MODELLING OF SEWAGE QUALITY DURING RAINY SEASON

Asian Jr. of Microbiol. Biotech. Env. Sc. Vol. 17, No. (3) : 2015 : 593-601 © Global Science Publications ISSN-0972-3005 MODELLING OF SEWAGE QUALITY DURING RAINY SEASON A.O. HUSSEIN*1, S. SHAHID1, K.N. BASIM2 AND S. CHELLIAPAN3 1 Department of Hydraulics and Hydrology, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia 2 Department of Civil Engineering, Karbala University, Karbala, Iraq 3 Razak School of Engineeringand Advance Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia Key words: Pollution loading, Storm Water Management Model, Wastewater concentrations, Geographic information systems. Abstract–In the previous four decades, methodical research has been concentrated on the protection of water resources, and especially on the contaminating consequence of the inner city of natural water bodies. One approximation to this investigation has concerned the advancement of instruments to illustrate the phenomena which takes place in the city watershed throughout both wet and dry times. This article aims to determine the significant differences of the changes of Biochemical Oxygen Demand (BOD), and total suspended solids (TSS) parameters in a wastewater in the sewer networks during rainy seasons for the historic rainfall time series using SWMM5. For this purpose, thirty years (1980-2010) of rainfall, temperature and sewer flow data of Karbala city of Iraq were collected, processed and employed for the modelling of the pollution loading. The effects of land-use, population and impervious surface cover on the quality of storm water were also analyzed. The results showed that the concentrations of BOD5 and TSS increase significantly after raining in the study area. The statistical tests showed the differences were significant (p<0.05). The increase of the BOD5 was very high compared to TSS. It can be expected that the long-term simulations of storm water pollution loading would be help to compare the benefits of different scenarios of the sewagetreatment plan for the reduction of the pollution in the storm discharge. INTRODUCTION Modelling pollution loading in urban storm water is important for non-point source pollution control and water treatment. Majority of older urban area centres including many old metropolitan towns in the U.S. and Europe (New York City, Washington, Paris, London, etc.) are discharged by merging sewer systems. The magnitude of wet weather flow (WWF) contaminant loads (Chebbo and Saget, 1995; Veldkamp and Wiggers, 1997; Brombach et al., 2005; Suarez and Puertas, 2005; Gasperi et al., 2010) and subsequently caused acute impact on receiving waters. With the advances of civilization, agricultural lands, forests, and wetlands are converted into municipal land uses. Urbanization adversely alters watershed hydrology, contributing to the deterioration of water resources and water quality. The urban runoff is one of the factors that have the most influence on the water quality of water bodies (Characklis and Wiesner, 1997). The cities where the rainfall is very sporadic and sparse, *Corresponding author’s email: hussein.alkaisy@gmail.com sudden rainfall after a long dry spell often causes a high level of pollution of the storm water. This alteration includes increased runoff volumes and peaks, decreased time of concentration, decreased base flow recharge (Burns et al., 2005), and increases as the non-point source (NPS) pollution in the runoff (Ying and Sansalone, 2010a; Ying and Sansalone, 2010b). Urban NPS pollutants include suspended and dissolved solids, nutrients, oxygendemanding organisms, bacteria, pesticides, metals, oil and grease. The transport of pollutants in the runoff from the land areas into water bodies is a natural process. However, urban city NPS pollution is intensified by activities associated with domestication. The most prevalent of these activities include increased impervious surfaces (e.g., roads, parking lots, sidewalks, roofs, and compacted areas), increased application of fertilizers, pesticides on municipal lawns and gardens, erosion from land disturbance due to the construction activities. Increasing use of vehicles also causes pollutant inputs into the air and subsequent atmospheric HUSSEIN ET AL. 594 deposition transferred to nearby aquatic systems by the runoff (Baird et al., 1996). The contaminated rainwater is often discharged untreated into water courses from the natural land within the watershed in the urban areas. Several studies have proven that the quality of water degradation in the receiving water bodies is caused by the storm-water runoff. As the runoff in the municipal areas plays key roles in the water-quality deterioration in the adjacent hydraulics components, an accurate estimate of nonpolluting loads, runoff from the city and quality of the water from the receiving water are important (Lee et al. 2012). The problem of the aquatic ecosystem degradation is directly related to the binomial industrialization/urbanization, as well as to the process of the political-economic development, which is determined, as they will consider the