Abstract
CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring.Article HighlightsThere is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring.The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control.Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field
Highlights
CBRN, or chemical, biological, radiological, and nuclear, is a label applied to hazardous events of a chemical, biological, radiological, or nuclear nature, in which an agent such as a chemical or a virus, or radiation, is dispersed into an area and poses a risk to individuals lives or health.The threat of CBRN events is an ever growing one in the modern world, brought to light by recent high-profile incidents such as the leaks from the Fukushima Daiichi Nuclear Plant following the earthquake which spread irradiated seawater into the pacific (World Nuclear Association 2020), and the release of the Novichok nerve agent in Salisbury (Carlsen 2018)
The Metop Satellites and several of the NOAA satellites are equipped with the MHS, or Microwave Humidity Sounder, and the Advanced Microwave Sounding Units (AMSUs) on older satellites, which measure the radiance in the microwave spectrum to produce vertical measurements of humidity and water content with the exception of small ice particles which are transparent to microwave frequencies (EUMETSAT 2020a)
Ground-based or aerial wind speed measuring services may be more applicable for low-altitude monitoring for CBRN purposes, such as measurements taken by wind turbines and modelling predictions, such as those provided in the UK by the Numerical Objective Analysis Boundary Layer, or NOABL, which predicts of wind velocity at heights of 10, 20, and 45 m (NOABL 2020)
Summary
CBRN, or chemical, biological, radiological, and nuclear, is a label applied to hazardous events of a chemical, biological, radiological, or nuclear nature, in which an agent such as a chemical or a virus, or radiation, is dispersed into an area and poses a risk to individuals lives or health. Any tools that can provide these will be of great value, and data from satellites can be useful in producing more accurate models and simulations to match the critical nature of these threats. The capabilities for high-resolution observations have improved over the past few years, allowing for improvements on existing model fidelity and utility. Using this information to improve inputs to CBRN modelling tools will produce valuable training and incident management capabilities to help combat CBRN events as they occur (Selva and Krejci 2012).
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