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The air quality monitor (AQM) began operations on the International Space Station (ISS) in March 2013 and was validated for operational use in January 2014. The AQM is a gas chromatograph-differential mobility spectrometer that currently monitors 22 target compounds in the ISS atmosphere. Data are collected twice per week, although data collection can be more frequent in contingency situations. In its second year, the AQM has provided data to decision-makers on several ISS contaminant related issues in both air and water. AQM has been used in strictly air incidents, such as a potential ammonia leak, and to investigate air contaminants affecting the water processing (excess ethanol). In the latter case data from water monitors and AQM were compared to understand the issue with the water processor. Additionally, the AQM has been moved to different ISS modules to determine whether air is sufficiently mixed between modules so that a central LAB module location is representative of the entire ISS atmosphere. Historic data on the ISS atmosphere in different modules from archival samples (ground lab analysis) suggest that the atmosphere is usually homogenous. This presentation will briefly describe the technical aspects of the AQM operations and summarize the validation results. The main focus of the presentation will be to discuss the results from the AQM survey of the ISS modules and to show how the AQM data has contributed to an understanding of environmental issues that have arisen on ISS. Presentation of a potential ammonia leak (indicated by an alarm) in 2015 will illustrate the use and value of the AQM in such situations.
Weld quality monitor for production testing of welding strength in electronic circuitry
The Weld Quality Monitor has provided online control through the application of linear transducers. The electronic systems must be upgraded to increase confidence level required in production. The Weld Quality Monitor has been used with a recent model solid state power supply to evaluate ability to differentiate between sound and defective weld joints. The Weld Quality Monitor has been evaluated on Dumet and Kovar to interconnect materials Nickel and Alloy 180 in weld joints used on microelectronic module fabrication. Distinct separation was achieved between low strength welds and sound welds by the Quality Monitor. Evaluation performed indicates a drift level at less than 2 percent on a series of 50 consecutive welds. Operation under shop conditions identified need for controlled electrode impact. This was achieved with pneumatic actuation.
Weld quality monitor tests of Dumet/nickel ribbon welded joints
The quality of contact lens samples depends strongly on their free volume fractions. It is expected that the free volume fraction of the test samples will also impact their attenuation coefficients for soft x rays. Samples with larger free volume fractions should have lower linear attenuation coefficients. Linear attenuation coefficients for Cd-109/Ag-109 x rays have been measured in five contact lens samples. It has been shown that the difference in the linear attenuation coefficients of discs from any given sample are entirely due to the difference in their respective volume fractions. It is therefore concluded that an x ray attenuation measurement system can serve as a quality monitor for contact lens polymers.
Recent studies have documented a correlation between air quality and drought in the United States, which has been linked with increased aerosols including airborne particulate matter (PM) during drought conditions. This study partnered with local health departments to evaluate trends in air quality in the Pacific Northwest during the evolution of drought conditions using aerosol optical depth (AOD) observations collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellites. These satellite data were analyzed in conjunction with ground-based PM2.5 and PM10 data sourced from the Environmental Protection Agency (EPA)’s network of ground-based monitors and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index. Based on recommendations by local health departments, this study examined air quality trends between 2015 and 2022 in 12 counties within Oregon and Washington that reflected diversity in population density, drought exposure, rural and urban status, and data availability from EPA monitors. Overall, results indicated variation in relationships among drought, satellite, and ground-based air quality data across the study area. This study did not control for the impact of wildfire events on air quality and also did not investigate shorter SPEI aggregation periods, both of which are avenues for future research. This project supplemented research into links between drought and human health and provided health departments with an objective foundation from which they can communicate public health risks to local communities.
Data quality monitoring is critical to all experiments impacting the quality of any physics results. Traditionally, this is done through an alarm system, which detects low level faults, leaving higher level monitoring to human crews. Artificial Intelligence is beginning to find its way into scientific applications, but comes with difficulties, relying on the acquisition of new skill sets, either through education or acquisition, in data science. This paper will discuss the development and deployment of the Hydra monitoring system in production at Gluex. It will show how “off-the-shelf” technologies can be rapidly developed, as well as discuss what sociological hurdles must be overcome to successfully deploy such a system. Early results from production running of Hydra will also be shared as well as a future outlook for development of Hydra.
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Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.
The primary reason for monitoring air quality aboard the International Space Station (ISS) is to determine whether air pollutants have collectively reached a concentration where the crew could experience adverse health effects. These effects could be near-real-time (e.g. headache, respiratory irritation) or occur late in the mission or even years later (e.g. cancer, liver toxicity). Secondary purposes for monitoring include discovery that a potentially harmful compound has leaked into the atmosphere or that air revitalization system performance has diminished. Typical ISS atmospheric trace pollutants consist of alcohols, aldehydes, aromatic compounds, halo-carbons, siloxanes, and silanols. Rarely, sulfur-containing compounds and alkanes are found at trace levels. Spacecraft Maximum Allowable Concentrations (SMACs) have been set in cooperation with a subcommittee of the National Research Council Committee on Toxicology. For each compound and time of exposure, the limiting adverse effect(s) has been identified. By factoring the analytical data from the Air Quality Monitor (AQM), which is in use as a prototype instrument aboard the ISS, through the array of compounds and SMACs, the risk of 16 specific adverse effects can be estimated. Within each adverse-effect group, we have used an additive model proportioned to each applicable 180-day SMAC to estimate risk. In the recent past this conversion has been performed using archival data, which can be delayed for months after an air sample is taken because it must be returned to earth for analysis. But with the AQM gathering in situ data each week, NASA is in a position to follow toxic-effect groups and correlate these with any reported crew symptoms. The AQM data are supplemented with data from real-time CO2 instruments aboard the ISS and from archival measurements of formaldehyde, which the AQM cannot detect.
