Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Zixiang Zhao; Yichen Lu; Yu Zhan; Yuan Cheng; Fumo Yang; Jeffrey R Brook; Kebin He

doi:10.1016/j.scitotenv.2023.166693

Long-term spatiotemporal variations in surface NO₂ for Beijing reconstructed from surface data and satellite retrievals

Sci Total Environ. 2023 Dec 15:904:166693. doi: 10.1016/j.scitotenv.2023.166693. Epub 2023 Aug 30.

Authors

Zixiang Zhao¹, Yichen Lu¹, Yu Zhan², Yuan Cheng³, Fumo Yang⁴, Jeffrey R Brook⁵, Kebin He⁶

Affiliations

¹ Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
² College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China. Electronic address: yzhan@scu.edu.cn.
³ State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
⁴ College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China.
⁵ Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
⁶ State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.

PMID: 37657553
DOI: 10.1016/j.scitotenv.2023.166693

Abstract

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO₂), but few studies have compared their differences for application in surface NO₂ reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO₂ vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO₂ and to apply the resulting data to revisit the spatiotemporal variations in surface NO₂ for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R² values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO₂ at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO₂ during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R² of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO₂, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO₂ level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO₂ dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Keywords: Beijing; Machine learning; NO(2); OMI; Spatial downscaling; TROPOMI.