Modeling of Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) in the Sirinhaém River, ,

Ana Flávia Araújo M. Sc .Environmental Technology, Institute of Technology Association of Pernambuco, , Brazil e-mail: [email protected]

Diogo Henrique Fernandes da Paz M.Sc. Student, Civil Engineering Postgraduate Program, University of Pernambuco, Recife, Brazil e-mail: [email protected]

Simone Rosa da Silva Professor, Civil Engineering Postgraduate Program, University of Pernambuco, Recife, Brazil Professor, Environmental Technology Master Degree, Institute of Technology Association of Pernambuco, Recife, Brazil e-mail: [email protected]

ABSTRACT Water resources are very important for the maintenance of life on earth, and water quality results from a relationship between natural phenomena, potentially degrading human activities as well as aspects related to the use and occupation of land. In this context, the role of water resources management is to seek viable alternatives to the solution of the qualitative and quantitative problems of these resources and one of the tools used is the monitoring of water quality in watersheds. The aim of this paper was to apply the QUAL-UFMG model of water quality to assess the water quality of the Sirinhaém river to subsidize a future framework of rivers in classes according to preponderant use. The QUAL-UFMG model was used to simulate the Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD), and the calibration was perfomed manually. For the application of mathematical modeling, the organic load of domestic origin from the cities of Cortês, , and Sirinhaém and from industrial effluents of the Pedrosa, Cucaú and Trapiche plants were considered. The flow and hydraulic characteristics data were obtained through the fluviometric stations of the National Water Agency - (ANA) while the average values of DO, BOD, and temperature came from the systematic monitoring of the water quality stations. Based on the observed data, the model proved to be efficient in simulating the parameters of the river in this stretch. In the model’s validation, a better result was obtained for the BOD than DO. KEYWORDS: Water quality modelling; Dissolved Oxygen, Biochemical Oxygen Demand.

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INTRODUCTION Watercourses are essential to the development of human activities, because in addition to their various uses, are also employed as a form of final destination of domestic and industrial sewage discharges, treated or untreated. Thus, pollution of surface fresh waters and deterioration of ecosystems due to human activities are one of the great challenges of contemporary society. Factors such as demographic and economic growth, changes in vegetation, and diversification of uses of water resources have generated great pressure on the hydrological cycle and on the reserves of surface water and groundwater. Associated with these factors, it is necessary to also consider the emissions of pollutants and nutrients in water bodies, causing several problems in aquatic ecosystems. The environmental management of water resources involves two significant dimensions, one referring to the quantity of water and the other related to the quality, and the search for viable alternatives to the solution of qualitative and quantitative problems of water resources is imperative, ensuring access to this resource to future generations. The management of water resources is presented as a complex task, and for effective implementation of the Brazilian National Water Resources Policy (Law 9.433/1997), it is necessary to use tools so that one can see if their goals are being reached or not, at what cost this may occur, and what the most appropriate measures to be taken are, and one of these tools is to conduct periodic campaigns for monitoring water quality in the watershed. Besides the periodic monitoring of water quality in the management plans of water resources, framing constitutes an important management tool, for it consists in setting the goals for water quality that must be achieved or maintained in a segment of water body, according to the intended preponderant uses over time. The use of tools that enable the analysis and prognosis of these water bodies assists in their management, control, and protection. For this purpose, mathematical models of water quality are an important tool. The modeling of water quality allows the visualization of the environment in an integrated manner, due to its ability to encompass hydrological, physical, chemical, and biological processes. Furthermore, it allows the simulation of self-purification processes of the river, assisting in the management of these resources. Thus, monitoring data on water quality of water courses has been fundamental in the management of water resources, as this is an activity that involves the process of planning and aligning the multiple uses of this resource. To assist in the management, control, and protection of water resources, it is important to use tools that enable the analysis and prognosis of water bodies given the imposed alterations. Mathematical models of water quality are used for such purposes, allowing the simulation of processes occurring in the river and therefore aiding in making decisions regarding their management (OPPA, 2007). Studies of this nature play an important part in watershed management by defining strategic actions that aim to maintain or achieve the water quality required for many existing and planned uses. The aim of this paper was to apply the QUAL-UFMG model of water quality to assess the water quality of the Sirinhaém river to subsidize a future framework of rivers in classes according to preponderant use. Vol. 19 [2014], Bund. P 3877

