Applications of Open-Path Fourier Transform Infrared

Tag : organic solvent

RESULTS
Performance of OP-FTIR at the Paint Manufacturing Plant
After signal processing work, 10 compounds (Table 1) emerged as theidentifiable main species. On the basis of an emissions inventory,methane, O3, and ammonia were not expected to be emitted from thepaint manufacturing processes; therefore, these data will not bediscussed for this scenario. Emission of toluene, m-xylene, p-xylene, styrene, methanol, acetone, and 2-butanone are all expectedfrom the paint manufacturing processes. Table 1 summarizes theperformance statistics of the OP-FTIR monitoring data for the paintmanufacturing study. It was partly cloudy with scattered rainduring the 3-day continuous monitoring period. Therefore, 295spectral data were not produced, or 13.6% of the 2160 expectedspectral data for the 2-min data resolution. As will be discussedlater, percentages of detectable points for some species were notsatisfied. Nevertheless, the continuous monitoring dataset providesample valuable information. Using toluene as an example, there were1865 data points, consisting of 1858 (99.6%) nondetectable (belowfield detection limit) points and 7 (0.38%) detectable points. Thenumber of detectable points was related to the process backgroundconditions and the field detection limits. The low percentage ofdetectable points indicated that OP-FTIR was relatively lesssensitive to toluene, as were other aromatic species (m-xylene, p-xylene, and styrene), corresponding to their high minimum reportedconcentrations. As for the cases of methane, methanol, acetone, 2-butanone, and ammonia, the percentage of detectable points was muchhigher, therefore more information was provided and betterrepresentation was expected. Note that the field detection limitsof the OP-FTIR monitoring system were corresponding to the fieldconditions such as moisture content and the particulate lightscattering effect. Therefore, the field detection limits of themeasurement could not be accurately determined and could only beapproximated to the minimum reported concentrations listed in Table1. Because of the variety of field detection limits, it was alsodecided not to take the nondetectable points into account whencalculating the averaged concentration presented in Tables 1 and 2.
Also shown in Table 1 are the maximum concentrations reportedduring the 3-day monitoring period. Except for methanol andacetone, the maximum concentrations of other species were higherthan their low odor threshold values, indicating that strongsolvent odors are expected in plants during the worst condition andrelated pollution prevention practices should be enhanced. For thecases of toluene, m- xylene, p-xylene, styrene, and 2-butanone, theaveraged concentrations (which did not take the nondetectablepoints into account) were still much higher than their odorthreshold values. Therefore, related odor problems should beexpected during the routine production processes and should betreated as priority pollutants for odor combat in the paintmanufacturing plant. Nevertheless, the averaged and maximumconcentrations did not exceed the time-averaged threshold limitvalues (TLVs); therefore the occupational exposures were incompliance for this particular example. Moreover, it is worthnoting that the recorded maximum concentration of toluene was 3166ppb, which exceeds the 2-ppm maximum allowable boundaryconcentration regulated by the Taiwan Environmental ProtectionAgency (EPA) stationary air pollutant emission standards. The aboveresults indicate that OP-FTIR can be of great assistance in bothindustrial hygiene and environmental air pollutant regulatoryenforcement.
Temporal Characterization of the Paint Manufacturing Activities
Temporal characterization of the paint manufacturing activities isbetter depicted using a 10-min averaged spectra data. As shown inFigure 3, methanol, 2-butanone, and m-xylene showed similar time-series patterns. It is clear that high concentrations emerge duringdaytime and diminish at night. The results correspond to thedaytimeonly operation of the plant.
