Full-coverage mapping and spatiotemporal variations of ground-level
ozone (O3) pollution from 2013 to 2020 across China
Abstract
Ozone (O3) is an important trace and greenhouse gas in the atmosphere
yet, and it threatens the ecological environment and human health at the
ground level. Large-scale and long-term studies of O3 pollution in China
are few due to highly limited direct measurements whose accuracy and
density vary considerably. To overcome these limitations, we employed
the ensemble learning method of the extremely randomized trees model by
utilizing the spatiotemporal information of a large number of input
variables from ground-based observations, remote sensing, atmospheric
reanalysis, and model simulation products to estimate ground-level O3.
This method yields uniform, long-term and continuous spatiotemporal
information of daily maximum eight-hour average (MDA8) O3 over China
(called ChinaHighO3) from 2013 to 2020 at a 10 km resolution without any
missing values (spatial coverage = 100%). Evaluation against
observations indicates that our O3 estimations and predictions are
reliable with an average out-of-sample (out-of-station) coefficient of
determination (CV-R2) of 0.87 (0.80) and root-mean-square error of 17.10
(21.10) μg/m3 [units here are at standard conditions (273K,
1013hPa)], and are also robust at varying spatial and temporal scales
in China. This high-quality and full-coverage O3 dataset allows us to
investigate the exposure and trends in O3 pollution at both long- and
short-term scales. Trends in O3 concentrations varied substantially but
showed an average growth rate of 2.49 μg/m3/yr (p < 0.001)
from 2013 to 2020 in China. Most areas show an increasing trend since
2015, especially in summer ozone over the North China Plain. Our dataset
accurately captured a recent national and regional O3 pollution event
from 23 April to 8 May in 2020. Rapid increase and recovery of O3
concentrations associated with variations in anthropogenic emissions
were seen during and after the COVID-19 lockdown, respectively. This
carefully vetted and smoothed dataset is valuable for studies on air
pollution and environmental health in China.