Estimating ground-level CO concentrations across China based on national monitoring network and MOPITT: Potentially overlooked CO hotspots in the Tibetan Plateau

Abstract

<p><strong>Abstract.</strong> Given its relatively long lifetime in the troposphere, carbon monoxide (CO) is commonly employed as a tracer for characterizing airborne pollutant distributions. The present study aims to estimate the spatiotemporal distributions of ground-level CO concentrations across China during 2013–2016. A refined random-forest-spatiotemporal-kriging model (RF-STK) is developed to simulate daily gridded CO concentrations (0.1° grid with 98 341 cells) based on the extensive CO monitoring data and the Measurements of Pollution in the Troposphere CO retrievals (MOPITT-CO). The refined RF-STK model alleviates the negative effects of sampling bias and variance heterogeneity on the model training, resulting in cross-validation <i>R<sup>2</sup></i> of 0.51 and 0.71 for predicting daily and spatial CO concentrations, respectively. The national population-weighted CO concentrations were predicted to be (0.99 ± 0.30) mg m<sup>−3</sup> (µ±σ) and showed decreasing trends over all regions of China at a rate of (−0.021 ± 0.004) mg m<sup>−3</sup> per year. The CO pollution was more severe in North China (1.19 ± 0.30) mg m<sup>−3</sup>, and the predicted spatial pattern was roughly consistent with the MOPITT-CO. The hotspots in the Central Tibetan Plateau which were overlooked by the MOPITT were revealed by the refined RF-STK predictions. This information has an implication for improving the MOPITT-CO derivation procedure and air quality management.</p>