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Water quality before and after deep tunnel operation in milwaukee, wisconsin
PII: S0043-1354(00)00556-X
Wat. Res. Vol. 35, No. 11, pp. 2683–2692, 2001 # 2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0043-1354/01/$ - see front matter
WATER QUALITY BEFORE AND AFTER DEEP TUNNEL OPERATION IN MILWAUKEE, WISCONSIN IRWAN A. AB RAZAK and ERIK R. CHRISTENSEN*,y Department of Civil Engineering and Mechanics, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA (First received 1 December 1999; accepted in revised form 1 October 2000) Abstract}The mitigative effects of the deep tunnel for temporary storage of storm water and sewage, on the water quality of the Milwaukee, Menomonee, and Kinnickinnic Rivers are investigated. The analysis is based on data from the Milwaukee Metropolitan Sewerage District’s overflow and surface-water quality monitoring program. Statistical analysis of water quality parameters (BOD, phosphorus, suspended solid, fecal coliform counts, zinc, and chloride) in the three rivers indicates that Menomonee River benefits the most from the deep tunnel. Fecal coliform counts inside the CSO area, and to a certain extent BOD and zinc levels, exhibit the most significant decline after 1994 when the tunnel came on line. These conclusions are based on t-test comparisons of regional averages incorporating spatial and temporal correlations from 1991 to 1993 and 1994 to 1997. The results from t-tests are complemented and confirmed with results from Mann–Kendall tests for trend. Suspended solids and chloride do not decrease after 1994. # 2001 Elsevier Science Ltd. All rights reserved Key words}water quality, combined sewer overflows (CSO), t-test, Mann–Kendall test
INTRODUCTION
The deep tunnel is part of the Milwaukee, WI USA metropolitan sewerage district’s (MMSDs) massive water pollution abatement program. The main purpose of the deep tunnel is to substantially reduce, if not eliminate combined sewer overflows (CSO) and overflows from sanitary sewers into the rivers, and ultimately Lake Michigan. This study was undertaken to systematically characterize water quality in the Milwaukee, Menomonee, and Kinnickinnic Rivers before and after the deep tunnel came online in 1994. While CSO control is now fairly common, there are only few studies of the resulting improvement in water quality of the receiving waters. By the mid-1970, the metropolitan interceptor sewer system had become insufficient to carry the load from the separate sewer system and the combined sewer system, particularly during wet weather. As a result, a decision was made to build an inline storage system, i.e., deep tunnel to absorb all but a few overflows per year from the metropolitan interceptor sewer. The inline storage system consists of four deep tunnels located approximately 91 m from the ground surface in the Niagara Dolomite bedrock. The tunnel *Author to whom all correspondence should be addressed. Tel.: +1-414-229-4968; fax: +1-441-229-6958; e-mail: [email protected] y Present address: Camp Dresser & McKee, Inc., 312 East Wisconsin Avenue, Milwaukee, WI 53202, USA
follows mainly the surface drainage patterns including the Menomonee, Milwaukee, and Kinnickinnic Rivers and Lincoln Creek (Fig. 1). The total length of the deep tunnel is 31.2 km. The tunnel extends primarily within the combined sewer overflow area, except that the north branch is aligned along the Lincoln Creek, and the west branch follows the Menomonee River to its confluence with Underwood Creek. The tunnel range in diameter from 1.83 to 9.84 m. The total capacity is 1.53 106 m3. When a volume of 5.68 105 is reached, combined sewage is no longer allowed to enter the tunnel in order to reserve the remaining capacity for the higher strength sewage from the separate sewer system. Sewers are connected to the tunnel through diversion structures, backed up with bypasses to the rivers for either separate or combined sewage. The objectives of the present work are to evaluate the pollution mitigating effects of the deep tunnel, and to develop recommendations for an optimal sampling strategy for future monitoring of the performance of the system. STUDY AREA
Figure 1 shows the locations of water quality sampling stations along the Milwaukee, Menomonee, and Kinnickinnic Rivers. The stations were classified based on their locations into two groups: outside and inside the CSO area. All sampling stations are located within the direct drainage area to the
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Fig. 1. Map showing water quality sampling locations along the Milwaukee, Menomonee, and Kinnickinnic River. Also shown is the extent of the combined sewer overflow are (MMSD, 1999).
Milwaukee Harbor estuary (SEWRPC, 1987). Referring to Fig. 1, sampling stations that are inside the Milwaukee Harbor estuary include RI-5 through RI7 in the Milwaukee River, and all sampling stations from the harbor to RI-9 in the Menomonee River. For the Kinnickinnic River, only sampling stations RI-13, 14, 18 and 19 are considered to be inside the Milwaukee Harbor estuary. As the focus of this study was the CSO area, only sampling station OH-3 in the Milwaukee’s Outer Harbor was examined. For a more complete analysis of the deep tunnel’s impacts on the Outer Harbor area, more sampling stations should be included. For each of the three rivers, the flow rates were obtained from instantaneous stream flow records
measured by the United States Geological Survey (USGS). The gage in the Milwaukee River is located at Estabrook Park, in the vicinity of river sampling station RI-4. For the Menomonee River, data from the gage located in Wauwatosa at the 70th street bridge was used. The gage is near sampling station RI-9 of the Menomonee River. Flow rates for the Kinnickinnic River were obtained from a gage by S. 11th Street between sampling stations RI-12 and RI-13. DATA ANALYSIS AND METHODOLOGY
Investigation of water-quality data ideally requires compensation of variations in stream flow as it could be
Water quality before and after deep tunnel operation
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Fig. 2. Total annual precipitation at selected rain gage stations.
one source of variability in the data set. The three river systems have different discharges, with the Milwaukee River having on average, the highest flow (106 m3/d), and the Kinnickinnic River the lowest (5 104 m3/d). The average flow for the Menomonee River is 2 105 m3/d. As the discharge increases, pollutants such as dissolved solids or metals decrease in concentration whereas for other pollutants, e.g., suspended solids, the levels may increase with discharge (Harned et al., 1981). However, in this study discharge normalization was not done because the rainfall did not vary greatly during the period investigated from 1991 to 1997 (Fig. 2). In addition, the variations within the watershed in any given year are comparable to the interannual variability. There is, however, a small tendency of lower rainfall for 1994–1997. This difference implies that our estimates for decreases after 1994 in concentrations of pollutants such as BOD and fecal coliform are conservative, i.e., discharge normalized decreases would have been higher. For statistical analyses, water-quality records were grouped into a pre-deep tunnel and a post-deep tunnel period: 1991–1993 and 1994–1997, respectively. Waterquality data from the Milwaukee, Menomonee, and Kinnickinnic River were examined separately. Furthermore, for each river, the sampling stations were grouped based on their locations in relation to the CSO and non-CSO areas. Quantification of the deep tunnel’s pollution mitigating effects was accomplished by comparing average pollutant levels from the two periods and the two areas (CSO and non-CSO). Major water-quality parameters that were analyzed include BOD, phosphorus, suspended solids, fecal coliform counts, zinc, and chloride.
For comparison purposes, statistical analyses were also conducted on Outer Harbor sampling stations OH-3 (Fig. 1).
Water quality data for the rivers and the Outer Harbor The river water quality sampling program (MMSD, 1999) is active from April 1 through November 15 of each year. There was biweekly sampling before 1994, and 15–20% less frequent sampling after that date. River water quality plots, as shown in Figs 3 and 4 for fecal coliform in the Menomonee River were prepared, in order to graphically compare the concentration levels, the degree of variations in the levels, and the overall trend at various river sampling stations inside and outside the CSO areas. The plots were prepared in the correct spatial order based on the sampling stations in the rivers from upstream to downstream towards the harbor area. The concentration levels that were examined were those sampled 1 m below the surface. In general, it can be observed that there is a certain seasonal similarity of water quality with respect to time. This is especially evident with phosphorus levels in all three rivers (Ab Razak, 1999) in contrast to fecal coliform counts (Figs 3 and 4), and to a certain extent BOD that exhibit distributions that are more random. Above the detection limit (2 mg/l), BOD also exhibits seasonality with higher levels from August to September. The cyclic nature of some of the pollutants can be seen for periods both before and after the deep tunnel came on line. There are lower pollutant levels in all three rivers towards the
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Fig. 3. Log(Fecal coliform) level 1991–1993 for Menomonee River (inside CSO area).
harbor areas (Figs 3 and 4), probably due to dilution with lake water. From Figs 3 and 4 it is obvious that the number of overflows at station WPDES 180 has been reduced drastically from more than 100 during 1991–1993 to only 3 after 1994. A resulting decrease in fecal coliform counts at sampling stations RI-20, 11, and 17 is also apparent. On August 28, 1995 and June 18, 1996, overflow and sampling were on the same day. In addition, there was overflow on June 21, 1997 and sampling two days later, June 23, 1997. Resulting elevated fecal coliform counts are clearly visible for stations RI-20, 11 and 17 (Fig. 4). However, in the majority of cases, samplings of river and Outer Harbor stations were done on days without overflow. The fractions of river samplings with overflow were 23, 18, and 27% for the Milwaukee, Menomonee and Kinnickinnic Rivers, respectively, for the 1991–1993 period. The corresponding numbers during 1994–1997 were 2, 4, and 0%, in the same order. A complete listing of sampling and overflow dates is given in Ab Razak (1999). Note that the regular sampling in time, i.e., every other week, coupled with the random occurrence of overflows, accurately reflects the average perceived risk of human contact with river water.
