Source Contributions of Sulfate Aerosol over East Asia Estimated by

Article pubs.acs.org/est

Source Contributions of Sulfate Aerosol over East Asia Estimated by CMAQ-DDM Syuichi Itahashi* Department of Earth System Science and Technology, Kyushu University Kasuga Park, Kasuga, Fukuoka, 816-8580, Japan

Itsushi Uno Research Institute for Applied Mechanics, Kyushu University Kasuga Park, Kasuga, Fukuoka, 816-8580, Japan

Soontae Kim Division of Environmental, Civil and Transportation Engineering, Ajou University Woncheon-dong,Yeongtong-gu, Suwon, 443-749, South Korea

ABSTRACT: We applied the decoupled direct method (DDM), a sensitivity analysis technique for computing sensitivities accurately and efficiently, to determine the source-receptor relationships of anthropogenic SO2 emissions to sulfate aerosol over East Asia. We assessed source contributions from East Asia being transported to Oki Island downwind from China and Korea during two air pollution episodes that occurred in July 2005. The contribution from China, particularly that from central eastern China (CEC), was found to dominate the sulfate aerosols. To study these contributions in more detail, CEC was divided into three regions, and the contributions from each region were examined. Source contributions exhibited both temporal and vertical variability, largely due to transport patterns imposed by the Asian summer monsoon. Our results are consistent with backward trajectory analyses. We found that anthropogenic SO2 emissions from China produce significant quantities of summertime sulfate aerosols downwind of source areas. We used a parametric scaling method for estimating anthropogenic SO2 emissions in China. Using column amounts of SO2 derived from satellite data, and relationships between the column amounts of SO2 and anthropogenic emissions, 2009 emissions were diagnosed. The results showed that 2009 emissions of SO2 from China were equivalent to 2004 levels.

1. INTRODUCTION Sensitivity analyses are essential in the evaluation of environmental models and their uncertainty, and in quantifying the responses of output variables to perturbations in input parameters.1 When examining the sensitivity of photochemical model output to variations in the model’s input, the decoupled direct method (DDM) provides an efficient and accurate means, especially when compared to the commonly used bruteforce method (BFM). The use of DDM for photochemical model sensitivity analyses is well established in the United States.2,3 Higher order DDM (or HDDM) is required for ozone due to its complex nonlinear chemical formation © 2012 American Chemical Society

processes and has been used with success in evaluating the effectiveness of control measures and in quantifying ozone formation potential.4,5 For example, HDDM was used to estimate the emission reductions required to attain the 8 h ozone air quality standard in the Dallas-Fort Worth region of Texas.6 DDM has also been in used for particulate matter (PM) applications.7,8 Received: Revised: Accepted: Published: 6733

March 6, 2012 May 24, 2012 May 29, 2012 May 29, 2012 dx.doi.org/10.1021/es300887w | Environ. Sci. Technol. 2012, 46, 6733−6741

Environmental Science & Technology

Article

Figure 1. Model domain of WRF-CMAQ including source regions (China, central eastern China, Korea, and Japan), the Oki Island receptor location, and subset areas (North China Plain, Yangtze Delta, eastern China, and Inner Mongolia) (inset).

between SO2 emissions and aerosol sulfate, we applied DDM techniques for East Asia. Here we begin with a brief description of model configuration and an explanation of the DDM techniques used to evaluate the source contributions. As a base case simulation, modeled results are compared with PM observations at a site downwind of China and Korea, and two pollutant episodes are found. We present the source contributions as evaluated by DDM, and validate our methods using the traditional BFM approach. We also propose using a new simplified inversion approach derived from DDM calculations to predict future, or current, emissions.

