For simplicity’s sake, let’s assume the term ‘water infrastructure’ refers to medium-scale decentralized water purification units and urban water distribution systems. The former choice is based on increasing the interest of government, private and public-private partnerships in such infrastructure. These treatment facilities provide about 500 to 3000 litres of clean water per hour. The government and private sector see it as a means to rapidly provide safe drinking water to the populace, especially in rural areas. This also serves as a quick temporary relief to vulnerable population (in terms of the water quality it is consuming) through a relatively small investment. The latter choice of urban water distribution systems comprise large-scale network of pipelines, valves and water supply sources such as tubewells. The reason for focusing on such distribution systems in this article is urbanization. According to estimates, almost 70 percent of the world’s population will be living in urban centres by 2050. This mass movement of people to cities places massive stress on water distribution systems. Hence, their future planning, operation, maintenance and management in general is extremely important.
So what do I mean by smart water infrastructure? In layman terms, it means remote monitoring, ‘intelligent’ decision-making on run-time and implementing those decisions (again in run-time) on water infrastructures. For example, faults in a water treatment units can be remotely diagnosed or predicted through this monitoring and ‘data analytics.’ Similarly, leakage in a pipeline or water theft can be timely and remotely detected, localized and mitigated. In engineering terms, broadly speaking, this is called upgrading built infrastructure to a ‘cyber-physical system.’ This involves collecting real-time data (from [embedded] sensor systems, mobile phones, people-as-sensors etc.), analyzing data through efficient and intelligent algorithms to eventually implement data-driven real-time effective control mechanisms.
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For example, real-time (second-by-second) demand and supply management can be one application. Another example can be intelligently and dynamically isolating pipelines through valves so a minimum number of users are affected and a minimum volume of water is wasted when there is a pipe leakage. Intelligence, just as in humans, can also mean finding patterns and trends and improving performance through experience, such as increasing accuracy to detect and localize faults in decentralized water treatment units or water theft in water distribution systems as more and more data comes in.
The reasons of going into the above-mentioned detail are two-fold:
Firstly, I want to establish the fact that such smart monitoring and control solutions aren’t expensive at all. For example, as it turns out, one can predict or at-least diagnose faults on a motor pump in a water treatment unit, just by analyzing data from an attached accelerometer (your smartphone uses this sensor to detect tilting motion or orientation). Installation of the sensor is very easy and the sensor itself costs less than $10. So, by having multiple cheap sensors retrofitted onto electro-mechanical equipment, one can monitor the whole infrastructure system’s ‘health’ remotely. Similarly, at no additional cost, smartphones can be used as data collectors or to provide intelligent instructions to local operators. The best part is that mobile phone networks have inherent capabilities to serve as communication platforms for these technological solutions (ICTs). As mobile networks have coverage in most remote areas even in developing countries, this makes deploying such solutions practical for areas that were previously difficult to serve.
Just one quick comment on Supervisory Control and Data Acquisition (SCADA) systems. Some readers from an engineering background might be of the view that this is exactly what SCADA systems do. SCADAs are far more expensive than these wireless and networked embedded systems leveraging mobile networks, and secondly, most SCADA systems are not capable of data analytics or even data storage. Hence, features necessary for predictive maintenance, timely fault localization or mitigation and other valuable applications such as consumer analytics, proactive supply chain management are generally unavailable.
Secondly, such solutions don’t just impact underlying technology but also greatly influence the associated socio-economic and political aspects of water service delivery. This is where the case for such solutions starts building up. To be able to show that this is a worthwhile investment for poor economies, financial feasibility and economic sustainability has to be evaluated. It’s been shown that smart infrastructures tend to work efficiently for longer periods. One can intuitively think that if something (let’s say a car) is maintained well, which is the case when timely monitoring is done, it has a longer life and operates more efficiently. Apart from serving these maintenance purposes, the live data collected is effectively used for Human Resource management and devising data-driven public policy in the developed world to achieve the Sustainable Development Goals (SDGs) outlined by the United Nations. Moreover, social scientists may use this monitoring data for impact evaluation of social experiments, like say for measuring the impact of an intervention in extant low-cost clean drinking water service for a low-income suburban community with the eventual goal of increasing adoption.
