Recipient Organization
GEORGIA STATE UNIVERSITY
UNIVERSITY PLAZA
ATLANTA,GA 30303
Performing Department
Computer Science
Non Technical Summary
The World Health Organization (WHO) states that exposure to radon gas is the second leading cause of lung cancer. Radon originates from the radioactive decay chain of uranium, and can be found particularly in regions with soils developed on uranium-rich geological substrates. Radon measurements today largely require manual methods or involve costly and bulky equipment which limits both the scale of measurements and the ability to generate informative online datastreams. At present there is no clear understanding of the spatio-temporal distribution of radon, the impact of soil and environmental factors on radon generation and penetration, and the spatio-temporal movement of radon gas in soil. This project develops a real-time radon measurement test-bed which will constitute a 200 node wireless sensor network to be deployed centered around the north-eastern suburbs of Atlanta in Georgia, USA, a metro area of 6 million residents with known high potential for radon exposure. This research will bring together an interdisciplinary team with complementary expertise in geoscience, soil science, chemistry, physics and computer science to measure and study soil radon penetration, distribution and movement in- situ at high temporal and spatial resolutions. Using the continuous time series measurements of radon in surficial soil (within few feet under surface) from 200 locations in the testbed site, along with intensive (static) and extensive (temporally and/or spatially variable) parameters, over long term and at high spatial resolution, this research will develop a radon prediction model that maps its distribution along space and time.This projectenhances national research infrastructure by building a sensor testbed and advances the understanding of the processes and factors that lead to radon build-up in soil and buildings. In addition to disseminating results through publications and presentations, the testbed access for experiments and all measurement data, will be made available to the community for advancing soil science and research. This project's interdisciplinary team will develop a set of course modules that will enable cross-disciplinary learning across computer science, engineering, geosciences, chemistry and physics. This project will create research experiences for undergraduate (REU) students and provide opportunities to cross-pollinate with other REU efforts in the university. This projectenables a broad participation of students at Georgia State University (GSU), the largest university in Georgia, with a large population of first-generation students, and designated as a Minority Serving Institution (Predominantly African-American and Hispanic serving Institution).
Animal Health Component
30%
Research Effort Categories
Basic
50%
Applied
30%
Developmental
20%
Goals / Objectives
The overarching goal of this research is to derive a clear understanding of the spatio-temporal distribution of radon, the impact of soil and environmental factors on radon generation and penetration, and the spatio-temporal movement of radon gas in soil. To address this, this research develops a real-time radon measurement test-bed which will constitute a 200 node wireless sensor network to be deployed centered around the north-eastern suburbs of Atlanta in Georgia, USA, a metro area of 6 million residents with known high potential for radon exposure. This research will bring together an interdisciplinary team with complementary expertise in geoscience, soil science, chemistry, physics and computer science to measure and study soil radon penetration, distribution and movement in- situ at high temporal and spatial resolutions. Using the continuous time series measurements of radon in surficial soil (within few feet under surface) from 200 locations in the testbed site, along with intensive (static) and extensive (temporally and/or spatially variable) parameters, over long term and at high spatial resolution, this research will develop a radon prediction model that maps its distribution along space and time.The objectives ofthis research are the following:1. Given a geologic bedrock system that emits radon gas into overlying soil and some internal radon generation within the soil, model radon concentration and exhalation across space and time as a function ofthe intensive (static: geological, soil porosity, bulk density, texture) and extensive (temporally and/or spatially variable: soil gas, soil water content, soil temperature, meteorological) parameters;2. Derivehigh resolution time-series measurements of soil radon with other intensive and extensive parameters, across space and time, and design data-driven modelsto predict radon concentration, exhalation and movement (dynamics) in soil;3. Design methodologies to obtain long term time-series measurements and conduct real-time prediction of soil radon by deploying sensors with low-power wireless communication capability, employing energy-efficient computing techniques, and leveraging edge and cloud computing.
Project Methods
The methods used by this project to achieve its goals are along the following research thrusts:Research Thrust 1: Soil, Sensor and Wireless Survey: This thrust involves (a) conducting soil characterization, geophysical survey, and soil gas survey; (b) designing the sensor-nest, calibrating the sensor-nest along with other site level environmental sensors; and (c) identifying and calibrating the most suitable off-the-shelf battery powered long-range low-power wireless technology for the testbed through on-site underground experiment trials.Research Thrust 2: Testbed Deployment and Radon Prediction: This thrust involves (a) implementing and deploying the soil radon wireless testbed in the Atlanta Dekalb county test site, in a sequential and an incremental fashion, and (b) designing empirical models, through statistical approaches and machine learning, to estimate and predict soil radon content across space and time.Research Thrust 3: Energy Efficient Protocols and Computing: This thrust involves (a) designing energy- efficient mechanisms and protocols to support battery-operated sensing and communication on the sensor-nests for long-term, yet maintain high fidelity in the data quality, and (b) develop a soil-to-cloud wireless computing framework to enable machine learning assisted soil radon prediction, and optimized data offloading to conserve computing resources on the battery operated sensing devices.The effectiveness of the research will be evaluated through testbed experimentation and its data analysis for (i) radon prediction model fidelity, (ii) energy efficiency, (iii) soil-to-cloud wireless performance, and (iv) environmental impact of the research.