alexa Evaluation of Wastewater Treatment Plants in El-Gharbia Governorate, Egypt | OMICS International
ISSN: 2161-0401
Organic Chemistry: Current Research

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Evaluation of Wastewater Treatment Plants in El-Gharbia Governorate, Egypt

Abd El-Motaleb M Ramadan1, Adel AH Abdel-Rahman2, Ali M Abdullah3* and Osama A Eltawab4

1Chemistry Department, Faculty of Science, Kafr El-Sheikh University, Kafr El-Sheikh, Egypt

2Chemistry Department, Faculty of Science, Menuofia University, Shebin El-Kome, Egypt

3Alexandria University, IGSR, Alexandria, Egypt

4Sector of Laboratories and Insurance, El-Gharbia Company for Water and Wastewater, Tanta, Egypt

*Corresponding Author:
Ali M Abdullah
Alexandria University
IGSR, Alexandria
Egypt
Tel: 002-012-292-480-37
E-mail: [email protected]

Received date: July 17, 2016; Accepted date: July 26, 2017; Published date: August 04, 2017

Citation: Ramadan AEMM, Rahman AAA, Abdullah AM, Eltawab OA (2017) Evaluation of Wastewater Treatment Plants in El-Gharbia Governorate, Egypt. Organic Chem Curr Res 6: 184. doi: 10.4172/2161-0401.1000184

Copyright: © 2017 Ramadan AEMM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

The present study has been undertaken to evaluate performance efficiency of wastewater treatment plants in El-Gharbia governorate in Egypt. The wastewater treatment plants using different biological treatment techniques (conventional activated sludge, oxidation ditch, extended aeration, rotating biological contactors and aerated lagoons processes). Wastewater samples were collected from both influent and effluent of each plant and the wastewater quality were determined at central laboratory of Garbyia Water Co. The performance of each plant was estimated based on the treated wastewater quality data. Correlations between influent and effluent TSS, COD and BOD5 were developed. Kotour WWTP operates with the oxidation ditch technology exhibits the highest performance efficiency, while Tanta WWTP operates with conventional activated sludge technology exhibits the lowest one. The results show that, all collected samples from Tanta, and El Mehala El Kobra WWTPs were exceeding the Egyptian Permissible limits (COD: 80 mg/l) while the samples collected from Mehalet Marhom, Mehalet Menof, Kotour, El Santa, Shernak and Zefta were complying the Egyptian regulations.

Keywords

Wastewater treatment; WQI; Plants; Garbyia Governorate

Introduction

There is no truer sign of civilization and culture than good sanitation. A good drain reflects the culture as much as a beautiful statue [1,2]. Wastewater is essentially the water supply of the community after it has been fouled by a variety of uses. The water supplied to a community receives a range of chemical substances and microbial flora during its use such that the wastewater acquires a polluting potential and becomes a health and environmental hazard. Communicable diseases of the intestinal tract such as cholera, typhoid, dysenteries and water borne diseases like infectious hepatitis etc., can be spread from uncontrolled disposal of wastewater, and therefore prevention of communicable diseases and protecting public health attracts the primary objective of sanitary wastewater disposal [2,3].

The sites for disposal of wastewater have traditionally been natural watercourses, land and the coastal waters. One of the major sources of organic pollution is effluents from sewage treatment works. Prevention of pollution of natural resources such as land and water by the wastewater and adequate preparation or renovation of the wastewater before reuse, are further important considerations in formulating and designing appropriate wastewater disposal arrangements [3,4].

Given the characteristics of raw wastewater and the requirements of disposal or reuse, the wastewater usually requires some type of preparation or treatment before it is rendered fit for disposal or reuse. Generally, in many situations involving domestic wastewater, the treatment consists of removal of suspended solids and 5-day, 20°C BOD, which are the two usual parameters of prime interest. The degree of treatment provided to the wastewater will largely be based on the effluent standards prescribed by the regulatory agencies when the treated effluent is to be discharged into a watercourse or land. If the effluent is to be reused, the quality of the effluent required to support such reuse will indicate the degree of treatment necessary. The complete treatment of wastewater is brought by a sequential combination of various physical unit operations, and chemical and biological unit processes. The general yardstick of evaluating the performance of sewage treatment plant is the degree of reduction of BOD, and suspended solids, which constitute organic pollution. The performance efficiency of treatment plant depends not only on proper design and construction but also on good operation and maintenance [5,6].

Performance evaluation of existing treatment plants is required (1) to assess the existing effluent quality and/or to meet higher treatment requirements and, (2) to know about the treatment plants whether it is possible to handle higher hydraulic and organic loadings. Performance appraisal practice of existing treatment plants is effective in generation of additional data which also can be used in the improvement in the design procedures to be followed for design of these plants. Existing facilities can be made to handle higher hydraulic and organic loads by process modifications, whereas meeting higher treatment requirements usually requires significant expansion and/or modification of existing facilities [7,8].

One of the primary considerations in evaluating an existing wastewater treatment plants is in the area of plant operation and control. A major tool required for proper process control is frequent and accurate sampling and laboratory analysis [9,10].

In the current wastewater treatment process, microorganisms play a significant role in the treatment of domestic sewage. Many different organisms live within the wastewater itself, assisting in the breakdown of certain organic pollutants [11,12]. The basis for using these EM species of microorganisms is that they contain various organic acids due to the presence of lactic acid bacteria, which secrete organic acids, enzymes, antioxidants and metallic chelates. The creation of an antioxidant environment by EM assists in the enhancement of the solidliquid separation, which is the foundation for cleaning water [13,14].

