SewerSnort: A drifting sensor for in situ Wastewater Collection System gas monitoring
Introduction
A Wastewater Collection System (WCS) collects and transports wastewater generated from households and industries to treatment plants or disposal sites by means of a system of underground pipelines. During the transportation process a periodic inspection and maintenance of sewer pipe must be performed particularly for aging pipes reaching or past its life expectancy since WCS components are prone to damage from aging, excessive traffic, and biochemical reactions [6]. In addition, untreated sewer escaped from WCS through leaks or overflow endangers public health by contaminating the source of drinking water and polluting natural environment [5].
Organic material transported by the sewer accumulates along the bottom (forming a sediment), and walls (forming a coating known as “bio-film”), of the pipeline. Due to anaerobic conditions, where insufficient electrons are available to accept ions, biochemical reactions that occur in these sediments and bio-film generates substantial amount of hydrogen sulfide (H2S), methane (CH4), and other volatile substances (collectively, in-sewer gases) [13]. Hydrogen sulfide, which is toxic and odorous gas, is a precursor to the formation of sulfuric acid (H2SO4) which is corrosive to metal and concrete and noxious to human [48]. On the other hand, methane gas is highly flammable and forms explosive mixtures with air. Also, methane gas is an asphyxiant and may displace oxygen in an enclosed space [38]. In addition, there is growing consensus that sewage systems contribute a significant fraction of greenhouse gases (GHG) such as carbon dioxide (CO2) and methane [34], [10].
Due to such an unfriendly and harsh environment direct sewer inspection and maintenance operations are life-threatening. A number of complicated and expensive indirect methods has developed in the past. For example, pipe leakage detection can be accomplished via the injection of smoke and fluorescent dyes [42], [33] or via remote inspection with cameras and sonar systems attached to tethered probes [42] or mobile robots [1]. Sewer flow monitoring can be achieved by installing flow meters at strategic locations so that the drainage system can be properly controlled to prevent or to minimize overflows [43]. Also, sediment control can be accomplished via localized flushing or chemical treatments [5].
While pipe damage detection and flow monitoring have been actively studied in both academia and industry, in-sewer gas monitoring has received little attention due to the difficulty of in situ measurements and the relative lack of sensor installations – mostly in treatment plants. Also, areas that are easily approached and instrumented are principally limited to manholes. However, manhole-based sensor readings are poor indicators of toxic gas concentration due to the fresh air flowing through the manhole (called the “chimney effect”). To fill the gap the United States Environmental Protection Agency (USEPA) recommends an analytical modeling to predict sediment buildups and gas concentrations [5]. Nonetheless, it is extremely difficult to model and fit a sewer system due to the large spatio-temporal variability and the lack of legitimate data. To this reason, a proper maintenance is not usually performed, or rather sewer flushing is performed only when odor complaints are received or legal disputes are occurred which results endangering public health and causing expensive litigations [2].
Thus, there is a strong rationale for in-sewer gas monitoring especially when sewer gas is a key indicator of sewer conditions (sediment buildups, corrosion, and explosion) [13]. Also, accurate and effective sewer gas monitoring can suggest areas for targeted supplemental study or corrective actions. In addition, the accurate information of toxic gases can reduce the occupational health and safety risks of personnel working in sewer pipes. As in-sewer fiber optical cable installations become prevalent, the safety issues become more of concern [49]. Moreover, researchers can have a better understanding in-sewer gas phases and can estimate accurate amount of GHG production in sewers [34], [10].
In this paper, we design a low cost in-sewer gas monitoring system. The proposed system would allow frequent WCS inspection, comprehensive WCS sewer gas measurement, and early detection of problems. In addition, the system allows targeting of accurate sewer flushing measure which substantially improves service uptime, reduces the maintenance expense, enhances illegal toxic chemical dumping enforcement, reduces the risks of contaminating the source of drinking water, and reduces the risks of polluting our natural environment.
To this end, we propose SewerSnort, a novel method involving drifting sensors that monitor in-sewer gases. A SewerSnort node is dispensed upstream of the WCS. It measures in-sewer gas concentrations while floating downstream and marks measurement readings along with their geographic location obtained from a set of beacons located beneath manholes. Upon the completion of journey, a SewerSnort node is extracted at a wastewater treatment plant, pumping station, or sewer manhole. The data acquired by SewerSnort can be collected through traditional public network infrastructure systems such as municipal Wi–Fi, an emerging low-power high-availability mesh networking system such as Streetline [40]. Additionally, the data in SewerSnort can be manually retrieved through short-range wireless communication upon retrieval since physical contact with the probe once deployed in the sewer is not advisable due to surface contamination and biohazard.
