Application of Standardized Quality Control Procedures to Open-Path

Apr 4, 2001 - Application of Standardized Quality Control Procedures to Open-Path Fourier Transform Infrared Data Collected at a Concentrated Swine Pr...
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Environ. Sci. Technol. 2001, 35, 1859-1866

Application of Standardized Quality Control Procedures to Open-Path Fourier Transform Infrared Data Collected at a Concentrated Swine Production Facility JEFFREY W. CHILDERS* ManTech Environmental Technology, Incorporated, P.O. Box 12313, Research Triangle Park, North Carolina 27709 EDGAR L. THOMPSON, JR., D. BRUCE HARRIS, AND DAVID A. KIRCHGESSNER National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711 MATTHEW CLAYTON AND DAVID F. NATSCHKE ARCADIS Geraghty and Miller, Incorporated, P.O. Box 13109, Research Triangle Park, North Carolina 27709 WILLIAM J. PHILLIPS SpectraSoft Technology, P.O. Box 1596, Tullahoma, Tennessee 37388

Open-path Fourier transform infrared (OP/FT-IR) spectrometry was used to measure the concentrations of ammonia, methane, and other atmospheric gases at a concentrated swine production facility. A total of 2200 OP/FT-IR spectra were acquired along nine different monitoring paths during an 8-day period between January 11 and 22, 1999. Standardized quality control (QC) procedures were applied to the archived OP/FT-IR spectra to verify that the instrument was set up and operating properly during the field study and to identify outliers in the concentration data. These QC procedures included measuring the random baseline noise, the signal strength, and the relative singlebeam intensity in selected wavenumber regions; inspecting the archived spectra for wavenumber shifts, changes in resolution, and evidence of detector saturation; and examining time series plots of the target gas concentrations and the uncertainty values reported by the classical leastsquares (CLS) analysis. Application of these QC procedures to the archived spectra identified 252 potential outliers. After a careful review of the original spectra, 41 of the 252 suspected outliers were designated as actual outliers. Of the QC criteria used during this study, the uncertainty values reported by the CLS analysis were the most reliable indicators of actual outliers.

regional environment (1). One concern is the emission and transport of atmospheric gases from these facilities (2). Measuring gaseous emissions from concentrated swine production operations is challenging because of the size and diversity of the emission sources at these sites. An openpath Fourier transform infrared (OP/FT-IR) system can be used to advantage in these applications because it is capable of measuring the concentrations of several target gases simultaneously over a long path. This capability eliminates the need for an extensive point monitor network. Also, the equipment is transportable, so several paths can be monitored sequentially during a field study. The Air Pollution Prevention and Control Division of the National Risk Management Research Laboratory of the U.S. Environmental Protection Agency conducted a field study from January 11 to 22, 1999, to evaluate OP/FT-IR spectrometry for measuring the concentrations of ammonia (NH3), methane (CH4), and other atmospheric gases at a commercial swine production operation in eastern North Carolina. This study was part of the state of North Carolina’s Trace Gas and Nutrient Dynamics Measurement Project (3). A description of the site, instrumental setup, and preliminary concentration data has been reported elsewhere (4). The goals of this study were to determine the proper procedures for setting up the OP/FT-IR system with respect to the complex site geometry and to evaluate the capabilities of single-path OP/FT-IR measurements for estimating emission factors for NH3 and CH4. For estimates of emission factors to be credible, the concentration data produced by the OP/FT-IR system must be of a known quality. Several groups have contributed to the development of quality control (QC) procedures for assessing the quality of OP/FT-IR data. Weber et al. (5) identified the need for performance characteristics in optical remote sensing and discussed the applicability of existing International Organization for Standardization methods to the remote sensing of air pollutants. Kricks et al. (6) discussed quality assurance (QA) issues related to the operation of OP/FT-IR spectrometers during field applications and identified several potential sources of error in the measurements. DuBois et al. (7) reviewed the results of QA programs implemented for OP/ FT-IR projects at 20 industrial facilities and hazardous waste sites throughout the U.S. over a 5-year period. Childers et al. (8) proposed QC procedures for a long-term OP/FT-IR monitoring program. An EPA-sponsored field audit based on many of these proposed QC procedures has been conducted (9). These combined efforts led to the development of standardized QC procedures for OP/FT-IR measurements that are described in a guidance document (10), an ASTM guide (11), and an ASTM standard practice (12). A method for producing concentration data from OP/FT-IR spectra, along with relevant QC procedures, has also been developed (13). The QC procedures described in these documents were applied to archived OP/FT-IR data collected at a concentrated swine production facility to verify that the instrument was set up and operating properly during the field study and to identify potential outliers in the spectral data.

