GH011-04
Air quality effects of COVID-19 lockdowns and curfews – Case studies from East Africa

Monday, 14 December 2020: 17:45
Virtual
Michael Giordano1, Daniel Westervelt2, V Faye McNeill3, Albert Presto4, Paulina Jaramillo4, Emilia Tjernström5, John Mungai6, Jimmy Gasore7, Paul Green8, Deo Okure9, Matthias Beekmann10 and R Subramanian4, (1)CNRS, Paris Cedex 16, France, (2)Columbia University of New York, Palisades, United States, (3)Columbia University of New York, Chemical Engineering, Palisades, NY, United States, (4)Carnegie Mellon University, Pittsburgh, PA, United States, (5)University of Sydney, Sydney, Australia, (6)Innovations for Poverty Action, Nairobi, Kenya, (7)University of Rwanda, Kigali, Rwanda, (8)AirQo, Kampala, Uganda, (9)Airqo, Kampala, Uganda, (10)Laboratoire Inter-universitaire des Systèmes Atmosphériques (LISA), UMR CNRS 7583, Université Paris-Est Créteil, Université de Paris, Institut Pierre Simon Laplace (IPSL), Créteil, France
Abstract:
Collectively, urban East Africa generally struggles with air quality issues; PM2.5 concentrations are often at or above WHO guidelines. COVID-19 related actions are interventions that could directly reduce anthropogenic air pollution. East Africa has been host to differing responses to the COVID-19 pandemic – from a business as usual approach in Addis Ababa, Ethiopia to lockdowns, shelter-in-place, and curfew orders of varying lengths and degrees of severity in Kampala, Uganda; Nairobi, Kenya; and Kigali, Rwanda. Here we analyze the impact that implementing actions to control COVID-19 have had on these cities utilizing both reference-grade data from US Embassy BAMs and low-cost sensor data from the AfriqAir network. While disentangling COVID-19 actions from interannual variability is difficult, retrospective time series analysis suggests that intervention actions did indeed contribute to lowering PM2.5 concentrations by 10% or more in the cities that took actions. In particular, curfews contributed the most to PM2.5 reductions with reductions of 20% or more from normal overnight baselines. Deeper analysis, including statistical causal modeling, will also be presented.