Rationalization of an Odor Monitoring System: A ... - ACS Publications

Environ. Sci. Technol. 2000, 34, 1166-1173

Rationalization of an Odor Monitoring System: A Case Study of Lin-Yuan Petrochemical Park LU-YEN CHEN,* FU-TIEN JENG, MING-WHEI CHANG, AND SHUI-HWAY YEN Graduate Institute of Environmental Engineering, National Taiwan University, 71 Chou-Shan Road, Taipei, Taiwan

The Lin-Yuan Petrochemical Industrial Park, located closely to a populated area in southern Taiwan, is considered to be a major source of odor events. To evaluate odor impact near the park, establishment of sampling stations specifically for monitoring odor is essential. This study is intended to identify the location of such odor-monitoring stations through actual monitoring of potential odor-causing substances. We also correlate the concentration of the characteristic chemical odorant to the odor concentration perceived by a panel and predicte time and spatial concentrations of the major odor-causing substances using a dispersion model. Air samples were obtained on four occasions from 10 sites that had experienced frequent odor problems in the past. The concentrations of 10 compounds of each sample were quantified with a GC/ MS unit. The impact index is introduced as the concentration of the individual compounds divided by the respective odor threshold concentration. In general, there is one specific compound with its impact index 2-3 orders of magnitude higher than other monitored compounds. Air samples were also subject to a 6-8-person panel for the odor threshold estimates using an olfactometer. There is some correlation between the concentration of the major species identified and the odor concentration experienced by the panel. The industrial source complex short-term model was then used to evaluate the concentration profiles of odorous compounds. The same dispersion model was also used to evaluate the odor event, in which the hourly event is defined as the occurrence if the predicted concentration is higher than the threshold value. On the basis of the event profiles, several stations for odor monitoring specifically are suggested.

Introduction The Lin-Yuan Industrial Park, located in the Kaohsiung area in southern Taiwan, is one of the major petrochemical industrial parks in Taiwan. Because petrochemical plants are often associated with serious pollution problems, the Industry Development Bureau of the Ministry of Economic Affairs of Taiwan Government has set up an environmental monitoring system around the Lin-Yuan Industrial Park. The system monitors the emission conditions of criteria pollutants (e.g., particulate matter, Nox, and SOx), toxic pollutants, and * Corresponding author phone: (886)2-2363-0231, ext 3041; fax: (886)2-2362-5946; e-mail: [email protected]. 1166

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FIGURE 1. Preliminary sampling sites and neighboring factories. some hazardous chemicals (e.g., HCN and HCl). In general, the emissions of the stacks of stationary sources can meet the regulatory standards. However, there are still some complaints by local inhabitants due to odor problems of the Lin-Yuan Industrial Park. The presence of odor is the most important factor of disharmony between the industrial park and its neighborhood. The raw materials and products of petrochemical plants are often volatile and semivoatile organics. The volatile organic compounds (VOCs) themselves are often responsible for odor problems; however, complex reactions resulting in serious odor annoyance are not quite understood. Perception and recognition of different odors are of course different from person to person. The samples from these monitoring stations are not representative for odor monitoring; hence, they cannot indicate the extent of odor problems. The need for selecting monitoring stations specifically for detecting an odor is apparent. Recently, several approaches have been developed for optimizing the air-monitoring station sites, e.g., DMSS method (1), FOM-SOI method (2, 3), and COVA method (4). The targets are primarily criteria air pollutants, and none is specifically used for odor monitoring. Furthermore, few cover the complex industrial park with more than 15 major industries. Consequently, this study is intended to use an EPA-developed model to evaluate time and spatial variations of individual compound responsible for typical odor problems encountered in the Lin-Yuan Industrial Park. 10.1021/es990180g CCC: $19.00

