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**Slaughterhouse Wastewater Characterization and Treatment: An Economic and Public Health Necessity of the Meat Processing Industry in Ontario, Canada** ()

The characteristics of the slaughterhouse effluents and current wastewater treatment practices in the province of Ontario, Canada are analyzed. Meat processing plants are found to produce large amounts of wastewater due to the slaughtering process and cleaning of their facilities. Furthermore, the composition of the wastewater varies according to the type and number of animals slaughtered and the water requirements of the process. However, the slaughterhouse wastewater usually contains high levels of organics and nutrients. Several slaughterhouses in Ontario discharge their wastewater into the municipal sewer system after primary pretreatment at the meat processing plant. Therefore, due to the high-strength characteristics of the slaughterhouse effluents, an extensive treatment for a safe discharge into the environment is required. Thus, the combination of biological processes and advanced oxidation technologies for slaughterhouse wastewater treatment is evaluated in this study. Results show that the application of combined biological and advanced oxidation processes is recommended for on-site slaughterhouse wastewater treatment.

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*Journal of Geoscience and Environment Protection*,

**4**, 175-186. doi: 10.4236/gep.2016.44021.

1. Introduction

The treatment of water and wastewater has become crucial due to the continuous growth of world population and the pollution of freshwater because of not adequately treated wastewater discharged into environment, especially in developing countries [1]. Besides, the decreasing availability of freshwater has redirected the objectives in the area of wastewater treatment to recycling and reuse. Nevertheless, diverse techniques are adopted for water and wastewater treatment depending on the differences in geographic location, financial resources, living standards, and life quality in different countries, as well as the characteristics of the wastewater effluents and pollutants [2].

The meat processing industry produces large volumes of Slaughterhouse Wastewater (SWW) from the slaughtering of animals and cleaning of the slaughterhouse facilities. Up to 24% of the water used in the food and beverage industry is from the meat processing [3]. Slaughterhouses and Meat Processing Plants (MPPs) are part of a large industry worldwide, where the composition of the wastewater depends on the diverse practices in the slaughtering process. Consequently, SWW requires significant treatment for a safe and sustainable release to the environment [1].

According to Mittal [4], slaughterhouses in Ontario, Canada, typically discharge the SWW into the municipal sewer system after a preliminary treatment. Thus, slaughterhouses commonly pay surcharges, penalties, or fines to dispose their effluents into receiving municipal wastewater treatment plants. Moreover, there are currently 134 MPPs in Ontario that can process 100 - 200 animals per month. Approximately 53% of Ontario’s slaughterhouses do not treat their wastewater on-site before disposal. Dissolved Air Flotation (DAF) or aeration is the typical method of preliminary treatment with 16% of Ontario’s slaughterhouses using it at their facilities. The rest of slaughterhouses (31%) use passive methods such as lagoons or storage tanks to settle solids (Figure 1) [1].

Direct discharge of untreated slaughterhouse effluents to a water body is not practical due to the high organic load of the SWW. Therefore, appropriated disposal and treatment is required. It may be also stated that in terms of operation and economics, it is beneficial to implement combined processes for the management of slaughterhouse effluents since it couples the benefit of different technologies to improve high strength industrial wastewater treatment [5].

Advantages of the combined processes include potential energy recovery from the conversion of organic pollutants into biogas with high overall treatment efficiency [5]. However, SWWs may contain toxic and non-bio- degradable organic substances which make biological treatment alone insufficient [1]. Thus, Advanced Oxidation Processes (AOPs) are used to improve the bio-treatability of wastewaters containing non-biodegradable organics, which are toxic to common microorganisms. AOPs are becoming an attractive alternative to conventional treatment methods and a complimentary treatment option to biological processes in SWW treatment. Furthermore, AOPs can inactivate microorganisms for disinfection while avoiding the formation of hazardous by- products [6].

