Changes in PM_2.5 sensitivity to NO_x and NH₃ emissions due to a large decrease in SO₂ emissions from 2013 to 2018

ABSTRACT

The authors evaluated and compared the behavior of PM_2.5 with respect to NO_x and NH₃ emission changes in high (the year 2013) and low (the year 2018) SO₂ emission cases. Two groups of simulations were conducted based on anthropogenic emissions from China in 2013 and 2018, respectively. In each group of simulations, a respective 25% reduction in NO_x and NH₃ emissions were assumed. A sensitivity factor (β) was defined as the relative change in PM_2.5 concentration due to 1% change in NO_x or NH₃ emissions. In the high SO₂ emissions case, PM_2.5 was more sensitive to NH₃ (0.31) emissions change than NO_x (0.21). Due to the significant decrease in SO₂ emissions from the high to low SO₂ emissions case, the sensitivity of PM_2.5 to NO_x increased to 0.33, while its sensitivity to NH₃ decreased to 0.22. The result implies that now and in the future, PM_2.5 is/will be less sensitive to NH₃ emissions change, while NO_x emissions control is more effective in reducing the surface PM_2.5 concentration. Seasonally, in the low SO₂ emissions case, the sensitivities of PM_2.5 to NO_x and NH₃ in winter were higher than those in summer, indicating that to deal with severe winter haze more attention should be paid to the emissions control of inorganic PM_2.5 precursors, especially NO_x.

Graphical abstract

摘要

随着大气污染防治计划的实施, 我国SO₂排放量从2013年的25.4 Tg下降至2018年的10.5 Tg。SO₂排放的大幅变化是否会影响PM_2.5对NO_x和NH₃排放的敏感性尚不清楚。为此, 本研究采用GEOS-Chem全球大气化学传输模式, 评估了中国东部不同SO₂排放水平下PM_2.5对NO_x和NH₃敏感性的变化。结果表明: 2013年, PM_2.5对NH₃的敏感性 (0.31) 比NO_x (0.21) 更强。2013~2018年, 由于SO₂排放大幅下降, PM_2.5对NO_x的敏感性上升至0.33, 而对NH₃的敏感性则下降到0.22。因此, 在较低的SO₂排放情景下, 考虑到PM_2.5对NH₃的敏感性降低, 进一步消减NO_x排放可能对降低PM_2.5浓度有更好的效果。

KEYWORDS:

• SO₂ emissions
• PM_2.5 sensitivity to NO_x
• PM_2.5 sensitivity to NH₃
• emissions control strategy

关键词:

• SO₂排放；PM_2.5对NO_x的敏感性；PM_2.5对NH₃的敏感性；减排控制措施

1. Introduction

Secondary inorganic aerosols (sulfate, nitrate, and ammonium; simplified to SNA) are important constituents of PM_2.5, occupying up to a half of the total PM_2.5 mass (Pan et al. 2016; Wang et al. 2014). Sulfur dioxide (SO₂), nitrogen oxides (NO_x), and ammonia (NH₃) are precursors of SNA. In the atmosphere, SO₂ and NO_x are oxidized to sulfuric acid (H₂SO₄) and nitric acid (HNO₃), and then they are neutralized by NH₃. NH₃ prefers to form ammonium sulfate ((NH₄)₂SO₄) first for its stability. Ammonium nitrate (NH₄NO₃) is semi-volatile, and the low temperature and high humidity are favorable for NH₄NO₃ staying in the condensation state. Emission changes of SO₂, NO_x, and NH₃ will lead to changes in SNA concentration and then PM_2.5 concentration.

The sensitivity of PM_2.5 to SO₂, NO_x, and NH₃ emission changes is estimated to evaluate emission control strategies and to design effective emission control policies (Wang et al. 2013; Zhang et al. 2019, 2015b). Wang et al. (2013) reported that from 2000 to 2006, a large increase in SO₂ (60%) and NO_x (80%) emissions resulted in a 60% lifting of the SNA concentration over China. Since then, from 2006 to 2015, changes in SO₂ (−16%) and NO_x (+16%) emissions have led to an overall decrease in the surface SNA concentration, while an increase in NH₃ emissions has offset the benefit of SO₂ and NO_x emissions control (Wang et al. 2013). Fu et al. (2017) and Wang et al. (2013) pointed out the necessity of NO_x and NH₃ emissions control for the high sensitivity of PM_2.5 with respect to NO_x and NH₃ emissions change. However, these studies were conducted at a relatively high SO₂ emissions level. From 2013 through 2017, observations from satellites have indicated a significant decrease in the SO₂ column over China (Figure S1), and SO₂ emissions from China were estimated to have decreased by 59% (Zheng et al. 2018). Considering the thermodynamic equilibrium among H₂SO₄, HNO₃, and NH₃, a sharp reduction in SO₂ emissions will alter the sensitivity of the PM_2.5 response to NO_x and NH₃ emission changes (Wang et al. 2013).

