Atmospheric Research 212 (2018) 106–119
Contents lists available at ScienceDirect
Atmospheric Research journal homepage: www.elsevier.com/locate/atmosres
Emission-driven changes in anthropogenic aerosol concentrations in China during 1970–2010 and its implications for PM2.5 control policy
T
Wenyuan Changa,⁎, Jianqiong Zhana, Ying Zhangb, Zhengqiang Lib, Jia Xingc, Jiandong Lid a
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China c State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China d State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
A R T I C LE I N FO
A B S T R A C T
Keywords: PM2.5 Historical aerosols Multi-model simulations Air quality
There are open debates on whether the amount of emission reduction could fulfil the anthropogenic PM2.5 (particulate matter smaller than 2.5 μm) mitigation in China. This study evaluated the long-term historical aerosol simulations for 1970–2010 in eastern China from three models (CACTUS, WRF-CMAQ, and GISS-E2-R). We introduced 95% Confidence Interval of n-year Moving Difference (n-year CIMD, n = 1…10) in the long-term simulated PM2.5 concentrations to determine how long PM2.5 change in history is significant and how many increases in historical emissions raised the amount of PM2.5 that we want to reduce today. The results show that the annual trends for the simulated PM2. 5 ranged from 0.42 to 0.72 μg m−3 year−1 lying within the 95% confidence intervals for the trend in the satellite-derived PM2.5. There was a reasonable change in PM2.5 chemical compositions with increasing nitrate and declining OA mass fractions from 1970 to 2010. Particulates were more neutralized as the quick increases in ammonia and the control on SO2 emissions. The significant analysis of changes indicates that at least a 5-year CIMD for PM2.5 could be distinguished from the PM2.5 fluctuations due to emission uncertainties and meteorological interannual variations. At least a 10-year CIMD for PM2.5 could be distinguished from the multi-model uncertainties. The historical relationship between PM2.5 and emissions suggests that the minimum PM2.5 reduction targeted in the China's 12th Five-Year Plan (FYP; 2011–2015) would require the emission changes compared with 2010 in SO2, NOx, and NH3 by 41%, 29%, and 42%, respectively. The amounts were larger than the emission reduction planned for the 12th FYP. This suggests that the past emission policies and PM2.5 control pledges were incompatible and a stricter emission reduction is needed to attain the 13th FYP (2016–2020).
1. Introduction Extreme haze events are a current threat to public health in China. Long-lasting winter haze events have seen PM2.5 concentrations of over 200 μg m−3 in northern cities (Wang et al., 2016; Hao et al., 2017; Huang et al., 2014a; Yang et al., 2015b; Yuan et al., 2015; Zhang et al., 2016). The Chinese State Council announced the Air Pollution Prevention and Control Action Plan (APPCAP) in September 2013, with the aim of decreasing 10% PM2.5 in major cities by the end of 2017 compared to 2012 levels (CAAC: Clean Air Alliance of China, 2013). An interim assessment report for the implementation of APPCAP was released in 2016. It reported improvements to air quality in most cities, but the goal of PM2.5 decrease in the Northern China Plain still requires ⁎
strong transregional collaboration (China Environment News, 2016). This indicates the current emissions control is not fully compatible with the PM2.5 control plan (Zhang et al., 2015a; Xu et al., 2017; Zhao et al., 2013). PM2.5 in China have been decreasing since 2008 (Zhang et al., 2017) due to the flue-gas desulfurization in power plants (Lu et al., 2011). Recent winter haze events have occurred in stagnant weather conditions that were supposed to link with the interactions between the atmosphere and Arctic sea ice (Wang et al., 2015a; Wang and Chen, 2016; Yin et al., 2017; Zou et al., 2017) and the air–sea oscillations (Hui and Xiang, 2015; Zhao et al., 2016). The cities that have successfully decreased PM2.5 face a challenge to maintain the improvements in the near future. Two questions raise in air quality management: (1) how
Corresponding author at: LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China. E-mail address:
[email protected] (W. Chang).
https://doi.org/10.1016/j.atmosres.2018.05.008 Received 6 November 2017; Received in revised form 11 April 2018; Accepted 21 May 2018 Available online 23 May 2018 0169-8095/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
meteorology and emission inventories.
many PM2.5 changes will be due to the emission changes alone, and (2) how significant is the change compared to the PM2.5 variation due to meteorology? The first question is not easy since PM2.5 does not have a simple relation with individual precursor for the nonlinear chemistry. PM2.5 level depends on an emission combination of all precursors. A few studies predicted PM2.5 based on emission scenarios for future decades (Li et al., 2016a; Liu et al., 2013), though the scenarios represent socioeconomic development that may not be consistent with real emission controls in China. A few short-term simulations were based on the actual FYP control policies (Zhang et al., 2015a; Wang et al., 2010; Zhao et al., 2013). The results only represent specific interannual differences and did not separate the important role of meteorology. Wang et al. (2013) discussed the effectiveness of emission controls in the Pearl River Delta region with daily PM2.5 in two years. Although they isolated the meteorological effects, the short-term results may not applicable for long-term PM2.5 control pledge. The aim of this study is to gain insights from long-term history. We raise a question: what quantity of historical emissions can raise the amount of PM2.5 that we want to reduce today. The advantage of using long-term historical records is to reveal the sensitivity of PM2.5 to emission changes, which reflects the impacts of the past changes in consumption and energy structures in China. We detected the extent to which changes in PM2.5 due to curtailing emission would be significant and assess the compatibility of the emission and PM2.5 control plans. Due to the lack of early aerosol data, long-term aerosol research based on observations is impossible at present. Many previous studies used individual models to track historical aerosols in China, focusing on the changes in aerosol radiative forcing and chemical composition (Geng et al., 2017; Gao et al., 2016b; Xing et al., 2015; Yang et al., 2015a, 2016). Aerosol simulations feature large uncertainties, even when based on similar emission inventories (Goto et al., 2015; Myhre et al., 2013). Therefore, beside to meteorology, discussion on PM2.5 changes due to emissions also need to evaluate simulation errors. This study analyses long-term aerosol simulations for the period 1970–2010 from three models with differences in resolutions, chemical mechanisms, meteorological conditions, and emission inventories. We firstly evaluate the quality of historical simulations, describe the common characteristics of historical aerosols in China. We then determine how many PM2.5 changes would be distinguishable from the simulation uncertainties and estimate the possible required emission reduction to fulfil the APPCAP mitigation target. The paper is organized as follows. Section 2 describes the three models, historical emission inventories, and the data used for model evaluations. Section 3 evaluates the model results. Section 4 describes the characteristics of historical changes in PM2.5, chemical composition and particulate acidity. Section 5 compares the historical changes in PM2.5 with the PM2.5 variations due to the interannual variations in meteorology and the uncertainties in emissions and models. This section also describes the result implication for the emission–PM2.5 control in China. Section 6 presents the conclusions.
2.1. CACTUS The Goddard Institute for Space Studies (GISS) general circulation model, version II, with Chemistry, Aerosols and Climate in Tropospheric Unified Simulation (CACTUS) (Liao et al., 2003, 2004, 2006; Liao and Seinfeld, 2005) is a fully coupled climate–chemistry model. It includes detailed tropospheric O3–NOx–hydrocarbon chemistry and aerosol chemistry, with 225 chemical species and 346 reactions. SOA formed from monoterpenes and isoprene is based on equilibrium partitioning and experimentally determined yield parameters. The heterogeneous reactions include the hydrolysis of N2O5 on aerosol surface, the update of SO2 on sea salt as well as the update of HNO3 and O3 on mineral dust (Liao and Seinfeld, 2005). The model has a horizontal resolution of 4° latitude by 5° longitude and nine sigma layers extending from the surface to 10 hPa. This coarse resolution ensures an efficient long-term simulation and meanwhile captures the aerosol characteristics over eastern China as the other two models (shown in Section 3). We carried out a set of CACTUS simulations with different emissions for each year during 1970–2008. Each chemical simulation was initialized from the same equilibrium climate in the year 2000 which was a result from a thousand-year climate simulation prior to this study. The equilibrium climate represents the model climate under the external forcing of greenhouse gases (GHGs) in 2000. The sea surface temperature was simulated with an online “Q-flux” ocean module with constant monthly horizontal heat fluxes. The setup allows the aerosol responds to emission changes alone. Each simulation ran for 24 months, with the second half used for analysis. 2.2. WRF-CMAQ WRF-CMAQ is a regional chemical model consisting of the Community Multiscale Air Quality Modeling System (CMAQ) (Hogrefe et al., 2015; Wong et al., 2012) coupled with the Weather Research and Forecasting (WRF) modelling system (Fast et al., 2006; Grell et al., 2005). The model applied here was developed to cover the entire northern hemisphere (Mathur et al., 2012, 2014). We used the model results for 1990–2010 based on the coupled system (WRF v3.4 coupled with CMAQ v5.0) that were simulated by Xing et al. (2015). The CMAQ model is configured with the CB05 (Sarwar et al., 2008) chemical mechanism and the AER06 aerosol module (Appel et al., 2013). The model domains were set on a polar stereographic projection with a grid resolution of 108 km and 44 vertical layers extending from the surface to 50 hPa. The chemical boundary conditions were the monthly results for 1990–2010 from GEOS-Chem chemical transport model. The driving meteorological fields were from the WRF simulations conducted on the same grid configuration, driven by NCEP (National Centers for Environmental Prediction)/NCAR (National Center for Atmospheric Research) reanalysis meteorological data every 6 h.
2. Models and datasets
2.3. GISS-E2-R
The long-term aerosol simulations were taken from three models which are CACTUS, WRF-CMAQ, and GISS-E2-R. All the models include comprehensive tropospheric chemistry and aerosols, and are capable of simulating concentrations of sulfate, nitrate, ammonium, black carbon (BC) and organic aerosol (OA) which is further divided into primary organic aerosol (POA) and secondary organic aerosol (SOA). Each model is suitable for assessing the PM2.5 trends in China. Mineral dust and sea salt were simulated with parameterizations in the models, which were dependent on the simulated meteorological conditions. Since they were natural coarse particulates, they were not discussed in this study. The differences among three models are related to the diverse chemical modules and the experimental setup using different
The GISS-E2-R global model is based on the E2 version of the GISS climate model coupled to a fully dynamic Russell ocean (Koch et al., 2006; Shindell et al., 2013). The model contains fully interactive chemistry and aerosols for the troposphere and stratosphere, with 156 chemical reactions among 51 species. The model includes heterogeneous chemistry of sulfate and nitrate on dust surface (Bauer et al., 2007; Bauer and Koch, 2005). NOx-dependent SOA chemistry is simulated with a two-product model from isoprene and terpenes (Tsigaridis and Kanakidou, 2007). We analyzed the model chemical results for 1970–2010 which were taken from the data archive of the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Lamarque et al., 2013). The multi-year model data were saved from the 107
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
As lack of control on agricultural sources (Lamarque et al., 2013), NH3 emissions steadily grow in 1970–2010 (Fig. 1c). The primary carbonaceous emissions had a slow increase in 1990–2000 and a rapid increase after 2000 (Fig. 1d), corresponding to the growth in energy consumption, industrial production, and vehicle population (Lei et al., 2011; Lu et al., 2011; Qin and Xie, 2012). The emission growth rates of SO2, NOx, and NH3 were slower in ACCMIP than those in the other two after 1990, reflecting the differences in the complication of regional emission inventories with varying emission factor and activity data. The ACCMIP emissions were lack of the interannual variations in SO2 and NOx in 1990–2000 because its annual emissions were linearly interpolated between decades.