Experimental Section

Introduction

Data Acquisition and Analysis Procedures. The experimental setup and data acquisition parameters are described in detail elsewhere (4). Briefly, the spectra were acquired with a

The proliferation of concentrated swine production operations in the coastal plains of North Carolina has led to concerns regarding the impact of these facilities on the

* Corresponding author phone: (919) 541-3005; fax: (919) 5413566; e-mail: [email protected].

10.1021/es001744f CCC: $20.00 Published on Web 04/04/2001

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monostatic OP/FT-IR system (AIL Systems, Inc., Deer Park, NY). Each spectrum was averaged for a 1-min scan time, zero-filled by an additional factor of 2, apodized with a triangular function, and then transformed to yield a singlebeam spectrum with a nominal 0.5-cm-1 resolution. The single-beam spectra were corrected for stray light, ratioed to a short-path background spectrum to produce transmittance spectra, and then converted into absorption spectra for further analysis. The concentrations of the target gases in the field spectra were determined by using the AutoQuant 3 (Rev. C) CLS software package (MIDAC Corporation, Irvine, CA). Three analysis regions were defined in the classical least-squares (CLS) method: (1) 901.6 to 980.7 cm-1 for NH3, water (H2O) vapor, and the tracer gas ethylene (C2H4) or sulfur hexafluoride (SF6); (2) 2075.5 to 2223.6 cm-1 for carbon monoxide (CO), carbon dioxide (CO2), nitrous oxide (N2O), and H2O vapor; and (3) 2888.9 to 2929.4 cm-1 for CH4 and H2O vapor. A linear baseline correction was included in the CLS method to account for linear baseline trends in the field spectra over each analysis region. Synthetic reference spectra for all of the target gases except SF6 were generated from the HITRAN database using Etrans (Ontar Corporation, North Andover, MA) to match the net absorbance of the target gases in the field spectra. The procedure for generating these reference spectra is described elsewhere (4). Reference spectra for SF6 were taken from the spectral library provided with AutoQuant 3. In addition to the CLS analysis method, an innovative nonlinear algorithm was used to determine the target gas concentrations in the field spectra (14). This algorithm fits a series of segmented polynomial curves to the envelope of each single-beam field spectrum to generate a synthetic background spectrum and then performs an iterative fit of the convolved spectral line data from Etrans to the background-fitted single-beam field spectra. Selected outputs from this full spectrum analysis algorithm were used for QC purposes. Quality Control Procedures. The QC procedures described in standard documents (10-13) were applied to the archived field spectra. Time series plots of each QC parameter were developed for each monitoring session. Control limits for these parameters were defined as x ( 2.7σ, where x and σ are the mean and standard deviation, respectively, of the selected QC parameter during each monitoring session. For data that follow a normal distribution, a value outside of these control limits has a probability of 0.007 of being a member of the data set (15). The original spectra associated with the data points identified as potential outliers by these tests were carefully reviewed to determine if they were actual outliers or should be included in the final data set. The outlier tests were applied manually to archived data in this study, but they could be automated to be included in real-time monitoring software. Random Baseline Noise. For this test, absorption spectra were created from the stray-light-corrected single-beam field spectra by using spectrum #1 as a background spectrum for spectrum #2, spectrum #2 as a background spectrum for spectrum #3, etc., until absorption spectra were created for each data file in the time series. The random baseline noise in these absorption spectra was estimated by calculating the root-mean-square (RMS) deviation over three wavenumber ranges: 979-991, 2494-2506, and 4394-4406 cm-1. The wavenumber ranges for these measurements were chosen to minimize the contribution of changing gas concentrations to the calculated RMS deviation. Signal Strength and Relative Single-Beam Intensity. The signal intensity in each stray-light-corrected single-beam spectrum was measured near 985, 2500, and 4400 cm-1. The single-beam intensities measured at 2500 and 4400 cm-1 1860

