Environ. Sci. Technol. 2000, 34, 4958-4962
Chlorophenols as Precursors of PCDD/Fs in Incineration Processes: Correlations, PLS Modeling, and Reaction Mechanisms KARI A. TUPPURAINEN,† P A¨ I V I H . R U O K O J A¨ R V I , ‡ ARJA H. ASIKAINEN,‡ MARJALEENA AATAMILA,‡ AND J U H A N I R U U S K A N E N * ,‡ Departments of Chemistry and Environmental Sciences, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland
Emissions of organic chlorinated compounds from municipal waste incineration, in particular polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs), have been a cause of public concern for many years because of the high toxicity of these compounds. PCDD/F formation in incineration processes is being studied widely using labscale apparatus, but pilot-plant investigations are quite rare. The correlation between TEQ-related PCDD/Fs and chlorophenols (ClPhs) was studied here using a pilotscale plant. The results suggest that almost all the ClPh isomers correlate strongly with PCDD/Fs in the gas phase, but only certain isomers, in particular 2,3,4,6- and 2,3,4,5,6ClPh, are of importance in the particle phase. The relationship of TEQ-related PCDD/Fs to ClPhs is so close that even predictive partial least-squares (PLS) modeling is feasible. In view of our results, some aspects of the mechanism of PCDD/F formation are discussed. From a practical point of view, the results suggest that ClPhs may be a good surrogate of TEQ-related PCDD/Fs in different incineration processes.
Introduction Combustion is an efficient way of disposing of waste, but, unfortunately, it is also a major source of toxic PCDD/F emissions in our environment (1). PCDD/Fs were discovered in municipal solid waste (MSW) incinerators in 1977 (2), and it has since been confirmed on several occasions that they are formed mainly in the postcombustion zone by various catalyzed mechanisms (for recent reviews see refs 3 and 4). Three main pathways have been proposed so far to explain the formation of PCDD/Fs during incineration: (i) pyrosynthesis, i.e., high-temperature gas phase formation (5), (ii) formation at low temperatures (250-350 °C) from macromolecular residual carbon and the organic or inorganic chlorine present in the fly ash matrix, often referred as the de novo mechanism (6-9), and (iii) through various organic precursors such as chlorophenols (ClPhs) or polychlorinated diphenyl ethers (PCDEs), which may be formed in the gas phase during incomplete combustion and combine heterogeneously and catalytically with the fly ash surface (10). * Corresponding author phone: +358-17-163227; fax: +358-17163230; e-mail:
[email protected]. † Department of Chemistry. ‡ Department of Environmental Sciences. 4958
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The mechanisms vary in relative importance according to the combustion conditions, but the following order is usually considered correct: (i) , (ii) < (iii), although the relative importance of the precursor and de novo mechanisms is still highly controversial (7). Moreover, the terminology used in this area has been somewhat halting, as pointed out by Iino et al. (9), for example. In any case, it has been proposed on the basis of simplified kinetic models that pyrosynthesis is actually of minor importance (11, 12) and that both postfurnace mechanisms are more important (13). Very recent findings suggest, however, that the potential contribution of gas-phase reactions has been underestimated in the past (14). Post-furnace formation of PCDD/Fs seems to take place at detectable rates only under the influence of metal catalysts, especially copper. Cu2+ cations have the strongest catalytic activity, enhancing the formation of the ring structure of PCDD by up to 3 orders of magnitude (15). It is not clear at this stage, however, why copper (rather than other transition metals) plays such a unique role in the formation of PCDD/ Fs. Although all these mechanisms have been known about for a number of years, only a few detailed reactions can be outlined at this stage. This is largely because we are always dealing only with minor side reactions with very low yields, and the extreme complexity of the fly ash matrix effectively obscures the identity of the active catalytic sites and the overall processes. In addition, a plethora of organic molecules are capable of forming PCDD/Fs (3, 4). As a consequence, only a few detailed reaction mechanisms have been presented for PCDD/F formation from macromolecular carbon (9) or even structurally closely related precursors, apart from the condensation of ClPhs (16). Indeed, the decisive role of ClPhs can hardly be