Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau
Estimating ground-level CO concentrations across China based on the national monitoring network and MOPITT: potentially overlooked CO hotspots in the Tibetan Plateau
复制标题
基于国家监测网络和MOPITT估算中国地面CO浓度：青藏高原可能被忽视的CO热点
DOI：
10.5194/acp-19-12413-2019
复制
发表时间：
2019-10-08
影响因子：
6.3
通讯作者：
Zhan, Yu
中科院分区：
文献类型：
--
作者：
Liu, Dongren;Di, Baofeng;Zhan, Yu
抽象的。考虑到碳在对流层中相对较长的寿命，
一氧化碳（CO）通常用作表征空气中
污染物分布本研究旨在评估
中国地面CO浓度时空分布特征
在2013-2016年期间。我们改进了随机森林时空克里格法
（RF-STK）模型，以模拟0.1 ℃的每日CO浓度
基于广泛的CO监测数据和
对流层CO污染反演（MOPITT CO）。RF-STK模型
缓解了抽样偏差和方差异质性的负面影响
在模型训练中，交叉验证R2为0.51和0.71，
分别预测日平均和多年平均CO浓度。
全国人口加权平均CO浓度预测为
为0.99±0.30 mg m−3（μ±σ），并显示
在中国所有地区呈下降趋势，下降率为-0.021 0. 004 mg m−3 yr−1。华北地区CO污染较严重
(1.19±0.30 mg m−3），预测模式通常为
与MOPITT公司一致。青藏高原中部的热点，
CO浓度被低估的MOPITT CO是明显的，
RF-STK预测。这一全面的地面CO数据集
对我国的空气质量管理具有一定的参考价值。
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.