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Monitoring the pollution of river Ganga by tanneries using the multiband ground truth radiometer
PHOTOGRAMMETRY & REMOTE SENSING
ELSEVIER
ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216
Monitoring the pollution of river Ganga by tanneries using the multiband ground truth radiometer Nitin Kumar Tripathi Ł , C. Venkobachar, Ramesh Kumar Singh, Shiv Pal Singh Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India Received 20 May 1997; accepted 11 June 1998
Abstract The feasibility of utilising the multiband ground truth radiometer (MGTR) for monitoring the pollution of the river Ganga by tanneries at Kanpur, India is explored. Parameters targeted in the study were Secchi depth (a measure of turbidity), turbidity, tannin concentration and chemical oxygen demand (COD). MGTR offers reflectance in 11 bands within the spectral range of 0.45 to 0.90 µm. The reflectance data has been utilised to develop empirical relationships with Secchi depth, turbidity and tannin concentration. The spectral reflectance data does not directly indicate the measure of the COD. However, an empirical relationship between tannin concentration and COD has been established which allows an indirect measurement of the COD. The conventional environmental engineering laboratory approach for determination of the above parameters is time consuming, expensive and slow. This results in serious constraints in monitoring pollution parameters at frequent intervals for a large number of sampling points. The outcome of the study shows the viability of MGTR as a means of quick, repetitive and handy remote sensing for monitoring pollution caused by tanneries in narrow surface streams. 1998 Elsevier Science B.V. All rights reserved. Keywords: environmental monitoring; river pollution; tanneries; multiband radiometer
1. Introduction The Ganga is the major river in Northern India. Many towns on the bank of the Ganga are highly industrialised. Most of the industries have inadequate effluent treatment facilities and dump their wastes directly into the river. A high concentration of tanneries in Kanpur has further aggravated the situation. Urgent monitoring steps need to be taken to prevent pollution because it has serious implications not only for public health, but also for the ecosystem and long term sustainability of urban and rural economy. Besides other chemical and textile industries, Ł Corresponding
author. E-mail: [email protected]
Kanpur has 151 tanneries located in a cluster at Jajmau along the southern bank of the Ganga with an estimated waste water discharge of 5.8 to 8.8 million litres per day. Out of 151 tanneries in Jajmau, 62 tanneries use exclusively the chrome tanning process, 50 tanneries use vegetable tanning processes, and 38 tanneries use both chrome and vegetable tanning. The Indian government under the Ganga Action Plan (GAP) has implemented several schemes for the abatement of pollution of the Ganga by tanneries. However, there are violations of the pollution control measures, and tannery effluents are still found in the river. The conventional monitoring methods of sampling, laboratory testing and analysis of river water are very costly and time consuming. Therefore,
0924-2716/98/$ – see front matter 1998 Elsevier Science B.V. All rights reserved. PII S 0 9 2 4 - 2 7 1 6 ( 9 8 ) 0 0 0 0 8 - 2
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government agencies find it very difficult to monitor the quality of river water over a long stretch at frequent intervals. This calls for a repetitive monitoring scheme so that the authorities may be in a position to implement remedial and control measures. This is the motivation behind this work. The remote sensing technique has the potential to repetitively monitor pollution of large water bodies. Several researchers (Moore, 1978; Lillesand and Kiefer, 1979; McKim et al., 1984; Khorram and Cheshire, 1985) have used satellite image interpretation for water quality analysis. Hand held radiometers have been used for in-situ monitoring of suspended solid concentration in river water and lakes (Ritchie et al., 1976; Bhargava and Mariam, 1990). The spatial resolution of multiband satellite imagery is very coarse, e.g., 30 m for Landsat-TM, which is not adequate to monitor the water quality of narrow surface streams. The costs involved and sensor limitations do not permit the use of multiband satellite imagery data on regular basis. The multiband ground truth radiometer (MGTR), which is manufactured in India, appears to be a good candidate for in-situ water quality monitoring of rivers. It offers around 0.20 m spatial resolution when operated from a height of around 1.2 m, which is very promising for monitoring of narrow surface streams. This study is aimed at exploring the feasibility of using the MGTR for monitoring of water quality parameters, in particular tannin concentration, turbidity and chemical oxygen demand (COD), of the river Ganga. The stretch of river Ganga selected for the study includes relatively unpolluted water at Bithur to the most polluted section at Jajmau and downstream. 2. Study area The stretch of Ganga river covering the entire southern periphery of Kanpur has been used as a case study. This river serves as a main source of domestic and industrial water supply to Kanpur. Both domestic and industrial effluents find their way after treatment into the river. The tannery effluent is one of the most polluting wastes among the industrial effluents. Around 30 kg of liquid effluent is produced per kg of leather processed. Thus, a substantial amount of effluent is discharged from tanneries, which affects the aquatic life and makes the water hazardous
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Table 1 Typical composite effluent characteristics Parameters
Values, water with tannin (in Ganga, at Kanpur)
Values, standard water a
Colour Odour Turbidity Temperature pH Suspended solid concentration Chloride Chromium COD BOD b Oil and grease Tannin
Greenish yellow Pungent tan Turbid Ambient 9.2
Dark yellow Mild Clear Ambient 7.5
40100 mg=l 8000 mg=l 150 mg=l 6854 mg=l 2496 mg=l 350 mg=l 100 mg=l
400 mg=l 1500 mg=l 130 mg=l 1500 mg=l 100 mg=l 50 mg=l 50 mg=l
a Allowable limit values set by Central Pollution Control Board, India. b Biological oxygen demand (BOD) is a measure of the quantity of dissolved oxygen necessary for the decomposition or organic matter by micro-organisms, such as bacteria.
for human consumption. The typical effluent characteristics according to Pandey (1988) are presented in Table 1. Tannery effluents contain considerable amount of proteins. These proteins are biodegradable and cause a very high oxygen demand, when discharged in watercourses. The salt and hydrogen sulphate present in tannery effluent may adversely affect stream quality and cause bad taste and odour. The effluent from vegetable tannery is highly coloured and when discharged into stream, the colour may persist for a very long period, whereas chrome tannery effluents are highly toxic to fish and other aquatic life. For the present study, samples from the river were collected from 28 points, starting from Bithur (used as a reference point with no industrial pollution) to downstream of Jajmau bridge covering all tanneries (locations of severe tannin concentration). A map showing all the sampling points across the stretch of the river Ganga is presented in Fig. 1. 3. Experimental methodology The objective of the present investigation was to evaluate the possibility of employing the MGTR to monitor the river Ganga at Jajmau. Empirical
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N.K. Tripathi et al. / ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216 Table 2 Description of MGTR spectral channels MGTR Satellites with similar Central wavelength Range bands wavelengths (µm) (µm) 1 2 3 4 5 6 7 8 9 10 11
Landsat-TM Landsat-TM Landsat-TM Landsat-TM IRS-1B IRS-1B IRS-1B IRS-1B SPOT SPOT SPOT
0.485 0.560 0.660 0.830 0.485 0.555 0.650 0.815 0.545 0.645 0.840
0.45–0.52 0.52–0.60 0.63–0.69 0.76–0.90 0.45–0.52 0.52–0.59 0.62–0.68 0.77–0.86 0.50–0.59 0.61–0.68 0.79–0.89
relations have been developed between reflectance versus Secchi depth 1 , turbidity and tannin concentration. An attempt has also been made to investigate the relationship between turbidity and Secchi depth and between tannin concentration and COD 2 . In the present study, both laboratory as well as field experiments have been conducted. Laboratory experiments have been conducted to understand the effect of tannin concentration on reflectance under controlled illumination. Reflectance from water, both in laboratory and field, has been observed employing the MGTR.
