Dispersion Modeling of the Transport and Dispersion of SO 2 Pollutants Emitted from a Power Plant in Tong Liang

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1 Final Report for the Project Dispersion Modeling of the Transport and Dispersion of SO 2 Pollutants Emitted from a Power Plant in Tong Liang Contractors: Dr. Darko Koracin, Dr. John Watson, 1. Executive summary We have performed a dispersion modeling study of the transport and dispersion of SO 2 pollutants emitted from the Tong Liang power plant. Results from the dispersion modeling study will be used to support an ongoing epidemiological study in this area. The dispersion modeling study was conducted using the EPA regulatory dispersion model Industrial Source Complex 3 Short Term (ISC3ST). The simulation period covered two years, 2002 and 2003, for which required input meteorological data sets were available. The modeling results consist of annual average, monthly maximum, daily maximum, 3-hr maximum, and all monthly averages during the power plant operation of SO 2 concentrations for 2002 and 2003 at each of 170 specified receptors. Additionally, we defined 216 polar receptors covering six distances (100, 500, 1000, 2000, 4000, and 7000 m) and each of them having 36 azimuth locations. These 386 receptors provide details on the spatial distribution of concentrations that helps to understand overall structure and evolution of the pollutant plume in the area. These results represent a basis for statistical and graphical analysis. 2. Project motivation A comprehensive study was conducted by Perera et al. (2005) to assess the health effects of industrial-origin pollution on newborns in Tong Liang, China. The main objective of the study was to investigate the extent to which reductions in coal-burning emissions might result in the improvements to the health of children. Chow et al. (2005) provided further insight into the pollution impact by analyzing measurements of speciated PM2.5, carbon, and particle-bound polycyclic aromatic hydrocarbon (PAH) at three community exposure sites. They found high concentrations compared to U.S. cities and that the highest concentrations occurred in winter. These high levels result from a mixture of contribution from several sources, including the Tong Liang power station, other industries (e.g., strontium), distant power stations, domestic coal use, burning and cooking, and vehicle exhaust. 1

2 3. Background Tong Liang County has approximately 800,000 inhabitants and is located about 60 km north of Chongqing City in the upper Yangtze River Valley. Most of the population is located in the downtown area with a radius of only about 3 km. The local generator of electricity (46,000 kw) is a coal-fired power plant, which is located only one kilometer south of the city center. The power plant is operational from mid-november to mid-may when the main need for electricity is present. Figure 1 shows topography surrounding of the power plant and Figure 2 shows the plant in relation to the city and monitoring stations. Fig. 1. Topographical setup of the Tong Liang power plant. 2

3 Fig. 2. Geographical setup of the Tong Liang County with indicated power plant and the monitoring stations (A, B, and C). During its annual operation, the power plant consumes 5,000 tons of coal per month. Since the coal contains 3-5% of sulfur by weight, it is important to investigate the extent to which SO2 that results from its combustion can contribute to the total pollution impact in the area. Other directly emitted pollutant contributions to ambient levels can be estimated if their ratios to SO2 in the stack emissions are known. 3