Statistical analysis As the water-quality parameters were sampled close in time and space, there could be problems with autocorrelation or tendency for neighboring observations to be alike (Berthouex and Brown, 1994; Gilbert, 1987). Among others, Durbin–Watson tests (Daniel, 1990) can be used to detect autocorrelation. Correlation can also be addressed, as was done here, by including spatial and temporal correlations when determining confidence limits about the regional means of the pollutants. The regional mean of a specific water-quality parameter such as BOD was calculated as follows. For a given group of river sampling stations, either outside or inside the CSO areas, with data over a given sampling period, for example can be expressed by from 1991 to 1993, the regional mean x ¼ x
ns X ns n 1 X 1X i x xij ¼ nns i¼1 j¼1 ns i¼1
ð1Þ
where n is the number of observations from each of ns i the stations, xij the j th observation at station i, and x estimated mean for the ith station.
Water quality before and after deep tunnel operation
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Fig. 4. Log(Fecal coliform) level 1994–1997 for Menomonee River (inside CSO area).
Data gathered from sampling stations located close in space may be highly correlated. In fact, this can be observed from the plots shown in Figs 3 and 4. Spatial correlation has to be taken into account when determining the variance, and ultimately the standard deviation of the regional mean. According to Gilbert (1987), the spatial correlation of two sampling points i and j can be calculated from PK i Þ xjk x j k¼1 ðxik x ð2Þ rij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ffi PK P i Þ2 K j k¼1 ðxik x k¼1 xjk x j are the average values at station i and j, i and x where x respectively, and K is the number of data, taken at the same dates, at each station. For subsequent calculations, we used ^c , obtained from all the average of the spatial correlations r ns ðns 1Þ=2 cross-correlations between the ns stations, e.g. for 1991–1993. Another correlation that is important is the one observed from samples taken at equal intervals along a line in time (Kachigan, 1986; Gilbert, 1987). The auto-
correlation rl , for example, denotes correlation of measurements taken l time lag apart i.e., between x1 and x2 , between x2 and x3 , etc., and the same is true with r2;3;4...:;n1 . Gilbert (1987) gives a method for estimating the lth lag autocorrelation: Pnl rl ¼
Þðxtþ1 x Þ ðxt x Pn Þ2 t¼1 ðxt x
t¼1
ð3Þ
is the average measurement at the particular where x location. Here, only autocorrelations up to 3 time lags apart were considered, assuming rl ¼ 0 for l > 3. In calculations involving confidence limits of the regional mean, we used ^l across sampling stations and the average of each time lag r time periods. After calculation of the spatial and temporal correlations, the 100(1 a)% confidence interval of the estimated mean can be found for data recorded at a given group of river sampling stations. The confidence interval when multiple
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Irwan A. Ab Razak and Erik R. Christensen
sampling stations are involved can be estimated from (Gilbert, 1987) t1a=2;nns 1 s x " #1=2 ! n1 1 2X ^c ðns 1ÞÞ 1þ ðn l Þ^ rl ð1 þ r ð4Þ nns n l¼1 where n is the total number of data points in time from a given sampling station, ns the number of sampling stations. " !#1=2 Pns i x Þ2 i¼1 ðx s ¼ nns ns ðns 1Þ ^l and r ^c as previously described. with r The confidence limits calculated from equation (4) are only for data recorded at multiple sampling stations. For the case where only one station was involved, for example at river sampling station RI-12 of the Kinnickinnic River, and Outer Harbor station OH-3, the following equation was used: " !#1=2 n1 1 2X t1a=2;n1 s 1þ ðn lÞ^ rl ð5Þ x n n l¼1 where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðxi x Þ2 : s¼t n1 i¼1 As was the case with multiple stations (equation (4)), the summation term involving the time lag in equation (5) only covers l =1 to 3. In equations (4) and (5), a 90% confidence interval of the regional mean or a ¼ 10% was used. To consider the question of improvement in water quality, records gathered from before and after the pollutant mitigation project have to be statistically analyzed. The t-test (Miller et al., 1990) is valuable in testing the hypothesis regarding improvement in river water quality after the deep tunnel came on line. Results from t-tests can be complemented with non-parametric tests such as the seasonal Kendall or Mann–Kendall test for trend. The most notable advantage of non-parametric tests is that they do not require the data to fit many of the distributional assumptions of parametric methods (McCuen, 1985; Gilbert, 1987). A t-test was performed to evaluate the null hypothesis of ðm1 m2 Þ ¼ 0, compared to the alternative hypothesis of ðm1 m2 Þ > 0. A suitable t-statistic was then used as an estimate of the unknown population variance. As the confidence intervals were calculated using a=10%, the statistic for test concerning differences between two means was modified by the following equation: 1 x 2 x ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t ¼ r q1 2 q2 2 þ a b
ð6Þ
where q1 , q2 are the term of equation (4) or (5) depending on the number of sampling stations involved, a, b is equal to 1.645 which is the quantile of the t distribution value at 1 degrees of freedom, and at t0:95 . As a complement to the t-test analysis, the data was also subjected to the Mann–Kendall test for trend. In the t-test, two averages computed from data sampled before, and after the commissioning of the deep tunnel in 1994, were compared. With the Mann–Kendall test, on the other hand, yearly averages of each water quality parameter from 1991 through 1997 were analyzed and inferences concerning the trend of these yearly average values were made. The null hypothesis was that there is no trend in the annual average values, while the alternative hypothesis was for a downward trend.
