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"Assessing the Causal Effects of Interventions on Air Quality Based on Machine Learning and Synthetic cControl Approaches" with Zongbo Shi, Professor, University of Birmingham.
"Assessing the Causal Effects of Interventions on Air Quality Based on Machine Learning and Synthetic cControl Approaches" with Zongbo Shi, Professor, University of Birmingham.
“Air quality interventions” include clean air actions and other measures that may affect air quality, such as COVID-19 lockdowns and climate policies. Understanding the impact “interventions” have on air quality is one of the key processes in air quality management. Observational data from monitoring networks are often used for assessing the air quality effectiveness of interventions. However, air pollution levels do not change linearly with emissions due to variations in weather conditions and chemical processes. Furthermore, they change on a seasonal and year by year basis at a specific location. In this seminar, I will present our studies on the changes in air pollutant concentrations arising from emission changes due to clean air actions and the COVID-19 lockdowns based on a machine learning technique and a “synthetic control” method. These methods are able to detect sudden decreases in air pollutant concentrations due to “interventions” such as COVID-19 lockdowns and holidays. They can also provide a quantitative evaluation of “causal” effects of the interventions.
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Contact: zhenqu@g.harvard.edu