Poor conditions of sewerage system, improper design of the plant and organizational problems are important factors that cause treatment plant not to meet the effluent standards [14]. Overloading due to increase in population and water use, discharge of trade effluents are other reasons of recent times for the poor performance of wastewater treatment plants [15]. The treatment efficiency may be badly affected if the system is hydraulically under loaded [14-18].

The main aims of the present study are to study and evaluate the wastewater treatment plants efficiency in Garbyia Governorate.

Methodology

Case of the study

The study aimed to evaluate the performance and efficiency of the Wastewater Treatment Plants (WWTPs) in El-Gharbia Governorate, middle of Delta, Egypt as shown in Figure 1. The survey of present study covers more than 90% of the WWTPs in El-Gharbia governorate (17 WWTPs; Tanta, Mahalet Marhom, Fesha, Nawag, Nefia, Mahalet Menof, Berma, Basyoun, Mashal and Kom Elnagar, Kafr Elzayat, Kotour, Neshyl, Segen Elcoom, Elsanta, Zefta, Elmahala Elkobra, and Saft Trab). El-Gharbia Governorate WWTPs were designed and constructed in order to receive an average of 493500 m3 of raw sewage wastewater per day aimed to manage it so as to minimize and/or remove organic matter, solids, nutrients, disease-causing organisms and other pollutants before it mixed with surface water bodies according to law No. 48 of 1982 and amendments. WQI were calculated as shown in Table 1.

organic-chemistry-El-Gharbia

Figure 1: Sampling sites, El-Gharbia Governorate.

WQI
Factor Weight Data WQI WQI
DO(mg/l) 0.17      
FC(CFU/100ml) 0.16      
pH 0.11      
BOD(mg/l) 0.11      
COD(mg/l) 0.15      
ΔT(°C) 0.1      
TP(mg/l) 0.1      
NO3(mg/l) 0.1      
Treated water WQI    

Table 1: Water Quality Index (WQI) weights and calculation.

Sampling

The collected samples were carried out during the study period (Jan. to Dec. 2016), collection and storage of samples were carried out according to APHA [19-21].

Performance appraisal has been carried out by comparing the concentrations of pollutants at the influent and effluent of the investigated treatment plants. The grab and composite samples were collected at the influent and effluent of the investigated treatment plants in clean polyethylene bottles. Composite samples were collected over 12 hours at a rate of one sample each hour. Residual chlorine (R.Cl2) was measured on site during sampling time. The composite samples were analysed for various parameters like BOD5, COD and TSS. The samples were analysed as outlined in the standard methods for the examination of water and wastewater APHA, Depending on the results, performance of each plant was evaluated. By regression analysis correlations between TSS, COD and BOD5 were established to improve treatment plants control and operation.

Results and Discussion

The evaluation of performance (pollutant removal efficiency) of the investigated wastewater treatment plants was undertaken in terms of effluent quality. The evaluation was based on measurements of TSS, BOD5, COD, R.Cl2, plant TSS removal efficiency (TSS%), plant BOD5 removal efficiency (BOD5%), and influent COD/BOD5 ratio. These parameters were estimated on monthly basis for the raw untreated wastewater (influent) and treated wastewater (effluent) for the period of 12 months from January to December, 2016.

TSS, BOD5 and COD

TSS, BOD5 and COD are indirect indicators for total suspended solids, fermentable and non-fermentable organic content. The obtained data show that, the physical (TSS), chemical (COD) and biochemical (BOD5) properties of the influent exhibits insignificant variations among the different investigated WWTPs. This variation trend was also detected for the single plant at different times. This variation may attribute to the different social, economic, geographic and climatic conditions in the studied communities. Significant variations of physical, chemical and biochemical properties of the different investigated WWTPs effluent were observed. This variation can be ascribed to the nature of incoming organic loading, the type of the operational conditions and mainly the difference in the efficiency of the treatment process.

The observed variability of the effluent concentrations and the removal efficiencies within all treatment plants operates with different technologies, considering all the analyzed constituents can be visualized in the data presented in the present study. These results are in agreement with the results obtained by Oliveira and Von Sperling.

The present results demonstrate that, Kotour WWTP operates with the oxidation ditch technology exhibits the highest performance efficiency, while Tanta WWTP operates with conventional activated sludge technology exhibits the lowest one.

TSS and TSS removal efficiency

TSS: The data of TSS are recorded in Tables 2 and 3. For the investigated WWTPs the average influent values of TSS are ranged from 253.167 mg/L at Mahalet Menof WWTP to 111.250 mg/L at El Mehala El Kobra WWTP. The average effluent values of TSS are ranged from 24.583 mg/L at Kotour WWTP to 144.583 mg/L at Tanta WWTP. These results reveal that the influent of the investigated WWTPs presents means of TSS significantly higher than that presented by the effluent. As well as the present results indicate that there is no significant variation in the influent mean TSS values while there is a significant variation in those presented by the effluent. A poor performance was observed for Tanta, Nefia, and Elmahala Elkobra, WWTPs. On the other hand, a good performance was detected for Mahalet Marhom, Fesha Sleem, Mahalet Menof, Berma, Basyoun, and Kom Elnagar, Kafr Elzayat, Kotour, Neshyl, Elsanta, and Elkorashia, Shenrak, Elgafaria, Zefta, Nawag, Saft Trab and Elhyatem WWTPs.

