Air quality in sub-Saharan Africa
With support from the National Science Foundation and the US State Department, we have started an air quality monitoring network in Kinshasa, DR Congo, a megacity with population over 11 million which suffers from poor air quality yet has no monitoring infrastructure. Other projects include air quality knowledge capacity building in Accra, Ghana (partner with Ghana EPA), sensor deployments in Nairobi, Kenya, Kampala, Uganda, and Lomé, Togo, and using models and remote sensing techniques in India, China, and sub-Saharan Africa.
Regional climate response to changes in regional aerosol emissions
Some topics we have worked on in the past include: 1) The impact of changes in emissions in specific regions on local and remote climate, 2) Aerosol impacts on clouds and precipitation and 3) The effect of absorbing aerosols on tropical monsoon systems. We use and develop three coupled chemistry-climate models: GFDL climate models, GISS ModelE, and CESM (NCAR).
Atmospheric chemistry modeling and remote sensing
Modeling of air quality globally and regionally, especially over sub-Saharan Africa and India. We use the GEOS-Chem model among others. We also use satellite retrievals of air quality-relevant properties to understand local and regional air quality.
Clean Air Monitoring and Solutions Network (CAMS-Net)
The Clean Air Monitoring and Solutions Network, or CAMS-Net, is a National Science Foundation-funded project aimed at creating an international “network of networks” that will facilitate the exchange of knowledge, ideas, and data in order to improve the usage and application of low-cost sensor air quality data.
More info: https://camsnet.org/
East Africa Air Quality Community of Practice
Our specific goals are threefold: 1) to establish a mutual exchange of knowledge and data between all project partners and key actors in East African cities; 2) develop innovative curricula for an air quality management certificate program which will train current policymakers, administrators and other key actors as well as more specialized learning materials for the next generation of East African scientists; and 3) build on and help coordinate efforts to co-produce key building blocks for Air Quality Management Plans (AQMPs) in each city and co-implement an AQMP for at least one East Afr
Machine Learning to evaluate the information content of satellite data for fine particle estimates in India
Motivation: Given the sparse ground-level measurements of fine particle (PM2.5) mass and composition over India, we explore the potential to glean insights into air pollution sources by combining satellite retrievals of tropospheric trace gases and aerosols.
Primary objective: Determine whether tropospheric trace gas columns retrieved from satellite instruments can increase the accuracy of ground-level fine particle estimates inferred from satellite aerosol optical depth.
Led by postdoc Dr. Zhonghua Zheng.