occupation and the varied uses of the environmental resources in each state or city. The concern stemming from non-point source NPS pollution has risen dramatically with the expanding urbanization. Therefore, multiple best management practices (BMPs) were identified and established to minimize the impact of storm events on surface water quality (Ferreira et al., 2005). The objective of the present study is to determine the significant differences of the changes of Biochemical Oxygen Demand (BOD), and total suspended solids (TSS) parameters in a wastewater in the sewer networks in Karbala city of Iraq using SWMM5. Through the process of rainfall, especially in areas where there are unpaved fully exposed sewage system in general, and private networks to increase the TSS and BOD5 concentrations because of the gaps, holes and areas of weakness in the sewerage network and the manholes in the areas, which contain the sewage systems are more susceptible than the others (Hussein et al., 2015; Obaid et al., 2014). The rainfall and hydraulic data, measured at the different catchments, were attained for the model calibration and verification. The water-quality data were available for the dry weather characterization, but only two to three rain events were sampled at each monitoring section. The main aim of this work is to simulate hydraulics and water quality in a residential area comprising of 34.8 hectares, which is divided into two catchments of different characteristics. MATERIALS AND METHODS In this study, we have used Geographical information system to generate the sewer network map, soil map, land use map and topography map. GIS is especially useful in analyzing water and wastewater flows (Shamsi and Shamsi, 2002). Storm water management model (SWMM5) used build-up and wash-off algorithms to determine how much pollution will be washed-off the land surface during a storm. This leads to the operational problems within the sewer system. Water Quality : The runoff pollutant loads may be modelled using event mean concentrations or using build-up and wash-off equations. The rating curve and exponential functions are available for the build-up. The different land uses inside each catchment, street sweeping and external and dry weather inflows may also be considered. Hydraulic Routing : Contrary to the catchment of undeveloped, watersheds in urban areas provide an additional component to be styled in the sewerage network. However, until there is a GIS based rings with all the links and their corresponding geometric features, modelling may be a whole network ineffectual big catchment. Since the task represents a large engineering effort to model the structure of each rain water drainage (i.e., Flow to the bottom drop inlet). Therefore, should be done with simplification and by being unrealistic. Some attempts have been made to obtain the sewerage network in comparison. Flow Routing : Natural streams are part and parcel of the sewer system. At the initial site, the investigations were conducted to determine the main features of the primary transfer streams. Field work has been during the month of Nov. 2005 to obtain transects in Major Channels using standard and rod, as described by Harrelson (Harrelson et al. 1994). Hydro Climatology : Hydrology is the scientific method that treats the water on the earth and its occurrence as rain, snow, and other forms of precipitation. It includes the study of the movement of water at the ground surface and underground to the sea, transpiration from vegetation, evaporation from land and water surface, back to where the atmosphere analysis has led to improving the understanding of the hydrology cycle. In the recent years, systems of physical and theoretical standpoint of the hydrology use of mathematical or computer simulation models, which can predict hydrologic events. Modelling of Sewage Quality in During a Rainy Season Precipitation : It is the primary source of water in streams, lakes, spring, and wells, which include rainfall, snow, hail, and sleet, and the engineer is concerned principally with the precipitation data in the absence of the stream-flow records. The U.S. Geological survey operates an extensive network of stream-flow measuring stations, though the country is not regularly calculated, although it uses its records, making it unnecessary to study the precipitation records and estimate the runoff there from. The smaller streams throughout the country normally are not gauged, although short-term records for the specific streams may be available, and the precipitation data must be obtained and the watershed studies as well, in order to determine the stream-flow characteristics. This is less desirable than the use of the actual runoff data and even a few years of stream-flow records are of great value in relating to the precipitation and runoff (Tchobanoglous, 1981). Rainfall : It is one of the significant climatic factors, where the rainfall period in the area of study is restricted to the months from October to January for the period from 1980 to 2010; the highest average rainfall recorded in January was 