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Spatially resolved monitoring for volatile organic compounds using remote sector sampling
Atmospheric Environment Vol. 27A, No. 5, pp. 739 747, 1993. Printed in Great Britain.
0004 ~,981/93 $6.00+0.00 Pergamon Press Ltd
SPATIALLY RESOLVED MONITORING FOR VOLATILE ORGANIC C O M P O U N D S USING REMOTE SECTOR SAMPLING JOACHIM D. PLEIL a n d WILLIAM A. McCLENNY U.S. EPA, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC 27711, U.S.A. MICHAEL W. HOLDREN a n d ALBERT J. POLLACK Battelle Memorial Institute, Columbus, OH 43201-2693, U.S.A. and KAREN D. OLIVER ManTech Environmental Technology Inc., Research Triangle Park, NC 27709, U.S.A. (First received 13 December 1991 and in final form 29 October 1992)
Abstract--Sector sampling for volatile organic compounds (VOCs) is conducted within an integrated sampling scheme and relies on a wind direction sensor. The wind sensor directs whole air, sampled at a constant rate, into either an "IN" sector canister or an "OUT" sector canister; when the wind comes from the suspected emissions area, sample is routed into the IN sector canister; otherwise, sample is collected in the OUT sector canister. This method is analogous to "upwind/downwind" sampling but does not require two distinct sites or manual sampler control. For this set of experiments, the IN and OUT sectors were 90 and 270 °, respectively, and the IN sector was centered on the VOC source. Two sampling sites were used. The first was about 2 miles north-northeast of a group of industrial facilities, and the second was located about 1 mile south-southeast of the same sources. Sites were operated concurrently with one sampler each; a third sampler was rotated between the sites to obtain duplicate samples. The air samples were analysed by gas chromatography for VOCs. The resultant data comparisons between IN and OUT duplicate samples show good correlation with expected VOC emissions, which were determined by grab samples within the target area. A t-test method for interpreting the sometimes subtle differences between IN and OUT sample data is presented. Key word index: Volatile organic compounds, wind direction, sector sampling, ambient air analysis, ambient air sampling.
INTRODUCTION
The U.S. Environmental Protection Agency (EPA) has developed methodology over the past 6 years for the trace-level determination of certain nonpolar volatile organic compounds (VOCs) in the ambient air. The methodology is based upon the use of S U M M A ®polished canisters for collecting whole-air samples for subsequent laboratory analysis. Details of the method are available in Method TO- 14 of E P A (1989). Typical applications of TO-14 involve time-integrated point monitoring, usually for 24-h periods, as in EPA's Toxic Air Monitoring System (TAMS) network (Smith and Holdren, 1989) or the U r b a n Air Toxics Monitoring Program (McAllister et al., 1989). Although point monitoring has proven to be useful in documenting V O C concentrations, more sophisticated monitoring strategies have been under development to produce additional information such as time variability of V O C concentrations and V O C source locations. This has involved semi-real-time, in situ 739
V O C determination with concurrent wind speed and direction measurement to allow calculation of back trajectories of VOCs (McClenny et al., 1989), timedependent measurements in indoor air situations (Pleil et al., 1989) and wind-direction-dependent V O C measurement (Pleil et al., 1988a). The correlation between wind direction (transport) and the concentration of specific airborne pollutants has been reported in the literature primarily for the study of aerosols. F o r instance, Lioy et al. (1980), Miller and Harris (1985) and Sperber (1987), as well as others, have dealt with the deposition of acidic species as a function of wind data. Kriews et al. (1988), Deyo et al. (1977) and Breiner and Buchanan (1984) have reported the use of automated wind-directionsensitive samplers for the selective collection of particulate matter. This work describes the use of a spatially resolved air monitoring strategy for inferring V O C emissions from suspect sources. The strategy is similar to the aerosol strategies mentioned above; concentrations
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are correlated with meteorology to infer source information. Ambient air is routed into one of two containers in the sampler: the " I N " container if the wind is blowing from a fixed spatial (directional) sector that includes the suspected V O C source(s), or the " O U T " container if it is blowing from another direction. The differences between the I N sector sample and the O U T sector sample at a single monitoring site indicate candidate V O C s emanating from the I N sector source. In effect, this "sector method" provides its own background data via the O U T sector sample and, as such, can be considered a substitute for the more conventional "upwind/downwind" sampiing, which would require relocating sampling sites and other operator intervention as a response to changing wind conditions. To corroborate the identification of source-related VOCs, additional sites can be operated simultaneously at other directions from the suspect source. This methodology was tested in an airshed containing an extended cluster of industrial V O C sources. Two fixed monitoring sites were used, one about 2 miles north-northeast, and the second about 1 mile south-southeast from the center of the industrial area. Each site was operated in duplicate for part of the time. In addition, various grab samples were taken close to the sources. The resulting samples were analysed for a variety of nonpolar volatile organic "toxics" from the M e t h o d TO-14 list (EPA, 1989) and for C 4 - C l o hydrocarbons. A data interpretation method based on t-test statistics is presented for the sector method to deduce the trace-level contributions of the suspected source area to the receptor sites. The grab sample results are presented to confirm these deductions.
EXPERIMENTAL All samples were collected in 6-d stainless steel SUMMA canisters, which have internally passivated surfaces. Canisters were cleaned prior to sample collection by evacuation to less than 50 #m Hg at an elevated temperature of 80-100°C. Quality assurance of the clean-up procedure was performed on a subset of these canisters by filling them with humidified zero air and analysing for residual contamination. For collecting field samples, the canisters were always used by starting with the clean-up vacuum ( < 50/~m Hg). Details of the use of various configurations of these sampling devices and the associated compound storage stability are available in Method TO-14 (EPA, 1989); some additional representative publications are given in the reference section (McClenny et al., 1987; Oliver et al., 1986, and references therein). The canisters used for this project were from two manufacturers: SIS Inc. (Moscow, ID) and Biospherics Inc. (Hillsboro, OR). The sector samplers consisted of a conventional actively pumped flow configuration (McClenny et al., 1987) with the exception that a three-way valve allowed the routing of the air sample flow to either of two canisters. The destination of the sample flow was dependent upon the wind direction; that is, its routing to one of the two sampling canisters depended upon a signal from a wind sensor that determined whether the air was coming from the suspect source (IN sector) or not (OUT sector). A diagram of this configuration is presented in Fig. 1. Three sector samplers from Xontech Inc. (Van Nuys, CA), were used in this study. Sample flow rate was maintained at about 10.5 cm 3 min- 1, which would result in a final canister pressure of 20 psig after 24 h if the wind sector never changed. Usually, when the wind was variable, more time was required to fill the IN sector canister because the respective sector was smaller than that for the OUT canister (90 vs 270°); sometimes two OUT sector samples were collected before a sufficient volume of IN sample was collected for analysis. For the first two tests, site 1 was operated in duplicate with samplers 1 and 3, while site 2 was concurrently operated with sampler 2. For the remaining two tests, sampler 3 was moved to site 2 and operated in duplicate with sampler 2, and sampler 1 was operated concurrently at site 1.
