Peat Water Treatment Using Combination of Cationic Surfactant Modified Zeolite, Granular Activated Carbon, and Limestone
Tweet MyronLMeters.com attempts to provide its customers with the latest in water quality research and industry updates. Find more at https://www.myronlmeters.com/. Abstract This research was conducted essentially to treat fresh peat water using a series of adsorbents. Initially, the characterization of peat water was determined and five parameters, including pH, colour, COD, turbidity, and iron ion […]
MyronLMeters.com attempts to provide its customers with the latest in water quality research and industry updates. Find more at https://www.myronlmeters.com/.
This research was conducted essentially to treat fresh peat water using a series of adsorbents. Initially, the characterization of peat water was determined and five parameters, including pH, colour, COD, turbidity, and iron ion exhibited values that exceeded the water standard limit. There were two factors influencing the adsorption capacity such as pH, and adsorbent dosages that were observed in the batch study. The results obtained indicated that the majority of the adsorbents were very efficient in removing colour, COD, turbidity at pH range 2-4 and Fe at pH range 6-8. The optimum dosage of cationic surfactant modified zeolite (CSMZ) was found around 2 g while granular activated carbon (GAC) was exhibited at 2.5 g. In column study, serial sequence of CSMZ, GAC, and limestone showed that the optimal reduction on the 48 hours treatment were found pH = 7.78, colour = 12 TCU, turbidity = 0.23 NTU, COD = 0 mg/L, and Fe= 0.11 mg/L. Freundlich isotherm model was obtained for the best description on the adsorption mechanisms of all adsorbents.
Keywords: cationic surfactant modified zeolite, granular activated carbon, limestone, peat water
Water is essential and fundamental to all living forms and is spread over 70.9% of the earth’s surface. However, only 3% of the earth’s water is found as freshwater, of which 97% is in ice caps, glaciers and ground water (Bhatmagar & Minocha, 2006). In Malaysia, more than 90% of fresh water supply comes from rivers and streams. The demand for residential and industrial water supply has grown rapidly coupled with an increase in population and urban growth (WWF Malaysia, 2004). Water demand in affected populations such as rural areas also demands that attention is paid to providing more sustainable solutions rather than transporting bottled water (Loo et al., 2012). For this reason, it is essential to ensure availability of local sources of water supply and even develop new potential sources of water such as from peat swamp forest to overcome future water shortages.
River water surrounded by peat swamp forest is defined as peat water and is commonly available as freshwater since it has a low concentration of salinity. The previous study shows that peat swamp forest has high levels of acidity and organic material depending on its region and vegetation types (Huling et al., 2001). Under natural conditions, tropical peat lands serve as reservoirs of fresh water, moderate water levels, reduce storm-flow and maintain river flows, even in the dry season, and they buffer against saltwater intrusion (Wosten et al., 2008).
Due to the acidity and high concentration of organic material, selective treatment of peat water must be conducted prior to its use as water supply. Recently, many methods have been designed and have proven their effectiveness in treating raw water such as coagulation and flocculation (Franceschi et al., 2002; Liu et al., 2011; Syafalni et al., 2012a), absorption (Ćurković et al., 1997), filtration (Paune et al., 1998) and combining (Hidaka et al., 2003). Careful consideration of the most suitable method is important to ensure that the adsorption process is the most beneficial, economically feasible method as well as easy to operate for producing high quality of water in a particular location.
Many researchers have shown that activated carbon is an effective adsorbent for treating water with high concentrations of organic compounds (Eltekova et al., 2000; Syafalni et al., 2012b). Its usefulness derives mainly from its large micropore and mesopore volumes and the resulting high surface area (Fu & Wang, 2011). However, its high initial cost makes it less economically viable as an adsorbent. Low cost adsorbent such as zeolite nowadays has been explored for its ability in many fields especially in water treatment. Natural zeolite has negative surface charge which gives advantages in absorbing unwanted positive ions in water such heavy metal. These ions and water molecules can move within the large cavities allowing ionic exchange and reversible rehydration (Jamil et al., 2010). The effectiveness of zeolite has been improvised by modified zeolite with surfactant in order to achieve higher performance in removing organic matter (Li & Bowman, 2001). Among tested cationic surfactants, hexa-decyl-tri-methyl ammonium (HDTMA) ions adsorbed onto adsorbent surfaces are particularly useful for altering the surface charge from negative to positive (Chao & Chen, 2012). Surfactant modified zeolite has been shown to be an effective adsorbent for multiple types of contaminants (Zhaohu et al., 1999).