objects. This instrument is manufactured by Optomech Engineering Pvt. Ltd., Hyderabad, India (Instruction Manual, 1991; Singh and Sirohi, 1994) in technical collaboration with the Space Application Centre, India. The MGTR is compatible with the Indian Remote Sensing satellite (IRS), Landsat-TM and SPOT. The operating spectral bands can be selected from visible to near infrared region (0.4 to 0.9 µm). The channel specifications are presented in Table 2. A calibration plate of 60 ð 60 cm is provided along with the instrument. This plate is uniformly coated with barium sulphate (BaSO 4 ) paint. The calibration plate is always taken to the field along with the equipment and is placed on the terrain surface=object under study. The spectral radiance of the plate as well as the study object are recorded for a particular band within 5 minutes, as sun angle changes by 1º every 5 minutes. Reflectance from barium sulphate is considered as 100%.
3.1. Multiband ground truth radiometer
3.2. Laboratory set-up
The MGTR is a rugged portable and battery-operated instrument for measuring radiance of various
A 50 ð 50 ð 75 cm tank of galvanised iron (GI) sheet of 22 gauges was fabricated for conducting laboratory studies. To minimise the bottom and side reflections, the interior surface of the tank was painted black. A 1000 W tungsten–halogen lamp was used to illuminate the tank uniformly from a height of 1 m from water surface to simulate the solar radiation. As the effective surface area of the tank is 0.25 m2 , the sensor (i.e. MGTR with 15º field of view) height was fixed at 1 m to ensure a large gap between area
Fig. 1. Map of sampling points along the Ganga river.
1 Secchi depth is a measure of water transparency (or turbidity). The depth of a Secchi disc, that is just visible by an observer at the water surface. 2 COD is a measure of the chemically oxidizable material in the water and furnishes an approximation of the amount of organic and reducing material present. The determined value may correlate with natural water colour or with carbonaceous organic pollution from sewage or industrial wastes.
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increases, the Secchi depth reduces. Water samples were collected from the sampling points in dark tight bottles for estimation of different water quality parameters. The samples collected were analysed for COD, tannin and turbidity according to Standard Methods (1992). 4. Results and discussion The results of the laboratory and field experiments and their analysis are presented in the following sections. 4.1. Reflectance for tannin monitoring
Fig. 2. Laboratory experimental set-up.
sensed and any possible background influence. Bhargava and Mariam (1990) have also used a similar set-up, shown in Fig. 2, to study turbidity induced by different types of soil. The angle of incident light was kept constant (45º with the surface of the water body) in this study and the MGTR was held vertically at approximately 1 m over the water surface. 3.3. Field study Sampling sites of low to extreme pollution have been selected along a stretch of Ganga, from Bithur to downstream of Jajmau bridge. The field study was conducted on a clear and sunny day, and samples were collected at locations, where potential bottom effects (mixing of reflectance from shallow river bed with water reflectance) could be avoided. The readings were taken when the sun angle was around 45º (between 9.30 to 11 a.m. and from 3.00 to 4.30 p.m.). The radiance of water and barium sulphate plate was recorded at the sampling points using the MGTR in all 11 bands. Secchi depth readings were taken as a measure of the in-situ turbidity. As the turbidity
A laboratory experiment was first conducted to determine the optimum wavelength of the MGTR for monitoring tannin. A known amount of tannin was added to the experimental tank of 180 l capacity, to achieve a tannin concentration of 8 mg=l, which is approximately the tannin concentration found at Jajmau. Fig. 3 shows the reflectance of water containing tannin in laboratory experiments, as well as at various locations of the Ganga at Jajmau. The results show higher reflectance in the wavelength band (0.76–0.90 µm) for both laboratory and field studies. This means that the presence of tannin in water influences the reflectance and shows maximum response in the near infrared region. The offset of the two curves in Fig. 3 is due to differences in illumination intensity (in the laboratory study a 1000 W tungsten halogen lamp was used). Bithur is situated upstream of Kanpur and the water there has negligible traces of tannin. Fig. 4 illustrates that there is no similarity between the reflectance obtained from Bithur and Jajmau samples. The reflectance of the Jajmau samples was smaller for all bands. Tannin affected water has highest reflectance in the wavelength band (0.76–0.90 µm), whereas clear water has peak reflectance between 0.61 to 0.69 µm and low reflectance in NIR. This establishes the strong influence of tannin over spectral reflectance. Thus, spectral reflectance contains vital information about the presence of tannin. It is clear that the MGTR offers a viable alternative to monitor tannery effluents from industry in surface water bodies. A detailed investigation to find the relationship between reflectance and water
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Fig. 3. Spectral reflectance characteristics of water containing tannin in laboratory and field study.
Fig. 4. Spectral reflectance of water in the Ganga river at Bithur (no tannin) and Jajmau (high tannin concentration).
quality parameters is described in the subsequent sections. 4.2. Chemical composition of Ganga water at Jajmau The tannin profile for several sampling stations is presented in Fig. 5. The results indicate that there is
an upsurge of tannin concentration from station 9 to 13, which corresponds to the location of tanneries in Jajmau. A maximum tannin concentration of 6 to 8 mg=l was obtained at these points. Thereafter, from stations 14 to 17 there is a continuous decline in tannin concentration due to dispersion. At stations 6 and 7, although located downstream of tanneries, the water contains less tannin than at stations 9–13, due to
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Fig. 5. Tannin concentration profile of the test area.
Fig. 6. Chemical oxygen demand profile of the test area.
storm water outfall and mixing effects respectively. The latter reason also explains the lower tannin at stations 14 and 15 as compared to the nearby stations 10 and 11. The COD for samples using the closed reflux method (Standard Methods, 1992) was determined and the profile is presented in Fig. 6. There
seems to be a strong correlation between tannin and COD, with an upsurge in COD concentration at the eighth sampling point. This is because tannin can be chemically oxidised. One point to be noted is that although the tannin concentration at samples beyond station 13 declined sharply, the decline in COD was
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Fig. 7. Turbidity profile of the test area (NTU D nephelometric turbidity units) .
Fig. 8. Secchi depth profile of the test area.
not so rapid. This indicates that water quality did not regenerate even after dilution of tannin. The turbidity of Ganga water at various sampling points was analysed in the laboratory and the profile is presented in Fig. 7. Again, a close correlation is found in upsurge of turbidity and increase in tannin and COD concentrations from sampling station 9 onwards. Just after station 13, decrease of turbidity is much steeper as compared to that of tannin and COD. In this region, the turbidity values almost reach the background levels of stations 1 to 8.
Secchi depth is used in the field for in-situ measurement of turbidity of the surface stream. The profile of Secchi depth along the various sampling points in the river is presented in Fig. 8. The turbidity of water increases as the Secchi depth decreases. Laboratory analysis of turbidity is more cumbersome than Secchi depth measurements. Thus, Secchi depth may be an alternative and convenient in situ measure of turbidity for surface streams.