WindDirectionSensor Sample Inlet
Electronics:Timer,SolenoidCont WindDirectionDecoderr~l~"~ -~
Pressure Regulator
Pump ~
Vent ExcessFlow
P P rue s a u r e G IIa ~
~ In
SUMMACane Out
Fig. 1. Schematic of sector sampler flow arrangement.
Spatially resolved monitoring for VOCs
741
1. Chlorinated Chemical Manufacturer 2. Power Plant (Coal) 3. Oil Refinery 4. Oil Tank Farm 5. Chemical Company 6. Liquified Gas Manufacturer 7. Plastics Manufacturer 8. Truck Depot
Fig. 2. Diagram of relative locations of sector sampling sites and the suspect sources.
The sites were provided by the state of Delaware for our experiments; both were state monitoring stations where our sampling inlets and wind gear could be mounted on the roof, about 12 ft off the ground. Each had electrical power available for the samplers. The sites were in the New Castle area near a variety of industrial sources including a coal-fired power plant, an oil refinery, a petroleum product storage and transfer facility, various chemical manufacturers and two plastics manufacturing plants. The samplers were operated with 90 ° IN sectors (with corresponding 270 ° OUT sectors). These sectors were chosen empirically to optimize collection of sample; a smaller IN sector would improve spatial selectivity but sacrifice the amount and time of sample collection (statistically), whereas a larger IN sector would have the opposite trade-off. A diagram of the relative locations of the sources, sampling sites and the IN sectors is given in Fig. 2. All analyses were performed by gas chromatography/mass spectrometry (GC/MS; Hewlett-Packard model HP-5880A gas chromatograph and model HP-5970B mass selective detector, Hewlett-Packard, Avondale, PA, and Palo Alto, CA, respectively) in the selected ion monitoring mode for the TO-14 (EPA, 1989) list of 40 nonpolar VOCs. In addition, a flame ionization detector (FID) was used in parallel at the column outlet to determine nontarget compounds, primarily the C4~2~o aikanes. The sample was preconcentrated with a Nutech 320-1 cryogenic focussing unit (Nutech Inc., Durham, NC) and separated on a 50 m, 0.32 mm i.d., HP-1 fusedsilica capillary column. Some of the grab samples taken close to the sources were analysed by GC/MS scan methods (28.5-350 amu) to identify all major organic constituents. The higher level compounds were quantified with a second GC (also an HP-5880A), equipped with an FID and an electron capture detector. The general analytical methods for these analyses are presented in the literature (McClenny et al., 1984; Pleil et al., 1987, 1988b); the specific analytical pro-
tocols used for the sector sample analyses are identical to those for EPA's TAMs network and are described in detail in the current TAMS report (Smith and Holdren, 1989). RESULTS AND DISCUSSION
Grab samples G r a b samples showed two specific groups of compounds, depending u p o n location; typical ranges of c o n c e n t r a t i o n s are given in Table 1 for samples t a k e n near the t a n k farm a n d near the chlorinated chemical manufacturer. These c o m p o u n d s were extremely variable in time, a n d we collected g r a b samples only when there were indications t h a t a n "event" was occurring, such as smells, d a t a from real-time i n s t r u m e n t a t i o n or obvious plumes. M o s t of these samples were t a k e n near the chlorinated chemical plant, a n d the results are from mass spectrometry data. The h y d r o c a r b o n d a t a are from two g r a b samples t a k e n o n a r o a d w a y near the oil storage facility. Only c o m p o u n d s t h a t exceeded 10 p p b v in at least one sample are listed in the table.
Sector samples--data reduction The d a t a from the sector samplers is b r o k e n d o w n into two categories; (1) mass spectrometry d a t a for "target c o m p o u n d s , " i.e. the toxic V O C s as listed in M e t h o d TO-14 (EPA, 1989); (2) d a t a for the h y d r o c a r b o n s that were t a k e n from F I D traces a n d quantified
742
J. D. PLEILet al. Table 1. Results (ppbv) from grab samples taken at the fenceline of two facilities: compounds found at levels higher than typical background concentrations Chlorinated c.hemical manufacturer (10 grab samples) Dichloromethane Benzene Chlorobenzene p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene
Petroleum products storage and handling facility (2 grab samples) 1-12 1-51 7-260 12-247 4-154 1-41
with respect to a calibrated benzene response. Per carbon benzene response for FID has historically been used in our laboratory because of the availability of a variety of NBS or NIST traceable standards. Cursory inspection of the concentration data reveals that differences between the IN and O U T paired sector samples are subtle and that the concentrations at the sites 1-3 miles away from specific sources are essentially at background levels, generally not exceeding 2 ppbv. To allow the extraction of useful information from this data set, a normalized ratio parameter for comparing paired IN and O U T sector data on a per compound basis was defined as R = ( I N - OUT)/(IN + OUT),
(1)
where IN and O U T are concentrations in ppbv for individual compounds. The parameter R can range from - 1 to +1. Positive R values indicate an IN sector prevalence, whereas negative values indicate an O U T sector prevalence. The absolute value of R gives a relative measure of the importance of a compound in a sector. Because this is a "ratio" parameter, some consideration must be given to dealing with very low concentration values, i.e. when the minimum quantifiable level is approached, the point must be determined at which a concentration should be considered zero rather than some minimum value. These decisions can easily change the calculated R from 0 to + 1. This was handled by rounding all data to'the nearest 0.1 ppbv (defined as our minimum quantifiable level for this project), generating all the R values, and then generating overall averages of concentrations for each compound from the sector samples. For compounds that exhibited average concentrations at or below the minimum quantifiable level, the R values were not considered strong indicators of sector prevalence. Also, when the (IN, OUT) concentration data in ppbv were (0, 0.1), (0, 0) or (0.1, 0), the R values were not included in the statistical treatment. Even in the absence of any sources, one expects scatter in the R values from the intrinsic sampling and analytical precision errors. Scatter in R for a specific compound from repeated measurements is also expected because the sources are likely to be variable in time.
Butane Isopentane Pentane 2-Methylpentane 3-Methylpentane Methylcyclopentane
22, 103 13, 444 7, 200 2, 50 2, 25 1, 13
To account for the combination of error and variability, we can determine a statistical level of significance for the sector assignment by calculating a standard deviation (SD) and a mean for various categories of R values and applying t-test statistics; i.e. different levels of confidence can be assigned to determinations of whether a compound is emanating from the source sector. In addition, the use of duplicate sampling allows direct comparisons between nominally identical samples to indicate any systematic problems with the samplers.
