Development of a decision-making methodology to design a water quality monitoring network.

The number of water quality monitoring stations in the USA has decreased over the past few decades. Scarcity of observations can easily produce prediction uncertainty due to unreliable model calibration. An effective water quality monitoring network is important not only for model calibration and water quality prediction but also for resources management. Redundant or improperly located monitoring stations may cause increased monitoring costs without improvement to the understanding of water quality in watersheds. In this work, a decision-making methodology is proposed to design a water quality monitoring network by providing an adequate number of monitoring stations and their approximate locations at the eight-digit hydrologic unit codes (HUC8) scale. The proposed methodology is demonstrated for an example at the Upper Colorado River Basin (UCRB), where salinity is a serious concern. The level of monitoring redundancy or scarcity is defined by an index, station ratio (SR), which represents a monitoring density based on water quality load originated within a subbasin. By comparing the number of stations from a selected target SR with the available number of stations including the actual and the potential stations, the suggested number of stations in each subbasin was decided. If monitoring stations are primarily located in the low salinity loading subbasins, the average actual SR tends to increase, and vice versa. Results indicate that the spatial distribution of monitoring locations in 2011 is concentrated on low salinity loading subbasins, and therefore, additional monitoring is required for the high salinity loading subbasins. The proposed methodology shows that the SR is a simple and a practical indicator for monitoring density.

Groundwater stress and vulnerability in rural coastal aquifers under competing demands: a case study from Sri Lanka.

Rural coastal aquifers are undergoing rapid changes due to increasing population, high water demand with expanding agricultural and domestic uses, and seawater intrusion due to unmanaged water pumping. The combined impact of these activities is the deterioration of groundwater quality, public health concerns, and unsustainable water demands. The Kalpitiya peninsula located northwest of Sri Lanka is one area undergoing such changes. This land area is limited and surrounded almost completely by sea and lagoon. This study consists of groundwater sampling and analysis, and vulnerability assessment using the DRASTIC method. The results reveal that the peninsula is experiencing multiple threats due to population growth, seawater intrusion, land use exploitation for intensive agriculture, groundwater vulnerability from agricultural and domestic uses, and potential public health impacts. Results show that nitrate is a prevalent and serious contaminant occurring in large concentrations (up to 128 mg/l NO(3)-N), while salinity from seawater intrusion produces high chloride content (up to 471 mg/l), affecting freshwater sources. High nitrate levels may have already affected public health based on limited sampling for methemoglobin. The two main sources of nitrogen loadings in the area are fertilizer and human excreta. The major source of nitrogen results from the use of fertilizers and poor management of intense agricultural systems where a maximum application rate of up to 11.21 metric tons N/km(2) per season is typical. These findings suggest that management of coastal aquifers requires an integrated approach to address both the prevalence of agriculture as an economic livelihood, and increasing population growth.

Sustainability of ground water quality considering land use changes and public health risks.

One of the major environmental issues of concern to policy-makers is the increased vulnerability of ground water quality (GWQ). Another issue of equal interest is the sustainability of natural resources for future generations. To understand the sustainability of the natural resources such as water in general, one needs to understand the impact of future land use changes on the natural resources. This work proposes a methodology to address sustainability of GWQ considering land use changes, aquifer vulnerability to multiple contaminants, and public health risks. The methodology was demonstrated for the Sumas-Blaine aquifer in Washington State. The land transformation model predicted that nearly 60 percent of the land use practices would change in the Sumas-Blaine Aquifer by the year 2015. The accuracy of the LTM model predictions increased to greater levels as the spatial resolution was decreased. Aquifer vulnerability analysis was performed for major contaminants using the binary logistic regression (LR) method. The LR model, along with the predicted future land use, was used to estimate the future GWQ using two indices-carcinogenic and non-carcinogenic ground water qualities. Sustainability of GWQ was then analyzed using the concept of 'strong' sustainability. The sustainability map of GWQ showed improvements in many areas where urbanization is expected to occur. The positive impact of urbanization on GWQ is an indication of the extensive damage caused by existing agricultural activities in the study area.

Aquifer vulnerability assessment to heavy metals using ordinal logistic regression.

