Quantification and evaluation of atmospheric ammonia emissions with different methods: a case study for the Yangtze River Delta region, China

Abstract

Abstract. To explore the effects of data and method on emission estimation, two inventories of NH3 emissions of the Yangtze River Delta (YRD) region in eastern China were developed for 2014 based on constant emission factors (E1) and those characterizing agricultural processes (E2). The latter derived the monthly emission factors and activity data integrating the local information of soil, meteorology, and agricultural processes. The total emissions were calculated to be 1765 and 1067 Gg with E1 and E2, respectively, and clear differences existed in seasonal and spatial distributions. Elevated emissions were found in March and September in E2, attributed largely to the increased top dressing fertilization and to the enhanced NH3 volatilization under high temperature, respectively. A relatively large discrepancy between the inventories existed in the northern YRD with abundant croplands. With the estimated emissions 38 % smaller in E2, the average of simulated NH3 concentrations with an air quality model using E2 was 27 % smaller than that using E1 at two ground sites in the YRD. At the suburban site in Pudong, Shanghai (SHPD), the simulated NH3 concentrations with E1 were generally larger than observations, and the modeling performance was improved, indicated by the smaller normalized mean errors (NMEs) when E2 was applied. In contrast, very limited improvement was found at the urban site JSPAES, as E2 failed to improve the emission estimation of transportation and residential activities. Compared to NH3, the modeling performance for inorganic aerosols was better for most cases, and the differences between the simulated concentrations with E1 and E2 were clearly smaller, at 7 %, 3 %, and 12 % (relative to E1) for NH4+, SO42-, and NO3-, respectively. Compared to the satellite-derived NH3 column, application of E2 significantly corrected the overestimation in vertical column density for January and October with E1, but it did not improve the model performance for July. The NH3 emissions might be underestimated with the assumption of linear correlation between NH3 volatilization and soil pH for acidic soil, particularly in warm seasons. Three additional cases, i.e., 40 % abatement of SO2, 40 % abatement of NOx, and 40 % abatement of both species, were applied to test the sensitivity of NH3 and inorganic aerosol concentrations to precursor emissions. Under an NH3-rich condition, estimation of SO2 emissions was detected to be more effective on simulation of secondary inorganic aerosols compared to NH3. Reduced SO2 would restrain the formation of (NH4)2SO4 and thereby enhance the NH3 concentrations. To improve the air quality more effectively and efficiently, NH3 emissions should be substantially controlled along with SO2 and NOx in the future.

Improvement from the satellite-derived NO<sub>x</sub> emissions on air quality modeling and its effect on ozone and secondary inorganic aerosol formation in Yangtze River Delta, China

“…NH -neutral or even NH 3 -poor condition in winter, consistent with the judgment through AQM based on an updated NH emission inventory (Zhao et al, 2020), as the NH volatilization in winter was much smaller than other seasons. Reducing NH 3 emissions was the most efficient way to control SNA pollution for the region in winter.…”

“…improved O simulation, i.e., the elevated O was an important reason for the enhanced the formation of SNA in winter (Huang et al, 2020). Similarly, the increased NO 3 was found for more than half of the YRD region in April, along with the growth of O 3 concentrations (Fig.…”

“…Shown in Table 2 from the reduced NO X emissions promoted it (Cai et al, 2017;Huang et al, 2020). In summer, the former dominated the process with the most evident improvement in NO 2 simulation (Figure 3), thus the reduced NO 3 concentrations that were closer to observation were simulated for all the cities.…”

“…The response of SNA concentrations to the changes in precursor emissions was influenced by various factors including the abundance of NH 3 , atmospheric oxidation, and the chemical regime of O 3 formation (Wang et al, 2013;Cheng et al, 2016;Zhao et., 2020). To explore the sensitivity of SNA formation to its precursor emissions, four cases were set besides the base scenario for January 2016, the month with the largest observed SNA concentrations.…”

“…For example, Wang et al (2019) found that the simulated O 3 concentrations in Shanghai (the most developed city in eastern China) could increase over 20% with a 60% reduction in NO X emissions in summer 2016, implying a clear "VOC-limit" pattern for the O 3 formation in the mega city. For the response of SIA to NO X emissions, the NH 4 + and SO 4 2concentrations at an urban site in another mega city Nanjing in eastern China were simulated to increase 1.9% and 2.8% with a 40% abatement of NO X emissions in autumn 2014, respectively, due to the weakened competition of SIA formation against SO 2 (Zhao et al, 2020). To our knowledge, however, the relatively new information from the inversed modeling of NO X emissions has not been sufficiently incorporated into the SIA and O 3 analyses with AQM in China.…”