This study aims to identify the characteristics of the slaughterhouse wastewater in Ontario, Canada and discuss possible treatment alternatives to minimize the impact of the discharge of these wastewaters to the environment, and to optimize processes for organics and nutrient removal, including combined biological treatment

Figure 1. Slaughterhouse wastewater treatment systems in Ontario.

and AOPs for water reuse. Consequently, the effects of the influent concentration of TOC, flow rate, pH, H_{2}O_{2} dosage, and their interactions on the overall treatment efficiency of the combined anaerobic-aerobic and UV/ H_{2}O_{2} process and the effluent H_{2}O_{2} residual concentration were investigated using the Design of Experiments (DOE) to optimize the combined processes in continuous mode at laboratory scale for SWW treatment. Statistical models were also developed to predict the percent TOC removal and the effluent concentration of H_{2}O_{2} as response variables. The statistical models were validated by an additional set of experiments at the optimum conditions in line with the DOE results.

2. Materials and Methods

2.1. Materials

Actual SWW samples were taken from selected provincially licensed meat processing plants directly from their source in Ontario, Canada [7]. A 30% w/w hydrogen peroxide solution was purchased from Sigma-Aldrich, whereas NaOH (99%) and H_{2}SO_{4} (99%) were obtained from EMD Millipore for pH adjustment.

2.2. Slaughterhouse Wastewater Characteristics

The main source of SWW is the feces, urine, blood, lint, fat, carcasses, and non-digested food in the intestines of the slaughtered animals, the production leftovers, and the cleaning of the facilities [8]. The SWW composition varies according to the industrial process and water demand. Nevertheless, they usually contain high levels of organics and nutrients, typically measured as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total suspended solids (TSS), total nitrogen (TN), and phosphorus (TP).

Slaughterhouse effluents are considered detrimental worldwide due to its complex composition of fats, proteins, and fibers, as well as the presence of organics, nutrients, pathogenic and non-pathogenic microorganisms, detergents and disinfectants used for cleaning activities, and pharmaceuticals for veterinary purposes [9]. Therefore, the treatment and disposal of wastewater from slaughterhouses and meat processing plants are an economic and public health necessity [10]. Table 1 attempts to summarize the typical characteristics of the slaughterhouse effluents in Ontario, Canada. The SWW features and common ranges are listed as BOD, COD, TOC, TSS, TN, and pH.

2.3. Experimental Setup and Procedure

An anaerobic baffled reactor (ABR), followed by an aerobic activated sludge (AS) reactor, and a UV/H_{2}O_{2} photoreactor, operated in continuous mode, were used in a combined system at the laboratory scale for SWW treatment. The schematic diagram of the experimental setup for the combined ABR-AS-UV/H_{2}O_{2} processes is illustrated in Figure 2.

Table 1. Common characteristics of slaughterhouse wastewater.

Figure 2. Schematic diagram of the combined anaerobic, aerobic and UV/H_{2}O_{2} processes for the treatment of SWW.

The 50 L combined ABR-AS-UV/H_{2}O_{2}system consisted of a 36-L ABR with five equal-volume chambers integrated with individual headspaces, biogas collection piping, and a 13-L aerobic AS reactor with a monitored air flow rate, and a 1-L photoreactor with recycle and uniform light distribution. A 45˚ slanted-edge baffle within each ABR chamber permits the down- and up-flow of the SWW, providing effective mixing and contact time between the SWW and the biomass. The AS air flow rate was set at 2 L/min to guarantee nitrifying bacteria growth and dissolved oxygen (DO) concentrations over 2.0 mg/L.

Anaerobic and aerobic sludge seeds were loaded into the anaerobic and aerobic bioreactors, respectively. The inoculum was acclimatized in two months by feeding the actual SWW continuously into the reactors at a constant flow rate (75 mL/min) while gradually increasing its concentration.

The stainless steel cylindrical photoreactor (Barrier SL-1S-Siemens Inc., Markham, ON) had an external diameter of 8 cm and a length of 34 cm with a 2.5 cm diameter UV-C lamp and output power of 6 W with 254 nm wavelength was inserted into the center of the photoreactor. A quartz sleeve covered the UV-C lamp to protect the lamp from fouling and maintain a uniform UV radiation emission.