4 4. Methodology 4.1 Dispersion model Industrial Source Complex Short Term 3 (ISCST3) model ISCST3 is a steady-state, Gaussian plume dispersion model designed for assessing pollutant concentrations from elevated point sources. According to the EPA Guideline on Air Quality Models (U.S. EPA, 1995), the ISCST3 model (classified as an A group preferred model) is recommended for industrial source complexes, rural or urban areas, flat or rolling terrain, transport distances less than 50 km, 1 hr to annual averaging time, and continuous pollutant emissions (Koracin et al., 2000). ISCST3 can be used to model primary pollutants from point, line, area, and volume sources. ISCST3 contains algorithms for effects such as building downwash and pollutant removal (wet scavenging and dry deposition), and is capable of modeling either gaseous species or particulate matter. 4.2 Basic concepts of atmospheric dispersion of pollutants After pollutants are emitted from a power plant stack, they form a plume and are transported by the wind. The central axis of the plume is aligned with the direction determined from the wind direction measured at a nearby weather station. The wind speed measured at the weather station is the model input that is used to determine the time of the plume arrival at each receptor. According to the Gausssian assumptions, the lateral and vertical spreads of the plume are selected according to atmospheric stability estimated from data provided by the weather station, upper-air stations, and the time of the day. High stability results in a less dispersed plume (usually during nighttime and or at lower temperatures). High stability causes high ground-level concentrations at receptors. On the other hand, during daytime and high temperatures with strong convective mixing of the atmosphere, the pollutants are efficiently mixed within a deep atmospheric boundary layer. This mixing results in lower ground-level concentrations. The concentrations are inversely proportional to the wind speed and consequently during low-wind conditions there is less atmospheric mixing and ground-level concentrations are high. With higher wind speeds, the pollutants are more mixed and the surface concentrations are low. Also, simulated concentrations are proportional to the emission rate the higher the emission rate the higher are the concentrations. Due to the continuous lateral and vertical dispersion, the pollutants are distributed in the space and the concentrations decrease with the distance from the power plant. The plume will affect receptors which are aligned with the wind direction measured at the weather station more than receptors that are down wind. 4.3 Model inputs We used an EPA approved dispersion model ISC3ST to simulate the transport and dispersion of pollutants emitted from the Tong Liang power plant ( N; E; 880 m MSL). Based on the given information, the emission parameters were set up as follows: 4

5 - Emission rate of SO 2 : g s -1 - Release height above the ground: 85 m - Stack gas exit temperature: 423 K - Stack gas exit velocity: 2.97 m s -1 - Stack diameter: 1.59 m The input surface meteorology was obtained from the nearest weather station ( N; E; 1107 m AGL). Unfortunately, the station reported only every 6 hours and we had to interpolate the data to hourly intervals as required by the dispersion model for wind speed, wind direction, temperature, and cloudiness. Using the wind speed, solar radiation, cloudiness, and the time of day/night, we computed the stability classes (A-F), which are also a required input. Required upper-air data were obtained from the nearest radiosonde station in Chongqing (29.52 N; E; elevation 351 m MSL). Within the global observational network, radiosonde stations send twice daily balloons with sensors to measure vertical profiles of the winds, temperature, humidity, and pressure from the surface to the top of the atmosphere. The main parameter obtained from the radiosonde measurements was the mixing depth (height of the atmospheric boundary layer in which the transport and dispersion of the emitted pollutants occur) as a required input to the dispersion model. 4.4 Required Model Setup Parameters According to specifications provided by the Columbia University (file DIST_BEAR.xls), coordinates and elevation of the source and 170 receptors used for the epidemiological study were specified. 5. Model results There are three major components, namely emissions, input meteorology, and the model structure, that influences the accuracy of the model results. We were given a constant emission rate; however, in reality the rate can change diurnally, monthly, and seasonally as well as due to unforeseen industrial operation changes. Since there is no documentation on these possible changes, the approximation of the constant emission rate has inherent uncertainty that influences magnitude of predicted concentrations in an unknown way. Regarding input meteorology, we have used available data. Hourly ground-level meteorological data from a weather station near the source as well as vertical sounding by radiosonde every 12 hours also near the source are preferred. Since the weather station data are recorded every six hours, we have interpolated the data into hourly intervals. This increases the uncertainty of estimated pollutant concentrations. Additionally, the data from the weather station should be quality assured, evaluated, and calibrated. The nearest radiosonde station is Chongqing, which is about 60 km southeast from the power plant. Considering complex terrain in the region, this separation can induce further uncertainty in the model results. Regarding the model, ISCST3 is an approved EPA model that can be applied to these conditions. Since ISCST3 is based on a Gaussian approximation of the plume spreading, it has uncertainties for complex terrain. 5