Zeolite is modified to improve its capability of exchanging the anion by cationic surfactants, called CSMZ. CSMZ adsorbs all major classes of water contaminants (anions, cations, organics and pathogens), thus making it reliable for a variety of water treatment applications (Bowman, 2003). Nowadays, interest in the adsorption of anions and neutral molecules by surfactant modified zeolite has increased (Zhang et al., 2002). Modification of zeolite by surfactant is commonly done by cationic or amphoteric surfactants. By introducing surfactant to the zeolite, an organic layer is developed on the external surfaces and the charge is reversed to positive (Li et al., 1998). However, the present study used zeolite that had been modified using Uniquat (QAC-50) as cationic surfactant (CSMZ) and their performance towards the removal of color, COD, turbidity and iron ion from peat water were investigated.
Four adsorbents were used in these experiments which are natural zeolite, zeolite modified by cationic surfactant, activated carbon and limestone. All adsorbents were prepared with equivalent sizes of 1.18 mm – 2.00 mm. Hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used for polishing zeolite during the preparation phase and for pH adjustment of the sample. Furthermore, potassium dichromate (K2CrO7), silver sulphate (Ag2SO4), sulphuric acid (H2SO4) and mercury (II) sulphate (HgSO4) were used as digestion solution reagents and acid reagents for COD analysis. Lastly, Uniquat (QAC-50) was used as cationic surfactant to modify the zeolite.
2.1 Preparation of Surfactant Modified Zeolite
In these studies, 100 g of prewashed natural zeolite was contacted with 5.6 ml/l Uniquat (QAC-50) as cationic surfactant (CSMZ). The mixture was then stirred at room temperature for 4 hours at 300 rpm (Karadag et al., 2007). The zeolite then was filtered and washed with distilled water several times. After that, the absorbent was dried in an oven at a temperature of 105 °C for 15 hours.
2.2 est Procedures
2.2.1 Batch Studies
Serial batch studies were conducted at room temperature (28 ± 1 °C) to investigate the influence of pH and dosage for removing colour, COD, turbidity and iron ion from peat water. Shaking speed of 200 rpm for 20 minutes were fixed and operated respectively. A working volume of 150ml peat water sample was set up in 250 ml conical flasks. Preceding the batch studies, initial concentration for those parameters was determined. The optimum pH and dosage of absorbent were determined. Subsequently, the percentage of removal was finally determined, plotted, and compared.
2.2.2 Batch Column Studies
Column studies were carried out using a plastic column with dimensions: 5.4 cm diameter and 48 cm length. Three adsorbents were filled inside the column at a specific depth with the supporting layers of marbles, cotton wool, and perforated net. Total volume of 2000 ml peat water was pumped in the up flow mode from the vessel into the column by using a Masterflex peristaltic pump at a minimum flow rate of (30, 60, 90) ml/min. In this study, however, column studies were performed un-continuously (batch) due to limitations of time. All parameters related to the column design are summarized in the following Table 1.
Table 1. Column studies parameters
|Horizontal Surface Area, A||cm2||
|Column volume, V||cm3||1099.3|
|Flowrate, Q||ml/min||30, 60, 90|
|Surface Loading Rate, SLR= Q/A||cm/min||1.31, 2.62, 3.93|
The serial sequence arrangements of adsorbents were conducted as shown in Figure 1 below. Effluent samples were collected at various time intervals, whilst maintaining room temperature, and analysed.
Figure 1. Schematic diagrams of lab-scale column studies
3. Results and Discussion
3.1 eat Water Characterization
Surface water originating from the peat swamp forest was taken from Beriah peat swamp river along the Kerian River on several occasions as the main sample. The characterization of peat water was carried out at the sampling point (in-situ measurement) using a multi-parameter probe as well as in the environmental laboratory of civil engineering, USM. Fundamentally, the characterization procedures were based on the Standard Methods for the Examination of Water and Wastewater (APHA, 1992). Table 2 represents the peat water characteristics in average value and the comparison to the standard drinking water quality in Malaysia.
Table 2. The characteristics of peat water sample from Beriah Peat Swamp Forest
|4.67 – 4.98|
Thirteen parameters were successfully determined where the first six parameters, including pH, temperature, TDS, DO, conductivity, and salinity were measured at the sampling point, whilst the rest of the parameters, including colour, turbidity, COD, iron ion, Ammoniacal Nitrogen, NH3-N, Ammonia (NH3), and Ammonium (NH4+) were examined from the sample brought to the environmental laboratory on the same day.
Acidic pH of the peat water was predicted due to the composition of the surrounding peat soil itself which had been formed by decaying material possessing humic substances (Rieley, 1992). Besides that, humic substances also lead to the high organic content as humic substances are comprised of numerous oxygen containing functional group and fractions (humic acid, fulvic acids and humin) with different molecular weights which mean yielding high concentration of turbidity and COD as well as coloured water (Torresday et al., 1996). Moreover, composition of peat soil may also have an impact on the iron ion concentration of peat water (Botero et al., 2010).
From the thirteen parameters, five parameters were indicated exceeding the standard limit. These parameters were pH, colour, turbidity, COD, and iron ion that showed values of 4.67 – 4.98, 224.7 TCU, 20.8 NTU, 33.3 mg/l, and
1.24 mg/l respectively while the standard limit of these parameters are 6.5 – 9.0, 15 TCU, 5 NTU, 10 mg/l, and 0.3 mg/l accordingly.
