approximations are involved. Similarly, Butler and Burke (16) applied Cropper and Kaminski’s approximate formula to an analysis of porous adsorption tube samplers, with the consequent result that their analysis is increasingly inaccurate as N is decreased below 30. The breakthrough volumes as determined from Figure 2 are derived from chromatographic theory assuming a standard Gaussian chromatographic response to a narrow pulse injection of a trace concentration of adsorbate. In some instances, as the concentration of adsorbate is increased, the response is no longer Gaussian but becomes a skewed Gaussian or some other functional form. Breakthrough volume can similarly be derived in these instances by the method given in Reilley ( 1 2 ) . However, it is felt that the breakthrough volume as derived from a trace concentration Gaussian response should represent a realistic breakthrough volume regardless of the actual non-Gaussian chromatographic response or adsorbate concentration of the adsorption sampler. This is supported by the observation that, in the majority of instances with increasing trace adsorbate concentration, the response can be expressed as a Gaussian skewed to the upstream side. However, in the instance when an adsorption tube sampler is required to collect larger concentrations of an adsorbate, Le., nontrace concentrations, it is recommended that the adsorbate retention time and the adsorbent number of theoretical plates be determined by the technique of inverse chromatography, as first detailed by Reilley (12). In this technique, a plug of pure carrier gas is injected onto an adsorbent column previously equilibrated with a carrier gas containing the maximum expected concentration of adsorbate. The resulting response appears as an inverse chromatograph, Le., having a negative peak. From this negative peak the adsorbate retention time and the adsorbent number of theoretical plates can be determined, allowing the breakthrough volume with a required collection efficiency to be calculated as described in the text. This breakthrough volume with its required collection efficiency is assured for the maximum expected adsorbate concentration. This argument applies also to the collection of many components with an adsorption sampler, and in these instances the derived breakthrough volume (for the component of interest with the least retention volume) should be regarded only as an approximate safe sampling volume. However, if the component retention volumes are determined at the con-
centrations actually expected during sampling and in the presence of all of the other components by an inverse chromatography experiment, then the calculated breakthrough volume can be regarded as a realistic measure of a safe sampling volume. Similarly, the Gaussian breakthrough volumes are valid for the collection of an adsorbate which varies in trace concentration over the sampling period, as long as the adsorbate concentration on an average extends throughout the entire sampling period. However, a change in the flow rate of air through the sampler can be expected to change the collection efficiency of the sampler since N is slightly dependent on flow. Consequently, N must be determined at the expected sampler flowrate. Literature Cited (1) Cropper, F. R.; Kaminsky, S. Anal. Chem. 1963,35,735. (2) Pellizzari, E. D.; Bunch, J. E.; Carpenter, B. H.; Sawicki, E. Enuiron. Sci. Technol. 1975,9, 555. (3) Parsons, J. S.; Mitzner, S. Enuiron. Sci. Technol. 1975, 9, 1053. (4) Russell, J. W. Enuiron. Sci. Technol. 1975,9, 1175. (5) Raymond, A.; Guiochon, G. J. Chromatogr. Sci. 1975,13,173. (6) Jones, P. W.; Grammer, A. D.; Strup, P. E.; Stanford, T. B. Enuiron,. Sci. Techrtol. 1976,10,806. ( 7 ) Holzer, G.; Shanfield, H.; Zlatkis, A.; Bertsch, W.; Juarez, P.; Mayfield, H.; Liebich, H. M. J. Chromatogr. 1977,142,755. ( 8 ) Tanaka, T. J. Chromatogr. 1978,153, 7. (9) Brown, R. H.; Purnell, C. J. J . Chromatogr. 1979,178,79. (10) Gallent, R. F.; King, J. W.; Levins, P. L., Piecewicz, J. F. “Characterization of Sorbent Resins for use in Environmental Sampling”, EPA Report 600/7-78-054, March 1978. (11) Piecewicz, J. F.; Harris, J. C.; Levins, P. L. “Further Characterizations of Sorbents for Environmental Sampling”, EPA Report 600/7-79-216, Sept 1979. (12) Reilley, C. N.; Hildebrand, G. P.; Ashley, J. W. Anal. Chem. 1962,34,1198. (13) Abramowitz,M., Stegun, I. A,, Eds. “Handbook of Mathematical Fupctions”; U.S. Government Printing Office: Washington, DC, 1964; Chapter 7. (14) Berlyand, 0. S.: Gavrilova, R. I.; Prudnikov, A. P. “Tables of Integral Error Functions and Hermite Polynomials”, Macmillan: New York, 1962. (15) Dietz, R., Brookhaven National Laboratory, private communcation, 1979. (16) Butler, L. D.; Burke, M. F. J . Chromatogr. Sci. 1976,14,117.
