Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO2 emissions from global GEOS-Chem adjoint model and OMI SO2 data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO2 and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO2 emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions.
This study analyzed the effectiveness of temporary emission control measures on air quality of Nanjing, China during the Jiangsu Development Summit (JDS). We employed a regional chemistry model WRF-Chem to simulate air pollutants in Nanjing and compared the results to surface observations and satellite retrievals. During the JDS, air pollutant emissions from industry and transportation sectors largely decreased by 50–67% due to the short-term emission control measures such as reducing coal combustions, shutting down factories, and partially limiting traffic. Benefiting from the emission control, the simulated concentrations of PM2.5, NO2, SO2, CO and VOCs in Nanjing decreased by 17%, 20%, 20%, 19%, and 15% respectively, consistent with the surface and satellite observations. However, both the observed and simulated O3 increased by 3–48% during the JDS, which was mainly due to the remarkable NOx emission reduction (26%) in the downtown of Nanjing where the O3 production regime was mainly VOC-controlled. In addition, the atmospheric oxidation capacity and further the sulfur oxidation ratio, were facilitated by the elevated O3, which led to variable mitigation efficiencies of different secondary PM2.5 compositions. Our study offers an opportunity for understanding the coordinated control of PM2.5 and O3 in typical city clusters, and can provide implications for future mitigation actions.
Abstract. PM2.5, generated via both direct emission and secondary formation,
can have varying environmental impacts due to different physical and
chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online or offline observations and numerical models, which are generally high economic cost- or labor-intensive. In this study, we develop a new method,
named Multi-Tracer Estimation Algorithm (MTEA), to identify the primary and
secondary components from routine observation of PM2.5. By comparing
with long-term and short-term measurements of aerosol chemical
components in China and the United States, it is proven that MTEA can
successfully capture the magnitude and variation of the primary PM2.5
(PPM) and secondary PM2.5 (SPM). Applying MTEA to the China National Air Quality Network, we find
that (1) SPM accounted for 63.5 % of the PM2.5 in cities in southern China
on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China,
and at the same time the secondary proportion in regional background regions
was ∼ 19 % higher than that in populous regions; (2) the
summertime secondary PM2.5 proportion presented a slight but consistent
increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly
because of the recent increase in O3 pollution in China; (3) the
secondary PM2.5 proportion in Beijing significantly increased by 34 %
during the COVID-19 lockdown, which might be the main reason for the observed
unexpected PM pollution in this special period; and finally, (4) SPM and
O3 showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but
the correlations between total PM2.5 and O3 in these two regions, as determined from PPM levels,
were quite different. In general, MTEA is a promising
tool for efficiently estimating PPM and SPM, and has huge potential for
future PM mitigation.
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