The project is a collaboration between UAE Space Agency, the American University of Ras Al Khaimah and Khalifa University of Science and Technology (KUST).
The MeznSat nanosatellite that Ras Al Khaimah students are helping to develop, build and test, is planned for launch on a Soyuz-2 rocket from Russia in June 2020, Abdul-Halim Jallad, Director and Assistant Professor, Center of Information, Communication and Networking Education and Innovation (ICONET) said.
The nanosatellite is designed to detect greenhouse gas concentrations from an orbit of 565km above the Earth. The project successfully passed the Critical Design Review stage with the satellite currently undergoing the final stages of construction in the purpose-built cleanroom at AURAK’s Space Lab was moved on to the testing phase in March 2020.
Once in orbit, the team of students will then monitor, process and analyse the data from a ground station in the UAE. The processes and expertise involved in monitoring the atmosphere are similar to those employed during conventional Earth Observation programs. The project looks to support Emirati young people in developing the skillsets necessary for the UAE’s ambitious National Space Program and its future projects.
MeznSat will be the first student-built scientific satellite in the UAE. The project aims at providing the UAE space industry with well-trained graduates through hands-on experience, while at the same time opening windows for advanced space-oriented research relevant to the UAE.
The project has seen undergraduate students design and construct the MeznSat which will be used to collect and analyse data on carbon dioxide and methane levels, using a visible camera as well as a shortwave infrared spectrometer onboard. The project seeks to realise the Space Agency’s strategic goals of capacity development, promoting scientific research and coordinating national space sector activities.
It will also provide valuable insight into the concentration of nutrients in the coastal waters of the Arabian Gulf, which will allow for more accurate predictions of algal blooms and supports the timely implementation of relevant precautionary measures.