The first step of the Mann–Kendall test as described by Gilbert (1987), involves arranging n specific average annual pollutant values in chronological order from 1991 to 1997. The difference xj-xk, where j > k for all nðn 1Þ=2 possible differences of the average values was then calculated. Only the sign, and not the magnitude of the difference between consecutive values are important. In mathematical form, this can be written 8 < 1 if xj xk > 0 ð7Þ sgnðxj xk Þ 0 if xj xk ¼ 0 : : 1 if xj xk 50 The Mann–Kendall statistic was then obtained from S¼
n1 X n X
sgnðxj xk Þ
ð8Þ
k¼1 j¼kþ1
RESULTS AND DISCUSSION
Spatial correlation of river sampling stations Focusing first on river sampling stations RI-1–4 of the Milwaukee River from outside the CSO area, the overall average spatial correlations between the sampling stations are of similar magnitude for all water quality parameters. From the 1991–1993 results, it appears that the average spatial correlation value is lowest for zinc (0.113) and highest for phosphorus (0.889) with intermediate values for the rest of the parameters. The high value for phosphorus (P) may be explained by the P source, which may be upstream farm fertilizer runoff. By contrast, Zn is likely to originate from local street runoff that is highly variable depending on locations of streets and discharge points. For BOD from 1991 to 1997, many values were at the detection limit of about 2 mg/l. This has an impact on the correlation calculations for BOD. The results for the period from 1994 through 1997 show that the correlation is highest for BOD (0.832) and lowest for zinc (0.465). The correlation coefficient for P is 0.635. For sampling stations RI-5, 6, and 7 inside the CSO area (1991–1993, and 1994–1997), spatial correlations are also of comparable magnitude for most of the water quality parameters. The results for 1991–1993 indicate that the highest correlation is associated with fecal coliform (0.696), and the lowest with zinc (0.137). After 1994, the highest average correlation is that of chloride (0.905), and the lowest, suspended solids (0.387). Comparing spatial correlations between two specific stations, for most of the water quality parameters (1991–1993, and 1994–1997), the results suggest that stations RI-6 and 7 provide nearly the same amount of information (Fig. 4). Most correlations r6;7 are between 0.7 and 0.9, with an average of 0.68. However, there may be other factors that are important in continuing with sampling at both of these two stations such as the fact that overflow station WPDES 147 is located between RI-6 and 7. Based on data from 1991–1993, for Menomonee River stations outside the CSO area (RI-16, 21, 22, 9),
Water quality before and after deep tunnel operation
the spatial correlation is highest with respect to suspended solids (0.731), and lowest for zinc (0.427). Data from 1994 through 1997 show highest average correlation for fecal coliform (0.739), and lowest for zinc (0.459). Similar to the case for Milwaukee River, many of the BOD data for the Menomonee River are at the detection limit. Therefore, less weight should be given to spatial correlation results for BOD. Comparing spatial correlations between two specific stations, the results for most parameters during 1991–1997 suggest that stations RI-21 and RI-22 provide almost the same amount of information (Fig. 3). Also here, most correlations r21;22 are between 0.7 and 0.9. The average r21;22 is 0.74. It may be possible to discontinue sampling at one of these stations based on this result, although RI-22 includes the impact of the Little Menomonee River (Fig. 1). For stations RI-20, 11, and RI-17, inside the CSO area, the spatial correlation is highest for chloride (0.746) and lowest for zinc (0.381) during 1991–1993. From 1994 to 1997, the results also indicate chloride (0.831) and zinc (0.169) as having the highest and lowest average spatial correlations, respectively. The results suggest that station RI-20, and 11 provide the same level of information especially with regard to phosphorus and fecal coliform values. For chloride, on the other hand, the same level of information can be gathered either from station RI-11 or 17. From 1991 to 1993, the overall average spatial correlation values associated with Kinnickinnic River stations inside the CSO area are highest for chloride (0.584) and lowest for BOD (0.183). Another observation is that despite the relatively small physical distance between stations, the results for inside the CSO area stations indicate little spatial correlation for most of the water quality parameters. Comparable results for data collected from 1994 through 1997 show highest average correlation for chloride (0.832) and lowest for BOD (0.344). The results indicate that data from station RI-18 and 19 exhibit a higher degree of similarity compared to data from other sampling stations in this area. The average r18;19 correlation is 0.78. Considering all three rivers, the high spatial correlations for chloride all occur inside the CSO area probably because of runoff from a few salt storage piles. On the other hand, the most common low spatial correlation is for zinc. This may be related to the variability in street runoff that contains zinc from automobile tires and galvanized iron. Time correlations Overall, the average time correlation results for data from both periods (1991–1993 and 1994–1997) exhibit decreasing values for time lag 1 to 3, e.g., r1 ¼ 0:382, r2 ¼ 0:172, r3 ¼ 0:043 inside the CSO area of Milwaukee River for phosphorus during 1994–1997. This is seen for most water quality
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parameters from stations outside, as well as inside CSO areas. However, it should be noted that water quality data from 1994 through 1997 were sampled more sporadically compared to prior years. This means that the average time correlation values from 1991 to 1993 are higher compared to values from 1994 to 1997. For example, r1 ¼ 0:416, r2 ¼ 0:212, r3 ¼ 0:053 for phosphorus inside the CSO area of Milwaukee River for 1991–1993 are higher than the above 1994–1997 values. The highest overall time correlations are for phosphorus in Milwaukee and Menomonee Rivers, and chloride in the Kinnickinnic River. The lowest time correlations occur for zinc in Milwaukee and Kinnickinnic Rivers, and for suspended solids and zinc in the Menomonee River. Time correlations for the Outer Harbor station OH-3 are not as important because of the moderating influence of the lake. Here, chloride has the highest correlation, while phosphorus, fecal coliform, and zinc have the lowest. Because of the many values of BOD near the detection limit at OH-3, the time correlations for BOD at this station are of limited value. Regional means with confidence limits Results for the Milwaukee, the Menomonee, and the Kinnickinnic Rivers for 1991–1993 and 1994–1997, are shown in Table 1. Also included are results of the Lake Michigan OH-3 sampling station. Comparing the results from the two periods for Milwaukee River, average regional levels for most pollutants are similar in magnitude. This can be seen for both groups of stations, inside and outside the CSO areas. For certain pollutants such as suspended solids and chloride, there is an increase (19.3–51.3%) in the average levels after 1994. By contrast, for pollutants such as fecal coliform sampled at stations inside the CSO area (RI-5–7), the regional average values are lower (22.4%) after 1994, as expected. It is likely that regional average values inside the CSO area were influenced by elevated pollutant levels from upstream. As mentioned earlier, most samplings were done on days without overflow. Before 1994 there was a greater chance, i.e., about 23% that overflow occurred on the day of sampling, and after 1994 only a 2% probability. Except for the effect on the fecal coliform count on August 28, 1995 and June 18, 1996 (Ab Razak, 1999), overflow events are seldom directly noticeable in the graphs displaying water-quality data. However, the effect of overflows may still be present due to delayed response through mixing, or to memory effect, especially for bacteria that multiply in the rivers. For Milwaukee River, two further factors that may influence pollutant levels inside the CSO area. One is the removal of the North Avenue dam (Fig. 1) in August 1997. The other is the flushing of the lower reaches of the river, i.e., downstream of station RI-5 with lake water before and after 1991. However, because of the timing of these events, they have little
0.0190 0.0025 (0.0190 0.0052) 0.0244 0.0030 (0.0225 0.00073) 0.0266 0.0097 (0.0248 0.0058) 0.0259 0.0036 (0.0244 0.0011) 0.0364 0.0117 (0.0314 0.0053) 0.0358 0.0114 (0.0300 0.0080) 0.0039 0.0011 (0.00493 0.00122) 24.4 3.4 (29.1 9.8) 27.2 7.6 (34.6 0.98) 2.21 6.8 (2.73 5.3) 14.8 1.3 (17.7 2.0) 19.7 8.5 (19.3 9.8) 12.8 2.6 (15.8 4.5) 1.98 0.56 (1.99 0.78) OH-3 Lake Michigan
RI-13, 14, 18, 19 inside CSO
RI-12 outside CSO Kinnickinnic River
RI-20, 11, 17 inside CSO
RI-5, 6, 7 inside CSO
RI-16, 21, 22, 9 outside CSO Menomonee River
Milwaukee River
RI-1–4 outside CSO
3.15 0.22 (3.16 0.22) 2.79 0.44 (3.04 0.31) 2.37 0.10 (2.32 0.22) 2.87 0.27 (2.27 0.17) 4.23 0.61 (4.79 0.69) 3.00 0.48 (2.73 0.83) 0.509 0.105 (0.415 0.066)
0.136 0.008 (0.131 0.0082) 0.114 0.026 (0.137 0.0089) 0.0927 0.0148 (0.0964 0.0070) 0.0899 0.0019 (0.0894 0.0095) 0.0855 0.0221 (0.0912 0.0181) 0.0763 0.0114 (0.0739 0.0147) 0.0492 0.0038 (0.0598 0.0368)
2.38 0.068 (2.54 0.013) 2.59 0.085 (2.48 0.18) 2.71 0.22 (2.84 0.21) 3.01 0.18 (2.61 0.17) 3.33 0.19 (3.17 0.17) 2.72 0.32 (2.46 0.48) 1.68 0.21 (1.08 0.14)
Zinc (mg/l) Log (Fecal coliform) Susp. Solids (mg/l) Phosphorous (mg/l) BOD (mg/l) Sampling stations
Table 1. Average regional pollutant values with 90% confidence interval about the mean, 1991 through 1993 and 1994 through 1997 in parentheses
49.5 1.8 (65.3 1.5) 42.7 7.5 (64.6 1.9) 89.3 13.0 (130 21) 70.8 15.0 (82.9 14.8) 130 15 (184 29) 73.3 38.6 (93.8 58.8) 31.4 2.5 (32.8 2.9)
Irwan A. Ab Razak and Erik R. Christensen Chloride (mg/l)
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or no influence on the comparison of pollutant concentrations before and after 1994. The results for Menomonee River show that except for suspended solids and chloride, the regional average levels of most of the other pollutants are lower after 1994. In particular, there is a 60.2% decrease in the fecal coliform count, and a 20.9% decrease for BOD, inside the CSO area. The results indicate that overall, the deep tunnel seems to have the most impact on improving water quality levels in the Menomonee River. The fact that the decrease can be detected inside the CSO area supports this claim. For suspended solids and chloride, the results in Table 1 show an increase (11.8–45.6%) of regional average values for river sampling stations outside the CSO area (compared to similar stations during 1991– 1993). This may in part explain the elevated levels (17.1–19.6%) observed inside the CSO area. Similar calculations were done with data from the Kinnickinnic River and the Lake Michigan (OH-3) sampling stations. For the Kinnickinnic River, the average values with 90% confidence intervals show a decrease for data collected after 1994 similar to the case for Menomonee River. For fecal coliform and BOD inside the CSO area, these decreases were 45.1 and 9.0%, respectively. It must be added, however, that the decrease for BOD and fecal coliform is less than the one observed for the Menomonee River. From the only representative sampling station in Lake Michigan that was analyzed (OH-3), all pollutants, except BOD and fecal coliform, exhibit an increase in average values after 1994. The decreases for fecal coliform and BOD are 74.9 and 18.5%, respectively. Trends determined from t-tests Table 2 shows the summary results of confidence levels (based on calculated t-values) at which the null hypothesis can be rejected, and the alternative hypothesis of lower pollutant values after the deep tunnel, can be accepted. With data from the Menomonee River, the results suggest that the null hypothesis can be rejected for several water quality parameters. The level of confidence is especially high with regard to BOD, and fecal coliform data (99.9 and 99.7%, respectively) for stations inside the CSO area. For the regional average fecal coliform value, the null hypothesis can be rejected at the 99.7% level of confidence despite the fact that the null hypothesis cannot be rejected with data from outside the CSO area. Had there not been an increase outside the CSO area, the decrease in fecal coliform inside the CSO area is likely to have been even larger. This indicates the effectiveness of the deep tunnel in improving the Menomonee River water quality inside the CSO area. For phosphorus (inside CSO area stations), the alternative hypothesis can only be accepted with a 53.2% level of confidence, while for stations outside the CSO area, the null hypothesis cannot be rejected.