16.0 mm while the lowest average has been recorded in July and August as it was (zero mm). The rainfall over the area under study is characterized by heavy rainfall for short periods of time. One of the important factors affecting the quantities of rainfall, other than the difference in location, is the distinctive frequency from one year to another (Iraqi Meteorological Organization, 2011). Temperature : It represents an important factor in the evaporation and evapotranspiration, which results in the warming air. The annual average temperature for the period from 1980 to 2010 in the Karbala city is high in summer; the highest degree of temperature has been recorded in August 2010, which was 47.4°C. The lowest degree has been recorded in January 1983, which was 1.6° (Iraqi Meteorological Organization, 2011). Evaporation : The process of evaporation is normally related to the temperatures, where it increases as the temperature increases, and inversely with the rainfall and the relative humidity. The amount of water evaporated from the land surface depends on the amount of moisture available, which depends upon the evaporation is one of the significantly important climatologically factors that influence the environment and is strongly connected 595 to the other factors (temperature, relative humidity, wind speed, air pressure, evaporation surfaces, and nature of evaporation surface). The process of evaporation affects the chemistry of the ground water. The strong evaporation leads to depositing the salts in the soil like gypsum, calcite and chlorides. The monthly evaporation rates in the Iraqi General Atmospheric Karbala station, for the period from 1980 to 2010, were between (483.9mm) in July and (64.7mm) in January (Iraqi Meteorological Organization, 2011). Infiltration : Infiltration is the water that enters the sewers through poor joints, cracked pipes and the walls of manholes. The inflow enters through the perforated manhole covers; roof drains and drains from the flooded cellars during runoff events. Because infiltration may be non-existent during the dry weather, the dry- weather flow may be considered as the sanitary sewage plus the industrial wastes. Catchment description The area where the study was based on is the Shohada Al Maudfeen residential district in the centre in the city of Karbala. It has a population of 8505 people in 2011. Karbala is one of Iraq’s wealthiest cities in Iraq, which has an area 5,034 km2, is located about 100 km (62 miles), (Lat:32° 36’ 51o N, Lon: 044° 01’ 29’’E) in the south- west of Baghdad that profits both from devout visitors and agricultural production. It is made up of two districts, the “Old Karbala,” which is a godly centre, and the “New Karbala,” which is the residential district containing Islamic schools and government buildings. These two districts had an estimated population of 1,066,600 people in 2011. Karbala experiences a hot desert climate with extremely heated dry summers and cool winters. Almost all the yearly precipitation is received between November and April, though no month is truly moist. The modelling- area comprehends as shown in Figure 1 that the two sewer sections were monitored for flow and sampled at pre-set intervals over dry and wet weather periods. The characteristics of the monitoring sections and the irrupt stream catchments are described in Table 1. The widths of most sub-catchments were considered the square root of its area. The impermeable areas of each sub-catchment were initially defined by the basis of the soil occupation analysis. All paved, and roof areas and part of the backyard area was considered as HUSSEIN ET AL. 596 BOD5 and TSS Sampling Fig. 1. Map of study area in Karbala. Table 1. Main characteristics of the monitored sections. Sub catchment A Monitoring section: interceptor sewer from separate systems. The interceptor surcharges for wet weather; upstream there is some overflow Pipes at the manholes that discharges to downstream. The estimated area served by the interceptor: 20.8 ha. Impervious area: 16.5 ha. Interceptor sewer length: 5500 m. Interceptor sewer diameters: 250 and 300 mm. Average interceptor sewer slope: 3%. It has 102 manholes. Average dry weather flow (DWF): 31 l/ s, including some infiltration flow. Sub catchment B Monitoring section: interceptor sewer from separate systems. Upstream catchment area: 14 ha. Connected impervious area: 9.8 ha. Total sewers length: 2800 m. Circular sewer shapes, from 250 to300 mm. Average ground slope: 5%. Average sewer slope: 2%. It has 62 manholes. Average DWF: 47 l/ s, including infiltration and/or underground water. invulnerable. However, this representation had to be changed due to the difficulties faced in calibrating the model. The weekdays and weekend dry weather average flow patterns were determined for each monitoring section and were introduced into the models. Manning numbers of n = 0.011 s. m-1/3 were used for overland on the impervious areas and of n = 0.015 s. m-1/3 for flow in the pipes. Samples for the Biochemical Oxygen Demand (BOD5) analysis were collected during a major storm event