Journal of Geophysical Research: Atmospheres

HCFC-22 flux estimates over East Asia by inverse modeling from hourly observations at Hateruma monitoring station



[1] A series of high-frequency observations of chlorodifluoromethane (HCFC-22) at Hateruma Island (latitude 24.1°N, longitude 123.8°E) was used to evaluate the strength of various sources in East Asia by a tracer transport inversion. The forward calculation was conducted with a regional meteorological model using an online tracer transport scheme, and the inversion calculation used a Bayesian approach. On the basis of winter observations during 2005–2007, we estimated the annual HCFC-22 emission from China to be 32 Gg, which is nine times current estimates. The total uncertainty of the Chinese emission was reduced from 50% (a priori) to 15% (a posteriori) by the inversion calculation. A sensitivity study showed that the a posteriori values for China showed little dependency on the a priori values, whereas those for Japan, Korea, and Taiwan were considerably affected by the a priori values used. This can be explained by the more frequent high-concentration events from China observed at the Hateruma site. The a posteriori emission estimates from central China accounted for half of the total emissions from China.

1. Introduction

[2] Hydrochlorofluorocarbons (HCFCs) are commonly substituted for chlorofluorocarbons (CFCs) because they have less ozone-depleting potential than CFCs and used as refrigerants, aerosol propellants, foam-blowing agents, and solvents. Their production started to increase rapidly in the 1980s as the use of CFCs in developed countries was phased out under the Montreal Protocol on Substances That Deplete the Ozone Layer [Aucott et al., 1999; McCulloch et al., 2003; Alternative Fluorocarbons Environmental Acceptability Study (AFEAS),]. Compared with CFCs, which have no natural mechanisms for removal from the troposphere, HCFCs have shorter lifetimes because they are decomposed by reaction with OH radicals in the troposphere. Nevertheless, HCFCs contain chlorine and consequently have ozone-depleting potential, and they also contribute significantly to radiative forcing [Forster et al., 2007]; their global warming potential over 100 years is as high as 2000 [Nakicenovic et al., 2000] despite their very low atmospheric concentrations in the parts per trillion (ppt) range. HCFCs are not regulated as greenhouse gases under the Kyoto Protocol. Instead, under the Montreal Protocol, industrialized nations were required to begin limiting production of HCFCs in 1996, working toward a total phase-out by 2030. In contrast, the Montreal Protocol allows developing countries to increase production of HCFCs until 2016 and continue production at 2015 levels until 2040. Global emissions of HCFCs, as inferred from the global surface means and simple two- and three-box models, showed a 60% increase between 2004 and 2007 [Montzka et al., 2009]. The Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios [Nakicenovic et al., 2000] predicts that the production and use of halocarbons and other halogenated compounds will continue to grow in developing regions throughout this century. In view of recent economic growth in East Asia, it is important to monitor the atmospheric concentrations of these halocarbons to better understand their potential contribution to global warming and to control their emissions.

[3] The aim of this study was to estimate the distribution of regional sources of these halocarbons and their magnitudes in East Asia from observed atmospheric concentrations by combining the use of a forward calculation, which simulated the observed concentrations, with an inversion calculation used to determine the most probable emission distribution from the results of the forward calculation.

[4] The CO2 research community has developed a tracer transport inversion technique to quantitatively constrain sources and sinks by using atmospheric concentrations [Enting and Mansbridge, 1989; Press et al., 1999; Enting, 2002; Tarantola, 2005]. Conventionally, global transport models are used for forward simulation. This study focused on regional emissions and used observational data solely from the Hateruma Global Environment Monitoring Station on Hateruma Island, the southernmost Japanese island. Global simulations should be constrained by various observational data sets, but the use of data sets from areas that cannot be well constrained may introduce additional uncertainty. In addition, a high horizontal resolution is required to retrieve emission distributions on a regional scale, and high-resolution temporal sampling is needed to resolve concentration variations on a synoptic time scale. We decided therefore to use a regional meteorological model that calculates meteorological parameters and the transport of tracers using a high temporal resolution (i.e., 20 seconds). Accurate simulation of meteorological conditions and tracer transport is important because the timing and shape of the sharp peaks in observation data that represent signals from nearby sources are controlled mostly by local meteorological conditions.