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Table 2. Summary results of the t-test, indicating the confidence level at which the null hypothesis can be rejected Sampling stations BOD Phosphorus Suspended solids log (Fecal coliform) Zinc Chloride a
Milwaukee a
Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO
incr incra 74.9% incra incra incra incra 81.3% 50.0% 85.0% incra incra
Menomonee 65.0% 99.9% incra 53.2% incra incra incra 99.7% 59.9% 74.2% incra incra
Kinnickinnic
Outer harbor station, OH-03
a
incr 68.0% incra 58.3% 51.6% incra 86.0% 76.7% 74.2% 75.2% incra incra
89.8% incra incra 99.9% incra incra
Increase after 1994. Table 3. Mann–Kendall statistics of log(fecal coliform) for Menomonee River inside CSO sampling stations
Year Yearly ave.
1991 3.06
1992 3.06
1993 2.93
1994 2.89
1995 2.57
1996 2.68
1997 2.30
No. of+signs
0.00
0.13 0.13
0.17 0.18 0.04
0.49 0.50 0.36 0.32
0.39 0.39 0.25 0.21 0.11
0.76 0.76 0.63 0.58 0.26 0.37
1 0 0 0 1 0 s=219 n
Therefore, value from Table A18 (Gilbert, 1987) Year
1991 1992 1993 1994 1995 1996 1997
Sampling station yearly average Value Rl-20
Rl-11
Rl-17
3.40 3.34 2.93 3.24 2.70 2.82 2.33
2.96 2.98 2.89 2.80 2.44 2.59 2.26
2.82 2.87 2.97 2.62 2.56 2.61 2.32
From Table 2, it can also be concluded that the deep tunnel has the most impact on reducing regional average fecal coliform levels inside the CSO area of all three rivers. Zinc levels exhibit the same tendency inside the CSO area. Although for zinc, the t-test indicates that the null hypothesis can be rejected for data outside the CSO area as well, the levels of confidence are lower compared to levels inside the CSO area. The lower confidence level shows smaller decrease in zinc regional average values after 1994 for areas not served by the deep tunnel. The pollutants that do not appear to be mitigated by the deep tunnel are suspended solids and chloride. The null hypothesis could not be rejected at any level inside the CSO area, and inspection of Table 1 supports this conclusion. Regarding suspended solids, the reason for the increased levels may be upstream construction activities and more intense rainstorms after 1994. For chloride, the reason could be leaching from salt storage piles. Mann–Kendall test In the Mann–Kendall test, the analysis focuses on yearly average values across all the years from 1991
No. ofsigns 5 5 4 3 1 1 =17 =7 =0.0054
Average
3.06 3.06 2.93 2.89 2.57 2.68 2.30
through 1997. This is in contrast to the t-test where two averages from different group of years were compared. The objective is to conclusively state whether the yearly average values exhibit any trend. Example calculations involving the Mann–Kendall statistic S are shown in Table 3 for log(fecal coliform) in the Menomonee River inside the CSO area. For the alternative hypothesis of a downward trend in the average annual values of the pollutants, the calculated Mann–Kendall statistic S (from n values of annual averages, Table 3), has to be negative. The probability corresponding to an absolute value of S, as shown in Table A18 by Gilbert (1987) is then equal to a specified a value. The final results stated in terms of confidence levels at which the null hypothesis can be rejected are shown in Table 4. The results show that inferences concerning yearly average values based on the Mann–Kendall test support the results obtained with the t-test (Table 2). In other words, like the t-test, the Mann–Kendall test indicates a downward trend of average annual fecal coliforms, zinc, and to some extent BOD, inside the CSO area after 1994.
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Table 4. Summary results of the Mann–Kendall test, indicating the confidence level at which the null hypothesis can be rejected Sampling stations BOD Phosphorus Suspended solids log (Fecal coliform) Zinc Chloride a
Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO
Milwaukee 50.0% 50.0% 93.2% incra incra incra incra 71.9% 61.4% 71.9% incra incra
Menomonee 61.4% 96.5% incra incra incra incra incra 99.5% 71.9% 50.0% incra incra
Kinnickinnic
Outer harbor station, OH-03
a
incr 80.9% incra incra incra incra 96.5% 88.1% 61.4% 50.0% incra incra
93.2% incra 50.0% 88.1% incra incra
Increase after 1994.
CONCLUSIONS
Based on statistical analyses of the data, the following can be concluded: 1. The Menomonee River gains the most benefits from the deep-unnel pollution mitigative function. This is based on the number of river water-quality parameters exhibiting a decline in average levels following the deep tunnel. The decline is less for data associated with the Milwaukee and Kinnickinnic Rivers. 2. Compared to all the other water-quality parameters analyzed in all three rivers, the fecal coliform count inside the CSO area declines the most. This is despite the fact that outside the CSO area, it does not decline after 1994, except perhaps in the Kinnickinnic River. However, the outside CSO area fecal coliform count for the Kinnickinnic River is based on only one station (RI-12) which is fairly close to the CSO boundary, and the values may, therefore, be influenced by the lower counts inside the CSO area. The results point to the beneficial effects of the deep tunnel. Without the increase in the fecal coliform count outside, one could expect an even more substantial decrease inside the CSO area. 3. Besides fecal coliform counts, BOD and zinc levels also decline to a certain extent after 1994. On the other hand, suspended solids and chloride levels increase. 4. Phosphorus levels inside the CSO area of the Menomonee and Kinnickinnic Rivers show only modest decline. Although the calculations indicate some phosphorus reduction outside the CSO area of the Milwaukee River, the same was not observed downstream in the CSO area. 5. Based on results from spatial correlation calculations of adjacent river sampling stations, for example, Menomonee River stations RI-21 and 22 and Kinnickinnic River stations RI-18 and 19, it appears that these stations provide nearly the same level of information. Excluding other
factors, this suggests that some of these stations are redundant. 6. Biweekly sampling, carried out before 1994, but not quite maintained after that date, should be resumed. More samplings should be done close to overflow points and near the time of overflow events. The episodic impact of pollutants during overflows may be underestimated in the current river sampling program.
Acknowledgements}This study was sponsored in part by the Milwaukee Metropolitan Sewerage District. REFERENCES
Ab Razak I. A. (1999) Combined sewer overflows and water quality before and after deep tunnels operation in Milwaukee, Wisconsin. Ph.D. Thesis, Department of Civil Engineering and Mechanics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin. Berthouex P. M. and Brown L. C. (1994) Statistics for Environmental Engineers. CRC Press, Inc., Boca Raton, FL. Daniel W. W. (1990) Applied Nonparametric Statistics. PWS-Kent Publishing Company, Boston. Gilbert R. O. (1987) Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York. Harned D. A., Daniel III C. C. and Crawford J. K. (1981) Methods of discharge compensation as an aid to the evaluation of water quality trends. Water Resources Research 17, 1389–1400. Kachigan S. A. (1986) Statistical Analysis. An Interdisciplinary Introduction to Univariate & Multivariate Methods. Radius Press, New York. McCuen R. H. (1985) Statistical Methods for Engineers. Prentice-Hall, Inc., Englewood Cliffs, NJ. Miller I., Freund J. E. and Johnson R. A. (1990) Probability and Statistics for Engineers. Prentice-Hall, Englewood Cliffs, NJ. MMSD (1999) Surface Water Quality Monitoring Program, Milwaukee, WI. Milwaukee Metropolitan Sewerage District, Milwaukee, WI. Southeastern Wisconsin Regional Planning Commission (SEWRPC) (1987) A Water Resources Management Plan for the Milwaukee Harbor Estuary, Vols. 1, 2. Southeast Regional Planning Commission, Planning Report #37, Milwaukee, WI.