There is a concrete example of this. Researchers from the University of Oxford conducted a study in Kenyan villages where they retrofitted accelerometers on hand-pumps to make them smart. They transmitted measurements from accelerometer sensors via SMS over mobile phone networks to get measurements of handpump usage and associated volumetric water use to monitor service delivery, remote surveillance of maintenance service delivery and down-time to guide performance-based contracts and objective data that can improve infrastructure planning and investment, and promote sector accountability.
The researchers were able to show that an operation and maintenance model powered by smart technology as mentioned above led to a ten-fold reduction in handpump downtime from 27 days per year to only three days per year. This was a shift from 70 to 98 percent of handpumps functioning at all times, people paying fairer prices based on actual pump usage for better service, five times higher revenue collection, new and objective metrics to guide water service regulatory reform and a revised financial architecture shaped by an output-based payment model.
Similarly, researchers from Portland State University concluded that GSM (wireless technology) sensor-driven maintenance significantly increased average handpump functionality and reduced repair time compared to traditional maintenance models.
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So without going deeper, it can be safely said that reliable and timely information on infrastructure functionality could improve institutional, operational and financial performance without incurring additional costs. Infact, some costs might be saved through prolonged infrastructure lifetime, proactive supply chain management, reduced operation and maintenance costs (spare parts, labour, transport, electricity and information collection). For readers unaware of proactive supply chain management, one concrete example could be that in case of faults in water treatment units installed in a remote area, a person with the right expertise and right equipment (fault nature already known through remote fault diagnosis) can be sent off to the site location, days before the fault actually occurs to avoid or reduce downtime. This saves labor and transport cost involved in multiple visits required in traditional maintenance models.
On the flip side, what happens when such solutions are not considered? Let’s consider the case for decentralized water treatment facilities in Pakistan, a South Asian country with the sixth largest population in the world and a weak economy that is currently facing a severe water security and quality challenge.
The government of Pakistan’s current vision is to install decentralized water purification systems, mostly standalone medium-capacity Reverse Osmosis or Ultra-Filtration with Arsenic Removal Units, to immediately supply safe drinking water to the masses. The government had planned on installing 3,307 such facilities in two provinces of the country by June 2018. While the plan has been altered many times till now (primarily due to political interventions), nevertheless, more than 900 units are already installed and running. A year after commissioning the plants, once they are handed over to the local governments in urban areas or Community Based Organizations (CBOs) in rural areas, they are not maintained properly and usually end up either dysfunctional or supplying contaminated water. This is mainly due to lack of technical capacity of local officials to run proper operations and conduct maintenance, and low community ownership due to minimal focus on its involvement and development. Moreover, with improper maintenance, membrane recovery falls and power consumption increases, increasing the financial and environmental cost of produced water units. Frequent breakdowns and inefficient supply chain management increases downtime and adversely affects service delivery. Water quality monitoring is substandard. It takes almost three months to test a sample, that is, if the sample is being tested. Based on my first hand observations, consumers don’t trust water availability and its quality from water treatment units due to these reasons. Hence, there is under-utilization and low willingness-to-pay, making the model economically unsustainable.
This is also shown by government failures in the past. Many decentralized water treatment plants were set-up in the last two decades as part of the Clean Drinking Water Act and other schemes. However, most of them are in ruins now. This may be due to weak sector governance, lax policy implementation mechanisms, a culture of patronage, fragmented service delivery, unclear delegation of responsibility, inadequate technical capacity, poor service delivery or all of these factors combined. But based on previous discussions and referenced research, we can see that all of these issues can be addressed through timely and reliable information, and intelligent control offered by smart infrastructures.
This is why fourth generation technologies should be applied to the water sector. Governments all across the world should start seeing value in smart solutions and invest in their application to the water sector to achieve clean water and sanitation as well as other associated SDGs.
Hassan Iqbal is an electrical engineer and the founder of Sionser, a startup that utilizes remote sensing technology to monitor drinking water quality. His startup has won several national and international business and technology competitions. Iqbal aims to leverage monitoring technologies for data-driven planning and management in order to improve water service delivery in low-income communities. He can be contacted at email@example.com.