Wastewater treatment plants IN/E January February March April May June July August September October November December Mean SD
Tanta IN 312 244 290 234 394 310 252 340 342 362 270 298 304.00 49.45
E 164 64 208 192 122 150 149 240 98 92 152 104 144.58 51.73
Mahalet Marhom IN 252 420 304 322 224 314 298 304 254 297 402 245 303.00 59.27
E 24 43 24 30 26 28 29 29 31 33 39 24 30.00 5.95
Nawag IN 216 294 262 308 296 188 236 310 342 218 222 262 262.83 47.48
E 45 91 42 85 36 63 36 69 56 25 28 22 49.83 23.05
Nefia IN 296 234 316 202 302 208 234 218 488 352 360 290 291.67 82.40
E 43 47 39 102 30 45 21 35 93 45 84 64 54.00 25.95
Mahalet Menof IN 202 274 210 236 258 282 212 294 412 208 236 214 253.17 59.18
E 19 21 20 31 36 30 24 28 25 24 22 22 25.17 5.11
Berma IN 238 212 312 292 226 282 301 448 360 324 248 242 290.42 66.50
E 29 20 88 25 24 24 27 39 31 36 25 44 34.33 18.30
Basyoun IN 208 392 284 304 308 436 432 208 259 308 412 214 313.75 85.85
E 20 31 22 31 26 24 26 32 24 37 37 62 31.00 11.22
Mashal and Kom Elnagar IN 280 264 322 308 270 214 452 296 364 216 354 226 297.17 69.48
E 32 38 102 27 30 24 35 36 21 33 27 25 35.83 21.48
Kafr Elzayat IN 236 389 330 306 268 216 368 306 270 268 318 288 296.92 50.43
E 34 22 21 43 27 30 66 30 25 28 41 24 32.58 12.58
Kotour IN 392 312 316 336 242 238 230 290 328 326 310 198 293.17 55.41
E 32 31 29 28 20 17 23 18 27 24 20 26 24.58 5.05
Neshyl IN 376 320 232 373 374 286 364 270 221 298 282 228 302.00 59.16
E 36 65 66 32 27 22 28 20 36 41 24 30 35.58 15.25
Segen Elcoom IN 164 196 294 372 254 232 220 330 300 216 230 302 259.17 60.70
E 28 68 78 26 24 34 29 54 35 24 25 20 37.08 19.02
Elsanta IN 315 272 324 205 248 254 228 284 328 215 256 230 263.25 42.11
E 38 35 21 28 25 29 21 28 23 29 23 22 26.83 5.46
Shenrak IN 258 316 316 270 288 202 268 256 220 230 358 298 273.33 44.74
E 27 25 23 27 27 23 25 26 25 21 35 28 26.00 3.49
Zefta IN 268 320 246 216 240 272 236 238 262 267 396 326 273.92 50.44
E 22 34 28 35 26 19 22 22 29 36 24 28 27.08 5.63
Elmahala Elkobra IN 288 306 288 254 328 242 260 224 384 346 248 348 293.00 50.05
E 99 130 80 131 134 106 106 94 103 110 92 142 110.58 19.35
Saft Trab and Elhyatem IN 275 338 276 302 295 280 202 206 282 262 214 302 269.50 42.12
E 20 126 32 21 21 43 25 32 24 31 78 22 39.58 31.60

Table 2: TSS data (mg/L) of the influent and effluent of the WWTPs. All data represents means of five replicates ± Stander Deviation (SD), TSS: Total suspended solids, IN: Influent (untreated raw wastewater) and E: Effluent (treated wastewater).

Wastewater treatment plants (WWTPs) TSS removal efficiency (%)
%Range Mean ± SD
Tanta 17.9-94.5 51.1 ± 19.7
Mahalet Marhom 87.8-92.1 90 ± 1.2
Nawag 66.5-91.6 81 ± 8.1
Nefia 49.5-91 8.7 ± 11
Mahalet Menof 86-93.9 89.8 ±2.2
Berma 71.8-91.5 88.1 ± 5.1
Basyoun 71-94.5 89.2 ± 6.3
Mashal and Kom Elnagar 68.3-94.2 87.6 ± 6.7
Kafr Elzayat 82.1-94.3 88.9 ±3.6
Kotour 86.9-93.8 91.5± 1.9
Neshyl 71.6-92.8 87.6 ±6.5
Segen Elcoom 65.3-93.4 85.1 ±8.2
Elsanta 86.3-93.5 89.6 ±2.4
Shenrak 88.6-92.7 90.4 ± 1.2
Zefta 83.8-93.9 89.8 ± 2.8
Elmahala Elkobra 48.4-73.2 61.7 ± 7.1
Saft Trab and Elhyatem 62.7-93 85.2 ±10.8
ANOVA F 23.548
P-value <0.001*

Table 3: Mean TSS removal efficiency of the thirty investigated WWTPs. All data represents means of 12 replicates per year ± Standard Deviation (SD), TSS: Total suspended solids, IN: Influent (untreated raw wastewater), E: Effluent (treated wastewater) and *: Significant variation.

The results show that, all collected samples from Tanta, El Mehala El Kobra and Nawag WWTPs were exceeding the Egyptian Permissible limits (TSS: 40 mg/l) while the samples collected from Mehalet Menof, Kotour, El Santa, Shernak and Zefta were complying the Egyptian regulations as indicated in Table 10.

