Environmental, Water Resources, and Coastal Engineering Seminar Series

Fitts-Woolard Hall 2331

Air pollution and climate change in Africa: From low-cost sensors to supercomputers

Abstract: In Africa, air pollution exposure has been linked to 1 million premature deaths annually and, without intervention, these numbers are likely to climb. Sparse pollutant monitoring across the continent makes these estimates uncertain and also hinders the development of mitigation policies and regulations. Additionally, some of the most severe climate impacts are also felt in Africa, despite the continent’s relatively small contribution to global greenhouse gas emissions. This talk will highlight some recent efforts to close the air pollution data gap in Africa using a variety of methods spanning from traditional reference monitors, to consumer-grade low-cost sensors, to satellite retrievals, and to air quality and climate models. In particular we demonstrate the effectiveness of well-calibrated low cost particulate matter sensors in several previously-unmonitored megacities including Kinshasa (DRC), Lomé (Togo), Accra (Ghana), Nairobi (Kenya), and more. We find that many consumer-grade Plantower-based low-cost PM2.5 monitoring devices, such as PurpleAir, Clarity, and QuantAQ, perform well (r-squared ~ 0.6, MAE ~ 7 µg m-3) compared to locally available reference monitors, but can be improved dramatically (r-squared ~ 0.8, MAE ~ 2) using a variety of statistical methods, including linear regression, random forest regression, and Gaussian mixture regression. These well-calibrated sensors form the basis of dense urban networks of PM2.5 monitors in several African megacities, for example in Kinshasa (DRC), where the annual mean PM2.5 in 2019 was approximately 45 µg m-3, or ~8 times the WHO annual guideline. This talk will also present some research that leverages well-calibrated low-cost sensors and reference monitors to evaluate and improve the Africa region GEOS-Chem nested high-resolution model (25km x 25km). We also demonstrate the potential of fusing satellite data with ground-based observations using machine learning to develop high spatiotemporal resolution PM2.5 datasets, which may be useful in policy and health settings in addition to model evaluation. Finally, I will present climate modeling results that quantify how both local and remote aerosol emissions changes can have a substantial impact on African climate, in particular rainfall in the drought-stricken Sahel region of Africa.

Biography: Daniel Westervelt is currently a Lamont Assistant Research Professor at Columbia University’s Lamont Doherty Earth Observatory in Palisades, NY. In addition, he is a Columbia University Climate and Life Fellow leading a project on air pollution and climate change in Africa. He is also an affiliated scientist with the NASA Goddard Institute of Space Studies in New York, NY, and an air pollution advisor to the US State Department. I am an affiliate faculty of the Columbia University Data Science Institute. Prior to Lamont, Dr. Westervelt worked as a Science, Technology, and Environmental Policy (STEP) postdoctoral research associate at Princeton University. He completed his PhD degree in May 2013 in Civil and Environmental Engineering from Carnegie Mellon University.