N.K. Tripathi et al. / ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216 Table 3 Correlation coefficients for linear and nonlinear models relating tannin concentration and reflectance Central wavelength (µm)
Linear model
Nonlinear model
0.485 0.545 0.555 0.560 0.645 0.650 0.660 0.815 0.830 0.840
0.83 0.82 0.80 0.89 0.86 0.89 0.88 0.50 0.41 0.55
0.83 0.94 0.92 0.96 0.96 0.94 0.91 0.57 0.42 0.77
However, a second order polynomial expression is found to improve the fitting of the data significantly. It is observed (Table 3) that the values of the correlation coefficients of the nonlinear model have increased compared to those of the linear model. Thus, it is proposed to use a second order polynomial relationship of the form Tn D A0 C A1 .R½ / C A2 .R½ /2
4.3. Empirical models between tannin and reflectance The correlation coefficients of linear as well as nonlinear models for all central wavelengths are given in Table 3. In this case, all samples were used, while in Tables 4 and 6, three samples used in the model verification (see Section 5) were left out. In case of the linear model, the correlation coefficients were maximum for the red wavelength band 0.61 to 0.69 µm. Lower correlations were observed for the blue and NIR regions. Bands 3, 7 and 10 are compatible to the red channels of Landsat, IRS and SPOT respectively. Thus, they were selected for determining the empirical relationship between reflectance and tannin. The following linear empirical relation between tannin concentration (Tn ) and reflectance (R½ ) has been employed: Tn D A0 C A1 .R½ /:
211
(1)
(2)
where A0 , A1 and A2 are the regression coefficients. The regression coefficients along with standard error of estimates are presented in Table 4. It is noted that the standard error of the nonlinear model is smaller. The effect of any extraneous factors affecting all bands equally can be self-compensated by spectral ratioing, whereby band ratios compensate only multiplicative effects (Lillesand and Kiefer, 1979). Therefore, in order to compensate the effect of multiplicative effects, e.g. illumination differences, on the apparent reflectances, the ratio of red to green band has been utilised. This has been done taking into account the colour of tannin which is reddish brown and has yielded high reflectance in the red and low in the green band (Fig. 4). A regression equation of the form Tn D A0 C A1 .BR/
(3)
where BR the ratio of red to green band, is employed to describe the empirical relationship between tannin and reflectance. The results of the band ratio model are shown in Table 4. As it can be seen in the table, a reasonably high correlation coefficient and low standard error are obtained. However, the best results are achieved with the nonlinear model (Fig. 9).
Table 4 Results of regression analysis for empirical models relating tannin concentration and reflectance Regression coefficients, correlation, standard error A0 A1 A2 R S.E.
Linear model
Nonlinear model
Band ratio model
Central wavelength (µm)
Central wavelength (µm)
Ratio of central wavelengths (µm)
0.660
0.650
0.645
0.660
0.660=0.560
16.770 5.259 – 0.87 0.123
12.468 3.668 – 0.89 0.154
16.006 5.196 – 0.86 0.143
33.535 43.763 11.844 0.94 0.073
0.650 24.541 34.402 9.414 0.94 0.091
0.645 8.815 19.387 5.915 0.92 0.073
1.000 5.499 – 0.80 0.127
0.650=0.555 1.970 7.499 – 0.88 0.153
0.645=0.545 1.946 7.051 – 0.77 0.207
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Fig. 9. Quadratic regression model between tannin and reflectance.
4.4. Empirical models between Secchi depth and reflectance Correlation coefficients between Secchi depth and reflectance are given in Table 5. It is observed that the correlation coefficients were maximum for the green wavelength region (0.5–0.6 µm). A simple linear regression relationship between Secchi depth (Sd) and spectral reflectance (R½ ) of the form Sd D A0 C A1 .R½ /
(4)
was employed. A quadratic model was also tested. Regression coefficients, correlation coefficients and Table 5 Correlation coefficients for linear and nonlinear models relating Secchi depth and reflectance Central wavelength (µm)
Linear model
Nonlinear model
0.485 0.545 0.555 0.560 0.645 0.650 0.660 0.815 0.830 0.840
0.88 0.95 0.95 0.96 0.94 0.93 0.92 0.72 0.68 0.72
0.90 0.97 0.96 0.96 0.94 0.94 0.92 0.83 0.72 0.74
standard errors are presented in Table 6. As Table 6 shows, the difference between linear and quadratic model was very small, fact which is also verified by the nearly linear form of the quadratic model in Fig. 10. In addition, a band ratio model of the form as in Eq. 3 was used (see Table 6). Sometimes, environmental variations, like complex effect of air density, air temperature, water vapour in atmosphere, wave turbulence etc., have a serious influence on the recorded data. If the environmental variations are spectrally independent over a large range of a few hundred nanometres, subtraction of radiance (L ½ ) in two bands can remove these effects (Curran and Novo, 1988; Gitelson and Kondratyev, 1991). For the same reason, derivative spectroscopy may be used to achieve better results. Derivative spectroscopy uses the first or second derivatives of spectral radiance (L ½ ) or reflectance (R½ ) to separate the effects of several parameters on the signal, and thus detect spectral characteristics, which can be solely attributed to a single parameter (Demetriades-Shah et al., 1990; Chen et al., 1992; Goodin et al., 1993; Han et al., 1994). A model of the form Sd D A0 C A1 .DR/
(5)
is used, where DR is derivative reflectance. DR was computed as difference of the reflectance in two wavelengths. The maximum correlation between derivative reflectance for wavelengths 0.66, 0.83 and 0.65, 0.815 µm were 0.93 and 0.94 respectively. The results of the regression analysis are shown in Table 6. The derivative model led to lower standard errors than the other models. 4.5. Empirical model between turbidity and Secchi depth As observed in Figs. 7 and 8, Secchi depth is related to turbidity. A linear model was not used in regression analysis since it led to low correlation between Secchi depth and turbidity (0.78). Using regression analysis and a second order polynomial, a relationship between turbidity (Tu) and Secchi depth of the form Sd D 42:1126
1:40873.Tu/ C 0:013614.Tu/2 (6)
Regression coefficients, correlation, standard error
Linear
Nonlinear
Derivative reflectance
Band ratio model
Central wavelength (µm)
Central wavelength (µm)
Central wavelengths (µm)
Ratio of central wavelengths (µm)
0.560
0.555
0.545
0.560
0.660–0.830
0.650–0.815
0.660-0.830
0.650-0.815
0.645-0.840
A0 A1 A2 R S.E.
5.458 2.681 – 0.96 0.418
6.207 2.973 – 0.95 0.476
6.688 3.368 – 0.95 0.450
7.011 0.266 – 0.93 0.155
8.180 0.294 – 0.94 0.156
58.920 25.930 – 0.94 0.250
60.052 29.998 – 0.91 0.153
76.838 21.794 – 0.77 0.178
37.59 46.105 4.940 0.96 0.428
0.555 57.76 63.259 8.568 0.96 0.427
0.545 85.52 89.923 14.188 0.97 0.397
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Table 6 Results of regression analysis for empirical models relating Secchi depth and reflectance
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4.6. Empirical model between turbidity and reflectance
Fig. 10. Quadratic regression model between Secchi depth and reflectance.