Over the past three decades, East Asia has experienced rapid economic and population growth and consequent increases in anthropogenic emissions. Air pollutants from East Asia can impact air quality, biogeochemical cycles, and climate change at regional to global scales.9 China is of particular interest. China’s growth and increased energy demand, combined with relatively weak emission control regulations, have resulted in significant emission increases.10 Analysis of satellite observations from 1996 to 2004 revealed significant incremental increases in the atmospheric column amounts of nitrogen dioxides (NO2) over China.11 Steadily increasing column amounts of NO2 occurred until February 2010, except during the economic downturn from late 2008 to mid-2009.12 However, new regulations imposed by the Chinese government (11th Five-Year Plan) mean that air pollutants, particularly sulfur dioxide (SO2), are on the decline. Satellite data and modeling results have shown that the variability in submicrometer-size (fine-mode) aerosol optical depth (AOD) over the oceans adjacent to East Asia increased from 2000 to 2005, peaked from 2005 to 2006, and then subsequently decreased. Such fluctuations in fine-mode AOD are thought to reflect the widespread installation of fluegas desulfurization (FGD) devices in coal-fired power plants in China, because aerosol sulfate is a major determinant of finemode AOD in East Asia,13 despite spatial variation of the FGD installation ratio across provinces.14 To most effectively address the problem of air pollution, the source-receptor (S-R) relationships of anthropogenic SO2 emissions and aerosol sulfate must be clarified for each country and/or region in East Asia. The S-R relationship for acidifying species has previously been examined through modeling over East Asia.15 It has also been found that the long-range transport of anthropogenic sulfur and reactive nitrogen from central China contributes a significant portion (more than 20%) of acid deposition in East Asia.16 However, S-R relationships have not been determined for sulfate aerosol, which is a major contributor to fine-mode AOD and a key climate forcer. To derive such relationships

2. MATERIALS AND METHODS 2.1. CMAQ. We used the U.S. Environmental Protection Agency’s Community Multiscale Air Quality (CMAQ) modeling system version 4.7.117 equipped with DDM.5,7 This model is driven by meteorological fields generated by the Weather Research and Forecasting (WRF; ver. 3.1.1) model with initial and boundary conditions defined by National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data with 1° × 1° resolution and 6 h intervals. The model had 98 × 78 horizontal grids with a resolution of 80 km, centered at 35°N, 115°E on a Lambert conformal projection, covering all of East Asia (Figure 1). The vertical grids extended from the surface to 50 hPa, including 20 layers within the lower 3 km. Anthropogenic emissions data were obtained from the Regional Emission Inventory in Asia (REAS).10 The most recent REAS emission inventory data were for 2005. Bottom-up emission estimates often suffer from time lags of several years because of the time needed to compile energy data, emission factors, and other socioeconomic information. Note that the REAS inventory contains annual emission totals with no temporal variations for individual sources or categories. Emissions from biogenic and biomass burning, with monthly variation, were obtained from the Model of Emissions of Gases and Aerosols from Nature (MEGAN)18 and the Reanalysis of the Tropospheric Chemical Composition 6734

dx.doi.org/10.1021/es300887w | Environ. Sci. Technol. 2012, 46, 6733−6741


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which has intensive industrial activity and dense population, was estimated to have produced 43.7% of China’s total SO2 emissions in 2005.10 Table 1 gives the anthropogenic SO2 emissions from these source regions. A receptor site was located downwind of China and Korea at a remote observation site in Oki, Japan (gray circle Figure 1), far from Japanese industry.

(RETRO) project (http://retro.enes.org/data_emissions. shtml), respectively. Data for volcanic activity were obtained from the Ace-Asia and TRACE-P Modeling and Emission Support System (ACESS). ACESS data are available at http:// www.cgrer.uiowa.edu/ACESS/acess_index.htm. We used the Statewide Air Pollution Research Center version 99 (SAPRC99) chemical mechanism with Euler Backward Iterative solver for producing gas phase chemistry, and CMAQ fifth-generation aerosol module (AERO5) for aerosol simulation. The model’s default initial and boundary conditions were used. CMAQ has been used to simulate air quality over East Asia.13,19,20 2.2. DDM. DDM enables accurate and computationally efficient calculations of the sensitivity coefficients required for evaluating the impact of parameter variations on output chemical concentrations. Sensitivity coefficients, Sj, represent the response of a chemical concentration, C, to the perturbations in a sensitivity parameter, pj (e.g., emissions, initial condition, boundary condition, reaction rate). A sensitivity parameter, pj, has the following relationship with Pj, an unperturbed (base case) sensitivity parameter: pj = εjPj = (1 + Δεj)Pj. Here, εj is a scaling factor with a nominal value of 1. The seminormalized first-order sensitivity coefficient, S(1) j , is defined as follows: Sj(1) = Pj