Horan et al. defined the activated sludge process as a suspended growth system comprising a mass of microorganisms constantly supplied with organic matter and oxygen. This process is widely used worldwide for the treatment of domestic and industrial wastewater, in situations where high effluent quality is necessary [22,23]. According to Francioso et al. a number of AS processes and design configuration have evolved due to new regulations for effluent quality, technological advances, better understanding of microbial processes and to reduce costs. We can have complete-mix activated sludge (CMAS), plug-flow (conventional, high-rate aeration, step feed, contact stabilization, twosludge, high-purity oxygen, Kraus process, conventional extended aeration), extended aeration (oxidation ditch, orbal, countercurrent aeration system, biolac process) and the sequentially operated systems such as sequentially batch reactor (SBR), cyclic activated sludge system (CAAS), Batch decant reactor- intermittent cycle extended aeration system (ICEAS) (Figures 2 and 3).

organic-chemistry-Tanta-WWTP

Figure 2: TSS of Tanta WWTP during the present study.

organic-chemistry-Elsanta-WWTP

Figure 3: TSS of Elsanta WWTP during the present study.

TSS removal efficiency

The data obtained for the TSS removal efficiency in Tables 3 and 4 illustrate that the average removal of TSS is ranged from 51.1% to 91.5% for Tanta and Kotour WWTPs respectively. Kotour WWTP is more efficient than Tanta WWTP in TSS removal by 40.38%. Poor efficiency for TSS removal is detected for Tanta, Nawag, Nefia, and Elmahala Elkobra, WWTPs. These results reveal a significant variation in the mean TSS removal efficiency for all investigated WWTPs.

Wastewater treatment plants IN/E January February March April May June July August September October November December Mean SD
Tanta IN 405 410 320 360 465 390 340 420 460 420 350 340 390.00 48.01
  E 200 90 250 220 169 190 150 280 115 154 210 150 181.50 54.60
Mahalet Marhom IN 310 495 370 390 375 370 340 460 340 380 470 320 385.00 59.89
  E 35 51 34 39 38 34 39 37 46 42 45 36 39.67 5.33
Fesha Sleem IN 345 390 390 390 395 320 280 390 280 290 330 350 345.83 45.67
  E 39 30 38 50 42 40 42 35 38 36 39 36 38.75 4.83
Nawag IN 285 395 330 380 410 280 290 410 450 280 290 310 342.50 62.29
  E 55 130 50 115 55 70 42 105 78 45 42 40 68.92 31.40
Nefia IN 390 395 380 310 465 310 290 300 510 420 420 320 375.83 70.93
  E 55 56 46 125 45 70 33 50 110 49 90 85 67.83 28.64
Mahalet Menof IN 250 320 250 430 372 360 290 320 570 260 310 330 338.50 90.10
  E 30 27 33 39 42 48 39 39 30 31 39 39 36.33 6.11
Berma IN 330 320 430 390 310 350 380 510 480 520 360 275 387.92 80.72
  E 38 39 96 38 39 40 39 46 45 43 41 54 46.50 16.26
Basyoun IN 300 465 320 380 495 510 510 270 310 370 450 290 389.17 92.12
  E 32 41 28 40 39 38 39 42 38 46 42 83 42.33 13.64
Mashal and Kom Elnagar IN 310 330 360 380 355 340 490 380 415 280 410 310 363.33 57.06
  E 39 52 110 45 40 35 41 50 32 46 39 34 46.92 20.80
Kafr Elzayat IN 335 445 360 390 310 310 420 340 360 310 510 360 370.83 61.31
  E 44 30 31 58 40 42 86 48 42 36 52 39 45.67 15.03
Kotour IN 495 375 395 380 380 330 270 380 390 430 370 270 372.08 61.88
  E 48 38 39 39 35 29 30 26 44 39 37 35 36.58 6.20
Neshyl IN 485 410 310 460 425 340 410 370 420 420 310 350 392.50 56.39
  E 50 80 73 45 46 28 36 31 52 52 35 44 47.67 15.71
Segen Elcoom IN 205 245 380 430 390 280 290 410 380 320 290 350 330.83 70.09
  E 48 82 105 42 45 38 35 70 46 38 38 36 51.92 22.09
Elsanta IN 465 365 390 380 355 320 280 350 390 310 290 310 350.42 52.37
  E 49 40 29 42 36 35 32 32 39 39 39 39 37.58 5.30
Shenrak IN 390 345 380 380 325 270 290 290 280 290 400 370 334.17 48.66
  E 36 30 29 38 40 25 36 29 36 37 44 38 34.83 5.46
Elgafaria IN 355 435 310 380 410 430 310 290 370 390 440 290 367.50 56.39
  E 41 45 42 58 38 42 39 53 54 38 45 36 44.25 7.11
Zefta IN 385 415 360 360 395 310 270 290 350 320 430 380 355.42 49.66
  E 29 48 38 44 30 26 30 38 34 45 38 34 36.17 6.94
Elmahala Elkobra IN 395 380 320 290 435 380 350 260 420 460 350 390 369.17 58.65
  E 105 160 100 160 100 160 140 180 190 150 170 190 150.42 32.92
Saft IN 325 390 290 470 340 390 250 280 345 370 290 370 342.50 60.73
  E 29 160 51 39 40 48 48 39 55 48 130 38 60.42 40.63

Table 4: Influent and effluent BOD5 values (mg/L) of the thirty investigated WWTPs.All data represents means of five replicates ± Stander Deviation (SD), BOD5: Biochemical oxygen.

