Effect of linguistic framing and information provision on attitudes towards induced seismicity and seismicity regulation.

Shale gas is an expanding energy source worldwide, yet 'fracking' remains controversial. Amongst public concerns is induced seismicity (tremors). The UK had the most stringent induced seismicity regulations in the world, prior to instating a moratorium on shale gas development. The Government cited induced seismicity as the key rationale for its November 2019 English moratorium. Yet, little is known about how the public perceives induced seismicity, whether they support regulatory change, or how framing and information provision affect perceptions. Across three waves of a longitudinal experimental UK survey (N = 2777; 1858; 1439), we tested whether framing of induced seismicity influences support for changing regulations. The surveys compared (1) quantitative versus qualitative framings, (2) information provision about regulatory limits in other countries and (3) seismicity from other industries, and (4) framing a seismic event as an 'earthquake' or something else. We find low support for changing current policy, and that framing and information provision made little difference to this. The one strong influence on perceptions of seismic events came from the type of activity causing the event; shale gas extraction clearly led to the most negative reactions. We discuss implications for future UK policy on shale gas and geothermal energy in an evolving energy landscape.

Effect of "finite pool of worry" and COVID-19 on UK climate change perceptions.

Research reveals that a "finite pool of worry" constrains concern about and action on climate change. Nevertheless, a longitudinal panel survey of 1,858 UK residents, surveyed in April 2019 and June 2020, reveals little evidence for diminishing climate change concern during the COVID-19 pandemic. Further, the sample identifies climate change as a bigger threat than COVID-19. The findings suggest climate change has become an intransigent concern within UK public consciousness.

Near real-time soil erosion mapping through mobile gamma-ray spectroscopy.

Soil erosion has been associated with various negative environmental impacts foremost of which is the potential pressure it could impose on global food security. The poor conditions of our agricultural soil can be attributed to years of unsustainable farming practices occurring throughout history that has placed significant pressure on the environment. Moreover, climate change scenarios indicate further intensification which is likely making prediction and assessment of erosion processes critical for long term agricultural sustainability. This study demonstrates the potential of mobile gamma-ray spectrometry with large volume NaI(Tl) detectors to identify, at high spatial resolution, changes in 137Cs soil concentration within the ploughed layer of soil and enabling the soil erosion processes to be quantified. This technique represents a significant advantage over conventional spatially-isolated point measurements such as soil sampling as it offers real time mapping at the field scale. However, spectral signal derived from measurements in the field are highly dependent on the calibration procedure used and are particularly sensitive to source-detector changes such as the presence of a vehicle, ground curvature and soil moisture content. Conventional calibration procedures tend to not consider these potential sources of uncertainty potentially leaving the system vulnerable to systematic uncertainties, especially when 137Cs concentrations are low. This study used Monte Carlo simulations to investigate such changes utilising additional information including a high-resolution digital terrain model. The method was demonstrated on a ploughed site in Scotland, revealing a mixture of tillage and water erosion patterns supported by soil core data. Findings showed that the sites topography had relatively little effect (<10%) on calculated erosion rates, but moisture content could be the determining factor, albeit very difficult to measure reliably throughout a survey.

Rapid in situ assessment of radiocesium wood contamination using field gamma-ray spectroscopy to optimise felling.

The Chernobyl nuclear power meltdown that took place in 1986 has left a radioactive contamination legacy that currently severely limits the economic potential of impacted regions including the Polessie State Radioecology Reserve in Southern Belarus. Extensive areas of forested land could potentially become economically viable for firewood and building materials if radioactive contamination, notably 137Cs, could be characterised faster, whilst closely adhering to regulatory limits. Currently, laboursome tree coring and unreliable transfer factors derived from limited soil sampling data are routinely employed in felling decision making, which has financial repercussions owed to the large amounts of waste produced and unnecessary transportation costs. In this study, it is demonstrated that a combination of targeted mobile gamma-ray spectrometry and a newly developed, lead shielded, in situ gamma-ray spectrometry method can significantly speed up the process of characterisation of 137Cs wood activity in the field. For the in situ method, Monte Carlo calibration routines were developed alongside spectral processing procedures to unfold spectra collected in the field allowing for separation of ground and tree spectral components. Isolated contributions from the tree could then be converted to activity. The method was validated at a test facility and then demonstrated at three separate sites with differing contamination levels. This technique showed that single trees could be measured within approximately 20% of the activity compared to conventional tree core data. However, some discrepancies were found which were attributed to under sampling using the tree corer and low count rates at the lowest activity site, prompting the need for further data collection to optimise the method. It was concluded that this real-time approach could be a valuable tool for management of contaminated forested areas, releasing valuable timber and ultimately reducing the risk associated with living and working in these areas.

Reconstructing the deposition environment and long-term fate of Chernobyl 137Cs at the floodplain scale through mobile gamma spectrometry.

Cs-137 is considered to be the most significant anthropogenic contributor to human dose and presents a particularly difficult remediation challenge after a dispersal following nuclear incident. The Chernobyl Nuclear Power Plant meltdown in April 1986 represents the largest nuclear accident in history and released over 80 PBq of 137Cs into the environment. As a result, much of the land in close proximity to Chernobyl, which includes the Polessie State Radioecology Reserve in Belarus, remains highly contaminated with 137Cs to such an extent they remain uninhabitable. Whilst there is a broad scale understanding of the depositional patterns within and beyond the exclusion zone, detailed mapping of the distribution is often limited. New developments in mobile gamma spectrometry provide the opportunity to map the fallout of 137Cs and begin to reconstruct the depositional environment and the long-term behaviour of 137Cs in the environment. Here, full gamma spectrum analysis using algorithms based on the peak-valley ratio derived from Monte Carlo simulations are used to estimate the total 137Cs deposition and its depth distribution in the soil. The results revealed a pattern of 137Cs distribution consistent with the deposition occurring at a time of flooding, which is validated by review of satellite imagery acquired at similar times of the year. The results were also consistent with systematic burial of the fallout 137Cs by annual flooding events. These results were validated by sediment cores collected along a transect across the flood plain. The true merit of the approach was confirmed by exposing new insights into the spatial distribution and long term fate of 137Cs across the floodplain. Such systematic patterns of behaviour are likely to be fundamental to the understanding of the radioecological behaviour of 137Cs whilst also providing a tracer for quantifying the ecological controls on sediment movement and deposition at a landscape scale.