TOC concentrations were analyzed for each sample using an automated TOC analyzer (Teledyne Tekmar Apollo 9000, Mason, OH). Temperature and pH were measured daily using a pH meter with atemperature probe (Thermo Scientific Orion 230A+, Ottawa, ON). The H_{2}O_{2} residuals were measured with a UV-Visible Spectrophotometer (Ultrospec 1100 pro-Amersham Biosciences, Amersham, UK) at 454 nm using neocuproine and copper [11]. All experiments were repeated in triplicates, and the average values were reported. Furthermore, three replicates were made for each analytical measurement.

2.4. Experimental Design and Optimization

A four-factor along with five-level CCD in conjunction with RSM was used to maximize percent TOC removal and minimize percent H_{2}O_{2} residuals in the effluent. The influent concentration of TOC (X_{1}), flow rate (X_{2}), H_{2}O_{2} dosage (X_{3}), and pH (X_{4}) were used as independent factors in the DOE; whereas, the percent TOC removal (Y_{1}) and H_{2}O_{2} residual (Y_{2}) were considered process responses. Thus, each factor was coded at five levels, from −2 to +2, as shown in Table 2. Previous studies [1] [7] [11] [12] were used to determine and select the critical ranges of the factors.

Equation (1) was used to predict the model responses as a quadratic model and estimate the parametrical

Table 2. Independent variables with coded levels based on a four-factor, five level CCD.

coefficients by correlating dependent and independent variables using the least-squares regression [11]:

(1)

where β_{o}, β_{i}, β_{ii}, and β_{ij} are the constant, linear, quadratic, and cross-factor interaction coefficients, respectively; X_{i} and X_{j} represent the independent variables; Y_{i} is the predicted response; and c and k are the residual term and the number of factors, respectively.

The Design-Expert 9.0.4.1 statistical software was employed for graphical and regression analysis to estimate the coefficients of the response functions. The significance of the independent variables, factor interactions, and model equations were examined by analysis of variance (ANOVA) at 95% confidence intervals (CI).

Three-dimensional (3D) surfaces and two-dimensional (2D) contour plots were obtained while keeping another factor constant in the quadratic models. Experiments were carried out to validate the statistical models for maximum percent TOC removal and minimum H_{2}O_{2} residual.

Optimal operating conditions were estimated using the numerical optimization method built in the software. Lastly, an additional experimental run was carried out to validate the predicted optimal conditions for both response functions, the percent removal of TOC, and H_{2}O_{2} residual.

The desirability multiple response method was used to combine the desirable ranges for each response to obtaining a simultaneous objective function that represents the geometric mean of all transformed responses as shown in Equation (2) [13]:

(2)

where D, d_{i}, and n are the desirability objective function, each response range, and the number of responses, respectively. If any of the analyzed responses is found to be outside of their desirability range, the overall desirability function becomes zero. Therefore, for a simultaneous optimization, each response is required to be assigned low and high values for optimization. In this case, the percent removal of TOC (d_{1}) is maximized while the H_{2}O_{2} residual (d_{2}) is minimized.

3. Results and Discussion

3.1. Experimental Design and Statistical Analysis

Table 3 portrays the four-factor, five-level CCD with observed and predicted values for both percent TOC removal and H_{2}O_{2} residual by the developed quadratic models related to the combined ABR-AS-UV/H_{2}O_{2} system in a continuous photoreactor for SWW treatment.

RSM was employed for parameter estimation, indicating the relationship between the input factors and the responses, as shown in Equation (2). Thus, to predict the response functions for percent TOC removal and H_{2}O_{2} residual, the second-order polynomial Equations (3) and (4) were developed, respectively:

(3)

(4)

Table 3. Four-factor, five-level CCD with observed and predicted percent TOC removal and H_{2}O_{2} residual.