In this paper, we make the following contributions to the field:
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We show the feasibility of a mobile drifting sensor by analyzing the sewer flow statistics and present the potential applications of in-sewer gas monitoring.
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We design an “inner-tube” shaped hull to handle the lateral force that pushes the drifter to the side of the sewers (known as the bank suction effect).
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We present the first single-supply differential ratiometric data acquisition architecture that targets electrochemical sensors for WCS monitoring applications. The design is implemented and evaluated. Controlled experiments confirm the accuracy of our gas sensor module.
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We propose a Received Signal Strength Indicator (RSSI) based localization scheme that provides meter-level accuracy in the underground GPS-denied sewer environment. Over ground experiments based on a programmable mobile robot emulator confirm the viability of proposed method.
This paper significantly enhances our earlier work [17] as follows. First, we include the description of environmental/health impact of sewer gas (Section 2). Second, we provide a detailed description of ratiometric signal conditioning (Section 4). Third, we elaborate the enhancement scheme for location estimation using flow velocity and discuss a method of handling the case when a beacon is unreachable or failed (Section 4.3). Fourth, we analyze the storage space requirement of SewerSnort (Section 4.4). Fifth, we discuss mechanisms for fault recovery using a convoy of drifters (Section 4.6). Finally, we present possible research directions such as drifter mobility modeling and networked drifters (Section 7).
Section snippets
Wastewater Collection System
A Wastewater Collection System collects wastewater generated from households or industries and transports them to treatment facilities or disposal sites. The system is categorized as a separate sewer system or a combined sewer system depending on whether sanitary wastewater is separated from storm water. The separate sewer system has two wastewater drainage systems in parallel; i.e., a sanitary sewer discharging wastewater to a wastewater treatment plant and a storm sewer discharging storm
System design requirements
Our main goal is to design an in-sewer gas monitoring system that considers the following requirements:
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The system should be independent of pipe profile (material, shape, or size). The most widely used image capture technologies such as Closed-Circuit Television (CCTV), Sewer Scanner and Evaluation Technology (SSET), and sonar only work with a limited set of the pipe materials and shapes deployed in WCS’s [42].
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The system should be scalable. A large metropolitan city like Los Angeles has a WCS
Hull design
When a surface vessel moves through a constricted waterway, the current velocity gradient pushes the craft toward the nearest bank ultimately resulting in a collision. Our drifter will suffer from this phenomenon, known as the “bank suction” effect [7]. As our drifter approaches the sewer wall the water channel size reduces and in turn increases the velocity of the water on that side. The asymmetric flow around the drifter causes pressure differences. As a result, a lateral force will push the
Experiments
We validate the end-to-end capability of our custom H2S sensor board by comparing it with that of QRAE PLUS Multi-Gas Monitor. QRAE is an off-the-shelf gas monitor that is equipped with the same type of RAE H2S electrochemical sensor element. Mounting our sensor system atop an Amigobot, a commercial mobile robot, and using it to mimic the sewer’s flow rate, we evaluate the overall system performance.
Related work
Wireless sensor networks have been widely utilized in various environmental monitoring systems. Among the wealth of research contributions, this section reviews only the few that are most significantly related to SewerSnort.
Conclusion and future work
This paper has presented an innovative sewer gas monitoring system based on a floating, drifting embedded sensor platform, the SewerSnort. We discussed the feasibility of a mobile drifting sensor by analyzing the sewer flow statistics and present the potential applications of in-sewer gas monitoring. Then, we designed an “inner-tube” shaped hull to handle the lateral force that pushes the drifter to the side of the sewers, presented the first single-supply differential ratiometric data
Acknowledgements
We thank RAE Systems for providing a QRAE gas detector and H2S electrochemical sensors (RAE 032-0102-000). We thank Woonghee Lee for providing 3D SewerSnort schematic. We appreciate the courtesy of Mr. Cowden, CEO of Rialto Concrete Pipe company, who provided us the experimental environment. This work is supported in part by the National Science Foundation under Grant No. 0722046.
Jihyoung Kim received M.S. degree in computer science from University of California, Los Angeles, in 2009. He is currently working toward Ph.D. degree in computer science at University of California, Los Angeles (UCLA). His research interests include sensor networks, social networks, cloud computing and network security.
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Jihyoung Kim received M.S. degree in computer science from University of California, Los Angeles, in 2009. He is currently working toward Ph.D. degree in computer science at University of California, Los Angeles (UCLA). His research interests include sensor networks, social networks, cloud computing and network security.