Wat. Res. Vol. 35, No. 11, pp. 2683–2692, 2001 # 2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0043-1354/01/$ - see front matter
WATER QUALITY BEFORE AND AFTER DEEP TUNNEL OPERATION IN MILWAUKEE, WISCONSIN IRWAN A. AB RAZAK and ERIK R. CHRISTENSEN*,y Department of Civil Engineering and Mechanics, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA (First received 1 December 1999; accepted in revised form 1 October 2000) Abstract}The mitigative effects of the deep tunnel for temporary storage of storm water and sewage, on the water quality of the Milwaukee, Menomonee, and Kinnickinnic Rivers are investigated. The analysis is based on data from the Milwaukee Metropolitan Sewerage District’s overflow and surface-water quality monitoring program. Statistical analysis of water quality parameters (BOD, phosphorus, suspended solid, fecal coliform counts, zinc, and chloride) in the three rivers indicates that Menomonee River benefits the most from the deep tunnel. Fecal coliform counts inside the CSO area, and to a certain extent BOD and zinc levels, exhibit the most significant decline after 1994 when the tunnel came on line. These conclusions are based on t-test comparisons of regional averages incorporating spatial and temporal correlations from 1991 to 1993 and 1994 to 1997. The results from t-tests are complemented and confirmed with results from Mann–Kendall tests for trend. Suspended solids and chloride do not decrease after 1994. # 2001 Elsevier Science Ltd. All rights reserved Key words}water quality, combined sewer overflows (CSO), t-test, Mann–Kendall test
INTRODUCTION
The deep tunnel is part of the Milwaukee, WI USA metropolitan sewerage district’s (MMSDs) massive water pollution abatement program. The main purpose of the deep tunnel is to substantially reduce, if not eliminate combined sewer overflows (CSO) and overflows from sanitary sewers into the rivers, and ultimately Lake Michigan. This study was undertaken to systematically characterize water quality in the Milwaukee, Menomonee, and Kinnickinnic Rivers before and after the deep tunnel came online in 1994. While CSO control is now fairly common, there are only few studies of the resulting improvement in water quality of the receiving waters. By the mid-1970, the metropolitan interceptor sewer system had become insufficient to carry the load from the separate sewer system and the combined sewer system, particularly during wet weather. As a result, a decision was made to build an inline storage system, i.e., deep tunnel to absorb all but a few overflows per year from the metropolitan interceptor sewer. The inline storage system consists of four deep tunnels located approximately 91 m from the ground surface in the Niagara Dolomite bedrock. The tunnel *Author to whom all correspondence should be addressed. Tel.: +1-414-229-4968; fax: +1-441-229-6958; e-mail: [email protected] y Present address: Camp Dresser & McKee, Inc., 312 East Wisconsin Avenue, Milwaukee, WI 53202, USA
follows mainly the surface drainage patterns including the Menomonee, Milwaukee, and Kinnickinnic Rivers and Lincoln Creek (Fig. 1). The total length of the deep tunnel is 31.2 km. The tunnel extends primarily within the combined sewer overflow area, except that the north branch is aligned along the Lincoln Creek, and the west branch follows the Menomonee River to its confluence with Underwood Creek. The tunnel range in diameter from 1.83 to 9.84 m. The total capacity is 1.53 106 m3. When a volume of 5.68 105 is reached, combined sewage is no longer allowed to enter the tunnel in order to reserve the remaining capacity for the higher strength sewage from the separate sewer system. Sewers are connected to the tunnel through diversion structures, backed up with bypasses to the rivers for either separate or combined sewage. The objectives of the present work are to evaluate the pollution mitigating effects of the deep tunnel, and to develop recommendations for an optimal sampling strategy for future monitoring of the performance of the system. STUDY AREA
Figure 1 shows the locations of water quality sampling stations along the Milwaukee, Menomonee, and Kinnickinnic Rivers. The stations were classified based on their locations into two groups: outside and inside the CSO area. All sampling stations are located within the direct drainage area to the
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Fig. 1. Map showing water quality sampling locations along the Milwaukee, Menomonee, and Kinnickinnic River. Also shown is the extent of the combined sewer overflow are (MMSD, 1999).
Milwaukee Harbor estuary (SEWRPC, 1987). Referring to Fig. 1, sampling stations that are inside the Milwaukee Harbor estuary include RI-5 through RI7 in the Milwaukee River, and all sampling stations from the harbor to RI-9 in the Menomonee River. For the Kinnickinnic River, only sampling stations RI-13, 14, 18 and 19 are considered to be inside the Milwaukee Harbor estuary. As the focus of this study was the CSO area, only sampling station OH-3 in the Milwaukee’s Outer Harbor was examined. For a more complete analysis of the deep tunnel’s impacts on the Outer Harbor area, more sampling stations should be included. For each of the three rivers, the flow rates were obtained from instantaneous stream flow records
measured by the United States Geological Survey (USGS). The gage in the Milwaukee River is located at Estabrook Park, in the vicinity of river sampling station RI-4. For the Menomonee River, data from the gage located in Wauwatosa at the 70th street bridge was used. The gage is near sampling station RI-9 of the Menomonee River. Flow rates for the Kinnickinnic River were obtained from a gage by S. 11th Street between sampling stations RI-12 and RI-13. DATA ANALYSIS AND METHODOLOGY
Investigation of water-quality data ideally requires compensation of variations in stream flow as it could be
Water quality before and after deep tunnel operation
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Fig. 2. Total annual precipitation at selected rain gage stations.
one source of variability in the data set. The three river systems have different discharges, with the Milwaukee River having on average, the highest flow (106 m3/d), and the Kinnickinnic River the lowest (5 104 m3/d). The average flow for the Menomonee River is 2 105 m3/d. As the discharge increases, pollutants such as dissolved solids or metals decrease in concentration whereas for other pollutants, e.g., suspended solids, the levels may increase with discharge (Harned et al., 1981). However, in this study discharge normalization was not done because the rainfall did not vary greatly during the period investigated from 1991 to 1997 (Fig. 2). In addition, the variations within the watershed in any given year are comparable to the interannual variability. There is, however, a small tendency of lower rainfall for 1994–1997. This difference implies that our estimates for decreases after 1994 in concentrations of pollutants such as BOD and fecal coliform are conservative, i.e., discharge normalized decreases would have been higher. For statistical analyses, water-quality records were grouped into a pre-deep tunnel and a post-deep tunnel period: 1991–1993 and 1994–1997, respectively. Waterquality data from the Milwaukee, Menomonee, and Kinnickinnic River were examined separately. Furthermore, for each river, the sampling stations were grouped based on their locations in relation to the CSO and non-CSO areas. Quantification of the deep tunnel’s pollution mitigating effects was accomplished by comparing average pollutant levels from the two periods and the two areas (CSO and non-CSO). Major water-quality parameters that were analyzed include BOD, phosphorus, suspended solids, fecal coliform counts, zinc, and chloride.
For comparison purposes, statistical analyses were also conducted on Outer Harbor sampling stations OH-3 (Fig. 1).
Water quality data for the rivers and the Outer Harbor The river water quality sampling program (MMSD, 1999) is active from April 1 through November 15 of each year. There was biweekly sampling before 1994, and 15–20% less frequent sampling after that date. River water quality plots, as shown in Figs 3 and 4 for fecal coliform in the Menomonee River were prepared, in order to graphically compare the concentration levels, the degree of variations in the levels, and the overall trend at various river sampling stations inside and outside the CSO areas. The plots were prepared in the correct spatial order based on the sampling stations in the rivers from upstream to downstream towards the harbor area. The concentration levels that were examined were those sampled 1 m below the surface. In general, it can be observed that there is a certain seasonal similarity of water quality with respect to time. This is especially evident with phosphorus levels in all three rivers (Ab Razak, 1999) in contrast to fecal coliform counts (Figs 3 and 4), and to a certain extent BOD that exhibit distributions that are more random. Above the detection limit (2 mg/l), BOD also exhibits seasonality with higher levels from August to September. The cyclic nature of some of the pollutants can be seen for periods both before and after the deep tunnel came on line. There are lower pollutant levels in all three rivers towards the
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Fig. 3. Log(Fecal coliform) level 1991–1993 for Menomonee River (inside CSO area).
harbor areas (Figs 3 and 4), probably due to dilution with lake water. From Figs 3 and 4 it is obvious that the number of overflows at station WPDES 180 has been reduced drastically from more than 100 during 1991–1993 to only 3 after 1994. A resulting decrease in fecal coliform counts at sampling stations RI-20, 11, and 17 is also apparent. On August 28, 1995 and June 18, 1996, overflow and sampling were on the same day. In addition, there was overflow on June 21, 1997 and sampling two days later, June 23, 1997. Resulting elevated fecal coliform counts are clearly visible for stations RI-20, 11 and 17 (Fig. 4). However, in the majority of cases, samplings of river and Outer Harbor stations were done on days without overflow. The fractions of river samplings with overflow were 23, 18, and 27% for the Milwaukee, Menomonee and Kinnickinnic Rivers, respectively, for the 1991–1993 period. The corresponding numbers during 1994–1997 were 2, 4, and 0%, in the same order. A complete listing of sampling and overflow dates is given in Ab Razak (1999). Note that the regular sampling in time, i.e., every other week, coupled with the random occurrence of overflows, accurately reflects the average perceived risk of human contact with river water.