GISS-E2-R transient climate simulations for the Coupled Model Intercomparison Project 5 (CMIP5). The transient simulations were spun up for 1000 years and simulated 1850–2005 with the ACCMIP historical emission inventories. The 2005–2010 aerosols were simulated with the emissions for representative concentration pathway (RCP) 4.5 (Thomson et al., 2011). The GISS-E2-R simulations were run at a resolution of 2° latitude by 2.5° longitude, with 40 vertical hybrid sigma layers extending from the surface to 0.1 hPa. As well as GISS-E2-R, there are additional 11 global climate/chemical transport models in the ACCMIP project that simulated aerosols per decade. Most of them only provide limited results on aerosol chemical components, particularly lack of nitrate and SOA. They are unsuitable for our discussion on yearly PM2.5. On the other hand, the ACCMIP results with the same emission inventories are useful for calculating the multi-model uncertainties in aerosol compositions, as shown in Section 5. The additional ACCMIP model information is listed in Table S1 in the Supplementary document.
2.5. Aerosol observations Three aerosol observation datasets were used for model evaluation. They are (1) the ground-based measurements of PM2.5 supported by China National Environmental Monitoring Centre (CNEMC); (2) the ground-based aerosol chemical composition measurements for PM10 from the Chinese Atmospheric Watch Network (CAWNET) affiliated with the China Meteorological Administration (Zhang et al., 2012); (3) and the satellite-derived PM2.5 dry mass concentrations estimated with aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) generated by Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. The CNEMC PM2.5 is an hourly dataset with a good spatial distribution covering hundreds of cities, but the data started in 2013 and is not suitable for evaluating our simulations for 1970–2010. The CAWNET data contains two-year (2006–2007) monthly concentrations of sulfate, nitrate, ammonium, black carbon, organic carbon (OC), and mineral dust in PM10 at 14 sites (7 urban and 7 regionally representative sites, with 11 sites in the eastern China which were used in the calculation). The satellite-derived PM2.5 was estimated from the MODIS products (AOD and fine-mode fraction) using a Particulate Matter Remote Sensing (PMRS) algorithm (Zhang and Li, 2015; Li et al., 2016b). Details on the algorithm is described in the supplementary material. The satellite-derived PM2.5 had an annual bias of −23.6% compared to the CNEMC PM2.5 for 2014–2015 (Fig. S1), with a spatial correlation coefficient of 0.5. The maximum negative bias was higher than 50% in January (Fig. S2), because (1) AOD is difficult to retrieve over high-reflectance surfaces (Levy et al., 2010) as fewer valid pixels for snowy winter in northern China, (2) some extreme haze events were misidentified as cloud by the MODIS cloud masking algorithm (Li et al., 2013), and (3) the large uncertainty of MODIS fine-mode fraction was found over land (Anderson et al., 2005). Apparently, none of the three datasets can be used individually to evaluate the model results for early years. Hence, we merged the datasets to represent the on-site PM2.5 and its chemical compositions in 2006–2007. Specifically, we averaged the satellite-derived PM2.5 converted from the MODIS products on-board the Terra and Aqua satellites to reduce the number of missing grids due to cloud contamination or low retrieval quality. The average was calculated if both sensor data were valid; otherwise keep one data if another is missing. After that, the two-year mean (2006–2007) simulations and the satellite-derived PM2.5 were interpolated to the CNEMC site locations. For evaluating the annual PM2.5, the simulations were compared with the satellite-derived PM2.5. For evaluating the monthly PM2.5, the simulations were compared with the satellite-derived PM2.5 multiplied with the monthly mean profiles of CNEMC PM2.5 for 2014–2016. This treatment avoids the adverse impacts of the strong low biases in the satellite data in winter. For evaluating the monthly chemical concentrations, the satellite-derived PM2.5 were interpolated into the CAWNET sites, and the results were multiplied with the monthly profile of nearby (within a radius of 90 km) CNEMC PM2.5 for 2014–2016 and the monthly chemical profiles (ignoring the dust contribution) of CAWNET for 2006–2007.
2.4. Emission inventories Two kinds of anthropogenic emission inventories were used for the simulations. The first is the Emissions Database for Global Atmospheric Research (EDGAR), version 4.2, compiled by the European Commission, Joint Research Centre/Netherlands Environmental Assessment Agency. The second is the unified, homogeneous, and comprehensive decadal emission inventories (Lamarque et al., 2010) compiled for the ACCMIP project (Lamarque et al., 2013). CACTUS and WRF-CMAQ had the same yearly EDGAR anthropogenic emissions of gas precursors (SO2, NOx, NH3, and CO) for 1970–2008. WRF-CMAQ additionally updated the emissions in 2009–2010 following the work of He (2012). The carbonaceous particulate emissions in CACTUS were the yearly interpolation between the decadal ACCMIP emissions in 1970–2000 and the RCP4.5 emissions for 2010. Anthropogenic emissions for non-methane volatile compounds (NMVOCs) in CACTUS were based on the Global Emission Inventory Activity (GEIA) datasets for 1996 with annual scaling based on the ACCMIP emission inventories. The carbonaceous aerosol emissions in WRF-CMAQ were estimated from the EDGAR PM10 emissions with a set of speciation profiles in the AERO6 aerosol module of CMAQ, while the NMVOC emissions were estimated from EDGAR NMVOCs with a speciation profiles for the CB05 chemical mechanism in CMAQ (Xing et al., 2015). The biomass burning emissions for precursors and primary particulates in CACTUS and WRFCMAQ were from EDGAR v4.2. GISS-E2-R used the yearly anthropogenic and biomass burning emissions in 1970–2010 from the interpolation based on the decadal ACCMIP emission inventories. The three models have different treatments for seasonal emissions. For CACTUS and GISS-E2-R, the anthropogenic emissions had no seasonal variation, and the biomass burning emissions varied on a monthly basis. For WRFCMAQ, the sectoral annual emissions were inputted each hour using the default EDGAR temporal profiles, which were based on western European data. Natural emissions of biogenic hydrocarbons and lightning NOx in WRF-CMAQ were constant values from the GEIA emissions, while they were online parameterized by meteorological parameters in CACTUS and GISS-E2-R. Fig. 1 shows the annual emissions of gas precursors and primary particulates from anthropogenic and biomass-burning sources in eastern China (100°–120°E, 20°–44°N). The regional SO2 and NOx emissions grew dramatically and attained the first peak values in the mid-1990s (Fig. 1a, b) for the increases in coal fuel consumption and vehicle population (Ohara et al., 2007; Streets et al., 2000; Zhang et al., 2007). The emissions declined during 1996–2000 as the slowdown in economic development, the flue-gas desulfurization started in power plants, and few consumptions of sulfur-bearing coal (Geng et al., 2017; Lei et al., 2011; Ohara et al., 2007). The emissions increased rapidly in the 2000s with the economic recovery following the Asian economic crisis in 1997. Agricultural sources account for 97% of NH3 emissions. 108
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Fig. 1. Annual emissions of gas precursors and primary particulates from anthropogenic and biomassburning sources in eastern China (100°–124°E, 20°–44°N). CACTUS and WRF-CMAQ used the EDGAR v4.2 emission inventories for SO2, NOx and NH3, and the WRF-CMAQ emissions are increased by 10% for clarity. The OC and BC emissions in WRFCMAQ were separated from the EDGAR PM10 emissions, which are unavailable in the data archive for Xing et al. (2015) and are not present in the figure. The emission units are Tg (SO2, NO, NH3, C, C) year−1 for SO2, NOx, NH3, BC, and OC, respectively.
via SO2 oxidation by homogeneous gas-phase reactions with OH and aqueous-phase reactions with O3 and H2O2. The aqueous reaction is more effective and accounts for half to two-thirds of sulfate formation (Adams et al., 1999; Zhang et al., 2015a). This explains why the models underestimated the sulfate in the dry air of winter. Recent studies have proposed other sulfate formation pathways in the cold season. One is heterogeneous oxidation of S (IV) in aqueous reactions catalysed by transition metal ions (TMIs) (Chen et al., 2016; Wang et al., 2012; Huang et al., 2014b; Zheng et al., 2015). It was confirmed to induce more sulfate, nitrate and ammonium (SNA) during haze episodes in winter China (Chen et al., 2016; Huang et al., 2014b; Zheng et al., 2015). Another is the coexistence of NO2 and SO2 enhances sulfate concentration. It is suited to winter haze episodes characterized by weak photochemical conditions, high ambient humidity, and NH3neutralized air (He et al., 2014; Liu et al., 2012; Xie et al., 2015). In this study, neither model simulated the synergistic effects of NO2 and SO2 on sulfate formation. CACTUS and GISS-E2-R simulated the heterogeneous reaction for SO2 uptake on dust surface and oxidized to sulfate by O3 (Bauer and Koch, 2005; Berglen et al., 2004; Liao and Seinfeld, 2005). Both models simulated negligible dust concentrations in winter, and GISS-E2-R used a lower uptake coefficient for SO2 on dust particles than those applied in other model studies (Bauer and Koch, 2005). The two models have no sulfate production with the TMIs catalytic chemistry either. WRF-CMAQ simulated the sulfur oxidation with predicted TMI, but it also strongly lowered winter sulfate. Seemingly, the missing winter sulfate cannot be explained by introducing one chemical mechanism. A deep discussion is beyond the scope of this work and deserves much more attention. In contrast to the sulfate, the simulated nitrate was close to the observations in the annual cycle (Fig. 3b). This is due to: (1) the gas–aerosol partitioning of HNO3 favouring the gas phase in the hot summer; and (2) the low modelled sulfate favouring more nitrate formation in winter as more NH3 can react with HNO3. GISS-E2-R simulated the highest nitrate concentrations (Table 1, Fig. 4), because the GISS-E2-R version in ACCMIP used the Henry value of ammonia instead of the effective Henry value, resulting in large amounts of ammonia and hence nitrate (Mezuman et al., 2016). The ammonium particulate concentration is similar to that of nitrate, with a winter-high and
This procedure takes advantage of the individual data: the precise of PM2.5 monthly profile at CNEMC sites, the chemical information in CAWNET PM10 and the satellite-derived PM2.5 in early years for which the simulations were carried out while the CNEMC data were not available. Although the treatment has uncertainties, for example, the datasets have different spatial and temporal representation and chemical mass fractions in PM10 are slightly different from those in PM2.5, the model biases evaluations (see Section 3) suggest that the merged data set is a reasonable option for evaluating the simulations for early years. 3. Model evaluation This section evaluates the average model results for 2006–2007. Anthropogenic PM2.5 in model hereinafter is defined as the sum of sulfate, nitrate, ammonium, BC, POA, and SOA because they constitute the major components of PM2.5 (Xiang et al., 2017; Wang et al., 2017). The annual biases in anthropogenic PM2.5 were −50.7% for CACTUS, −44.0% for WRF-CMAQ, and −47.0% for GISS-E2-R (Fig. 2). The low biases could be seen for each chemical composition. Since the satellitederived PM2.5 had a low bias itself, the merged observational data likely has a low error. The real model chemical biases thus could be larger than those shown in Fig. 2. Goto et al. (2015) observed a strong negative bias in sulfate in China using two global chemical models. They evaluated sulfate from a similar aerosol module coupled with two different host climate models. They found annual sulfate biases in the range of −77% to −63%, depending on the host model and the season. They argued that the large biases may be a common problem in chemical models and could not be fully explained by the coarse model resolution or emission errors. In this study, the largest aerosol biases were occurred in winter, particularly for sulfate (Fig. 3a). We ran an additional simulation in CACTUS with monthly varying anthropogenic emissions based on the monthly emission profiles suggested by Zhang et al. (2009). The regional SO2 emissions, for example, were 13% higher in January than in July. This additional emission did not lead to noticeable improvements in winter (Fig. S3), indicting the low winter biases cannot be attributed to the constant yearly emissions. It is commonly understood that anthropogenic sulfate is simulated 109
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Fig. 2. Comparison of the annual surface-layer aerosol concentrations (μg m−3) between the models and the merged observations for 2006–2007 at CNEMC sites to the east of 100°E. The model mean biases (μg m−3) and normalized mean biases (%, in brackets) are shown in the panels.
