International Journal of Environment Science and Technology Center for Environment and Energy Research and Studies (CEERS) ISSN: 1735-1472 EISSN: 1735-2630 Vol. 4, Num. 1, 2007, pp. 159-167

International Journal of Enviornmental Science and Technology, Vol. 4, No. 1, Winter 2007, pp. 159-167

Water quality parameters along rivers

¹Alam, Md. J. B.; ³ Islam, M. R.; ¹Muyen, Z.; ¹Mamun, M. & ²Islam, S.

¹Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh
²Chemistry Department, Shahjalal University of Science and Technology, Sylhet, Bangladesh
³North South University, Dhaka, Bangladesh
*Corresponding author, Email: jahiralam@yahoo.com Tel.: +8802 9114 285; Fax: +8802 1715 257

Received 31 August 2006;
Revised 28 November 2006;
Accepted 5 December 2006;
Available online 1 January 2007

Code Number: st07020

ABSTRACT: Water samples have been collected from a part of Surma River along different points and analyzed for various water quality parameters during dry and monsoon periods. Effects of industrial wastes, municipal sewage, and agricultural runoff on river water quality have been investigated. The study was conducted within the Chattak to Sunamganj portion of Surma River, which is significant due to the presence of two major industries-a paper mill and a cement factory. The other significant feature is the conveyors that travel from India to Chattak. This study involves determination of physical, biological and chemical parameters of surface water at different points. The river was found to be highly turbid in the monsoon season. But BOD and fecal coliform concentration was found higher in the dry season. The water was found slightly acidic. The mean values of parameters were Conductivity 84-805µs; DO: dry 5.52 mg/L, monsoon-5.72 mg/L; BOD: dry-1 mg/L, monsoon-0.878 mg/L; Total Solid: dry-149.4 mg/L, monsoon 145.7 mg/L. A model study was also conducted and values of different model parameters were estimated.

Key words: Conductivity, BOD, total solids, Surma River, DO, dry season, monsoon season

The Surma Basin, covering the eastern parts of the Bangladesh, contains at least 8 million people making it a populous river basin in Bangladesh. All these peoples depend on Surma Basin for their household, industrial and other purposes. However, the water quality of the Surma River is deteriorating day by day due to human activities and industrial effluents, which are built up on its bank. So it is of vital importance to monitor and simulate the water quality parameters of the Surma River to ascertain whether the water is still suitable for various uses. In a study, it was found that Texas has approximately 191,228 miles of streams and rivers. Overall rivers and stream water quality improved slightly between 1996 and 2002, as the number of miles not meeting their designated water quality uses fell from 4,290 to 3,568 miles. Of the 3,568 miles of rivers and streams that did not fully meet their designated use in the 2002 report, about 2,215 miles did not meet safe swimming (contact recreation) conditions, 455 miles did not meet standards for aquatic life, some 285 miles had fish consumption bans or advisories, and some 825 miles did not meet general uses due to high amounts of total dissolved solids and/or choride. It is important to note, however, that many miles of streams and rivers did not have sufficient data to determine if they met state water quality standards, and in fact, Texas Water QualityAuthority identified hundreds of miles of streams and rivers with water quality concerns but with insufficient data to meet their methodology for calling a stream or river "impaired". Between 1994 and 2002, overall use support in reservoirs declined from 98 to 70 percent, indicating a substantial decline in reservoir water quality. The decline in overall use support was caused by mercury deposition in reservoirs from atmospheric deposition, higher levels of dissolved oxygen, higher levels of metals and organic substances, and either high or low levels of pH, elevated levels of chloride and high levels of total dissolved solids (Texas Environmental Profiles, 2006). In another study, the Songhua river was found polluted. The river is considered as mother river of China as it provides water for industries, agriculture and human consumption. The river is polluted by both conventional pollutants (heavy metals, nitrate, phosphate, pesticides) and micro pollutants (ADB, 2006). Several studies (DOE 1993; Hossain, 2001) showed that surface water quality of the rivers of the country especially Surma River is moderately polluted in different locations. Hossain (2001) discussed a study to determine the organic and inorganic pollutant loads of the selected industrial effluent of Chattak Pulp and Paper Mill on the water quality of Surma River.
The objectives of the study are :
- To assess the present water quality, through analysis of some selected water quality parameters like pH, DS, TS, BOD, COD, DO, Fecal coliform, Sulfide, Phenol and Atrazine.
- To compare the results with the international and Bangladeshi standards.
- To predict the BOD, DO and nitrogen from the simulation model.
In this paper an attempt has been taken to study and simulate the environmental condition along the river and predict the pollution status. The study area is shown in Fig. 1.