[5] In this study, we applied a tracer transport inversion technique to HCFC-22 source estimation in East Asia in combination with a regional meteorological model using hourly observations at Hateruma station. We focused on emissions from China, which we consider the most uncertain, owing to recent rapid change in industrial activities there, and Hateruma station is well situated to monitor Chinese emissions downstream. First, the forward simulation used the initial flux distribution to calculate the time series of HCFC-22 concentrations at Hateruma station. We defined 12 source regions and tagged emissions from each region to establish the source-receptor relationship between East Asian emissions and HCFC-22 concentrations at Hateruma. We then compared these results with the observed time series and prepared a set of a priori flux values for the inversion. We used an inversion calculation with a Bayesian approach to derive the normalization factor for each source flux that resulted in the best fit between the modeled and observed peak areas. Here, we evaluate the a posteriori flux data and discuss remaining issues, such as errors associated with the calculations and dataset.

2. Methods

2.1. Observations

[6] In situ hourly measurements of HCFCs, hydrofluorocarbons, and other halocarbons have been conducted since 2004 at the ground monitoring station on Hateruma Island (latitude 24.1°N, longitude 123.8°E; see Figure 1). Ambient air is sampled at the top of a 40 m tower and analyzed by an automated measurement system consisting of a gas chromatograph/mass spectrometer with a cryogenic preconcentration module [Enomoto et al., 2005; Yokouchi et al., 2005].

Figure 1.

Locations of the Hateruma monitoring station and of major cities in China.

2.2. Forward Calculation

[7] For the forward calculation, we used the Regional Atmospheric Modeling System (RAMS version 4.4) regional meteorological model developed by Colorado State University [Pielke et al., 1992], which has already been successfully used for detailed analyses of the emission and transport of atmospheric tracers [Uno et al., 2005; Tanimoto et al., 2008; Uno et al., 2008; Yumimoto et al., 2008]. We used a rotated polar-stereographic (PS) projection for the horizontal grid, with the conversion center set as the domain center at 40°N and 130°E (Figure 2). The horizontal resolution was 40 km longitude by 40 km latitude, with 120 grid cells in both the east-west and north-south directions. The vertical component was based on a 20-layer σz terrain-following coordinate system, with the middle of the lowest layer 60 m above the surface and the top of the grid at ∼20 km. We used the Mellor and Yamada scheme [Mellor and Yamada, 1974], a simplified Kuo scheme [Kuo, 1974], and the Chen and Cotton scheme [Chen and Cotton, 1983] to parameterize vertical mixing, convection, and radiation, respectively. We used the “nudging” type data assimilation scheme implemented in RAMS. The European Centre for Medium-Range Weather Forecasting 2.5° mesh 6-hourly operational analysis data were used for the initial conditions and large-scale lateral boundary tendencies. We implemented a tagged tracer simulation, dividing the emission field in East Asia into 12 areas (Figure 2 and Table 1) utilizing the online-tracer transport scheme of RAMS. In this scheme, we assigned tags to tracers from different parts of the emission field, and the tracer transport was calculated as plural scalar values during the course of the simulation. The first simulation was conducted with 19 tracers, and a cross-correlation analysis was performed on the resulting 19 tracer time series at Hateruma to estimate the degree of correlation between each combination of two time series. The number of tags was reduced to 12 by aggregating those that were highly correlated in this analysis.

Figure 2.

The modeling domain and the source regions.

Table 1. The Source Regions Used for the Tagged Tracer Simulation
Area NumberLocation
2East Japan (longitude > 136°E)
3West Japan (longitude < 136°E)
7Northeast China (38°N < latitude < 45°N, longitude > 120°E)
8North China (38°N < latitude < 45°N, 110°E < longitude < 120°E)
9Mid-China (30°N < latitude < 38°N, longitude > 110°E)
10South China (20°N < latitude < 30°N, longitude > 115°E)
11Northeast region (45°N < latitude, longitude > 110°E)
12West region (longitude > 110°E)

[8] The lifetime of the target halocarbons (∼12 yr) is much longer than the transport time in the region (several days), so we could omit chemical reactions during transport and focus only on high-concentration events from source areas in the study region. The initial flux distribution was prepared by using the 1° mesh geographic distribution provided by the Global Emissions Inventory Activity (GEIA; and the annual emission estimates for 2005 reported by the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS; The 1° mesh geographic distribution was projected onto the PS grid, and fluxes in each source regions were aggregated (Figure 2) for the forward calculation. Emission was set to be constant over time. Emissions from outside the simulation domain were not taken into account. Zero-gradient conditions were applied to both inflow and outflow at the lateral boundaries. We set the period of the simulation from January 2005 to September 2007.