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were ratioed to that measured at 985 cm-1, which is the most intense region of the single-beam spectrum for this particular instrument, to determine if the profile of the single-beam spectrum had changed during each monitoring session. The signal intensity at 650 cm-1 was also inspected for evidence of detector saturation. Wavenumber Shifts and Changes in Resolution. The field absorption spectra were examined for wavenumber shifts relative to reference spectra of the target gases before conducting the CLS analyses. If necessary, the reference spectra were shifted by the appropriate amount of wavenumbers to match the positions of the absorption bands of the target gases in the field spectra. Wavenumber shifts and changes in resolution in the series of single-beam spectra collected during each monitoring session were identified by examining the output parameters of the nonlinear algorithm. As part of the fitting procedure for this algorithm the actual resolution of the field spectra and the magnitude of the wavenumber shift required to match the field spectra to the output of Etrans are calculated. Time Series Plots of Concentration Data. Time series plots of the concentrations of each target gas during each monitoring period were constructed. These plots were inspected for abrupt changes or unexpected trends. Anomalous data points or time intervals during which the instrument exhibited an irregular response were identified and annotated. The concentrations of the permanent gases, for example N2O and CH4, in the OP/FT-IR spectra measured at the ambient background sites and along specific monitoring paths were compared to the estimated global background average for that gas. The concentrations of the target gases were also examined for any correlation with changes in the concentrations of interfering species. Uncertainty Values Reported by the CLS Analysis. Potential outliers were also identified by examining the uncertainty values reported by the CLS algorithm. Uncertainty values that fell outside of the control limits were identified and annotated as suspected outliers. The residual spectra associated with the suspected outliers were inspected for evidence of excessive noise, wavenumber shifts, changes in resolution, baseline deviations, or the presence of unidentified interfering species. If inspection of the residual spectra did not reveal the cause of the high uncertainty values, the original absorption spectra, single-beam spectra, or interferograms were examined for anomalous features.

Results and Discussion The OP/FT-IR data were acquired along nine different monitoring paths, including those impacted by emissions from the farrowing/nursery barns, the finishing barns, and the waste lagoon, under a variety of meteorological conditions, during this 8-day study. Thirty-eight separate monitoring sessions were conducted in which a total of 2200 spectra were acquired. The concentrations of each target gas exhibited a wide dynamic range throughout the study and often fluctuated during any given monitoring period. The following QC procedures were applied to the archived OP/ FT-IR spectra to determine if these fluctuations were real or were caused by an instrument malfunction or an artifact in the analysis method. Random Baseline Noise. The magnitude of the RMS deviation represents a combination of the instrument noise (e.g., the detector preamplifier noise), intensity perturbations caused by scintillation in the atmosphere, and changing gas concentrations along the path. The mean RMS deviation (reported in absorbance units) measured for all of the spectra collected during the study was 0.0011 ( 0.0005 between 979 and 991 cm-1; 0.0010 ( 0.0004 between 2494 and 2506 cm-1; and 0.0109 ( 0.0056 between 4394 and 4406 cm-1. In general, the magnitude of the RMS deviation increased with increasing