∂C ∂C ∂C = Pj = ∂pj ∂(εjPj) ∂εj

Table 1. Summary of Modeled Sulfate Concentrations and Source Contributions from the Anthropogenic Source Regions, China, Central Eastern China (CEC), Korea, and Japan, During Each Episode at Oki and the Anthropogenic SO2 Emissions Used in This Studya episode A (5−7 July)

concentration (μg/m3) 9.73

China CEC Korea Japan

4.87 4.20 (86.3%) 1.47 0.09

6.95 5.60 (80.5%) 0.73 0.04

anthropogenic SO2 emissions in 2005 (Mt/yr) 50.9 22.2 (43.7%) 0.41 0.86

a

Note: The percentages of CEC contributions to China are shown in parentheses.

(1)

3. RESULTS AND DISCUSSION 3.1. Base Simulation. A base case CMAQ simulation was performed prior to DDM sensitivity simulation. Emissions data used in the base case simulation included anthropogenic, biogenic, biomass burning, and volcanic SO2 sources. It is wellknown that trans-boundary air pollution episodes occur over East Asia in the springtime.20 However in this study, we focused on summertime concentrations, when AOD peaks each year.13 This base case simulation was conducted for July 2005 with 10 days of spin-up time to remove the influence of the initial conditions. Model performance was evaluated by comparing output with PM observation data (Figure 2(a)) from the Acid Deposition Monitoring Network in East Asia (EANET) at Oki, derived using the tapered element oscillating microbalance method (TEOM). Observation data for PM are limited at Oki (36°17′N, 133°11′E) and Rishiri (45°07′N, 141°12′E) in the EANET sites. However, data from the northern part of Japan, including Rishiri, are affected by submicrometer particles originating from wildfires in Siberia. Such episodic events are not coverd in the emission inventory used in this study. Therefore, we used only the data from Oki in this study, but our CMAQ modeling system was well validated over East Asia in our previous study.13,19,20 CMAQ treats PM size distribution in three modes: Aitken and accumulation modes for particles with diameter under 2.5 μm, and a dynamic coarse mode. A summation of Aitken and accumulation modes was used for fine-mode aerosols. Although CMAQ underestimated the peak concentrations of observed PM2.5, the model was able to duplicate the observed temporal variation in PM levels. The observed monthly average concentration of PM2.5 was 15.6 μg/m3 and peaked on 22 July 2005 at 60 μg/m3. In evaluating model performance, we used mean fractional bias (MFB) and mean fractional error (MFE) to avoid the dominance of observations close to zero:

(2)

where C0 is the concentration in the base case simulation. The zero-out source contribution (ZOC) of an emission source is defined as the difference between C0 and the concentration that would occur if the source did not exist.5,6 A first-order approximation of ZOC can be calculated from DDM results by setting pj = 0 (i.e., Δεj = −1) in eq 2 and can also be expressed as ZOC(Pj) = C0 − C(pj = 0) = Sj(1)

15.14

source contributions (μg/m3)

In CMAQ chemical concentrations are calculated by solving the atmospheric diffusion equation. DDM calculates seminormalized sensitivity coefficients using equations with a form analogous to that of the atmospheric diffusion equation. The derivations of these equations are presented in more detail elsewhere.4,5 To project the fractional perturbation of Δεj from the base case simulation, the corresponding concentration can be approximated by a Taylor series expansion of the sensitivity coefficient: C(pj ) = C(Pj) + ΔεjSj(1) + ··· ≈ C0 + ΔεjSj(1)

episode B (20−22 July)

(3)

In this sense, the first-order approximation of ZOC is equal to the seminormalized first-order sensitivity itself, and we can investigate the S-R relationships based on the ZOC approach. Higher order sensitivities for PM are not yet included, and source contributions under nonlinear chemical processes for secondarily formed PM are not considered. However, the response of secondary inorganic PM has been reported to be reasonably linear for specific emission sources.21 Therefore, a first-order approximation of ZOC can provide the reasonably good predictions of the impact of emission sources. We chose three countries (China, Korea, and Japan), one main region (central eastern China; CEC), and four detailed subset areas of China as emissions source areas for CMAQ-DDM calculation (Figure 1). The CEC region (110°E−123°E, 30°N−40°N), 6735