Duplicate sampling The duplicate sampling throughout this experiment allowed direct comparison between 10 pairs of samples with up to 34 compounds per pair. In this case, corresponding IN samples were compared to each other, and corresponding OUT samples were compared to each other from side-by-side samplers operated over the same time period. The same calculation was applied to paired duplicate samples that was applied to the paired IN and O U T samples described above as Equation (1) (all data in ppbv): R =(IN1 - IN2)/(IN1 +IN2) or
R = (OUT 1 - OUT2)/(OUT 1 + OUT2) as appropriate. The data were filtered to remove concentration pairs at or below the nominal detection limit, which resulted in 296 data points that, in the absence of any sampling or analytical error, have an expected value of R = 0.
Data presentation Qualitative. The overall behavior of both the intrinsic errors and of the combined intrinsic errors plus source variability can be represented in graphic form with frequency distribution bar graphs. This requires that data from different compounds be composited to accumulate enough points to get a meaningful distribution. The resulting frequency distribution (R value interval vs number of occurrences) for the duplicate pairs is shown in Fig. 3; about 64% of the data is within the graph bin at the expected value of zero.
Spatially resolved monitoring for VOCs
743
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Fig. 3. Frequency distributions of R values of duplicate sample. R = 0 indicates no intrinsic sampling/analysis error. Mean R =0.016, SD =0.12
To compare the overall trends of the data, R values for corresponding I N / O U T samples were composited for those compounds that were found in the grab samples (see Table 1), and R values were separately composited for all other compounds. These frequency distributions are given in Figs 4a and 4b. As expected, the compounds in the grab samples exhibit a statistical shift to positive R (toward IN sector prevalence), whereas the distribution of the other compounds is centered about R = 0, indicating no sector prevalence. Quantitative. The probability that a given compound's average R value (R,ve) demonstrates a sector preference (is statistically different from the null hypothesis, R = 0) was calculated for confidences of 90, 95, and 99% for each site individually and for the combined data set. This was done by applying the t-test: t = Ravex/N/SD,
(2)
where N is the sample size and SD is the standard deviation of the R values. The t value can then be looked up in standard statistical tables to determine confidence. Compounds were then assigned to the appropriate sector, IN or OUT. The results are given in Table 2. A similar compilation of information for R values for the duplicate samples (comparing IN vs IN, and OUT vs O U T corresponding samples, as discussed above) is presented in Table 3. These data indicate sampling and analytical scatter and error trends. Table 4 contains the overall averages of concentrations for each compound to allow further interpretation of the statistical results.
Based upon Tables 2, 3 and 4 and the calculated means and standard deviations of the R values, the following additional filtering of the sector data was performed. • Dichloromethane data were deleted from final conclusions because duplicate precision data exhibited both a bias (Table 3), as well as high Rave and SD values; this indicates a systematic problem, possibly contamination in the samplers. • Benzene, toluene and 2-methylpentane data exhibit bias data in the 90 or 95% confidence intervals for their duplicate comparisons (Table 3). Because the corresponding R values were all less than 0.1 (very close to neutral bias), this indicates scatter in sideby-side sampling without appreciable systematic error. Also, examination of the raw data shows that these compounds were internally consistent within their respective samplers with respect to IN/OUT comparisons. Therefore, the data for these compounds were considered useful and included in the conclusions. • Ethyl chloride and vinylidene chloride were deleted from any final conclusions because their overall average concentrations throughout the study were well below the minimum quantifiable level (see Table 4) and they were found during only one sampling episode at low levels. • Though a few other compounds such as trichlorobenzene, n-octane and n-nonane exhibit low average concentrations in Table 4, these data were judged useful because these compounds were found in about half the samples at levels exceeding the minimum quantifiable level.
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tin - ourI R Value: R : Un + o . t j where In, Out are Concentrations of C o m p o u n d s from Canisters Corresponding to the In or Out Sector
Fig. 4. Frequency distributions of R values of IN vs OUT sector samples. Positive R values indicate IN sector prevalence. (A) Composite data for compounds found in grab samples near suspect source. (B) Composite data for all other detected compounds.
• Freon 113 measurements were anomalously high and exhibited large scatter; however, they showed no particular bias with respect to duplicates or sector preference. This could be a result of either highly variable local concentrations or a violent random sampling/analytical error. The data were retained because there was no unambiguous explanation to justify deletion. The final results indicate the following. • The most significant correlations are for site 2 (Table 2); this is expected because this site is closer to the sources, as seen in Fig. 2
e
e
M o s t of the positive correlation is for the aliphatic and aromatic hydrocarbons; the source area is dominated by petroleum product processing and storage. Freon 12 exhibits a definite O U T sector prevalence for site 1 at 95% confidence and a neutral sector prevalence for site 2. This points out the benefit of using at least two sites when sector-sampling air from a suspected source. In this case, the source of the Freon 12 is apparently somewhere in the O U T sector for site 1 and presumably far enough away from the source area to show no statistical relevance at site 2.
Spatially resolved monitoring for VOCs
745
Table 2. Sector sampling results: t-test confidences for IN or O U T sector assignments for individual compounds* Statistical significance Combined data 90% 95% 99%
Compound Freon 12 Dichloromethane Benzene Toluene Chlorobenzene Ethylbenzene m,p-Xylene o-Xylene 4-Ethyltoluene 1,3,5,-Trimethyl benzene p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene Isobutane Butane Isopentane Pentane Cyclopentane 2-Methylpentane 3-Methylpentane n-Hexane Methylcyclopentane Cyclohexane lsooctane n-Heptane n-Octane
--t . IN IN IN IN IN . .
-.
. .
IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN
--
OUT OUT . IN IN . . . . IN ----IN -. . . . . . .
IN IN IN IN IN
. IN IN -IN IN IN IN IN IN IN IN IN IN IN IN .
Site 1 95% 99%
90%
.
.
.
.
. IN . . --IN IN IN -IN IN -IN IN ---
IN IN IN IN IN IN -IN IN IN IN IN
.
.
-----
. IN . .
IN ---
IN IN IN -IN IN -IN
--
-IN
IN ---
-.
90% .
.
Site 2 95% 99%
IN IN IN IN IN IN IN IN
. -IN IN IN -IN IN --
IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN --
-IN IN -IN IN IN IN IN IN IN IN IN IN IN --
IN IN IN -IN --
IN IN IN IN IN IN IN IN IN --
.
*Compounds that were detected but displayed no significant (90%) sector prevalence: methyl chloride, ethyl chloride, Freon 11, vinylidene chloride, Freon 113, methylchloroform, carbon tetrachloride, 1,2,4-trimethylbenzene, n-nonane and n-decane. t No sector prevalence.