CASE STUDY

Characterization of the Studied Area The Sirinhaém River Watershed corresponds to the Water Resources Planning Unit UP4, according to the State Water Resources Plan of Pernambuco (1998), shown in Figure 1. This watershed is located between 277863 L and 9085501 N south latitude and 191977 L and 9031945 N west longitude (PERNAMBUCO, 2006). It is limited to the north by the River Watershed (UP3) and a group of watersheds of small coastal rivers GL3 (UP16); to the south by the Uma River Watershed (UP5) and the group of watersheds of small coastal rivers GL4 (UP17); to the east by the Atlantic Ocean and groups of watersheds GL3 and GL4, and to the west by the Watershed. The drainage area of the watershed is 2090.64 km², covering areas from 19 municipalities. Among them, Cortês and Ribeirão are fully inserted in the basin and the others are partially inserted. The Sirinhaém River is the main watercourse of the watershed, having its source in the municipality of Camocim de São Felix. Its length is approximately 185 km in the northwest- southeast direction and its main tributaries are: the stream of Sangue and the , , Tapiruçu, and Sibiró rivers on the left bank, the Seco, Tanque de Piabas, Várzea Alegre, Córrego do Sabiá streams and the Cuiambuca river on the right bank, as shown in Figure 1.

Figure 1: Map of the Sirinhaém River watershed – PE – Brazil. Source: Adapted from SECTMA (2006) Vol. 19 [2014], Bund. P 3878

Methodology The Sirinhaém river watershed has been monitored by the Pernambuco Environmental Agency (CPRH) in five stations since March 1992. However, for the modeling of water quality conducted in this study, we used monitoring data from 2001 to 2008 due to concomitant availability of flow monitoring data from fluviometric stations. Data for 2009 and 2010 were used to validate the mathematical model. Table 1 describes the monitoring sampling stations.

Table 1: Water quality monitoring stations STATION WATER LOCATION COORDINA BODY TES SI-03 Sirinhaém On the right bank of the Sirinhaém 25L 0207475 River River, downstream from the city of UTM 9068278 Barra de Guabiraba. SI-20 Amaraji Near the mouth of the Amaraji 25L 0236521 River River, at the bridge on PE-073, UTM 9052326 downstream from the Estreliana Plant, in Gameleira. SI-45 Sirinhaém After the location of Cucaú, on the 25L 0256656 River PE-073 bridge, downstream from the UTM 9043242 Cucaú Plant, in the city of . SI-51 Sirinhaém At the collection of COMPESA, 25L 0265192 River Camboinha in Sirinhaém. UTM 9049950 SI-55 Sirinhaém After the Tapicuru River, on the 25L 0268059 River PE-060 bridge, in the city of UTM 9051044 Sirinhaém.

As the fluviometric stations, Engenho Bento and Engenho Mato Grosso were considered. Along the way, the main river presents contributions from tributaries and releases of domestic and industrial sewage, which contribute to increase the flow, as the withdrawals of water in the river helps to decrease the flow. For the application of mathematical modeling, the organic load of domestic origin from the cities of Cortês, Gameleira, and Sirinhaém and industrial effluents from the Pedrosa, Cucaú, and Trapiche plants was considered. For manipulation of spatial data, ArcGis 9.2 software, produced by ESRI, which is a geographic information manager program, was used. The program offers tools needed to conduct research and analyze data, presenting the results so as to assist in decision making. With the aid of the Geoprocessing Unit of the Institute of Technology of Pernambuco (UGEO-ITEP/OS) was possible to delimit the area of study and the coordinates of the points of effluent discharge, enabling greater detailing of the elements that affect the water quality in the modeled stretch. The choice of the QUAL-UFMG model occurred due to use and applicability according to the following criteria: ● Ease of use, considering the interface and language; Vol. 19 [2014], Bund. P 3879

● Using basic parameters for the qualitative assessment of the water resource (DO, BOD, total phosphorus and thermotolerant coliforms); ● Availability of water quality monitoring data conducted by CPRH over 10 years, as well as flow data of the body being studied. The QUAL-UFMG model, based on the QUAL2E model, originally developed by the US. Environmental Protection Agency (USEPA), in the United States, enables faster and simpler simulations facilitating the initial contact of users with advanced modeling. Despite simulating up to 15 water quality organic constituents, the two main basic parameters used in water quality monitoring were used to conduct this study: dissolved oxygen (DO) and biochemical oxygen demand (BOD) (VON SPERLING, 2007). For the model of DO and BOD in water courses, there are the equations of mixture. The concentration and deficit of oxygen in the river after mixing with sewage are determined by the following equation, which is also used for BOD:

[Eq.1]

where: 3 Qr is the river flow upstream of the release of disposals (m /s); 3 Qe is the flow of sewage (m /s); DOr is the concentration of dissolved oxygen in the river, upstream from the release of the disposal (mg/L); DOe is the concentration of dissolved oxygen in sewage (mg/L).