2.3. Inversion Calculation

2.3.1. Events-Based Inversion

[9] Inverse modeling of the sources of atmospheric trace gases on a global scale with low spatial and temporal resolution is an established technique [Enting, 2002]. However, in regional scale modeling the domain is limited, and the inflow fluxes across lateral boundaries are usually much higher than the surface fluxes in the regional domain. These inflow fluxes can be treated as unknown parameters to be estimated in the inversion calculations, or they can be estimated from available measurements or from the results provided by larger-scale models, although the estimates of these inflow fluxes can introduce additional uncertainty into a regional scale inversion. In this study, we assumed that the fluxes from distant sources (i.e., Europe and North America) had become diffuse during transport and contributed only to a gradual baseline variation. We focused only on high-concentration events, which appeared as clear sharp peaks in the observation data and indicated emissions from relatively nearby sources. The observation matrix was constructed from the peak areas of each event, and the response matrix was constructed from the peak areas of the corresponding events as simulated by the model. Prior to the inversion, the raw observation data were smoothed by using a 6-hour running average, and then sharp peaks were selected on the basis of their having a signal-to-noise ratio (SNR) >5.

[10] The definition of the background level is critical when using enhancements relative to background [Ryall et al., 2001]. For the tagged tracer simulation results, we used the minimum value of the time series of the targeted period as the single baseline value, because we did not need to take into account baseline noise. For the observation data, we determined the baseline by the rectangle method using the peak analysis tool of the Origin Pro 8 software package (OriginLab Corporation, Northampton, MA, USA). In this method, a rectangle is manually positioned over each peak and the baseline is drawn between the two outermost points included in the rectangle. We positioned the rectangles by referring to the results of the offline global transport model NIES_TM (NIES Transport Model) [Maksyutov et al., 2008], using a 1° mesh horizontal resolution and the GEIA emission inventory and setting the emissions in the RAMS calculation region to zero. The model reproduced the synoptic scale (3–4 days) variation and the seasonal change in the baseline concentration at Hateruma. In winter, the baseline noise was sufficiently small compared with the peak height of the HCFC-22 observations to meet our selection criterion of SNR >5. To minimize the cost function, we selected a total of 39 events from each winter (January–March) from 2005 to 2007.

2.3.2. Inverse Model

[11] We used the Bayesian least squares method to derive the estimate of the source distribution achieving the best fit between observations and calculations [Enting, 2002]. With this method, the size of the uncertainty of the observational data and the a priori emission estimation and its uncertainty are used as input values.

[12] The observation vector c consists of a set of observed concentrations of a trace gas and can be expressed as shown in equation (1), where x is the state vector of the model parameters (source/sink), G is the Green function of transport operator, and equation image is the total observation error, which consists of the measurement error, the representation error, and the forward model error:

equation image

By expressing x in terms of the a priori error vector equation imagea as in equation (2),

equation image

the cost function J(x) is formulated to reduce the mismatch between the observations c and the model results G · x while preventing the model from deviating too far from the a priori emission estimation. In equation (3), SΣ is the sum of the covariance matrices of the observation error and Sa is the a priori parameter error covariance matrix for xa:

equation image

The solution to the inverse model provides optimal estimates of regional fluxes (equation image) and the a posteriori error covariance matrix (equation image) of the flux estimates, is shown in equations (4) and (5), respectively:

equation image
equation image

We used the inverse model program prepared for the TransCom 3 CO2 inversion [Gurney et al., 2003], which utilizes the singular value decomposition technique.

