Rochelle Schneider
Rochelle Schneider is a Research Fellow in AI4EO at the European Space Agency Φ-Lab. She is also visiting scientist at ECMWF and honorary Assistant Professor at London School of Hygiene & Tropical Medicine (LSHTM). Dr Schneider holds a PhD in Geospatial Analytics, MSc GIS & Science, and MRes in Remote Sensing. Rochelle is passionate about EO missions and an advocate of building opportunities to introduce the benefits of satellite technologies into public health research, unlocking and ensuring the generation of global impact.
Sessions
Exposure to fine particulate matter (PM2.5) is linked to adverse health outcomes. Usually, epidemiological studies rely on PM2.5 measurements collected from ground monitors. However, in many places such as Great Britain the existing monitoring network provides limited spatio-temporal coverage of PM2.5. Data from satellites, climate/atmospheric reanalysis models, and chemical transport models offer additional information that can be used to reconstruct PM2.5 concentrations, filling the gaps in the ground monitoring network. This study developed a multi-stage satellite-based machine learning (ML) model to estimate daily PM2.5 levels across Great Britain during 2008-2018. Stage-1 estimated PM2.5 concentrations in monitors with only PM10 records. Stage-2 imputed missing satellite aerosol-optical-depth due to cloudiness and bad retrievals. Stage-3 applied the Random Forest algorithm to estimate PM2.5 concentrations using a combined dataset from Stage-1, Stage-2, and a list of spatiotemporally synchronised predictors. Stage-4 estimated daily PM2.5 using Stage-3 model. The model performed well with an overall mean R2 of 0.77. The high spatio-temporal resolution and the relatively high precision allowed these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.