Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. In this paper, the focus is on algorithms for efficient measurement and sampling using a multi-robot, data-driven, water-sampling behavior, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles (ASVs), one equipped with a water quality sensor and the other equipped with a water-sampling apparatus. The ASV with the sensor acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The ASV equipped with the water sampling apparatus makes decisions in real time on where to sample the water based on the suggestions made by the explorer robot. We evaluate the system in the context of measuring chlorophyll distributions. We do this both in simulation based on real geophysical data from MODIS measurements, and on real robots in a water reservoir. We demonstrate the effectiveness of the proposed approach in several ways including in terms of mean error in the interpolated data as a function of distance traveled.
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Multi-Modal Lake Sampling for Detecting Harmful Algal Blooms
In this paper, we present a system for measuring water quality, with a focus on detecting and predicting Harmful Cyanobacterial Blooms (HCBs). The proposed approach includes stationary multi-sensor stations, Autonomous Surface Vehicles (ASVs) collecting water quality data, and manual deployments of vertical water sampling together with vertical water quality sensor data collection, in order to monitor the health of the lake and the progress of different types of algal blooms. Traditional water monitoring is performed by manual sampling, which is limited both in the spatial and the temporal domain. The proposed method will expand the range of measurements while reducing the cost. Human sampling is still included in order to provide a base of comparison and ground truth for the automated measurements. In addition, the collected data, over multiple years, will be analyzed to infer correlations between the different measured parameters and the presence of blooms. A detailed description of the proposed system is presented together with data collected during our first sampling season.
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- Award ID(s):
- 1923004
- PAR ID:
- 10431839
- Date Published:
- Journal Name:
- OCEANS 2022, Hampton Roads
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Freshwater cyanobacterial harmful algal blooms (CHABs) are a well‐known global public health threat. Monitoring and early detection of CHAB toxins are currently accomplished using labor‐intensive sampling techniques and subsequent shore‐based analyses, with results typically reported 24–48 h after sample collection. We have developed and implemented an uncrewed, autonomous mobile sampler‐analytical system capable of conducting targeted in situ toxin measurements in < 2 h. A surface plasmon resonance (SPR) instrument was combined with the environmental sample processor (ESP) to fully automate detection and quantification of particle‐associated cyanobacterial microcystins (pMC). This sensor‐sampler system was integrated with a long‐range autonomous underwater vehicle (LRAUV) and deployed in western Lake Erie for field trials in the summer of 2021. The LRAUV was remotely piloted to acquire samples at selected locations within and adjacent to a CHAB. Sixteen pMC measurements ranging from 0.09 to 0.55
μ g/L lake water were obtained over a 14‐day period without recovery of the LRAUV. The SPR/ESP/LRAUV system complements existing satellite, aerial, and manual sampling CHAB survey techniques, and could be used to enhance predictive models that underpin bloom and toxicity forecasts. This system is also extensible to detection of other algal toxins in freshwater and marine environments, with its near real‐time assessment of bloom toxin levels potentially offering additional socioeconomic benefits and public health protection in a variety of settings. -
A water treatment center (WTC) removes contaminants and unwanted components from the water and makes the water more acceptable to the end-users. A modern WTC is equipped with different water sensors and uses a combination of wired/wireless communication network. During the water treatment process, controllers periodically collect sensor measurements and make important operational decisions. Since accuracy is vital, a WTC also uses different data validation mechanisms to validate the incoming sensor measurements. However, like any other cyber-physical system, water treatment facilities are prone to cyberattacks and an intelligent adversary can alter the sensors measurements stealthily, and corrupt the water treatment process. In this work, we propose WTC Checker (WTC2), an impact-aware formal analysis framework that demonstrates the impact of stealthy false data injection attacks on the water treatment sensors. Through our work, we demonstrate that if an adversary has sufficient access to sensor measurements and can evade the data validation process, he/she can compromise the sensors measurements, change the water disinfectant contact time, and inflict damage to the clean water production process. We model this attack as a constraint satisfaction problem (CSP) and encode it using Satisfiability Modulo Theories (SMT). We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on different synthetic test cases.more » « less
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The increasing amount of data and the growing use of them in the information era have raised questions about the quality of data and its impact on the decision-making process. Currently, the importance of high-quality data is widely recognized by researchers and decision-makers. Sewer inspection data have been collected for over three decades, but the reliability of the data was questionable. It was estimated that between 25% and 50% of sewer inspection data is not usable due to data quality problems. In order to address reliability problems, a data quality evaluation framework is developed. Data quality evaluation is a multi-dimensional concept that includes both subjective perceptions and objective measurements. Five data quality metrics were defined to assess different quality dimensions of the sewer inspection data, including Accuracy, Consistency, Completeness, Uniqueness, and Validity. These data quality metrics were calculated for the collected sewer inspection data, and it was found that consistency and uniqueness are the major problems based on the current practices with sewer pipeline inspection. This paper contributes to the overall body of knowledge by providing a robust data quality evaluation framework for sewer system data for the first time, which will result in quality data for sewer asset management.
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The air quality at the Lake Michigan shoreline in southeastern Wisconsin is heavily influenced by the combination of Chicago area urban emissions and the meteorology over the lake. In June 2020, a multi-rotor DJI M600 Pro unmanned aerial system (UAS) equipped with a small ozone monitor (2B POM) and a meteorological sensor (iMET-XF) was flown on forecasted ozone exceedance days in the morning and evening to measure ozone, temperature, pressure and humidity profiles from 5-120 m AGL at the Chiwaukee Prairie State Natural Area in Southeastern Wisconsin. The Wisconsin DNR lakeshore air quality monitor at Chiwaukee Prairie in Kenosha, WI (AIRS ID 55-059-0019) sits 0.16 km from the shoreline and at the Wisconsin-Illinois boarder, near to where the UAS flights took place. The Chiwaukee Priaire monitoring station was equipped for an enhanced monitoring season, with a LIDAR Wind Profiler instrument. The combination of UAS measurements with the LIDAR meteorological measurements provide an understanding of the vertical structure in the meteorology of lake breeze and ozone during exceedance days. Temperature measurements aloft from the UAS show an atmospheric inversion at this site all sampling days (June 8, 9, 15-19). The ozone measurements trend with the temperature data, typically with higher ozone aloft than at the surface with a regular feature at 50-80 m AGL. We will discuss the results from the UAS with the LIDAR measurements to help understand the lake breeze influence on the local ozone measurements.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/OCEANS47191.2022.9977137
