Technical note: Reconstructing surface missing aerosol elemental carbon data in long-term series with ensemble learning

Abstract. Ground-based measurements of elemental carbon (EC) – classified under thermal-optical methods and considered as a surrogate for black carbon – are essential for assessing air quality and evaluating climate impacts. However, data gaps caused by technical challenges impede comprehensive analyses of long-term trends. This study proposes an ensemble learning method to address these challenges. The model uses readily accessible ground observation air pollutant data as proxies for EC-related tracers, along with meteorological parameters, to enhance prediction accuracy. It integrates outputs from Gradient Boosting Regression Trees, eXtreme Gradient Boosting, and Random Forest models, combining them through ridge regression to produce robust predictions. We applied this approach to reconstruct hourly EC concentrations from 2013 to 2023 for four cities in Eastern China, filling 45–79 % of missing data and improving prediction performance by 8–17 % compared to individual models. Over the 11-year period, EC exhibited an overall decline (-0.20 to -0.14 µg m^-3 a^-1), with a more significant decline from 2013 to 2020 (-0.24 to -0.15 µg m^-3 a^-1) from 3.26 µg m^-3 to 1.59 µg m^-3, followed by a noticeable slowdown from 2020 to 2023 (-0.12 to -0.04 µg m^-3 a^-1). Additionally, a fixed emission approximation method based on ensemble learning is proposed to quantitatively analyze the drivers of long-term EC trends. The analysis reveals that anthropogenic emission controls were the predominant contributors, accounting for approximately 92 % of the changes in EC trends from 2013 to 2020. However, their influence weakened post-2020, contributing approximately 80 %. These findings highlight that while China's Clean Air Actions implemented since 2013 have significantly reduced black carbon concentrations, sustained and enhanced strategies are still necessary to further mitigate black carbon pollution in the country.