Space agency-specific standards for crew dose and risk assessment of ionising radiation exposures for the International Space Station.

The Partner Agencies of the International Space Station (ISS) maintain separate career exposure limits and shared Flight Rules that control the ionising radiation exposures that crewmembers can experience due to ambient environments throughout their space missions. In low Earth orbit as well as further out in space, energetic ions referred to as galactic cosmic radiation (GCR) easily penetrate spacecraft and spacecraft contents and consequently are always present at low dose rates. Protons and electrons that are trapped in the Earth's geomagnetic field are encountered intermittently, and a rare energetic solar particle event (SPE) may expose crew to (mostly) energetic protons.

A commentary on the impact of modelling results to inform mission planning and shield design.

A correspondence has been received in reference to a recently published article titled "On the decision making criteria for cis-lunar reference scenarios". The intent of the paper was to demonstrate: (i) a novel methodology for calculating the dose from solar particle events (SPEs), and (ii) the impact of the SPE parametric model, shield thickness, dose metric, and radiation transport code on choosing a worst-case scenario. This effort assumed a spherical, aluminum spacecraft with an internal diameter of 3.

On the decision making criteria for cis-lunar reference mission scenarios.

Space agencies are currently developing reference mission scenarios to determine if occupational dose limits, already adopted for low-Earth orbit (LEO) missions to the International Space Station (ISS), are also applicable for deep space cis-lunar missions. These cis-lunar missions can potentially last upwards of a year, during which astronauts will experience a daily low-dose from galactic cosmic radiation (GCR) and a potentially high-dose from single, or multiple, solar particle events (SPEs). Unlike GCR exposure, SPEs are difficult to predict and model due to their sporadic nature.