Statistical analysis As the water-quality parameters were sampled close in time and space, there could be problems with autocorrelation or tendency for neighboring observations to be alike (Berthouex and Brown, 1994; Gilbert, 1987). Among others, Durbin–Watson tests (Daniel, 1990) can be used to detect autocorrelation. Correlation can also be addressed, as was done here, by including spatial and temporal correlations when determining confidence limits about the regional means of the pollutants. The regional mean of a specific water-quality parameter such as BOD was calculated as follows. For a given group of river sampling stations, either outside or inside the CSO areas, with data over a given sampling period, for example can be expressed by from 1991 to 1993, the regional mean x ¼ x
ns X ns n 1 X 1X i x xij ¼ nns i¼1 j¼1 ns i¼1
ð1Þ
where n is the number of observations from each of ns i the stations, xij the j th observation at station i, and x estimated mean for the ith station.
Water quality before and after deep tunnel operation
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Fig. 4. Log(Fecal coliform) level 1994–1997 for Menomonee River (inside CSO area).
Data gathered from sampling stations located close in space may be highly correlated. In fact, this can be observed from the plots shown in Figs 3 and 4. Spatial correlation has to be taken into account when determining the variance, and ultimately the standard deviation of the regional mean. According to Gilbert (1987), the spatial correlation of two sampling points i and j can be calculated from PK i Þ xjk x j k¼1 ðxik x ð2Þ rij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ffi PK P i Þ2 K j k¼1 ðxik x k¼1 xjk x j are the average values at station i and j, i and x where x respectively, and K is the number of data, taken at the same dates, at each station. For subsequent calculations, we used ^c , obtained from all the average of the spatial correlations r ns ðns 1Þ=2 cross-correlations between the ns stations, e.g. for 1991–1993. Another correlation that is important is the one observed from samples taken at equal intervals along a line in time (Kachigan, 1986; Gilbert, 1987). The auto-
correlation rl , for example, denotes correlation of measurements taken l time lag apart i.e., between x1 and x2 , between x2 and x3 , etc., and the same is true with r2;3;4...:;n1 . Gilbert (1987) gives a method for estimating the lth lag autocorrelation: Pnl rl ¼
Þðxtþ1 x Þ ðxt x Pn Þ2 t¼1 ðxt x
t¼1
ð3Þ
is the average measurement at the particular where x location. Here, only autocorrelations up to 3 time lags apart were considered, assuming rl ¼ 0 for l > 3. In calculations involving confidence limits of the regional mean, we used ^l across sampling stations and the average of each time lag r time periods. After calculation of the spatial and temporal correlations, the 100(1 a)% confidence interval of the estimated mean can be found for data recorded at a given group of river sampling stations. The confidence interval when multiple
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Irwan A. Ab Razak and Erik R. Christensen
sampling stations are involved can be estimated from (Gilbert, 1987) t1a=2;nns 1 s x " #1=2 ! n1 1 2X ^c ðns 1ÞÞ 1þ ðn l Þ^ rl ð1 þ r ð4Þ nns n l¼1 where n is the total number of data points in time from a given sampling station, ns the number of sampling stations. " !#1=2 Pns i x Þ2 i¼1 ðx s ¼ nns ns ðns 1Þ ^l and r ^c as previously described. with r The confidence limits calculated from equation (4) are only for data recorded at multiple sampling stations. For the case where only one station was involved, for example at river sampling station RI-12 of the Kinnickinnic River, and Outer Harbor station OH-3, the following equation was used: " !#1=2 n1 1 2X t1a=2;n1 s 1þ ðn lÞ^ rl ð5Þ x n n l¼1 where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðxi x Þ2 : s¼t n1 i¼1 As was the case with multiple stations (equation (4)), the summation term involving the time lag in equation (5) only covers l =1 to 3. In equations (4) and (5), a 90% confidence interval of the regional mean or a ¼ 10% was used. To consider the question of improvement in water quality, records gathered from before and after the pollutant mitigation project have to be statistically analyzed. The t-test (Miller et al., 1990) is valuable in testing the hypothesis regarding improvement in river water quality after the deep tunnel came on line. Results from t-tests can be complemented with non-parametric tests such as the seasonal Kendall or Mann–Kendall test for trend. The most notable advantage of non-parametric tests is that they do not require the data to fit many of the distributional assumptions of parametric methods (McCuen, 1985; Gilbert, 1987). A t-test was performed to evaluate the null hypothesis of ðm1 m2 Þ ¼ 0, compared to the alternative hypothesis of ðm1 m2 Þ > 0. A suitable t-statistic was then used as an estimate of the unknown population variance. As the confidence intervals were calculated using a=10%, the statistic for test concerning differences between two means was modified by the following equation: 1 x 2 x ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t ¼ r q1 2 q2 2 þ a b
ð6Þ
where q1 , q2 are the term of equation (4) or (5) depending on the number of sampling stations involved, a, b is equal to 1.645 which is the quantile of the t distribution value at 1 degrees of freedom, and at t0:95 . As a complement to the t-test analysis, the data was also subjected to the Mann–Kendall test for trend. In the t-test, two averages computed from data sampled before, and after the commissioning of the deep tunnel in 1994, were compared. With the Mann–Kendall test, on the other hand, yearly averages of each water quality parameter from 1991 through 1997 were analyzed and inferences concerning the trend of these yearly average values were made. The null hypothesis was that there is no trend in the annual average values, while the alternative hypothesis was for a downward trend.
The first step of the Mann–Kendall test as described by Gilbert (1987), involves arranging n specific average annual pollutant values in chronological order from 1991 to 1997. The difference xj-xk, where j > k for all nðn 1Þ=2 possible differences of the average values was then calculated. Only the sign, and not the magnitude of the difference between consecutive values are important. In mathematical form, this can be written 8 < 1 if xj xk > 0 ð7Þ sgnðxj xk Þ 0 if xj xk ¼ 0 : : 1 if xj xk 50 The Mann–Kendall statistic was then obtained from S¼
n1 X n X
sgnðxj xk Þ
ð8Þ
k¼1 j¼kþ1
RESULTS AND DISCUSSION
Spatial correlation of river sampling stations Focusing first on river sampling stations RI-1–4 of the Milwaukee River from outside the CSO area, the overall average spatial correlations between the sampling stations are of similar magnitude for all water quality parameters. From the 1991–1993 results, it appears that the average spatial correlation value is lowest for zinc (0.113) and highest for phosphorus (0.889) with intermediate values for the rest of the parameters. The high value for phosphorus (P) may be explained by the P source, which may be upstream farm fertilizer runoff. By contrast, Zn is likely to originate from local street runoff that is highly variable depending on locations of streets and discharge points. For BOD from 1991 to 1997, many values were at the detection limit of about 2 mg/l. This has an impact on the correlation calculations for BOD. The results for the period from 1994 through 1997 show that the correlation is highest for BOD (0.832) and lowest for zinc (0.465). The correlation coefficient for P is 0.635. For sampling stations RI-5, 6, and 7 inside the CSO area (1991–1993, and 1994–1997), spatial correlations are also of comparable magnitude for most of the water quality parameters. The results for 1991–1993 indicate that the highest correlation is associated with fecal coliform (0.696), and the lowest with zinc (0.137). After 1994, the highest average correlation is that of chloride (0.905), and the lowest, suspended solids (0.387). Comparing spatial correlations between two specific stations, for most of the water quality parameters (1991–1993, and 1994–1997), the results suggest that stations RI-6 and 7 provide nearly the same amount of information (Fig. 4). Most correlations r6;7 are between 0.7 and 0.9, with an average of 0.68. However, there may be other factors that are important in continuing with sampling at both of these two stations such as the fact that overflow station WPDES 147 is located between RI-6 and 7. Based on data from 1991–1993, for Menomonee River stations outside the CSO area (RI-16, 21, 22, 9),
Water quality before and after deep tunnel operation