To perform the modeling the following input data are required: ● The river flow upstream of the release(Qr) and sewage flow (Qe); ● Dissolved oxygen in the river, upstream of the release (QDr); ● Dissolved oxygen in sewage (QDe); ● BOD in the river upstream of the release (BODr) and sewage BOD (BODe); ● Deoxygenation coefficient (K1) and decomposition coefficient (Kd); ● Reaeration coefficient (K2); ● Travel speed of the river (v) and depth of the river (H); ● Course time of the river (t) and liquid temperature (T); ● Saturation concentration of DO (Cs); ● Minimum permissible DO (DOmín) and maximum permissible BOD (BODmáx). The flow of the water course that receives the disposals is of substantial importance in mathematical modeling, as apart from influencing the hydraulic behavior of the river, it is directly associated with the dilution capacity of the affluent disposals. According to Von Sperling (2007), of all variables that influence the ability to assimilate pollutants, the river flow is the most important. The flow data were obtained from the Hydrological Information System - Hidroweb, of the National Water Agency and treated in the Hidro 1.2 software, also provided by this Agency. In general, according to Von Sperling (1996) apud Paz and Barbosa (2011), the production of sewage corresponds approximately to water consumption. However, the fraction of sewage that reaches the collection system may vary. The fraction of water supplied that enters the collection Vol. 19 [2014], Bund. P 3880 network in the form of sewage is known as the feedback coefficient and has the usual value of 80%. The sewage flow considered in studies of self-purification is usually the average flow without coefficients for the time and day of highest consumption. The sewage flow from the municipalities of Cortez, Gameleira, and Sirinhaém was considered in modeling, calculated by the following formula:(2)

[Eq.2]

where Qe is the average domestic sewage flow (m³/s); QPC is the per capita quota of water (250 l/hab.d); R is the feedback coefficient; 86,400 is the number of seconds per day. The calculation of the average population of cities between the years 2001 to 2008 was conducted using data from IBGE (2000). The flow of effluents in the Pedrosa, Cucaú, and Trapiche plants, however, was calculated based on the amount of sugarcane processed per day according to existing estimates in the literature. According to Von Sperling (2007), in raw sewage the levels of dissolved oxygen are usually zero or near zero. This is due to the large amount of organic matter, resulting in a high oxygen consumption by microbial decomposers. As the sewage from the municipalities and the slaughterhouse are not treated, sewage DO is denoted as 0 mg/L in the self-purification calculations. To calculate the concentration of BOD5 of domestic sewage, we used the methodology of Von Sperling (1996):

[Eq.3] Obs.: g/m³ = mg/L.

The mean data for DO, BOD, and temperature during dry months along the stretch studied were obtained from the monitoring stations SI-03, SI-20, SI-45, and SI-55 of CPRH, which performs the collection and analysis of water every two months. According to Von Sperling (2007), the minimum dissolved oxygen levels to be maintained in the water bodies are stipulated by the law of the country or region. In Brazil, the values vary according to the class in which the water body is framed. The minimum DO and maximum BOD permissible levels in bodies of fresh water according to the class to which they belong are shown in Table 1, according to the CONAMA Resolution 357/2005 from National Council of Environment.

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Table 2: Minimum DO and maximum BOD permissible levels (CONAMA Resolution 357/2005). Class Minimum DO (mg/L) Maximum BOD (mg/L) Special Even treated releases are not Even treated releases are not allowed. allowed. 1 6 3.0 2 5 5.0 3 4 10.0 4 2 - Source: Von Sperling (2007).