Received for reuieu September 29,1980. Accepted May 12,1981.This research was performed under the auspices of the United States Department of Energy under Contract No. DE-AC02-76CH00016.
Comparison of Trihalomethane Levels and Other Water Quality Parameters for Three Treatment Plants on the Ottawa River Rein Otson,” David T. Williams, and Peter D. Bothwell Bureau of Chemical Hazards, Environmental Health Directorate, Health and Welfare Canada, Ottawa, Canada
Tony K. Quon Bureau of Management Consulting, Management Services, Supply and Services Canada, Ottawa, Canada
Introduction
Potable water supplies which contain chlorine as a disinfectant may also contain halogenated organics at levels potentially hazardous to human health. Reactions of chlorine with naturally occurring organic materials (1, 2 ) and with model compounds ( 3 ) in water have been demonstrated to yield halogenated products such as the trihalomethanes (THM). The factors controlling production of trihalomethanes during potable water treatment have been investigated 0013-936X/81/0915-1075$01.25/0 @ 1981 American Chemical Society
for a number of water supplies (4-6). Water parameters, such as, temperature, total organic carbon content (TOC), pH, color, chlorine demand, turbidity, and water type (surface water or groundwater) have been shown to affect trihalomethane production. Also, water treatment practices, such as chlorination, filtration, coagulation, and storage, have considerable effect on the extent of formation of halogenated organics. Studies (4,6-9) have been conducted wherein water quality parameters and treatment practices for potable water treatVolume 15, Number 9, September 1981
1075
Water quality in three potable water treatment systems obtaining raw water from the Ottawa River was compared over a period of 13 months. Treated water from two plants was similar but was significantly ( p < 0.05) different from treated water in the third treatment plant which differed in design and applied different chlorination practices. Pearson correlation coefficients showed a complex relationship among several water quality parameters. Multiple regression analysis showed that chlorine dosages and demands were dominant
in determining chloroform levels, and other parameters displayed a minor effect which varied from system to system and according to stage of treatment. Pronounced seasonal variation was observed for temperature, chlorine dosages and demands, trihalomethane (THM) levels, alkalinity measurements, and alum dosages. A significant ( p < 0.05) variation was found in T H M levels over a 24-h period at any particular point in a system.
ment systems have been determined on a limited number of occasions for a large number of municipalities. Investigations of factors determining T H M production in a potable water treatment system have been conducted over a period of several months (5, 10). Such studies have focused on a limited number of water quality parameters or topics, and in order to complement these studies a detailed, 13-month survey of water in three treatment systems on the Ottawa River was carried out.
