Saudi Electricity Company (SEC) and KAUST have launched a pilot study at SEC’s Rabigh power plant to demonstrate a cryogenic technology that captures multiple pollutants and greenhouse gases, including carbon dioxide. The technology captures over 98% of carbon dioxide from flue gas, as well as sulfur dioxide, nitrogen oxides, and particulate matter, using a single system, unlike current technologies. The streamlined post-processing has a smaller environmental footprint and lower costs. Why it matters: This project supports Saudi Arabia's net-zero carbon goals and offers a potentially more efficient and cost-effective method for retrofitting existing power plants.
KAUST researchers are investigating the sources and chemistry of airborne particles to tackle urban air pollution. The research integrates laboratory simulations of atmospheric reactions with field measurements to understand the formation mechanisms of particulate matter (PM). They are also developing cellular and animal models to test how different air pollutants affect human health, in collaboration with the Center of Excellence for Smart Health. Why it matters: This research can inform targeted control strategies to manage emissions and improve air quality in Saudi Arabia and other countries facing similar pollution challenges.
KAUST has developed AirGo, a hybrid air quality monitoring system using mobile and stationary sensors. The system measures gases (carbon dioxide, carbon monoxide, sulfur dioxide, ozone, etc.) and particulate matter, providing real-time environmental data. AirGo is at technology readiness level 6 and is being scaled up for broader use through partnerships with manufacturers. Why it matters: This technology directly supports Saudi Vision 2030's environmental sustainability goals and the development of smart cities by providing granular air quality insights.
This paper introduces neural Bayes estimators for censored peaks-over-threshold models, enhancing computational efficiency in spatial extremal dependence modeling. The method uses data augmentation to encode censoring information in the neural network input, challenging traditional likelihood-based approaches. The estimators were applied to assess extreme particulate matter concentrations over Saudi Arabia, demonstrating efficacy in high-dimensional models. Why it matters: The research offers a computationally efficient alternative for environmental modeling and risk assessment in the region.
KAUST researchers reviewed 570 papers on alcohol combustion dating back to the early 1900s, synthesizing existing knowledge and identifying gaps in the literature. They developed a model that simulates alcohol combustion, gathering specific aspects to better understand combustion in engines. The study revealed properties of alcohol fuels, including high resistance to autoignition and decreased particulate matter emissions, but also increased emissions of carcinogenic aldehydes. Why it matters: This comprehensive study provides valuable insights for designing more efficient internal combustion engines operating on alcohols and addresses implications for air quality regulations.