 2000 American Chemical Society Published on Web 03/02/2000

TABLE 1. Concentration of Primary Odorous Speciesa

odor threshold (µg/m3)

sampling time: April 5, 10:00-12:00 sampling station acetic acid cumene methyl acrylate ethyl acrylate methyl methacrylate styrene benzene toluene ethyl benzene m-xylene, p-xylene odor concn (ou/m3)

wind direction: W S10 NDb 19 ND ND ND ND ND 14 6.4 14 64

sampling time: April 4/5, 23:00-01:00 sampling station acetic acid cumene methyl acrylate ethyl acrylate methyl methacrylate styrene benzene toluene ethyl benzene m-xylene, p-xylene odor concn (ou/m3)

S4 51 144 ND ND ND ND ND 11 3.2 57 13

S1 91 ND ND ND ND ND 7.3 17 1.0 234 81

S7 ND ND 3.5 1.9 ND 18 ND 3.2 11 4.8 51

S5 ND ND 6.6 ND ND 25 ND 10 10 12 49

S3 103 ND ND ND ND 17 13 26 7.8 17 66

S2 220 ND ND ND ND ND ND ND 5.9 250 71

S8 ND ND 67 2.5 ND 21 ND 21 7.3 18 42

S4 58 5.0 ND ND ND 30 ND 17 8.6 42 23

wind direction: W, NW S5 51 ND ND ND ND 13 1.3 1.4 3.9 10 424


S6 25 10 ND ND ND ND ND 12 2.5 9.5 33

wind direction: NW, NNW


TABLE 2. Odor Thresholds of the Target Species

S9 185 17 ND ND ND 111 ND 15 116 40 273

S8 ND ND 9.7 2.2 1.3 27 ND 9.1 ND 18 158

S4 157 ND ND ND ND 29 56 47 5.7 58 316

wind direction: W, N S2 158 ND ND ND ND 29 13 25 9.9 151 277

S6 ND ND ND ND ND 143 ND 12 26 18 833

S7 ND ND 11 5.4 ND 35 ND 7.4 4.8 9.4 249

S5 ND ND 22 3.9 ND 42 ND 19 8.2 16 365

a All values in µg/m3, except odor concentration (odor unit/m3). b ND, not detected.

The present study consists of three phases. The purposes of the Phase I study is to monitor some specific VOCs in the ambient air around Lin-Yuan area and to characterize the distinct and potential odor-causing components. The concentrations of a major chemical compound is then correlated with the odor concentrations determined in an olfactometer, a practice seldom performed before. The Phase II study involves the use of a dispersion model to evaluate the impact of odor-causing compounds within the Lin-Yuan Industrial Park. The eventual goal of this study is to identify the location

compound

literature (6)

exptl value

acetic acid cumene methyl acrylate ethyl acrylate methyl methacrylate styrene benzene toluene ethyl benzene m-xylene, p-xylene

181 160 3200 1 200 594 196000 6008 2300 3160, 9100

12

122 112

of proposed monitoring stations for monitoring VOCs and potential odor problems. In Phase III, the monitoring data of the two stations selected are used to evaluate their effectiveness.