6 The analysis of the modeled ground-level concentrations at specified receptors and dense polar grid for each year (2002 and 2003) includes: 1. Annual average SO 2 concentrations 2. Annual maximum monthly SO 2 concentrations 3. Average SO 2 concentrations for each month of the power plant operation 4. Annual maximum daily SO 2 concentrations 5. Annual maximum 3-hr SO 2 concentrations In order to understand and interpret the model results and concentration patterns, we analyzed input wind direction and wind speed measured at the weather station. Figure 3 shows frequency distributions of the wind direction for 2002 and 2003, respectively. Both figures show dominant winds from the north which implies that the concentrations of the transported and dispersed and pollutants will be at maximum mainly to the south of the power plant. It should be also noticed that the flows are mainly channeled in the north-south direction, so the highest concentrations will be along this line. This will be obvious for long-term averages such as for an annual cycle. For shorter times, high concentrations can be present for other directions which might occur with less frequency within the annual cycle, but still can make an impact over shorter intervals. Figure 3. Frequency distribution of the wind direction at the Tong Liang weather station for 2002 (upper panel) and 2003 (lower panel). 6

7 The analysis of the wind speed shows that during 2002 and 2003 the station recorded zero winds (calm conditions) for 53% and 60% of the hours, respectively, and that significantly influences the model results. This number of calm conditions is quite unusual. Future studies should provide more accurate hourly meteorological parameters that could improve model results. 5.1 Study-area receptors and additionally specified receptors in a polar system The use of an additional dense receptor grid in a polar system allows for a symmetric coverage of the entire area around the power plant and helps understanding of the main spatial characteristics of the simulated concentrations. Notice that this polar receptor grid is added to the already specified grid of 170 study-area receptors. These 170 study-area receptors would not generally cover the entire area around the source. Figures 4 and 5 show locations of the 170 study-area receptors and additional receptors in a polar system. The polar receptors were selected in the following way. At six distances from the source (100, 500, 1000, 2000, 4000, and 7000 m), receptors were chosen for each 10 degrees of azimuth. Therefore for each of the distances, there are 36 receptors. There are 216 additional polar receptors to cover area where there are no field study receptors. In summary, there are 170 receptors to represent the residency of study subjects and 216 polar receptors that we specified to examine the spatial distribution of plume impact. Consequently, there are total of 386 receptors that were used for the analysis. Figure 4. Locations of the 170 study-area receptors within the 5 km from the source. The power plant is positioned in the origin. 7

8 Figure 5. Locations of the additional 216 receptors in a polar system within 7 km from the source. These additional receptors facilitate spatial analysis. The power plant is positioned in the origin. 5.2 Statistics of extreme simulated SO 2 concentrations Table 1 summarizes highest SO 2 concentrations simulated for 2002 and Notice that the highest values occur at receptors downwind and in close proximity to the power plant. Almost all highest concentrations are at spatial location 35 with bearing 180 from the north and 377 m from the power plant. Notice that the bearing 180 is along of the prevailing wind direction, as noted above (see the following sections ). Locations 35 and c1125 create favorable conditions for extremely high concentrations. A study subject c1125 at which one of the extremes is simulated is located 786 m from the power plant and also is aligned with the prevailing flow channel (bearing is 189 ). SO2 statistics of concentrations are listed in Appendix A for 2002, and Appendix B for Table 1. Extreme SO 2 concentrations simulated for 2002 and Concentrations SO2(ug/m3) Location Location Max. annual mean Study area c1125 Max. monthly mean Study area Study area 35 Max. daily mean Study area Study area 35 Max. 3-hr mean Study area Study area 35 8

9 5.3 Annual average SO 2 concentrations Figure 6 shows spatial distribution of the annual average ground-level concentrations of SO 2 for 2002 and 2003, Figure 6. Simulated annual average SO 2 ground-level concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel). 9

10 The highest concentrations are mainly located to the south of the source and in the proximity of the source for several other directions as a consequence of the prevailing wind direction. Notice that the extreme concentrations are simulated in a narrow band due to the channeling of the winds in the area. Also, the receptors selected for the epidemiological health-impact study are almost entirely located to the north of the power plant where concentrations are generally lower. Due to the frequent calm conditions, high concentrations are also found near the power plant for other directions. During the calm conditions, the wind direction is undefined and the pollutants accumulate near the source. Appendices A and B show values of annual mean concentrations for 2002 and 2003, respectively for all receptors. 10