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Figure 2. (a) Temporal variation in measured PM2.5 versus modeled fine-mode aerosol concentration at Oki during July 2005. (b) Monthly average − + concentration of observed and modeled PM2.5 (left axis) and each PM2.5 component (SO2‑ 4 , NO3 , and NH4 , right axis) at Oki. The error bars for the observations represent the daily and biweekly maximum/minimum concentrations for PM2.5 and each PM2.5 component, respectively. The error bars for model simulation represent the one standard deviation.

MFB =

2 N

N

∑ i=1

Mi − Oi 2 , MFE = Mi + Oi N

N

∑ i=1

|Mi − Oi| Mi + Oi

3.2. Sensitivity Simulation. In Section 3.1, we showed that sulfate made up the main aerosol fraction over East Asia in July 2005. A set of CMAQ-DDM simulations was designed to examine the S-R relationships of anthropogenic SO2 emissions and downwind sulfate concentrations using ZOC (eq 3). In the DDM simulation, we did not initialize sensitivity coefficients on the first day, and the last hour’s sensitivity coefficients for the previous day were used for the next day of simulation. Considering the long-range of transport of pollutants over East Asia, 5 day spin-ups were used for both episodes. The modeled results were averaged across the surface layer, from the first model layer (up to 42 m in height) to the third layer (127 m). 3.2.1. Evaluation. To assess the sensitivity of the simulation using DDM, we compared DDM and BFM simulations of the response of aerosol sulfate concentrations to 20% reductions in SO2 emissions across the domain for both episodes (A and B). DDM uses the parametric scaling technique to provide sensitivity estimates (eq 2). A 20% emissions reduction was chosen to be large enough to prevent numerical noise but small enough to capture local variation from the emissions base case. The choice of a 20% reduction in achieving these goals has been well demonstrated.7 We used linear regression to compare the BFM and DDM techniques for evaluation. The regression slopes were near 1.0 (1.04 and 1.02 for episodes A and B, respectively), and the intercepts were near 0.0 (−0.01 and 0.01 for episodes A and B, respectively). The multiple correlation coefficient was above 0.997 for both episodes. The sensitivity of sulfate aerosol concentrations to SO2 emissions was highly correlated between BFM and DDM because sulfate has a more direct relationship with its precursor emissions rather than other secondarily formed species (e.g., photochemical ozone) as reported in previous study.7 Here DDM was in good agreement with BFM, even during the typhoon season,

(4)

where N is the total number of valid pairs of modeled (M) and observed (O) concentrations. Throughout July 2005, MFB and MFE were −35.2% and 63.6%, respectively, well within the range for PM model performance.22 We also validated monthly mean concentrations of PM2.5 and examined the main components of PM2.5 individually. − + Specifically, the observed concentrations of SO2− 4 , NO3 , NH4 , and other inorganic components that make up PM2.5 were analyzed using filter packs followed by ion chromatography. The observed monthly mean concentrations were compared with modeled fine-mode aerosols (Figure 2b). CMAQ performed well in simulating the concentrations of all PM components, with a slightly negative bias. Sulfate was the dominant component of PM throughout July 2005. During the summer, high temperatures and humidity coupled with strong atmospheric oxidation favor sulfate formation from SO2. In contrast, nitrate tends to remain in gas phase at higher temperatures, resulting in a lower PM contribution. Therefore, in the S-R analysis we focused on the source contributions of anthropogenic SO2 emissions to aerosol sulfate. Meteorological parameters (wind fields, temperature, and humidity) were also well simulated by the WRF-CMAQ model. Two peaks of PM were captured (Figure 2a). We targeted those peak events and analyzed the S-R relationships using CMAQ-DDM for the periods of 5−7 July (episode A) and 20−22 July (episode B). During those peaks, the PM concentration was high because of typical summertime meteorological conditions. The Oki site had mainly stagnant weather conditions characterized by highpressure systems with light westerly synoptic winds. 6736



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Figure 3. Temporal variation of modeled sulfate concentration and source contributions of anthropogenic SO2 to aerosol sulfate at Oki calculated by ZOC during episode A (top) and episode B (bottom).

