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A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

文献类型： 外文期刊

作者： Wu, Yangyang 1 ; Di, Baofeng 1 ; Luo, Yuzhou 3 ; Grieneisen, Michael L. 3 ; Zeng, Wen 1 ; Zhang, Shifu 1 ; Deng, Xunfe 1 ;

作者机构： 1.Sichuan Univ, Dept Environm Sci & Engn, Chengdu 610065, Sichuan, Peoples R China

2.Sichuan Univ, Inst Disaster Management & Reconstruct, Chengdu 610200, Sichuan, Peoples R China

3.Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA

4.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

5.China Geol Survey, Nat Resources Comprehens Survey Command Ctr, Beijing 100055, Peoples R China

6.Natl Engn Res Ctr Flue Gas Desulfurizat, Chengdu 610065, Sichuan, Peoples R China

7.Sichuan Univ, Yibin Inst Ind Technol, Yibin Pk, Yibin 644000, Peoples R China

关键词： Nitrogen dioxide; Long term; Back extrapolation; Machine learning; Concept drift; Exposure assessment

期刊名称：ENVIRONMENT INTERNATIONAL （ 影响因子：9.621； 五年影响因子：10.72 ）

ISSN： 0160-4120

年卷期： 2021 年 154 卷

页码：

收录情况： SCI

摘要： Background: Long-term surface NO2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO2 observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift. Objective: This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO2 levels across China during 2005-2018. Methods: On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO2 levels. Results: The validation against Taiwan's NO2 observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 mu g/m 3 , 7.1 to 4.3 mu g/m 3 , and 6.1 to 2.9 mu g/m 3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO2([NO2](pw)) during 2005-2012 was estimated as 40.2 and 50.9 mu g/m 3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO2](pw) increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO2> 40 mu g/m(3) were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively. Conclusion: With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO2 across China during 2005-2018, which is valuable for environmental management and epidemiological research.

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