Standard methods were adapted for the analysis of various water quality parameters APHA-AWWA-WPCF (1989). 1 liter polypropylene bottles were used for water quality parameter analysis and 1 L glass bottles were used for pesticide analysis. Prior to sample collection, all bottles were washed with dilute acid followed by distilled water and were dried in an oven. At each sampling location, water samples were collected in two polypropylene and one-glass bottles. Before taking final water samples, the bottles were rinsed three times with the water to be collected. The sample bottles were labeled with date and sampling source. Information was also gathered about the types of pesticides and fertilizers being used near sampling points. Samples were collected from September 2001 to July 2003. The study was conducted by a group of students. An interactive river water quality model has been developed to simulate the fate and transport of pollutants through a river system using "C"language. At present, it helps a user to predict the variation of BOD, DO, Nitrogen, Phosphorus and Heavy Metal as these move through a river system. The sub-models have been validated with the data readily available during the field experiment.

The maximum concentration of iron for the present study was found to be 3.16 ppm; minimum concentration found to be 0.26ppm at Chattak Cement Factory effluent (Sunamaganj)-discharge point. These values were less than the values of iron in the Meuse and the Rhine River water. The concentration of Surma River exceeds the permissible limit of iron recommended by EU (Claes, et. al., 1997) and BD (Bangladesh Gazzet, 1997). During monsoon, it was found to be higher than the permissible level of iron; whereas in the dry season, it was within the permissible limit. The lead concentration was found within the 13 ppb in our present study. Majid and Sharma (1999) found lead concentration of Kernofully River was 0.04 ppb. The level of lead is much below the permissible limit for irrigation and livestock drinking recommended by EU (Claes, et. al., 1997) and BD (Bangladesh Gazzet, 1997). The standard limit of lead for domestic and irrigation water is 50 ppb. In the respect, the level of lead causes no matter of concern. The concentration of chromium was found within the 38.2 ppb in our present study. Shiddiky (2002) reported that concentration of chromium of Buriganga and Shitallahkha River was 20.6 ppb. The level of chromium is much below the permissible limit for irrigation and livestock drinking recommended by EU (Claes, et. al., 1997) and BD (Bangladesh Gazzet, 1997). The standard limit of lead for domestic and irrigation water is 50 ppb. In the respect, the river of Surma is not polluted in terms of Cr. The concentration of zinc is higher in the dry season than the value of monsoon. Zinc concentration was found maximum at downstream during dry season (1.48 ppm) and zinc concentration was minimum at upstream during monsoon (0.0022 ppm). The effluent discharge from industries, various domestic and household sources enhance the concentration of zinc during dry season. The concentration of copper was found within the 4.2 ppb in our present study. Shiddiky (2002) reported that concentration of copper of Indus river of Pakistan was within 91ppb. The level of copper is much below the permissible limit for irrigation and livestock drinking recommended by EU (Claes, et. al., 1997) and BD (Bangladesh Gazzet, 1997). The standard limit of copper for domestic and irrigation water is 1ppm. In the respect, the river of Surma is not polluted in terms of copper Table 2 shows the concentration of trace elements in water of Surma River.

River modeling
The proposed model is capable of simulating one dimensional steady state behavior of pollutant transport in the river system. At present, it consists of four sub-models. BOD-DO sub model and nitrification sub model.

Formulation BOD-DO sub model
The need for the BOD-DO model of any river is predicting the pollution status at different locations. The impact of low DO concentration or of anaerobic conditions is reflected in an unbalanced ecosystem, fish mortality, odors and other aesthetic nuisances. The DO problem has its origin with the input of waste into a river body. Re-aeration from the atmosphere and DO in the incoming tributaries or effluents have been included as sources of DO and the oxidation of carbonaceous waste material and oxidation of nitrogenous waste material have been considered as sinks.

The net equation for DO deficit is

dD/dt = K₁ L_t -K₂ D + 4.57 K₂₃ N₂ (1)

The solution for the differential equation is

D = [ K₁L₀(exp(-K₁t)-exp(-K₂t)/(K₂-K₁)- D₀ exp(-K₁t)) +4.57 {K₁₂K₂₃(((1-e^-k ₁₁^t)/K₁₁)-((1-e^-k ₂₂^t)/K₂₂)/ (K₂₂-K₁₁)N₀₁) +K₂₃/K₂₂(1-e^-k ₂₂^t)N₀₂}] (2)

Where D is oxygen deficit, C_s is oxygen concentration at saturation level, C is oxygen concentration at any time t, L is amount of carbonaceous at any time t, K₁ is rate of BOD exertion, K₂ is the rate of re-aeration, N₁ is concentration of organic N, N₂ is concentration of NH₃ N, N₃ is concentration of NO₂^-, NO₃^--N, K₁₂ is rate of decomposition of organic N to NH₃, K₁₁ is rate of decomposition of organic N to NH₃ and its settling, K₂₃ is rate of oxidation of NH₃ to NO₂^-, NO₃^-, K₂₂ is rate of oxidation of NH₃ and the rate of uptake of NH₃ by aquatic plants. When CBOD of the waste is very high, then DO would approach complete depletion and anaerobic conditions would result. The end of anaerobic reach is given by

x_f = x_i + U(K₁L_i-K₂C_s)/K₁K₂C_s (3)

From the end of the anaerobic reach, the original equation of oxygen deficit can be used with L₀- ultimate BOD at the end of anaerobic reach (L_f) and initial deficit, D₀ = D _s (Muyan, and Mamun, 2003).