Attempts were made to correlate turbidity and reflectance, and a second order polynomial led to a good fit. Besides this model, relationships between reflectance ratio and turbidity, and derivative reflectance and turbidity were also explored. For both reflectance ratio and DR, the red and NIR bands were used. Details are omitted, since the results were very similar to those of Secchi depth and reflectance (see Section 4.4). The values of reflectance obtained from water bodies contain a composite effect of various pollution parameters. Taking tannin concentration and Secchi depth as a measure of turbidity, a multivariate model of the following form was used: R½ D þ0 C þ1 .Tn / C þ2 .Sd/:
(7)
Correlation coefficients are high for wavelengths 0.56, 0.555 and 0.545 µm. The values of þ0 , þ1 and þ2 , correlations and standard errors are presented in Table 7. 4.7. Empirical model between tannin and chemical oxygen demand As tannin exerts COD in river water, an empirical relation between them was sought. A nonlinear equation of the following form was found to relate the two well (a linear model was not used since it led to low correlation between tannin and COD). Tn D 4:3658
0:0982.COD/ C 0:00930.COD/2 : (8)
Fig. 11. Quadratic regression model between Secchi depth and turbidity.
led to a high correlation coefficient of 0.975 and a standard error of 0.257. A high correlation between turbidity and Secchi depth suggests that Secchi depth alone may be observed in the field and later related to turbidity. This will decrease the time and effort involved in sampling and laboratory testing for turbidity estimation. The relationship between turbidity and Secchi depth is presented in Fig. 11.
The correlation coefficient was 0.959 and the standard error 0.156, and their relation is presented in Fig. 12. The study reveals a good correlation between tannin and COD. 5. Model verification The Chi-square test, based on three samples that were not included in the regression modelling, was used for verification of various models proposed. Thereby, emphasis was placed to five models for tannin and Secchi depth (see Table 8). The Chi-square
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Table 7 Results of multivariate regression analysis using tannin concentration, Secchi depth and reflectance in three bands Central wavelength (µm) 0.560 0.555 0.545
þ0
þ1
0.877 0.233 0.160
0.010 0.100 0.073
þ2
R
S.E.
0.035 0.053 0.043
0.96 0.96 0.96
0.169 0.166 0.135
6. Conclusions
Fig. 12. Quadratic regression model between tannin and chemical oxygen demand.
equation is n X .E 1 2 comp D 1
O1 /2
(9)
E1
where E is the predicted value, O is the observed 2 value, n is the number of sampling points, and comp is the computed value of Chi-square. For 5% level of significance and two degrees of 2 should be less than or equal to 5.59. freedom, comp 2 The comp of various models is shown in Table 8. Obviously, additional tests with a higher number of independent samples are needed.
This study shows that the MGTR is an effective tool for rapid determination of Secchi depth, turbidity and tannin concentration in narrow surface streams. The turbidity measurement in the field involves considerably more time and infrastructure than Secchi depth measurement. The investigations revealed that Secchi depth can be conveniently used for in situ representation of the turbidity (Fig. 11), as their empirical relation shows high correlation coefficient (0.975) and small standard error (0.257). The reflectance has very high correlation with Secchi depth in the wavelength region 0.50 to 0.60 µm. A nonlinear model between Secchi depth and reflectance, with a correlation of 0.967, led to an improvement over a linear model. Since reflectance from water is dependent on composition of suspended matter and also environmental parameters, a multivariate regression analysis was attempted to correlate the reflectance with tannin as well as with Secchi depth. This model allows estimation of tannin concentration given availability of Secchi depth and reflectance data. High correlation coefficients were found using spectral reflectance in the wavelength region of 0.50 to 0.60 µm. Tannin concentration has been modelled as a function of reflectance using a second order polynomial. A very high coefficient of correlation ranging between 0.92 to 0.94 has been obtained in the wavelength region 0.62–0.69 µm. The COD does
Table 8 Results of Chi-square test for different models Parameter
Tannin
Secchi depth
Model
Tn vs. R½ nonlinear
Tn vs. BR
Sd vs. R½ nonlinear
Sd vs. BR
Sd vs. DR
2 comp
5.51
4.01
3.98
3.50
2.01
Tn : Tannin concentration, BR: Band ratio, Sd: Secchi depth, R½ : Reflectance, DR: Derivative reflectance.
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not influence the spectral reflectance of water. COD is dependent on presence of organic or inorganic wastes in water. Tannin presence in water is found to augment the COD. A second order polynomial relation between tannin concentration and COD has been established. It led to a high correlation coefficient of 0.959 and a standard error of 0.156. Thus, COD may be indirectly monitored by determining tannin concentration. The proposed models have been verified using the Chi-square test and were found satisfactory at a 5 percent level of significance. However, it should be noted that the empirical relations between different water quality parameters and reflectance have been derived based on data from a small study area, with high pollution level. Their validity needs to be verified in additional test areas with varying levels of pollution. Reflectance observed in the eleven bands of the MGTR can be successfully utilised for quick and repetitive determination of turbidity, tannin concentration and COD for a large number of points. This remote sensing approach is an effective alternative to tedious, time consuming conventional environmental laboratory techniques. Although some satellite sensors have wavelengths similar to the ones of MGTR, the latter has the distinct advantages of higher spatial resolution and more frequent coverage, especially since it is not affected by clouds. A further possibility could be the use of sensors with spectral characteristics similar to those of MGTR and sufficient geometric resolution on low-flying aircrafts or even satellites. The methods and empirical relationships presented here can be used for Secchi depth, turbidity and COD determination in many applications, apart from tannery pollution, involving water quality monitoring. Acknowledgements The help of the Environmental Engineering Laboratory, I. I.T. Kanpur, India, in carrying out chemical analysis is gratefully acknowledged. We gratefully acknowledge the help of U.P. Jal Nigam, Kanpur towards conducting the environmental survey of the river for this study. The encouragement received from U.P. Pollution Control Board, India during discussions is thankfully acknowledged. The authors
also feel indebted to an anonymous referee for his useful suggestions. References Bhargava, D.S., Mariam, D.W., 1990. Spectral reflectance relationships to turbidity generated by difference clay materials. Photogramm. Eng. Remote Sensing 56 (2), 225–229. Chen, Z., Curran, P.J., Hanson, J.D., 1992. Derivative reflectance spectroscopy to estimate suspended sediment concentration. Remote Sensing Environ. 40 (1), 67–77. Curran, P.J., Novo, E.M., 1988. The relationship between suspended sediment concentration and remotely sensed spectral radiance: a review. J. Coastal Res. 4 (3), 351–368. Demetriades-Shah, T., Steven, M., Clark, J., 1990. High resolution derivative spectra in remote sensing. Remote Sensing Environ. 33, 55–64. Gitelson, A.A., Kondratyev, K.Y., 1991. Optical models of mesotrophic and eutrophic waterbodies. Int. J. Remote Sensing 12 (3), 373–386. Goodin, D., Han, L., Fraser, R., Rundquist, D.C., Stebbins, W., Schalles, J., 1993. Analysis of suspended solids in water using remotely sensed high resolution derivative spectra. Photogramm. Eng. Remote Sensing 59 (4), 505–510. Han, L., Rundquist, D.C., Liu, L., Fraser, R., Schalles, J., 1994. The spectral responses of algal chlorophyll in water with varying levels of suspended sediment. Int. J. Remote Sensing 15 (18), 3707–3718. Instruction manual of multiband ground truth radiometer — model-041, 1991. Optomech Engineers Pvt. Ltd., Hyderabad, India. Khorram, S., Cheshire, H.M., 1985. Remote sensing of water quality in the Neuse river estuary North California. Photogram. Eng. Remote Sensing 51 (3), 329–341. Lillesand, T.M., Kiefer, R.W., 1979. Remote Sensing and Image Interpretation. Wiley, New York. McKim, H.C., Merry, C.J., Layman, R.W., 1984. Water quality monitoring using an airborne spectroradiometer. Photogramm. Eng. Remote Sensing 50 (3), 353–360. Moore, G.K., 1978. Satellite surveillance of physical water-quality characteristics. Proc. of 12th Int. Symposium on Remote Sensing of Environment 1, 445–462. Pandey, B.K., 1988. Monitoring the pollution of Ganga river by tannery effluent. Project report, Government Leather Institute, Kanpur, pp. 25–30. Ritchie, J.C., Schiebe, F.R., McHenry, J.R., 1976. Remote sensing of suspended sediment in surface waters. Photogrammm. Eng. Remote Sensing 42, 1539–1545. Standard Methods, 1992. Standard Methods for Examination of Water and Wastewater, 18th ed., American Public Health Assoc., Washington D.C. Singh, R.P., Sirohi, A., 1994. Spectral reflectance properties of different types of soil surfaces. ISPRS J. Photogramm. Remote Sensing 49 (4), 34–40.