In water bodies where there is no framework, as is the case of Sirinhaém river, it is considered to be Class 2, according to the CONAMA Resolution nº 357/2005, and in this case the minimum DO and maximum BOD levels should be 5.0 mg/L. Historical data of speed and depth of the river were obtained from the ANA website, through the Hydrological Information System - Hidroweb, of the National Water Agency and treated in the Hidro 1.2 software, also provided by this Agency. Through the fluviometric stations of Engenho Bento and Engenho Mato Grosso, we obtained the historical values of speed and depth and with these data an estimate of the speed and depth was performed using the correlation with the observed flow in fluviometric stations located in the studied stretch, as indicated by Von Sperling (2007), when estimating the velocity of the liquid mass in a watercourse. Model calibration is performed by adjusting the coefficients, which may vary in certain ranges, allowing to adjust mathematical equations to the physical realities of the study area (IDE and RIBEIRO, 2008). The K1 coefficient represents the decomposition rate of biodegradable organic material, which, in turn, depends on a number of physical, chemical and biological factors. Among such factors, mainly temperature and composition of the effluent stand out. The Kd coefficient is the incorporation of the decomposition of organic matter by the biomass suspended in the liquid mass, as well as the biomass in the bottom sludge. The Ks represents the ratio of the sedimenting velocity of the sedimentable organic material (sedimentable BOD) and the depth of the river (CHAPRA apud VON SPERLING, 2007). The determination of the three coefficients were made according to tabulated values (Table 3). Table 3: Typical coefficients of BOD removal (K1 and Kd) (20ºC).

Shallow rivers Deep rivers

Origin K1 Removal Kr Removal Kr Decomp. Kd Sediment. Ks Decomp. Kd Sediment. Ks (=Ks+kd) (=Ks+kd) Watercourse 0,35-0,45 0,50-1,00 0,10-0,35 0,60-1,35 0,35-0,50 0,05-0,20 0,40-0,70 receiving concentrated raw sewage Watercourse 0,30-0,40 0,40-0,80 0,05-0,25 0,45-1,05 0,30-0,45 0,00-0,15 0,30-0,60 receiving low concentration raw sewage Watercourse 0,30-0,40 0,40-0,80 0,05-0,10 0,45-0,90 0,30-0,45 0,00-0,05 0,30-0,50 receiving primary effluent Watercourse 0,12-0,24 0,12-0,24 - 0,12-0,24 0,12-0,24 - 0,12-0,24 receiving secondary effluent Watercourse with 0,08-0,20 0,08-0,20 - 0,08-0,20 0,08-0,20 - 0,08-0,20 clean waters Source: Fair et al. (1973), Arceivala (1981), apud Von Sperling (2007). Vol. 19 [2014], Bund. P 3882

All initial data obtained were entered in the QUAL-UFMG model Excel spreadsheet. After completing the spreadsheet, the calibration of the model, one of the most difficult steps in modeling, was performed. For Von Sperling (2007), the calibration consists of obtaining a good adjustment between the observed and estimated data (calculated by the model) by varying the coefficients of the model. Reis (2006) also stated that calibration depends on a combination of hydraulic, hydrologic, and water quality data. The calibration can be performed in two ways: manually, varying the values of parameters so that the sum of the squared errors decreases, until a satisfactory adjustment is obtained, or an automated manner by means of optimization methods that search for possible values of the coefficients and lead to the smallest sum of squared errors (VON SPERLING, 2007). Manual calibration was performed in order to obtain appropriate values for each situation using the Coefficient of Determination (CD), which is one of the most useful statistical indicators of adjustment of estimated data to observed data, and is expressed as:

[Eq.4]

where Yobs is the observed value;

Yest is the estimated value;

Yobsméd is the the mean of the values observed. The values of CD can vary between - ∞ and +1. CD equals 1 indicates perfect adjustment between the observed and estimated data.

Results and Discussion Mathematical modeling of water quality occurred from completing the initial data, where the spreadsheet simulated all other data along the stretch, and, from the CPRH water quality monitoring stations, calculated the values of DO and BOD between the years 2001 and 2008. Nevertheless, the calibration was performed, to make the values suitable to local conditions. Manual calibration proved to be quite efficient, as it obtained, for each stretch of river, a value for K1, Kd, Ks, and K2, as shown in Table 4. Following manual calibration, we observed a good agreement between the estimated and observed values for both DO and for BOD with a CD of 1.0 and 0.9, respectively, as shown in Figures 2 and 3. Thus, it follows that the model is capable of explaining, respectively, 100% and 90% of the variance of the experimental data.

Table 4: K1, Kd, Ks, and K2 values obtained by manual calibration of the model for the three stretches. Stretch of the river K1 Kd Ks K2 SI-03/SI-20 1.20 1.74 0.75 4.90 SI-20/SI-45 1.20 1.50 1.00 7.50 SI-45/SI-55 1.00 3.70 2.60 4.30

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Figure 2: DO simulation for the stretch from SI-03 to SI-55 in the Sirinhaém river.

Figure 3: BOD simulation for the stretch from SI-03 to SI-55 in the Sirinhaém river.