Table I. Parameter Measurements and Mean Values Over 13 Months in Treatment Systems A-C
Experimental Section Treatment Systems a n d Water Parameter Monitoring. Three potable water treatment systems, which apply complete water treatment (Table I), are situated near and obtain raw water from the Ottawa River. Plants A and C, which are of similar design, applied conventional coagulation and sedimentation with rapid sand and anthracite filtration, whereas a Degremont system ( 1 1 ) with a Pulsator clarifier and an Aquazur “V” filter was used at plant B. Plants B and C are 5.1 and 6.7 km, respectively, downstream from plant A. The average residence time for water in each plant was estimated at 4.5 h and the rated daily capacities for plants A-C are 190 X lo3, 90 X lo3, and 190 X lo3 m3, respectively. Breakpoint chlorination is applied a t plant B from the end of May to mid-September. Water quality parameters were monitored a t locations shown in Table I and at station 2 for system C, at ca. 9:00 a.m. on Wednesdays, a t 2-week intervals, and over a 13-month period starting in July 1977 and ending in August 1978. Only grab samples were obtained from cold water taps situated at station 1 , l . S km from the treatment plant, and, for system C, at stations 2-5 established at distances of 2.6,3.2,4.5, and 5.1 km from plant C, respectively. For 5 consecutive days in August 1978, starting on Monday, grab samples from treatment system C were analyzed for total available residual chlorine (TARC),TOC, and T H M content after sample collection each morning at ca. 9:00 a.m., and every 4 h for 24 h starting Wednesday morning. Temperature, pH, color, turbidity, conductivity, alkalinity, chlorine residual, fluoride residual, and bacteria count values were obtained for grab samples analyzed (12) at the plants. Plant effluent water pH, chlorine residual, and fluoride residual values were obtained from on-line, continuous recorder charts. Plants A and C applied the same in-house developed method for percent settling determination, whereas plant B used a different in-house method. Additional raw, filter effluent, plant effluent, and distribution system water grab samples were collected in duplicate for T H M (quenched and stored 1 day) (13),TOC (stored 0.72). The anomalous relationships for the chlorine dosages and demands, and the TARC levels, can be explained by the difference in chlorination practices a t the plants. At plants A and C, prechlorine was adjusted to maintain a minimum chlorine residual in the filter effluent water. Hence, the dosage was largely dependent on chlorine demand but usually did not fully satisfy the demand. At both plants, postchlorine dosage Volume 15, Number 9, September 1981
1077
was adjusted with the objective of maintaining a particular TARC (-0.9 ppm) in the plant effluent water. Consequently, the larger the prechlorine dosage, the more the chlorine demand had been satisfied, and the smaller the postchlorine dosage required to meet the TARC objective. At plant B, the pre- and postchlorine dosages were adjusted roughly in unison and generally according to the time of year, rather than to meet a specific plant effluent TARC objective. As a result, both the filter effluent and plant effluent TARC values varied widely. Turbidity, TOC, color, and p H measurements did not generally show very strong correlation with T H M levels but often showed strong ( r > 10.701) correlation with parameters, such as temperature, prechlorine dosage, and alum dosage. Alum dosage showed negative correlation with chloroform levels ( r d -0.68), raw water temperature (r d -0.90), and filter effluent TOC ( r < -0.70), and positive correlation with raw water alkalinity ( r 3 0.80). Raw water temperature ( r 2 0.87) and alkalinity ( r d -0.74) in turn showed a strong effect on chloroform levels in system B and a similar, but weaker, effect in systems A and C. Filter effluent TOC showed good correlation ( r 3 0.71) with plant effluent and station 1 chloroform levels in system B, but weak correlation in systems A and C where good correlation ( r d -0.70) with plant effluent TARC was evident. Some correlation ( r 2 0.50) occurred between temperature and filter effluent TOC a t all plants. Parameters such as conductivity, water volume leaving and entering the plants, and silicate dosage, a t times, also showed some ( r 3 )0.50])correlation with each other and with other parameters. A complex interdependence of pH, alkalinity, conductivity, turbidity, and temperature was observed. Alum, silicate, lime, and chlorine dosages are thus always to some extent dependent on water quality as described by many parameters. The interdependence of a large number of water quality parameters suggests that the use of correlation coefficients for identification of parameters which control T H M production is limited. Mean Chloroform Level Models. Best fitting models for mean chloroform levels in filter effluent water, the change in mean chloroform levels from filter effluent to plant effluent water and from plant effluent to station 1water, and the mean chloroform level in station 1 water allowed identification of parameters which were statistically significant in predicting chloroform levels. Table I11 summarizes some modeling results obtained with appropriate chlorine dosages or chlorine demands as part of the independent variables. Unlisted models, which gave similar results, were also considered in the following discussion.
