Mapping the daily nitrous acid (HONO) concentrations across China during 2006-2017 through ensemble machine-learning algorithm.

Abstract

Nitrous acid (HONO) is a major source of the hydroxyl radical (OH) and plays a key role in atmospheric photochemistry. The lack of spatially resolved HONO concentration information results in large knowledge gaps of HONO and its role in atmospheric chemistry and air pollution in China. In this work, an ensemble machine learning model comprising of random forest, gradient boosting, and back propagation neural network was proposed, for the first time, to estimate the long-term (2006-2017) daily HONO concentrations across China in 0.25° resolution. Further, the key factors controlling the space-time variablity of HONO concentrations were analyzed based on variable importance values. The ensemble model well characterized the spatiotemporal distribution of daily HONO concentrations with the sampled-based, site-based and by-year cross-validation (CV) R2 (RMSE) of 0.7 (0.36 ppbv), 0.67 (0.36 ppbv), and 0.62 (0.40 ppbv), respectively. HONO hotspots were mainly distributed in the Beijing-Tianjin-Hebei (BTH), Pearl River Delta (PRD), Yangtze River Delta (YRD), and several sites of Sichuan Basin, in line with the distribution patterns of the tropospheric NO2 columns and assimilated surface NO3- levels. The national HONO levels stagnated during 2006-2013, then declined after 2013 benefiting from the implementation of the Action Plan for Air Pollution Prevention and Control. The NO3- concentration, urban area, NO2 column density ranked as important variables for HONO prediction, while agricultral land, forest and grassland played minor roles in affecting HONO concentrations, suggesting the significant role of heterogeneous HONO production from anthropogenic precursor emissions. Leveraging the ground-level HONO observations, this study fills the gap of statistically modelling nationwide HONO in China, which provides essential data for atmospheric chemistry research.