summer-low cycle, in agreement with the observations (Fig. 3c). Huang et al. (2014a) proposed that SOA accounts for 73% of OA during high pollution periods in winter in northern cities. The models
largely underestimated wintertime OA concentrations (Fig. 3d) because the biases in SOA were higher than 90% in three models. The large model biases indicate our limited knowledge on SOA chemistry and the
Fig. 3. Comparison of the monthly surface-layer aerosol concentrations (μg m−3) between the models and the merged observations for 2006–2007 at CNEMC sites to the east of 100°E. The OC monthly concentration for WRF-CMAQ is unavailable in the data archive for Xing et al. (2015), and thus the monthly OC and PM2.5 from this model are not present. 110
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
supported our finding that primary particulate emissions are likely underestimated in model, probably because many emission inventories share common emission factors and activity data (Granier et al., 2011). Table 1 shows the annual percentages of chemical composition in PM2.5 for 2006–2007. The models basically reproduced the PM2.5 compositions. They agree that the maximum composition in PM2.5 was sulfate (31.8% to 39.2%), followed by OA (19.8 to 31.6%), nitrate (15.4 to 16.9%), ammonium (10.7 to 18.9%), and BC (2.8 to 9.0%). It should be noted that this evaluation is indicative rather than conclusive because the PM2.5 chemical data for model evaluation have uncertainties. Due to the large biases in seasonality, the following sections only discuss the annual mean aerosols rather than their monthly mean values. This suggests that it is hard to say which model is substantially better than the others because the simulations were not stringently performed with unified experimental setup as those did in the ACCMIP project. The common model errors identified in this section should be kept in mind in the next discussion.
Table 1 Annual PM2.5 concentrations and the percentages of chemical compositions at the CAWNET sites for 2006–2007.
CAWNET CACTUS WRF-CMAQ GISS-E2-R
PM2.5 (μg m−3)
SO42−
NO3−
NH4+
OA
BC
43.2 23.7 26.9 25.5
37.1% 39.2% 35.2% 31.8%
15.8% 16.2% 15.4% 16.9%
11.6% 18.9% 15.4% 10.7%
28.2% 19.8% 31.2% 31.6%
7.4% 5.9% 2.8% 9.0%
possible missing anthropogenic sources of SOA (Hallquist et al., 2009; Tsigaridis et al., 2014). The models underestimated BC concentrations (Fig. 3e). Fu et al. (2012) simulated carbonaceous particulates and added 60% more primary particulate emissions to ensure the simulated OC/BC concentrations approaching the observations. Their work was based on regional emission inventories different from this study but
Fig. 4. Annual surface-layer aerosol concentrations at CNEMC sites to the east of 100°E. (OA = 1.4 × OC for other chemical components, e.g., oxygen, nitrogen, and metal ions during oxidative aging).
111
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
4. Historical changes in PM2.5
derived estimations. Boys et al. (2014) separated the satellite-derived PM2.5 trends into chemical components with the chemical rations from the GEOS-Chem simulations. Our three models agreed with their finding that SNA increased more rapidly than carbonaceous particulates (Table 3). Thus, annual trends were well represented in the models which were applied to the discussion on the significant changes in PM2.5 in Section 5.
4.1. PM2.5 annual trends The surface-layer aerosol simulations were interpolated to the CNEMC sites because the routine measurements are officially used for monitoring air quality in China. The conclusions drawn on these sites could be readily extended in the future comparison. Fig. 4 shows the simulated annual trends averaged over the sites in eastern China (east of 100°E). Due to the coarse model resolution, we focus on the mean aerosol changes over the sites. The emissions determine the long-term changes in concentrations, which are consistent with the conclusion that the aerosol decadal variations are mainly due to emissions (Gao et al., 2016b; Yang et al., 2015a). The three models had different meteorological conditions. WRF-CMAQ was driven by reanalysis meteorological data, which were more realistic than the equilibrium and transient climate in CACTUS and GISS-E2-R. The agreement in the increasing trends indicates that meteorology was a minor factor in the aerosol decadal simulations. This is not a contradiction to the previous findings that weak monsoon modifies haze weather in northern China (Niu et al., 2010; Zhu et al., 2012), because we care about the annual PM2.5 while the monsoon signal could be more significant in the monthly aerosols. The annual trends in PM2.5 were more consistent than the aerosol chemical compositions across the models because the biases in chemical compositions complement each other (e.g. more sulfate with less nitrate). As the stricter emissions control since 2008 for the Beijing Olympic Games, PM2.5 concentrations in China peaked in 2006–2007 and decreased afterwards. This decreasing trend has been detected in the satellite AOD in the Chinese coastal outflow region (Zhang et al., 2017) and the satellite-derived PM2.5 (Boys et al., 2014; Geng et al., 2017; Ma et al., 2016). This decrease was not reproduced in CACTUS, as the EDGAR v4.2 inventories did not contain the recent emission changes in China; and neither was it reproduced in GISS-E2-R, as the interpolated emissions smoothed out the interannual variations. WRF-CMAQ added the emissions for 2009–2010 and captured the noticeable decreases in SNA after 2008. The historical trends in PM2.5 for China have been estimated using satellite AOD, where the AOD–PM2.5 relationship was either built on statistical models (Ma et al., 2016) or simulated with a chemical transport model (Boys et al., 2014; Geng et al., 2017; van Donkelaar et al., 2015). Previous studies used different sampling ranges and periods that were not all specified in their publications. Here, we calculated the aerosol trends covering 100°–120°E and 20°–44°N approximately matching the area illustrated in figure 3 of Boys et al. (2014). The comparison of the annual trends of PM2.5 and the chemical compositions from this and previous studies were summarized in Tables 2 and 3, respectively. Compared to the previous satellite-derived estimations, the three models underestimated the increasing trends of PM2.5 (Table 2). CACTUS showed the highest increasing trends for it had the maximum PM2.5 concentration in 2008, partly offsetting the model's negative errors in concentrations. In spite of this, during 1998–2008/2010 (the overlapping period of these data), the model annual trends were within the 95% confidence intervals of the satellite-
4.2. PM2.5 chemical compositions Fig. 5 shows the changes in the PM2.5 chemical compositions, and the percentages of the compositions in each decade are shown in Table S2. During 1990–2008, nitrate in PM2.5 increased from 9.4% to 17.6% in CACTUS, 7.4% to 12.7% in WRF-CMAQ, and 13.0% to 16.8% in GISS-E2-R. At the same time, the OA fractions in PM2.5 decreased from 27.6% to 18.6% in CACTUS, 40.6% to 29.7% in WRF-CMAQ, and 37.6% to 31.4% in GISS-E2-R. The sulfate fraction was almost unchanged, despite the effective reduction in SO2 emissions. The BC fraction showed no change at all, implying BC emissions catch up the PM2.5 growths. The ammonium fraction showed a slight increase, corresponding to the stable growth in NH3 emissions. These model results are qualitatively consistent with previous measurements. In Beijing, Yang et al. (2011b) observed that SNA in PM2.5 had increased from 29% to 36% during 2002–2007, and nitrate showed a solid growth of 20%. Han et al. (2015) found that sulfates in PM2.5 were lower in 2011–2013 than in 2002–2004, but the nitrate and ammonium fractions increased. In the non-refractory PM1 of Beijing, nitrate (sulfate) was 22% (25%) in July 2006 (Sun et al., 2010) and increased (decreased) to 25% (18%) by June–August 2011 (Sun et al., 2012). A higher nitrate fraction was also observed in submicron particles at a downwind coastal site in eastern China (sulfate 19%; nitrate 28%) (Hu et al., 2013) and at the Yangtze River delta during the summer harvest (sulfate 12%; nitrate 23%) and autumn periods (sulfate 11%; nitrate 20%) (Zhang et al., 2015b). In southern China, the nitrate to sulfate ratios increased from 0.31 to 0.69 during 2007–2011 in Guangzhou (Fu et al., 2014), and from 0.73 to 0.92 during 2008–2012 in Foshan (Tan et al., 2016). The observations confirmed less OA in PM2.5. Yang et al. (2011a) found that the OC fractions during 2005–2008 decreased by 13.7% and 27.1% at urban and rural sites in Beijing, respectively. He et al. (2001) demonstrated that the OC fraction in PM2.5 decreased by 13–33% in 2005–2008 compared to the data for 1999–2000. 4.3. PM2.5 acidity Changes in chemical compositions alter the PM2.5 acidity. We considered three indexes that measure the acidity: the degree of particulate neutralization (DON), the degree of sulfate neutralization (DSN), and the gas ratio (GR) of free ammonia to total nitrate. The definitions are given in Appendix A. PM2.5 is fully neutralized with DON = 1 and is acidic with DON lower than 1. Sulfuric acid is fully neutralized with DSN = 2; otherwise, there is a coexistence of sulfate and bisulfate. A GR value greater than 1 indicates that ammonium nitrate formation is limited to nitric acid; otherwise, it is limited to free ammonia. Note that
Table 2 Annual trends (mean ± 95% confidence interval) for surface-layer PM2.5 (μg m−3 yr−1) in eastern China (EC; 100°–120°E, 20°–44°N), the North China Plain (NCP), and East Asia (EA). Data source
Methodology
Period
Region
Annual trend
CACTUS WRF-CMAQ GISS-E2-R Boys et al. (2014) van Donkelaar et al. (2015) Ma et al. (2016)
Model Model Model MISR + SeaWiFS AOD & GEOS-Chem MODIS + MISR + SeaWiFS AOD & GEOS-Chem MODIS AOD & Linear Regression
1998–2008 1998–2010 1998–2010 1998–2012 1998–2012 2004–2013
EC EC EC EC EA NCP
0.72 ± 0.24 0.51 ± 0.15 0.42 ± 0.04 0.79 ± 0.27 1.63 ± 0.54 0.75 ± 0.59
112
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Table 3 Annual trends (mean ± 95% confidence interval) for surface-layer aerosol chemical compositions (μg m−3 yr−1) in eastern China (100°–120°E, 20°–44°N). Data source
Period
SO42−
NO3−
NH4+
OA
BC
SNA
OA + BC
CACTUS WRF-CMAQ GISS-E2-R Boys et al. (2014)
1998–2008 1998–2010 1998–2010 1998–2012
0.30 ± 0.13 0.25 ± 0.08 0.19 ± 0.03
0.19 ± 0.05 0.09 ± 0.02 0.09 ± 0.03
0.17 ± 0.06 0.09 ± 0.03 0.06 ± 0.01
0.04 ± 0.02 0.07 ± 0.04 0.05 ± 0.01
0.02 ± 0.01 0.01 ± 0.00 0.03 ± 0.00
0.66 ± 0.23 0.44 ± 0.12 0.34 ± 0.03 0.78 ± 0.27
0.06 ± 0.02 0.07 ± 0.04 0.08 ± 0.01 0.06 ± 0.16
Fig. 5. Annual changes in PM2.5 chemical compositions at CNEMC sites to the east of 100°E.
