PM2.5 during severe winter haze in Beijing, China, has reached levels as high as 880 μg/m3, with sulfur compounds contributing significantly to PM2.5 composition. This sulfur has been traditionally assumed to be sulfate, although atmospheric chemistry models are unable to account for such large sulfate enhancements under dim winter conditions. Using a 1‐D model, we show that well‐characterized but previously overlooked chemistry of aqueous‐phase HCHO and S(IV) in cloud droplets to form a S(IV)‐HCHO adduct, hydroxymethane sulfonate, may explain high particulate sulfur in wintertime Beijing. We also demonstrate in the laboratory that methods of ion chromatography typically used to measure ambient particulates easily misinterpret hydroxymethane sulfonate as sulfate. Our findings suggest that HCHO and not SO2 has been the limiting factor in many haze events in Beijing and that to reduce severe winter pollution in this region, policymakers may need to address HCHO sources such as transportation.
Abstract. In his study, we use a combination of multivariate statistical methods to
understand the relationships of PM2.5 with local meteorology and
synoptic weather patterns in different regions of China across various
timescales. Using June 2014 to May 2017 daily total PM2.5 observations
from ∼ 1500 monitors, all deseasonalized and detrended to focus
on synoptic-scale variations, we find strong correlations of daily PM2.5
with all selected meteorological variables (e.g., positive correlation with
temperature but negative correlation with sea-level pressure throughout
China; positive and negative correlation with relative humidity in northern
and southern China, respectively). The spatial patterns suggest that the
apparent correlations with individual meteorological variables may arise from
common association with synoptic systems. Based on a principal component
analysis of 1998–2017 meteorological data to diagnose distinct
meteorological modes that dominate synoptic weather in four major regions of
China, we find strong correlations of PM2.5 with several synoptic modes
that explain 10 to 40 % of daily PM2.5 variability. These modes
include monsoonal flows and cold frontal passages in northern and central
China associated with the Siberian High, onshore flows in eastern China, and
frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH)
region as a case study, we further find strong interannual correlations of
regionally averaged satellite-derived annual mean PM2.5 with annual mean
relative humidity (RH; positive) and springtime fluctuation frequency of the
Siberian High (negative). We apply the resulting PM2.5-to-climate
sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled
Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict
future PM2.5 by the 2050s due to climate change, and find a modest
decrease of ∼ 0.5 µg m−3 in annual mean
PM2.5 in the BTH region due to more frequent cold frontal ventilation
under the RCP8.5 future, representing a small “climate benefit”, but the
RH-induced PM2.5 change is inconclusive due to the large inter-model
differences in RH projections.
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