Value of Information in Design of Groundwater Quality Monitoring Network under Uncertainty

The increasing need for groundwater as a source for fresh water and the continuous deterioration in many places around the world of that precious source as a result of anthropogenic sources of pollution highlights the need for efficient groundwater resources management. To be efficient, groundwater resources management requires efficient access to reliable information that can be acquired through monitoring. Due to the limited resources to implement a monitoring program, a groundwater quality monitoring network design should identify what is an optimal network from the point of view of cost, the value of information collected, and the amount of uncertainty that will exist about the quality of groundwater. When considering the potential social impact of monitoring, the design of a network should involve all stakeholders including people who are consuming the groundwater. This research introduces a methodology for groundwater quality monitoring network design that utilizes state-of-the-art learning machines that have been developed from the general area of statistical learning theory. The methodology takes into account uncertainties in aquifer properties, pollution transport processes, and climate. To check the feasibility of the network design, the research introduces a methodology to estimate the value of information (VOI) provided by the network using a decision tree model. Finally, the research presents the results of a survey administered in the study area to determine whether the implementation of the monitoring network design could be supported. Applying these methodologies on the Eocene Aquifer, Palestine indicates that statistical learning machines can be most effectively used to design a groundwater quality monitoring network in real-life aquifers. On the other hand, VOI analysis indicates that for the value of monitoring to exceed the cost of monitoring, more work is needed to improve the accuracy of the network and to increase people’s awareness of the pollution problem and the available alternatives

On-The-Road Testing of the Effects of Driver’s Experience, Gender, Speed, and Road Grade on Car Emissions

On-road vehicles have become a dominant source of air pollution and energy consumption in many parts of the world. As a result, estimating the amount of pollution from these vehicles and analyzing the factors affecting their emission is necessary to understand and manage ambient air quality. Traditionally, automobile emissions have been measured with dynamometer tests using representative driving cycles. A review of the related literature shows that there is a lack of real life, on-the-road testing of automobile emissions. Moreover, a few previous studies have directly discussed the impact of driver variability on emissions from the vehicles. This research analyzes the impacts of driver experience, gender, speed, and road grade on vehicle emissions through on-the-road testing experiment in Logan, Utah, USA during summer of 2016. The methodology of the research starts by selecting a representative car to perform the tests on. The next step was to choose test drivers representing four groups: young males, young females, experienced males, and experienced females. After that, the drivers were assigned a specified route that has different speed limits and grades. Emissions from the car exhaust (specifically carbon monoxide-CO, hydrocarbons-HC, and nitrogen oxides-NOx) in addition to the engines rotational speed (rpm), car speed, and exhaust temperature, were measured every second while driving on the specified route. Statistical analysis of the results shows that contrary to the common stereotypes, experienced drivers emitted 52% more HC and 49% more NOx than young drivers and female drivers, and male drivers emitted 14% more HC and 44% more NOx than female drivers. It also shows that CO emission is not significantly dependent on age, gender, nor driving conditions. Finally, driving through low-speed segments emits significantly higher HC (79%), while driving through uphill segments emits significantly higher (98%) NOx than driving through downhill segment

A decision tree model to estimate the value of information provided by a groundwater quality monitoring network

Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current methemoglobinemia and bottled water costs of $150/person and$ 0.6/baby/day, the decision tree results show that the expected cost of establishing the proposed groundwater quality monitoring network exceeds the expected costs of the uninformed alternatives and there is no value to the information the monitoring system provides. However, the monitoring system will be preferred to ignoring the health risk or using alternative sources if the methemoglobinemia cost rises to $300/person or the bottled water cost increases to$ 2.3/baby/day. Similarly, the monitoring system has value if the system can more accurately report actual aquifer concentrations and the public more fully abides by manager recommendations to use/not use the aquifer. The system also has value if it will serve a larger population or if its installation costs can be reduced, for example using a smaller number of monitoring wells. The VOI analysis shows how monitoring system design, accuracy, installation and operating costs, public awareness of health risks, costs of alternatives, and demographics together affect the value of implementing a system to monitor groundwater quality