BOD5 and BOD5 removal efficiency

BOD5: Table 4 presents the BOD5 values (mg/L) for the influent and effluent of the investigated WWTPs. Table 5 shows the mean BOD5 values (mg/L) for the influent and effluent of the thirty investigated WWTPs. For the investigated WWTPs, the average influent values of BOD5 are ranged from 330.833 mg/L to 399.167 mg/L for Segen Elcoom and El Moutamadia WWTPs respectively. The average effluent values are ranged from 34.833 mg/L to 181.500 mg/L for Shenrak and Tanta stage 2 WWTPs respectively. These results indicate poor performance for Tanta, and Elmahala Elkobra WWTPs. A good performance is detected for Mahalet Marhom, Fesha Sleem, Mahalet Menof, Berma, Basyoun Mashal and Kom Elnagar, Kafr Elzayat, Kotour, Neshyl, Elsanta, Shenrak, and Zefta, WWTPs.

Wastewater treatment plants January February March April May June July August September October November December Mean SD
Tanta 51 78 22 39 64 51 56 33 75 63 40 56 52 17
Mahalet Marhom 89 90 91 90 90 91 89 92 86 89 90 89 90 1
Fesha Sleem 89 92 90 87 89 88 85 91 86 88 88 90 89 2
Nawag 81 67 85 70 87 75 86 74 83 84 86 87 80 7
Nefia 86 86 88 60 90 77 89 83 78 88 79 73 81 9
Mahalet Menof 88 92 87 91 89 87 87 88 95 88 87 88 89 2
Berma 88 88 78 90 87 89 90 91 91 92 89 80 88 4
Basyoun 89 91 91 89 92 93 92 84 88 88 91 71 88 6
Mashal and Kom
 Elnagar
87 84 69 88 89 90 92 87 92 84 90 89 87 6
Kafr Elzayat 87 93 91 85 87 86 80 86 88 88 90 89 88 3
Kotour 90 90 90 90 91 91 89 93 89 91 90 87 90.1 1.5
Neshyl 90 80 76 90 89 92 91 92 88 88 89 87 88 5
Segen Elcoom 77 67 72 90 88 86 88 83 88 88 87 90 84 8
Elsanta 89 89 93 89 90 89 89 91 90 87 87 87 89 2
Met Yazed and Elkorashia 91 91 91 89 91 91 91 81 89 89 86 89 89 3
Shenrak 91 91 92 90 88 91 88 90 87 87 89 90 89 2
Elgafaria 88 90 86 85 91 90 87 82 85 90 90 88 88 3
Zefta 92 88 89 88 92 92 89 87 90 86 91 91 90 2
Shershaba 93 87 89 81 92 89 84 88 83 83 90 81 87 4
Elmahala Elkobra 73 58 69 45 77 58 60 31 55 67 51 51 58 13
Saft Trab 91 59 82 92 88 88 81 86 84 87 55 90 82 12

Table 5: BOD5 removal efficiency (%) of the thirty investigated WWTPs.All data represents means of five replicates ± Stander Deviation (SD), BOD5: Biochemical oxygen demand after five days, IN: Influent (untreated raw wastewater) and E: Effluent (treated wastewater).

It is obvious that the influent of the investigated WWTPs presents means of BOD5 significantly higher than that presented by the effluent. No significant variation in the influent mean BOD5 values can be detected while there is a significant variation in those presented by the effluent.

The results show that, all collected samples from Tanta, and El Mehala El Kobra WWTPs were exceeding the Egyptian Permissible limits (BOD: 60 mg/l) while the samples collected from Mehalet Menof, Nawag, Menof, Kotour, El Santa, Shernak and Zefta were complying the Egyptian regulations as indicated in Table 12.

BOD5 removal efficiency

Table 5 presents the BOD5 removal efficiency (%) of the investigated WWTPs. Table 6 presents the mean BOD5 removal efficiency of the thirty studied WWTPs. The average values of BOD5 removal efficiency are ranged from 52.3% to 90.1% for Tanta and Kotour WWTPs respectively. Kotour WWTP is more efficient than Tanta WWTP in BOD5 removal by 37.72%. Poor BOD5 removal efficiency is observed for Tanta, Nawag, Nefia, Elmahala Elkobra, Saft Trab and Elhyatem WWTPs. A significant variation in the mean BOD5 removal efficiency for the investigated WWTPs is observed (Figures 4 and 5).