Materials and Methods Area Description. Lin-Yuan Industry Park, about 388 ha, is one of several petroleum industrial complexes in southern Taiwan. Its eastern border connects with the Kao-Pin River. In the western, southern, and northern terrain, there are some villages with a large population (about 50 000 residents surrounding the industrial park). In this industrial park, most of the plants are related to petrochemical processes, which are characterized with their potential emission of odorous air pollutants. In the studied area, the daily variation of the prevailing wind directions is significant. For example, the west and southwest winds dominate in the daytime (6 am-6 pm) in the spring, and northeast winds predominate in the nighttime. The seasonal variation of the wind direction, however, is not noticeable. Wind speed also varies with time; the probability of velocity less than 1 m/s is approximately 20% in daytime and 33% in nighttime. The height of the mixing layers also differs significantly within 1 day; the highest mixing layer is greater than 800 m near noon, whereas the lowest is below 400 m in the midnight. The atmospheric stability in this area is under neutral (class D) to highly stable (classes E and F) conditions. Sampling Sites. Sampling stations were set up to collect air samples at sites that experienced frequent and intensive odor events in 1994. Figure 1 shows the sampling sites and neighboring factories; the sampling sites are prefixed with S, while the factories are prefixed with F. The UTM (universal transverse mercator) coordinate is used to indicate the location and studied area; the scale is in meters, and the N arrow is directly pointing north direction. Air samples were taken at the breathing-zone level at each of 10 sampling stations, using a Tedlar sampling bag placed in a vacuum vessel. Whenever there was an occurrence of odor, the valve controlling the air to the bag was immediately turned on by personnel driving around the park. The sampling normally lasted about 2-5 min. The air samples were subsequently analyzed by GC/MS to quantify the concentrations of the potential odorous compounds. Analytic Method. The air samples collected were concentrated by Tenax-TA adsorbent and analyzed with the TO-1 method (5) in a Hewlett-Packard GC unit (5890 series II) with a thermal desorption cold temperature injector (Chrompack) and a DB-624 fused silica capillary column (0.32 mm I.d., 30 m long). The initial GC operating temperature was 50 °C. The temperature program (temperature rate, final temperature, holding time) was as follows: level 1, 6 °C/ min, 90 °C, 10 min; level 2, 3 °C/min, 120 °C, 3 min; and level 3, 10 °C/min, 180 °C, 6 min. The carrier gas He was operated at the flow rate of 2 mL/min. VOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Evaluated Odorous Contribution of Odorous Species (%)a sampling time: April 5, 10:00-12:00 sampling station acetic acid cumene methyl acrylate ethyl acrylate methyl methacrylate styrene benzene toluene ethyl benzene m-xylene, p-xylene odor concn (ou/m3)

wind direction: W S10 NDb 12 ND ND ND ND ND 2 × 10-1 3 × 10-1 2 × 10-1 64

S4 425 90 ND ND ND ND ND 2 × 10-1 1 × 10-1 9 × 10-1 13

sampling time: April 4/5, 23:00-01:00 sampling station acetic acid cumene methyl acrylate ethyl acrylate methyl methacrylate styrene benzene toluene ethyl benzene m-xylene, p-xylene odor concn (ou/m3)

S3 858 ND ND ND ND 15 7 × 10-3 4 × 10-1 3 × 10-1 3 × 10-1 66

a

S9 1542 11 ND ND ND 99 ND 2 × 10-1 5 7 × 10-1 273

S2 1317 ND ND ND ND 26 7 × 10-1 4 × 10-1 4 × 10-1 2 277

S6 ND ND ND ND ND 128 ND 2 × 10-1 1 3 × 10-1 833

S8 ND ND 2 250 ND 19 ND 3 × 10-1 3 × 10-1 3 × 10-1 42

S4 483 3 ND ND ND 27 ND 3 × 10-1 4 × 10-1 7 × 10-1 23

S8 ND ND 3 × 10-1 230 1 24 ND 2 × 10-1 ND 3 × 10-1 158

S4 1308 ND ND ND ND 26 3 × 10-2 8 × 10-1 2 × 10-1 9 × 10-1 316

wind direction: W, N

b

S7 ND ND 3 × 10-1 540 ND 31 ND 1 × 10-1 2 × 10-1 2 × 10-1 249

S5 ND ND 7 × 10-1 390 ND 38 ND 3 × 10-1 4 × 10-1 3 × 10-1 365

ND, not detected.

Determination of Odor Concentration. The air samples were subject to odor quantification with 2 h delay after sampling by passing samples through a dynamic olfactometer (Eurogenic, The Netherlands). A panel of 6-8 persons, who had passed the standard 1-butanol test, sniffed the diluted field samples (2nd series dilution) as well as odor-free blank air samples through the vent. A software package (Nose for Windows) was used to analyze the individual duplicate results (best estimate threshold) and the panel results. The odor concentration is then reported as odor unit/m3 (ou/m3). 9

S2 1833 ND ND ND ND ND ND ND 3 × 10-1 4 71

S5 425 ND ND ND ND 12 7 × 10-4 2 × 10-2 2 × 10-1 2 424

All values in µg/m3, except odor concentration (odor unit/m3).

