Keyong Huang, Qingyang Zhu, Xiangfeng Lu, Dongfeng Gu, Yang Liu. (2023). Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO₂ Concentrations and Attributable Health Burdens in China From 2005 to 2020. GeoHealth, 2023, 7(5): e2023GH000798. doi: 10.1029/2023GH000798.

 

Read online: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GH000798

 

Previous studies about air pollution in China focused on fine particulate matter and ozone. As one of the major NO_x emission countries worldwide, China has experienced serious air pollution in last decades. However, the long-term spatiotemporal distribution of NO₂ levels, especially before 2013, was still unknown. In the current study, we firstly gap filled the missing OMI satellite NO2 values, then developed an ensemble machine learning model to estimate the spatiotemporal pattern of monthly mean NO₂ concentrations at 0.05° spatial resolution from 2005 to 2020 in China and further estimated its attributable disease burden. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R² were 0.88, 0.82 and 0.73, respectively. In addition, our model can provide accurate historical NO₂ concentrations, with both by-year CV R² and external separate year validation R² achieving 0.80. The estimated national NO₂ levels showed increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The annual mortality burden attributable to NO₂ exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. Our model could provide reliable long-term NO₂ predictions at a high spatial resolution for epidemiological studies in China. Our results also highlighted the heavy disease burden by NO₂ and call for policies to reduce the emission of NOx in China.

 

By-year cross validation and external validation using data from year 2020.

 

Annual mean mortality burden attributable to long-term NO₂ exposure in China. (a), annual NO₂ related mortality burden per 100,000 persons at provincial level in 2005, 2010, 2015, and 2020. (b), national mean NO₂ related mortality burden from 2005 to 2020.

Wenhao Wang, Xiong Liu, Jianzhao Bi, Yang Liu, A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology, Environment International, Volume 158, 2022, 106917
https://doi.org/10.1016/j.envint.2021.106917

Abstract: Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)’s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R2 of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

Keywords: Surface ozone; TROPOMI; OMI; Ozone profile; HRRR; Random forest; Spatiotemporal distribution