the spatial correlation is highest with respect to suspended solids (0.731), and lowest for zinc (0.427). Data from 1994 through 1997 show highest average correlation for fecal coliform (0.739), and lowest for zinc (0.459). Similar to the case for Milwaukee River, many of the BOD data for the Menomonee River are at the detection limit. Therefore, less weight should be given to spatial correlation results for BOD. Comparing spatial correlations between two specific stations, the results for most parameters during 1991–1997 suggest that stations RI-21 and RI-22 provide almost the same amount of information (Fig. 3). Also here, most correlations r21;22 are between 0.7 and 0.9. The average r21;22 is 0.74. It may be possible to discontinue sampling at one of these stations based on this result, although RI-22 includes the impact of the Little Menomonee River (Fig. 1). For stations RI-20, 11, and RI-17, inside the CSO area, the spatial correlation is highest for chloride (0.746) and lowest for zinc (0.381) during 1991–1993. From 1994 to 1997, the results also indicate chloride (0.831) and zinc (0.169) as having the highest and lowest average spatial correlations, respectively. The results suggest that station RI-20, and 11 provide the same level of information especially with regard to phosphorus and fecal coliform values. For chloride, on the other hand, the same level of information can be gathered either from station RI-11 or 17. From 1991 to 1993, the overall average spatial correlation values associated with Kinnickinnic River stations inside the CSO area are highest for chloride (0.584) and lowest for BOD (0.183). Another observation is that despite the relatively small physical distance between stations, the results for inside the CSO area stations indicate little spatial correlation for most of the water quality parameters. Comparable results for data collected from 1994 through 1997 show highest average correlation for chloride (0.832) and lowest for BOD (0.344). The results indicate that data from station RI-18 and 19 exhibit a higher degree of similarity compared to data from other sampling stations in this area. The average r18;19 correlation is 0.78. Considering all three rivers, the high spatial correlations for chloride all occur inside the CSO area probably because of runoff from a few salt storage piles. On the other hand, the most common low spatial correlation is for zinc. This may be related to the variability in street runoff that contains zinc from automobile tires and galvanized iron. Time correlations Overall, the average time correlation results for data from both periods (1991–1993 and 1994–1997) exhibit decreasing values for time lag 1 to 3, e.g., r1 ¼ 0:382, r2 ¼ 0:172, r3 ¼ 0:043 inside the CSO area of Milwaukee River for phosphorus during 1994–1997. This is seen for most water quality
2689
parameters from stations outside, as well as inside CSO areas. However, it should be noted that water quality data from 1994 through 1997 were sampled more sporadically compared to prior years. This means that the average time correlation values from 1991 to 1993 are higher compared to values from 1994 to 1997. For example, r1 ¼ 0:416, r2 ¼ 0:212, r3 ¼ 0:053 for phosphorus inside the CSO area of Milwaukee River for 1991–1993 are higher than the above 1994–1997 values. The highest overall time correlations are for phosphorus in Milwaukee and Menomonee Rivers, and chloride in the Kinnickinnic River. The lowest time correlations occur for zinc in Milwaukee and Kinnickinnic Rivers, and for suspended solids and zinc in the Menomonee River. Time correlations for the Outer Harbor station OH-3 are not as important because of the moderating influence of the lake. Here, chloride has the highest correlation, while phosphorus, fecal coliform, and zinc have the lowest. Because of the many values of BOD near the detection limit at OH-3, the time correlations for BOD at this station are of limited value. Regional means with confidence limits Results for the Milwaukee, the Menomonee, and the Kinnickinnic Rivers for 1991–1993 and 1994–1997, are shown in Table 1. Also included are results of the Lake Michigan OH-3 sampling station. Comparing the results from the two periods for Milwaukee River, average regional levels for most pollutants are similar in magnitude. This can be seen for both groups of stations, inside and outside the CSO areas. For certain pollutants such as suspended solids and chloride, there is an increase (19.3–51.3%) in the average levels after 1994. By contrast, for pollutants such as fecal coliform sampled at stations inside the CSO area (RI-5–7), the regional average values are lower (22.4%) after 1994, as expected. It is likely that regional average values inside the CSO area were influenced by elevated pollutant levels from upstream. As mentioned earlier, most samplings were done on days without overflow. Before 1994 there was a greater chance, i.e., about 23% that overflow occurred on the day of sampling, and after 1994 only a 2% probability. Except for the effect on the fecal coliform count on August 28, 1995 and June 18, 1996 (Ab Razak, 1999), overflow events are seldom directly noticeable in the graphs displaying water-quality data. However, the effect of overflows may still be present due to delayed response through mixing, or to memory effect, especially for bacteria that multiply in the rivers. For Milwaukee River, two further factors that may influence pollutant levels inside the CSO area. One is the removal of the North Avenue dam (Fig. 1) in August 1997. The other is the flushing of the lower reaches of the river, i.e., downstream of station RI-5 with lake water before and after 1991. However, because of the timing of these events, they have little
0.0190 0.0025 (0.0190 0.0052) 0.0244 0.0030 (0.0225 0.00073) 0.0266 0.0097 (0.0248 0.0058) 0.0259 0.0036 (0.0244 0.0011) 0.0364 0.0117 (0.0314 0.0053) 0.0358 0.0114 (0.0300 0.0080) 0.0039 0.0011 (0.00493 0.00122) 24.4 3.4 (29.1 9.8) 27.2 7.6 (34.6 0.98) 2.21 6.8 (2.73 5.3) 14.8 1.3 (17.7 2.0) 19.7 8.5 (19.3 9.8) 12.8 2.6 (15.8 4.5) 1.98 0.56 (1.99 0.78) OH-3 Lake Michigan
RI-13, 14, 18, 19 inside CSO
RI-12 outside CSO Kinnickinnic River
RI-20, 11, 17 inside CSO
RI-5, 6, 7 inside CSO
RI-16, 21, 22, 9 outside CSO Menomonee River
Milwaukee River
RI-1–4 outside CSO
3.15 0.22 (3.16 0.22) 2.79 0.44 (3.04 0.31) 2.37 0.10 (2.32 0.22) 2.87 0.27 (2.27 0.17) 4.23 0.61 (4.79 0.69) 3.00 0.48 (2.73 0.83) 0.509 0.105 (0.415 0.066)
0.136 0.008 (0.131 0.0082) 0.114 0.026 (0.137 0.0089) 0.0927 0.0148 (0.0964 0.0070) 0.0899 0.0019 (0.0894 0.0095) 0.0855 0.0221 (0.0912 0.0181) 0.0763 0.0114 (0.0739 0.0147) 0.0492 0.0038 (0.0598 0.0368)
2.38 0.068 (2.54 0.013) 2.59 0.085 (2.48 0.18) 2.71 0.22 (2.84 0.21) 3.01 0.18 (2.61 0.17) 3.33 0.19 (3.17 0.17) 2.72 0.32 (2.46 0.48) 1.68 0.21 (1.08 0.14)
Zinc (mg/l) Log (Fecal coliform) Susp. Solids (mg/l) Phosphorous (mg/l) BOD (mg/l) Sampling stations
Table 1. Average regional pollutant values with 90% confidence interval about the mean, 1991 through 1993 and 1994 through 1997 in parentheses
49.5 1.8 (65.3 1.5) 42.7 7.5 (64.6 1.9) 89.3 13.0 (130 21) 70.8 15.0 (82.9 14.8) 130 15 (184 29) 73.3 38.6 (93.8 58.8) 31.4 2.5 (32.8 2.9)
Irwan A. Ab Razak and Erik R. Christensen Chloride (mg/l)
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or no influence on the comparison of pollutant concentrations before and after 1994. The results for Menomonee River show that except for suspended solids and chloride, the regional average levels of most of the other pollutants are lower after 1994. In particular, there is a 60.2% decrease in the fecal coliform count, and a 20.9% decrease for BOD, inside the CSO area. The results indicate that overall, the deep tunnel seems to have the most impact on improving water quality levels in the Menomonee River. The fact that the decrease can be detected inside the CSO area supports this claim. For suspended solids and chloride, the results in Table 1 show an increase (11.8–45.6%) of regional average values for river sampling stations outside the CSO area (compared to similar stations during 1991– 1993). This may in part explain the elevated levels (17.1–19.6%) observed inside the CSO area. Similar calculations were done with data from the Kinnickinnic River and the Lake Michigan (OH-3) sampling stations. For the Kinnickinnic River, the average values with 90% confidence intervals show a decrease for data collected after 1994 similar to the case for Menomonee River. For fecal coliform and BOD inside the CSO area, these decreases were 45.1 and 9.0%, respectively. It must be added, however, that the decrease for BOD and fecal coliform is less than the one observed for the Menomonee River. From the only representative sampling station in Lake Michigan that was analyzed (OH-3), all pollutants, except BOD and fecal coliform, exhibit an increase in average values after 1994. The decreases for fecal coliform and BOD are 74.9 and 18.5%, respectively. Trends determined from t-tests Table 2 shows the summary results of confidence levels (based on calculated t-values) at which the null hypothesis can be rejected, and the alternative hypothesis of lower pollutant values after the deep tunnel, can be accepted. With data from the Menomonee River, the results suggest that the null hypothesis can be rejected for several water quality parameters. The level of confidence is especially high with regard to BOD, and fecal coliform data (99.9 and 99.7%, respectively) for stations inside the CSO area. For the regional average fecal coliform value, the null hypothesis can be rejected at the 99.7% level of confidence despite the fact that the null hypothesis cannot be rejected with data from outside the CSO area. Had there not been an increase outside the CSO area, the decrease in fecal coliform inside the CSO area is likely to have been even larger. This indicates the effectiveness of the deep tunnel in improving the Menomonee River water quality inside the CSO area. For phosphorus (inside CSO area stations), the alternative hypothesis can only be accepted with a 53.2% level of confidence, while for stations outside the CSO area, the null hypothesis cannot be rejected.