1168

S5 ND ND 2 × 10-1 ND ND 22 ND 2 × 10-1 4 × 10-1 2 × 10-1 49

wind direction: W, NW


S7 ND ND 1 × 10-1 190 ND 16 ND 5 × 10-2 5 × 10-1 8 × 10-2 51

wind direction: NW, NNW S1 758 ND ND ND ND ND 4 × 10-3 3 × 10-1 4 × 10-2 4 81


S6 208 6 ND ND ND ND ND 2 × 10-1 1 × 10-1 2 × 10-1 33


The odor threshold concentrations of several compounds were taken from previous EPA results (6), except styrene, acetic acid, and methyl methacrylate (MMA). The odor thresholds of these three compounds were determined as described above for air samples. Further information can be found in a previous work (7).

Results and Discussion Odorous Species. A total of 18 air samples were taken from four occasions: both daytime and nighttime in two different

FIGURE 3. Impact index of odorous species from sampling site near factory F5 (S7 and S8). FIGURE 2. Impact index of odorous species from sampling site near factory F12 (S1-S4). days in April. The concentration variations of different compounds are shown in Table 1. The composition of air samples at each station differs significantly due to the emission characteristics of different plants within this park. The concentration of each monitored compound also differs as industrial processes may vary within a day. Nonetheless, m- and p-xylene are present in all 18 samples; ethyl benzene and toluene are present in 17 samples; and MMA is only present in one sample. The highest concentrations of these compounds measured are as follows (all in µg/m3): xylenes, 250; acetic acid, 220; styrene, 143; ethyl benzene, 116; methyl acrylate, 67; toluene, 47; cumene, 144; and ethyl acrylate, 5. Ambient air level goals of the United States are as follows (all in µg/m3): xylenes, 5200, 8-h TWA (time-weighted average); acetic acid, 2450, 8-h TWA; styrene, 26.3, annual TWA; ethyl benzene, 130, 24-h TWA; methyl acrylate, 3500, 8-h TWA; toluene, 1400, 8-h TWA; cumene, 2900, 8-h TWA; and ethyl acrylate, 65, 24-h TWA (7). Except styrene, the concentration of all detected compound are below the ambient air level goals. To estimate the influence of these potential odor-causing species on the overall odor intensity, the ratio of the individual organic concentration to its odor threshold concentration (Table 2) was used as the impact index on the neighboring inhabitants. Because the composition of odor-causing compounds in air samples is complex, the necessity of finding a suitable way to express the odor intensity with some key pollutants is essential. Thus, the impact index can be used to provide the weighted contribution of each compound. For example, based on the acetic acid concentration of 51 µg/m3 and its odor threshold of 12 µg/m3, the impact index is 4.3. On the other hand, the impact index for styrene is only 1.3 because of its relatively high odor threshold concentration (112 µg/m3), although it exhibits a much higher concentration of 143 µg/m3. The information about odorous pollutants and their impact index is shown in Table 3. Those compounds with higher values of impact index are labeled as the potential key odorous pollutants. In general, there is one specific compound at each site with its impact index 2 or 3 orders of magnitude higher than other compounds. On the basis of the results shown in Figures 2 and 3, the most significant odor-causing compounds are acetic acid or ethyl acrylate depending on the sampling site. The other compounds with less impact index may exert potential synergistic effect on the overall odor intensity. However, the exact role of this synergistic impact is unclear and beyond the scope of this study. Of course, compounds that are not monitored may contribute to the odor intensity even as a trace level. The regressions between the concentration of the major odor-causing compound and odor concentrations deter-