FIGURE 1. Plot of the RMS deviation between 2494 and 2506 cm-1 including the ( 2.7σ control limits for OP/FT-IR data collected along a 228-m physical path between the farrowing/nursery barns and the finishing barns on January 11, 1999. path length. A total of 120 different spectra were identified as potential outliers by the combined RMS deviation tests in all three spectral regions: 60 spectra were identified as outliers from the RMS deviation measured between 979 and 991 cm-1, 42 were found between 2494 and 2506 cm-1, and 40 were identified between 4394 and 4406 cm-1. Failing this test in one spectral region did not necessarily mean that a particular spectrum would also fail it in another region. For example, of the 60 spectra that were identified as potential outliers in the 979 to 991-cm-1 region, only 11 also failed the test in the 2494 to 2506-cm-1 region. Overall, only eight spectra failed the RMS deviation test in all three wavenumber regions. An example of a time series plot of the RMS deviation between 2494 and 2506 cm-1, for data files collected along a monitoring path between the farrowing/nursery barns and the finishing barns on January 11, 1999, is shown in Figure 1. The RMS deviation for the data pair representing spectra #14 and #15 in this series slightly exceeded the upper control limit. However, these two spectra did not fail any of the other QC tests. A careful review of the original spectra and other QC parameters revealed that the concentration data generated from these two spectra were most likely valid. As in this example, many of the potential outliers identified by this test narrowly exceeded the control limits. In most cases, a slightly elevated RMS deviation value did not have a deleterious effect on the ability of the CLS algorithm to produce valid concentration data. Of the 120 potential outliers identified from the combined RMS deviation tests, only 14 were later designated actual outliers. These results are consistent with the general observation that random baseline noise frequently is not the limiting factor in CLS analysis and indicate that the criterion for identifying outliers from this test could be made less stringent. For example, changing the control limits from x + 2.7σ to x + 3σ reduced the number of potential outliers from 120 to 59 spectra. Signal Strength and Relative Single-Beam Intensity. Measurements of the signal strength and relative single-beam intensity can be used to determine if the output power of the IR source, the transmitting or reflecting properties of the optics, the alignment of the retroreflector and the transmitting/receiving telescope, or the alignment of the interferometer have changed, or if other problems, such as icing on the detector window or detector saturation, occurred during the monitoring session. Measurements of the relative single-

beam intensity in different wavenumber regions can be used to detect a change in the shape of the single-beam spectrum. Such a change in shape relative to the background spectrum can affect the baseline in the absorption spectrum, which, if not modeled properly, can cause errors in the CLS analysis. The maximum intensity of the single-beam spectra at 985 cm-1 ranged from 0.4 to 2.6 × 10-5 (arbitrary units) during the study and generally decreased with increasing path length. The single-beam intensity at 2500 cm-1 relative to that at 985 cm-1 was relatively constant throughout the study (0.9442 ( 0.0821) and did not depend on path length. The relative single-beam intensity measured at 4400 cm-1 was lower than that measured at 2500 cm-1, exhibited more variability (0.0878 ( 0.0182), and also did not depend on path length. Nonphysical energy below 650 cm-1 was not observed in any of the spectra, which indicates that the detector was not saturated. Fifty different spectra were identified as potential outliers by these tests. Twenty-three spectra were identified as potential outliers by the test measuring the single-beam intensity at 985 cm-1, whereas 23 and 21 spectra failed the tests measuring the relative intensities at 2500 and 4400 cm-1 compared to that at 985 cm-1, respectively. There was no correlation between failing the signal-strength test at 985 cm-1 and failing the relative single-beam intensity tests in the other wavenumber regions. However, 12 of the 23 spectra that failed the relative intensity test at 2500 cm-1 also failed the test at 4400 cm-1. In addition, a significant number (12 of 21) of the spectra that failed the relative single-beam intensity test at 4400 cm-1 also failed the RMS deviation test at 4400 cm-1. However, except for extreme cases, failing these tests did not prevent valid concentration data from being produced by the CLS method. An abrupt decrease in the maximum single-beam intensity did not necessarily cause a change in the relative singlebeam intensities. For example, the signal strength measured at 985 cm-1 in the single-beam spectrum exhibited a sharp decrease for spectrum #28 during a monitoring session between the farrowing/nursery barns and the finishing barns on January 11, 1999 (Figure 2A). However, the relative singlebeam intensities measured at 2500 and 4400 cm-1 did not change over this time period (Figure 2B). Also, the RMS deviation did not reflect the decrease in the single-beam intensity for this spectrum (Figure 1). As a result, after VOL. 35, NO. 9, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Plots of (A) the single-beam intensity at 985 cm-1 (9) and (B) the ratio of single-beam intensity at 2500 ([) and 4400 cm-1 (2) to that at 985 cm-1 for OP/FT-IR spectra collected along a 228-m physical path between the farrowing/nursery barns and the finishing barns on January 11, 1999. inspection of the actual spectrum and a review of the other QC parameters, this data point was not designated as an actual outlier despite the sudden decrease in the overall signal intensity. Wavenumber Shifts and Changes in Resolution. Wavenumber shifts and changes in resolution in the field spectra relative to the reference spectra cause errors in the CLS analysis. Russwurm (16) has shown that wavenumber shifts can be the most important source of error in CLS analyses. A shift of 1/10th of the instrument resolution can produce a 10% error in the reported gas concentration. Because of the magnitude of this potential error, each field spectrum must be shifted to agree with the reference spectrum. Wavenumber shifts and changes in resolution will affect narrow, structured bands more significantly than they will affect broad bands. Thus, broad absorbers are not useful analytes for this QC test. Wavenumber shifts or changes in resolution between two absorption spectra can be determined by scaling the absorption features in the two spectra to the same intensity and then subtracting one spectrum from the other. If a wavenumber shift occurs between the two spectra, the subtraction result will exhibit a feature that appears to be the first derivative of the line shape. If the resolution has changed, but there is no wavenumber shift, the subtraction result will exhibit a feature that has the shape of an “M” or 1862