The multiple-mode concentration behavior agrees with the batchoperation behavior of the plant. Using data from day 2 as anexample, concentrations of all three species increased quicklyafter the beginning of daytime office hours, and the first peakconcentrations arose between 8:00 a.m. and 9:00 a.m. 2-Butanone andm-xylene demonstrated an even closer relationship; their secondpeak concentrations appeared around 11:00 a.m., whereas the thirdpeak concentration was shown at around 4:00 p.m. It is also clearthat 2- butanone and m-xylene concentrations were not elevatedduring the lunch hour (12:00 to 1:00 p.m.) of the first and secondday. The above results correspond well with the three daily batchoperation activities of the plant. Methanol is mainly used as asolvent for cleaning reactor tanks, whereas 2-butanone and m-xyleneare base solvents to produce paints. It is clearly shown in Figure3 that 2- butanone and m-xylene exhibited very similar time-seriespatterns, indicating that they were likely emitted from similarproduction lines where 2-butanone is used as a co-solvent todissolve flaxseed oil into m-xylene.18 However, some discrepancieswere observed on the first and third day between 4:00 p.m. and12:00 a.m., in which m- xylene concentrations were not shown. Themost likely explanation was that m-xylene concentrations were belowthe field detection limit (approximated with the minimum reportedconcentration of 92 ppb in Table 1) during the period, thus notreported. This finding reveals one of the system's majorchallenges, if the field detection limits are not improved, thesystem can only be used in plant areas where concentrations arehigher, but not in urban or residential ambient air quality studieswith lower concentrations.
Spatial Characterization of the Paint Manufacturing Facilities
Taking advantage of the continuous monitoring capability of OP-FTIR and the meteorological data recorded, one can distinguishsources of emissions. The pollutant detected wind-rose percentageplots in Figure 1 show that methanol, 2-butanone, acetone, m-xylene, and toluene are mainly from southeast (SE) windward sourcesthat correspond to manufacturing processes M3, M4, and M5, whichutilize them as cleaning or base solvents. It is also clear that M7(solvents preparation/mixing process) releases significant amountof methanol, 2-butanone, and acetone from the west-southwest (WSW)direction. For the case of acetone, in addition to the SE and WSWsources, a new source of emission was identified from the east-northeast (ENE) direction. This was not an unusual event, becauseacetone is used more extensively in M2 for blending-tank cleaningpurposes.
It is of merit to mention that styrene was used as one of the majorsolvents along with methyl methacrylate (MMA) to produce acrylicresin in M1. Styrene is a suspected carcinogenic compound,19 andits low odor threshold value (5 ppb)20 often irritates the nearbyneighborhood. In the study planning stage, we were hoping to findevidence of styrene coming from the north-northeast (NNE) direction(M1). However, only four data points were reported above fielddetection limit during the 3-day monitoring period. Therefore, noconclusive result could be drawn. As discussed earlier, for somearomatic compounds such as styrene, the detection limit of OP-FTIRmay not have sufficient sensitivity. Alternatively, a pointsampling method such as the U.S. Environmental Protection Agency'sTO- 1521,23 can be used in conjunction with OP-FTIR for monitoringthese compounds.
Data Performance of OP-FTIR in the Petrochemical Complex
In the second scenario, the OP-FTIR was set up in a lubricantmanufacturing plant to assess VOC emissions in the petrochemicalcomplex (Figure 2). The single path length in between the IR sourceand the retroreflector was 83 m. The plant uses heavy oil as rawmaterial and goes through vacuum distillation, air stripping, and adrying process to produce lubricant and base oil. Few VOC emissionsare expected in these processes, and if produced, they should becollected and treated in a flare.
After signal processing work, eight major VOCs (Table 2) emerged asthe identifiable species, including ethylene, propylene, toluene,2-butanone, propane, n-butane, methanol, and ammonia. Table 2summarizes the performance statistics of the OP-FTIR monitoringdata. It was sunny and less humid during the 3 days and 8-hrcontinuous monitoring period. Therefore, all 2388 spectral datawere produced for the 2-min data resolution. This was an example inwhich weather conditions favored the operation of OP-FTIR, whereasat the first scenario was not.
Using ethylene as an example, the total effective data were 2388points, consisting of 1738 (73%) nondetectable (below detectionlimit) points and 650 (27%) detectable points. The number ofdetectable points was much higher than in the first scenario.Moreover, the minimum concentrations detected in Table 2 were lowerthan that of in Table 1, taking toluene, 2-butanone, methanol, andammonia as examples. Therefore, better sensitivity and moreexplainable results could be provided by the OP-FTIR in a favorableweather condition that has less interference.
Also shown in Table 2 are the concentration statistics reportedduring the 3-day monitoring period. Except for toluene, theaveraged concentrations of all other species were lower than theirlow odor threshold values, indicating that strong solvent odors arenot expected in the plant during regular operations. Furthermore,the maximum concentrations recorded did not exceed the TLVs.
