Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014

Abstract China is the largest anthropogenic aerosol-generating country worldwide; however, few studies have analyzed the PM 2.5 chemical components and their underlying precursor emissions over long periods and across the national domain. First, global 3-D tropospheric chemistry and transport model (GEOS-Chem)-integrated satellite-retrieved aerosol optical depth (AOD) and vertical profiles were used to estimate near-surface sulfate and nitrate levels at 10-km resolution over China from 2006 to 2014. Ground measurement validation of our satellite model yielded correlation coefficients (r) of 0.7 and 0.73 and normalized mean bias (NMB) values of −37.96% and − 32.73% for sulfate and nitrate, respectively. Second, analyses of the spatiotemporal distributions of sulfate and nitrate as well as the vertical density Ozone Monitoring Instrument (OMI)-measured SO 2 (PBL_SO 2 ) and NO 2 (TVCD_NO 2 ) indicated that the highest nitrate and sulfate levels occurred in the North China Plain (~25 μg/m 3 ) and Sichuan Basin (SCB) (~30 μg/m 3 ), respectively. The long-term variations in the estimated components and precursor gases indicated that the large sulfate decline was positively correlated with the SO 2 emission reduction due to the mandatory desulfurization implemented in 2007. The annual growth rate of sulfate relative to the national mean was −6.19%/yr, and the concentration decreased by 17.10% from 2011 to 2014. Energy consumption increases and a lack of control measures for NO 2 resulted in persistent increases in NO 2 emissions and nitrate concentrations from 2006 to 2010, particularly in the SCB. With energy consumption structure advancements, reductions in NO 2 emissions and corresponding nitrate levels over three typical regions were prominent after 2012. Third, the estimated national-scale uncertainties of satellite datasets at 0.1° × 0.1° were 26.88% for sulfate and 25.55% for nitrate. Differences in the spatial distributions and temporal trends between our estimated components and precursor gases were mainly attributed to the dataset accuracy, the data pre-processing strategy, inconsistent column density and near-surface mass concentration, meteorological variables and complex chemical reactions.