Water quality before and after deep tunnel operation
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Table 2. Summary results of the t-test, indicating the confidence level at which the null hypothesis can be rejected Sampling stations BOD Phosphorus Suspended solids log (Fecal coliform) Zinc Chloride a
Milwaukee a
Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO
incr incra 74.9% incra incra incra incra 81.3% 50.0% 85.0% incra incra
Menomonee 65.0% 99.9% incra 53.2% incra incra incra 99.7% 59.9% 74.2% incra incra
Kinnickinnic
Outer harbor station, OH-03
a
incr 68.0% incra 58.3% 51.6% incra 86.0% 76.7% 74.2% 75.2% incra incra
89.8% incra incra 99.9% incra incra
Increase after 1994. Table 3. Mann–Kendall statistics of log(fecal coliform) for Menomonee River inside CSO sampling stations
Year Yearly ave.
1991 3.06
1992 3.06
1993 2.93
1994 2.89
1995 2.57
1996 2.68
1997 2.30
No. of+signs
0.00
0.13 0.13
0.17 0.18 0.04
0.49 0.50 0.36 0.32
0.39 0.39 0.25 0.21 0.11
0.76 0.76 0.63 0.58 0.26 0.37
1 0 0 0 1 0 s=219 n
Therefore, value from Table A18 (Gilbert, 1987) Year
1991 1992 1993 1994 1995 1996 1997
Sampling station yearly average Value Rl-20
Rl-11
Rl-17
3.40 3.34 2.93 3.24 2.70 2.82 2.33
2.96 2.98 2.89 2.80 2.44 2.59 2.26
2.82 2.87 2.97 2.62 2.56 2.61 2.32
From Table 2, it can also be concluded that the deep tunnel has the most impact on reducing regional average fecal coliform levels inside the CSO area of all three rivers. Zinc levels exhibit the same tendency inside the CSO area. Although for zinc, the t-test indicates that the null hypothesis can be rejected for data outside the CSO area as well, the levels of confidence are lower compared to levels inside the CSO area. The lower confidence level shows smaller decrease in zinc regional average values after 1994 for areas not served by the deep tunnel. The pollutants that do not appear to be mitigated by the deep tunnel are suspended solids and chloride. The null hypothesis could not be rejected at any level inside the CSO area, and inspection of Table 1 supports this conclusion. Regarding suspended solids, the reason for the increased levels may be upstream construction activities and more intense rainstorms after 1994. For chloride, the reason could be leaching from salt storage piles. Mann–Kendall test In the Mann–Kendall test, the analysis focuses on yearly average values across all the years from 1991
No. ofsigns 5 5 4 3 1 1 =17 =7 =0.0054
Average
3.06 3.06 2.93 2.89 2.57 2.68 2.30
through 1997. This is in contrast to the t-test where two averages from different group of years were compared. The objective is to conclusively state whether the yearly average values exhibit any trend. Example calculations involving the Mann–Kendall statistic S are shown in Table 3 for log(fecal coliform) in the Menomonee River inside the CSO area. For the alternative hypothesis of a downward trend in the average annual values of the pollutants, the calculated Mann–Kendall statistic S (from n values of annual averages, Table 3), has to be negative. The probability corresponding to an absolute value of S, as shown in Table A18 by Gilbert (1987) is then equal to a specified a value. The final results stated in terms of confidence levels at which the null hypothesis can be rejected are shown in Table 4. The results show that inferences concerning yearly average values based on the Mann–Kendall test support the results obtained with the t-test (Table 2). In other words, like the t-test, the Mann–Kendall test indicates a downward trend of average annual fecal coliforms, zinc, and to some extent BOD, inside the CSO area after 1994.
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Table 4. Summary results of the Mann–Kendall test, indicating the confidence level at which the null hypothesis can be rejected Sampling stations BOD Phosphorus Suspended solids log (Fecal coliform) Zinc Chloride a
Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO Outside CSO Inside CSO
Milwaukee 50.0% 50.0% 93.2% incra incra incra incra 71.9% 61.4% 71.9% incra incra
Menomonee 61.4% 96.5% incra incra incra incra incra 99.5% 71.9% 50.0% incra incra
Kinnickinnic
Outer harbor station, OH-03
a
incr 80.9% incra incra incra incra 96.5% 88.1% 61.4% 50.0% incra incra
93.2% incra 50.0% 88.1% incra incra
Increase after 1994.
CONCLUSIONS
Based on statistical analyses of the data, the following can be concluded: 1. The Menomonee River gains the most benefits from the deep-unnel pollution mitigative function. This is based on the number of river water-quality parameters exhibiting a decline in average levels following the deep tunnel. The decline is less for data associated with the Milwaukee and Kinnickinnic Rivers. 2. Compared to all the other water-quality parameters analyzed in all three rivers, the fecal coliform count inside the CSO area declines the most. This is despite the fact that outside the CSO area, it does not decline after 1994, except perhaps in the Kinnickinnic River. However, the outside CSO area fecal coliform count for the Kinnickinnic River is based on only one station (RI-12) which is fairly close to the CSO boundary, and the values may, therefore, be influenced by the lower counts inside the CSO area. The results point to the beneficial effects of the deep tunnel. Without the increase in the fecal coliform count outside, one could expect an even more substantial decrease inside the CSO area. 3. Besides fecal coliform counts, BOD and zinc levels also decline to a certain extent after 1994. On the other hand, suspended solids and chloride levels increase. 4. Phosphorus levels inside the CSO area of the Menomonee and Kinnickinnic Rivers show only modest decline. Although the calculations indicate some phosphorus reduction outside the CSO area of the Milwaukee River, the same was not observed downstream in the CSO area. 5. Based on results from spatial correlation calculations of adjacent river sampling stations, for example, Menomonee River stations RI-21 and 22 and Kinnickinnic River stations RI-18 and 19, it appears that these stations provide nearly the same level of information. Excluding other
factors, this suggests that some of these stations are redundant. 6. Biweekly sampling, carried out before 1994, but not quite maintained after that date, should be resumed. More samplings should be done close to overflow points and near the time of overflow events. The episodic impact of pollutants during overflows may be underestimated in the current river sampling program.
Acknowledgements}This study was sponsored in part by the Milwaukee Metropolitan Sewerage District. REFERENCES
Ab Razak I. A. (1999) Combined sewer overflows and water quality before and after deep tunnels operation in Milwaukee, Wisconsin. Ph.D. Thesis, Department of Civil Engineering and Mechanics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin. Berthouex P. M. and Brown L. C. (1994) Statistics for Environmental Engineers. CRC Press, Inc., Boca Raton, FL. Daniel W. W. (1990) Applied Nonparametric Statistics. PWS-Kent Publishing Company, Boston. Gilbert R. O. (1987) Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, New York. Harned D. A., Daniel III C. C. and Crawford J. K. (1981) Methods of discharge compensation as an aid to the evaluation of water quality trends. Water Resources Research 17, 1389–1400. Kachigan S. A. (1986) Statistical Analysis. An Interdisciplinary Introduction to Univariate & Multivariate Methods. Radius Press, New York. McCuen R. H. (1985) Statistical Methods for Engineers. Prentice-Hall, Inc., Englewood Cliffs, NJ. Miller I., Freund J. E. and Johnson R. A. (1990) Probability and Statistics for Engineers. Prentice-Hall, Englewood Cliffs, NJ. MMSD (1999) Surface Water Quality Monitoring Program, Milwaukee, WI. Milwaukee Metropolitan Sewerage District, Milwaukee, WI. Southeastern Wisconsin Regional Planning Commission (SEWRPC) (1987) A Water Resources Management Plan for the Milwaukee Harbor Estuary, Vols. 1, 2. Southeast Regional Planning Commission, Planning Report #37, Milwaukee, WI.