Table 3. Duplicate results: t-test confidences for comparing duplicate s a m p l e s - - I N vs IN or O U T vs O U T - - f r o m sector canisters for individual compounds
1,3,5-trimethylbenzene
Statistical significance
Compound Dichloromethane Benzene Toluene 2-Methylpentane
90%
Combined data 95%
99%
* * * *
* * -*
* ----
*Statistically significant error in side-by-side comparisons for these compounds. All other compounds in Table 2 demonstrate no such error.
CONCLUSIONS AND RECOMMENDATIONS G i v e n the d a t a f r o m T a b l e 2 a n d the d i s c u s s i o n a b o v e , o n e c a n c o n c l u d e r e a s o n a b l y t h a t the following V O C s are c a n d i d a t e e m i s s i o n s f r o m the s u s p e c t s o u r c e area. benzene toluene
chlorobenzene ethylbenzene m,p-xylene
isopentane pentane
p-dichlorobenzene o-dichlorobenzene trichlorobenzene isobutane butane
cyclopentane 2-methylpentane 3-methylpentane n-hexane methylcyclopentane cyclohexane isooctane n-heptane n-octane
W h e n this list is c o m p a r e d to the listing of d o m i n a n t V O C s f o u n d in the g r a b s a m p l e s (Table 1), we see t h a t every c o m p o u n d f r o m T a b l e 1 is identified as a n e m i s s i o n c a n d i d a t e by the s e c t o r s a m p l e r m e t h o d (with the e x c e p t i o n of d i c h i o r o m e t h a n e , w h i c h h a d b e e n eliminated f r o m c o n s i d e r a t i o n b e c a u s e of p o t e n tial p r o b l e m s m e n t i o n e d above). T h e a d d i t i o n a l c o m p o u n d s are likely either l o w e r level s t e a d y - s t a t e s o u r c e e m i s s i o n s t h a t did n o t exhibit g r e a t e r t h a n 1 0 - p p b v levels in the g r a b s a m p l e s , o r event releases t h a t were m i s s e d by the g r a b s a m p l e s . T h e sector s a m p l i n g m e t h o d o l o g y a n d the described d a t a r e d u c t i o n t e c h n i q u e s c a n p r o v i d e V O C d a t a t h a t indicates p r o b a b l e s o u r c e e m i s s i o n identifications, even at distances o f 1-3 miles. T h i s is a useful
746
J. D. PLEIL et al. Table 4. Average concentrations (ppbv) measured over the total data set* Compound Freon 12 Methyl chloride Ethyl chloride Freon 11 Vinylidene chloride Dichloromethane Freon 113 Methylchloroform Benzene Carbon tetrachloride Toluene Chlorobenzene Ethylbenzene m,p-Xylene o-Xylene 4-Ethyltoluene 1,3,5-Trimethylbenzene 1,2,4-Trimethylbenzene
Average (ppbv) 0.52 0.49 0.02 0.33 0.00 0.99 19.98 0.48 0.58 0.14 0.99 0.25 0.18 0.56 0.23 0.09 0.09 0.27
Compound Isobutane Butane Isopentane Pentane Cyclopentane 2-Methylpentane 3-Methylpentane n-Hexane Methylcyclopentane Cyclohexane Isooctane n-Heptane n-Octano n-Nonane n-Decane p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene
Average (ppbv) 1.20 2.22 2.37 1.05 0.09 0.53 0.37 0.32 0.17 0.15 0.19 0.11 0.07 0.05 0.22 0.61 0.13 0.04
*For compounds that were detected at least twice in the study; the average concentrations include zero values.
technique for b o t h s h o r t - t e r m V O C screening of suspect sources a n d for long-term m o n i t o r i n g of the c o n t r i b u t i o n s from a specific source. T h e m e t h o d is optimized by the use of multiple sites a n d by minimizing the distance between the receptor site a n d the suspect source; distance of ! mile or less are recommended. Acknowledgements--The authors thank the following individuals for their invaluable assistance during the performance of this work. Our hosts, Captain Joseph J. Kliment and Terri H. Brixen of the State of Delaware Department of Natural Resources, made all arrangements for field sites, provided office space for meetings, and made us feel welcome in Delaware. Matthias Yoong of Xontech Inc. provided his expert assistance in preparing the samplers and calibrating flows in the field. George M. Russwurm of ManTech Environmental Technology Inc. assisted in grab sampling, in operating the field sites and in preparing daily meteorological reports. The research described in this article has been funded in part by the United States Environmental Protection Agency through contract 68-02-4127 to Battelle Memorial Institute and contracts 68-02-4444 and 68-D0-0106 to ManTech Environmental Technology Inc. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. REFERENCES
Breiner S. J. and Buchanan J. W. (1984) Particle source assessment using wind switched high volume samplers and X-ray diffraction analysis. J. Air Pollut. Control Ass. 34, 1052-1055. Deyo J., Toma J. and King R. B. (1977) Development and testing of a portable wind sensitive directional air sampler. d. Air Pollut. Control Ass. 27, 142-144. EPA (1989) Method TO-14: the determination of volatile organic compounds (VOCs) in ambient air using SUMMA passivated canister sampling and gas chromatographic
analysis. In Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air. EPA600/4-89/017, U.S. Environmental Protection Agency, Research Triangle Park, NC. Kriews M., Nauman K. and Dannecker W. (1988) Aerosol specification in the southern North Sea region by wind dependent sampling and multielement analysis. J. Aerosol Sci. 19, 1051-1054. Lioy P. J., Sampson P. J., Tanner R. L., Minnich T. and Lyons W. (1980) The distribution and transport of sulfate species in the New York metropolitan area during the 1977 summer aerosol study. Atmospheric Environment 14, 1391-1407. McAllister R. A., Moore W. H., Rice J., Dayton D. P., Jongteaux R. F., O'Hara P. L., Merrill R. G. and Bursey J. (1989) 1988 Nonmethane organic compound and urban air toxics monitoring programs, final report. Contract 68-D80014 to EPA-AREAL, Radian Corporation, Research Triangle Park, NC. McClenny W. A., Pleil J. D., Holdren M. W. and Smith R. N. 0984) Automated cryogenic preconcentration and gas chromatographic determination of volatile organic compounds. Analyt. Chem. 56, 2947-2951. McClenny W. A., Pleil J. D., Lumpkin T. A. and Oliver K. D. (1987) Toxics monitoring with canister-based systems. 80th Annual Meeting of the Air Pollution Control Association, New York, 21-26 June. McClenny W. A., Oliver K. D. and Pleil J. D. (1989) A field strategy for sorting volatile organics into source related groups. Envir. Sci. Technol. 23, 1373-1379. Miller J. M. and Harris J. M. (1985) The flow climatology to Bermuda and its implication for long range transport. Atmospheric Environment 19, 409-414. Oliver K. D., Pleil J. D. and McClenny W. A. (1986) Sample integrity of trace level volatile organic compounds in ambient air stored in SUMMA polished canisters. Atmospheric Environment 20, 1403-1411. Pleil J. D., Oliver K. D. and McClenny W. A. (1987) Enhanced performance of Nation dryers in removing water from air samples prior to gas chromatogaphic analysis. J. Air Pollut. Control Ass. 37, 244-248. Pleil J. D., McClenny W. A. and Oliver K. D. (1988a) Wind direction dependent whole-air sampling for ambient
Spatially resolved monitoring for VOCs VOCs. 81st Annual Meeting and Exhibition of the Air Pollution Control Association, Dallas, TX, 19-24 June. Pleil J. D., Oliver K. D. and McClenny W. A. (1988b) Ambient air analyses using nonspecific flame ionization and electron capture detection compared to specific detection by mass spectroscopy. J. Air Pollut. Control Ass. 38, 1006-1010. Pleil J. D., McClenny W. A. and Oliver K. D. (1989) Temporal variability measurement of specific volatile or-
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ganic compounds. Int. J. envir, analyt. Chem. 37, 263-275. Smith D. L. and Holdren M. W. (1989) Method evaluation of TAMS network sampling, final report. Contract 68-024127 to EPA-AREAL, work assignment 66, Battelle Memorial Institute, Columbus, OH. Sperber K. R. (1987) The concentration and deposition of nitrate, sulfate, and ammonium as a function of wind direction from precipitation samples. Atmospheric Environment 21, 2629-2641.