0
2140wI
I/\ 0
U
2 120 -
a
[r
5 100
X'
I
x-x
0-0
0 '
I
AUG
NCV
FEB
MAY
Seasonal changes in water temperature (e),plant effluent chloroform levels, raw water TOC, and total chlorine dosages at plants A (X) and B (0). Figure 1.
Table II. Correlation between Chloroform Levels and Values of Some Parameters Measured Over 13 Months in Treatment Systems A-C flller effluent water parameler
prechlorine dose postchlorine dose total chlorine dose alum dose chlorine demand 1 chlorine demand 2 total chlorine demand raw water, temperature TOC filter effluent, TOC plant effluent, TOC TARC
1078
A
B
C
+0.83 -0.75 f0.77 -0.52 fO.81 -0.12 f0.79 f0.50 -0.43 +0.28 4-0.27 -0.79
4-0.92 +0.89 f0.93 -0.88 f0.94 f0.75 +0.93
4-0.95 -0.50 fO.90 -0.58 +0.94 +0.28 f0.92 +0.60 -0.36 4-0.35 $0.40 -0.77
+0.91 -0.13 +0.90 -0.64 f0.92 f0.52 +0.91 $0.62 -0.32 $0.56 4-0.52 -0.35
+OB8 -0.43 4-0.69 f0.59 +0.36
Environmental Science & Technology
station 1 water
plant effluent water A +B + C
A
4-0.93 -0.68 +0.90 -0.66 4-0.92 f0.05 4-0.92 f0.68 -0.13 $0.37 +0.35 -0.82
B
+0.88 +0.91
+0.90 -0.89 +0.90 +0.83 +0.91 $0.90 -0.42 $0.72 4-0.66 f0.26
C
+0.89 -0.33 +0.88 -0.61 fO.90 +0.35 +0.89 $0.61 -0.32 4-0.39 4-0.43 -0.68
A+B +C
$0.88 +0.02 fO.91 -0.69 +0.91 4-0.65 +0.91 f0.67 -0.23
+0.60 $0.56 -0.30
A
$0.92 -0.51 +0.93 -0.73 4-0.93 4-0.07 f0.93 +0.77 +0.03 +0.43 f0.42 -0.71
B
-I-0.87 4-0.89 +0.89 -0.88 +0.89 f0.78 +0.90 +0.87 -0.37 $0.71 f0.69 4-0.30
C
$0.91 -0.34 $0.91 -0.68 $0.92 +0.37 +0.92 +0.70 -0.37 +0.41 4-0.47 -0.72
A + B +C
f0.83 fO.11 +0.88 -0.76 4-0.87 +0.60 +0.88 4-0.73 -0.22 $0.58 f0.57 -0.22
~~
Table 111. Best Fitting Mean Chloroform Level Models a from Results of Multiple Regression Analysis regression coeflicients ( R ) and CumUiatiVe treatment stage and variables
filter effluent prechlorine dosage raw TOC raw temp raw color raw bacteria raw conductivity raw alkalinity raw pH alum dosage silicate dosage filter to plant effluentC prechlorine dosage postchlorine dosage raw TOC raw temp filter TOC silicate plant effluent to station 1 e raw temp demand 1 demand 2 demand 3 plant TOC station 1 prechlorine dosage postchlorine dosage raw turbidity raw alkalinity raw pH raw color raw bacteria raw temp alum silicate
system A R
%
16.gd -5.53d -0.503d
65.8 80.8 81.3
-0.218d
R
2.80d
-4.49 0.944d -24.2 -54.4
33.6d 33.5
83.4
1.98d
87.8
87.7
R
%
14.7d
90.5
-0.526d 0.002 -0.146
91.5 91.6 91.6
0.306
91.9
91.6 91.6
14.3d
91.3
52.Bd
75.7
1.45 9.45
80.8 81.4
10.6
85.0 88.3
12.2d
82.1
3.22
86.9
8.91
85.6
0.658d -1.80 -21.9 -30.5
28.9 40.7 35.6 39.7
33.5d
82.7
88.7 41.4 62.4 60.9
83.9 86.8
-1.59
87.3
-0.098
85.3
-10.6
system C
%
9.08d
0.443 7.74
15.4d
explained variation ( % )
system B
85.8
9.53d
97.9d
-4.39d 23.8
77.7
85.3 86.4
2.95 5.92d
89.2 85.1
0.005
89.4
4.65 31.2d
86.0 85.0
Minimumresidual error variance as a criterion. * For each variable,all other variables with smaller explained variationvalues are included. Model with chlorine dosages as part of the independent variables. Significant at p < 0.05. e Model with chlorine demands as part of the independent variables.
