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. Given its relatively long lifetime in the troposphere, carbonmonoxide (CO) is commonly employed as a tracer for characterizing airbornepollutant distributions. The present study aims to estimate thespatiotemporal distributions of ground-level CO concentrations across Chinaduring 2013–2016. We refined the random-forest–spatiotemporal kriging(RF–STK) model to simulate the daily CO concentrations on a 0.1 ∘ grid based on the extensive CO monitoring data and the Measurements ofPollution in the Troposphere CO retrievals (MOPITT CO). The RF–STK modelalleviated the negative effects of sampling bias and variance heterogeneityon the model training, with cross-validation R2 of 0.51 and 0.71 forpredicting the daily and multiyear average CO concentrations, respectively.The national population-weighted average CO concentrations were predicted tobe 0.99±0.30  mg m −3 ( μ±σ ) and showeddecreasing trends over all regions of China at a rate of - 0.021 ± 0.004  mg m −3  yr −1 . The CO pollution was more severe in North China( 1.19±0.30  mg m −3 ), and the predicted patterns were generallyconsistent with MOPITT CO. The hotspots in the central Tibetan Plateau wherethe CO concentrations were underestimated by MOPITT CO were apparent in theRF–STK predictions. This comprehensive dataset of ground-level COconcentrations is valuable for air quality management in China.

atmospheric chemistry and physics

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