Applying Instrumental Neutron Activation Analysis (INAA) to the Study of Heavy Metals in Spanish Moss (Tillandsia Usneoides) for Biomonitoring Air Pollution in Lowcountry of South Carolina
Spanish Moss (Tillandsia usneoides) is an epiphyte that grows upon larger trees in tropical and subtropical climates. It is a common plant in the lowcountry region of South Carolina. It absorbs nutrients and water through its leaves from the air and rainfall, which makes it ideal for serving as a bio-indicator of local air pollution. Similar projects and analytical methods have been applied in other places (e.g., Romania, Argentina, etc.), but none of them is conducted in South Carolina.
The PI’s research group proposes to study the samples of Spanish Moss with a sensitive radioanalytical method—instrumental neutron activation analysis (INAA). INAA has been a versatile tool in multi-element analysis for more than half a century, but it was not widely applied to studying environmental toxicology in South Carolina. After irradiation of the samples with nuclear reactors, qualitative and quantitative information of the elements can be obtained from the decay spectra recorded by gamma-ray spectrometers. By analyzing samples of Spanish Moss with INAA, it is possible to determine the level of heavy metal elements with high accuracy and extreme sensitivity, and establish a relationship between the concentrations of trace elements, especially heavy metals in the Spanish moss and the air pollution in the lowcountry of South Carolina.
This study will be the pilot stage of a long-term project. It will start with the lowcountry of South Carolina, particularly in Berkely, Charleston, Dorchester, and Orangeburg counties. The Spanish Moss near the industry and heavy traffic sites will be given special attention. Hundreds of Spanish Moss samples will be collected from these counties. All samples will be irradiated by thermal and epithermal neutrons from PULSTAR reactor. Both short-lived isotopes and long-lived isotope spectra will be measured after irradiation. Our focus will be on concentrations of heavy metals, such as Cd, Zn, Hg, As, Pb, etc. In order to deal with the extensive data of gamma-ray spectra, an online spectra analysis program (Nuclear Activation Analysis System: NAAS) will be applied. Neutron flux of the PULSTAR reactor will be simulated with MCNPX to mimic the real situation in neutron irradiation.
This study will support the mission of natural resource management programs in 1890 research and train highly-skilled, competent and well-prepared students in the fields of Nuclear Engineering and radiochemistry. The immediate outcome of the project is a detailed survey map of heave metal elements in Spanish Moss in the lowcountry of South Carolina, which will give the public a better understanding of the environmental impact of human activities on air pollution toxicology. Pertinent information from the research will be disseminated to local farmers through 1890 extension, particularly to those classified as small limited resource farmers. This project will contribute to enhancing the environmental monitoring, environmental toxicology, air quality of plantations, and improve the economic vitality of rural communities in South Carolina.
Developing a Temporal Data Mining (TDM) System for In-Situ Decommissioning (ISD) Sensor Network Test Bed
Big Data, a concept widely popular nowadays, is defined as extremely large and complex data sets that need to be analyzed computationally to reveal patterns, trends, and associations, and is difficult to process using non-computational methods. Temporal Data Mining (TDM) is an active and rapidly evolving area in the Big Data science. In 2006, Laxman and Unnikrishnan firstly gave a complete survey on TDM theories and developed new algorithms for discovering frequency episodes in event stream. Their new algorithms are, both space-wise and time-wise, significantly more efficient than the earlier algorithms reported. These new TDM techniques soon found their applications in the real world, such as neuronal network studies and the automobile industry.
Is it possible to borrow TDM concepts/algorithms and apply them to nuclear sciences, especially to the practice in nuclear site monitoring and restoration? In-situ monitoring and decommissioning, like an automobile assembly line, generate a large amount of data which is time-specific, age-specific, and developmental stage-specific. This large amount of data may be useful to find unknown patterns of material failure, system breakdown, radiation field change, liquid leaking, with tempera data mining techniques.
At SRNL, researchers have established an ISD Sensor Network Test Bed, a unique, small-scale, and configurable environment, for the assessment of prospective sensors on actual ISD system material, at a minimal cost. The extensive data collected by the ISD sensors are ideal for temporal data mining to validate ISD system performance and predict possible system failures (or future accidents). A fast, robust, and efficient real-time data acquisition and data mining system based on current computer technology is urgently needed. We propose:
- Design and implement a real-time data acquisition system for ISD sensor network testbed located in SRNL. Through the data acquisition system, real-time data from various sensors (temperate, pressure, humidity, radiation field, leakage, etc.) will be synchronized and then stream into the data server with time stamps. The data acquisition system will also have the capability to control the devices remotely according to the feedbacks from data analysis and temporal data mining (TDM).