Wastewater treatment plants IN/E January February March April May June July August September October November December Mean SD
Tanta IN 524 632 653 708 633 690 675 899 651 622 630 572 657.42 90.80
E 239 168 330 285 220 239 177 378 159 198 271 198 238.50 67.25
Mahalet Marhom IN 475 795 731 623 416 616 547 762 578 776 783 704 650.50 127.80
E 67 74 68 74 65 68 68 67 74 66 61 60 67.67 4.62
Nawag IN 422 695 575 597 694 556 625 550 742 517 464 514 579.25 96.77
E 74 176 73 173 71 110 63 143 122 68 61 61 99.58 43.92
Nefia IN 564 725 716 687 717 665 618 677 752 761 593 422 658.08 96.48
E 76 75 70 163 63 106 80 71 144 62 107 127 95.33 33.86
Mahalet Menof IN 468 479 522 689 523 510 520 451 870 524 634 642 569.33 120.23
E 50 69 54 54 61 60 55 60 65 57 60 62 58.92 5.25
Berma IN 667 527 690 598 620 652 563 722 876 849 636 550 662.50 109.70
E 68 55 112 65 69 67 61 66 66 70 69 75 70.25 14.03
Basyoun IN 599 778 625 682 664 737 734 499 683 546 613 584 645.33 82.92
E 50 67 66 79 64 74 66 60 51 79 63 118 69.75 17.76
Mashal and KomElnagar IN 525 714 662 677 467 550 885 741 572 574 656 500 626.92 118.93
E 65 64 192 78 66 66 69 66 66 69 65 68 77.83 36.14
Kafr Elzayat IN 426 616 761 632 440 693 659 505 692 656 781 502 613.58 119.08
E 69 51 62 71 71 69 121 71 68 67 76 63 71.58 16.78
Kotour IN 716 465 764 673 742 758 536 578 731 649 528 536 639.67 105.99
E 72 55 62 74 60 66 43 60 57 67 59 53 60.67 8.51
Neshyl IN 752 598 681 795 602 723 656 607 779 638 489 637 663.08 88.13
E 71 128 96 78 77 65 60 55 66 69 52 72 74.08 20.56
Segen Elcoom IN 329 382 764 730 624 561 676 639 872 559 662 500 608.17 154.59
E 79 125 156 75 74 63 58 98 64 66 56 56 80.83 30.98
Elsanta IN 775 654 638 749 427 564 424 605 532 750 459 637 601.17 123.50
E 72 64 65 70 64 68 67 65 77 65 60 53 65.83 5.98
Shenrak IN 678 686 664 627 459 528 409 535 639 716 552 588 590.08 95.50
E 58 54 51 62 70 53 59 57 55 55 63 52 57.42 5.45
Elgafaria IN 525 790 756 613 664 872 663 592 899 627 560 527 674.00 127.81
E 59 57 64 68 68 63 68 76 80 62 71 76 67.67 7.13
Zefta IN 647 700 752 642 580 509 528 627 626 643 630 500 615.33 75.24
E 45 69 66 62 61 46 61 63 67 58 60 56 59.50 7.50
Elmahala Elkobra IN 684 631 611 638 767 634 713 539 686 646 482 522 629.42 82.16
E 146 225 167 238 196 418 202 371 328 222 259 268 253.33 81.85
Saft Trab IN 540 727 595 722 684 620 475 645 589 624 459 614 607.83 85.16
E 52 207 71 78 64 78 68 59 79 63 197 67 90.25 52.86

Table 6: Influent and effluent COD values (mg/L) of the thirty investigated WWTPs. All data represents means of five replicates ± Stander Deviation (SD), COD: Chemical oxygen demand, IN: Influent (untreated raw wastewater) and E: Effluent (treated wastewater).

organic-chemistry-BOD-Tanta

Figure 4: BOD of Tanta WWTP during the present study.

organic-chemistry-BOD-Elsanta

Figure 5: BOD of Elsanta WWTP during the present study.

Dissolved organics are generally treated with biological processes. The more common systems are aerobic (with oxygen) and include aerobic or facultative pond, biofilm reactor, and activated sludge processes. All these processes rely on the ability of microorganisms to convert organic wastes into stabilized, low-energy compounds [15].

COD of the investigated WWTPs

Table 6 shows the influent and effluent COD values (mg/L) of the investigated WWTPs. Table 6 reports the mean influent and effluent COD values (mg/L) of the investigated WWTPs. The average values of the influent COD are ranged from 657.42 mg/L to 663.08 mg/L for Tanta and Neshyl WWTPs respectively. The average effluent values are ranged from 57.417 mg/L to 253.333 mg/L for Shenrak and Elmahala Elkobra WWTPs respectively.

These results show a poor performance for Tanta, Nefia, Elmahala Elkobra, Nawag, Saft Trab WWTPs. A good performance is detected for Mahalet Marhom, Fesha Sleem, Mahalet Menof, Berma, Basyoun Mashal and Kom Elnagar, Kafr Elzayat, Kotour, Neshyl, Elsanta, Shenrak, and Zefta, WWTPs.

The present results illustrate that the influent of the investigated WWTPs presents means of COD significantly higher than that presented by the effluent. No significant variation in the influent mean COD values is observed, while there is a significant variation in those presented by the effluent.

The results show that, all collected samples from Tanta, and El Mehala El Kobra WWTPs were exceeding the Egyptian Permissible limits (COD: 80 mg/l) while the samples collected from Mehalet Marhom, Mehalet Menof, Kotour, El Santa, Shernak and Zefta were complying the Egyptian regulations as indicated in Table 11.

Chemical oxygen demand (COD) is a measure of the amount of oxygen required to chemically oxidize reduced minerals and organic matter [22,23]. Higher levels of COD were observed in influent but were reduced, with a mean percentage removal efficiency of 38.9 (± 62.22) % in effluent in average. This explains the significant difference between influent and effluent values of BOD as a result of plants performance (P>0.05). Furthermore, COD effluent concentrations were above the recommend EPA standard of 250 mg/L despite high percentage removal efficiency. This is due to very low algal populations to cause chemical activity that will reduce the COD [24,25] (Figures 6 and 7).

organic-chemistry-COD-Elsanta

Figure 6: COD of Elsanta WWTP during the present study.

organic-chemistry-COD-WWTP

Figure 7: COD of Elsanta WWTP during the present study.

Residual chlorine (R.Cl2)

Table 7 reveals the effluent R.Cl2 values (mg/L) of the investigated WWTPs. Data in Table 8 provides the mean values of the effluent R.Cl2 (mg/L) of the investigated WWTPs. Mean effluent R.Cl2 values below the reference value is observed for Tanta, Kom Elnagar, Neshyl, Shenrak, and Saft Trab WWTPs. A significant difference in the effluent R.Cl2 values of the investigated WWTPs were noticed.