11 5.4 Annual maximum monthly SO 2 concentrations Annual maximum SO 2 monthly concentrations are shown in Fig. 7. Figure 7. Simulated maximum monthly average SO 2 ground-level concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel). The highest monthly average during the plant operation is included in Table 1. The maximum monthly concentration pattern follows the structure of the annual average concentrations (Fig. 6); however, the magnitude is higher and the band of the maximum concentrations is wider compared to the annual average pattern. This reflects 11

12 the differences in monthly wind frequencies as shown in Fig. 3. Since the concentrations can be smaller during other months with higher winds and less stable conditions, annual concentrations can be still fairly small. However, in most of the cases, receptors which have high monthly maximum concentration will also have high concentrations for other periods. Due to seasonal effects of the meteorology, most of the variability is present for individual monthly averages as shown in the appendices A and B and the supplemental data base. 5.5 Annual maximum daily SO 2 concentrations Figure 8 shows simulated maximum daily-averaged SO 2 ground-level concentrations. Figure 8. Simulated maximum daily average SO 2 ground-level concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel). 12

13 The figure indicates that on a daily interval there can be a significant contribution to the concentration field around the source. Notice also that the magnitude is generally an order of magnitude greater than the monthly average maximum. One can see that there are noticeable differences in maximum daily averages between the 2002 and 2003 results due to more variability in the daily meteorology. Also, the differences in the meteorology between 2002 and 2003 led to occurrence of some simulated concentrations to the north of the power plant which are not significant for Annual maximum 3-hour SO 2 concentrations Figure 9 shows simulated maximum 3-hr averaged SO 2 ground-level concentrations for 2002 and Figure 9. Simulated maximum 3-hour average SO 2 ground-level concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel). 13

14 Three-hour average concentrations are more than twice the average daily concentrations for most directions within two kilometers from the source. During the calm no-wind conditions, the pollutants accumulate near the source and contribute to very high concentrations. For the dominant flow channeling in the southern region, high concentrations are estimated even beyond 6 km from the source. 14

15 6. Additional presentation of the simulation results in the proximity of the power plant overlaid with the Tong Liang city map In order to present detailed dispersion modeling results for the main city center around the power plant, we overlaid the same dispersion results (shown in Section 5) on the city map for a 2 km radius around the power plant. By this process, one can see the distribution of the concentrations with respect to the city infrastructure and locations of monitoring stations. Figure 10 shows the distribution of the mean annual SO 2 concentrations for 2002 and 2003 overlaid over the city map. Fig. 10. Annual average SO 2 concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel) overlaid on the Tong Liang city map. 15

16 The distribution of simulated concentrations is similar for both years to the south an west from the plant. However, in 2003 there was more plume transport over the northeast residential area. - Maximum monthly mean SO 2 concentrations Fig. 11. Maximum monthly average SO 2 concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel) overlaid over the Tong Liang city map. 16

17 For shorter time periods compared with the annual, there are more chances to transport and disperse pollutants in more sectors around the power plant. - Maximum daily mean SO 2 concentrations Fig. 12. Maximum daily average SO 2 concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel) overlaid over the Tong Liang city map. For daily averages, power plant contributions are evident at nearly every location. Notice that the concentrations are more uniform around the power plant (more in 2003 than in 2002) and they drop off quite rapidly with distance for all segments except in the southern section. 17

18 - Maximum 3-hr mean SO 2 concentrations Fig. 13. Maximum 3-hr average SO 2 concentrations (μg m -3 ) for 2002 (upper panel) and 2003 (lower panel) overlaid over the Tong Liang city map. For the shortest intervals, concentrations are uniform around the power plant. Concentrations are more uniform for 2003 than for The central area is mostly pollutant free since the plume from an elevated source needs to travel for some distance before dispersing to ground-level. 18

