Physical and chemical sensors are often proxies in environmental health studies but do not always provide a comprehensive picture. Microorganism dynamics are complex and often result from many different spatiotemporally dynamic factors. Furthermore, stream quality impairments and health-related illnesses commonly result from microorganisms. Due to the complexity of microorganisms and their environmental and public health importance, it is critical to be able to measure biotic response in addition to physicochemical conditions. Our poster will be highlighting two methods that are aimed towards measuring and quantifying different microbiological species as well as their response towards changes in physiochemical conditions. These microbial components hold value as practical and applicable indicators, as they are already organisms are that commonly used to assess and study the quality of different aquatic environments; for example, fecal indicator bacteria, namely E.coli and Enterococci, are used as the standard organisms for assessing recreational beach and freshwater quality because they have been linked to pathogenic/disease-causing organisms. To look at physicochemical and algal dynamics in streams an algal biosensor is under investigation, which can be used to integrate stream conditions over an exposure period.

Increasing demands on water supplies, along with concerns about non-point source pollution, and water quality–based ecological factors all point to the need for observing stream flow perturbations and pollutant discharges at higher resolution than has been practical until now. This work presents a rapidly deployable Networked Infomechanical System (NIMS RD) technology for observing spatiotemporal variability in hydraulic and chemical properties across stream channels. NIMS RD is comprised of two supporting towers and a suspension cable delivering power and Internet connectivity for controlling and actuating the tram-like NIMS unit. The NIMS unit is capable of raising and lowering a payload of sensors, allowing a preprogrammed or data-actuated adaptive scan to be completed across a stream channel. In this paper, NIMS RD is demonstrated in two relevant cases: (1) elucidating spatiotemporal variations in nutrients and other biologically significant stream constituents in Medea Creek, a small urban stream in Southern California; and (2) using high-resolution synoptic sampling of steady velocity and salinity distributions across the San Joaquin River in Central California to provide quantitative salt load estimates. For Medea Creek, temperature and specific conductivity (SC) exhibited varying cross-sectional patterns throughout each of three 24 hour scans carried out over three summer months. Both temperature and SC displayed repeating sinusoidal diel fluctuations independent of the spatial variation. For each of the months the cross-sectional variation was less during the late nighttime and morning hours than during the afternoon and early nighttime hours. For the San Joaquin River, high-resolution velocity distributions from NIMS RD were successfully reproduced in separate deployments and quantitatively matched stage-based volumetric flow rates at the site. The product of the velocity and associated SC distributions yielded total salt load estimates similar to previously reported values, but no basis for direct comparison was available.

The exponential progress of technology development, driven in many cases by Moore’s Law, has enabled the combination of sensing, computation and wireless communication in small, low-power devices that can be embedded directly in the physical environment. Recent research has resulted in several new classes of embedded networked sensing systems that can be rapidly distributed in the environment to study phenomena with unprecedented detail. Embedded networked sensing systems are transforming the way in which physical, biological and chemical changes are detected and quantified. These results are leading to new mechanistic understanding of the environment and, consequently, to new models and predictions for better assessment and management of environmental challenges.

This white paper describes the emerging technologies used in distributed sensing systems and the opportunities these systems present for environmental management, and in particular, water quality protection. A team of faculty, students, and staff at the Center for Embedded Networked Sensing (CENS) wrote the report. CENS is a National Science Foundation sponsored Science and Technology Center, headquartered at the University of California, Los Angeles (UCLA). In addition to UCLA, the California Institute of Technology, the Riverside and Merced campuses of the University of California, and the University of Southern California are partners in the center. CENS is developing embedded networked sensing systems and applying this technology to critical scientific and social applications. The Foresight and Governance Project at the Woodrow Wilson International Center for Scholars edited and finalized this document for the U.S. Environmental Protection Agency’s Office of Water.

This paper first briefly describes the potential applications of sensing systems to four common water quality management problems. This potential includes: (1) providing early warning for septic systems, (2) allowing for the trading of credits for non-point source runoff, (3) monitoring beach water quality, and (4) management of combined sewer overflows. Section 4 describes these scenarios in further detail.