Negative coefficients for the model components X_{1}, X_{2}, X_{3}, X_{4}, X_{1}X_{2}, X_{1}X_{3}, X_{1}X_{4}, X_{3}X_{4}, , , and in Y_{1} and X_{1}, X_{1}X_{2}, X_{1}X_{3}, X_{1}X_{4}, X_{2}X_{3}, and in Y_{2}, indicate unfavorable effects on the percent TOC removal and the H_{2}O_{2} residual, respectively. Whereas, positive coefficients for X_{2}X_{3}, X_{2}X_{4}, and in Y_{1} and X_{2}, X_{3}, X_{4}, X_{2}X_{4}, X_{3}X_{4}, , , and in Y_{2} indicate favorable effects on the percent TOC removal and the H_{2}O_{2} residual, respectively. Since the coefficients with values close to zero represent lower relative intensity, do not intensely affect the TOC removal while X_{1}, X_{1}X_{2}, X_{2}X_{3}, and do not intensely affect H_{2}O_{2} residual.

Although this evaluation provides a rapid analysis of the parametrical effect on the response variables, ANOVA with 95% CI was also applied to evaluate the statistical significance of the developed quadratic models for the percent TOC removal and the H_{2}O_{2} residual. Thus, the statistical significance of each factor coefficient, as shown in Equations (3) and (4), was determined by the Fisher’s (F) exact test, comparing probability (p) values greater than F. Consequently, the model F-values of 287.69 and 116.90 for TOC removal and H_{2}O_{2} residual, respectively, imply the models are significant.

Besides, small probability values (p < 0.05) indicate significant model terms, which confirm the accuracy of the developed models to predict the response functions. Conversely, p-values > 0.10 indicate the model terms are not significant, in this case, is not significant for both TOC removal and H_{2}O_{2} residual. If the quadratic effect is not significant, then the optimal levels of the parameter are in the extremes of the experimental region [14].

The goodness of fit of the developed models was validated by the determination coefficient (R^{2}) and the adjusted R^{2} that ensures an adequate variation of the quadratic model to the experimental values. The values of R^{2} and adjusted R^{2} were found to be 0.9963 and 0.9928 for the percent TOC removal and 0.9909 and 0.9824 for the H_{2}O_{2} residual, respectively, representing an adequate model’s significance.

Moreover, the adequate precision for the percent TOC removal and H_{2}O_{2} residual models were found to be 77.49 and 51.54, respectively (Table 4). Since both values were greater than 4.00, the model can be used to navigate the CCD design space [15]. The lack of fit was calculated to assess how well the model fits the data. The lack of fit p-values of the percent TOC removal and the H_{2}O_{2} residual were found to be 0.6059 and 0.1145, respectively. A not significant lack of fit (p > 0.10) indicates that the model fits the data well.

On the other hand, the assumption of the constant variance was verified by plotting the internally studentized residual versus predicted values (Figure 3(a) and Figure 3(b)). The studentized residuals were found dividing the residuals by their standard deviations showing a randomly scattered pattern within the outlier detection limits −3 and +3. Therefore, model predictions, described in Equations (3) and (4), for both the percent TOC removal and the H_{2}O_{2} residual, respectively, are satisfactory.

Moreover, the normal probability plot of residuals, shown in Figure 4(a) and Figure 4(b) for the TOC removal

Figure 3. Internally studentized residuals versus predicted values for (a) percent TOC removal and (b) H_{2}O_{2} residual.

Figure 4. Internally studentized residuals versus normal probability for (a) percent TOC removal and (b) H_{2}O_{2} residual.

and the H_{2}O_{2} residual, respectively, showed a straight line pattern followed by the points on the plot, not an S-shaped curve. Consequently, a transformation of the response is not required because of the normal distribution of the residuals [11].

The correlation between the observed and predicted values is shown in Figure 5(a) and Figure 5(b) for the TOC removal and the H_{2}O_{2} residual, respectively. As a result, minor discrepancies are represented by a straight line trend, which indicates a good agreement between observed and predicted values. Hence, the quadratic model predictions for both percent TOC removal and H_{2}O_{2} residual responses are satisfactory.