challenged. Their conversion to PCDDs at ca. 300 °C on the surface of catalytically active fly ash particles has been verified experimentally (17), and the most abundant PCDD congeners have been shown recently to be condensation products of the thermodynamically most stable ClPhs (18-20). These condensation reactions are postulated to take place via Smiles rearrangement, with a dioxaspiro-type compound as an intermediate (18). Some possible routes from ClPhs to PCDD/Fs are well documented both experimentally (21) and theoretically (22-24). In view of these results, it is not surprising that the condensation reactions of ClPhs have attracted much attention, as they might constitute a relatively elementary mechanism for the formation of PCDD/Fs in combustion processes. However, previous results have been based for the most part on laboratory-scale studies or theoretical considerations. During recent years, we have performed a series of pilot-plant experiments to determine whether injecting a liquid inhibitor (urea) directly into the flue gases would significantly reduce PCDD/F formation. The results will be published separately, but the work did lead to the collection of a comprehensive data set (altogether 53 samples up to now) which is also suitable for exploring ClPh-PCDD/F relations. The aim here is to describe these relations employing correlation analysis and partial least-squares (PLS) modeling. In addition, some possible reaction mechanisms will be discussed. Recently, there is a growing tendency to find surrogates of PCDD/Fs, e.g. for online estimation of PCDD/F emissions; in particular chlorobenzenes have attracted much attention (25, 26). Since the route from ClPhs to PCDD/Fs is more straightforward than that from chlorobenzenes, the use of ClPhs as surrogates of PCDD/Fs will be explored. 10.1021/es991429x CCC: $19.00
2000 American Chemical Society Published on Web 10/26/2000
FIGURE 1. Scheme of the pilot plant used in the test runs.
Experimental Section The tests were carried out in a 50-kW pilot plant (Figure 1) consisting of an oil or stoker burner and a furnace from which the flue gases were directed through a delay chamber and economizer to the stack. Sampling was performed isokinetically after the economizer. The basic fuels in the tests were light heating oil (LHO) with a feed rate of 4.7 L/h (23 samples) and refuse-derived fuel (RDF pellets, 30 samples) with a feed rate of 10.5 kg/h. In the heating oil tests, chlorine and copper (in the form of tetrachloroethylene and copper nitrate, respectively) were added to the fuel (chlorine) or flue gas (copper) and adjusted to correspond to 0.5% of the total fuel flow (by mass). These additives (p.a., purchased from Merck) were selected as a consequence of previous studies (27) and were used to increase the PCDD/F formation in the tests to the level corresponding to MSW incinerator emissions. The inhibitor (urea) was dissolved in water-methanol or water, and the amounts injected were adjusted to correspond to 0.1%, 0.5%, and 1.0% of the total fuel flow. Only copper nitrate (as a catalyst) was injected into the flue gases in the activator test and only a pure water solution in the blank test. In the RDF tests the amounts of urea were only 0.05%, 0.22%, and 0.45%. The residence times of the flue gases were 4.5-5.0 s (16 samples; no delay chamber), 7.0-8.5 s (26 samples; the flue gases were conducted via the upper part of the delay chamber), and 12-16 s (11 samples; detours were made for the flue gases using partitions in the delay chamber). The flue gas temperatures at different locations in the pilot plant (shown in Figure 1) were (mean values, range) as follows: T1 (930 °C, 856-1045 °C), T2 (720 °C, 654-744 °C), and T3 (460 °C, 311-590 °C). Two blank tests were carried out for each residence time to check the absence of “memory effects”, i.e., some amounts of the catalysts, inhibitors, precursors, and PCDD/Fs may remain on the incinerator walls having a disturbing effect on the next runs. ClPh and PCDD/F samples were collected in glassfiber filters (Schleicher & Schuell) at locations T4 (200 °C) and T5 (180 °C) for examination of the particulate phase (kept in a box at ca. 130 °C during the sampling period to prevent water condensing onto the filter) and in XAD-2 resin (Amberlite, 20-50 mesh) for the gas phase after removing the condensation water from the sample stream. The particulate mass collected on the filter was measured by weighing the filter at constant temperature and relative humidity (20 ( 1 °C and 45 ( 5%, respectively) before and after sampling. The flue gas parameters O2, CO, and CO2 were analyzed continuously at the same location just after the PCDD/F sampling. The water/gas ratio was estimated by direct calculation; the amount of water was measured by condensing it at the end of analysis. Details of the analytical procedure have been described elsewhere (28, 29). It consists of Soxhlet extraction with toluene, ClPh extraction with potassium carbonate, and PCDD/F purification with concentrated sulfuric acid and multistep columns. The particulate and gaseous phases were analyzed separately. Finally, the samples were analyzed using a HP 6890 gas chromatograph with a HP 5973 mass selective