0004 ~,981/93 $6.00+0.00 Pergamon Press Ltd
SPATIALLY RESOLVED MONITORING FOR VOLATILE ORGANIC C O M P O U N D S USING REMOTE SECTOR SAMPLING JOACHIM D. PLEIL a n d WILLIAM A. McCLENNY U.S. EPA, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC 27711, U.S.A. MICHAEL W. HOLDREN a n d ALBERT J. POLLACK Battelle Memorial Institute, Columbus, OH 43201-2693, U.S.A. and KAREN D. OLIVER ManTech Environmental Technology Inc., Research Triangle Park, NC 27709, U.S.A. (First received 13 December 1991 and in final form 29 October 1992)
Abstract--Sector sampling for volatile organic compounds (VOCs) is conducted within an integrated sampling scheme and relies on a wind direction sensor. The wind sensor directs whole air, sampled at a constant rate, into either an "IN" sector canister or an "OUT" sector canister; when the wind comes from the suspected emissions area, sample is routed into the IN sector canister; otherwise, sample is collected in the OUT sector canister. This method is analogous to "upwind/downwind" sampling but does not require two distinct sites or manual sampler control. For this set of experiments, the IN and OUT sectors were 90 and 270 °, respectively, and the IN sector was centered on the VOC source. Two sampling sites were used. The first was about 2 miles north-northeast of a group of industrial facilities, and the second was located about 1 mile south-southeast of the same sources. Sites were operated concurrently with one sampler each; a third sampler was rotated between the sites to obtain duplicate samples. The air samples were analysed by gas chromatography for VOCs. The resultant data comparisons between IN and OUT duplicate samples show good correlation with expected VOC emissions, which were determined by grab samples within the target area. A t-test method for interpreting the sometimes subtle differences between IN and OUT sample data is presented. Key word index: Volatile organic compounds, wind direction, sector sampling, ambient air analysis, ambient air sampling.
INTRODUCTION
The U.S. Environmental Protection Agency (EPA) has developed methodology over the past 6 years for the trace-level determination of certain nonpolar volatile organic compounds (VOCs) in the ambient air. The methodology is based upon the use of S U M M A ®polished canisters for collecting whole-air samples for subsequent laboratory analysis. Details of the method are available in Method TO- 14 of E P A (1989). Typical applications of TO-14 involve time-integrated point monitoring, usually for 24-h periods, as in EPA's Toxic Air Monitoring System (TAMS) network (Smith and Holdren, 1989) or the U r b a n Air Toxics Monitoring Program (McAllister et al., 1989). Although point monitoring has proven to be useful in documenting V O C concentrations, more sophisticated monitoring strategies have been under development to produce additional information such as time variability of V O C concentrations and V O C source locations. This has involved semi-real-time, in situ 739
V O C determination with concurrent wind speed and direction measurement to allow calculation of back trajectories of VOCs (McClenny et al., 1989), timedependent measurements in indoor air situations (Pleil et al., 1989) and wind-direction-dependent V O C measurement (Pleil et al., 1988a). The correlation between wind direction (transport) and the concentration of specific airborne pollutants has been reported in the literature primarily for the study of aerosols. F o r instance, Lioy et al. (1980), Miller and Harris (1985) and Sperber (1987), as well as others, have dealt with the deposition of acidic species as a function of wind data. Kriews et al. (1988), Deyo et al. (1977) and Breiner and Buchanan (1984) have reported the use of automated wind-directionsensitive samplers for the selective collection of particulate matter. This work describes the use of a spatially resolved air monitoring strategy for inferring V O C emissions from suspect sources. The strategy is similar to the aerosol strategies mentioned above; concentrations
740
J.D. PLEILet al.
are correlated with meteorology to infer source information. Ambient air is routed into one of two containers in the sampler: the " I N " container if the wind is blowing from a fixed spatial (directional) sector that includes the suspected V O C source(s), or the " O U T " container if it is blowing from another direction. The differences between the I N sector sample and the O U T sector sample at a single monitoring site indicate candidate V O C s emanating from the I N sector source. In effect, this "sector method" provides its own background data via the O U T sector sample and, as such, can be considered a substitute for the more conventional "upwind/downwind" sampiing, which would require relocating sampling sites and other operator intervention as a response to changing wind conditions. To corroborate the identification of source-related VOCs, additional sites can be operated simultaneously at other directions from the suspect source. This methodology was tested in an airshed containing an extended cluster of industrial V O C sources. Two fixed monitoring sites were used, one about 2 miles north-northeast, and the second about 1 mile south-southeast from the center of the industrial area. Each site was operated in duplicate for part of the time. In addition, various grab samples were taken close to the sources. The resulting samples were analysed for a variety of nonpolar volatile organic "toxics" from the M e t h o d TO-14 list (EPA, 1989) and for C 4 - C l o hydrocarbons. A data interpretation method based on t-test statistics is presented for the sector method to deduce the trace-level contributions of the suspected source area to the receptor sites. The grab sample results are presented to confirm these deductions.