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a “W”, depending on which of the two spectra contains the broader line. If there are no wavenumber shifts or changes resolution, the subtraction result will be random noise. Although this interactive subtraction procedure is effective for identifying wavenumber shifts or changes in resolution between individual spectra, manual application of this test to all of the spectra in the entire data set is impractical. However, the nonlinear analysis algorithm used as an alternative analysis method during this study automatically outputs the wavenumber shift relative to the spectral line data generated from Etrans and the calculated spectral resolution for each single-beam field spectrum. Forty-two spectra were identified as potential outliers because of wavenumber shifts or changes in resolution by examining these outputs from the nonlinear algorithm. Twenty-three potential outliers were identified from the wavenumber shift parameter and 25 different spectra were identified as potential outliers from the resolution check. The mean wavenumber shift calculated by the nonlinear algorithm during each monitoring session ranged from 0.24 to 0.39 cm-1, whereas the mean wavenumber shift for a composite of all of the field spectra collected throughout the study was 0.34 ( 0.04 cm-1. The magnitude of this parameter did not depend on the path length. More than half of the potential outliers that

FIGURE 3. Plot of the path-averaged concentrations of NH3 (9) and CH4 (2) along a 258-m physical path along the eastern berm of the waste lagoon on January 20, 1999. exceeded the control limits for the wavenumber shift parameter resulted from shifts of only (0.01 cm-1 from the mean for each respective monitoring period. Only spectra that exhibited wavenumber shifts g0.03 cm-1 from this mean were later designated as actual outliers by reviewing the other QC parameters. This result indicates that the control limits can be relaxed for this test to reduce the number of potential outliers that must be reviewed. The mean resolution of the field spectra throughout the entire study, as calculated by the full spectrum nonlinear analysis algorithm, was 0.56 ( 0.03 cm-1. In general, the measured resolution did not vary with path length. As with the wavenumber shift parameter, many of the spectra identified as potential outliers from the resolution test exhibited changes in resolution of only 0.01 cm-1 from the mean during any particular monitoring session. However, in some cases, these spectra were later designated as actual outliers, which indicates that the control limits used to identify potential outliers from changes in resolution were adequate for this study. Time Series Plots of Estimated Concentrations of the Target Gases. An inspection of the time series plots of the target gas concentrations occasionally revealed an obvious outlier. However, these plots do not always unambiguously identify outliers. For example, time series plots of the concentrations of CH4 and NH3 along the eastern berm of the waste lagoon on January 20, 1999, are shown in Figure 3. In this example, the concentrations of CH4 and NH3 exhibit a noticeable decrease at time 1351. The data files collected during this time period were examined for high uncertainty values reported by the CLS algorithm, the impact of interfering species, excessive random noise, or unusual single-beam intensities. In this case, no obvious reasons for the sudden decrease were found and all of the QC parameters were within the control limits. Therefore, the changes in concentrations of these target gases most likely represent actual changes in concentration along the path. The concentrations of permanent atmospheric gases, such as N2O, can be used to evaluate the performance of the OP/ FT-IR monitor. Ambient concentrations of N2O are detectable in most OP/FT-IR spectra, provided that the concentrationpath length product is sufficient. The average global concentration of N2O is approximately 310 ppb, and the ambient concentrations of N2O are generally constant from site to site. Some swine production operations are a source of N2O