Beijing. However, Liu et al. (2017) found that fine particles in Beijing were moderately acidic, with an average pH value of 4.2. It indicates the measured acidity was highly dependent on the sampling location and period. Although the simulations were not representative of any real case, our results provided emissions-based evidence for more neutralized PM2.5 in the atmosphere. GR was only calculated in CACTUS because the concentrations of nitric acid or ammonia were not available in the other two model results. According to the GR variation, nitrate formation was limited to free ammonia for most of the simulation period. The air gradually became nitric-acid-limited in the 2000s, and at that time NOx control was crucial to mitigating nitrate pollution (Fig. 6c). This transition corresponds to the peak in NH3 emissions relative to SO2 and NOx (Fig. 6d). At the end of the simulation period, the rapid growths in SO2 and NOx turned the atmosphere back to free-ammonia-limited conditions. The results disclose that NH3 control may not be an effective way in mitigating PM2.5 pollution until the NH3-limited air took place in recent years. Note that our results apply to the annual PM2.5 in eastern China. The GR changes can be highly variable across the region in different seasons. For example, Zhao et al. (2013) ran simulations for the North
our aerosol acidity is not the particulate acidity in the real atmosphere, as we ignored the compositions of ionic sodium and chloride in the models. The acidity was calculated based on dry mass concentrations without the effects of aerosol water content. Fig. 6 shows the strong model differences in DON and DSN variations. CACTUS showed increases in DON and DSN, and DSN approached the neutralizing value of 2. It indicates less aerosol acidity and more ammonium sulfate formation because the quick increase in ammonia and emission control on SO2. WRF-CMAQ showed a similar decrease in acidity, but at a more moderate acidic level. The two models show different trends after 2003. WRF-CMAQ showed a slight increase in acidity probably because it employed an updated yearly emission inventories reflecting the recent control implementation. GISS-E2-R simulated strong aerosol acidity and remained the acidity level since 1990 as it simulated high nitrate. Generally, the DON increases in CACTUS and WRF-CMAQ were consistent with a few measurements. Sun et al. (2010) found that aerosols in Beijing were more neutral in 2006, as compared to earlier measurements in 2001. G. Wang et al. (2016) found that wintertime fine particulates were almost completely neutralized by ammonia, with pH values of ~7 in Xi'an and 113
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Fig. 6. Annual changes in the degree of neutralization (DON), the degree of sulfate neutralization (DSN), the gas ratio (GR), and the emission ratio between NH3 and SO2 plus NOx in eastern China (dimensionless units). Fig. 7. The annual moving differences in PM2.5 concentrations (μg m−3) at CNEMC sites to the east of 100° as a function of n-year sampling intervals increasing from 1 year to 10 years. The horizontal dashed lines denote aerosol standard deviations (std) due to the uncertainties in the models (red), emission inventories (blue), and meteorological interannual variations (orange). (IQR = interquartile range – the 1st quartile subtracted from the 3rd quartile). The shading in the boxplots denotes a 95% confidence level. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
meteorological variation and uncertainties in simulations. Specifically, we calculated the 95% Confident Interval of n-year Moving Difference (nyear CIMD, n = 1…10) in the long-term simulated PM2.5 concentrations at CNEMC sites. Calculations of the moving differences were repeated 10 times with the PM2.5 sampling interval increasing from 1 year to 10 years. We constructed each set of the moving differences with a probability density function (PDF) and calculated the 95% CI for each PDF. A PM2.5 change for n-year sampling interval is significant when the low edge of nyear CIMD is higher than a given uncertain value. Further details on nyear CIMD can be found in Appendix B. We determined the significance of PM2.5 changes by comparing with three uncertainty sources. These were: (1) emission uncertainties
China Plain and Yangtze River Delta regions in January. They found the air was NH3-rich (HNO3-limited) and the control of NOx emissions alone could increase sulfate and PM2.5. 5. Implications for China's PM2.5 control policies 5.1. Expected time for a significant change in PM2.5 Since the simulated annual trends were within the 95% confidence interval (CI) for the annul trends of the satellite-derived PM2.5, the historical simulations were capable to determine the extent to which PM2.5 changes to be significant as distinct from the PM2.5 variations due to 114
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Since the beginning of 13th FYP (2016–2020), the emission control target is regulated to province level for operation. The documented emission control target for the entire country was drawn up for 12th FYP (2011–2015). Thus, we used the 12th FYP plan as a reference point. The 12th FYP required the national emissions to decrease by 10% in NOx and 8% in SO2 in 2010–2015. According to the emission inventories of EDGAR for Hemispheric Transport of Air Pollution project, the emissions in 2010 were 13.9 Tg (NOx) and 23.8 Tg (SO2) in eastern China. Related to the 2010 level, the mean 10-year moving differences in emissions were 13% in NOx and 19% in SO2. Apparently, the 12th FYP reduction for NOx was comparable to the 10-year moving difference in NOx emission, but the FYP reduction for SO2 was smaller than the 10-year moving difference. Since the 10-year CIMD in PM2.5 is lower than the APPCAP PM2.5 control target, the 12th FYP emission control was not sufficient for the APPCAP PM2.5 control pledge either. This inference is consistent with conclusions from aerosol simulations for the 12th FYP emissions control scenario (Zhang et al., 2015a; Zhao et al., 2013). Today, the real emission control in China has exceeded the requirement for the 12th FYP (de Foy et al., 2016). Due to the lack of emission inventories after 2013 when the work was done, we cannot directly assess the effect of the recent emission reduction. Instead, we extrapolated the low edges of n-year CIMD for PM2.5 and found at least a 22-year CIMD had a low edge of PM2.5 higher than 8 μg m−3. We then extrapolated the mean moving differences in emissions and found that the expected emissions changes for the 22-year interval would require up to 41%, 29%, and 42% changes (relative to the 2010 level) in SO2, NOx, and NH3, respectively. This implies that such emission reductions could lead to PM2.5 changes equivalent to the APPCAP conservative target for 2017. How reliable is this inference? Wang et al. (2015b) conducted a WRF-CHEM simulation of haze days in January 2013 based on regional emission inventories for 2010. They showed that 30% reductions of SO2, NOx, NH3, and VOCs decreased PM2.5 by 5–41 μg m−3 (2–17%), which is comparable to our extrapolation that 29–42% emission growths can, at the 95% confidence level, increase PM2.5 by at least 10% compared to the 2010 levels. We do not infer the n-year interval to achieve the desired reduction because extrapolation of CIMD for the desired target yields a time longer than the total years in 1970–2010, which was unlikely reliable. Certainly, the practical emission control is not simply to curtail historical increases and requires cost–benefit analysis (Gao et al., 2016a). Our analysis thus is instructive rather than conclusive.
represented by the PM2.5 standard deviations in CACTUS from four emission inventories for 2006 (Chang et al., 2015); (2) meteorological variations represented by the interannual variations of PM2.5 from the GEOS-CHEM simulation, with varying driving meteorology and fixed the year 2006 emissions during 1986–2006 (Yang et al., 2015a); and (3) the model uncertainties represented by the standard deviations of PM2.5 in the ACCMIP model results based on a uniform emissions inventory for 2000. Table S1 shows the available aerosol results in the ACCMIP models, which were used for calculating the uncertainties of (3). The PM2.5-required components (SNA, OA, and BC) were only fully available in three ACCMIP models. Due to the limit model numbers, the multi-model uncertainty was defined to the differences between the maximum and minimum model results. Fig. 7 shows the CIMD for PM2.5 as a function of the n-year sampling intervals. WRF-CMAQ PM2.5 was not shown as it was close to CACTUS and had a short record. The moving differences for PM2.5 was approximately a linear function of the n-year sampling interval. The low edges of 5-year CIMD (1.73 μg m−3 in CACTUS and 1.89 μg m−3 in GISS-E2-R) were larger than the PM2.5 interannual variation due to meteorology (1.41 μg m−3) and the uncertainty due to emission inventories (1.65 μg m−3). Thus, a PM2.5 change at least equivalent to the 5-year CIMD is significant (with 95% confidence), compared with the emission uncertainties and meteorological interannual variation. This suggests that for studies of the effectiveness of emission controls on PM2.5, at least 5 years data set is needed to distinguish the changes from fluctuations induced by emission uncertainties and meteorology. The model difference was the largest source of PM2.5 uncertainty (3.89 μg m−3) and was comparable to the 10-year CIMD (3.67–4.70 μg m−3 in CACTUS and 3.83–4.72 μg m−3 in GISS-E2-R). This suggests that a PM2.5 change at least equivalent to 10-year CIMD is significant (with 95% confidence) to be distinguished among different models. This indicates that, concerning current model uncertainties, simulated data set used to determine the significant of PM2.5 to emissions changes should have data for at least 10 years. The point is that when comparing multi-model results or evaluating the effects of emission controls with model, one should be careful about attributing aerosol differences to emissions. For example, it is hard to say that a PM2.5 change is due emissions control alone if the control yields an amount of PM2.5 reduction less than the low edge of 5-year CIMD in PM2.5. Either different meteorological conditions or errors in emission inventories could yield a PM2.5 change which is comparable to the effects of emission control. Table S3 in the supplementary document shows the mean and 95% CI for the moving differences for PM2.5 at the CNEMC sites in the east of 100°E, which can be used for checking significance of aerosol changes in other simulations.
