Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China

Abstract

Continuously monitoring surface ozone (O₃) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O₃ estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a Deep Forest 21 (DF21) model to interpolate O₃ concentration by learning spatial patterns and a Long Short-Term Memory (LSTM) model to forecast O₃ concentration by learning data from the past. We used sites of city-level <i>in-situ</i> networks as the control sites to manifest short-distance O₃ transportation. Satellite-based observations of O₃ precursor indicators were incorporated to capture O₃ photochemical reactions. DF21 explained a larger fraction of O₃ variability (90 %) with a mean bias error of smaller than 1 &#x03BC;<i>g/m³</i>. We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas (<i>R²</i> &#x003E; 0.85 and bias &#x003C; 2 &#x03BC;<i>g/m³</i>). The forecasted O₃ concentration via LSTM showed a good and stable agreement (<i>R²</i> &#x2248; 0.85 and bias &#x003C; 5 &#x03BC;<i>g/m³</i>) with ground-based measurements for 8-hour, 24-hour, 28-hour, and 72-hour time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O₃ estimates for broad users.