Florida Transportation & Infrastructure: Monitoring Water Quality Along Southern Florida Seaports to Assess Impact on Coral Reef Tracts from Harbor Deepening Projects
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Benjamin Rocha↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Yampa Water Resources: Monitoring Water Quality and Evaluating Potential Drivers of Algae Blooms in the Upper Yampa River Watershed
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Samrin Sauda↗
Hilo Bay Water Resources: Monitoring Water Quality in Hilo Bay, Hawai'i to Support Future Community Planning
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Dani Sonobe↗
Hilo Bay Water Resources: Monitoring Water Quality in Hilo Bay, Hawaiʻi to Support Future Community Planning
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Dani Sonobe↗
Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures
Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.
Air quality↗
Enhanced detection of volatile organic compounds (VOCs) by caffeine modified carbon nanotube junctions
Detection of vapor phase analytes and gas molecules is primarily essential for numerous applications including medical diagnostic, environmental monitoring, chemical threat detection, food quality control, and chemical warfare agents. Here, we developed Caffeine functionalized carbon nanotubes (CNTs) random network based vapor quantum resistive sensors (vQRSs) for volatile organic compounds (VOCs) detection. The localized transmission electron microscopy (TEM) image and energy dispersive X-ray spectroscopy (EDS) confirm migration of caffeine to CNT–CNT junction (CNTj). We have recorded more than one order of magnitude higher sensitivity and unique selectivity towards selective VOCs (e.g., acetone, toluene, ethanol, methanol, etc.) with short response time (few seconds) for caffeine modified CNT junction (Caf-CNTj) vQRSs. Molecular dynamics (MD) calculations suggest that the sensitivity of the sensor is diffusion limited, with an advective contribution for ethanol. Furthermore, the designed Caf-CNTj sensors can potentially be important candidate to be a part of electronic nose used for various application such as breath analysis and food quality monitoring.
36 MATERIALS SCIENCE↗
Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
Carl Malings↗
Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine
High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.
Carl Malings↗
Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana
Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.
Sensors↗
NASA JSC water monitor system: City of Houston field demonstration
A water quality monitoring system with on-line and real time operation similar to the function in a spacecraft was investigated. A system with the capability to determine conformance to future high effluent quality standards and to increase the potential for reclamation and reuse of water was designed. Although all system capabilities were not verified in the initial field trial, fully automated operation over a sustained period with only routine manual adjustments was accomplished. Two major points were demonstrated: (1) the water monitor system has great potential in water monitoring and/or process control applications; and (2) the water monitor system represents a vast improvement over conventional (grab sample) water monitoring techniques.
Taylor, R. E.↗
Use of the JPL Electronic Nose to detect leaks and spills in an enclosed environment
An electronic nose to be used as an air quality monitor in human habitats in space has been developed at the Jet Propulsion Laboratory. This device is capable of detecting, identifying and quantifying several organic and inorganic chemical species which might be present as contaminants in spacecraft air. The complete portable device, including sensors, electronics, and software for data analysis, has been extensively tested.
environmental monitoring↗
Synergistic TEMPO Air Quality Science (STAQS)
In June-August 2023, STAQS seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science and increase societal benefit. Study philosophy: - Build an integrated observing system consisting of ground-, airborne-, and satellite-based platforms and air quality models. - Prioritize repeated systematic sampling in a predefined domain during morning, midday, and afternoon times over at least 4 days in each primary target areas. - Assemble a collaborative research team through leveraging multiple activities occurring in summer 2023 with federal and academic partners.
Laura Margaret Judd↗
On multidisciplinary research on the application of remote sensing to water resources problems
This research is directed toward development of a practical, operational remote sensing water quality monitoring system. To accomplish this, five fundamental aspects of the problem have been under investigation during the past three years. These are: (1) development of practical and economical methods of obtaining, handling and analyzing remote sensing data; (2) determination of the correlation between remote sensed imagery and actual water quality parameters; (3) determination of the optimum technique for monitoring specific water pollution parameters and for evaluating the reliability with which this can be accomplished; (4) determination of the extent of masking due to depth of penetration, bottom effects, film development effects, and angle falloff, and development of techniques to eliminate or minimize them; and (5) development of operational procedures which might be employed by a municipal, state or federal agency for the application of remote sensing to water quality monitoring, including space-generated data.
Source record↗
Merging of the Case 2 Regional Coast Colour and Maximum‑Peak Height Chlorophyll‑a Algorithms: Validation and Demonstration of Satellite‑derived Retrievals Across US Lakes
Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.