6. Conclusions This study analyzed anthropogenic aerosol simulations in eastern China during 1970–2010 using three global model results. All the three models underestimated the aerosol concentrations, with annual biases of −42% to −49% for sulfate, −37% to −44% for nitrate, −10% to −46% for ammonium, −51% to −73% for OC, and − 28% to 77% for BC. The large negative biases in wintertime sulfate and OC resulted in strong negative biases (−44% to −50.7%) in annual PM2.5. However, the models agreed on the annual trends of PM2.5 in the range of 0.42 to 0.72 μg m−3 year−1, within the 95% confidence level of the satellite data estimation. The models agreed on increasing nitrate (3.3–6.6% to 12.5–15.6%) and declining OA mass fractions (38.1–47.5% to 29.9–31.5%) in PM2.5 from 1970 to 2010, which qualitatively agreed with measurements. These agreements indicate the model results were suitable for the discussion on the significance of changes in PM2.5. The 95% confidence intervals of n-year moving differences in PM2.5 were calculated and compared with the variations of PM2.5 due to meteorology and simulation uncertainties. We found that the 5-year CIMD in PM2.5 was smaller than the effects of emission uncertainties and interannual variations of meteorology, and the 10-year CIMD in PM2.5 was comparable to the multi-model uncertainties. One must be careful in drawing conclusions on the effectiveness of emission controls
5.2. Compatibility of control plans for emissions and PM2.5 This section uses the n-year CIMD to evaluate the compatibility of past emission policy and PM2.5 control pledge in China. The Chinese State Council announced the APPCAP in September 2013, in which the annual PM2.5 concentration in major cities was expected to decrease by 10% by 2017 compared to the 2012 levels. The reduction target was 25% for the North China Plain, 20% for the Yangtze River Delta, and 10% for the Pearl River. According to the annual PM2.5 concentrations at 31 provincial capital cities during 2013–2014 (Wang et al., 2014), the median concentrations were in the range of 64–80 μg m−3. Roughly speaking, the conservative target (10% reduction) required annual PM2.5 decreases of 6.4–8 μg m−3, and the desired target (25% reduction) required decreases of 16–20 μg m−3. These required reductions are higher than the 10-year CIMD in PM2.5 (4.18 ± 0.52 μg m−3 in CACTUS; 4.28 ± 0.45 μg m−3 in GISS-E2-R, Table S3) and the multimodel uncertainties (3.89 μg m−3). That is, if the PM2.5 control is successfully achieved, the reductions should be significantly resolved by the models. How many emission reductions can fulfil such PM2.5 control target? 115
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
emissions for carbonaceous particulates in CACTUS and GISS-E2-R were linearly interpolated. The EDGAR v4.2 emission inventories did not concern the emission reductions in SO2 after 2008 and the stable NOx emissions after 2012 (Ronald et al., 2017). SOA chemistry is insufficient to reproduce observations in current models, and it was not individually discussed in this study. Multi-model simulations with better emission inventories and improved sulfur and SOA chemistry are in demand.
with air quality model based on short-term PM2.5 records. Knowing the minimum requirement for emissions reductions helps in arranging phased PM2.5 controls. China's conservative PM2.5 control target for 2017 is 10% decrease in PM2.5 (8 μg m−3) compared to 2012. Our simulations indicated that national mean emissions should decrease by as much as their increases (41% for SO2, 29% for NOx, and 42% for NH3) compared to their 2010 levels. The expected emission reductions exceed the control plan for China's 12th FYP (2011–2015), indicating a bigger cut in pollution and an aggressive emission regulation is needed for 13th FYP (2016–2020). There are a few limitations in this study. First of all, the low biases could underestimate the impacts of emission changes on PM2.5, and hence overestimate the required emission reductions to achieve the APPCAP target. Secondly, as the coarse model resolutions, we focus on the entire eastern China and ignore differences in sub-regional aerosols. Xiang et al. (2017) found higher nitrate in summer than in winter in Beijing, different from our higher nitrate in winter. It indicates regional discrepancies of aerosols and customized emissions policies are more welcome and feasible. The regional discussion requires newly fine emission inventories with high-temporal-resolution. Finally, the
Acknowledgments This work was jointly supported by the basic research program of the LAPC (Grant No. 7-082999) and the External Cooperation Program of BIC, Chinese Academy of Sciences (Grant No. 134111KYSB20150016). The model data outputs from ACCMIP were downloaded from the National Center for Atmospheric Science British Atmospheric Data Center (http://badc.nerc.ac.uk). We are grateful to CAWNET, affiliated with the China Meteorological Administration, for kindly providing the aerosol observations.
Appendix A (i) Degree of neutralization (DON) is defined by Adames et al. (1999)
DON =
[NH+4 ] + [NO−3 ]
2[SO24−]
(1)
(ii) Degree of sulfate neutralization (DSN) is defined by Pinder et al. (2008):
DSN =
[NH+4 ]–[NO−3 ] [SO24−]
(2)
(iii) The gas ratio (GR) is defined by Ansari and Pandis (1998):
GR =
[NH3T ]–DSN∙ [SO24−] free ammonia = total nitrate [HNO3T ]
where
[HNO3T]
(3) T
is the total nitric acid and [NH3 ] is the total ammonia (gas plus aerosol). All the aerosol species are in molar units.
Appendix B The significance of PM2.5 changes is inferred by calculating the Confidence Interval of n-year Moving Difference (n-year CIMD) in the long-term simulated PM2.5. The n-year moving difference in PM2.5 is a series of differences in PM2.5 concentrations (C) between two years with an interval of n years (Eq. (4)). The first element in the series is the PM2.5 difference between the initial year (y = 1971) and the n year forward (y + n). The second element is the difference between the second year (y = 1972) and the n year forward (y + n). The successive mean is calculated by moving forward. That is, picking up the next concentrations at the two edges of the n-year interval in the long-term simulations and calculating their difference. By repeating this calculation, we get a series of PM2.5 differences which constructs a probability density function (PDF) for the interval of n. By varying the interval from 1 year to 10 years, we get 10 PDFs and each has a 95% confidence interval for the corresponding n-year moving difference (n-year CIMD, n = 1, 2, …, 10). If a low edge of n-year CIMD is higher than a PM2.5 uncertain value (either due to meteorological interannual variation or simulation error), we say with 95% confidence that the PM2.5 changes per n years are significant compared with the uncertainty source, and the corresponding n-year mean moving differences in emissions are necessary to yield the significant changes in PM2.5
∆Cn = Cy + n − Cy
(4)
n = 1, 2, …, 10
y = 1971, 1972, … This approach is little bit analogous to estimate a CI range of dependent variables in the regression analysis. But a regression model is sensitive to the length of samples, particularly extreme sample values. This approach calculates the PM2.5 difference between two years, which is supposed to partially offset the adverse impacts of model biases in a specific year. Appendix C. Supplementary document Supplementary document to this article can be found online at https://doi.org/10.1016/j.atmosres.2018.05.008.
