A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation. (September 2021)

• Record Type: Journal Article
• Authors: Wu, Yangyang; Di, Baofeng; Luo, Yuzhou; Grieneisen, Michael L.; Zeng, Wen; Zhang, Shifu; Deng, Xunfei; Tang, Yulei; Shi, Guangming; Yang, Fumo; Zhan, Yu
• Is Part Of: Environment international. Volume 154(2021)
• Publication Date: 2021-09
• Subjects: Nitrogen dioxide -- Long term -- Back extrapolation -- Machine learning -- Concept drift -- Exposure assessment; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106576
• Languages: English
• ISSNs: 0160-4120

Abstract

Graphical abstract: Highlights: Long-term daily NO2 are derived for post-policy evaluation and exposure assessment. A common modeling approach (Base-RF) gives biased estimation in back-extrapolation. We propose a novel approach named RBE-RF for the bias correction. Average NO2 levels for China in 2011 can be underestimated by 22.4% by Base-RF. National population exposed to NO2 > 40 µg/m 3 is 18.5% by Base-RF and 33.0% by RBE-RF. Abstract: 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 µg/m 3, 7.1 to 4.3 µg/m 3, and 6.1 to 2.9 µg/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 µg/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 µg/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.