Sustainable Development and Future Trends in Desalination Technology

INTRODUCTION

The high cost of energy is one of the main challenges facing the economic sustainability of desalination plants. Energy generation is also associated with air pollution and environmental degradation (Fletcher et al., 2019; Margulis et al., 2010). Furthermore, the scientific community and much of the public, now recognize that climate change is likewise a major factor in sustainable economic development. This change is not only influenced by human activity but it is also accelerating, thus becoming a foremost hazard to the world's economic growth and to environmental stability (Cox et ah, 2019). Consequently, water resource planners must consider that recent climate history may not be an adequate predictor of the future. Fluctuating climate necessitates the need for us to understand how these changes occur to be able to plan appropriately. Adaptive infrastructure planning, for example, may be employed to help address the problems resulting from climate change uncertainty. Effective planning models play a critical role in targeting resources, thus saving financial resources (Fletcher et ah, 2019). A United Nations report estimated that the cost of climate change adaptation investments in the developing world may reach S500 billion per year by 2050. It is therefore essential to target infrastructure investments efficiently to reach the widest number of vulnerable communities (Puig et al., 2016).

Pramanik et al. (2017) reported that hypersaline brines are of growing environmental concern. Prominent examples of such high-salinity brines include water produced from the oil and gas industry, waste streams of minimum/zero liquid discharge operations, inland desalination concentrate, landfill leachate and flue gas desulfurization wastewater. Very high total dissolved solids (TDS) >60,000 ppm pose considerable technical challenges in treatment. While reverse osmosis (RO) is the most energy-efficient and cost-effective technique for desalinating seawater, exceedingly high operating pressures are needed to overcome the osmotic pressure of hypersaline brines, which prohibits the application of RO.

The influence of continuous discharge of high-salinity brine from desalination plants, both membrane and thermal, into coastal environments are also a matter of grave public concern (Petersen et al., 2019). The results of Petersen et al. (2019) indicated that to minimize environmental impacts, discharge should target waters where a long history of human activity has already compromised the natural setting. Aside from concerns over brine discharge, effective metering of potable water also plays a role in the sustainable management of drinking water distribution systems (Maiolo et al., 2019). In this case, information is required on the operating status of system components to identify the best operational management measures. Fantini et al. (2016) found that smart metering networks in the energy sector allow operators and companies to improve production efficiency and offer customers enhanced service.

The aim of this chapter is to provide a critical review and analysis of sustainable developments and future trends as they relate to desalination technology. Emphasis is placed on infrastructure planning and management, the problem of brine discharge, energy efficiency and costs, membrane desalination and applications of renewable energy technology.

FLEXIBLE INFRASTRUCTURE PLANNING AND SUSTAINABLE DEVELOPMENT

Uncertainty in climate change projections poses a challenge to infrastructure planning, owing to the high cost of investments for any future developments such as increased desalination capacity (Fletcher et al., 2019; Margulis et al., 2010). Effective planning models thus play a critical role in targeting capital, consequently saving financial resources. Getting ready for climate change by adding extra desalination capacity, for example, incurs a high risk of expensive overbuilding in resource-scarce areas. However, enabling flexibility often requires substantial proactive planning or upfront investment. In the case of water resources, it is difficult for planners to know if and when to take action. Problems also arise if a water resources infrastructure, such as a water distribution network, cannot be adapted quickly. Fletcher et al. (2019) argued that techniques or models are needed to weigh the risks and benefits of static, as opposed to flexible, infrastructure approaches in responding to climate change uncertainty.

A technique called adaptive infrastructure planning may be employed to help address the problems resulting from climate change uncertainty. For instance, robust decision making uses iterative scenario development to minimize remorse from both overbuilding excessive infrastructure and being unprepared (Lempert et al., 2006). Furthermore, adaptive management requires ability to learn over time as more information is collected (Pahl-Wostl, 2007). For example, suppose current regional projections estimate a range between 0.5 and 1.5°C of change over the next 20 years. If after two decades, a 1.5°C of change is observed, then it can be argued that the climate is warming in this region more rapidly than expected. The temperature projections can then be shifted upward for the subsequent two decades. Fletcher et al. (2019) reported on a planning framework that explicitly models the potential to learn about climate uncertainty over time. They used probable learning to develop and evaluate flexible planning strategies in comparison to static approaches. A comprehensive set of virtual climate observations were developed that reflected many possible future regional climates, some of which were drier and some of which were wetter. Updated estimates reflected what would be learned if the virtual observation came to pass. Fletcher et al. (2019) employed this framework model to evaluate flexible infrastructure planning approaches and compared them to static approaches.