Figure 4. Spatial distribution of the episode-averaged source contribution from China calculated by ZOC during episode A (top) and episode B (bottom). Thick gray lines represent 5-day HYSPLIT backward trajectories. The numbers at the end of trajectories represent the order of the day of each episode (e.g., T1 on top panel means the backward trajectory for 5 July 2005, the first day of the episode).

indicating that the conversion of SO2 to sulfate in aqueous phase is well captured by the DDM extension. 3.2.2. Source Contribution. In Section 3.2.1, DDM performance was validated against the traditional BFM approach. In this section, we focus on the source contributions

from four regions across East Asia (China, CEC, Korea, and Japan) and present the time-series of modeled sulfate concentrations from each source region as estimated by ZOC (eq 3) in Figure 3. Source contributions were also averaged by episode (Table 1). In Figure 4, the episode-averaged spatial 6737



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Figure 5. Temporal variation in the source contributions of anthropogenic SO2 to aerosol sulfate at Oki calculated by ZOC during episode A (top) and episode B (bottom).

1). This CEC dominance is due to the distinctive atmospheric transport patterns of the summer Asian monsoon. This finding is important in understanding the role of Chinese emissions in the air quality of East Asia because previous studies have focused on springtime (rather than summertime) transboundary air pollution episodes. Using ZOC we also examined the source contributions of long-range transport to specific Chinese regions. Regions included the North China Plain, eastern China, the Yangtze Delta, and Inner Mongolia (Figure 1). Inner Mongolia is particularly relevant because many power plants were newly constructed there between 2005 and 2007.24 Southwest China, including Chongqing and Sichuan, is another important region due to its high-sulfur emissions and widespread implementation of FGD.14 However, southwest China’s contribution to sulfate at the Oki site was quite low during both episodes, likely due to its basin geography and the prevailing monsoon wind fields in summer. SO2 emissions from southwest China experienced minimal transport and persisted locally (Figure 4). Although source contributions from China, especially CEC, dominated long-range transport to the Oki site, contribution ratios of the four regions varied considerably over time (Figure 5). In episode A, three regions in CEC had almost the same ratio of influence on the first peak (21:00 local time (LT), 5 July), whereas the Yangtze Delta region only contributed slightly to the second peak (18:00 LT, 6 July) in episode A. For the first peak of episode A, the main contributing category was

distribution of source contribution from China evaluated by ZOC is illustrated. The contribution from China was dominant during both periods. Prevailing winds meant that contributions from China exceeded those from the Yellow Sea, Korea, and western Japan. Korea’s contribution was evident, particularly on 21 July. Throughout the study period, Japan’s contribution of SO2 to sulfate aerosol formation at the Oki Island site was quite low due to prevailing wind patterns. We developed 5 day HYSPLIT backward trajectories23 (T1−T3) from Oki beginning at noon on each day of each episode (Figure 4). Results from trajectory analyses corresponded well to the source contributions estimated by DDM. All backward trajectories originated in China, revealing the dominant Chinese source contribution for both episodes. However, other regions also played a role. For instance, the T2 trajectory (bottom panel of Figure 4), started in the Yangtze Delta region, moved to the Liaodong peninsula, and finally arrived at Oki. In this case, the air parcel passed over Seoul and Busan, which are principal emission source regions in Korea, and sulfate contributions from Korea were relatively high on 21 July. We found that the CEC region plays an important role in the long-range transport of pollutants over ocean areas of East Asia. Although anthropogenic SO2 emissions from CEC represent less than half of China’s total SO2 emissions, the region provided 80% of the source contribution against total contribution from China to Oki during both episodes (Table 6738