[13] The reduction in the uncertainty of the emission estimation, ΔSa = Saequation image, can be used to formally compare and evaluate different sampling strategies or datasets with regard to the information about surface emissions that can be retrieved from the data. This method is a very useful tool because no concentration data are required to calculate the uncertainty equation image. However, it must be used with caution because it does not provide any information as to whether the inversion calculations were successful; that is, low uncertainty equation image estimates might be accompanied by large errors in unknown parameters, often caused by a large model-data mismatch. Posterior uncertainty estimated with the covariance matrix should be complemented with a measure of the posterior model-data fit. In the present study, we evaluated the match between the model results and the observations by using the root mean square error (RMSE). The RMSE is defined as the square root of the mean squared error between observed and modeled peak areas for all events.

3. Results and Discussion

3.1. Forward Simulation-Meteorological Conditions

[14] We conducted the forward simulation for January 2005 to September 2007 and compared the result with the observed time series of HCFC-22 at Hateruma station. In general, high-concentration events were more frequent from late autumn to spring than during summer in both the observation and simulation data. We attribute this result to meteorological conditions: in winter and spring, Hateruma is in the path of outflow from the Asian continent, whereas in summer it is covered by maritime air. As the simulation only took into account emissions from a limited area (Figure 2), it was not able to reproduce the baseline variation. Because our aim was to estimate emissions from East Asia, we selected the winter results for our estimation, when continental outflow most frequently affects Hateruma and when the baseline data were also the most stable. We judged that the annual HCFC-22 trend was negligible within winter from recent observation results.

[15] Comparison of the wind speed and wind direction time series observed at Hateruma station with calculated values (Figure 3) showed that the observation and calculation data agreed quite well overall. Detailed comparison of the wind vectors, however, indicated that either a slight time lag of the change of wind velocity or a small variance in wind direction between observed and calculated data, or both, resulted in a large discrepancy between observed and calculated HCFC-22 concentrations. Generally, changes in the wind accompanying the passage of a pressure system were well reproduced, whereas moderate changes occurring under stagnant atmospheric conditions caused the model to have problems reproducing the local wind field.

Figure 3.

Observed and simulated time series of (a) wind direction and (b) wind speed at Hateruma station from 17 January to 31 March 2005 (circles, observation data; lines, simulation results). Wind direction is plotted as the incidence angle of the wind (north = 0°; positive, clockwise).

3.2. Inverse Calculation

3.2.1. Manual Fitting

[16] We estimated the relative contribution from each source area during the experimental period from the tagged tracer simulation results. Figure 4 shows the time series of observed HCFC-22 and calculated tracer concentrations at Hateruma for January–March 2005 with the tagged simulation results shown by source region. For each sharp observed peak, except for several minor peaks, the tagged simulation results show one or more corresponding peaks at the same time. In February, more plumes arrived at Hateruma from Japan and Korea, whereas in March more plumes originated in China. However, when we compared intensities between observed and calculated peaks, the sum of the simulated regional peaks did not agree with the observed peak intensity. In particular, the calculated peaks for plumes originating in China tended to be significantly smaller than the observed peaks, suggesting a large discrepancy between the actual source distribution and the emission inventory employed for this calculation.

Figure 4.

Observed and simulated time series of chlorodifluoromethane (HCFC-22) concentrations at Hateruma for January to March 2005. The tagged tracer simulation results show the concentration contributed by each source region. The simulation results have been multiplied by a factor of 5, and 12 pptv has been added to the original mixing ratios. The factor (5) and the offset (12 pptv) were chosen arbitrarily to make the comparison of the simulation results with the observation data easier to visualize.

[17] We also compared the sum of the simulated values from all regions with the observation peaks (Figure 5), with the observed time series smoothed by 6-hourly adjacent averaging. Only emissions from China, multiplied by a normalization factor of 15, were enhanced in January and March (Figure 5, dotted line), whereas emissions from all sources, multiplied by a normalization factor of 5, were enhanced in February (Figure 5, dashed line). The time evolution of the plumes as determined by examining the 3-D concentration field resulting from the RAMS tagged tracer simulation showed that more plumes arriving at Hateruma originated in China in January and March and that in February, more of the arriving plumes originated in Japan, Korea, or Taiwan. By comparing the areas of calculated peaks originating exclusively in China, judged from the tagged simulation results (peaks 1–4 in Figure 5) with those of the observed peaks, we estimated the difference between the emissions values used in the simulation and the actual emissions. On average, the areas of the calculated and observed peaks differed by a factor of 14.8 (±4.6) in 2005. For the winters of 2006 and 2007 (Figure 6), we similarly estimated the observed and simulated emissions from China to differ by a factor of ∼10.