mined by the panel show some correlation. For example, in the case of four sampling sites near F12, the linear regression between odor concentration and acetic acid concentration yields an R 2 value of 0.28 for all sampling data, but with a better correlation (R 2 ) 0.86) for acetic acid concentration less than 105 µg/m3 (Figure 4). F12 uses the raw materials of p-xylene and acetic acid for generation of the major product terephthalic acid. Thus, the effect of acetic acid on odor concentration is expected at a lower concentration level. High acetic acid concentration with a serial dilution may provide the unreliable panel results. By monitoring acetic acid concentration, the extent of odor problems can be quantified, and it may serve a preliminary tool for detecting the eventual occurrence of an odor event. For air samples from two sites near F5, the linear regression between odorous concentration and ethyl acrylate concentration, however, yields an R 2 value of 0.54 (Figure 5). The poor correlation may be due to the fact that F5 is a complex industry and produces many products and that the contribution of other unmonitored odorous compounds to the overall odor concentration may be significant. Air Dispersion Modeling. The recent ISCST3 (industrial source complex short-term) model (8) was used to evaluate the concentration distribution of acetic acid and ethyl acrylate. The model assumptions are as follows: (i) chemical transformation is assumed to be exponential decay and (ii) Gaussian plume model is used to calculate short-term impacts (i.e., 1-24 h) and annual average concentration. The 1994 meteorological data were used in the simulation. Source Strength Evaluation. The odor emission sources are classified into stacks, process equipment components, and storage tanks. Depending on the manufacturing process, emitted odor-causing substances from stacks may be significant as compared to pollutants emitted from the other two sources. The amount emitted from equipment components can be estimated from the emission factor table (Table 4) (10). However, the values in Table 4 are for the total emission rate. Thus, a correlation factor to account for the contribution of the individual compound must be used. On the basis of the ratio of acetic acid and ethyl acrylate to the total raw materials consumed in F5 and F12, the correlation factors of 8% and 64%, respectively, were used to account for the fraction of the total hydrocarbon emission rate listed in Table 4. The storage emission loss (breathing and working loss) used in this study was estimated using the method described in ref 11. The major emission parameters used in the model are summarized in Table 5. We treated tanks as point sources, while equipment emissions were summed up into area sources within the plants they belong to. Concentration Profiles (Seasonal Average). The results for seasonal average concentrations are based on the simulated average concentrations of 6 h, which are then merged into the average daytime or nighttime concentrations VOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Correlation of acetic acid concentration and the odor concentration near factory F12.

FIGURE 5. Correlation of ethyl acrylate concentration and the odor concentration near factory F5.

TABLE 4. Emission Factors of Emission from Equipment Components equipment component

fluid type

valve light liquid heavy liquid pump seal heavy liquid compressor seal pressure relief valve flange open-ended line sampling connection

gas 0.0071 0.00023 light liquid 0.0214 gas gas overall overall

emission factor (kg h-1 source-1) 0.0056 0.0494 0.228 0.104 0.00083 0.0017 0.015

for different seasons. The simulated results in the nighttime are characterized with a larger influenced area and higher concentrations, in contrast to their counterparts in the daytime. That is expected since there is a less diffusion potential due to the lower wind speed and lower diffusion layer in the nighttime. Also, the pollutant diffusion behavior 1170

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TABLE 5. Emission Rates Used in the ISCST3 Model compound stack equipment source

factory 12

factory 5

acetic acid 15, ranging from 0.05 to 0.30 kg/h 5 sets, each 0.88 kg/h 5, ranging from 0.23 to 0.31 kg/h

ethyl acrylate 0 7.11 kg/h 0

for each season varies with changing wind direction. The average concentration contours of acetic acid in winter are shown in Figures 6 and 7 for daytime and nighttime, respectively. The expansion of pollutant contour toward southwest in the nighttime (Figure 7), as compared to those contour lines in daytime (Figure 6), clearly results from the prevailing sea-land breathing with southwestern wind in the day and northeastern wind in the night. Since the measured odor threshold concentration for acetic acid is only 12 µg/m3, which is less than the EPA-reported threshold value (6), the area of the 12 µg/m3 contour lines in the nighttime is approximately 4 times larger than that in the daytime. The current regulatory standard for odor near the

FIGURE 8. Winter average concentrations of ethyl acrylate in daytime (µg/m3). FIGURE 6. Winter average concentrations of acetic acid in daytime (µg/m3).

