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

Dongren Liu,Michael L Grieneisen,Yuzhou Luo,Xunfei Deng,Fumo Yang,Baofeng Di,Hanyue Zhang,Yu Zhan

doi:10.5194/acp-19-12413-2019

Abstract

Abstract. 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. 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 of Pollution in the Troposphere CO retrievals (MOPITT CO). The RF–STK model alleviated the negative effects of sampling bias and variance heterogeneity on the model training, with cross-validation R2 of 0.51 and 0.71 for predicting the daily and multiyear average CO concentrations, respectively. The national population-weighted average CO concentrations were predicted to be 0.99±0.30 mg m−3 (μ±σ) and showed decreasing 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 generally consistent with MOPITT CO. The hotspots in the central Tibetan Plateau where the CO concentrations were underestimated by MOPITT CO were apparent in the RF–STK predictions. This comprehensive dataset of ground-level CO concentrations is valuable for air quality management in China.

Highlights

Ground-level carbon monoxide (CO) is a worldwide atmospheric pollutant posing risks to human health and the environment (White et al, 1990; Reeves et al, 2002)
The national average concentrations would be overestimated if they were determined as the averages of all the monitoring data, as the CO concentrations were generally lower in remote areas
As most of the monitoring sites were in urban areas, we refined the random-forest–spatiotemporal kriging (RF–spatiotemporal kriging model (STK)) model through inversely weighting the training samples with the surrounding population densities

Summary

Introduction

Ground-level carbon monoxide (CO) is a worldwide atmospheric pollutant posing risks to human health and the environment (White et al, 1990; Reeves et al, 2002). While CO is formed naturally from the oxidation of methane and nonmethane volatile organic compounds, anthropogenic emissions from incomplete combustion of fossil fuels and biofuels contribute to approximately 42 % of the total atmospheric CO (Holloway et al, 2000; Pommier et al, 2013). In spite of the slow decrease in CO concentrations in recent years based on satellite retrievals (Xia et al, 2016; Zheng et al, 2018), China is still one of the countries with the most severe CO pollution in the world, and the combustion of fossil fuels is the dominant source of anthropogenic CO emissions (Wang et al, 2004; Duncan et al, 2007a). The national air pollution monitoring network in mainland China has been regularly observing ground-level CO concentrations since 2013 (MEPC, 2017) by the non-dispersive infrared absorption method and the gas filter correlation in-

Methods

Results

Conclusion