The purpose of this paper is to assess the influence of the large SO₂ emissions reduction from 2013 to 2018 on the sensitivity of PM_2.5 to NO_x and NH₃ emissions control and make recommendations for future emission control policies with respect to PM_2.5 precursors. We introduce the model and simulation scenarios in section 2. In section 3, we discuss the sensitivity of PM_2.5 to NO_x and NH₃ emission changes at different SO₂ emission levels. Concluding remarks are given in section 4.

2. Model and simulation

2.1. Model description

We used the GEOS-Chem global chemical transport model (http://www.geos-chem.org), v11-1. The global version with a horizontal resolution of 2° × 2.5° was employed, driven by the MERRA-2 meteorology field. All simulations in this study used meteorology fields from December 2012 to December 2013, and the first month was for spin-up. The aerosol species considered in the GEOS-Chem model include SNA aerosol, carbonaceous aerosol, dust aerosol, sea salt, and marine POA (primary organic aerosols). SNA aerosol simulation coupled to gas-phase chemistry was originally described by Park et al. (2004). SNA thermodynamics are calculated using the module of ISORROPIA II, developed by Fountoukis and Nenes (2007). The default anthropogenic emissions in GEOS-Chem are from EDGAR v4.2 (EC-JRC/PBL, http://edgar.jrc.ec.europa.eu), and NH₃ emissions are from the MASAGE inventory developed by Paulot et al. (2014). We overwrote the anthropogenic emissions in East Asia with the MIX inventory (Li et al. 2017).

GEOS-Chem has been used to simulate PM_2.5 and its constituents in China, and it has been proven to have good skill in simulating the spatiotemporal distribution and concentration level of aerosols. Zhang et al. (2015a) reported a spatial correlation coefficient (R) of 0.65 between modeled and simulated PM_2.5 in China, and Zhang et al. (2018) reported an R of 0.85 for the day-to-day variation of PM_2.5. Both found an underestimation of ~40% in the PM_2.5 concentration simulated by the model, but the bias was within a reasonable performance level for chemical models to simulate PM_2.5 (Eder and Yu 2006; Stewart et al. 2019). GEOS-Chem also performs well in simulating SNA aerosols. Wang et al. (2013) and Zhang et al. (2015b) reported a good ability of GEOS-Chem to simulate SO₄²⁻ at weekly, monthly, and seasonal scales. However, the model was found to underpredict the SO₄²⁻ concentration during severe haze days for a large percentage of them (Wang et al. 2014). It was improved through adding a heterogeneous oxidation procedure of SO₂ to H₂SO₄ in the model, which was included in the simulations carried out in this work. NO₃⁻ and NH₄⁺ overestimation is a typical problem of chemical models (Stewart et al. 2019; Wang et al. 2013). Heald et al. (2012) recommended to reduce the HNO₃ concentration when it is read into the SNA aerosol chemical module in the model, which was also included in this study.

2.2 Simulation scenarios

Two groups of simulations were conducted based on the emissions in the years 2013 and 2018, which are referred to as the high and low SO₂ emission cases, respectively. We assumed a separate 25% decrease in NO_x and NH₃ emissions from China and calculated the sensitivity of PM_2.5 to them.

As mentioned, the default MIX inventory in GEOS-Chem is for the year 2010, and we scaled it up to obtain the emissions for 2013 and 2018. According to Zheng et al. (2018), from 2010 to 2013, the emissions of the main PM_2.5 precursors, i.e. SO₂, NO_x, non-methane volatile organic carbon (NMVOC), NH₃, elemental carbon (EC), and organic carbon (OC), changed by −9%, 5%, 9%, 4%, 1%, and −3%, respectively. After the Air Pollution Prevention and Control Action Plan was delivered in 2013, emissions of the above species dropped largely from 2013 through 2017, especially for SO₂. The relative emissions change of the six precursors were −59% for SO₂, −21% for NO_x, 2% for NMVOC, −3% for NH₃, −28% for EC, and −32% for OC. We assumed emission changes were linear from 2010 to 2013 and from 2013 to 2017, and then performed a linear extrapolation for the year 2018. The scale factors are displayed in Figure 1.

Figure 1. Emission scale factors based on the year 2010 for the main PM_2.5 precursors.

3. Sensitivity of PM2.5 to NO_x and NH₃ emissions at different SO₂ emission levels and in different seasons

Here, we discuss the sensitivity of PM_2.5 with respect to NO_x and NH₃ emissions control in different SO₂ emission cases and different seasons. We defined an efficiency factor (β) following the work of Zhang et al. (2019), Zhang et al. (2015b) to quantify and compare the PM_2.5 sensitivities to NO_x emissions change. β is defined as follows:

$\frac{\mathrm{\Delta }X}{X}=\beta \times \frac{\mathrm{\Delta }E}{E},$

where $\frac{\mathrm{\Delta }X}{X}$ represents the relative changes in the PM_2.5 concentration and $\frac{\mathrm{\Delta }E}{E}$ denotes the relative change in NO_x or NH₃ emissions. In this study, $\frac{\mathrm{\Delta }E}{E}$ was 0.25 for all the simulations.