ELSEVIER
ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216
Monitoring the pollution of river Ganga by tanneries using the multiband ground truth radiometer Nitin Kumar Tripathi Ł , C. Venkobachar, Ramesh Kumar Singh, Shiv Pal Singh Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India Received 20 May 1997; accepted 11 June 1998
Abstract The feasibility of utilising the multiband ground truth radiometer (MGTR) for monitoring the pollution of the river Ganga by tanneries at Kanpur, India is explored. Parameters targeted in the study were Secchi depth (a measure of turbidity), turbidity, tannin concentration and chemical oxygen demand (COD). MGTR offers reflectance in 11 bands within the spectral range of 0.45 to 0.90 µm. The reflectance data has been utilised to develop empirical relationships with Secchi depth, turbidity and tannin concentration. The spectral reflectance data does not directly indicate the measure of the COD. However, an empirical relationship between tannin concentration and COD has been established which allows an indirect measurement of the COD. The conventional environmental engineering laboratory approach for determination of the above parameters is time consuming, expensive and slow. This results in serious constraints in monitoring pollution parameters at frequent intervals for a large number of sampling points. The outcome of the study shows the viability of MGTR as a means of quick, repetitive and handy remote sensing for monitoring pollution caused by tanneries in narrow surface streams. 1998 Elsevier Science B.V. All rights reserved. Keywords: environmental monitoring; river pollution; tanneries; multiband radiometer
1. Introduction The Ganga is the major river in Northern India. Many towns on the bank of the Ganga are highly industrialised. Most of the industries have inadequate effluent treatment facilities and dump their wastes directly into the river. A high concentration of tanneries in Kanpur has further aggravated the situation. Urgent monitoring steps need to be taken to prevent pollution because it has serious implications not only for public health, but also for the ecosystem and long term sustainability of urban and rural economy. Besides other chemical and textile industries, Ł Corresponding
author. E-mail: [email protected]
Kanpur has 151 tanneries located in a cluster at Jajmau along the southern bank of the Ganga with an estimated waste water discharge of 5.8 to 8.8 million litres per day. Out of 151 tanneries in Jajmau, 62 tanneries use exclusively the chrome tanning process, 50 tanneries use vegetable tanning processes, and 38 tanneries use both chrome and vegetable tanning. The Indian government under the Ganga Action Plan (GAP) has implemented several schemes for the abatement of pollution of the Ganga by tanneries. However, there are violations of the pollution control measures, and tannery effluents are still found in the river. The conventional monitoring methods of sampling, laboratory testing and analysis of river water are very costly and time consuming. Therefore,
0924-2716/98/$ – see front matter 1998 Elsevier Science B.V. All rights reserved. PII S 0 9 2 4 - 2 7 1 6 ( 9 8 ) 0 0 0 0 8 - 2
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government agencies find it very difficult to monitor the quality of river water over a long stretch at frequent intervals. This calls for a repetitive monitoring scheme so that the authorities may be in a position to implement remedial and control measures. This is the motivation behind this work. The remote sensing technique has the potential to repetitively monitor pollution of large water bodies. Several researchers (Moore, 1978; Lillesand and Kiefer, 1979; McKim et al., 1984; Khorram and Cheshire, 1985) have used satellite image interpretation for water quality analysis. Hand held radiometers have been used for in-situ monitoring of suspended solid concentration in river water and lakes (Ritchie et al., 1976; Bhargava and Mariam, 1990). The spatial resolution of multiband satellite imagery is very coarse, e.g., 30 m for Landsat-TM, which is not adequate to monitor the water quality of narrow surface streams. The costs involved and sensor limitations do not permit the use of multiband satellite imagery data on regular basis. The multiband ground truth radiometer (MGTR), which is manufactured in India, appears to be a good candidate for in-situ water quality monitoring of rivers. It offers around 0.20 m spatial resolution when operated from a height of around 1.2 m, which is very promising for monitoring of narrow surface streams. This study is aimed at exploring the feasibility of using the MGTR for monitoring of water quality parameters, in particular tannin concentration, turbidity and chemical oxygen demand (COD), of the river Ganga. The stretch of river Ganga selected for the study includes relatively unpolluted water at Bithur to the most polluted section at Jajmau and downstream. 2. Study area The stretch of Ganga river covering the entire southern periphery of Kanpur has been used as a case study. This river serves as a main source of domestic and industrial water supply to Kanpur. Both domestic and industrial effluents find their way after treatment into the river. The tannery effluent is one of the most polluting wastes among the industrial effluents. Around 30 kg of liquid effluent is produced per kg of leather processed. Thus, a substantial amount of effluent is discharged from tanneries, which affects the aquatic life and makes the water hazardous
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Table 1 Typical composite effluent characteristics Parameters
Values, water with tannin (in Ganga, at Kanpur)
Values, standard water a
Colour Odour Turbidity Temperature pH Suspended solid concentration Chloride Chromium COD BOD b Oil and grease Tannin
Greenish yellow Pungent tan Turbid Ambient 9.2
Dark yellow Mild Clear Ambient 7.5
40100 mg=l 8000 mg=l 150 mg=l 6854 mg=l 2496 mg=l 350 mg=l 100 mg=l
400 mg=l 1500 mg=l 130 mg=l 1500 mg=l 100 mg=l 50 mg=l 50 mg=l
a Allowable limit values set by Central Pollution Control Board, India. b Biological oxygen demand (BOD) is a measure of the quantity of dissolved oxygen necessary for the decomposition or organic matter by micro-organisms, such as bacteria.