A methodology using ordinal logistic regression is proposed to predict the probability of occurrence of heavy metals in ground water. The predicted probabilities are defined with reference to the background concentration and the maximum contaminant level. The model is able to predict the occurrence due to different influencing variables such as the land use, soil hydrologic group (SHG), and surface elevation. The methodology was applied to the Sumas-Blaine Aquifer located in Washington State to predict the occurrence of five heavy metals. The influencing variables considered were (1) SHG; (2) land use; (3) elevation; (4) clay content; (5) hydraulic conductivity; and (6) well depth. The predicted probabilities were in agreement with the observed probabilities under existing conditions. The results showed that aquifer vulnerability to each heavy metal was related to different sets of influencing variables. However, all heavy metals had a strong influence from land use and SHG. The model results also provided good insight into the influence of various hydrogeochemical factors and land uses on the presence of each heavy metal. A simple economic analysis was proposed and demonstrated to evaluate the cost effects of changing the land use on heavy metal occurrence.

Multi-criteria decision analysis for the optimal management of nitrate contamination of aquifers.

We present an integrated methodology for the optimal management of nitrate contamination of ground water combining environmental assessment and economic cost evaluation through multi-criteria decision analysis. The proposed methodology incorporates an integrated physical modeling framework accounting for on-ground nitrogen loading and losses, soil nitrogen dynamics, and fate and transport of nitrate in ground water to compute the sustainable on-ground nitrogen loading such that the maximum contaminant level is not violated. A number of protection alternatives to stipulate the predicted sustainable on-ground nitrogen loading are evaluated using the decision analysis that employs the importance order of criteria approach for ranking and selection of the protection alternatives. The methodology was successfully demonstrated for the Sumas-Blaine aquifer in Washington State. The results showed the importance of using this integrated approach which predicts the sustainable on-ground nitrogen loadings and provides an insight into the economic consequences generated in satisfying the environmental constraints. The results also show that the proposed decision analysis framework, within certain limitations, is effective when selecting alternatives with competing demands.

Applicability of risk-based management and the need for risk-based economic decision analysis at hazardous waste contaminated sites.

Decision analysis in subsurface contamination management is generally carried out through a traditional engineering economic viewpoint. However, new advances in human health risk assessment, namely, the probabilistic risk assessment, and the growing awareness of the importance of soft data in the decision-making process, require decision analysis methodologies that are capable of accommodating non-technical and politically biased qualitative information. In this work, we discuss the major limitations of the currently practiced decision analysis framework, which evolves around the definition of risk and cost of risk, and its poor ability to communicate risk-related information. A demonstration using a numerical example was conducted to provide insight on these limitations of the current decision analysis framework. The results from this simple ground water contamination and remediation scenario were identical to those obtained from studies carried out on existing Superfund sites, which suggests serious flaws in the current risk management framework. In order to provide a perspective on how these limitations may be avoided in future formulation of the management framework, more matured and well-accepted approaches to decision analysis in dam safety and the utility industry, where public health and public investment are of great concern, are presented and their applicability in subsurface remediation management is discussed. Finally, in light of the success of the application of risk-based decision analysis in dam safety and the utility industry, potential options for decision analysis in subsurface contamination management are discussed.

Risk assessment at hazardous waste-contaminated sites with variability of population characteristics.

Risk assessment is considered to be an effective scientific tool which enables decisionmakers to manage hazardous waste-contaminated sites in a cost-effective manner while preserving public health. However, the current risk assessment framework proposed by the US Environmental Protection Agency (US EPA) has limitations in addressing the true variability of population characteristics. This study proposed a methodology that is different from the existing framework by accounting for the true variability of population characteristics. The key differences of the proposed methodology and the existing framework are the (1) use of the transient exposure concentration; (2) use of the entire population rather than a representative ideal individual; (3) use of age- and gender-based population subgroups to represent population characteristics; (4) use of a population growth model to represent growth dynamics; and (5) presentation of risk through a risk profile with risk summarized through a single indicator, potential cancer incidences (PCI). The proposed methodology was applied in a ground water contamination scenario due to benzene to determine its applicability. The results of the study showed that age-based variability of population characteristics is important in predicting the population risk while gender played a small role. The existing US EPA methodology and its variation using age-independent variability of population characteristics overestimate the risk given by PCI substantially, and therefore, the decisions can lead to costly cleanup goals. Population risk is not a single value but a distribution due to the contribution from ditferent individuals of the exposed population. Hence, the decision criterion proposed in this study, PCI, is found to be a useful indicator to describe population carcinogenic risk to the society under a variety of conditions and scenarios.