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may have increased. An investigation of the area’s contribution over recent years is of interest in future research. 3.2.3. Simplified Inversion of Emission. We used a simplified inversion method based on the sensitivities evaluated by DDM to predict future year emissions. The parametric scaling method (eq 2) is generally used to investigate the response of chemical concentrations to perturbations in model parameters. DDM calculates sensitivity coefficients, revealing a perturbation ratio and allowing concentrations to be predicted. In our previous study,13 we applied a simplified inversion method25 for estimating anthropogenic SO2 emissions from China. This method combines the use of modeled sensitivity analyses and satellite-retrieved SO2 vertical column density (VCD) data. The VCD of SO2 is assumed to be representative of changes in SO2 emissions. The basic equation is

other areas of China, including the Pearl River Delta area in the south. During the second peak, the North China Plain and eastern China played dominant roles. Inner Mongolia made a large contribution to the second peak. Based on backward trajectory analyses, pollutants originating from the Yangtze Delta or southern China did not contribute to the latter part of episode A (T2 and T3 top panel Figure 4). A significant contribution from the Yangtze Delta was seen in episode B. After a decrease in sulfate at 23:00 LT on 21 July, contributions from the North China Plain increased. In contrast to episode A, during episode B Inner Mongolia did not make a large source contribution. The temporal variability of source contributions was well illustrated by dividing the CEC region into smaller areas. Vertical profiles of episode-averaged source contributions (surface to 5000 m above sea level) were also examined (Figure 6). The contribution of sulfate from China peaked between 750

ΔE ΔΩ =β× E Ω

(5)

where E is emissions, Ω is the VCD, and ΔΩ is the change in VCD under the change of emissions ΔE (a 15% emission perturbation was chosen in our previous study). The term β represents the local sensitivity of VCD to changes in emission levels. To normalize the units of emissions and VCD, these terms were divided by themselves. Once the term β was determined by model simulation, emissions for the year j were projected based on the year i emissions, and satellite-derived VCD (Ω) in years i and j, as follows: ⎛ Ω j − Ωi ⎞ Ej = ⎜1 + β ⎟E i Ωi ⎠ ⎝

(6)

In this process, it is required to calculate model response with perturbed emission. This approach is based on the traditional BFM. By using the sensitivities calculated by DDM, a more accurate, effective, and straightforward inversion method was developed. We applied parametric scaling method to estimate the anthropogenic SO2 emissions in 2009. Based on eq 2, the perturbation ratio of anthropogenic SO2 emissions (Δεj) has the following relationships:

Δεj = Figure 6. Vertical profiles of episode-averaged source contributions of anthropogenic SO2 emissions to aerosol sulfate at Oki calculated by ZOC during episode A (top) and episode B (bottom).

Ω − Ω0 Sj(1)

(7)

where Ω0 is the SO2 VCD in the base year (i.e., 2005), Ω is the SO2 VCD for the target year of the inversion estimate (i.e., 2009), and S(1) is the sensitivity coefficient of anthropogenic j SO2 emissions to the VCD of SO2. On the analogous form of eq 6, emissions for the target year j are estimated using the following equation.

and 1500 m, partly due to removal through dry and wet deposition at lower levels, and also due to strong vertical summertime mixing. We found the variation in vertical profile contributions between Chinese regions to be as important as the temporal variations. Of all CEC regions, only the Yangtze Delta showed decreasing contributions with increasing height. Due to strong westerly winds aloft, most Yangtze Delta contributions above 1500 m were transported toward the Pacific Ocean. Conversely, the North China Plain exhibited increasing contributions at higher altitudes, brought on by stronger winds, which facilitated longer range transport. The nearest source region to Oki, eastern China, showed more stable contributions with some reductions as height increased. During both episodes, particularly episode A, Inner Mongolia’s contribution increased with height. Since Inner Mongolia’s new power plants were not well established in 2005 (the year of the emissions inventory used here), the contribution of this region

Ej = (1 + Δεj)Ei

(8)

The procedure of our simplified inversion method is as follows: 1 On the basis of the base-case CMAQ simulation, the VCD of SO2 over CEC was calculated for both episodes in 2005 (Ω0 in eq 7). 2 The VCDs of SO2 over CEC were projected from 2005 to 2009 (Ω in eq 7), assuming linearity between modeled SO2 VCD and SO2 VCD derived from satellite observations. Satellite derived SO2 VCD showed a strong correlation with both anthropogenic SO2 emissions and modeled SO2 VCD. Therefore, satellite-derived data are 6739