emissions, so fluctuations in the N2O concentration might be expected when monitoring nonbackground paths. The mean concentrations of N2O measured during each monitoring period at the concentrated swine production facility ranged from 0.296 to 0.328 ppm (4). The relative standard deviation (RSD) for the ambient N2O concentrations was typically less than 1% during a monitoring session, except for one session along the eastern berm of the waste lagoon during which the concentrations ranged from 0.314 to 0.411 ppm and exhibited a 5% RSD. An example of the use of N2O to determine the stability of the instrument is given in Figure 4, which is a plot of the concentrations of NH3 and N2O along a 228-m physical path between the farrowing/nursery barns and the finishing barns on January 11, 1999. The NH3 concentration ranged from 0.150 to 0.500 ppm, with a mean and standard deviation of 0.287 ( 0.066 ppm, and exhibited periodic fluctuations about the mean. This observation initially indicated that the instrument was unstable and that the source of the instability was periodic. However, the N2O concentrations were stable over this monitoring session (0.306 ( 0.001 ppm), which indicates that the instrument was functioning properly over this time period. This observation, however, does not rule out the possibility of interfering species contributing to the variability in the NH3 concentrations. Further examination of the data showed that the NH3 concentrations were not correlated with changes in the H2O vapor or tracer gas (i.e., C2H4) concentrations over this time period, and no additional interfering species were detected along the path. These results indicate that the instrument was operating properly and that the analysis method did not produce any artifacts during this monitoring session. Therefore, the fluctuations in the NH3 concentrations most likely reflected real changes in the ambient concentrations. The changes in NH3 concentrations were also correlated with changes in CH4 concentrations, which indicates that the NH3 and CH4 emissions were from the same source; i.e., the farrowing/nursery barns. The concentrations of these gases were possibly related to the on/off cycles of the vent fans in these barns, although records of the fan use were not available to verify this assumption. Uncertainty Values Reported by the CLS Analysis. The CLS analysis method reports an estimate of the uncertainty, reported as ( values in units of ppm, that is associated with each concentration measurement for each absorption spectrum. The reported uncertainty values are related to the VOL. 35, NO. 9, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Plot of the path-averaged concentrations of NH3 (9) and N2O ([) along a 228-m physical path between the farrowing/nursery barns and the finishing barns on January 11, 1999. spectral residual, which is calculated by subtracting the predicted spectrum that is generated from the set of reference spectra during the CLS analysis from the field spectrum. Although these values are not indicative of the method detection limit or the accuracy of the concentration measurement, they can be used as a QC check while developing the analysis method and for identifying outliers in the field spectra. For example, when developing the analysis method, large uncertainties could indicate that all of the interfering species have not been identified and included in the calibration model, deviations in the baseline have not been accounted for, or the field spectra exhibit a wavenumber shift or change in resolution relative to the reference spectra. Abrupt changes in the magnitude of the reported uncertainties during the analysis of an individual data set can indicate the presence of previously unidentified interfering species, a large change in the concentration of a target gas or an interfering species along the path, or a malfunction in the instrument. Time series plots of the reported uncertainty values for each target gas were created for each monitoring period. Application of the x ( 2.7σ control limits to these plots identified 88 different spectra as potential outliers. The total numbers of potential outliers detected for each target gas were NH3, 34; C2H4, 25; SF6, 12; H2O-Region 1, 38; CO, 17; CO2, 15; N2O, 12; H2O-Region 2, 17; CH4, 35; and H2ORegion 3, 19. If the uncertainty value for one of the target gases exceeded the control limits for a given spectrum, then the uncertainty values for the other gases coanalyzed in the same wavenumber region more than likely also failed this test for that spectrum. For example, 32 of the 34 spectra that were identified as potential outliers by failing the QC test for the uncertainty values associated with NH3 also failed this test for H2O vapor in Region 1. Similar relationships were also observed between the uncertainty values for C2H4 or SF6 and H2O in Region 1; CO, CO2, N2O, and H2O in Region 2; and CH4 and H2O in Region 3. Failing the QC test for uncertainty values of some gases was also related to other QC parameters. For example, more than half of the spectra that failed the test for the uncertainty values for CO2, N2O, and H2O in Region 2 also failed the RMS deviation test between 2494 and 2506 cm-1. Other than this example, there were no strong correlations between the uncertainty values and the other QC parameters. An example of the use of the uncertainty values to detect outliers is illustrated for a monitoring session along a 238-m 1864

