Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy
Authors: Park Jincheol , Jung Jia , Choi Yunsoo , Lim Hyunkwang , Kim Minseok , Lee Kyunghwa , Lee Yun Gon , Kim Jhoon 
Published: June 2023

Abstract. In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded
by the Geostationary Environment Monitoring Spectrometer (GEMS) and its
sister instruments, this study aims to establish a top-down approach for
adjusting aerosol precursor emissions over East Asia. This study involves a
series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by
sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of
NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions
adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result
of the improvement in the quantity of available data. Taking advantage of
the data fusion product, we perform sequential emissions adjustments during
the spring of 2022, the period during which the substantial reductions in
anthropogenic emissions took place accompanied by the COVID-19 pandemic
lockdowns over highly industrialized and urbanized regions in China. While
the model initially overestimates surface PM2.5 concentrations by
47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx
and primary PM emissions by 7.84 % and 9.03 %, respectively,
substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of
emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model
biases in simulations of aerosol loading over East Asia; in particular, the
model performance tends to improve to a greater extent on the condition that
spatiotemporally more continuous and frequent observational references are
used to capture variations in bottom-up estimates of emissions. In addition
to reconfirming the close association between aerosol precursor emissions
and AOD as well as surface PM2.5 concentrations, the findings of this
study could provide a useful basis for how to most effectively exploit
multisource top-down information for capturing highly varying anthropogenic emissions.