FIGURE 9. Winter average concentrations of ethyl acrylate in nighttime (µg/m3).

FIGURE 7. Winter average concentrations of acetic acid in nighttime (µg/m3). manufacturer boundary line is 50 ou/m3; it corresponds to approximately acetic acid concentration of 79 µg/m3 (Figure 4). Thus, this particular manufacturer may not meet the odor standard in the nighttime. A similar impact of the daytime and nighttime variation for ethyl acrylate is shown in Figures 8 and 9. Although the model predicts a larger influenced area in the nighttime, the highly localized concentration still prevails in the daytime. Due to wind direction, the pollutant concentration profiles in other seasons shift to other directions (not shown), e.g., to the north in the summer and to the southwest and northeast in the fall. Again, the purpose of these plots is not intended to show the exact concentration profiles; rather they can be used to pinpoint the potential monitoring sites. Event Consideration. In this study, the number of possible odor events derived from individual odorous compound is

used for selecting potential sampling sites. All of the 8760 h (365 × 24) in 1 year of simulation were investigated for the hourly concentration. For each simulated grid point, if the average concentration of individual odorous compound is larger than its odorous threshold concentration, the particular time (hour) is then labeled as the occurrence of one odor event. The total number of events are then summarized and plotted in Figures 10 (acetic acid) and 11 (ethyl acrylate). The influence area of the predicted hourly odor event for these two compounds is completely different. For example, for approximately 23% (2010 events in 1 year) of the time that the odor event will occur due to acetic acid, the area is located in the south of the industrial park and covers mainly F12, F13, and F17 and, to some extent, F11 and F16 (Figures 1 and 10). On the other hand, the area for the same 2010 events/year due to ethyl acrylate is shifted to the north and covers mainly F5 and F2. The difference of odor event distribution patterns is due to the different locations of the odorants’ emission sources. After checking the wind direction patterns for events corresponding to both compounds, we did not find an obvious difference. The source arrangement is therefore viewed as a major factor of odor event distribution pattern. Thus, the selection of many monitoring stations to cover a wild area is essential. VOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 6. Sampling Results in Recommended Stations B and Ea station B, near S4, F12 chemical species

day

acetic acid methyl acrylate ethyl acrylate mma toluene ethyl benzene m, p-xylene styrene benzene cumene a

All values in µg/m3.

55 ND** ND ND 1 4 48 ND ND ND b

53 ND ND ND 13 4 15 ND 17 339

station E, near S7, F5 night

ND ND ND ND 6 4 11 ND ND ND

39 ND ND ND 13 2 26 ND ND ND

NDb ND ND ND 18 4 9 13 3 4

day ND 13 14 ND 10 11 8 55 ND ND

ND 3 ND ND 8 9 4 11 ND ND

night ND ND ND ND 12 5 11 11 1 ND

ND ND 1 2 16 3 6 69 ND ND

ND 5 10 ND 6 4 7 13 ND ND

ND, not detected.

FIGURE 10. Annual number of odor events for acetic acid.

FIGURE 12. Monitoring station candidates.

