The goal of the project is twofold: 1) to better understand and further improve the use of low cost air pollution sensors and 2) to analyze and characterize air pollution data in sub-Saharan Africa. Air pollution kills an estimated 700,000 people per year in Africa, but existing air pollution data in Africa is extremely sparse and estimates of the associated mortality are uncertain. Low cost air pollution sensors have the potential to rapidly revolutionize air quality awareness and data availability in data-sparse areas of the world, including sub-Saharan Africa. However, use of low cost sensors requires careful calibration, performance evaluation, and other quality assurance before the data can be fully trusted to the same degree as regulatory-grade monitors. As part of a larger project led by Dr. Westervelt, fine particulate matter (PM2.5) sensors have already been deployed in several African megacities, including Kinshasa, Democratic Republic of Congo; Nairobi, Kenya; Kampala, Uganda; Accra, Ghana, and Lomé, Togo. In Kampala and Accra, sensors are co-located with a regulatory-grade PM2.5 instrument for several months, allowing for a direct comparison between low cost and regulatory-grade PM2.5 measurements, and also allowing for the development of calibration factors.
The objective is to use new large cloud-resolving simulations to try and better represent cloud processes in coarse-resolution climate models (~100km in horizontal resolution). Those simulations are global (spanning the entire globe) at 2km resolution and 30-minute output. The data will be hosted on google cloud platform (Pangeo) (the data size is about 50TB). We will in particular evaluate the impact of using Constitutional Neural Network (in time and space) and the capacity for out of sample prediction.
The ocean has absorbed the equivalent of 41% of industrial-age fossil carbon emissions. In the future, this rate of this ocean carbon sink will determine how much of mankind’s emissions remain in the atmosphere and drive climate change. To quantify the ocean carbon sink, surface ocean pCO2 must be known, but cannot be measured from satellite; instead it requires direct sampling across the vast and dangerous oceans. Thus, there will never be enough observations to directly estimate the carbon sink as it evolves. Data science can fill this gap by offering robust approaches to extrapolate from sparse observations to full coverage fields given auxiliary data that can be measured remotely.