- Design and implement a web-based temporal data mining system for the ISD sensor network testbed. This web-based temporal data analysis and data mining system frequently visit the data server located off-site through web GUIs. The system can be accessed over the internet through a safe membership authorization from anywhere. The sketch of the data acquisition and data mining system is shown below.
A new algorithm for sequence prediction over long categorical event streams will be applied on this big dataset. In this, the set of significant frequent episodes associated with each target event type is obtained based on formal connections between frequent episodes and Hidden Markov Models (HMMs), and a mixture of such HMMs are used for estimating the likelihood for every target event type (e.g. material failure or accidents).
This project is unique in that it is the first time in the world to combine computer real-time data gathering, temporal data mining and possibly remote control in a nuclear deactivation and decommission scene. It applies contemporary computer concepts (big data/data mining/machine learning) to traditional areas of nuclear sciences (permanent entombment of contaminated, large nuclear structures via in-situ decommissioning). If this project can be implemented, it will greatly improve the speed of emergency response as well as diminish the needs of nuclear personals, which then will significantly reduce the cost of the whole nuclear industry.
Applying Nuclear Activation Analysis to the Study of Toxic Elements in Cotton Seeds
Cotton has been an important cash crop in the Palmetto State since revolutionary times to current day. Its seeds are about 15% of the value of the crop and used widely in making oil and feeding animals. Throughout the growing season, cotton assimilates numerous trace elements from the soil, including the toxic ones. Some of these trace elements are accumulated or enriched in cotton seeds.
Nuclear activation analysis has been a versatile tool in multi-elemental analysis for more than half a century. After irradiation of the samples, qualitative and quantitative information of the elements can be obtained from the decay spectra recorded by gamma-ray spectrometers. By analyzing samples of cotton seeds and the corresponding local soil with nuclear activation analysis, it is possible to determine the level of trace elements with high accuracy and extreme sensitivity and establish a relationship between the number of toxic trace elements in the cotton seeds and the level of heavy metal contamination in the local soil.
(1) Cotton extract numerous trace elements from the local soil, including toxic ones (Arsenic, Cadmium, Lead, Mercury, etc.). These toxic trace elements are accumulated/enriched in seeds.
(2) The amount of toxic elements in cotton seeds is an indicator of the heavy metal contamination of the local soil where the cotton grows.
(1) Analyzing samples of cotton seeds and their corresponding local soil with nuclear activation analysis;
(2) Investigate the relationship of toxic elemental level in the crop with heavy metal contamination of local soil.
(1) A detailed survey map of toxic elements level in cotton seeds in South Carolina;
(2) A detailed survey map of toxic elements level in cotton farm soil in South Carolina;
(3) A survey map of the toxic elements level in cotton seeds in the United States/World.
(1) One of the first attempts to study crops with nuclear activation analysis;
(2) Data collected can be used for future references;
(3) Demonstrate the environmental impact of human behavior on traditional cash crops and food industry.
(1) Sample Collection and Preparation: cotton seeds samples are collected in the South Carolina/United States/World; soil samples are collected in cotton farms at South Carolina; All the samples are prepared in the Applied Radiation Sciences Laboratory (APSL) at the South Carolina State University (SCSU).
(2) Irradiation and Measurement: irradiations are conducted at the PULSTAR Reactor at the North Carolina State University. Spectra measurements and gamma counting of samples are conducted at the ARSL at SCSU.
(3) Data Analysis: peak fitting and spectra analysis are performed by computer clusters at the ARSL. Some statistical models (Linear discriminant analysis, principal components analysis, etc.) are adopted in the data processing.
Developing Smart Container Network for Hazardous Materials Transportation
- Design and implement smart containers dedicated to hazardous material shipping.
- Design and implement a real-time data acquisition and data mining system for smart containers in hazardous material transportation.
- A new algorithm (HMMs) for sequence prediction over long categorical event streams will be applied to this big dataset in order to find an early indicator of future catastrophic events.
Laboratory and Office Space Request
Transit Research Center: office (150 ft2), Test Bay 1 (950 ft2);
Research and Conference Complex: Data/Cloud Server Room (200 ft2), Transportation Sensor Laboratory (500 ft2)
Gain new research equipment and train students in NE, EE, and CS.
Secure external funding and expand the research team in the transportation center.
Industry outreach: Orange-box (smart container)