EXPERIMENTAL All samples were collected in 6-d stainless steel SUMMA canisters, which have internally passivated surfaces. Canisters were cleaned prior to sample collection by evacuation to less than 50 #m Hg at an elevated temperature of 80-100°C. Quality assurance of the clean-up procedure was performed on a subset of these canisters by filling them with humidified zero air and analysing for residual contamination. For collecting field samples, the canisters were always used by starting with the clean-up vacuum ( < 50/~m Hg). Details of the use of various configurations of these sampling devices and the associated compound storage stability are available in Method TO-14 (EPA, 1989); some additional representative publications are given in the reference section (McClenny et al., 1987; Oliver et al., 1986, and references therein). The canisters used for this project were from two manufacturers: SIS Inc. (Moscow, ID) and Biospherics Inc. (Hillsboro, OR). The sector samplers consisted of a conventional actively pumped flow configuration (McClenny et al., 1987) with the exception that a three-way valve allowed the routing of the air sample flow to either of two canisters. The destination of the sample flow was dependent upon the wind direction; that is, its routing to one of the two sampling canisters depended upon a signal from a wind sensor that determined whether the air was coming from the suspect source (IN sector) or not (OUT sector). A diagram of this configuration is presented in Fig. 1. Three sector samplers from Xontech Inc. (Van Nuys, CA), were used in this study. Sample flow rate was maintained at about 10.5 cm 3 min- 1, which would result in a final canister pressure of 20 psig after 24 h if the wind sector never changed. Usually, when the wind was variable, more time was required to fill the IN sector canister because the respective sector was smaller than that for the OUT canister (90 vs 270°); sometimes two OUT sector samples were collected before a sufficient volume of IN sample was collected for analysis. For the first two tests, site 1 was operated in duplicate with samplers 1 and 3, while site 2 was concurrently operated with sampler 2. For the remaining two tests, sampler 3 was moved to site 2 and operated in duplicate with sampler 2, and sampler 1 was operated concurrently at site 1.
WindDirectionSensor Sample Inlet
Electronics:Timer,SolenoidCont WindDirectionDecoderr~l~"~ -~
Pressure Regulator
Pump ~
Vent ExcessFlow
P P rue s a u r e G IIa ~
~ In
SUMMACane Out
Fig. 1. Schematic of sector sampler flow arrangement.
Spatially resolved monitoring for VOCs
741
1. Chlorinated Chemical Manufacturer 2. Power Plant (Coal) 3. Oil Refinery 4. Oil Tank Farm 5. Chemical Company 6. Liquified Gas Manufacturer 7. Plastics Manufacturer 8. Truck Depot
Fig. 2. Diagram of relative locations of sector sampling sites and the suspect sources.
The sites were provided by the state of Delaware for our experiments; both were state monitoring stations where our sampling inlets and wind gear could be mounted on the roof, about 12 ft off the ground. Each had electrical power available for the samplers. The sites were in the New Castle area near a variety of industrial sources including a coal-fired power plant, an oil refinery, a petroleum product storage and transfer facility, various chemical manufacturers and two plastics manufacturing plants. The samplers were operated with 90 ° IN sectors (with corresponding 270 ° OUT sectors). These sectors were chosen empirically to optimize collection of sample; a smaller IN sector would improve spatial selectivity but sacrifice the amount and time of sample collection (statistically), whereas a larger IN sector would have the opposite trade-off. A diagram of the relative locations of the sources, sampling sites and the IN sectors is given in Fig. 2. All analyses were performed by gas chromatography/mass spectrometry (GC/MS; Hewlett-Packard model HP-5880A gas chromatograph and model HP-5970B mass selective detector, Hewlett-Packard, Avondale, PA, and Palo Alto, CA, respectively) in the selected ion monitoring mode for the TO-14 (EPA, 1989) list of 40 nonpolar VOCs. In addition, a flame ionization detector (FID) was used in parallel at the column outlet to determine nontarget compounds, primarily the C4~2~o aikanes. The sample was preconcentrated with a Nutech 320-1 cryogenic focussing unit (Nutech Inc., Durham, NC) and separated on a 50 m, 0.32 mm i.d., HP-1 fusedsilica capillary column. Some of the grab samples taken close to the sources were analysed by GC/MS scan methods (28.5-350 amu) to identify all major organic constituents. The higher level compounds were quantified with a second GC (also an HP-5880A), equipped with an FID and an electron capture detector. The general analytical methods for these analyses are presented in the literature (McClenny et al., 1984; Pleil et al., 1987, 1988b); the specific analytical pro-
tocols used for the sector sample analyses are identical to those for EPA's TAMs network and are described in detail in the current TAMS report (Smith and Holdren, 1989). RESULTS AND DISCUSSION
Grab samples G r a b samples showed two specific groups of compounds, depending u p o n location; typical ranges of c o n c e n t r a t i o n s are given in Table 1 for samples t a k e n near the t a n k farm a n d near the chlorinated chemical manufacturer. These c o m p o u n d s were extremely variable in time, a n d we collected g r a b samples only when there were indications t h a t a n "event" was occurring, such as smells, d a t a from real-time i n s t r u m e n t a t i o n or obvious plumes. M o s t of these samples were t a k e n near the chlorinated chemical plant, a n d the results are from mass spectrometry data. The h y d r o c a r b o n d a t a are from two g r a b samples t a k e n o n a r o a d w a y near the oil storage facility. Only c o m p o u n d s t h a t exceeded 10 p p b v in at least one sample are listed in the table.
Sector samples--data reduction The d a t a from the sector samplers is b r o k e n d o w n into two categories; (1) mass spectrometry d a t a for "target c o m p o u n d s , " i.e. the toxic V O C s as listed in M e t h o d TO-14 (EPA, 1989); (2) d a t a for the h y d r o c a r b o n s that were t a k e n from F I D traces a n d quantified
742
J. D. PLEILet al. Table 1. Results (ppbv) from grab samples taken at the fenceline of two facilities: compounds found at levels higher than typical background concentrations Chlorinated c.hemical manufacturer (10 grab samples) Dichloromethane Benzene Chlorobenzene p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene
Petroleum products storage and handling facility (2 grab samples) 1-12 1-51 7-260 12-247 4-154 1-41
with respect to a calibrated benzene response. Per carbon benzene response for FID has historically been used in our laboratory because of the availability of a variety of NBS or NIST traceable standards. Cursory inspection of the concentration data reveals that differences between the IN and O U T paired sector samples are subtle and that the concentrations at the sites 1-3 miles away from specific sources are essentially at background levels, generally not exceeding 2 ppbv. To allow the extraction of useful information from this data set, a normalized ratio parameter for comparing paired IN and O U T sector data on a per compound basis was defined as R = ( I N - OUT)/(IN + OUT),
(1)
where IN and O U T are concentrations in ppbv for individual compounds. The parameter R can range from - 1 to +1. Positive R values indicate an IN sector prevalence, whereas negative values indicate an O U T sector prevalence. The absolute value of R gives a relative measure of the importance of a compound in a sector. Because this is a "ratio" parameter, some consideration must be given to dealing with very low concentration values, i.e. when the minimum quantifiable level is approached, the point must be determined at which a concentration should be considered zero rather than some minimum value. These decisions can easily change the calculated R from 0 to + 1. This was handled by rounding all data to'the nearest 0.1 ppbv (defined as our minimum quantifiable level for this project), generating all the R values, and then generating overall averages of concentrations for each compound from the sector samples. For compounds that exhibited average concentrations at or below the minimum quantifiable level, the R values were not considered strong indicators of sector prevalence. Also, when the (IN, OUT) concentration data in ppbv were (0, 0.1), (0, 0) or (0.1, 0), the R values were not included in the statistical treatment. Even in the absence of any sources, one expects scatter in the R values from the intrinsic sampling and analytical precision errors. Scatter in R for a specific compound from repeated measurements is also expected because the sources are likely to be variable in time.