19 7. Additional statistics helpful for the health study Owing to the uncertainties in input data and model formulation, SO 2 concentration estimates are best used in a relative rather than in an absolute sense. Monthly and annual averages are also more accurate than shorter averaging periods. For example, one could look at the power station at-home exposure of a study during the first month or two of pregnancy and compare this with the exposure of the study subjects. These relations can be easily examined using the supplemental spread sheets. In Appendix C, we sorted the model results at all receptors by the magnitude of the simulated concentrations (highest to lowest) as a summary of the ranking for each type of average. In numbers, this means that the receptor with highest concentrations is assigned number 1, the next highest with 2, and so on. The receptor with the lowest concentration is assigned number 170. This represents the summary ranking of the receptors depending on the strength of the pollution impact taking into consideration each type of the average. For each category (annual mean, monthly maximum, daily maximum, and 3-hr maximum concentrations), we assign this ranking and sum up all ranks for each receptor. By that, we come up with the final ranking in which the lower the sum the higher overall impact can be expected at that particular receptor. We calculate the ranking for 2002 and 2003 and sum up the total ranking for both years. This information can be useful since the health data can be also ranked in the same manner and the rankings for the pollution impact can be correlated with the ranking for the health data. The details of the final ranking computation are: Start with the supplemental excel files with estimated concentrations for 2002 and Sort all receptors by the magnitude of annual average concentrations from the highest to the lowest values. Assign ranking numbers to all receptors: the receptor with the highest concentration is assigned 1, next highest concentration is assigned 2, and so on. The receptor with the lowest concentration is assigned with 170. Store these numbers for each receptor as annual ranking number. In the next step repeat the ranking procedure explained above, but for monthly maximum concentrations. Assign this new monthly maximum ranking number to each receptor. Add this monthly maximum ranking number to the annual average ranking number obtained in the previous step. In the next step repeat the ranking procedure explained above, but for daily maximum concentrations. Assign this new daily maximum ranking number to each receptor. Add this daily maximum ranking number to the annual average ranking number and monthly maximum ranking number obtained in the previous two steps. In the final step repeat the ranking procedure explained above, but for 3-hr maximum concentrations. Assign this new 3-hr maximum ranking number to each receptor. Add this daily maximum ranking number to the annual average ranking number, monthly maximum ranking number obtained in the previous three steps. The final ranking number for each receptor is the sum of the annual average ranking number, the monthly maximum average ranking number, the daily maximum ranking number, and the 3-hr maximum ranking number. 19

20 Consequently, the smaller the final ranking number at a receptor the higher the impact at that receptor. 8. Conclusions In order to support an ongoing epidemiological health-effect study, we have performed a dispersion modeling study of the transport and dispersion of SO 2 emitted from the Tong Liang power plant. The dispersion modeling study for the periods of power plant operation in 2002 and 2003 was conducted using the EPA regulatory dispersion model Industrial Source Complex 3 Short Term (ISC3ST). The inputs for the dispersion model were data from a nearby meteorological station and from the nearest upper-air radiosonde station in Chongqing located approximately 60 km from the power plant. The modeling results consist of annual average, monthly maximum, daily maximum, 3-hr maximum, and monthly averages of SO 2 concentrations for 2002 and 2003 at each of 170 study subject receptors. Additionally, we defined 216 polar receptors covering six distances (100, 500, 1000, 2000, 4000, and 7000 m) with 36 azimuth locations. These 386 receptors provide details on the spatial distribution of the concentrations that helps us to understand the overall structure and evolution of the plume for the entire area. Monthly averages are shown for the months when the power plant was operational (December through May). For the months when the power plant was not in operation there were no emissions and the simulated concentrations are equal to zero. Whatever concentrations are measured during the time when the power plant was not operational are due to other emission sources in the area and in the region. Due to the prevailing winds blowing from the north to the south, most of the plume is transported and dispersed in the area south of the plant. This is mainly evident for the longer term averages (annual). Since most of the study subject receptors were north of the plant, they do not show concentrations as high as those south of the plant. The other important meteorological condition is a large occurrence of calm and low wind speeds. This causes high concentrations in the vicinity of the plant where most of the emissions accumulate. Whether these meteorological conditions are accurately represented by the nearby weather station should be evaluated with additional colocated measurements and other stations. The analysis of the wind speed shows that during 2002 and 2003 the station recorded zero winds (calm conditions) for 53% and 60% of hours, respectively. The consequence of these calm conditions is that some of the highest concentrations are estimated in the vicinity of the power plant. Monthly averages show a wider spatial distribution of high concentrations than the annual average. This is due to the natural variability (for short time intervals days or hours) of the wind direction and atmospheric stability superimposed on the prevailing winds. This is even more evident when the winds are light and of variable directions. In these cases the dispersion is less pronounced and higher concentrations can occur in many directions around the plant. The simulated maxima in the monthly averages, daily averages, and 3-hr averages are over 100, 300, and 600 μg m -3, respectively. High concentrations are simulated with similar values for both years. As shown in Section 5, the meteorology (mainly through 20