on 1st January, 2013 (30mm a rainfall). Sample sites were selected to represent a variety of conditions, for example, urban vs. Agricultural and Amish vs. conventional farms. Composite samples were also made from the water collected by mechanized pump samplers at two locations (S-EX and S-EX2). These motorized stations were equipped with calibrated weirs and stream stage recorders and provided data on the stream discharge. One foot, the automated pump samplers collected water samples every 30 minutes during the storm event. Combined samples in the BOD5 analysis were made at the end of the event by merging 50 mL of water from each sample taken by the pump sampler. The BOD5 loading was calculated by multiplying the BOD5 of the composite sample by the volume of runoff measured during the time interval the pump samplers were operating, where the average results of BOD5 and TSS were 180 and 200 mg /L, respectively. Water Quality Coefficients Adaptation The water-quality coefficients for the exponential build-up and the wash-off equations were adjusted for the existing TSS and BOD 5 data at the several monitoring sections. Data was available for the two successive rain events in sections, sub catchment A&B, and two rain events at the outfall. The exponential buildup equation is given by B=C1. (1e-C2. t), where B is the pollutant buildup mass per unit of sub-catchment area (kg/ha); C1 is the maximum buildup possible (kg/ha); C2 is the buildup rate constant (1/days); and t is the time (days). The exponential wash-off equation is given by W=C3. q C4.B, where W is the pollutant wash-off rate per unit area (kg/ (hour. ha)); q is the runoff rate per unit area (mm/hour); C3 is the wash-off coefficient; and C4 is the wash-off exponent. After a sensitivity analysis of the coefficients, these were interactively adjusted through a series of simulations for the events. The same values of the calibration coefficients were assumed for all sub catchments of each monitoring catchment. For some calibration sections, more than one combination of coefficients was found to adjust the modeled pollution graphs to the measured concentrations. The adjustment was concluded when satisfactory results were obtained using the same values for Modelling of Sewage Quality in During a Rainy Season three calibration coefficients in all the catchments, and only one coefficient characterizes each catchment, as presented in Table 2. Table 2. Values obtained for the empirical water quality coefficients Build-up equation Sub-catchment A Sub-catchment B 140 170 0.09 0.06 Wash-off equation 0.15 0.12 1.24 1.23 Modelling during wet climate discharges Modelling results may be seen or exported in the form of reports, tables, hydro-graphs, longitudinal profiles and other graphs, for several parameters at nodes or links, such as water depths, heads, flows, volumes, flooding and pollutant loads. SWMM5 also provides statistics (mean, peak, total, duration and inter-even time), histograms and the frequency plots of the variables for annual, monthly, daily or event-dependent periods. 597 shown in Figure 2 was prepared by digitizing from the soil map of the Karbala city in Arc GIS. A soil map is classified into seven types of soil; (i) water body, (ii) Mixed Gypiferou’s desert land, (iii) saline lake bottom land, (iv) river layer soils, (v) poorly drained phase, (vi) river basin soils silted and (vii) sand dune soils. The soil map helped to know and identify the type of soil in the area of study, and thus the amount of water filtration coming from the sewage system. Slope Map : The slope is one of the important factors for the direct effect on the flow size and runoff velocity. If the slope is steep, the runoff will be higher and infiltration will be lesser. The slope map of the study area was prepared from the ASTER DEM data in Arc GIS, as shown in Fig. 3 and also RESULTS AND DISCUSSION Preparation of Maps : The maps of slope, soil, sewer and storm networks within the study area were prepared in Arc GIS 9.3. The maps are described below. Soil Map : The rate of infiltration is a function of soil properties within the drainage area, ground slopes, and ground cover. Soil texture has a significant effect on groundwater potential. The soils with high porosity are considered good for the extraction of groundwater. The soil map of the study area is Fig. 3. Slope map of the city center of Karbala generated by GIS Fig. 2. Soil map of the city center of Karbala generated by GIS 598 HUSSEIN ET AL. land use map in the Karbala city centre as shown in Fig. 4. Fig. 4. Land-use map of the city centre of Karbala Figure 5 compares measured and modelling results at the sub-catchment; a monitoring and at the outfall of the sub catchment B. In both cases, the wet-weather TSS and BOD5 concentrations significantly exceeded the raw sewage TSS and BOD concentrations, showing the importance of the build-up, wash-off and transport processes. However, even if the acceptable results were observed, the number of available events was not adequate to allow a full calibration of the four empirical coefficients of each water-quality