for human consumption. The typical effluent characteristics according to Pandey (1988) are presented in Table 1. Tannery effluents contain considerable amount of proteins. These proteins are biodegradable and cause a very high oxygen demand, when discharged in watercourses. The salt and hydrogen sulphate present in tannery effluent may adversely affect stream quality and cause bad taste and odour. The effluent from vegetable tannery is highly coloured and when discharged into stream, the colour may persist for a very long period, whereas chrome tannery effluents are highly toxic to fish and other aquatic life. For the present study, samples from the river were collected from 28 points, starting from Bithur (used as a reference point with no industrial pollution) to downstream of Jajmau bridge covering all tanneries (locations of severe tannin concentration). A map showing all the sampling points across the stretch of the river Ganga is presented in Fig. 1. 3. Experimental methodology The objective of the present investigation was to evaluate the possibility of employing the MGTR to monitor the river Ganga at Jajmau. Empirical
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N.K. Tripathi et al. / ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216 Table 2 Description of MGTR spectral channels MGTR Satellites with similar Central wavelength Range bands wavelengths (µm) (µm) 1 2 3 4 5 6 7 8 9 10 11
Landsat-TM Landsat-TM Landsat-TM Landsat-TM IRS-1B IRS-1B IRS-1B IRS-1B SPOT SPOT SPOT
0.485 0.560 0.660 0.830 0.485 0.555 0.650 0.815 0.545 0.645 0.840
0.45–0.52 0.52–0.60 0.63–0.69 0.76–0.90 0.45–0.52 0.52–0.59 0.62–0.68 0.77–0.86 0.50–0.59 0.61–0.68 0.79–0.89
relations have been developed between reflectance versus Secchi depth 1 , turbidity and tannin concentration. An attempt has also been made to investigate the relationship between turbidity and Secchi depth and between tannin concentration and COD 2 . In the present study, both laboratory as well as field experiments have been conducted. Laboratory experiments have been conducted to understand the effect of tannin concentration on reflectance under controlled illumination. Reflectance from water, both in laboratory and field, has been observed employing the MGTR.
objects. This instrument is manufactured by Optomech Engineering Pvt. Ltd., Hyderabad, India (Instruction Manual, 1991; Singh and Sirohi, 1994) in technical collaboration with the Space Application Centre, India. The MGTR is compatible with the Indian Remote Sensing satellite (IRS), Landsat-TM and SPOT. The operating spectral bands can be selected from visible to near infrared region (0.4 to 0.9 µm). The channel specifications are presented in Table 2. A calibration plate of 60 ð 60 cm is provided along with the instrument. This plate is uniformly coated with barium sulphate (BaSO 4 ) paint. The calibration plate is always taken to the field along with the equipment and is placed on the terrain surface=object under study. The spectral radiance of the plate as well as the study object are recorded for a particular band within 5 minutes, as sun angle changes by 1º every 5 minutes. Reflectance from barium sulphate is considered as 100%.
3.1. Multiband ground truth radiometer
3.2. Laboratory set-up
The MGTR is a rugged portable and battery-operated instrument for measuring radiance of various
A 50 ð 50 ð 75 cm tank of galvanised iron (GI) sheet of 22 gauges was fabricated for conducting laboratory studies. To minimise the bottom and side reflections, the interior surface of the tank was painted black. A 1000 W tungsten–halogen lamp was used to illuminate the tank uniformly from a height of 1 m from water surface to simulate the solar radiation. As the effective surface area of the tank is 0.25 m2 , the sensor (i.e. MGTR with 15º field of view) height was fixed at 1 m to ensure a large gap between area
Fig. 1. Map of sampling points along the Ganga river.
1 Secchi depth is a measure of water transparency (or turbidity). The depth of a Secchi disc, that is just visible by an observer at the water surface. 2 COD is a measure of the chemically oxidizable material in the water and furnishes an approximation of the amount of organic and reducing material present. The determined value may correlate with natural water colour or with carbonaceous organic pollution from sewage or industrial wastes.
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increases, the Secchi depth reduces. Water samples were collected from the sampling points in dark tight bottles for estimation of different water quality parameters. The samples collected were analysed for COD, tannin and turbidity according to Standard Methods (1992). 4. Results and discussion The results of the laboratory and field experiments and their analysis are presented in the following sections. 4.1. Reflectance for tannin monitoring
Fig. 2. Laboratory experimental set-up.
sensed and any possible background influence. Bhargava and Mariam (1990) have also used a similar set-up, shown in Fig. 2, to study turbidity induced by different types of soil. The angle of incident light was kept constant (45º with the surface of the water body) in this study and the MGTR was held vertically at approximately 1 m over the water surface. 3.3. Field study Sampling sites of low to extreme pollution have been selected along a stretch of Ganga, from Bithur to downstream of Jajmau bridge. The field study was conducted on a clear and sunny day, and samples were collected at locations, where potential bottom effects (mixing of reflectance from shallow river bed with water reflectance) could be avoided. The readings were taken when the sun angle was around 45º (between 9.30 to 11 a.m. and from 3.00 to 4.30 p.m.). The radiance of water and barium sulphate plate was recorded at the sampling points using the MGTR in all 11 bands. Secchi depth readings were taken as a measure of the in-situ turbidity. As the turbidity
A laboratory experiment was first conducted to determine the optimum wavelength of the MGTR for monitoring tannin. A known amount of tannin was added to the experimental tank of 180 l capacity, to achieve a tannin concentration of 8 mg=l, which is approximately the tannin concentration found at Jajmau. Fig. 3 shows the reflectance of water containing tannin in laboratory experiments, as well as at various locations of the Ganga at Jajmau. The results show higher reflectance in the wavelength band (0.76–0.90 µm) for both laboratory and field studies. This means that the presence of tannin in water influences the reflectance and shows maximum response in the near infrared region. The offset of the two curves in Fig. 3 is due to differences in illumination intensity (in the laboratory study a 1000 W tungsten halogen lamp was used). Bithur is situated upstream of Kanpur and the water there has negligible traces of tannin. Fig. 4 illustrates that there is no similarity between the reflectance obtained from Bithur and Jajmau samples. The reflectance of the Jajmau samples was smaller for all bands. Tannin affected water has highest reflectance in the wavelength band (0.76–0.90 µm), whereas clear water has peak reflectance between 0.61 to 0.69 µm and low reflectance in NIR. This establishes the strong influence of tannin over spectral reflectance. Thus, spectral reflectance contains vital information about the presence of tannin. It is clear that the MGTR offers a viable alternative to monitor tannery effluents from industry in surface water bodies. A detailed investigation to find the relationship between reflectance and water
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Fig. 3. Spectral reflectance characteristics of water containing tannin in laboratory and field study.
Fig. 4. Spectral reflectance of water in the Ganga river at Bithur (no tannin) and Jajmau (high tannin concentration).
quality parameters is described in the subsequent sections. 4.2. Chemical composition of Ganga water at Jajmau The tannin profile for several sampling stations is presented in Fig. 5. The results indicate that there is
an upsurge of tannin concentration from station 9 to 13, which corresponds to the location of tanneries in Jajmau. A maximum tannin concentration of 6 to 8 mg=l was obtained at these points. Thereafter, from stations 14 to 17 there is a continuous decline in tannin concentration due to dispersion. At stations 6 and 7, although located downstream of tanneries, the water contains less tannin than at stations 9–13, due to
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Fig. 5. Tannin concentration profile of the test area.