Nitrification sub model

The nitrogen concentration in the river at various points along the flow direction is to be monitored. The conditions of nitrification include the presence of nitrifying bacteria, optimum _PH in the alkaline range (_pH=8), DO concentration is more than 1 mg/L. If N₁ is concentration of organic N, N₂ is concentration of NH₃-N, N₃ is concentration of NO₂^- and NO₃^- N, K₁₂ is rate of decomposition of organic N to NH₃, K₁₁ is overall loss co-efficient of organic N due to settling of particulate forms and hydrolysis and bacterial decomposition of NH₃, K₂₃ is rate of oxidation of NH₃ to NO₂^-, NO₃^-, K₂₂ is tare of oxidation of NH₃ and rate of uptake of NH₃ by aquatic plants, K₃₃ is rate at which NO₂^-, NO₃ is lost due to uptake by aquatic plants or through denitrification, then nitrification kinetics can be described by following equations:

dN₁/dt = -K₁₁N₁ (4)

dN₂/dt = K₁₂N₁- K₂₂N₂ (5)

dN₃/dt = K₂₃N₂- K₃₃N₃ (6)

Solution for the above differential equations is

N₁=N₀₁e^k₁₁^t (7)

N₂={K₁₂N₀₁(e^-k₁₁^t-e^-k₂₂^t)/(K₂₂-K₁₁)}.. N₀₂e^k₂₂^t (8)

N₃=K₁₂K₂₃ N₀₁{(e^-k₁₁^t -e^-k₃₃^t)/(K₃₃- K₁₁) -(e^-k₂₂^t- e^-k₃₃^t)/ (K₃₃- K₂₂)}/(K₂₂- K₁₁)+ K₂₃N₀₂(e^-k₂₂^t -e^-k₃₃^t)/(K₃₃- K₂₂) + N₀₃ e^-k₃₃^t (9)

Where N₀₁, N₀₂, N₀₃ are initial concentration of organic N, NH₃-N and NO₂^-, NO₃^--N respectively (Thomann and Mueller, 1987; Himesh, et al., 2000).

Case study-1
Stream Discharge 0.3 m³/s, DO concentration 5.62 mg/L, BOD 0.95 mg/L. Stream reparation and deaeration rate respectively 0.4/day and 0.15/day. Constant K_11, K, K, K, andKare respectively 0.11, 0.09, 0.14, 0.13 and 0.16 (Hossain, 2001) and the model output is shown in the following Fig. 10 for a distance of 100 km. The river profile industrial wastewater is being discharged having the following characteristics shown in Table 3.

Case study-2

Stream discharge 0.3 m³ /s, Organic N concentration 5 mg/l, NH₃-N concentration 0.15 mg/L and NO₂, NO₃ N concentration 0.03 mg/L. Constant K_11, K₁₂, K₂₂, K₂₃, and K₃₃ are respectively 0.11, 0.09, 0.14,0.13 and 0.16 (Hossain, 2001).

Waste water characteristics are as follows Organic N concentration 4 mg/l, NH3-N concentration 0.2 mg/L and NO2, NO3-N concentration 0.01 mg/L. And the model output is shown in the following Fig. 11 for a distance of 300 km.

The results from data analysis show that, the water is certainly unfit for drinking purposes without any form of treatment, but for various other surface water usage purposes, it still could be considered quite acceptable. But as we know, once a trend in pollution sets in, it generally accelerates to cause greater deterioration. So few years from now, serious water quality deterioration could take place. However, there could be gross differences in the test results of some samples at different laboratories in the country, which could limit the use of these data for sensitive policy issues. The differences might be attributed to the approach adopted by laboratories in sample preservation, quality of chemicals used, testing method applied or qualification or expertise of the technicians or test performers. This study involves determination of physical, biological and chemical parameters of surface water at different points. The river was found to be highly turbid in the monsoon season. But BOD and fecal coliform concentration was found higher in the dry season. The water was found slightly acidic. The mean values of parameters were Conductivity 84805 µs; DO: dry-5.52 mg/L, monsoon-5.72 mg/L; BOD: dry-1mg/L, monsoon-0.878 mg/L; Total solid: dry-149.4 mg/L, monsoon-145.7 mg/L. The model also predicts the parameter very well. Moreover, it is clear from the analysis that industries have negative impact on ground water resources near the industrial area. It is clear from the analysis compared with Table 4 that the maximum concentration of iron for the present study was found to be 3.16 ppm; minimum concentration found to be 0.26 ppm at Chattak Cement Factory effluent (Sunamaganj) discharge point. Moreover it was found that total hardness of the Surma River increases along the downstream. Hardness values of water samples varied from 30.20 to 70.20 ppm as CaCO₃, which is fit for drinking use (Table 4).

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