Value of information analysis for groundwater quality monitoring network design Case study: Eocene Aquifer, Palestine

Value of information (VOI) analysis evaluates the benefit of collecting additional information to reduce or eliminate uncertainty in a specific decision-making context. It makes explicit any expected potential losses from errors in decision making due to uncertainty and identifies the “best” information collection strategy as one that leads to the greatest expected net benefit to the decision-maker. This study investigates the willingness to pay for groundwater quality monitoring in the Eocene Aquifer, Palestine, which is an unconfined aquifer located in the northern part of the West Bank. The aquifer is being used by 128,000 Palestinians to fulfill domestic and agricultural demands. The study takes into account the consequences of pollution and the options the decision maker might face. Since nitrate is the major pollutant in the aquifer, the consequences of nitrate pollution were analyzed, which mainly consists of the possibility of methemoglobinemia (blue baby syndrome). In this case, the value of monitoring was compared to the costs of treating for methemoglobinemia or the costs of other options like water treatment, using bottled water or importing water from outside the aquifer. And finally, an optimal monitoring network that takes into account the uncertainties in recharge (climate), aquifer properties (hydraulic conductivity), pollutant chemical reaction (decay factor), and the value of monitoring is designed by utilizing a sparse Bayesian modeling algorithm called a relevance vector machine

Use of a Relevance Vector Machine for Groundwater Quality Monitoring Network Design Under Uncertainty

This paper presents a methodology for groundwater quality monitoring network design. This design takes into account uncertainties in aquifer properties, pollution transport processes, and climate. The methodology utilizes a statistical learning algorithm called relevance vector machines (RVM), which is a sparse Bayesian framework that can be used for obtaining solutions to regression and classification tasks. Application of the methodology is illustrated using the Eocene Aquifer in the northern part of the West Bank, Palestine. The procedure presented in this paper utilizes a Monte Carlo (MC) simulation process to capture the uncertainties in recharge, hydraulic conductivity, and nitrate reaction processes through the application of a groundwater flow model and a nitrate fate and transport model. This MC modeling approach provides several thousand realizations of nitrate distribution in the aquifer. Subsets of these realizations are then used to design the monitoring network. This is done by building a best-fit model of nitrate concentration distribution everywhere in the aquifer for each Monte Carlo subset using RVM. The outputs from the RVM model are the distribution of nitrate concentration everywhere in the aquifer, the uncertainty in the characterization of those concentrations, and the number and locations of “relevance vectors” (RVs). The RVs form the basis of the optimal characterization of nitrate throughout the aquifer and represent the optimal locations of monitoring wells. In this paper, the number of monitoring wells and their locations where chosen based on the performance of the RVM model runs. The results from 100 model runs show the consistency of the model in selecting the number and locations of RV‘s. After implementing the design, the data collected from the monitoring sites can be used to estimate nitrate concentration distribution throughout the entire aquifer and to quantify the uncertainty in those estimates

Birzeit University Students’ Perception of Bottled Water Available in the West Bank Market

Water bottling industry has negative environmental impacts due to exploitation and possible pollution of water resources and due to solid waste problems related to the use of plastic bottles. To mitigate these impacts, it is important to study the link between consuming bottled drinking water and the perception of its quality. The objective of the study is to assess the perception of Birzeit University students’ of the bottled water marketed in the West Bank and its impact on the humans and the environment. Universities play an important role in providing awareness about environmental issues and sustainability, and university students are thought to be more environmentally conscious about these issues. A quantitative survey was used to analyze the behaviors and perceptions of Birzeit University students. The sample size was 375 students, distributed according to the college, gender, and the academic year at the university. The results show that the factors that affect the perception of the students are mainly the educational year at the university, the income, the family size, and the community type