3.2. Individual and Cross-Factor Interaction Effects of Model Parameters

The significance of each model factor was also evaluated using the F-exact test and p-values for each factor including linear, quadratic, and cross-factor interaction. All four independent variables including influent TOC (X_{1}), flow rate (X_{2}), H_{2}O_{2} dosage (X_{3}), and pH (X_{4}) have a significant effect on both responses since their p- values are lower than 0.05. Besides, the cross-factor interactions of all model parameters, including the influent TOC concentration and flow rate (X_{1}X_{2}), influent TOC concentration and H_{2}O_{2} dosage (X_{1}X_{3}), influent TOC concentration and pH (X_{1}X_{4}), flow rate and H_{2}O_{2} dosage (X_{2}X_{3}), flow rate and pH (X_{2}X_{4}), and H_{2}O_{2} dosage and pH (X_{3}X_{4}) showed a significant effect on both TOC removal and H_{2}O_{2} residual. The cross-factor interaction effects with the highest significance as per their p-values < 0.0001 are illustrated in Figure 6.

Figure 5. Observed experimental data versus predicted values for (a) percent TOC removal and (b) H_{2}O_{2} residual.

Figure 6. 3D surfaces and 2D plots of the interaction effects of: (a) influent TOC concentration and H_{2}O_{2} dosage (X_{1}X_{3}), (b) flow rate and H_{2}O_{2} dosage (X_{2}X_{3}), and (c) H_{2}O_{2} dosage and pH (X_{3}X_{4}) on the TOC removal; and (d) influent TOC concentration and H_{2}O_{2} dosage (X_{1}X_{3}), (e) flow rate and pH (X_{2}X_{4}), and (f) H_{2}O_{2} dosage and pH (X_{3}X_{4}) on H_{2}O_{2} residual.

3.3. Optimization of Operating Conditions

The RSM was used to calculate the optimum conditions for the four independent variables to get maximum percent TOC removal and minimum H_{2}O_{2} residual. Equations (3) and (4) were defined as objective functions for percent TOC removal and H_{2}O_{2} residual, respectively, and the independent factors in their range were used as model constraints. Thus, the following optimum conditions to reach a maximum TOC removal of 98.9% and minimum H_{2}O_{2} residual of 1.4% were found: influent TOC of 50 mg/L, flow rate of 15 mL/min, H_{2}O_{2} dosage of 344 mg/L, and pH of 7.2. The obtained optimal operating conditions were used in an additional run to validate the predicted values. Obtaining a TOC removal of 97.8% and H_{2}O_{2} residual of 1.3% were obtained experimentally, confirming the reliability of the model since the values are within the 95% CI.

Table 4. ANOVA of the prediction results for the percent TOC and H_{2}O_{2} residual by quadratic modeling.

^{a}Degrees of freedom (df). ^{b}Fisher’s (F) exact test value. ^{c}A probability value (p) < 0.05 is considered to be significant, a p-value > 0.10 is considered not significant. ^{d}Total sum of squares corrected for the mean.

^{*}Corresponding author.

The interaction effects of the influent TOC concentration, flow rate, H_{2}O_{2} dosage, and pH had a significant effect on both TOC removal and H_{2}O_{2} residual. Optimum conditions were found for each variable to achieve maximum TOC removal with minimum H_{2}O_{2} residual. The developed mathematical models provided a comprehensive exploration of the cross-factor interactive effects of the independent variables on the responses. The proposed models explaining the treatment of SWW by the continuous ABR-AS-UV/H_{2}O_{2} system were found suitable for future studies on reactor design, modeling, and scale-up.

Acknowledgements

The financial support of Natural Sciences and Engineering Research Council of Canada (NSERC), Ontario Trillium Scholarship (OTS) program, Colciencias, University of Cartagena, and Ryerson University is greatly appreciated.

NOTES

^{*}Corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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