Jonathan Friedman has spent most of his professional career split between IT/MIS administrative duties and mixed-signal PCB design. He was the Director of Database Support Services for Sonic Associates (an IMAX company), Director of US Technological Cooperation Students at the Chernigov State Institute for Economics and Management (Chernigov, Ukraine, 2002), and is the Founder of HalcyonIT, an IT outsourcing firm for many small-to-medium size businesses. In his research at the Networked and Embedded Systems Laboratory, University of California, Los Angeles (UCLA), he is interested in improving the physical sensor layer of wireless mobile embedded sensor networks through more advanced implementations and designs. Specially, he is targeting the problem of location of a sensed entity by implementing a new architecture for robust (noise-immune), low-latency (many positional fixes per second), high-accuracy localization for mobile nodes. Additional interests lie in relaxing the deployment constraints and requirements for static (nonmobile) beacons. Application areas of interest lie in entertainment. In collaboration with UCLA’s School of Theater, Film, and Television, his work is being shaped and adapted to fit within the unique constraints of this application space.
Uichin Lee is an assistant professor in the Department of Knowledge Service Engineering at Korea Advanced Institute of Science and Technology (KAIST). He received a B.S. in computer engineering from Chonbuk National University in 2001, an M.S. degree in computer science from KAIST in 2003, and a Ph.D. degree in computer science from the University of California at Los Angeles (UCLA) in 2008. Before joining KAIST, he was a member of technical staff at Bell Laboratories, Alcatel-Lucent until 2010. His research interests include distributed systems and mobile/pervasive computing.
Luiz F.M. Vieira is an Associate Professor at the Computer Science Department at the Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil. Dr. Vieira holds a Ph.D. in Computer Science from the University of California, Los Angeles (UCLA), 2009. His research areas are ad hoc and sensor networks, network coding, computer networks and distributed systems.
Diego Rosso is an Assistant Professor in the Civil and Environmental Engineering Department at the University of California, Irvine. During the past ten years he has studied wastewater treatment processes, energy conservation in treatment processes, and carbon footprint models. Since January 2008 he has been directing the UCI Environmental Processes Laboratory, leading a team of 12 student researchers focusing on carbon- and energy- footprint analyses and the water-energy nexus. Previously, he was at UCLA, where he received his Ph.D. in Environmental Engineering in 2005. He is also a Chemical Engineering Laureate from the University of Padua in Italy. He is an active member and campus adviser of Engineers Without Borders.
Mario Gerla is a Professor in the Computer Science at UCLA. He holds an Engineering degree from Politecnico di Milano, Italy and the Ph.D. degree from UCLA. He became IEEE Fellow in 2002. At UCLA, he was part of the team that developed the early ARPANET protocols under the guidance of Prof. Leonard Kleinrock. At Network Analysis Corporation, New York, from 1973 to 1976, he helped transfer ARPANET technology to Government and Commercial Networks. He joined the UCLA Faculty in 1976. At UCLA he has designed and implemented network protocols including ad hoc wireless clustering, multicast (ODMRP and CodeCast) and Internet transport (TCP Westwood). He has lead the $12M, 6 year ONR MINUTEMAN project, designing the next generation scalable airborne Internet for tactical and homeland defense scenarios. He is now leading two advanced wireless network projects under ARMY and IBM funding. His team is developing a Vehicular Testbed for safe navigation, urban sensing and intelligent transport. A parallel research activity explores personal communications for cooperative, networked medical monitoring (see http://www.cs.ucla.edu/NRL for recent publications).
Mani Srivastava is on the faculty at UCLA, where he is Professor and Vice Chair in the Electrical Engineering Department, with a joint appointment in the Computer Science Department. He is also associated with the NSF Center for Embedded Networked Sensing, and his research at CENS since the early days of Sensor Networking research has addressed multiple aspects of embedded, pervasive, and participatory sensing technologies and applications. His current research interests include: sensing and control technologies for energy management in smart buildings, smart grids, and computing/communication systems at multiple scales; mobile and wearable sensing for biomedical and psychosocial applications; underwater bio-mimetic sensing and communications; privacy and data quality in sensory information; and, emerging hardware-software platform technologies. Before joining UCLA, Mani received his BTech from IIT Kanpur, M.S. and Ph.D. from Berkeley, and worked in Networked Computing Research at Bell Laboratories for several years. He is a Fellow of the IEEE, and recently served as the Editor-in-Chief of the IEEE Transactions on Mobile Computing.