Fig. 6. Chemical oxygen demand profile of the test area.
storm water outfall and mixing effects respectively. The latter reason also explains the lower tannin at stations 14 and 15 as compared to the nearby stations 10 and 11. The COD for samples using the closed reflux method (Standard Methods, 1992) was determined and the profile is presented in Fig. 6. There
seems to be a strong correlation between tannin and COD, with an upsurge in COD concentration at the eighth sampling point. This is because tannin can be chemically oxidised. One point to be noted is that although the tannin concentration at samples beyond station 13 declined sharply, the decline in COD was
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Fig. 7. Turbidity profile of the test area (NTU D nephelometric turbidity units) .
Fig. 8. Secchi depth profile of the test area.
not so rapid. This indicates that water quality did not regenerate even after dilution of tannin. The turbidity of Ganga water at various sampling points was analysed in the laboratory and the profile is presented in Fig. 7. Again, a close correlation is found in upsurge of turbidity and increase in tannin and COD concentrations from sampling station 9 onwards. Just after station 13, decrease of turbidity is much steeper as compared to that of tannin and COD. In this region, the turbidity values almost reach the background levels of stations 1 to 8.
Secchi depth is used in the field for in-situ measurement of turbidity of the surface stream. The profile of Secchi depth along the various sampling points in the river is presented in Fig. 8. The turbidity of water increases as the Secchi depth decreases. Laboratory analysis of turbidity is more cumbersome than Secchi depth measurements. Thus, Secchi depth may be an alternative and convenient in situ measure of turbidity for surface streams.
N.K. Tripathi et al. / ISPRS Journal of Photogrammetry & Remote Sensing 53 (1998) 204–216 Table 3 Correlation coefficients for linear and nonlinear models relating tannin concentration and reflectance Central wavelength (µm)
Linear model
Nonlinear model
0.485 0.545 0.555 0.560 0.645 0.650 0.660 0.815 0.830 0.840
0.83 0.82 0.80 0.89 0.86 0.89 0.88 0.50 0.41 0.55
0.83 0.94 0.92 0.96 0.96 0.94 0.91 0.57 0.42 0.77
However, a second order polynomial expression is found to improve the fitting of the data significantly. It is observed (Table 3) that the values of the correlation coefficients of the nonlinear model have increased compared to those of the linear model. Thus, it is proposed to use a second order polynomial relationship of the form Tn D A0 C A1 .R½ / C A2 .R½ /2
4.3. Empirical models between tannin and reflectance The correlation coefficients of linear as well as nonlinear models for all central wavelengths are given in Table 3. In this case, all samples were used, while in Tables 4 and 6, three samples used in the model verification (see Section 5) were left out. In case of the linear model, the correlation coefficients were maximum for the red wavelength band 0.61 to 0.69 µm. Lower correlations were observed for the blue and NIR regions. Bands 3, 7 and 10 are compatible to the red channels of Landsat, IRS and SPOT respectively. Thus, they were selected for determining the empirical relationship between reflectance and tannin. The following linear empirical relation between tannin concentration (Tn ) and reflectance (R½ ) has been employed: Tn D A0 C A1 .R½ /:
211
(1)
(2)
where A0 , A1 and A2 are the regression coefficients. The regression coefficients along with standard error of estimates are presented in Table 4. It is noted that the standard error of the nonlinear model is smaller. The effect of any extraneous factors affecting all bands equally can be self-compensated by spectral ratioing, whereby band ratios compensate only multiplicative effects (Lillesand and Kiefer, 1979). Therefore, in order to compensate the effect of multiplicative effects, e.g. illumination differences, on the apparent reflectances, the ratio of red to green band has been utilised. This has been done taking into account the colour of tannin which is reddish brown and has yielded high reflectance in the red and low in the green band (Fig. 4). A regression equation of the form Tn D A0 C A1 .BR/
(3)
where BR the ratio of red to green band, is employed to describe the empirical relationship between tannin and reflectance. The results of the band ratio model are shown in Table 4. As it can be seen in the table, a reasonably high correlation coefficient and low standard error are obtained. However, the best results are achieved with the nonlinear model (Fig. 9).
Table 4 Results of regression analysis for empirical models relating tannin concentration and reflectance Regression coefficients, correlation, standard error A0 A1 A2 R S.E.
Linear model
Nonlinear model
Band ratio model
Central wavelength (µm)
Central wavelength (µm)
Ratio of central wavelengths (µm)
0.660
0.650
0.645
0.660
0.660=0.560
16.770 5.259 – 0.87 0.123
12.468 3.668 – 0.89 0.154
16.006 5.196 – 0.86 0.143
33.535 43.763 11.844 0.94 0.073
0.650 24.541 34.402 9.414 0.94 0.091
0.645 8.815 19.387 5.915 0.92 0.073
1.000 5.499 – 0.80 0.127
0.650=0.555 1.970 7.499 – 0.88 0.153
0.645=0.545 1.946 7.051 – 0.77 0.207
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Fig. 9. Quadratic regression model between tannin and reflectance.
4.4. Empirical models between Secchi depth and reflectance Correlation coefficients between Secchi depth and reflectance are given in Table 5. It is observed that the correlation coefficients were maximum for the green wavelength region (0.5–0.6 µm). A simple linear regression relationship between Secchi depth (Sd) and spectral reflectance (R½ ) of the form Sd D A0 C A1 .R½ /
(4)
was employed. A quadratic model was also tested. Regression coefficients, correlation coefficients and Table 5 Correlation coefficients for linear and nonlinear models relating Secchi depth and reflectance Central wavelength (µm)
Linear model
Nonlinear model
0.485 0.545 0.555 0.560 0.645 0.650 0.660 0.815 0.830 0.840
0.88 0.95 0.95 0.96 0.94 0.93 0.92 0.72 0.68 0.72
0.90 0.97 0.96 0.96 0.94 0.94 0.92 0.83 0.72 0.74
standard errors are presented in Table 6. As Table 6 shows, the difference between linear and quadratic model was very small, fact which is also verified by the nearly linear form of the quadratic model in Fig. 10. In addition, a band ratio model of the form as in Eq. 3 was used (see Table 6). Sometimes, environmental variations, like complex effect of air density, air temperature, water vapour in atmosphere, wave turbulence etc., have a serious influence on the recorded data. If the environmental variations are spectrally independent over a large range of a few hundred nanometres, subtraction of radiance (L ½ ) in two bands can remove these effects (Curran and Novo, 1988; Gitelson and Kondratyev, 1991). For the same reason, derivative spectroscopy may be used to achieve better results. Derivative spectroscopy uses the first or second derivatives of spectral radiance (L ½ ) or reflectance (R½ ) to separate the effects of several parameters on the signal, and thus detect spectral characteristics, which can be solely attributed to a single parameter (Demetriades-Shah et al., 1990; Chen et al., 1992; Goodin et al., 1993; Han et al., 1994). A model of the form Sd D A0 C A1 .DR/
(5)
is used, where DR is derivative reflectance. DR was computed as difference of the reflectance in two wavelengths. The maximum correlation between derivative reflectance for wavelengths 0.66, 0.83 and 0.65, 0.815 µm were 0.93 and 0.94 respectively. The results of the regression analysis are shown in Table 6. The derivative model led to lower standard errors than the other models. 4.5. Empirical model between turbidity and Secchi depth As observed in Figs. 7 and 8, Secchi depth is related to turbidity. A linear model was not used in regression analysis since it led to low correlation between Secchi depth and turbidity (0.78). Using regression analysis and a second order polynomial, a relationship between turbidity (Tu) and Secchi depth of the form Sd D 42:1126
1:40873.Tu/ C 0:013614.Tu/2 (6)
Regression coefficients, correlation, standard error
Linear
Nonlinear
Derivative reflectance
Band ratio model
Central wavelength (µm)
Central wavelength (µm)
Central wavelengths (µm)
Ratio of central wavelengths (µm)
0.560
0.555
0.545
0.560
0.660–0.830
0.650–0.815
0.660-0.830
0.650-0.815
0.645-0.840
A0 A1 A2 R S.E.