Figure 5.

(upper) Observed and (lower) simulated time series of HCFC-22 concentrations at Hateruma for January to March 2005. The summed results for the 12 source regions by the tagged tracer simulation are shown by the solid line (factor = 1; the original emission values are used). The dashed line is the result of the simulation using the input emissions all factored by 5. The dotted line is the result of the simulation using the input emissions, of which only the emission from China was factored by 15, but emissions from other regions remained as original magnitudes.

Figure 6.

Observed and simulated time series of HCFC-22 concentrations at Hateruma for the winters of (a) 2006 and (b) 2007. The dotted line shows the simulated results when sources in China only were multiplied by a factor of 10.

3.2.2. Inversion (Optimal Estimation)

[18] As described in section 3.2.1, manual fitting yielded a rough factor for Chinese sources from a limited number of events (n = 3–4) per year. An optimal inverse calculation was conducted by the Bayesian least squares technique to extract additional information from events that were not used in the manual fitting because of their more complicated source-receptor relationship. For the a priori data set, we used the forward simulation results for the Chinese sources multiplied by a factor of 10 in accordance with the manual fitting results. We prepared a covariance matrix for both the observation error and the a priori parameter error as the prior information required as input for the Bayesian estimation. The observation error consisted of the HCFC-22 measurement error, the forward model transport error, the peak-fitting error, and representativeness error. The representativeness error was estimated from the difference in the simulation result between a horizontal resolution of 40 km and one of 80 km. The measurement error was negligibly small (<1%) compared with the transport error and the peak fitting error. However, the transport error and the peak fitting error are not easy to estimate. In this study, we estimated the transport error as the RMSE between the calculated and observed peak areas after a preliminary inverse calculation with a single initial transport error value, under the assumption that any mismatch of the model and the observation after the source is improved by inversion is due to transport error. We estimated the peak fitting error as the difference between the smallest and largest peak areas obtained by different fitting procedures (∼30%). The overall observation error for each event ranged from 1% to 80% during the experimental period. The peaks with large transport error tended to have a large peak fitting error and vice versa, resulting in the wide range of observation error. In the inverse calculation, peaks with a large assigned error received much less weight than those with a small assigned error, with the result that the inversion solution was mainly constrained by the better modeled peaks. We set the a priori flux error to 5% for fluxes from Japan, Korea, and Taiwan, to 50% for fluxes from China, and to 10% for the northeast and west regions in the calculation domain (Figure 2). The a priori flux error for China was set to 10 times that for Japan, Korea, and Taiwan because the a priori flux itself was set to 10 times the initial inventory value for China on the basis of the manual fitting results (see section 3.2.1). We consider the larger error resulting from the large scaling factor (10) obtained by the manual fitting to be reasonable, given the assumed much faster change in the emission rate in China, owing to the recent rapid development compared with developed countries and regions, which have been emitting considerable amounts of HCFCs since the 1990s [Nakicenovic et al., 2000].

[19] Figure 7 and Table 2 show the inversion results for the winters of 2005–2007, with the combined data of all three winters inverted at once. In these results, the relative standard deviation (r.s.d.; Table 2) for the different Chinese regions ranged from 28% to 56%, reflecting the differences in their contributions to the concentration events at Hateruma among the 3 years. The results for 2005–2007 did not show a clear increasing or decreasing trend over these 3 years. Since HCFC-22 is predominantly used for refrigeration and air conditioning, it should be released mainly from urban or industrialized areas. However, the timing of the release may be irregular because it is released primarily when refrigeration and air conditioning equipment is produced, installed or uninstalled, or decommissioned. If the production of HCFC-22 has been monotonically increasing during the past decade, as suggested by McCulloch et al. [2006], then the large annual variation (indicated by the r.s.d. values in Table 2) also suggests that the inversion results depend greatly on the selection of the events used for the calculation. As the proportion of emissions picked up from different source regions and contributing to high-concentration events observed at Hateruma vary each year, we presume that the error due to the variation in transport between years is large. Therefore, to increase the sample size (number of events) and reduce the standard error, we present the inversion results for the events of all 3 years together (Figure 7 and Table 2).