Section 1 provides an overview of sensors (i.e., the devices that convert environmental phenomena into an electronic response) and actuators (i.e., the devices that convert electrical signals into mechanical responses). Sensors have the potential to detect physical, chemical, biological, and radiation properties in the environment. A variety of sensors is currently available for networked environmental sensing, while others are still in early research and development phases. Physical sensors for water quality monitoring are generally the most field-ready and scalable to distributed applications, followed by chemical and then biological sensors. The costs for these sensors depend on the physical, chemical, or biological parameter of interest. Indicator sensors and event-triggering sampling can be used when direct detection sensors are not ready for field deployment. To more extensively detect environmental properties, even more sophisticated sensors and sensing strategies are needed, including: (1) hardening novel sensors types (such as lab-on-a-chip technology) to withstand harsh conditions for extended periods, and (2) devising integrated sensing systems for higher order observations, such as quantifying materials fluxes in the environment.

Section 2 on Deployment Platforms discusses three new sensing system classes: static, mobile robotic, and mobile handheld. These sensing systems differ from traditional measurement systems in that sensors are attached to wireless radios that enable real-time communication of the data collected. For any particular situation, the best system class to use depends on the environment’s spatial and temporal variation. Among the three classes of sensing systems, mobile handheld systems are best used when the environmental phenomena of interest cover a broad area and do not require great spatial resolution. Static sensing systems are best used over smaller areas when high spatial resolution is not required, and mobile robotic systems are appropriate for intensive measurement of very small areas. To improve overall sensing efficiency (e.g., time or cost), adaptive sampling allows the system to dynamically adjust its measurement location or frequency to meet spatial or temporal variation in the environment. Sensing platforms can also be combined such that different platforms can provide information at different scales. This type of multi-scale system can also often help improve the efficiency of a monitoring effort. Despite the opportunities these sensing systems present, the ability to deploy them in the field can be limited by power availability and faults that interfere with communication or sensing hardware.

To help address some of the challenges facing the effective implementation of sensing systems and the interpretation of the acquired data, section 3 discusses the usefulness of considering the entire “life cycle” of data in a sensing system. This life cycle consists of three distinct phases: design and deployment of the observing system; operation and monitoring; and analysis, modeling and data sharing.

The final section of the report offers recommendations for future research. In spite of the substantial success in research and development activities that has given rise to existing sensing systems, relatively few have been deployed in real-world applications. The time is ripe to expand the range of applications where embedded sensing systems are used. Some of the key recommendations outlined in section 5 for novel uses of embedded sensing systems include:

Sensors and Actuators • (1) Long-term research and development for sensors where new or improved detection methods are needed and (2) short-term market incentives targeted at moving already well-developed sensing technology from research prototypes (e.g., biological and chemical sensors) to commercially available products. • Long-term research to develop detection methods for carbonaceous compounds, heavy metals, large molecular mass molecules such as dissolved organic compounds and dissolved organic nitrogen compounds, pathogenic organisms, biologically-active compounds, biomarkers, and lab-on-achip sensors. • Research on methods to minimize sensor maintenance in the field. • Investments to bring prototype technologies, such as small robust nitrate sensors that can be deployed for long periods, to market in forms suitable for environmental sensing.

Deployment Platforms • Investments in a range of pilot studies to determine specific deployment and analysis methodologies for target systems (e.g., septic system or sewage discharge monitoring). • Definition of requirements of large scale uses of the technology to encourage the production of user-friendly systems.

The Data Life Cycle • The encouragement of pilot deployments to test and refine data management tasks for specific applications. • Continued research and testing of tools to improve system robustness and ensure high-quality data. • Additional focus on the integration of sensing systems with external data sources and third-party applications, especially map-based visualization with tools for both rigorous GIS techniques and more public friendly web applications.

Training • Training at multiple levels (school systems and professional development) to ensure that a ready workforce exists that is prepared to use these new sensing technologies.

Embedded networked sensing systems will form a critical infrastructure resource for society—they will monitor and collect information on such diverse subjects as plankton colonies, endangered species, soil and air contaminants, medical patients, and buildings, bridges and other manmade structures. Investments in further research to help bring the sensing technologies discussed in this report into practice will transform the way we monitor and manage the health of our natural resources and predict and respond to crises.