Wastewater treatment plants January February March April May June July August September October November December Mean SD
Tanta 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00
Mahalet Marhom 1 0.6 1 3 0.6 0.6 0.7 0.8 0.8 0.6 0.5 0 0.85 0.73
Fesha Sleem 1 2 1.5 1.3 0.8 0.8 0.5 1 0.8 3 0.2 2 1.24 0.78
Nawag 0 0.2 0.6 0.6 1 1 0.8 0 0.9 0 1 0 0.51 0.44
Nefia 1 0.8 1 0.7 0.5 0.6 0.6 0.5 0.6 0.5 1 0 0.65 0.28
Mahalet Menof 0.5 0.5 0.6 0.5 0.6 1 0.9 0.2 3 0.5 0.5 0.8 0.80 0.72
Berma 3 0.5 0 0 1 0 0 1.5 2 2.5 0.8 0 0.94 1.08
Basyoun 0.8 0.8 1 0.5 0.6 0 0.6 0.8 0 0.8 2 0.7 0.72 0.51
Mashal 0.5 0 0 0.5 0.1 0.5 0.7 0.5 0.5 0.6 0.6 0.3 0.40 0.24
Kafr Elzayat 0 0 0.8 0.5 0.5 0.6 0.4 0.8 1 0.8 1 0.6 0.58 0.33
Kotour 0 4 1.5 0.5 2.5 0.5 0.1 3.3 0.5 0.7 0.5 0.8 1.24 1.32
Neshyl 0 0.6 0 0 0.6 0.5 0.5 0.3 0.5 1.7 0 0.5 0.43 0.47
Segen Elcoom 0.3 0 0 0 0 0 0 0 0.1 1.3 1.5 0 0.27 0.54
Elsanta 0.2 1 3 0.8 0.5 0.6 0.6 2.5 0.6 0.5 0.8 0.2 0.94 0.88
Met Yazed 0.5 3 0.6 0.5 0 0.5 0.6 0.5 2 0.7 1.5 2 1.03 0.89
Shenrak 0.6 0.6 0.6 0.5 0.5 0.7 0.1 0.5 0.5 0.6 0 0 0.43 0.25
Elgafaria 0.8 0 0.6 0.8 1 0.6 0.5 0.8 0.5 0.5 0.6 0.78 0.62 0.25
Zefta 1.5 0.5 0.6 0.5 0.5 1.5 0.8 0.8 0.6 1.6 3.4 1.2 1.13 0.83
Shershaba 0.8 1 1.5 0.8 1.2 0.7 0.9 1 0.8 0.5 1.3 0.8 0.94 0.28
Elmahala Elkobra 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00
Saft Trab 0.8 0.8 0.8 0 0.8 0.8 0.6 0 0 0 0 0 0.38 0.40

Table 7: Effluent R.Cl2values (mg/L) of the thirty investigated WWTPs. All data represents means of five replicates ± Stander Deviation (SD), R.Cl2: Residual chlorine, IN: Influent (untreated raw wastewater) and E: Effluent (treated wastewater).

Influent COD/BOD5 ratio: Table 8 presents the influent COD/ BOD5 ratio of the investigated WWTPs. Data in Table 9 reports the mean influent COD/BOD5 ratio of the investigated WWTPs. The values of the influent COD/BOD5 ratio of the investigated WWTPs are within the normal range and does not exceeded the upper reference value reported by Wentzel et al. [11] (1.25-2.5). This indicates that the incoming influents to these investigated WWTPs are human wastes in nature and is not industrial wastes. Industrial wastes are characterized by the presence of slowly biodegradable organic suspended solids or refractory substances for biodegradation or both of them. These results indicate a non-significant variation in the mean influent COD/BOD5 ratio of the studied WWTPs.

Wastewater treatment plants (WWTPs) Influent COD/BOD5 ratio
Range Mean
Tanta 1.3-2.14 1.708±0.287
Mahalet Marhom 1.11-2.2 1.695±0.28
Fesha Sleem 1.5-2.5 1.807±0366
Nawag 1.34 -2.16 1.709±0.218
Nefia 1.32-2.26 1.785±0.35
Mahalet Menof 1.41-2.09 1.719±0.27
Berma 1.42 -2.02 1.733±0.211
Basyoun 1.34-2.2 1.712±0.294
Mashal and Kom Elnagar 1.32-2.16 1.735±0.251
Kafr Elzayat 1.3-2.24 1.674±0.332
Kotour 1.24-2.3 1.742±0.316
Neshyl 1.42-2.2 1.713±0.248
Segen Elcoom 1.43-2.33 1.843±0.325
Elsanta 1.2-2.42 1.726±0.322
Met Yazed 1.54-2.16 1.763±0.217
Shenrak 1.38-2.47 1.786±0.344
Elgafaria 1.27-2.44 1.861±0.365
Zefta 1.32-2.16 1.757±0.262
Shershaba 1.38-2.47 1.805±0.368
Elmahala Elkobra 1.34-2.2 1.728±0.278
Saft Trab 1.54-2.3 1.791±0.235
ANOVA F 0.495
P-value 0.988

Table 8: Mean influent COD/BOD5 ratio of the thirty investigated WWTPs.All data represents means of 12 replicates per year ± Standard Deviation (SD), BOD5: Biochemical oxygen demand after five days, COD: Chemical oxygen demand, IN: Influent (untreated raw wastewater).

It can be observed that, the influent COD/BOD5 ratio are lower than 3. This indicates that these influent wastewaters can usually be successfully treated with biological processes because of their high biodegradability and this meets the data reported by Ng Wun [25].