Figure 7.

Results of the inverse calculation. The inventory data are the emission data for each region from Global Emissions Inventory Activity (GEIA), the a priori values are those with Chinese emissions multiplied by a factor of 10, and the a posteriori values are those after the inversion calculation. Error bars on the a priori and the a posteriori values indicate the covariance before and after the inversion.

Table 2. Inversion Results for Data From the Winter of 2005–2007a
 HCFC-22 (Mg)post./pri.SDunc. red. (%)2005–2007 r.s.d.(%)
a prioria posterioria pri.a post.
  • a

    a pri., a priori; a post., a posteriori; post./pri., ratio of a posteriori to a priori values; unc. red., uncertainty reduction from a priori to a posteriori source fluxes resulting from the inverse calculation, = [(1−σa priori/σa posteriori) × 100]; r.s.d., relative standard deviation of the inversion results (n = 3) when the inverse calculation was conducted independently for each year (2005–2007). The italicized copy indicates results that depended on the a priori setting, thus having a lower confidence level.

East Japan12246122841.0030.
West Japan803480891.0070.
Northeast China242334241.4130.500.2844.335.93
North China303765312.1510.500.2746.246.46
Central China13297162201.2200.500.0981.328.08
South China781161030.7810.500.098255.48
Northeast region124111440.9220.
West region102610130.9870.

[20] We examined how the RMSE (used to estimate the transport error) was changed by the inversion by comparing the fitted peak areas of 39 selected events from the winters of 2005–2007 (Figure 8). Prior values and posterior values are from the forward model results and the inverse model results, respectively, and the error bars of the prior values represent the observation errors. As described above in this section, a large observation error was assigned to peaks whose RMSE was not reduced by the preliminary inversion, which resulted in a large RMSE remaining between the observation and posterior values of those peaks.

Figure 8.

Fitted values of peak areas for 39 events selected from the HCFC-22 time series for winter 2005 (events 1–11), 2006 (events 12–25), and 2007 (events 26–39). Prior and posterior values are from the forward model and the inverse model results, respectively, and the error bars represent the data uncertainty assigned to each event.

[21] Among the 12 source regions, only the uncertainties of the fluxes from China were significantly reduced by the inverse calculation. This result was expected, owing to the experimental design of this study, which uses the winter data. Most of the high-concentration events observed at Hateruma originated in China in winter, so the information extracted from those events mostly reflects the source fluxes from China (Table 3). The uncertainty reduction was largest for southern and central China, where most of the plumes observed at Hateruma during winter originated. The much higher a priori uncertainty level used for the Chinese source regions (a priori errors were set to 10 times those of other regions) also contributed to the larger uncertainty reduction. The total uncertainty for Chinese emissions was reduced from 50% (a priori) to 15% (a posteriori) by the inverse calculation. To examine the influence of different a priori values, we examined the sensitivity of the inversion results to different a priori emission values and assumed uncertainties. We examined a priori emission values for China between 10 and 160 Gg, with an a priori uncertainty range from 50% to 300%. The resulting a posteriori values ranged from 30–36 Gg, indicating that the inversion results for China were not constrained very much by the a priori values. In contrast, the a posteriori values for other source regions (Japan, Korea, and Taiwan) varied greatly when the a priori values were changed; they tended to stay close to the a priori values, but they exhibited more variation as the uncertainty level increased (Table 2, shaded parts). A possible cause of this result is that the plumes from these three regions during the calculation period often became mixed before their arrival at Hateruma, resulting in relatively large transport errors that prevented the inversion from constraining the individual source regions.

Table 3. The Estimated Relative Contributions (%) of the Source Regions to the High-HCFC-22 Concentration Events Observed in the Winters of 2005–2007 at Hateruma Station
Location3 Yearsa200520062007
  • a

    Column represents the combined contributions for all 3 winters.

East Japan1.
West Japan4.
Northeast China4.
North China9.68.910.39.4
Central China34.839.329.136.9
South China26.536.227.820.7
Northeast region6.20.74.510.3
West region6.