Satellite↗
Mars Ascent Vehicle-Propellant Aging
This project is to develop and test a new propellant formulation specifically for the Mars Ascent Vehicle (MAV) for the robotic Mars Sample Return mission. The project was initiated under the Planetary Sciences Division In-Space Propulsion Technology (ISPT) program and is continuing under the Mars Exploration Program. The two-stage, solid motor-based MAV has been the leading MAV solution for more than a decade. Additional studies show promise for alternative technologies including hybrid and bipropellant options, but the solid motor design has significant propellant density advantages well suited for physical constraints imposed while using the SkyCrane descent stage. The solid motor concept has lower specific impulse (Isp) than alternatives, but if the first stage and payload remain sufficiently small, the two-stage solid MAV represents a potential low risk approach to meet the mission needs. As the need date for the MAV slips, opportunities exist to advance technology with high on-ramp potential. The baseline propellant for the MAV is currently the carboxyl terminated polybutadiene (CTPB) based formulation TP-H-3062 due to its advantageous low temperature mechanical properties and flight heritage. However, the flight heritage is limited and outside the environments, the MAV must endure. The ISPT program competed a propellant formulation project with industry and selected ATK to develop a new propellant formulation specifically for the MAV application. Working with ATK, a large number of propellant formulations were assessed to either increase performance of a CTPB propellant or improve the low temperature mechanical properties of a hydroxyl terminated polybutadiene (HTPB) propellant. Both propellants demonstrated potential to increase performance over heritage options, but an HTPB propellant formulation, TP-H-3544, was selected for production and testing. The test plan includes propellant aging first at high vacuum conditions, representative of the Mars transit, followed by an additional year at simulated Mars surface conditions. The actual Mars surface environment is based on the igloo design, actively maintains the propellant at or above -40 degC, 95% carbon dioxide at Mars surface pressure. The NASA Marshall Space Flight Center (MSFC) Mars environment test facility is shown in figure 1 and located in the East Test area of Redstone Arsenal due to storage of live propellants. The facility consists of a vacuum chamber placed inside a large freezer unit. The facility includes pressure and temperature monitoring equipment in addition to a vacuum quality monitoring system spectrometer to record any outgassing products.
Dankanich, John↗
Assessment of Ethanol Trends on the ISS
The International Space Station (ISS) Environmental Control and Life Support System (ECLSS) provides a working environment for six crewmembers through atmosphere revitalization and water recovery systems. In the last year, elevated ethanol levels have presented a unique challenge for the ISS ECLSS. Ethanol is monitored on the ISS by the Air Quality Monitor (AQM). The source of this increase is currently unknown. This paper documents the credible sources for the increased ethanol concentration, the monitoring provided by the AQM, and the impact on the atmosphere revitalization and water recovery systems.
Perry, Jay↗
Amine-functionalized metal-organic framework ZIF-8 toward colorimetric CO 2 sensing in indoor air environment
Carbon dioxide (CO 2 ) has been shown to contribute to human health consequences indoors, such as shortness of breath, nasal and optic irritation, dizziness, and nausea. Here in this work, we explore the potential of metal–organic frameworks (MOFs) as highly-porous, crystalline sorbents for sensitive colorimetric CO 2 detection. In particular, the zeolitic imidazolate framework (ZIF-8) is chosen as the sorptive material due to its chemical stability and tunable CO 2 affinity. The colorimetric gas sensor is developed in methanol with three components: (i) MOF ZIF-8 as a high surface area adsorbent; (ii) ethylenediamine (ED) as the CO 2 -affinitive basic function; and (iii) phenolsulfonpthalein (PSP) as the pH indicator. Colorimetric assays and ratiometric analysis confirm a colorimetric response to variable CO 2 concentrations of relevance to indoor air quality. The color response is attributed to a zwitterion mechanism whereby ED reacts with CO 2 to form a zwitterionic intermediate. This intermediate is then deprotonated by the pH indicator, shifting the pH and inducing a color change. Given its simple fabrication, rapid and obvious response, and stability in ambient environment, the ZIF-8-based colorimetric sensor provides a promising route for an improved indoor air quality monitoring.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improved Detection Technique for Solvent Rinse Cleanliness Verification
The NASA White Sands Test Facility (WSTF) has an ongoing effort to reduce or eliminate usage of cleaning solvents such as CFC-113 and its replacements. These solvents are used in the final clean and cleanliness verification processes for flight and ground support hardware, especially for oxygen systems where organic contaminants can pose an ignition hazard. For the final cleanliness verification in the standard process, the equivalent of one square foot of surface area of parts is rinsed with the solvent, and the final 100 mL of the rinse is captured. The amount of nonvolatile residue (NVR) in the solvent is determined by weight after the evaporation of the solvent. An improved process of sampling this rinse, developed at WSTF, requires evaporation of less than 2 mL of the solvent to make the cleanliness verification. Small amounts of the solvent are evaporated in a clean stainless steel cup, and the cleanliness of the stainless steel cup is measured using a commercially available surface quality monitor. The effectiveness of this new cleanliness verification technique was compared to the accepted NVR sampling procedures. Testing with known contaminants in solution, such as hydraulic fluid, fluorinated lubricants, and cutting and lubricating oils, was performed to establish a correlation between amount in solution and the process response. This report presents the approach and results and discusses the issues in establishing the surface quality monitor-based cleanliness verification.
Hornung, S. D.↗