5.458 2.681 – 0.96 0.418
6.207 2.973 – 0.95 0.476
6.688 3.368 – 0.95 0.450
7.011 0.266 – 0.93 0.155
8.180 0.294 – 0.94 0.156
58.920 25.930 – 0.94 0.250
60.052 29.998 – 0.91 0.153
76.838 21.794 – 0.77 0.178
37.59 46.105 4.940 0.96 0.428
0.555 57.76 63.259 8.568 0.96 0.427
0.545 85.52 89.923 14.188 0.97 0.397
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Table 6 Results of regression analysis for empirical models relating Secchi depth and reflectance
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4.6. Empirical model between turbidity and reflectance
Fig. 10. Quadratic regression model between Secchi depth and reflectance.
Attempts were made to correlate turbidity and reflectance, and a second order polynomial led to a good fit. Besides this model, relationships between reflectance ratio and turbidity, and derivative reflectance and turbidity were also explored. For both reflectance ratio and DR, the red and NIR bands were used. Details are omitted, since the results were very similar to those of Secchi depth and reflectance (see Section 4.4). The values of reflectance obtained from water bodies contain a composite effect of various pollution parameters. Taking tannin concentration and Secchi depth as a measure of turbidity, a multivariate model of the following form was used: R½ D þ0 C þ1 .Tn / C þ2 .Sd/:
(7)
Correlation coefficients are high for wavelengths 0.56, 0.555 and 0.545 µm. The values of þ0 , þ1 and þ2 , correlations and standard errors are presented in Table 7. 4.7. Empirical model between tannin and chemical oxygen demand As tannin exerts COD in river water, an empirical relation between them was sought. A nonlinear equation of the following form was found to relate the two well (a linear model was not used since it led to low correlation between tannin and COD). Tn D 4:3658
0:0982.COD/ C 0:00930.COD/2 : (8)
Fig. 11. Quadratic regression model between Secchi depth and turbidity.
led to a high correlation coefficient of 0.975 and a standard error of 0.257. A high correlation between turbidity and Secchi depth suggests that Secchi depth alone may be observed in the field and later related to turbidity. This will decrease the time and effort involved in sampling and laboratory testing for turbidity estimation. The relationship between turbidity and Secchi depth is presented in Fig. 11.
The correlation coefficient was 0.959 and the standard error 0.156, and their relation is presented in Fig. 12. The study reveals a good correlation between tannin and COD. 5. Model verification The Chi-square test, based on three samples that were not included in the regression modelling, was used for verification of various models proposed. Thereby, emphasis was placed to five models for tannin and Secchi depth (see Table 8). The Chi-square
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Table 7 Results of multivariate regression analysis using tannin concentration, Secchi depth and reflectance in three bands Central wavelength (µm) 0.560 0.555 0.545
þ0
þ1
0.877 0.233 0.160
0.010 0.100 0.073
þ2
R
S.E.
0.035 0.053 0.043
0.96 0.96 0.96
0.169 0.166 0.135
6. Conclusions
Fig. 12. Quadratic regression model between tannin and chemical oxygen demand.
equation is n X .E 1 2 comp D 1
O1 /2
(9)
E1
where E is the predicted value, O is the observed 2 value, n is the number of sampling points, and comp is the computed value of Chi-square. For 5% level of significance and two degrees of 2 should be less than or equal to 5.59. freedom, comp 2 The comp of various models is shown in Table 8. Obviously, additional tests with a higher number of independent samples are needed.
This study shows that the MGTR is an effective tool for rapid determination of Secchi depth, turbidity and tannin concentration in narrow surface streams. The turbidity measurement in the field involves considerably more time and infrastructure than Secchi depth measurement. The investigations revealed that Secchi depth can be conveniently used for in situ representation of the turbidity (Fig. 11), as their empirical relation shows high correlation coefficient (0.975) and small standard error (0.257). The reflectance has very high correlation with Secchi depth in the wavelength region 0.50 to 0.60 µm. A nonlinear model between Secchi depth and reflectance, with a correlation of 0.967, led to an improvement over a linear model. Since reflectance from water is dependent on composition of suspended matter and also environmental parameters, a multivariate regression analysis was attempted to correlate the reflectance with tannin as well as with Secchi depth. This model allows estimation of tannin concentration given availability of Secchi depth and reflectance data. High correlation coefficients were found using spectral reflectance in the wavelength region of 0.50 to 0.60 µm. Tannin concentration has been modelled as a function of reflectance using a second order polynomial. A very high coefficient of correlation ranging between 0.92 to 0.94 has been obtained in the wavelength region 0.62–0.69 µm. The COD does
Table 8 Results of Chi-square test for different models Parameter
Tannin
Secchi depth
Model
Tn vs. R½ nonlinear
Tn vs. BR
Sd vs. R½ nonlinear
Sd vs. BR
Sd vs. DR
2 comp
5.51
4.01
3.98
3.50
2.01
Tn : Tannin concentration, BR: Band ratio, Sd: Secchi depth, R½ : Reflectance, DR: Derivative reflectance.
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not influence the spectral reflectance of water. COD is dependent on presence of organic or inorganic wastes in water. Tannin presence in water is found to augment the COD. A second order polynomial relation between tannin concentration and COD has been established. It led to a high correlation coefficient of 0.959 and a standard error of 0.156. Thus, COD may be indirectly monitored by determining tannin concentration. The proposed models have been verified using the Chi-square test and were found satisfactory at a 5 percent level of significance. However, it should be noted that the empirical relations between different water quality parameters and reflectance have been derived based on data from a small study area, with high pollution level. Their validity needs to be verified in additional test areas with varying levels of pollution. Reflectance observed in the eleven bands of the MGTR can be successfully utilised for quick and repetitive determination of turbidity, tannin concentration and COD for a large number of points. This remote sensing approach is an effective alternative to tedious, time consuming conventional environmental laboratory techniques. Although some satellite sensors have wavelengths similar to the ones of MGTR, the latter has the distinct advantages of higher spatial resolution and more frequent coverage, especially since it is not affected by clouds. A further possibility could be the use of sensors with spectral characteristics similar to those of MGTR and sufficient geometric resolution on low-flying aircrafts or even satellites. The methods and empirical relationships presented here can be used for Secchi depth, turbidity and COD determination in many applications, apart from tannery pollution, involving water quality monitoring. Acknowledgements The help of the Environmental Engineering Laboratory, I. I.T. Kanpur, India, in carrying out chemical analysis is gratefully acknowledged. We gratefully acknowledge the help of U.P. Jal Nigam, Kanpur towards conducting the environmental survey of the river for this study. The encouragement received from U.P. Pollution Control Board, India during discussions is thankfully acknowledged. The authors
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