Throughout systems A and C, prechlorination dosage was dominant in determining mean chloroform levels. Chlorine demand 1 was also a dominant factor in the initial stages of treatment, and water treatment showed some influence after water left the treatment plant, but chloroform levels in the distribution system were predominantly determined by the prechlorine dosage. At plant B, although mean chloroform levels in filter effluent water were determined by prechlorine dosage and chlorine demand 1,postchlorine dosage and water temperature were influential in determining subsequent changes in chloroform levels. Overall, the mean chloroform levels in distribution system B were determined chiefly by postchlorine dosage. Some raw water parameters and parameters which were influenced by, or associated with, the sedimentation and filtration processes, e.g., alum and silicate dosages, showed a mixed and usually minor influence on chloroform levels a t all three treatment plants. Values of the correlation coefficients (e.g., Table 11)suggested a substantial effect of parameters such as alum dosage and temperature on T H M levels and also demonstrated a complex interrelationship between parameters. As determined by multiple re-
gression analysis, chlorine dosages and demands were the principal factors in determining T H M levels, and most other parameters were not significant.
Summary Water quality in three potable water treatment systems, which obtain raw water from the Ottawa River, was compared over a 13-month period. Treated waters from the two similar treatment plants were not significantly ( p > 0.05) different, but they were significantly ( p < 0.05) different from treated water at the third plant, which was somewhat different in design and applied different chlorination practices. Also, the nature of correlations found for water parameters in the two similar systems was quite often quite different from those in the third system. However, the interdependence observed for a large number of parameters suggested that the use of correlation coefficients for identification of parameters which control T H M production is limited and may lead to erroneous conclusions. Chlorine dosage (16) and chlorine demand (17) have been reported to be the dominant factors in predicting T H M levels Volume 15, Number 9, September 1981
1079
after multiple regression analysis of data from one-time sampling studies of many water systems. A study (7) of 10 treatment plants over a 10-month period concluded that no single factor always dominated T H M formation but that total chlorine dosage was a principal factor. In the present study, when data for all three plants were combined, multiple regression analysis showed that chlorine dosages and demands were dominant in determining mean chloroform levels. Seasonal trends, with minimum T H M levels observed during the cold winter months ( 3 , 7 , 8 , 1 8 - 2 0 ) and with maximum T H M levels during spring (18),summer ( 8 , 1 9 , 2 0 ) ,and fall ( 7 ) ,have been reported. Water temperature ( 5 , 2 1 ) and variations in the organic content of the raw water supply ( 5 , 7 , 18) have been suggested as factors influencing seasonal T H M level fluctuations. In the present study, seasonal variation, with low values in the winter and high values in the summer, was observed for temperature, prechlorine and total chlorine dosages, chlorine demand 1 and total demand, and CHC13 and CHBrClz levels. High values in the winter and low values in the summer were evident for alkalinity measurements and alum dosages. The need to consider temporal and spatial variation in T H M levels ( 2 2 ) during planning of sampling schedules and in the interpretation of monitoring results was emphasized by the finding of a significant ( p < 0.05) variation in T H M levels during a 24-h period and from day to day in water from a tap in the distribution system. Although changes in T H M levels after water leaves a treatment plant have been reported ( 4 , 7 , I O ) , chloroform levels did not change significantly ( p > 0.05) between stations 2 and 5 in the present study. Over 13 months, trihalomethane levels in station 1 water for the three treatment systems ranged from 7.0 to 187 pg/L for CHCl3 and
