Abstract. Quantification of emission changes is a prerequisite for the assessment of
control effectiveness in improving air quality. However, the traditional
bottom-up method for characterizing emissions requires detailed
investigation of emissions data (e.g., activity and other emission
parameters) that usually takes months to perform and limits timely
assessments. Here we propose a novel method to address this issue by using a
response model that provides real-time estimation of emission changes based
on air quality observations in combination with emission-concentration
response functions derived from chemical transport modeling. We applied the
new method to quantify the emission changes on the North China Plain (NCP)
due to the COVID-19 pandemic shutdown, which overlapped the Spring Festival (also known as Chinese New Year)
holiday. Results suggest that the anthropogenic emissions of NO2,
SO2, volatile organic compound (VOC) and primary PM2.5 on the NCP were reduced by 51 %, 28 %,
67 % and 63 %, respectively, due to the COVID-19 shutdown, indicating
longer and stronger shutdown effects in 2020 compared to the previous Spring
Festival holiday. The reductions of VOC and primary PM2.5 emissions are
generally effective in reducing O3 and PM2.5 concentrations.
However, such air quality improvements are largely offset by reductions in
NOx emissions. NOx emission reductions lead to increases in
O3 and PM2.5 concentrations on the NCP due to the strongly VOC-limited
conditions in winter. A strong NH3-rich condition is also suggested
from the air quality response to the substantial NOx emission
reduction. Well-designed control strategies are recommended based on the air
quality response associated with the unexpected emission changes during the
COVID-19 period. In addition, our results demonstrate that the new
response-based inversion model can well capture emission changes based on
variations in ambient concentrations and thereby illustrate the great
potential for improving the accuracy and efficiency of bottom-up emission
inventory methods.
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