Validation is used as a verification of the already calibrated model using a different set of data from the one used in calibration. The calibration of the model is appropriate when the values observed are similar throughout the set (OPPA, 2007). The validation was performed using data from the dry season of 2009 and 2010. Figures 4 and 5 show the DO and BOD validation results.

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Figure 4: DO validation for the years of 2009 and 2010.

Figure 5: BOD validation for the years of 2009 and 2010.

The results of the validation of the parameters show how the coefficients of the model influence in the system response. The validation proved to be quite satisfactory for the BOD, as the deoxygenation coefficient (K1), which is the most important factor in the calibration of the BOD is not very sensitive to variations, allowing for greater ease of validation. The reaeration coefficient (K2), however, for being the most sensitive coefficient, evidenced by the sensitivity analysis, makes each year a specific situation, and a small change completely alters the model. Thus, validation has been less satisfactory for DO than for BOD.

CONCLUSIONS One of the major limitations encountered when conducting mathematical modeling wasthe lack of available data on the Sirinhaém river Watershed, which forced the use of approximate values and means consistent with the literature, and do not always accurately represent the local reality. Nevertheless, despite estimates used as input data, the model proved to be very efficient and can well represent what actually occurs in this stretch of river. However, there is a clear need for Vol. 19 [2014], Bund. P 3885 more water quality monitoring points, so that scenarios even closer to the real conditions of the river can be simulated. Modeling results clearly show that, despite having stretches with large rates of disconformities with the maximum permitted levels for the parameters of DO and BOD, the Sirinhaém river presents a good capacity for self-purification along its course, even during the dry season, considered the most critical. With a good calibration, models of water quality are of great help in times of defining strategies for management of water resources and should always be part of the studies to award grants and charges for water use, for being able to predict and assess changes in the water quality resulting from new developments in the watershed. Therefore, there is an unquestionable need for interventions in the area with regard to the control of water pollution, especially in adapting to environmental standards for the sugarcane plants and also the collection and treatment of domestic sewage from the municipalities Preventive measures, which are those that, when applied, prevent or minimize the release of pollutants into water resources, and corrective measures, which aim to correct an existing situation, seeking, through its application, the improvement of the quality of water resources, so that they can mitigate the consequences of this pollution, should be taken.

REFERENCES 1. BRASIL. Conselho Nacional de Meio Ambiente. Resolução CONAMA nº 357 de 17 de marçode 2005. Dispõe sobre a classificação dos corpos de água e diretrizes ambientais para o seu enquadramento, bem como estabelece as condições e padrões de lançamento de efluentes, e dá outras providências. Brasília, DF, 17 de março de 2005. Disponível em:< http://www.mma.gov.br/port/conama/res/res05/res35705.pdf> Acesso em 20 Jan. 2012.

2. BRASIL. Lei Federal nº. 9.433, de 08 de Janeiro de 1997. Institui a Política Nacional de Recursos Hídricos e cria o Sistema Nacional de Recursos Hídricos. Disponível em: . Acesso em 20 Abr 2011.

3. IDE, William R.; RIBEIRO, Maria Lúcia. Calibração do modelo de qualidade de água QUAL-UFMG para o Rio Taquarizinho em período de estiagem. Caderno de Recursos Hídricos, 2009.

4. OPPA, L.F. Utilização de modelo matemático de qualidade da água para análise de alternativas de enquadramento do rio Vacacaí Mirim. Santa Maria, 2007. Dissertação de Mestrado Pós-Graduação em Engenharia Civil, Universidade Federal de Santa Maria. 2007.

5. PAZ,Diogo Henrique Fernandes; BARBOSA, Ioná Maria Beltrão Rameh. Estudo da redução da carga orgânica lançada em um trecho do rio Capibaribe para atendimento à legislação ambiental. XIX Simpósio Brasileiro de Recursos Hídricos – 2011.

6. PERNAMBUCO. Secretaria de Ciência, Tecnologia e Meio Ambiente. Atlas de bacias hidrográficas de Pernambuco. Secretaria de Ciência, Tecnologia e Meio Ambiente; 2006. Vol. 19 [2014], Bund. P 3886

7. REIS, J. S. A. Modelagem matemática da qualidade de água para o Alto Rio das Velhas/ MG. Dissertação (Mestrado). Universidade Federal de Ouro Preto. Programa de Pós-graduação em Engenharia Ambiental. 2006.

8. VON SPERLING, M. Estudos e modelagem da qualidade da água de rios. Belo Horizonte: Departamento de Engenharia Sanitária e Ambiental; Universidade Federal de Minas Gerais, 2007.

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