The 2016 Adaptation Finance Gap Report from the United Nations Environment Program evaluated the expenses of meeting adaptation requirements and assessed the funding that was available for doing so (Puig et al., 2016). The report suggested that although international public funding for adaptation has increased in recent years, previous assessments of the costs of adaptation have been significantly underestimated. This left a gap, the adaptation finance gap, which needs to be filled if societies (i.e., nations) are to meet the goals of the Paris Agreement. The United Nations report estimated that the cost of climate change adaptation investments in the developing world may reach $500 billion per year by 2050. It is therefore essential to target infrastructure investments efficiently so that they reach the widest number of susceptible people.

Flexible planning strategies can substantially reduce the cost of infrastructure investments. Fletcher et al. (2019) claimed that theirs was the first framework that values the ability of flexible approaches to respond to climate learning. Their results showed that climate change uncertainty can be reduced over the lifetime of an infrastructure project across different climate change trajectories. Flexibility was effective in preventing unnecessary infrastructure additions while maintaining reliability.

In a related study by Margulis et al. (2010) for the World Bank, the authors concluded that as developing countries weigh how best to revitalize their economies using a sustainable development path, they will have to factor in the reality that the global annual average temperature is expected to be 2°C above preindustrial levels by 2050. A 2°C warmer world will experience more intense rainfall, as well as more frequent and more intense droughts, floods, heat waves and other extreme weather events. This will have a significant effect on how nations manage their economies, care for their people, and design their development paths. It was argued that countries will need to adopt measures to adapt to climate change, similar to what was recommended by Fletcher et al. (2019). Hopefully, this strategy will offer a way to make the effects of climate change less disruptive and spare the poor and the vulnerable from shouldering an unduly high burden.

Margulis et al. (2010) further noted that given the uncertainty surrounding both climate outcomes and longer-term projections of social and economic development, countries should try to delay adaptation decisions as much as possible and focus on low-regret actions. They should also enhance the resilience of vulnerable sectors. In agriculture, for example, this would mean better management of water resources by giving policymakers greater flexibility in handling either droughts or waterlogging caused by floods.

The instinctive approach to cost adaptation related to climate change was also described by Margulis et al. (2010). This methodological approach consists of two tracks, comparing a future world without climate change with a future world with climate change. The differences between the two biospheres necessitate a series of actions to adapt to the new world conditions. The costs of these additional actions are the costs of adapting to climate change. Figure 13.1 summarizes the methodological approaches of the two tracks. Margulis et al. (2010)

Economics of adaptation to climate change. Summary of methodological approaches of two tracks

FIGURE 13.1 Economics of adaptation to climate change. Summary of methodological approaches of two tracks: global and country. The intuitive approach to costing adaptation involves comparing a future world without climate change with a future world with climate change. The difference between the two worlds entails a series of actions to adapt to the new world conditions, and the costs of these additional actions are the costs of adapting to climate change (Margulis et al., 2010).

explained that overall, the global study estimated that the cost between 2010 and 2050 of adapting to an approximately 2°C warmer world by 2050 is in the range of S70 billion to $100 billion per year. Similar results were reported by Puig et al. (2016) for the United Nations were the estimated cost of climate change adaptation investments in the developing world was predicted as $500 billion per year by 2050. However, this was more than five times greater than that estimated by Margulis et al. (2010).

Cox et al. (2019) reported that global climate model (GCM) projections are generally considered the best source of information for predicting future weather and hydrologic conditions in the face of a changing environment. Understanding and interpreting GCM projections is therefore critical for water resources planning. Regrettably, this can be a challenging task as climate model data, particularly precipitation data, have a large scatter and often lack apparent trends, as likewise observed by Fletcher et al. (2019) and Margulis et al. (2010). The paper by Cox et al. (2019) demonstrated a simple, practical method for synthesizing climate model data into more informative metrics using case studies. Their results identified significant increasing trends in expected 21st century temperatures for most GCM projections. Significant trends were also identified for probable future monthly and 24-hour maximum precipitation and drought severity. Implications of this work for water resources planning were discussed.

Scientific communities, and much of the public, recognize that climate change is not only influenced by human activity, but that it is also accelerating Cox et al. (2019). Consequently, water resources planners must consider that recent climate history is not an adequate predictor of the future. Fluctuating climate necessitates the need for mankind to understand how these changes occur to be able to plan properly. As Fletcher et al. (2019) and Margulis et al. (2010) concluded, the variability, nonstationarity and trends in climate data must be assessed and then incorporated into resource planning models so that future trends may be more reliably predicted. This conclusion is correct whether planning emphasis is on typical conditions such as available water resources, or on extreme conditions such as floods and droughts. A wealth of data now exists, including 20th century observations and 21st century climate model projections, to help scientists comprehend these dynamic forces. Cox et al. (2019) concluded that the methodology presented in their study could serve as a useful, low-cost, initial step in long-term planning.

 
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