FIGURE 11. Annual number of odor events for ethyl acrylate. The key point of rationalization of establishing any monitoring system is the appropriateness of the monitoring station arrangement, i.e., to collect the representative samples. In this study, the simulated number of odor events is used to establish the potential monitoring candidates. With 1172

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the aid of event numbers distribution, the monitoring stations can be identified and are shown in Figure 12. Because of the characteristics of local sea-land breathing, the importance of each monitoring station should be ranked accordingly. The stations located downwind, such as B, C, D, E, G, and H, are more important in the daytime. For the nighttime, stations A, D, F, and G are more important. Consequently, the overlap stations of D and G should be viewed as the primary monitoring candidates. Monitoring Stations Result. Air samples were taken using Tenax-TA adsorption tubes from stations B and E on six occasions. The sampling time was preset to turn on a low flow (50-100 mL/min) sample pump (Gilian model LFS 113), and the sample apparatus was placed on the top of brick walls (2-2.5 m height). The results (Table 6) indicate that the station B samples indeed contain high acetic acid concentration (up to 55 µg/m3) and that station E samples include one sample with 14 µg/m3 of ethyl acrylate in the daytime.

Summary The present study monitors 10 organic compounds present in the air samples of random 10 stations. The concentrations of these determined compounds are coupled with the odor threshold concentrations to calculate the impact index to determine the potential contribution factor of the respective compound to the measured odor concentration. In general, there is one specific compound with its impact index 2-3 orders of magnitude higher than those of other compounds. Furthermore, there is some correlation between acetic acid concentration and the odor concentration, and to some extent, between ethyl acrylate and odor concentrations. Since odor events impact inhabitants within short periods, the investigation of concentration variation, especially peak values and their occurrence frequency, is of practical importance. Subsequently, a dispersion model is used to predict the average concentration of acetic acid and ethyl acrylate both in daytime and nighttime in four seasons. The concentration profiles of characteristic species varies with time and season, due to local sea-land breathing, different mixing layers, and wind speed. Eventually, on the basis of the predictions of odor events of these two compounds, the potential stations for odor monitoring are identified.

Acknowledgments The financial assistance from the Industry Development Bureau of the Ministry of Economic Affairs is appreciated.

Literature Cited (1) Noll, K. E.; Mitsutomi, S. Atmos. Environ. 1983, 17, 2583-3590.

(2) Liu, M. K.; Avrin, J.; Pollack, R. I.; Behar, J. V.; McElroy, J. L. Environ. Monit. Assess. 1986, 6, 1-11. (3) McElroy, J. L.; Behar, J. V.; Dunn, L. M.; Pitchford, A. M.; Pitchford, A. M.; Lem, P. N.; Fisher, N. T.; Meyers, T. C.; Liu, M. K. Environ. Monit. Assess. 1986, 6, 13-34. (4) Tsai, J. H.; Lee, Y. Y. J. Chin. Inst. Eng. 1993, 16, 543-555. (5) U.S. EPA. Guideline for determination of laboratory acceptability for analysis of volatile organic pollutants collected on Tenax GC (trade name) adsorbant; Government Printing Office: Washington, DC, 1984; EPA-600/4-84-035. (6) U.S. EPA. Reference Guide to Odor Thresholds for Hazardous Air Pollutants Listed in the Clean Air Act Amendments of 1990; Government Printing Office: Washington, DC, 1992; EPA/600/ R-92/047. (7) Dravnieks, A.; Jake, F, J. Air Pollut. Control Assoc. 1980, 30, 12841289. (8) Calabrese, E. J.; Kenyon, E. M. Air Toxics and Risk Assessment; Lewis Publishers: Ann Arbor, MI, 1991. (9) U.S. EPA. User Guide for the Industrial Source Complex (ISC3) Dispersion Models, Office of Air Quality Planning and Standards; Government Printing Office: Washington, DC, 1995; EPA-454/ B-95-003b. (10) U.S. EPA. Protocol for Equipment Leak Emission Estimates; Government Printing Office: Washington, DC, 1995; EPA453R95017. (11) U.S. EPA. Compilation of Air Pollutant Emission Factors; Government Printing Office: Washington, DC, 1985; AP-42.

Received for review February 16, 1999. Revised manuscript received July 22, 1999. Accepted November 30, 1999. ES990180G

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Rationalization of an Odor Monitoring System: A ... - ACS Publications

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