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considered to be adequate indicators of SO2 emission changes, and an 18.6% reduction of the annual-averaged retrieved SO2 VCD over CEC from 2005 to 2009 was evaluated.13 We did not consider seasonal variations in anthropogenic emissions, and therefore this method may be a more reliable approach to use for the inversed estimation of annual emissions. 3 The sensitivity of anthropogenic SO2 emissions to SO2 VCD was calculated by CMAQ-DDM (S(1) in eq 7), j along with the sensitivity of anthropogenic SO 2 emissions to aerosol sulfate. 4 The perturbations in emissions from 2005 to 2009 (Δεj) were calculated using eq 7. 5 Finally, year 2009 anthropogenic SO2 emissions from China were estimated using the ratio of emission change (Δεj) from eq 8. This procedure and its results are also shown in Table 2 with the same numbering. China’s anthropogenic SO2 emissions in

We evaluated the S-R relationships based on summertime year 2005 emissions, when SO2 emissions from China were still relatively high because FGD use at power plants was at an early stage. The widespread installation of FGD from 2005 to 2010 has resulted in SO2 emission reductions and is thus expected to reduce secondary aerosol formation downwind. Future research is required to understand how these changes may have influenced S-R relationships in East Asia such as those presented here. This will also require an accurate emission inventory, and the simplified inversion estimation approach presented here can provide the basis for its development.

Table 2. Procedures and Results of the Simplified Inversion Method

ACKNOWLEDGMENTS This work was partly supported by Research Fellowships from the Japan Society for the Promotion of Science (JSPS) for Young Scientists program (22-3510), and a Grant-in-Aid for Scientific Research No. 21241003 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The authors would like to thank EANET for providing the measurement data set.

■

*Phone: +81-92-583-7775; fax: +81-92-583-7775; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

■

episode A episode B (5−7 July) (20−22 July) 1 2 3 4 5

Ω0: SO2 VCD above CEC in 2005 (DU) Ω: SO2 VCD above CEC estimated for 2009 (DU) S(1) j : sensitivity coefficient of anthropogenic SO2 emissions to SO2 VCD above CEC (DU) Δεj: ratio of anthropogenic SO2 emission changes from 2005 to 2009 estimated anthropogenic SO2 emissions from China on 2009 (Mt/yr)

1.34 1.09

0.75 0.61

1.75

1.07

- 14.31%

- 13.09%

43.6

AUTHOR INFORMATION

Corresponding Author

■

REFERENCES

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44.3

2005 were 50.9 Mt/yr (Table 1), and estimated SO2 emissions in 2009 from each episode were 43.6 and 44.3 Mt/yr, respectively. These values are equivalent to those for year 2004 emissions and are consistent with the previously reported value of 42.5 Mt/yr.13 Differences likely arose because this study examined two summer episodes, whereas previously an entire year of CMAQ data were employed. Uncertainties exist in emission inventories, and REAS emission inventory used in this study has some uncertainties mainly arising from differences in fuel consumption values. We set the uncertainty of SO2 emission as ±20%.10 The DDM can calculate the perturbed concentrations under the fractional perturbation through Taylor expansions (eq 2), and we calculated the estimated SO2 emissions using the same process by considering the uncertainty of emissions. The uncertainty range of estimated SO2 emissions is evaluated as ±1.9 Mt/yr. Given the uncertainty of the SO2 emission inventory, the estimated SO2 emissions for 2009 were between 41.7 and 46.2 Mt/yr. Although more sophisticated inversion methods such as a Kalman filter and/or adjoint inverse modeling are required for emission inventory optimization,26−28 a more efficient means of updating emissions inventories is also required to keep pace with rapid changes in anthropogenic emissions resulting from economic shifts and control measure advances. Currently, emissions inventory updates generally take a number of years to complete. Our simplified inversion estimation that combines DDM-based sensitivity analysis and satellite retrieval can serve as a more rapid approach to emissions inventory updates. 6740