An in situ method for the high resolution mapping of 137Cs and estimation of vertical depth penetration in a highly contaminated environment.

The Chernobyl nuclear power plant meltdown has to date been the single largest release of radioactivity into the environment. As a result, radioactive contamination that poses a significant threat to human health still persists across much of Europe with the highest concentrations associated with Belarus, Ukraine, and western Russia. Of the radionuclides still prevalent with these territories 137Cs presents one of the most problematic remediation challenges. Principally, this is due to the localised spatial and vertical heterogeneity of contamination within the soil (~10's of meters), thus making it difficult to accurately characterise through conventional measurement techniques such as static in situ gamma-ray spectrometry or soil cores. Here, a practical solution has been explored, which utilises a large number of short-count time spectral measurements made using relatively inexpensive, lightweight, scintillators (sodium iodide and lanthanum bromide). This approach offers the added advantage of being able to estimate activity and burial depth of 137Cs contamination in much higher spatial resolution compared to traditional approaches. During the course of this work, detectors were calibrated using the Monte Carlo Simulations and depth distribution was estimated using the peak-to-valley ratio. Activity and depth estimates were then compared to five reference sites characterised using soil cores. Estimates were in good agreement with the reference sites, differences of ~25% and ~50% in total inventory were found for the three higher and two lower activity sites, respectively. It was concluded that slightly longer count times would be required for the lower activity (<1MBqm-2) sites. Modelling and reference site results suggest little advantage would be gained through the use of the substantially more expensive lanthanum bromide detector over the sodium iodide detector. Finally, the potential of the approach was demonstrated by mapping one of the sites and its surrounding area in high spatial resolution.

Mapping the spatial distribution and activity of (226)Ra at legacy sites through Machine Learning interpretation of gamma-ray spectrometry data.

Radium ((226)Ra) contamination derived from military, industrial, and pharmaceutical products can be found at a number of historical sites across the world posing a risk to human health. The analysis of spectral data derived using gamma-ray spectrometry can offer a powerful tool to rapidly estimate and map the activity, depth, and lateral distribution of (226)Ra contamination covering an extensive area. Subsequently, reliable risk assessments can be developed for individual sites in a fraction of the timeframe compared to traditional labour-intensive sampling techniques: for example soil coring. However, local heterogeneity of the natural background, statistical counting uncertainty, and non-linear source response are confounding problems associated with gamma-ray spectral analysis. This is particularly challenging, when attempting to deal with enhanced concentrations of a naturally occurring radionuclide such as (226)Ra. As a result, conventional surveys tend to attribute the highest activities to the largest total signal received by a detector (Gross counts): an assumption that tends to neglect higher activities at depth. To overcome these limitations, a methodology was developed making use of Monte Carlo simulations, Principal Component Analysis and Machine Learning based algorithms to derive depth and activity estimates for (226)Ra contamination. The approach was applied on spectra taken using two gamma-ray detectors (Lanthanum Bromide and Sodium Iodide), with the aim of identifying an optimised combination of detector and spectral processing routine. It was confirmed that, through a combination of Neural Networks and Lanthanum Bromide, the most accurate depth and activity estimates could be found. The advantage of the method was demonstrated by mapping depth and activity estimates at a case study site in Scotland. There the method identified significantly higher activity (<3 Bq g(-1)) occurring at depth (>0.4m), that conventional gross counting algorithms failed to identify. It was concluded that the method could easily be employed to identify areas of high activity potentially occurring at depth, prior to intrusive investigation using conventional sampling techniques.

Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.

The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application.

Development of a neural network approach to characterise (226)Ra contamination at legacy sites using gamma-ray spectra taken from boreholes.

There are a large number of sites across the UK and the rest of the world that are known to be contaminated with (226)Ra owing to historical industrial and military activities. At some sites, where there is a realistic risk of contact with the general public there is a demand for proficient risk assessments to be undertaken. One of the governing factors that influence such assessments is the geometric nature of contamination particularly if hazardous high activity point sources are present. Often this type of radioactive particle is encountered at depths beyond the capabilities of surface gamma-ray techniques and so intrusive borehole methods provide a more suitable approach. However, reliable spectral processing methods to investigate the properties of the waste for this type of measurement have yet to be developed since a number of issues must first be confronted including: representative calibration spectra, variations in background activity and counting uncertainty. Here a novel method is proposed to tackle this issue based upon the interrogation of characteristic Monte Carlo calibration spectra using a combination of Principal Component Analysis and Artificial Neural Networks. The technique demonstrated that it could reliably distinguish spectra that contained contributions from point sources from those of background or dissociated contamination (homogenously distributed). The potential of the method was demonstrated by interpretation of borehole spectra collected at the Dalgety Bay headland, Fife, Scotland. Predictions concurred with intrusive surveys despite the realisation of relatively large uncertainties on activity and depth estimates. To reduce this uncertainty, a larger background sample and better spatial coverage of cores were required, alongside a higher volume better resolution detector.