Butane Isopentane Pentane 2-Methylpentane 3-Methylpentane Methylcyclopentane
22, 103 13, 444 7, 200 2, 50 2, 25 1, 13
To account for the combination of error and variability, we can determine a statistical level of significance for the sector assignment by calculating a standard deviation (SD) and a mean for various categories of R values and applying t-test statistics; i.e. different levels of confidence can be assigned to determinations of whether a compound is emanating from the source sector. In addition, the use of duplicate sampling allows direct comparisons between nominally identical samples to indicate any systematic problems with the samplers.
Duplicate sampling The duplicate sampling throughout this experiment allowed direct comparison between 10 pairs of samples with up to 34 compounds per pair. In this case, corresponding IN samples were compared to each other, and corresponding OUT samples were compared to each other from side-by-side samplers operated over the same time period. The same calculation was applied to paired duplicate samples that was applied to the paired IN and O U T samples described above as Equation (1) (all data in ppbv): R =(IN1 - IN2)/(IN1 +IN2) or
R = (OUT 1 - OUT2)/(OUT 1 + OUT2) as appropriate. The data were filtered to remove concentration pairs at or below the nominal detection limit, which resulted in 296 data points that, in the absence of any sampling or analytical error, have an expected value of R = 0.
Data presentation Qualitative. The overall behavior of both the intrinsic errors and of the combined intrinsic errors plus source variability can be represented in graphic form with frequency distribution bar graphs. This requires that data from different compounds be composited to accumulate enough points to get a meaningful distribution. The resulting frequency distribution (R value interval vs number of occurrences) for the duplicate pairs is shown in Fig. 3; about 64% of the data is within the graph bin at the expected value of zero.
Spatially resolved monitoring for VOCs
743
190 180 170 160 150 140 130 120 ¢1 110 100 0 90 '5 80 70 ,,Q E 60 z 50 40 30 2O 10
oo
0
- I .00
--0.80-o.oo-0.40--o.2o
o.oo
0.20
0.40
0.60
o.oo
1.00
R value: R = ~ J w h e r e S 1, S 2 are concentrations of compounds collected in duplicate
Fig. 3. Frequency distributions of R values of duplicate sample. R = 0 indicates no intrinsic sampling/analysis error. Mean R =0.016, SD =0.12
To compare the overall trends of the data, R values for corresponding I N / O U T samples were composited for those compounds that were found in the grab samples (see Table 1), and R values were separately composited for all other compounds. These frequency distributions are given in Figs 4a and 4b. As expected, the compounds in the grab samples exhibit a statistical shift to positive R (toward IN sector prevalence), whereas the distribution of the other compounds is centered about R = 0, indicating no sector prevalence. Quantitative. The probability that a given compound's average R value (R,ve) demonstrates a sector preference (is statistically different from the null hypothesis, R = 0) was calculated for confidences of 90, 95, and 99% for each site individually and for the combined data set. This was done by applying the t-test: t = Ravex/N/SD,
(2)
where N is the sample size and SD is the standard deviation of the R values. The t value can then be looked up in standard statistical tables to determine confidence. Compounds were then assigned to the appropriate sector, IN or OUT. The results are given in Table 2. A similar compilation of information for R values for the duplicate samples (comparing IN vs IN, and OUT vs O U T corresponding samples, as discussed above) is presented in Table 3. These data indicate sampling and analytical scatter and error trends. Table 4 contains the overall averages of concentrations for each compound to allow further interpretation of the statistical results.
Based upon Tables 2, 3 and 4 and the calculated means and standard deviations of the R values, the following additional filtering of the sector data was performed. • Dichloromethane data were deleted from final conclusions because duplicate precision data exhibited both a bias (Table 3), as well as high Rave and SD values; this indicates a systematic problem, possibly contamination in the samplers. • Benzene, toluene and 2-methylpentane data exhibit bias data in the 90 or 95% confidence intervals for their duplicate comparisons (Table 3). Because the corresponding R values were all less than 0.1 (very close to neutral bias), this indicates scatter in sideby-side sampling without appreciable systematic error. Also, examination of the raw data shows that these compounds were internally consistent within their respective samplers with respect to IN/OUT comparisons. Therefore, the data for these compounds were considered useful and included in the conclusions. • Ethyl chloride and vinylidene chloride were deleted from any final conclusions because their overall average concentrations throughout the study were well below the minimum quantifiable level (see Table 4) and they were found during only one sampling episode at low levels. • Though a few other compounds such as trichlorobenzene, n-octane and n-nonane exhibit low average concentrations in Table 4, these data were judged useful because these compounds were found in about half the samples at levels exceeding the minimum quantifiable level.
744
J.D. PLEILet al. 34 32 3O
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-0.40
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o
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E
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tin - ourI R Value: R : Un + o . t j where In, Out are Concentrations of C o m p o u n d s from Canisters Corresponding to the In or Out Sector
Fig. 4. Frequency distributions of R values of IN vs OUT sector samples. Positive R values indicate IN sector prevalence. (A) Composite data for compounds found in grab samples near suspect source. (B) Composite data for all other detected compounds.
• Freon 113 measurements were anomalously high and exhibited large scatter; however, they showed no particular bias with respect to duplicates or sector preference. This could be a result of either highly variable local concentrations or a violent random sampling/analytical error. The data were retained because there was no unambiguous explanation to justify deletion. The final results indicate the following. • The most significant correlations are for site 2 (Table 2); this is expected because this site is closer to the sources, as seen in Fig. 2
e
e
M o s t of the positive correlation is for the aliphatic and aromatic hydrocarbons; the source area is dominated by petroleum product processing and storage. Freon 12 exhibits a definite O U T sector prevalence for site 1 at 95% confidence and a neutral sector prevalence for site 2. This points out the benefit of using at least two sites when sector-sampling air from a suspected source. In this case, the source of the Freon 12 is apparently somewhere in the O U T sector for site 1 and presumably far enough away from the source area to show no statistical relevance at site 2.