detector (ClPhs) and also with a HRGC/HRMS (VG 70 250 SE), operating the MS at a resolution of 10 000 (PCDD/Fs). Computational Methods. PLS (partial least-squares or projections to latent structures) is a projection method to model complex relationships in a set of data. A lucid presentation of its mathematical details is given by Geladi and Kowalski (30), for example. PLS decomposes two data matrices, X (independent variables) and Y (dependent variables), into new latent variables (PLS components), and creates simultaneously a predictive relationship between them. The optimum number of PLS components is determined by cross-validation. LOO (leave-one-out) crossvalidation proceeds by omitting one sample of input data, rederiving the PLS model, and predicting the Y values of the omitted sample; this cycle continues until all Y values have been predicted exactly once. The cross-validated correlation coefficient Q2 is calculated from Q2 ) 1 - ∑(ypred - yobs)2/ ∑(yobs - ymean)2, where ypred ) predicted dependent variable, yobs ) observed dependent variable, and ymean ) mean of observed dependent variables. In general, PLS is more resistant against change correlations than the conventional regression analysis. The final, non-cross-validated model will be derived using the optimum number of PLS components, and its statistical significance is evaluated using conventional indicators, i.e., correlation coefficient r 2, standard error SE, and F statistic. PLS modeling was carried out using the Molecular Spreadsheet facility of the SYBYL molecular modeling program package (31) with appropriate options (VALIDATION ) LEAVE-ONE-OUT, SCALING ) AUTOSCALE). The model reported here is selected according to the crossvalidated standard error minimum, subject to the constraint that the maximum number of PLS components should not exceed N/4, where N is the number of samples (32). Preliminary correlation analyses were performed with the MATLAB program package (33).
Results and Discussion An X-block matrix consists of all the ClPh isomers (measured in both particle and gas phases) together with 10 operating parameters (Table 1). The most abundant ClPh isomers throughout the test runs were 2-, 4-, 2,4- (+ coeluted 2,5-), 2,4,6-, 2,3,4,6-, and 2,3,4,5,6-ClPh (Figure 2), i.e., the effect of the ortho-para controlling OH-group is clearly seen. The concentrations of other ClPh isomers were practically zero in the particle phase. As typical for different combustion sources (34-36), PCDF-to-PCDD ratio was greater than one (Figure 3). Overall, the isomeric distribution of PCDD/Fs is similar to that found in MSW incinerators, i.e., highly chlorinated PCDD congeners predominated throughout the tests, while PCDFs showed the highest amounts for hexaand hepta congeners (34, 35, 37, 38). Since this study deals only with 18 TEQ-related PCDD/Fs, the experiments resulted in a Y-block matrix with 53 samples, each described in terms of 36 measured PCDD/F responses (Table 2). The levels of the individual PCDD/Fs were converted to one value of toxic equivalent quantity I-TEQ ) ∑(concentration × I-TEF), and I-TEQ values of PCDDs, PCDFs, and PCDD/Fs were added to the Y-block matrix. The International TEF factors were taken from ref 39. Correlation Analysis. Pearson correlation coefficients were calculated separately for the gas and particle phases. The results (given only for I-TEQs of PCDDs and PCDFs as an illustrative example, Table 1) indicated that the relation between ClPhs and PCDD/Fs in the gas phase is completely different from that in the particulate phase. In the gas phase PCDD/Fs correlate either closely (PCDFs) or at least moderately (PCDDs) with all the ClPh isomers. Interestingly, the correlation coefficients for 3,5-ClPh are very low, which may be interpreted as indirect evidence of the importance of ortho VOL. 34, NO. 23, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. X-Block (Nos. 1-27) Variables and Their Correlations with I-TEQ Values of PCDDs and PCDFs in the Gas and Particle Phase gas phase
particles
no.
