A robust approach to deriving long-term daily surface NO levels across China: Correction to substantial estimation bias in back-extrapolation.

BACKGROUND

Long-term surface NO data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO 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 NO 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 NO 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 NO levels.

RESULTS

The validation against Taiwan's NO 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, 7.1 to 4.3 µg/m, and 6.1 to 2.9 µg/m in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO ([NO]) during 2005-2012 was estimated as 40.2 and 50.9 µg/m by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO] 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 NO > 40 µg/m 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 NO across China during 2005-2018, which is valuable for environmental management and epidemiological research.