Spatially resolved monitoring for VOCs
745
Table 2. Sector sampling results: t-test confidences for IN or O U T sector assignments for individual compounds* Statistical significance Combined data 90% 95% 99%
Compound Freon 12 Dichloromethane Benzene Toluene Chlorobenzene Ethylbenzene m,p-Xylene o-Xylene 4-Ethyltoluene 1,3,5,-Trimethyl benzene p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene Isobutane Butane Isopentane Pentane Cyclopentane 2-Methylpentane 3-Methylpentane n-Hexane Methylcyclopentane Cyclohexane lsooctane n-Heptane n-Octane
--t . IN IN IN IN IN . .
-.
. .
IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN
--
OUT OUT . IN IN . . . . IN ----IN -. . . . . . .
IN IN IN IN IN
. IN IN -IN IN IN IN IN IN IN IN IN IN IN IN .
Site 1 95% 99%
90%
.
.
.
.
. IN . . --IN IN IN -IN IN -IN IN ---
IN IN IN IN IN IN -IN IN IN IN IN
.
.
-----
. IN . .
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IN IN IN -IN IN -IN
--
-IN
IN ---
-.
90% .
.
Site 2 95% 99%
IN IN IN IN IN IN IN IN
. -IN IN IN -IN IN --
IN IN IN IN IN IN IN IN IN IN IN IN IN IN IN --
-IN IN -IN IN IN IN IN IN IN IN IN IN IN --
IN IN IN -IN --
IN IN IN IN IN IN IN IN IN --
.
*Compounds that were detected but displayed no significant (90%) sector prevalence: methyl chloride, ethyl chloride, Freon 11, vinylidene chloride, Freon 113, methylchloroform, carbon tetrachloride, 1,2,4-trimethylbenzene, n-nonane and n-decane. t No sector prevalence.
Table 3. Duplicate results: t-test confidences for comparing duplicate s a m p l e s - - I N vs IN or O U T vs O U T - - f r o m sector canisters for individual compounds
1,3,5-trimethylbenzene
Statistical significance
Compound Dichloromethane Benzene Toluene 2-Methylpentane
90%
Combined data 95%
99%
* * * *
* * -*
* ----
*Statistically significant error in side-by-side comparisons for these compounds. All other compounds in Table 2 demonstrate no such error.
CONCLUSIONS AND RECOMMENDATIONS G i v e n the d a t a f r o m T a b l e 2 a n d the d i s c u s s i o n a b o v e , o n e c a n c o n c l u d e r e a s o n a b l y t h a t the following V O C s are c a n d i d a t e e m i s s i o n s f r o m the s u s p e c t s o u r c e area. benzene toluene
chlorobenzene ethylbenzene m,p-xylene
isopentane pentane
p-dichlorobenzene o-dichlorobenzene trichlorobenzene isobutane butane
cyclopentane 2-methylpentane 3-methylpentane n-hexane methylcyclopentane cyclohexane isooctane n-heptane n-octane
W h e n this list is c o m p a r e d to the listing of d o m i n a n t V O C s f o u n d in the g r a b s a m p l e s (Table 1), we see t h a t every c o m p o u n d f r o m T a b l e 1 is identified as a n e m i s s i o n c a n d i d a t e by the s e c t o r s a m p l e r m e t h o d (with the e x c e p t i o n of d i c h i o r o m e t h a n e , w h i c h h a d b e e n eliminated f r o m c o n s i d e r a t i o n b e c a u s e of p o t e n tial p r o b l e m s m e n t i o n e d above). T h e a d d i t i o n a l c o m p o u n d s are likely either l o w e r level s t e a d y - s t a t e s o u r c e e m i s s i o n s t h a t did n o t exhibit g r e a t e r t h a n 1 0 - p p b v levels in the g r a b s a m p l e s , o r event releases t h a t were m i s s e d by the g r a b s a m p l e s . T h e sector s a m p l i n g m e t h o d o l o g y a n d the described d a t a r e d u c t i o n t e c h n i q u e s c a n p r o v i d e V O C d a t a t h a t indicates p r o b a b l e s o u r c e e m i s s i o n identifications, even at distances o f 1-3 miles. T h i s is a useful
746
J. D. PLEIL et al. Table 4. Average concentrations (ppbv) measured over the total data set* Compound Freon 12 Methyl chloride Ethyl chloride Freon 11 Vinylidene chloride Dichloromethane Freon 113 Methylchloroform Benzene Carbon tetrachloride Toluene Chlorobenzene Ethylbenzene m,p-Xylene o-Xylene 4-Ethyltoluene 1,3,5-Trimethylbenzene 1,2,4-Trimethylbenzene
Average (ppbv) 0.52 0.49 0.02 0.33 0.00 0.99 19.98 0.48 0.58 0.14 0.99 0.25 0.18 0.56 0.23 0.09 0.09 0.27
Compound Isobutane Butane Isopentane Pentane Cyclopentane 2-Methylpentane 3-Methylpentane n-Hexane Methylcyclopentane Cyclohexane Isooctane n-Heptane n-Octano n-Nonane n-Decane p-Dichlorobenzene o-Dichlorobenzene Trichlorobenzene
Average (ppbv) 1.20 2.22 2.37 1.05 0.09 0.53 0.37 0.32 0.17 0.15 0.19 0.11 0.07 0.05 0.22 0.61 0.13 0.04
*For compounds that were detected at least twice in the study; the average concentrations include zero values.
technique for b o t h s h o r t - t e r m V O C screening of suspect sources a n d for long-term m o n i t o r i n g of the c o n t r i b u t i o n s from a specific source. T h e m e t h o d is optimized by the use of multiple sites a n d by minimizing the distance between the receptor site a n d the suspect source; distance of ! mile or less are recommended. Acknowledgements--The authors thank the following individuals for their invaluable assistance during the performance of this work. Our hosts, Captain Joseph J. Kliment and Terri H. Brixen of the State of Delaware Department of Natural Resources, made all arrangements for field sites, provided office space for meetings, and made us feel welcome in Delaware. Matthias Yoong of Xontech Inc. provided his expert assistance in preparing the samplers and calibrating flows in the field. George M. Russwurm of ManTech Environmental Technology Inc. assisted in grab sampling, in operating the field sites and in preparing daily meteorological reports. The research described in this article has been funded in part by the United States Environmental Protection Agency through contract 68-02-4127 to Battelle Memorial Institute and contracts 68-02-4444 and 68-D0-0106 to ManTech Environmental Technology Inc. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. REFERENCES
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