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 and Saudi Aramco collaborated to develop a laser-based sensor for detecting trace amounts of gas leaks in petrochemical plants. The sensor uses machine learning to identify specific gases, differentiating it from previous sensors that only detect large leaks. The technology can differentiate between closely related industrial gases like benzene, toluene, ethyl benzene and xylene (BTEX). Why it matters: This innovation enables proactive monitoring and rapid pinpointing of leaks, enhancing safety, environmental protection, and operational efficiency in the petrochemical industry.
The AQABA project, a collaboration involving KAUST and international institutions, studies air quality and climate change in the Arabian Basin using a marine research vessel. The vessel traveled from France through the Suez Canal, around the Arabian Peninsula, and stopped at KAUST. Researchers presented findings on atmospheric dust, air pollution, and aerosol measurements, highlighting the impact of dust on renewable energy and air pollution on health. Why it matters: The project provides crucial data for understanding and addressing climate challenges and air quality issues in the Middle East.
KAUST is hosting Junfeng (Jim) Zhang from Duke University to study air pollution's impact on health in Saudi Arabia. Zhang will collaborate with KAUST faculty to assess the health effects of environmental stressors using epidemiology and toxicology. Air pollution causes significant premature deaths and loss of life expectancy in Saudi Arabia. Why it matters: This research will inform evidence-based policies and treatment strategies to combat respiratory illnesses linked to air pollution in Saudi Arabia and the broader region.
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.
MBZUAI researchers developed AirCast, a novel AI model for improved air pollution forecasting, which won the best paper award at the TerraBytes workshop during ICML. AirCast fuses weather and chemistry data using a Vision Transformer and frequency-weighted MAE to better predict extreme events like Saharan dust storms. In tests across the Middle East and North Africa, AirCast reduced PM2.5 error by 33% compared to a persistence baseline and outperformed the CAMS physics model. Why it matters: Accurate air pollution forecasting is critical for public health in the GCC region, and this research demonstrates a significant advancement using AI to address this challenge.
KAUST collaborated with NASA's Langley Research Center to launch six weather balloons from KAUST's Coastal & Marine Laboratory, reaching an altitude of 35 kilometers. The balloons were equipped with instruments to measure meteorological properties and characterize the optical properties of aerosols, including a Compact Optical Backscatter Aerosol Detector (COBALD). The research focuses on understanding the impact of dust aerosols on the Arabian Peninsula, including their effects on climate, air quality, and solar energy. Why it matters: This collaboration advances understanding of atmospheric aerosols in the region, with implications for climate modeling, solar energy efficiency, and Red Sea ecosystems.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.