116
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
References
Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., Eder, B., 2005. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975. http://dx.doi.org/10.1016/j.atmosenv.2005.04.027. Hallquist, M., Wenger, J.C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N.M., George, C., Goldstein, A.H., Hamilton, J.F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.E., Jimenez, J.L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, Th.F., Monod, A., Prévôt, A.S.H., Seinfeld, J.H., Surratt, J.D., Szmigielski, R., Wildt, J., 2009. The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmos. Chem. Phys. 9, 5155–5236. http://dx.doi.org/10.5194/acp-9-5155-2009. Han, L., Cheng, S., Zhuang, G., Ning, H., Wang, H., Wei, W., Zhao, X., 2015. The changes and long-range transport of PM2.5 in Beijing in the past decade. Atmos. Environ. 110, 186–195. http://dx.doi.org/10.1016/j.atmosenv.2015.03.013. Hao, T., Han, S., Chen, S., Shan, X., Zai, Z., Qiu, X., Yao, Q., Liu, J., Chen, J., Meng, L., 2017. The role of fog in haze episode in Tianjin, China: a case study for November 2015. Atmos. Res. 194, 235–244. http://dx.doi.org/10.1016/j.atmosres.2017.04. 020. He, H., Wang, Y., Ma, Q., Ma, J., Chu, B., Ji, D., Tang, G., Liu, C., Zhang, H., Hao, J., 2014. Mineral dust and NOx promote the conversion of SO2 to sulfate in heavy pollution days. Sci. Rep. 4. http://dx.doi.org/10.1038/srep04172. He, K., Yang, F., Ma, Y., Zhang, Q., Yao, X., Chan, C.K., Cadle, S., Chan, T., Mulawa, P., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35 (29), 4959–4970. http://dx.doi.org/10.1016/s1352-2310(01)00301-6. He, K.B., 2012. Multi-Resolution Emission Inventory for China (MEIC): Model Framework and 1990–2010 Anthropogenic Emissions, International Global Atmospheric Chemistry Conference, 17–21 September, Beijing, China, S1-I-2. Hogrefe, C., Pouliot, G., Wong, D., Torian, A., Roselle, S., Pleim, J., Mathur, R., 2015. Annual application and evaluation of the online coupled WRF-CMAQ system over North America under AQMEII phase 2. Atmos. Environ. 115, 683–694. http://dx.doi. org/10.1016/j.atmosenv.2014.12.034. Hu, W.W., Hu, M., Yuan, B., Jimenez, J.L., Tang, Q., Peng, J.F., Hu, W., Shao, M., Wang, M., Zeng, L.M., Wu, Y.S., Gong, Z.H., Huang, X.F., He, L.Y., 2013. Insights on organic aerosol aging and the influence of coal combustion at a regional receptor site of central eastern China. Atmos. Chem. Phys. 13, 10095–10112. http://dx.doi.org/10. 5194/acp-13-10095-2013. Huang, R.-J., Zhang, Y., Bozzetti, C., Ho, K.-F., Cao, J.-J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., SchnelleKreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I.E., Prévôt, A.S.H., 2014a. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222. http://dx.doi.org/10.1038/ nature13774. Huang, X., Song, Y., Zhao, C., Li, M., Zhu, T., Zhang, Q., Zhang, X., 2014b. Pathways of sulfate enhancement by natural and anthropogenic mineral aerosols in China. J. Geophys. Res. 119, 14165–14179. http://dx.doi.org/10.1002/2014jd022301. Hui, G., Xiang, L., 2015. Influences of El Nino Southern Oscillation events on haze frequency in eastern China during boreal winters. Int. J. Climatol. 35, 2682–2688. http://dx.doi.org/10.1002/joc.4133. Koch, D., Schmidt, G.A., Field, C.V., 2006. Sulfur, sea salt, and radionuclide aerosols in GISS ModelE. J. Geophys. Res. 111, D06206. http://dx.doi.org/10.1029/ 2004JD005550. Lamarque, J.-F., Bond, T.C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M.G., Shindell, D., Smith, S.J., Stehfest, E., Van Aardenne, J., Cooper, O.R., Kainuma, M., Mahowald, N., McConnell, J.R., Naik, V., Riahi, K., van Vuuren, D.P., 2010. Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys. 10, 7017–7039. http://dx.doi.org/10.5194/acp-10-70172010. Lamarque, J.-F., Shindell, D.T., Josse, B., Young, P.J., Cionni, I., Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W.J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S.J., Horowitz, L.W., Lee, Y.H., MacKenzie, I.A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S.T., Schulz, M., Skeie, R.B., Stevenson, D.S., Strode, S., Sudo, K., Szopa, S., Voulgarakis, A., Zeng, G., 2013. The atmospheric chemistry and climate model Intercomparison project (ACCMIP): overview and description of models, simulations and climate diagnostics. Geosci. Model Dev. 6, 179–206. http://dx.doi.org/10.5194/gmd-6-179-2013. Lei, Y., Zhang, Q., He, K.B., Streets, D.G., 2011. Primary anthropogenic aerosol emission trends for China, 1990-2005. Atmos. Chem. Phys. 11, 931–954. http://dx.doi.org/10. 5194/acp-11-931-2011. Levy, R.C., Remer, L.A., Kleidman, R.G., Mattoo, S., Ichoku, C., Kahn, R., Eck, T.F., 2010. Global evaluation of the collection 5 MODIS dark-target aerosol products over land. Atmos. Chem. Phys. 10, 10399–10420. http://dx.doi.org/10.5194/acp-10-103992010. Li, K., Liao, H., Zhu, J., Moch, J.M., 2016a. Implications of RCP emissions on future PM2.5 air quality and direct radiative forcing over China. J. Geophys. Res. 121, 12985–13008. http://dx.doi.org/10.1002/2016jd025623. Li, S.S., Chen, L.F., Xiong, X.Z., Tao, J.H., Su, L., Han, D., Liu, Y., 2013. Retrieval of the haze optical thickness in North China plain using MODIS data. IEEE Trans. Geosci. Remote Sensing 51 (5), 2528–2540. http://dx.doi.org/10.1109/TGRS.2012. 2214038. Li, Z., Zhang, Y., Shao, J., Li, B., Hong, J., Liu, D., Li, D., Wei, P., Li, W., Li, L., Zhang, F., Guo, J., Deng, Q., Wang, B., Cui, C., Zhang, W., Wang, Z., Lv, Y., Xu, H., Chen, X., Li, L., Qie, L., 2016b. Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: method validation using ground-based measurements. Remote Sens. Environ. 173, 59–68. http://dx.doi.org/10.1016/j.rse.2015.11.019. Liao, H., Adams, P.J., Chung, S.H., Seinfeld, J.H., Mickley, L.J., Jacob, D.J., 2003.
Adams, J.P., Seinfeld, J.H., Koch, D.M., 1999. Global concentrations of tropospheric sulfate, nitrate, and ammonium aerosol simulated in a general circulation model. J. Geophys. Res. 104 (D11), 13791–13823. http://dx.doi.org/10.1029/1999jd900083. Anderson, T.L., Wu, Y.H., Chu, D.A., Schmid, B., Redemann, J., Dubovik, O., 2005. Testing the MODIS satellite retrieval of aerosol fine-mode fraction. J. Geophys. Res. 110, D8. http://dx.doi.org/10.1029/2005jd005978. Ansari, A.S., Pandis, S.N., 1998. Response of inorganic PM to precursor concentrations. Environ. Sci. Technol. 32 (18), 2706–2714. http://dx.doi.org/10.1021/es971130j. Appel, K.W., Pouliot, G.A., Simon, H., Sarwar, G., Pye, H.O.T., Napelenok, S.L., Akhtar, F., Roselle, S.J., 2013. Evaluation of dust and trace metal estimates from the community multiscale air quality (CMAQ) model version 5.0. Geosci. Model Dev. 6, 883–899. http://dx.doi.org/10.5194/gmd-6-883-2013. Bauer, S.E., Koch, D., 2005. Impact of heterogeneous sulfate formation at mineral dust surfaces on aerosol loads and radiative forcing in the Goddard Institute for Space Studies general circulation model. J. Geophys. Res. 110, D17202. http://dx.doi.org/ 10.1029/2005JD005870. Bauer, S.E., Mishchenko, M.I., Lacis, A.A., Zhang, S., Perlwitz, J., Metzger, S.M., 2007. Do sulfate and nitrate coatings on mineral dust have important effects on radiative properties and climate modeling? J. Geophys. Res. 112, D06307. http://dx.doi.org/ 10.1029/2005JD006977. Berglen, T.F., Berntsen, T.K., Isaksen, I.S.A., Sundet, J.K., 2004. A global model of the coupled sulfur/oxidant chemistry in the troposphere: the sulfur cycle. J. Geophys. Res. 109, D19310. http://dx.doi.org/10.1029/2003JD003948. Boys, B.L., Martin, R.V., van Donkelaar, A., MacDonell, R.J., Hsu, N.C., Cooper, M.J., Yantosca, R.M., Lu, Z., Streets, D.G., Zhang, Q., Wang, S.W., 2014. Fifteen-year global time series of satellite-derived fine particulate matter. Environ. Sci. Technol. Lett. 48 (19), 11109–11118. http://dx.doi.org/10.1021/es502113p. CAAC: Clean Air Alliance of China, October 2013. State Council Air Pollution Prevention and Control Action Plan, Issue II. Available at: http://en.cleanairchina.org/product/ 6346.html, Accessed date: 30 August 2017 (English translation). Chang, W., Liao, H., Xin, J., Li, Z., Li, D., Zhang, X., 2015. Uncertainties in anthropogenic aerosol concentrations and direct radiative forcing induced by emission inventories in eastern China. Atmos. Res. 166, 129–140. http://dx.doi.org/10.1016/j.atmosres. 2015.06.021. Chen, D., Liu, Z., Fast, J., Ban, J., 2016. Simulations of sulfate-nitrate-ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014. Atmos. Chem. Phys. 16, 10707–10724. http://dx.doi.org/10.5194/acp-16-107072016. China Environment News, July 6, 2016. Interim assessment report for the implementation of Air Pollution Prevention and Control Action Plan. Available at: http://www. cenews.com.cn/sylm/hjyw/201607/t20160706_806932.html, Accessed date: 30 August 2017 (in Chinese). van Donkelaar, A., Martin, R.V., Brauer, M., Boys, B.L., 2015. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123, 135–143. http://dx.doi.org/10.1289/ehp.1408646. Fast, J.D., Gustafson, W.I., Easter Jr., R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., Grell, G.A., Peckham, S.E., 2006. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorologychemistry-aerosol model. J. Geophys. Res. 111, D21305. http://dx.doi.org/10.1029/ 2005JD006721. de Foy, B., Lu, Z.F., Streets, D.G., 2016. Satellite NO2 retrievals suggest China has exceeded its NOx reduction goals from the twelfth five-year plan. Sci. Rep. 6, 35912. http://dx.doi.org/10.1038/srep35912. Fu, T.-M., Cao, J.J., Zhang, X.Y., Lee, S.C., Zhang, Q., Han, Y.M., Qu, W.J., Han, Z., Zhang, R., Wang, Y.X., Chen, D., Henze, D.K., 2012. Carbonaceous aerosols in China: topdown constraints on primary sources and estimation of secondary contribution. Atmos. Chem. Phys. 12, 2725–2746. http://dx.doi.org/10.5194/acp-12-2725-2012. Fu, X., Wang, X., Guo, H., Cheung, K., Ding, X., Zhao, X., He, Q., Gao, B., Zhang, Z., Liu, T., Zhang, Y., 2014. Trends of ambient fine particles and major chemical components in the Pearl River Delta region: observation at a regional background site in fall and winter. Sci. Total Environ. 497-498, 274–281. http://dx.doi.org/10.1016/j.scitotenv. 2014.08.008. Gao, J., Yuan, Z., Liu, X., Xia, X., Huang, X., Dong, Z., 2016a. Improving air pollution control policy in China-a perspective based on cost-benefit analysis. Sci. Total Environ. 543, 307–314. http://dx.doi.org/10.1016/j.scitotenv.2015.11.037. Gao, M., Carmichael, G.R., Saide, P.E., Liu, Z.F., Yu, M., Streets, D.G., Wang, Z.F., 2016b. Response of winter fine particulate matter concentrations to emission and meteorology changes in North China. Atmos. Chem. Phys. 16, 11837–11851. http://dx.doi. org/10.5194/acp-16-11837-2016. Geng, G., Zhang, Q., Tong, D., Li, M., Wang, S., He, K., 2017. Chemical composition of ambient PM2.5 over China and relationship to precursor emissions during 2005-2012. Atmos. Chem. Phys. 17, 9187–9203. http://dx.doi.org/10.5194/acp-17-9187-2017. Goto, D., Nakajima, T., Dai, T., Takemura, T., Kajino, M., Matsui, H., Takami, A., Hatakeyama, S., Sugimoto, N., Shimizu, A., Ohara, T., 2015. An evaluation of simulated particulate sulfate over East Asia through global model intercomparison. J. Geophys. Res. Atmos. 120, 6247–6270. http://dx.doi.org/10.1002/2014JD021693. Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., van der Gon, H.D., Frost, G.J., Heil, A., Kaiser, J.W., Kinne, S., Klimont, Z., Kloster, S., Lamarque, J.-F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T., Raut, J.-C., Riahi, K., Schultz, M.G., Smith, S.J., Thompson, A., van Aardenne, J., van der Werf, G.R., van Vuuren, D.P., 2011. Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980-2010 period. Clim. Chang. 109, 163–190. http:// dx.doi.org/10.1007/s10584-011-0154-1.