variable
PCDD
PCDF
PCDD
PCDF
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
2-ClPh 342,62,4 (+2,5) 3,52,33,42,4,62,3,62,3,42,4,52,3,53,4,52,3,4,62,3,4,52,3,4,5,6temp T1 (°C) temp T2 (°C) temp T3 (°C) O2 (%) CO (ppm) CO2 (ppm) particles (mg/Nm3) urea (%) water/gas ratio (kg/kg) residence time (s)
0.30 0.41 0.38 0.37 0.49 0.06 0.48 0.38 0.53 0.38 0.47 0.45 0.51 0.36 0.71 0.42 0.42 0.03 -0.41 0.08 -0.13 0.23 0.08 0.22 0.03 0.17 -0.10
0.66 0.82 0.79 0.76 0.85 0.33 0.88 0.79 0.76 0.80 0.82 0.80 0.90 0.78 0.75 0.75 0.58 -0.13 -0.22 0.01 0.01 0.03 -0.05 -0.07 -0.15 0.02 0.00
-0.14
-0.08
-0.14
-0.06
-0.15
-0.11
0.42
0.38
0.87
0.75
0.89 0.14 -0.82 0.31 -0.38 0.26 0.37 0.38 -0.05 0.37 -0.37
0.76 0.07 -0.71 0.31 -0.22 0.11 0.23 0.25 -0.13 0.19 -0.47
FIGURE 2. Average chlorophenol isomer pattern (gas phase and particles). chlorine atoms (see below). The correlation with the thermodynamically most stable ClPh (2,3,4,5,6-ClPh) was clearly lower than that for the other ClPh isomers, in particular for PCDFs; this is probably caused by the resistance to radical formation. The importance of the supplementary X-variables nos. 18-27 (process parameters) seems to be remarkably small, and the differences between PCDDs and PCDFs were small in this respect. In the particle phase, the role of particlebound (2,4,6-), 2,3,4,6-, and 2,3,4,5,6-ClPh becomes decisive. Furthermore, some process parameters, especially the temperature T2 together with O2, CO, CO2, particles, water/gas ratio and residence time, are clearly of some importance. Considering individual TEQ-related PCDD/F congeners, the results of the preliminary correlation analysis indicate that the amounts of some chlorophenols, in particular 2,3,4,6and 2,3,4,5,6-ClPh, correlate with those of PCDD/Fs, especially hexa- and heptaisomers. Instead, the correlations with 4960
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FIGURE 3. Average PCDD/F congener pattern (gas phase and particles).
TABLE 2. PLS Analysis Derived by Fitting the Gas (g) and Particle (p) Phase Concentrations Simultaneously to the Modela Y-variable
Q2
2,3,7,8-TeCDD (g) 1,2,3,7,8-PeCDD (g) 1,2,3,4,7,8-HxCDD (g) 1,2,3,6,7,8-HxCDD (g) 1,2,3,7,8,9-HxCDD (g) 1,2,3,4,6,7,8-HpCDD (g) 1,2,3,4,6,7,9-HpCDD (g) OCDD (g) 2,3,7,8-TeCDF (g) 1,2,3,7,8-PeCDF (g) 2,3,4,7,8-PeCDF (g) 1,2,3,4,7,8-HxCDF (g) 1,2,3,6,7,8-HxCDF (g) 2,3,4,6,7,8-HxCDF (g) 1,2,3,7,8,9-HxCDF (g) 1,2,3,4,6,7,8-HpCDF (g) 1,2,3,4,7,8,9-HpCDF (g) OCDF (g) 2,3,7,8-TeCDD (p) 1,2,3,7,8-PeCDD (p) 1,2,3,4,7,8-HxCDD (p) 1,2,3,6,7,8-HxCDD (p) 1,2,3,7,8,9-HxCDD (p) 1,2,3,4,6,7,8-HpCDD (p) 1,2,3,4,6,7,9-HpCDD (p) OCDD (p) 2,3,7,8-TeCDF (p) 1,2,3,7,8-PeCDF (p) 2,3,4,7,8-PeCDF (p) 1,2,3,4,7,8-HxCDF (p) 1,2,3,6,7,8-HxCDF (p) 2,3,4,6,7,8-HxCDF (p) 1,2,3,7,8,9-HxCDF (p) 1,2,3,4,6,7,8-HpCDF (p) 1,2,3,4,7,8,9-HpCDF (p) OCDF (p) I-TEQ ∑ PCDDs (p+g) I-TEQ ∑ PCDFs (p+g) I-TEQ ∑ PCDD/Fs (p+g)