Figure 2 shows the annual mean PM_2.5 distribution in the high and low SO₂ emission cases and the difference between them. The PM_2.5 concentration was much higher in the east than in the northeast or west, for both the high and low SO₂ emission cases. Therefore, we focused on the east of China (22°–42°N, 102°–122°E) where the PM_2.5 concentrations were highest. The annual mean PM_2.5 concentration over East China decreased by 10.3 μg m⁻³ (19.6%) due to the emissions reduction from the high to low SO₂ emissions case, 55% of which was driven by a reduction in the sulfate concentration. Seasonally, the reduction in the PM_2.5 concentration was lower in winter (January, February, and December) than in summer (June, July, and August). Relatively, the PM_2.5 reduction rate was 17.8% in winter, which was lower than the annual mean level; and it was 28.2% in summer, which was much higher than the annual mean rate of decrease. This indicates that the formation of aerosols is more easily affected by the emissions of precursors in summer.

Figure 2. Annual mean PM_2.5 concentrations in the high and low SO₂ emission cases, and their difference. The black box denotes the region of interest.

As shown in Figure 3, in the high SO₂ emissions case, the annual mean PM_2.5 sensitivity with respect to NO_x emissions reduction (β) was 0.21 over the region of interest. Seasonally, β in winter (0.22) and summer (0.23) was close to that of the annual mean. There was a sharp increase in β (53.3%) from the high to low SO₂ emissions case at the annual mean level. In other words, under low SO₂ emissions, the behavior of PM_2.5 becomes more sensitive to NO_x emissions change. This indicates that now and in the future, NO_x emissions control is/will be more effective in reducing the PM_2.5 concentration. The increase in β from the high to low SO₂ emissions case varied from season to season. For the East China regional mean, β increased by more than 80% in winter, while in summer the rate of increase was approximately 20%. Different to that in the high SO₂ emissions case, the wintertime β was much higher than the summertime one in the low SO₂ emissions case. In winter, a reduction in SO₂ emissions would release more free NH₃ into the atmosphere, making NH₄NO₃ more sensitive to NO_x emissions change. This explains the large increase in β in winter. The results imply that now and in the future, NO_x emissions control is/will be more effective in dealing with severe winter haze than in previous years.

Figure 3. PM_2.5 sensitivity to NO_x emissions change in the high (left-hand column) and low (middle column) SO₂ emission cases, and their difference (right-hand column). The rows show the annual (top), winter (middle), and summer (bottom) mean.

The behavior of PM_2.5 with respect to NH₃ emissions control is displayed in Figure 4. In the high SO₂ emissions case, PM_2.5 was 47.6% more sensitive to NH₃ than to NO_x emissions change, with an annual mean β of 0.31. Seasonally, β was higher in winter (0.43) than in summer (0.24), in contrast to that in the NO_x emissions control condition. The sensitivity of PM_2.5 to NH₃ emissions change fell by 30.1% from the high to low SO₂ emissions case, and the decrease was more significant in winter (32.6%) than in summer (20.7%). Notably, in the low SO₂ emissions case, the sensitivity of PM_2.5 to NH₃ emissions change (0.22) was 32.7% lower than it was to NO_x emissions change. The result indicates that (1) when SO₂ emissions decrease, more free NH₃ is released to the atmosphere, making NH₃ emissions control less effective at reducing the surface PM_2.5 concentration; and (2) now and in the future, it is/will be more effective to reduce the PM_2.5 concentration by controlling NO_x rather than NH₃ emissions.

Figure 4. PM_2.5 sensitivity to NH₃ emissions change in the high (left-hand column) and low (middle column) SO₂ emission cases, and their difference (right-hand column). The rows show the annual (top), winter (middle), and summer (bottom) mean.

4. Conclusion

The reduction in emissions of the precursors of PM_2.5 from 2013 to 2018 resulted in a 19.6% decrease in the surface PM_2.5 concentration over East China (22°–42°N, 102°–122°E), most of which was driven by the significant decrease in the sulfate concentration. The substantial decrease in SO₂ emissions and the sulfate concentration were expected to have altered the sensitivity of PM_2.5 to NO_x and NH₃ emission changes. Therefore, we conducted model sensitivity studies to assess the changes in PM_2.5 sensitivities to NO_x and NH₃ emission changes owing to the large decrease in SO₂ emissions from 2013 to 2018.

In the high SO₂ emissions case (the year 2013), PM_2.5 was 47.6% more sensitive to NH₃ emissions change than NO_x, which was mostly driven by winter. From the high to low SO₂ emissions case, the PM_2.5 sensitivity factor (β) for NH₃ decreased by 30.1%, while for NO_x it increased by 53.3%, and the PM_2.5 sensitivity was 48.6% higher to NO_x than NH₃. Seasonally, in the low SO₂ emissions case, the PM_2.5 sensitivities to both NO_x and NH₃ were higher in winter than in summer.

Based on the results of this work, we conclude that now and in the future, the behavior of PM_2.5 is/will become less sensitive to NH₃ emission changes, while NO_x emissions control is more effective at reducing the surface PM_2.5 concentration, especially in winter.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 41805098].