Abstract. The contribution of meteorology and emissions to
long-term PM2.5 trends is critical for air quality management but has
not yet been fully analyzed. Here, we used the combination of a machine learning
model, statistical method, and chemical transport model to quantify the
meteorological impacts on PM2.5 pollution during 2000–2018.
Specifically, we first developed a two-stage machine learning PM2.5
prediction model with a synthetic minority oversampling technique to improve
the satellite-based PM2.5 estimates over highly polluted days, thus
allowing us to better characterize the meteorological effects on haze
events. Then we used two methods to examine the meteorological contribution to
PM2.5: a generalized additive model (GAM) driven
by the satellite-based full-coverage daily PM2.5 retrievals and
the Weather Research and Forecasting/Community Multiscale Air Quality
(WRF/CMAQ) modeling system. We found good agreements between GAM estimations and
the CMAQ model estimations of the meteorological contribution to PM2.5 on
a monthly scale (correlation coefficient between 0.53–0.72). Both methods
revealed the dominant role of emission changes in the long-term trend of
PM2.5 concentration in China during 2000–2018, with notable influence
from the meteorological condition. The interannual variabilities in
meteorology-associated PM2.5 were dominated by the fall and winter
meteorological conditions, when regional stagnant and stable conditions were
more likely to happen and when haze events frequently occurred. From 2000 to
2018, the meteorological contribution became more unfavorable to PM2.5
pollution across the North China Plain and central China but were more
beneficial to pollution control across the southern part, e.g., the Yangtze
River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in
2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the
long-term PM2.5 trends, the meteorology-driven anomalies also
contributed −3.9 % to 2.8 % of the annual mean PM2.5
concentrations in eastern China estimated from the GAM. The
meteorological contributions were even higher regionally, e.g., −6.3 %
to 4.9 % of the annual mean PM2.5 concentrations in the
Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain,
−4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3
% in the Pearl River Delta. Considering the remarkable meteorological
effects on PM2.5 and the possible worsening trend of meteorological
conditions in the northern part of China where air pollution is severe and
population is clustered, stricter clean air actions are needed to avoid haze
events in the future.
Remote impacts from the tropical Indian Ocean on haze pollution in January over the Yangtze River Delta