An automated water quality monitoring system was developed by Langley Research Center to meet a need of the Environmental Protection Agency (EPA). Designed for unattended operation in water depths up to 100 feet, the system consists of a subsurface buoy anchored in the water, a surface control unit (SCU) and a hydrophone link for acoustic communication between buoy and SCU. Primary functional unit is the subsurface buoy. It incorporates 16 cells for water sampling, plus sensors for eight water quality measurements. Buoy contains all the electronic equipment needed for collecting and storing sensor data, including a microcomputer and a memory unit. Power for the electronics is supplied by a rechargeable nickel cadmium battery that is designed to operate for about two weeks. Through hydrophone link the subsurface buoy reports its data to the SCU, which relays it to land stations. Link allows two-way communications. If system encounters a problem, it automatically shuts down and sends alert signal. Sequence of commands sent via hydrophone link causes buoy to release from anchor and float to the surface for recovery.
The preprototype water quality monitor (WQM) subsystem was designed based on a breadboard monitor for pH, specific conductance, and total organic carbon (TOC). The breadboard equipment demonstrated the feasibility of continuous on-line analysis of potable water for a spacecraft. The WQM subsystem incorporated these breadboard features and, in addition, measures ammonia and includes a failure detection system. The sample, reagent, and standard solutions are delivered to the WQM sensing manifold where chemical operations and measurements are performed using flow through sensors for conductance, pH, TOC, and NH3. Fault monitoring flow detection is also accomplished in this manifold assembly. The WQM is designed to operate automatically using a hardwired electronic controller. In addition, automatic shutdown is incorporated which is keyed to four flow sensors strategically located within the fluid system.
Hydra: Computer Vision for Data Quality Monitoring Mar 13, 2024, 4:15 PM 30m Charles B. Wang Center, Stony Brook University Poster Track 1: Computing Technology for Physics Research Poster session with coffee break Speaker Ayan Roy Description Hydra is an extensible framework for training, managing, and deploying machine learning models for near real-time data quality monitoring. It is designed to take some of the burden off of shift crews by providing ‘round-the-clock’ monitoring of plots representing the data being collected. The Hydra system is backed by a database which is leveraged for near push button training and is primarily controlled and viewed through a set of web interfaces. This web interface contains a simple-to-use web based GUI for labeling of the datasets used in training; making it possible to label thousands of images quickly and efficiently. To aid in the analysis of Hydra inferences gradCAM, a method of interpretability, is performed and overlaid on the target image, highlighting regions of interest, making diagnosis of problems much faster. Development began in 2019 for the GlueX Experiment in Hall-D, the Hydra system has grown to encompass all of the experimental halls at Jefferson Laboratory. This talk will focus on the features of Hydra as well as provide details of the challenges present with deploying to disparate experimental halls. With a roadmap of development established, Hydra aims to continue to grow in richness of feature set and expand to encompass other monitoring tasks in differing communities.
Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).
A small, helicopter-borne water-quality monitoring package is being developed by the NASA/EPA using a combination of basic in situ water quality sensors and physical sample collector technology. The package is a lightweight system which can be carried and operated by one person as a passenger in a small helicopter typically available by rental at commercial airports. Real-time measurements are made by suspending the water quality monitoring package with a cable from the hovering helicopter. Designed primarily for use in rapidly assessing hazardous material spills in inland and coastal zone water bodies, the system can survey as many as 20 data stations up to 1.5 kilometers apart in 1 hour. The system provides several channels of sensor data and allows for the addition of future sensors. The system will also collect samples from selected sites with sample collection on command. An EPA Spill Response Team member can easily transport, deploy, and operate the water quality monitoring package to determine the distribution, movement, and concentration of the spilled material in the water body.
In August 2009, an experimental water quality monitoring kit based on Colorimetric Solid Phase Extraction (CSPE) technology was delivered to the International Space Station (ISS) aboard STS-128/17A. The kit, called the Colorimetric Water Quality Monitoring Kit (CWQMK), was flown and deployed as a Station Development Test Objective (SDTO) experiment on the ISS. The goal of the SDTO experiment is to evaluate the acceptability of CSPE technology for routine water quality monitoring on the ISS. This paper provides an overview of the SDTO experiment, as well as a detailed description of the CWQMK hardware and a summary of the testing and analysis conducted to certify the CWQMK for use on the ISS. The initial results obtained from the SDTO experiment are also reported and discussed in detail
In August 2009, an experimental water quality monitoring kit based on Colorimetric Solid Phase Extraction (CSPE) technology was delivered to the International Space Station (ISS) aboard STS-128/17A. The kit, called the Colorimetric Water Quality Monitoring Kit (CWQMK), was developed by a team of scientists and engineers from NASA s Habitability and Environmental Factors Division in the Space Life Sciences Directorate at Johnson Space Center, the Wyle Integrated Science and Engineering Group in Houston, Texas, the University of Utah, and Iowa State University. The CWQMK was flown and deployed as a Station Development Test Objective (SDTO) experiment on ISS. The goal of the SDTO experiment was to evaluate the acceptability of CSPE technology for routine water quality monitoring on ISS. This paper provides an overview of the SDTO experiment, as well as a detailed description of the CWQMK hardware and a summary of the testing and analysis conducted to certify the CWQMK for use on ISS. The results obtained from the SDTO experiment are also reported and discussed in detail.