model. In accordance with that, they obtained outputs underline the model’s uncertainties whenever the number of event’s data is not sufficient for calibration. It contains eleven types, which are religious, green area, public building and administration services, proposed commercial, development, proposed exurban, proposed industrial, residential, cemetery, proposed grassy area and mixed use. The model used land-use percentages directly to calculate how much pollutant build-up there will be in a catchment. Land-use percentages by the catchment were also Fig. 5. Modelling results at sub catchment-A and sub catchment-B Modelling of Sewage Quality in During a Rainy Season used to determine the percentage of the impervious areas and manning roughness coefficients, which were then used to calculate the total runoff flow. The results showed that in the same rain event the pollutant loads (Biochemical Oxygen Demand, BOD5 and Total Suspended Solids, TSS) in the sampling areas increase significantly and the calibration was so matching. The increase of TSS is very high compared to BOD5. It is expected that the long-term simulations of storm water pollution loading will help to compare the benefits of different scenarios of the sewage-treatment plan for the reduction of the pollution in the storm discharge in the study area. In the process of the SWMM5 running, TSS was identified in all the manholes of the sewerage network in the study area. The mean concentration of TSS was 350 mg / L, especially in 599 the outfall of sub-catchment A as shown in Figure 6. While the TSS in the outfall of watershed B was 380 mg/L. As for the BOD5, the concentration was 435 mg /L in the outfall of sub-catchment A and in the outfall of sub-catchment B, the maximum BOD5 was 475mg/L. These results considered near from the results in the Table 3 because rainfall often reduces wastewater concentration, and that it depends on the strength of sewage networks as well depends on the land-use type which carries the surface runoff. Table 3. Typical municipal wastewater Characterisation adopted by Rossman, (2010) Parameter BOD TSS High Medium Low 560 600 350 400 230 250 Fig. 6. Statistical results of sub-catchments overflow HUSSEIN ET AL. 600 And also the Significance test for concentration change of BOD5 ranging 222 to 426 mg/L and for TSS concentrations were between 184 to 316 mg/L (Muserere et al., 2014). CONCLUSION In our research, the modelling provided useful results for comparing non-point pollutant loadings originating from various areas within the two watersheds. In addition to the general capacities of the hydrodynamic models. The result data series may also be easily exported, allowing the treatment of the data by external software. A maximum of about thirty days data could be loaded from a 5minute interval inflow time series. It took 20 hours to run a 5-year long simulation with the full dynamic wave routing, for a model with 2 catchments, 146 nodes, 144 conduits and water quality parameters for one pollutant substance. The impervious areas of each sub catchment were initially modelled based on the soil occupation by pavements and roofs. However, the only losses used for impervious areas are the depression storage losses, which were not enough to allow the necessary flow reductions for the model’s calibration. Those results, when integrated with other watershed assessment findings, provide valuable information for targeting general areas (sub-watersheds) where water quality improvement opportunities would be most effective. This will assist the country in identifying potential areas for implementing new structural BMPs, retrofits to existing structures, stream restoration projects, or other site-specific improvements. Further application of model would involve modelling to help select specific locations and types of structural BMPs for implementation. In this extended application, the baseline model presented here (which estimates the current and future pollutant loads) would be augmented with simulations of pollutant loading reductions from potential BMPs in new locations or from retrofitting existing structures. Running various “what-if ” scenarios would help identify specific locations for the most cost effective BMPs that would result in the greatest improvement in water quality in watershed streams and lakes. This extended application would refine the model output and support more detailed recommendations and cost estimates for the watershed planning. For the present case study, continuous modelling allowed to compare the benefits of different scenarios of storage and sewage treatment plant capacities for the reduction of the overflow discharges. The benefits in the reduction of the discharged BOD5 and TSS loads were very similar to the benefits in the reduction of the overflowed water volumes, showing that, in addition to the increases in the difficulties and uncertainties associated with the water quality model, no relevant benefits were obtained by its use, at least within the scope of the present study. 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