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physical path on the eastern berm of the lagoon on January 13, 1999. The H2O vapor concentration measured in the NH3 spectral region exhibited erratic behavior during this time period (Figure 5A). However, the H2O vapor concentration measured in Regions 2 (shown) and 3 (not shown) did not exhibit this behavior. Likewise, the H2O vapor concentration calculated from the meteorological data was steady during this time period (not shown). Examination of the uncertainty values for H2O vapor concentrations in Region 1 revealed that increases in these values corresponded with increases in the tracer gas (e.g., SF6) concentrations (Figure 5B). These results indicate that interference from strongly absorbing compounds can be a limiting factor in CLS analyses. This effect is amplified for interfering species that exhibit a nonlinear response with respect to changes in concentration (17). However, in this case, the NH3 concentrations were apparently not affected by increases in SF6 concentrations. Valid NH3 concentrations were obtained even though the uncertainty values for H2O were erratic in this analysis region. Designation of Suspect Spectra as Actual Outliers. A total of 252 spectra from the original data set containing 2,200 field spectra were identified as potential outliers by the various QC tests described above. Each of these 252 spectra was reviewed for anomalous characteristics, such as excessive noise, low signal strength, changes in the shape of the singlebeam spectrum, wavenumber shifts or changes in resolution, baseline abnormalities, or the presence of unidentified interfering species. Based on this review, 41 spectra were designated as actual outliers. This value represents less than 2% of the total number of spectra acquired during this study. The spectra designated as actual outliers were excluded from the final data set, so that the reported concentrations would not be skewed by erroneous data points. The effect of excluding the outliers from the final data set varied for each monitoring session. For many sessions, the average concentration and standard deviation of a particular target gas changed less than 1% when the outliers were excluded. However, in some monitoring sessions the average concentrations of the target gases changed as much as 6%, with the standard deviation changing more than 100%. These results indicate that excluding outliers has the most significant effect on the standard deviation of the concentration measurements. No single outlier test provided a definitive determination of actual outliers. For example, valid concentration data were

FIGURE 5. Plot of (A) the H2O vapor concentration measured from OP/FT-IR spectra analyzed in Region 2 (2) and in Region 1 ([), and (B) the uncertainty values from H2O in Region 1 ([) and the concentration (× 105) of SF6 (9) measured along a 238-m path on the eastern berm of the lagoon on January 13, 1999. obtained for most data files for which the RMS deviation, the signal strength, or the relative single-beam intensities were outside of the control limits. Although these tests did not allow for an independent identification of actual outliers, they are of diagnostic value when assessing the data. Of the different QC tests used in this study, the magnitude of the uncertainty values reported by the CLS analysis was the most reliable indicator of an actual outlier. For example, 39 of the 41 spectra designated as actual outliers exhibited elevated uncertainty values for one or more target gases. Because the uncertainty values are related to the spectral residual, abnormalities in the field spectra, such as high noise levels, wavenumber shifts, and changes in resolution, will be reflected in the magnitude of the uncertainty value. However, this test can produce false positive outlier detections. For example, rapidly changing gas concentrations along the path can cause the uncertainty values to exceed the control limits even though the spectra might pass all of the other QC tests. These QC tests were designed to establish objective criteria for assessing the quality of OP/FT-IR data and to eliminate the need for reviewing each field spectrum. Although some subjective judgment is required to determine that a suspect spectrum is an actual outlier, these tests reduced the number of spectra that had to be reviewed. In this study, slightly

more than 10% of the field spectra required additional review. Relaxing the control limits for some of the QC tests could reduce the number of spectra to be reviewed. Even though these QC tests are effective in assessing the performance of the instrument and the data analysis method, they do not provide an estimate of the absolute errors in the reported concentrations. More effort is needed to understand the causes and magnitudes of the errors involved in OP/FT-IR measurements.

