Imputation Strategies for Predicting Tropospheric Ozone
Abstract
: Tropospheric Ozone (TO) prediction is essential for air-quality management; however, the construction of robust and accurate predictive models is hindered by missing data in Air-Quality Station (AQS) time series. The development of reliable predictive models depends on uninterrupted datasets to properly capture the underlying dynamics and ensure stable parameter estimation. AQS time series frequently contain missing data due to sensor malfunctions, maintenance activities, power interruptions, communication failures, and equipment disruption caused by wildlife. This study evaluates two imputation strategies, Multivariate Imputation by Chained Equations (MICE) and the incorporation of data from nearby Weather Stations (WS), to mitigate missing data in AQS time series. Using multi-year real data from an AQS in Paran´a, Brazil, we compare the performance of XGBoost models trained with each imputation strategy for TO prediction. The results show that, although both methods are robust, imputation using nearby WS data yields similar performance (R² = 0.87, RMSE = 2.60 ppb) compared to MICE (R² = 0.86, RMSE = 2.66 ppb). These findings suggest that different imputation strategies can yield comparable performance when developing predictive models for TO.