21 the analysis of the wind speed and direction) appears to be similar in 2002 and This implies that the impact of the power plant can be expected to be similar from year to year. Sorting the model results for all receptors by the magnitude of the simulated concentrations (highest to lowest) can be used as a measure of exposure to the power station emissions. The ranking was done for each category (annual mean, monthly maximum, daily maximum, and 3-hr maximum concentrations), and then all ranks were summed up for each receptor and for both years. That way, we developed a ranking in which the lower the sum the higher overall impact can be expected at that particular receptor. We believe that this information can be useful since the health-impact data can be also ranked in the same manner and the rankings for the pollution impact can be correlated with the ranking for the health data. Similar rankings can be performed with additional information about the study subjects, e.g., plume exposure during the first month of pregnancy vs. exposure of other subjects. We believe that the results of this study can be further analyzed together with health data in possible future projects. This study results could be also used in the future to determine risk assessment and health exposure levels due to pollution from the power plant. References Chow, J. C., J. G. Watson, L.-W. A. Chen, D. Koracin, B. Zielinska, D. Tang, F. Perera, J. Cao, and S.C. Lee, 2006: Exposure to PM2.5 and PAHs from the Tong Liang, China - Epidemiological Study. J. Environ. Sci. Health, Part A (in print). Koracin, D., D. Podnar, V. Isakov, J. Chow, Y. Dong, A. Miller, and M. McGown, 2000: PM 10 dispersion modeling for Treasure Valley, Idaho. J. Air Waste Manage. Assoc., 50, Perera, F., D. Tang, R. Whyatt, S. A. Lederman, W. Jedrychowski, 2005: DNA damage from polycyclic aromatic hydrocarbons measured by benzo[a]pyrene-dna adducts in mothers and newborns from Northern Manhattan, the World Trade Center Area, Poland, and China. Cancer Epidemiol. Biomarkers Prev., 14(3), U.S. Environmental Protection Agency, 1995: Guideline on Air Quality Models. EPA- 450/ R; Research Triangle Park, NC. 21

22 Appendix A Spatial grid (preceded by c), study subject (no prefix), monitoring location coordinates, annual average, maximum 3-hr average, maximum daily average, maximum monthly average, and monthly average SO 2 contribution (μg m -3 ) for June through November are not included because the power station was not operating during this period Tongliang ISC3 CONC CONC CONC CONC MAX CONC CONC CONC CONC CONC CONC Rec.# STUDY_ID WAYP. Distance Bearing ANNUAL 3hr MAX 24hr MAX MONTH Jan-02 Feb-02 Mar-02 Apr-02 May-02 Dec-02 (m) (deg) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) 1 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

23 Appendix A - Simulated SO 2 concentrations (μg m -3 ) for 2002 continued (II). 70 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

24 Appendix A - Simulated SO 2 concentrations (μg m -3 ) for 2002 continued (III). 141 c c c c c c c c c c UN HOTEL STATION A STATION C STATION B TINQING (S ANFONG ( ARLONG (F NGLONG ( ANJIU (SI ATHER ST UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA UDY AREA

25 Appendix B - Spatial grid (preceded by c), study subject (no prefix), monitoring location coordinates, annual average, maximum 3-hr average, maximum daily average, maximum monthly average, and monthly average SO 2 contribution (μg m -3 ) for June through November are not included because the power station was not operating during this period Tongliang ISC3 CONC CONC CONC CONC CONC CONC CONC CONC CONC CONC Rec.# STUDY_ID WAYP. Distance Bearing ANNUAL 3hr MAX 24hr MAX MXMONTH Jan-02 Feb-02 Mar-02 Apr-02 May-02 Dec-02 (m) (deg) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) (ug/m3) 1 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

26 Appendix B - Simulated SO 2 concentrations (μg m -3 ) for 2003 continued (II). 71 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c