Acknowledgments The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and managed the research described here under contract 68-D5-0049 to ManTech Environmental Technology, Inc., and collected the data that are the subject of this analysis. The research 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. The authors thank George M. Russwurm of ManTech Environmental and Jack C. Suggs of the U.S. Environmental Protection Agency for helpful discussions regarding this manuscript. VOL. 35, NO. 9, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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Literature Cited (1) Paerl, H. W. Ophelia 1995, 41, 237. (2) Aneja, V. P.; Murray, G. C.; Southerland, J. EM 1998, 4, 22. (3) Status Report on Emissions and Deposition of Atmospheric Nitrogen Compounds from Animal Production in North Carolina; North Carolina Department of Environment and Natural Resources, Division of Air Quality: Raleigh, NC, 1999. (4) Childers, J. W.; Thompson, E. L., Jr.; Harris, D. B.; Kirchgessner, D. A.; Clayton, M.; Natschke, D. F.; Phillips, W. J. Atmos. Environ. 2001, 35 (11), 1923-1936. (5) Weber, K.; van de Wiel, H. J.; Junker, A. C. F.; de LaRiva, C. In Proceedings of Optical Remote Sensing. Applications to Environmental and Industrial Safety Problems; SP-81; Air & Waste Management Association: Pittsburgh, PA, 1992; pp 30-42. (6) Kricks, R. J.; Scotto, R. L.; Pritchett, T. H.; Russwurm, G. M.; Kagann, R. H.; Mickunas, D. B. In Proceedings of Optical Remote Sensing. Applications to Environmental and Industrial Safety Problems; SP-81; Air & Waste Management Association: Pittsburgh, PA, 1992; pp 224-231. (7) Dubois, A. E.; Engle, J. W.; McKane, P. L.; Perry, S. H. In Proceedings of Optical Remote Sensing for Environmental and Process Monitoring; VIP-55; Air & Waste Management Association: Pittsburgh, PA, 1996; pp 139-146. (8) Childers, J. W.; Russwurm, G. M.; Thompson, E. L., Jr. In Proceedings of Optical Sensing for Environmental and Process Monitoring; VIP-37; Air & Waste Management Association: Pittsburgh, PA, 1995; pp 389-395. (9) Childers, L. O. In Proceedings of Optical Remote Sensing for Environmental and Process Monitoring; VIP-55; Air & Waste Management Association: Pittsburgh, PA, 1996; pp 127-138. (10) FT-IR Open-Path Monitoring Guidance Document; EPA/600/ R-96/040. U.S. Environmental Protection Agency, Office of

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Research and Development, National Exposure Research Laboratory: Research Triangle Park, NC, 1996. Standard Guide for Open-Path Fourier Transform Infrared (OP/ FT-IR) Monitoring of Gases and Vapors in Air, E 1865-97. In Annual Book of ASTM Standards, Vol. 03.06.; American Society for Testing and Materials: West Conshohocken, PA, 1997. Standard Practice for Open-Path Fourier Transform Infrared (OP/FT-IR) Monitoring of Gases and Vapors in Air, E 1982-98. In Annual Book of ASTM Standards, Vol. 03.06.; American Society for Testing and Materials: West Conshohocken, PA, 1998. Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air: Compendium Method TO-16; EPA/ 625/R-96/010b. U. S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Research Information: Cincinnati, OH, 1997. Phillips, B.; Moyers, R.; Lay, L. T. In Proceedings of Optical Sensing for Environmental and Process Monitoring; VIP-37; Air & Waste Management Association: Pittsburgh, PA, 1995; pp 374-388. Jobson, J. D. Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design; Springer-Verlag: New York, 1991; p 57. Russwurm, G. M. In Optical Remote Sensing: Applications to Environmental and Industrial Safety Problems; SP-81; Air & Waste Management Association: Pittsburgh, PA, 1992; pp 105111. Russwurm, G. M.; Phillips, B. Appl. Opt. 1999, 38, 639.

Received for review October 10, 2000. Revised manuscript received February 15, 2001. Accepted February 19, 2001. ES001744F