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(13) Itahashi, S.; Uno, I.; Yumimoto, K.; Irie, H.; Osada, K.; Ogata, K.; Fukushima, H.; Wang, Z.; Ohara, T. Interannual variation in the fine-mode MODIS aerosol optical depth and its relationship to the changes in sulfur dioxide emissions in China between 2000 and 2010. Atmos. Chem. Phys. 2012, 12, 2631−2640. (14) Zhao, Y.; Wang, S.; Duan, L.; Lei, Y.; Cao, P.; Hao, J. Primary air pollutant emissions of coal-fired power plants in China: Current status and future prediction. Atmos. Environ. 2008, 42, 8442−8452. (15) Carmichael, G. R.; Calori, G.; Hayami, H.; Uno, I.; Cho, S. Y.; Engardt, M.; Kim, S. B.; Ichikawa, Y.; Ikeda, Y.; Woo, J. H.; Ueda, H.; Amann, M. The MICS-Asia study: Model intercomparison of longrange transport and sulfur deposition in East Asia. Atmos. Environ. 2002, 36, 175−199. (16) Lin, M.; Oki, T.; Bengtsson, M.; Kanae, S.; Holloway, T.; Streets, D. G. Long-range transport of acidifying substances in East AsiaPart II Source-receptor relationships. Atmos. Environ. 2008, 42, 5956−5967. (17) Byun, D. W.; Schere, K. L. Review of the governing equations, computational algorithms, and other components of the Model-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 2006, 59, 51−77. (18) Guenther, A.; Karl., T.; Harley, P.; Wiedinmyer, C.; Palmer, P. I.; Geron, C. Estimates of global terrestrial isoprene emissions using MEGAN (model of emissions of gases and aerosols from nature). Atmos. Chem. Phys. 2006, 6, 3181−3210. (19) Uno, I.; He, Y.; Ohara, T.; Yamaji, K.; Kurokawa, J.; Katayama, M.; Wang, Z.; Noguchi, K.; Hayashida, S.; Richter, A.; Burrows, J. P. Systematic analysis of interannual and seasonal variations of modelsimulated tropospheric NO2 in Asia and comparison with GOMEsatellite data. Atmos. Chem. Phys. 2007, 7, 1671−1681. (20) Itahashi, S.; Yumimoto, K.; Uno, I.; Eguchi, K.; Takemura, T.; Hara, Y.; Shimizu, A.; Sugimoto, N; Liu, Z. Structure of dust and air pollutant outflow over East Asia in the spring. Geophys. Res. Lett. 2010, 37, L20806. (21) Koo, B. S.; Wilson, G. M.; Morris, R. E.; Dunker, A. M.; Yarwood, G. Comparison of source apportionment and sensitivity analysis in a particulate matter air quality model. Environ. Sci. Technol. 2009, 43, 6669−6675. (22) Boylan, J. W.; Russell, A. G. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmos. Environ. 2006, 40, 4946−4959. (23) Draxler, R. R.; Hess, G. D. An overview of the HYSPLIT_4 modelling system for trajectories, dispersion, and deposition. Aust. Meteorol. Mag. 1998, 47, 295−308. (24) Li, C.; Zhang, Q.; Krotkov, N. A.; Streets, D. G.; He, K.; Tsay, S.; Gleason, J. F. Recent large reduction in sulfur dioxide emissions from Chinese power plants observed by the Ozone Monitoring Instrument. Geophys. Res. Lett. 2010, 37, L08807. (25) Lamsal, L. N.; Martin, R. V.; Padmanabhan, A.; van Donkelaar, A.; Zhang, Q.; Sioris, C. E.; Chance, K.; Kurosu, T. P.; Newchurch, M. J. Application of satellite observations for timely updates to global anthropogenic NOx emission inventories. Geophys. Res. Lett. 2011, 38, L05810. (26) Yumimoto, K.; Uno, I. Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation. Atmos. Environ. 2006, 40, 6836−6845. (27) Napelenok, S. L.; Pinder, R. W.; Gilliland, A. B.; Martin, R. V. A method for evaluating spatially-resolved NOx emissions using Kalman filter inversion, direct sensitivities, and space-based NO2 observations. Atmos. Chem. Phys. 2008b, 8, 5603−5614. (28) Kurokawa, J.; Yumimoto, K.; Uno, I.; Ohara, T. Adjoint inverse modeling of NOx emissions over eastern China using satellite observations of NO2 vertical column densities. Atmos. Environ. 2009, 43, 1878−1887.

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Source Contributions of Sulfate Aerosol over East Asia Estimated by

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