Source Identification of VOCs in the Petrochemical Complex
Among the eight VOCs monitored in Table 2, ethylene, propylene, andmethanol were the major pollutants that showed unique time- seriespatterns (Figure 4). It is clear that ethylene and propylene havesimilar behavior, indicating that they originated from the samesource, whereas methanol was emitted from different sources in theopposite wind direction. However, on the first and second daybetween 4:00 p.m. and 8:00 a.m. a different pattern was observed incomparison with ethylene and propylene. A possible explanation isthat the ambient concentration of propylene was approximately 5 to10 times lower than that of ethylene. Therefore, during this periodpropylene concentrations may have been too low to be recorded. Itis also possible that the minimum reported concentration ofethylene (13 ppb) was lower than that of propylene (27 ppb);therefore, more ethylene data points were detected (27.2%) than forpropylene (5.3%), as shown on Table 2. Alternatively, one can alsoreason that a minor amount of ethylene was emitted together withmethanol from a northern wind direction. Figure 2 shows thepollutant-detected wind- rose percentage plots for thecorresponding VOCs. It is clear that ethylene, propylene,2-butanone, and toluene mainly originated from the southwarddirection where the tank storage area of the petroleum refinery islocated. On the other hand, n-butane mainly originated from thenorthward direction where the butadiene manufacturing process ofthe refinery is located. Ammonia also originated from the northwarddirection, and its origin is assumed to be the gasolinedesulfuration plant. This was expected because ammonia is anaccompanying reduction product of hydrogen sulfide in thedesulfuration process. Finally, propane originated from both thesouth and north, and methanol originated from various sources fromthe SSE to the north. Therefore, no definite conclusions could bemade for the identification of propane and methanol sources.
CONCLUSIONS
An OP-FTIR system was set up for continuous VOC monitoring with thepurpose of characterizing source emission behavior and to identifyemission sources. In the paint manufacturing plant study, OP-FTIRsuccessfully identified toluene, m-xylene, acetone, 2- butanone,methanol, styrene, and p-xylene as the process-related emissionspecies. Continuous on-scene monitoring produces highqualitytime-series concentration plots that correspond well with thedaytime-only batch production activities of the plant. With thehelp of wind rose plots we were able to distinguish the specificmanufacturing process responsible for certain VOC emissions. In thesecond scenario, OP-FTIR was set up to assist in distinguishingsources of certain VOCs in a multi-industrial complex, and theorigins of many VOCs were reasonably uncovered. Weather played animportant role in producing high-quality OPFTIR monitoring data,favoring clear and less humid conditions. The OP-FTIR real-timecontinuous VOC line monitoring technique allowed monitoring andidentification of emission sources of selected VOCs.
IMPLICATIONS
With the help of meteorological data, the OP-FTIR system was shownto be an effective tool to depict spatial variations in identifyingsources of VOC emissions. The continuous monitoring capability ofOP-FTIR also provides useful time-series concentration data todepict temporal emission behavior. Such information can be veryuseful for engineers to identify and control VOC emissions.
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Chitsan Lin and Naiwei Liou
Department of Marine Environmental Engineering, National KaohsiungMarine University, Kaohsiung, Taiwan, Republic of China
Endy Sun
Environmental Science Corporation, Taipei, Taiwan, Republic ofChina
About the Authors
Chitsan Lin is an associate professor at the National KaohsiungMarine University (NKMU) in Kaohsiung, Taiwan. Naiwei Liou was agraduate student at NKMU during this study, and is now a doctoralstudent in the Environmental Institute of the National Sun Yet-ShanUniversity in Kaohsiung, Taiwan. Endy Sun is CEO of EnvironmentalScience Corporation in Taipei, Taiwan. Please addresscorrespondence to: Chitsan Lin, Ph.D., Department of MarineEnvironmental Engineering, National Kaohsiung Marine University,142 Haijhuan Road, Nanzih District, Kaohsiung 81143, Taiwan,Republic of China; phone: +886-7-3651472; fax: +886-7-3651472;e-mail: ctlin@mail.nkmu.edu.tw.
Copyright Air and Waste Management Association Jun 2008
(c) 2008 Journal of the Air & Waste Management Association.Provided by ProQuest Information and Learning. All rights Reserved.

Source: Journal of the Air & Waste Management Association
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