117
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
aerosol chemical speciation monitor. Atmos. Environ. 51, 250–259. http://dx.doi. org/10.1016/j.atmosenv.2012.01.013. Tan, J., Duan, J., Ma, Y., He, K., Cheng, Y., Deng, S., Huang, Y., Si-Tu, S., 2016. Long-term trends of chemical characteristics and sources of fine particle in Foshan City, Pearl River Delta: 2008–2014. Sci. Total Environ. 565, 519–528. http://dx.doi.org/10. 1016/j.scitotenv.2016.05.059. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Chang. 109, 77–94. http://dx.doi. org/10.1007/s10584-011-0151-4. Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P.J., Artaxo, P., Bahadur, R., Balkanski, Y., Bauer, S.E., Bellouin, N., Benedetti, A., Bergman, T., Berntsen, T.K., Beukes, J.P., Bian, H., Carslaw, K.S., Chin, M., Curci, G., Diehl, T., Easter, R.C., Ghan, S.J., Gong, S.L., Hodzic, A., Hoyle, C.R., Iversen, T., Jathar, S., Jimenez, J.L., Kaiser, J.W., Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y.H., Lin, G., Liu, X., Luo, G., Ma, X., Mann, G.W., Mihalopoulos, N., Morcrette, J.-J., Müller, J.-F., Myhre, G., Myriokefalitakis, S., Ng, N.L., O'Donnell, D., Penner, J.E., Pozzoli, L., Pringle, K.J., Russell, L.M., Schulz, M., Sciare, J., Seland, Ø., Shindell, D.T., Sillman, S., Skeie, R.B., Spracklen, D., Stavrakou, T., Steenrod, S.D., Takemura, T., Tiitta, P., Tilmes, S., Tost, H., van Noije, T., van Zyl, P.G., von Salzen, K., Yu, F., Wang, Z., Wang, Z., Zaveri, R.A., Zhang, H., Zhang, K., Zhang, Q., Zhang, X., 2014. The AeroCom evaluation and intercomparison of organic aerosol in global models. Atmos. Chem. Phys. 14, 10845–10895. http://dx.doi.org/10.5194/acp-14-10845-2014. Tsigaridis, K., Kanakidou, M., 2007. Secondary organic aerosol importance in the future atmosphere. Atmos. Environ. 41, 4682–4692. http://dx.doi.org/10.1016/j.atmosenv. 2007.03.045. Wang, G., Zhang, R., Gomez, M.E., Yang, L., Zamora, M.L., Hu, M., Lin, Y., Peng, J., Guo, S., Meng, J., Li, J., Cheng, C., Hu, T., Ren, Y., Wang, Y., Gao, J., Cao, J., An, Z., Zhou, W., Li, G., Wang, J., Tian, P., Marrero-Ortiz, W., Secrest, J., Du, Z., Zheng, J., Shang, D., Zeng, L., Shao, M., Wang, W., Huang, Y., Wang, Y., Zhu, Y., Li, Y., Hu, J., Pan, B., Cai, L., Cheng, Y., Ji, Y., Zhang, F., Rosenfeld, D., Liss, P.S., Duce, R.A., Kolb, C.E., Molina, M.J., 2016. Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. U. S. A. 113 (48), 13630–13635. http://dx.doi.org/10.1073/ pnas.1616540113. Wang, H., Chen, H.-P., 2016. Understanding the recent trend of haze pollution in eastern China: roles of climate change. Atmos. Chem. Phys. 16, 4205–4211. http://dx.doi. org/10.5194/acp-16-4205-2016. Wang, H.J., Chen, H.P., Liu, J.P., 2015a. Arctic Sea ice decline intensified haze pollution in eastern China. Atmos. Ocean. Sci. Lett. 8, 1–9, 2015. Wang, J., Zhang, J.-S., Liu, Z.-J., Wu, J.-H., Zhang, Y.-F., Han, S.-Q., Zheng, X.-J., Zhou, L.-D., Feng, Y.-C., Zhu, T., 2017. Characterization of chemical compositions in sizesegregated atmospheric particles during severe haze episodes in three mega-cities of China. Atmos. Res. 187, 138–146. http://dx.doi.org/10.1016/j.atmosres.2016.12. 004. Wang, L., Zhang, Y., Wang, K., Zheng, B., Zhang, Q., Wei, W., 2015b. Application of weather research and forecasting model with chemistry (WRF/Chem) over northern China: sensitivity study, comparative evaluation, and policy implications. Atmos. Environ. 124, 337–350. http://dx.doi.org/10.1016/j.atmosenv.2014.12.052. Wang, L.T., Jang, C., Zhang, Y., Wang, K., Zhang, Q.A., Streets, D., Fu, J., Lei, Y., Schreifels, J., He, K.B., Hao, J.M., Lam, Y.F., Lin, J., Meskhidze, N., Voorhees, S., Evarts, D., Phillips, S., 2010. Assessment of air quality benefits from national air pollution control policies in China. Part II: evaluation of air quality predictions and air quality benefits assessment. Atmos. Environ. 44, 3449–3457. http://dx.doi.org/ 10.1016/j.atmosenv.2010.05.058. Wang, X., Liu, H., Pang, J., Carmichael, G., He, K., Fan, Q., Zhong, L., Wu, Z., Zhang, J., 2013. Reductions in sulfur pollution in the Pearl River Delta region, China: assessing the effectiveness of emission controls. Atmos. Environ. 76, 113–124. http://dx.doi. org/10.1016/j.atmosenv.2013.04.074. Wang, X., Wang, W., Yang, L., Gao, X., Nie, W., Yu, Y., Xu, P., Zhou, Y., Wang, Z., 2012. The secondary formation of inorganic aerosols in the droplet mode through heterogeneous aqueous reactions under haze conditions. Atmos. Environ. 63, 68–76. http:// dx.doi.org/10.1016/j.atmosenv.2012.09.029. Wong, D.C., Pleim, J., Mathur, R., Binkowski, F., Otte, T., Gilliam, R., Pouliot, G., Xiu, A., Young, J.O., Kang, D., 2012. WRF-CMAQ two-way coupled system with aerosol feedback: software development and preliminary results. Geosci. Model Dev. 5, 299–312. http://dx.doi.org/10.5194/gmd-5-299-2012. Xiang, P., Zhou, X., Duan, J., Tan, J., He, K., Yuan, C., Ma, Y., Zhang, Y., 2017. Chemical characteristics of water-soluble organic compounds (WSOC) in PM2.5 in Beijing, China: 2011-2012. Atmos. Res. 183, 104–112. http://dx.doi.org/10.1016/j.atmosres. 2016.08.020. Xie, Y., Ding, A., Nie, W., Mao, H., Qi, X., Huang, X., Xu, Z., Kerminen, V.-M., Petäjä, T., Chi, X., Virkkula, A., boy, M., Xue, L., Guo, J., Sun, J., Yang, X., Kulmala, M., Fu, C., 2015. Enhanced sulfate formation by nitrogen dioxide: implications from in situ observations at the SORPES station. J. Geophys. Res. 120, 12679–12694. http://dx. doi.org/10.1002/2015jd023607. Xing, J., Mathur, R., Pleim, J., Hogrefe, C., Gan, C.-M., Wong, D.C., Wei, C., Gilliam, R., Pouliot, G., 2015. Observations and modeling of air quality trends over 1990-2010 across the Northern Hemisphere: China, the United States and Europe. Atmos. Chem. Phys. 15, 2723–2747. http://dx.doi.org/10.5194/acp-15-2723-2015. Xu, F., Xiang, N., Higano, Y., 2017. How to reach haze control targets by air pollutants emission reduction in the Beijing-Tianjin-Hebei region of China? PLoS One 12 (3). http://dx.doi.org/10.1371/journal.pone.0173612. Yang, F., Huang, L., Duan, F., Zhang, W., He, K., Ma, Y., Brook, J.R., Tan, J., Zhao, Q., Cheng, Y., 2011a. Carbonaceous species in PM2.5 at a pair of rural/urban sites in Beijing, 2005-2008. Atmos. Chem. Phys. 11, 7893–7903. http://dx.doi.org/10.5194/ acp-11-7893-2011.