[22] The estimated annual emission of HCFC-22 from all four source regions within China ranged from 3 to 16 Gg, and the estimated total annual emission was 32 Gg. This result is consistent with the annual emission from China of 52 (±34) Gg estimated from the observed concentrations of HCFC-22 and CO at Hateruma in 2005 by the tracer-ratio technique based on an independent CO emission inventory [Yokouchi et al., 2006]. Stohl et al. [2009] recently estimated annual HCFC-22 emissions from China to be 59 and 71 Gg for 2005 and 2006, respectively, by an inversion procedure based on the Lagrangian particle dispersion model. They used an a priori value of 166 Gg for the inversion, which they obtained from the United Nations Environment Programme (UNEP; inventory. This value is about six times the a priori value used in our study. After the inversion, a posteriori values decreased by ∼60% from their a priori value for China [Stohl et al., 2009] but were still approximately twice as high as the a posteriori value in our study. The difference between these two estimates is likely due to the different models and approaches used for each study, since our sensitivity study indicated little dependency on the a priori values used. In addition, the two studies selected and processed the observational data differently, which may account for the different results: Stohl et al. [2009] used a 3-hourly time series for 2005 and 2006, whereas we used only the winter peak areas for 2005–2007. The significant discrepancy between the HCFC-22 emissions from China estimated by this study and the available inventories indicates that emissions of this compound have been increasing so rapidly that they are difficult to estimate by a conventional bottom-up approach. A top-down approach is very efficient at detecting signals of a rapid emission change when reliable observation data are available, although it is critically important to use a well-validated model because the transport error can significantly affect the results. It is important that we continue our efforts to increase the quality of observation data and to reduce the transport error to improve the results obtained by top-down approaches.

[23] Among the four defined source regions in China, the absolute emissions derived for central China were largest. This area includes the Yangtze River delta area, one of the most densely populated areas in the world and an economic center of China. The a posteriori emissions from this area accounted for half of the total emissions from China. The North China region showed the largest increase after the inversion (a posteriori/a priori ratio = 2.2; Table 2), indicating that HCFC-22 emissions are more concentrated in this region in relation to the population size. The North China region includes the Beijing-Tianjing area, where industrial and economic activities, along with the population, have been growing very rapidly, especially in recent years in preparation for the Olympic Games in 2008. This growth led to a great demand for resources and energy, which may explain the significant emission increase. In contrast, the a posteriori/a priori ratio was lowest for South China among the source regions in China. South China includes the Pearl River delta area, which has been a leading economic region and a major Chinese manufacturing center since the late 1970s. Nevertheless, the annual variability of the inversion results for 2005–2007 was particularly large for both North China and South China (r.s.d. values; Table 2). To evaluate their recent emission trends quantitatively, observations are required for several more years. Higher resolution bottom-up inventory data will also help to reduce the uncertainty in this type of top-down estimation approach, because halocarbon emissions tend to be more point-source-like than the distribution of population. In addition, introducing more constraints for other parts of East Asia (e.g., Japan, Korea, Taiwan) would also allow more precise estimation of regional flux distributions. For this purpose, the use of additional observation points, which would compensate for the transport error at Hateruma station, might be most effective.

4. Conclusions

[24] A tracer-transport inversion technique, used in combination with the RAMS regional meteorological model and hourly observation data at Hateruma station, was applied to HCFC-22 source estimation in East Asia. A tagged tracer simulation was conducted with 12 defined source regions using the RAMS tracer-transport scheme. An inverse calculation using the Bayesian inversion technique and the results of the forward simulation was applied to derive the best fit factor for each source flux. Winter observations during 2005–2007 were used to extract information about emissions from China most efficiently, because they were least affected by changes in background air or extreme meteorological conditions such as often occur in other seasons.

[25] Comparison of the observed HCFC-22 time series with the forward simulation results indicated that the discrepancy between actual and reported source fluxes from China was larger than that for other parts of East Asia. The estimated total annual emission from China was 32 Gg, which is nine times the initial inventory used for the forward calculation. The relative contribution of the source flux from each defined region to the high-concentration events observed at Hateruma during the winters of 2005–2007 showed dominant contributions coming from central and South China; thus, the uncertainty reduction was largest for these regions. The uncertainty of the source flux from China was reduced by the inversion from 50% to 15%.


[26] This work was supported by the Global Environment Research Fund (Ministry of the Environment of Japan). We thank T. Ohara and S. Taguchi for useful discussions.