyses, such as forecasting or monitoring network design.

Efficiency of species survey networks can be improved by integrating different monitoring approaches in a spatial prioritization design

AbstractPublic participation to monitoring programs is increasingly advocated to overcome scarcity of resources and deliver important information for policy‐making. Here, we illustrate the design of optimal monitoring networks for bird species of conservation concern in Catalonia (NE Spain), under different scenarios of combined governmental and citizen‐science monitoring approaches. In our case study, current government efforts, limited to protected areas, were insufficient to cover the whole spectrum of target species and species‐threat levels, reinforcing the assumption that citizen‐science data can greatly assist in achieving monitoring targets. However, simply carrying out both government and citizen‐science monitoringad hocled to inefficiency and duplication of efforts: some species were represented in excess of targets while several features were undersampled. Policy‐making should concentrate on providing an adequate platform for coordination of government and public‐participatory monitoring to minimize duplicated efforts, overcome the biases of each monitoring program and obtain the best from both.

Pollution Plume Development in the Primary Aquifer at the Atlantis Historical Solid Waste Disposal Site, South Africa

The monitoring of pollution plumes from municipal landfills is essential in order to control and, where necessary, remediate aquifer contamination. The Atlantis historical landfill was established in 1975 and was unlined as it preceded the promulgation of the Minimum Requirements by the Department of Water and Sanitation. As the underlying, unconfined sandy aquifer serves as a water supply source to the town of Atlantis, regular quarterly hydrochemical monitoring was carried out from 1989 to 1997, at irregular intervals until 2003, and resumed in 2015 when new, deep boreholes were drilled. Groundwater monitoring over nearly three decades provided valuable information on the nature of the chemical reactions that take place in the subsurface and the extent of transport of chemical constituents. Ammonium and organic carbon, which are subject to redox reactions, were lagging compared to chloride and sodium, which are transported advectively. The most recent data indicated the plume consisted mainly of salinity (electrical conductivity (EC) > 200 mS m−1) in the form of sodium, calcium, chloride and bicarbonate ions 350–400 m down-gradient of the landfill, and it is still expanding at a maximum rate of about 25 m a−1, with local deviations from the regional flow pattern. It also became evident that the plume migrated to greater depth as it was transported further from the waste pile. The breakthrough of contaminants being observed at different depths highlights the importance of suitably designed monitoring networks.

The use of hierarchical clustering for the design of optimized monitoring networks

Abstract. Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity and can be used to assess the efficacy and design of air quality monitoring networks. We describe here our use of Kolmogorov–Zurbenko filtering and hierarchical clustering of NO2 and SO2 passive and continuous monitoring data to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1−R, R being the Pearson correlation coefficient, and the Euclidean distance; we find that both should be used in evaluating monitoring site similarity. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We demonstrate that clustering results depend on the air contaminant analysed, reflecting the difference in the respective emission sources of SO2 and NO2 in the region under study. Our work shows that much of the signal identifying the sources of NO2 and SO2 emissions resides in shorter timescales (hourly to daily) due to short-term variation of concentrations and that longer-term averages in data collection may lose the information needed to identify local sources. However, the methodology identifies stations mainly influenced by seasonality, if larger timescales (weekly to monthly) are considered. We have performed the first dissimilarity analysis based on gridded air quality model output and have shown that the methodology is capable of generating maps of subregions within which a single station will represent the entire subregion, to a given level of dissimilarity. We have also shown that our approach is capable of identifying different sampling methodologies as well as outliers (stations' time series which are markedly different from all others in a given dataset).

Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.