Interactions between tropospheric chemistry and aerosols in a unified general circulation model. J. Geophys. Res. 108 (D1), 4001. http://dx.doi.org/10.1029/ 2001JD001260. Liao, H., Chen, W.-T., Seinfeld, J.H., 2006. Role of climate change in global predictions of future tropospheric ozone and aerosols. J. Geophys. Res. 111, D12304. http://dx.doi. org/10.1029/2005JD006852. Liao, H., Seinfeld, J.H., 2005. Global impacts of gas-phase chemistry-aerosol interactions on direct radiative forcing by anthropogenic aerosols and ozone. J. Geophys. Res. 110, D18208. http://dx.doi.org/10.1029/2005JD005907. Liao, H., Seinfeld, J.H., Adams, P.J., Mickley, L.J., 2004. Global radiative forcing of coupled tropospheric ozone and aerosols in a unified general circulation model. J. Geophys. Res. 109, D16207. http://dx.doi.org/10.1029/2003JD004456. Liu, C., Ma, Q., Liu, Y., Ma, J., He, H., 2012. Synergistic reaction between SO2 and NO2 on mineral oxides: a potential formation pathway of sulfate aerosol. Phys. Chem. Chem. Phys. 14, 1668–1676. http://dx.doi.org/10.1039/c1cp22217a. Liu, H., Wang, X.M., Pang, J.M., He, K.B., 2013. Feasibility and difficulties of China's new air quality standard compliance: PRD case of PM2.5 and ozone from 2010 to 2025. Atmos. Chem. Phys. 13, 12013–12027. http://dx.doi.org/10.5194/acp-13-120132013. Liu, M., Song, Y., Zhou, T., Xu, Z., Yan, C., Zheng, M., Wu, Z., Hu, M., Wu, Y., Zhu, T., 2017. Fine particle pH during severe haze episodes in northern China. Geophys. Res. Lett. 44, 5213–5221. http://dx.doi.org/10.1002/2017GL073210. Lu, Z., Zhang, Q., Streets, D.G., 2011. Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996-2010. Atmos. Chem. Phys. 11, 9839–9864. http://dx.doi.org/10.5194/acp-11-9839-2011. Ma, Z., Hu, X., Sayer, A.M., Levy, R., Zhang, Q., Xue, Y., Tong, S., Bi, J., Huang, L., Liu, Y., 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 20042013. Environ. Health Perspect. 124, 184–192. http://dx.doi.org/10.1289/ehp. 1409481. Mathur, R., Gilliam, R., Bullock Jr., O.R., Roselle, S., Pleim, J., Wong, D., Binkowski, F., Streets, D., 2012. Extending the applicability of the Community Multiscale Air Quality Model to hemispheric scales: motivation, challenges, and progress. In: Steyn, D.G., Trini, S. (Eds.), Air Pollution Modeling and its Applications XXI. Springer, Dordrecht, pp. 175–179. http://dx.doi.org/10.1007/978-94-007-1359-8-30. Mathur, R., Roselle, S., Young, J., Kang, D., 2014. Representing the effects of long-range transport and lateral boundary conditions in regional air pollution models. In: Air Pollution Modeling and its Application XXII NATO Science for Peace and Security Series C: Environmental Security. Springer, Heidelburg, Germany, pp. 303–308 Chapter 51. Mezuman, K., Bauer, S.E., Tsigaridis, K., 2016. Evaluating secondary inorganic aerosols in three dimensions. Atmos. Chem. Phys. 16, 10651–10669. http://dx.doi.org/10.5194/ acp-16-10651-2016. Myhre, G., Samset, B.H., Schulz, M., Balkanski, Y., Bauer, S., Berntsen, T.K., Bian, H., Bellouin, N., Chin, M., Diehl, T., Easter, R.C., Feichter, J., Ghan, S.J., Hauglustaine, D., Iversen, T., Kinne, S., Kirkevåg, A., Lamarque, J.-F., Lin, G., Liu, X., Lund, M.T., Luo, G., Ma, X., van Noije, T., Penner, J.E., Rasch, P.J., Ruiz, A., Seland, Ø., Skeie, R.B., Stier, P., Takemura, T., Tsigaridis, K., Wang, P., Wang, Z., Xu, L., Yu, H., Yu, F., Yoon, J.-H., Zhang, K., Zhang, H., Zhou, C., 2013. Radiative forcing of the direct aerosol effect from AeroCom phase II simulations. Atmos. Chem. Phys. 13, 1853–1877. http://dx.doi.org/10.5194/acp-13-1853-2013. Niu, F., Li, Z., Li, C., Lee, K.-H., Wang, M., 2010. Increase of wintertime fog in China: potential impacts of weakening of the eastern Asia monsoon circulation and increasing aerosol loading. J. Geophys. Res. 115, D00K20. http://dx.doi.org/10.1029/ 2009JD013484. Ohara, T., Akimoto, H., Kurokawa, J., Horii, N., Yamaji, K., Yan, X., Hayasaka, T., 2007. An Asian emission inventory of anthropogenic emission sources for the period 19802020. Atmos. Chem. Phys. 7, 4419–4444. http://dx.doi.org/10.5194/acp-7-44192007. Pinder, R.W., Dennis, R.L., Bhave, P.V., 2008. Observable indicators of the sensitivity of PM2.5 nitrate to emission reductions—part I: deviation of the adjusted gas ratio and applicability at regulatory-relevant time scale. Atmos. Environ. 42, 1275–1286. http://dx.doi.org/10.1016/j.atmosenv.2007.10.039. Qin, Y., Xie, S.D., 2012. Spatial and temporal variation of anthropogenic black carbon emissions in China for the period 1980-2009. Atmos. Chem. Phys. 12, 4825–4841. http://dx.doi.org/10.5194/acp-12-4825-2012. Ronald, J., van der, A., Mijling, B., Ding, J., Koukouli, M.E., Liu, F., Li, Q., Mao, H., Theys, N., 2017. Cleaning up the air: effectiveness of air quality policy for SO2 and NOx emissions in China. Atmos. Chem. Phys. 17, 1775–1789. http://dx.doi.org/10.5194/ acp-17-1775-2017. Sarwar, G., Luecken, D., Yarwood, G., Whitten, G.Z., Carter, W.P.L., 2008. Impact of an updated carbon bond mechanism on predictions from the CMAQ modeling system: preliminary assessment. J. Appl. Meteorol. Climatol. 47 (1), 3–14. http://dx.doi.org/ 10.1175/2007JAMC1303.1. Shindell, D.T., Pechony, O., Voulgarakis, A., Faluvegi, G., Nazarenko, L., Lamarque, J.-F., Bowman, K., Milly, G., Kovari, B., Ruedy, R., Schmidt, G.A., 2013. Interactive ozone and methane chemistry in GISS-E2 historical and future climate simulations. Atmos. Chem. Phys. 13, 2653–2689. http://dx.doi.org/10.5194/acp-13-2653-2013. Streets, D.G., Tsai, N.Y., Akimoto, H., Oka, K., 2000. Sulfur dioxide emissions in Asia in the period 1985-1997. Atmos. Environ. 34, 4413–4424. http://dx.doi.org/10.1016/ s1352-2310(00)00187-4. Sun, J., Zhang, Q., Canagaratna, M.R., Zhang, Y., Ng, N.L., Sun, Y., Jayne, J.T., Zhang, X., Zhang, X., Worsnop, D.R., 2010. Highly time- and size-resolved characterization of submicron aerosol particles in Beijing using an aerodyne aerosol mass spectrometer. Atmos. Environ. 44, 131–140. http://dx.doi.org/10.1016/j.atmosenv.2009.03.020. Sun, Y., Wang, Z., Dong, H., Yang, T., Li, J., Pan, X., Chen, P., Jayne, J.T., 2012. Characterization of summer organic and inorganic aerosols in Beijing, China with an
118
Atmospheric Research 212 (2018) 106–119
W. Chang et al.
Zhang, Q.Q., Wang, Y., Ma, Q., Yao, Y., Xie, Y., He, K., 2015a. Regional differences in Chinese SO2 emission control efficiency and policy implications. Atmos. Chem. Phys. 15, 6521–6533. http://dx.doi.org/10.5194/acp-15-6521-2015. Zhang, X.Y., Wang, Y.Q., Niu, T., Zhang, X.C., Gong, S.L., Zhang, Y.M., Sun, J.Y., 2012. Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos. Chem. Phys. 12, 779–799. http://dx.doi.org/10.5194/acp-12-779-2012. Zhang, Y., Huang, W., Cai, T.Q., Fang, D.Q., Wang, Y.Q., Song, J., Hu, M., Zhang, Y.X., 2016. Concentrations and chemical compositions of fine particles (PM2.5) during haze and non-haze days in Beijing. Atmos. Res. 174, 62–69. http://dx.doi.org/10.1016/j. atmosres.2016.02.003. Zhang, Y., Li, Z., 2015. Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sens. Environ. 160, 252–262. http://dx.doi.org/10.1016/j.rse.2015.02.005. Zhang, Y.J., Tang, L.L., Wang, Z., Yu, H.X., Sun, Y.L., Liu, D., Qin, W., Canonaco, F., Prévôt, A.S.H., Zhang, H.L., Zhou, H.C., 2015b. Insights into characteristics, sources, and evolution of submicron aerosols during harvest seasons in the Yangtze River delta region, China. Atmos. Chem. Phys. 15, 1331–1349. http://dx.doi.org/10.5194/ acp-15-1331-2015. Zhao, B., Wang, S., Wang, J., Fu, Joshua S., Liu, T., Xu, J., Fu, X., Hao, J., 2013. Impact of national NOx and SO2 control policies on particulate matter pollution in China. Atmos. Environ. 77, 453–463. http://dx.doi.org/10.1016/j.atmosenv.2013.05.012. Zhao, S., Li, J., Sun, C., 2016. Decadal variability in the occurrence of wintertime haze in central eastern China tired to the Pacific Decadal Oscillation. Sci. Rep. 6. http://dx. doi.org/10.1038/srep27424. Zheng, B., Zhang, Q., Zhang, Y., He, K.B., Wang, K., Zheng, G.J., Duan, F.K., Ma, Y.L., Kimoto, T., 2015. Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China. Atmos. Chem. Phys. 15, 2031–2049. http://dx.doi.org/10. 5194/acp-15-2031-2015. Zhu, J., Liao, H., Li, J., 2012. Increases in aerosol concentrations over eastern China due to the decadal-scale weakening of the east Asian summer monsoon. Geophys. Res. Lett. 39, L09809. http://dx.doi.org/10.1029/2012gl051428. Zou, Y.F., Wang, Y.H., Zhang, Y.Z., Koo, J.H., 2017. Arctic Sea ice, Eurasia snow, and extreme winter haze in China. Sci. Adv. 3, e1602751. http://dx.doi.org/10.1126/ sciadv.1602751.
Yang, F., Tan, J., Zhao, Q., Du, Z., He, K., Ma, Y., Duan, F., Chen, G., Zhao, Q., 2011b. Characteristics of PM2.5 speciation in representative megacities and across China. Atmos. Chem. Phys. 11, 5207–5219. http://dx.doi.org/10.5194/acp-11-5207-2011. Yang, Y., Liao, H., Lou, S., 2016. Increase in winter haze over eastern China in the past decades: roles of variations in meteorological parameters and anthropogenic emissions. J. Geophys. Res. 121, 13050–13065. http://dx.doi.org/10.1002/ 2016JD025136. Yang, Y., Liao, Y., Lou, S., 2015a. Decadal trend and interannual variation of outflow of aerosols from East Asia: roles of variations in meteorological parameters and emissions. Atmos. Environ. 100, 141–153. http://dx.doi.org/10.1016/j.atmosenv.2014. 11.004. Yang, Y.R., Liu, X.G., Qu, Y., Wang, J.L., An, J.L., Zhang, Y., Zhang, F., 2015b. Formation mechanism of continuous extreme haze episodes in the megacity Beijing, China, in January 2013. Atmos. Res. 155, 192–203. http://dx.doi.org/10.1016/j.atmosres. 2014.11.023. Yin, Z., Wang, H., Chen, H., 2017. Understanding severe winter haze events in the North China plain in 2014: roles of climate anomalies. Atmos. Chem. Phys. 17, 1641–1651. http://dx.doi.org/10.5194/acp-17-1641-2017. Wang, Yungang, Ying, Q., Hu, J., Zhang, H., 2014. Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014. Environ. Int. 73, 413–422. http://dx.doi.org/10.1016/j.envint.2014.08.016. Yuan, Q., Li, W.J., Zhou, S.Z., Yang, L.X., Chi, J.W., Sui, X., Wang, X.W., 2015. Integrated evaluation of aerosols during haze-fog episodes at one regional background site in North China plain. Atmos. Res. 156, 102–110. http://dx.doi.org/10.1016/j.atmosres. 2015.01.002. Zhang, J., Reid, J.S., Alfaro-Contreras, R., Xian, P., 2017. Has China been exporting less particulate air pollution over the past decade? Geophys. Res. Lett. 44, 2941–2948. http://dx.doi.org/10.1002/2017GL072617. Zhang, Q., Streets, D.G., Carmichael, G.R., He, K.B., Huo, H., Kannari, A., Klimont, Z., Park, I.S., Reddy, S., Fu, J.S., Chen, D., Duan, L., Lei, Y., Wang, L.T., Yao, Z.L., 2009. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. 9, 5131–5153. http://dx.doi.org/10.5194/acp-9-5131-2009. Zhang, Q., Streets, D.G., He, K., Wang, Y., Richter, A., Burrows, J.P., Uno, I., Jang, C.J., Chen, D., Yao, Z., Lei, Y., 2007. NOx emission trends for China, 1995-2004: the view from the ground and the view from space. J. Geophys. Res. 112, D22306. http://dx. doi.org/10.1029/2007JD008684.
119