Correlations developed between TSS, COD and BOD5: Establishment of constant relationships among the various measures of organic content depends primarily on the nature of the wastewater and its source. Variations of both influent and effluent BOD5 with the influent and effluent TSS and COD were achieved using regression analysis (Table 9). As the experimental determination of BOD5 requires relatively long time (5 days), this theoretical correlation gives a fast expectation for the corresponding BOD5 values. Once the correlation has been established, TSS and COD measurements can be used to provide a good advantage for treatment plant control and operation. This will improve the performance efficiency of the investigated WWTPs [26,27].

Correlation between Expression Correlation coefficient
Variation of influent BOD5with the influent TSS and COD X=52.876+(0.755) Y+(0.154) Z
X: influent BOD5, Y: influent TSS and Z: influent COD
R Square=69.7%
or =0.697
Variation of effluent BOD5with the effluent TSS and COD X=-0.700+(0.698) Y+(0.324) Z
X: effluent BOD5, Y: effluent TSS and Z: effluent COD
R Square=97.4%
or =0.974

Table 9: Correlations developed between TSS, COD and BOD5 of the thirty investigated WWTPs. TSS: Totalsuspended solids, BOD5: Biochemical oxygen demand after five days, COD: Chemical oxygen demand and R Square: Coefficient of determination.

Treated water quality index and data analysis: Table 13 and Figure 8 show the calculated values of WQI for treated wastewater of the investigated WWTPs in the Garbyia Governorate. The values of WQI ranged from 69 (Neshyl WWTP) to 143 (Mehala El Kobra WWTP).

Wastewater treatment plants WQI Notes
Tanta 151
Marhom 88
Nawag 112  -
Nefia 106  -
Mahalet Menof 82  -
Berma 91  -
Basyoun 84  -
Kom Elnagar 81  -
Kafr Elzayat 77  -
Kotour 74  -
Neshyl 69  -
Segen Elcoom 98  -
Elsanta 79  -
Shenrak 82  -
Zefta 83  -
Elmahala 143  -
Saft Trab 96  -

Table 13: WQI for WWTPs.

organic-chemistry-WQI-WWTP

Figure 8: WQI for WWTPs.

Data Analysis

Tables 10-12 shows the number of collected samples and didn’t comply with Egyptian guidelines values for TSS, BOD and COD (Figure 8).

Wastewater Treatment Plant Number of non-comply Samples from 12 collected samples %
Tanta 12 100.0
Marhom 1 8.3
Nawag 7 58.3
Nefia 8 66.7
Mahalet Menof 0 0.0
Berma 2 16.7
Basyoun 1 8.3
Kom Elnagar 1 8.3
Kafr Elzayat 3 25.0
Kotour 0 0.0
Neshyl 3 25.0
Segen Elcoom 3 25.0
Elsanta 0 0.0
Shenrak 0 0.0
Zefta 0 0.0
Elmahala 12 100.0

Table 10: TSS data analysis.

Wastewater Treatment Plant Number of non-comply Samples from 12 collected samples %
Tanta 12 100.0
Marhom 0 0.0
Nawag 5 41.7
Nefia 4 33.3
Mahalet Menof 0 0.0
Berma 1 8.3
Basyoun 1 8.3
Kom Elnagar 1 8.3
Kafr Elzayat 1 8.3
Kotour 0 0.0
Neshyl 2 16.7
Segen Elcoom 3 25.0
Elsanta 0 0.0
Shenrak 0 0.0
Zefta 0 0.0
Elmahala 12 100.0

Table 11: COD data analysis.

Wastewater Treatment Plant Number of non-comply Samples from 12 collected samples %
Tanta 12 100.0
Marhom 0 0.0
Nawag 0 0.0
Nefia 5 41.7
Mahalet Menof 0 0.0
Berma 1 8.3
Basyoun 1 8.3
Kom Elnagar 1 8.3
Kafr Elzayat 1 8.3
Kotour 0 0.0
Neshyl 2 16.7
Segen Elcoom 3 25.0
Elsanta 0 0.0
Shenrak 0 0.0
Zefta 0 0.0
Elmahala 12 100.0

Table 12: BOD data analysis.

Conclusion

The performance studies on the investigated sewage treatment plants located in El-Gharbia governorate in Egypt conducted for a period of 12 months reveal that the overall performance achieved by some of the investigated plants is lower than the expected performance. This shows that improvements in the current situation are possible, thus serving as an incentive to designers and plant operators. Nonsignificant variation in the influent’s mean TSS, BOD5, COD and COD/ BOD5 ratios are observed. While significant variations in the removal efficiencies and the effluent concentrations considering all the analyzed constituents are obtained during the experimental period within all investigated treatment plants. The influents of the investigated WWTPs are human wastes in nature and can usually be successfully treated with biological processes because of their high biodegradability. Theoretical correlations between influents and effluents TSS, COD and BOD5 were determined. These correlations can be used to provide a good advantage for treatment plant control and operation. A probabilistic model has been used for determining achievable effluent BOD5, COD and TSS concentrations. This probabilistic approach provides a theoretical basis for the analysis of reliability. The reliability measures are expressed in probability terms, that is, the probability of success or adequate performance as a function of mean values and effluent variability. The effluent variability has been described by the coefficient of variation. The performance variability in some WWTPs are observed, this is because there are many factors that affect wastewater treatment plant performance (reliability). Flow variability and their characteristics, the inherent variability of the behavior of wastewater treatment processes (inherent reliability), the variability caused by failures, in addition the lack of experience of the wastewater treatment plant operators especially in the developing countries.

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