3.2 Effect of Initial pH on the Efficiency of Colour, COD, Turbidity, and Iron Ion (Fe) Removal
Influence of initial pH on the adsorption capacity for removing colour, COD, turbidity, and iron ion were investigated.
Figure 2(a) to Figure 2(d) below, displayed the percentage removal of colour, COD, turbidity, and iron ion against pH of adsorbents respectively.
Figure 2(a) shows the maximum removal percentage of colour that was removed by natural zeolite, CSMZ, and granular activated carbon (GAC) which were 79%, 90%, 82% respectively. This adsorption is depended on the characteristic of adsorbents itself. For zeolite and CSMZ were related to the amount of cationic ions (Al3+) increased, resulting in high reaction activity and GAC was related to the adsorption capacity. It was observed that the adsorption capacity was highly dependent on the pH of the solution, and indicated that the colour removal efficiencies decreased with the increase of solution pH.
The pH of the system exerts profound influence on the adsorptive uptake of adsorbate molecules presumably due to its influence on the surface properties of the adsorbent and ionization or dissociation of the adsorbate molecule. Figure 2(b) represents the percentage removal of natural zeolite and CSMZ where they reach optimum efficiency in removing organic compound (COD) at pH 2 with efficiency of 53% and 60% respectively. Meanwhile, the highest percentage removal of COD for GAC was achieved at pH 4 with efficiency obtained about 61%. Identical trends in colour removal were exhibited in percentage removal of COD for natural zeolite, CSMZ and GAC. In fact, this result also reveals that GAC has the highest percentage removal among natural zeolite and CSMZ yet optimum in difference pH solution. Neutralization mechanism occurs in low pH makes color removal, COD removal and Turbidity removals at pH 2 are higher for most of absorbents in this process.
In Figure 2(c), percentage turbidity removal against pH for each adsorbent revealed that optimal reduction of turbidity was obtained in an acidic environment with efficiency removal of 96%, 98%, 95% for natural zeolite, CSMZ, and GAC respectively. When the pH of the solution was adjusted above pH 6 to pH 12, the tendencies of all adsorption performances were gradually decreased. Moreover, it also showed that the lowest efficiency for the three adsorbents were identified at pH 12 with percentage values removal 55%, 61%, and 59% for natural zeolite, CSMZ, and GAC respectively.
Figure 2(d) demonstrates the removal efficiencies of iron ion as a function of the influent pH. The maximum removal of iron ion was observed at pH 8 for both natural zeolite and CSMZ whereas GAC had its optimum removal at pH 6. Natural zeolite and CSMZ only yielded 73% and 62% removal efficiency while GAC had more significant removal with removal efficiency of 80% to the iron ion concentration. Further, it is evident from the graph that gradual increment of removal efficiency for natural zeolite, CSMZ, and GAC occurred when the initial pH of the solution was increased to higher values. Somehow, at pH values greater than 6 the removal efficiency of GAC reduced slightly while for natural zeolite and CSMZ the reduction occurred from pH values above 8.
3.3 Effect of Adsorbent Dosage on the Efficiency of Colour, COD, Turbidity, and Iron Ion (Fe) Removal
The effect of adsorbent dosage was studied for all adsorbents employed on colour, COD, turbidity, and iron ion removal by varying the dosage of adsorbent and keeping all other experimental conditions constant. The pH was set to acidic conditions which were most favourable in obtaining the highest removal efficiency. In this study, to find optimal adsorbent dosage of natural zeolite and CSMZ, the appropriate experiments were carried out at adsorbent dosages in the range of 0.5 g to 5.0 g while for GAC, the adsorbent dosage was varied from 0.01 g to 4.0
- The experimental results for all the adsorbents are represented by Figure 3(a) to Figure 4(d).
Figure 3. Percentage of color (a), COD (b), turbidity (c), and Fe (d) removal against pH for NZ, and CSMZ
Figure 3(a) displays the relationship between the amount of adsorbent mass (dosage) and adsorption efficiency for natural zeolite and CSMZ in terms of removing colour. The colour removal of peat water increased from about 25% to 52% with increasing adsorbent dosage of natural zeolite from 0.5 g to 3.5 g whereas for CSMZ, removal percentage increased from 41% to 53% with increasing adsorbent dosage from 0.5 g to 2.0 g. However, further increase in adsorbent dosage to 5.0 g only led to slight degradation of removal efficiency to 50% and 41% for natural zeolite and CSMZ respectively. This degradation with further increases in adsorbent dosage was due to the unsaturated adsorption active sites during the adsorption process since the adsorbates in the vessel were only shaken for 20 minutes (insufficient time). Besides, modification of zeolite by cationic surfactant had proven to have better colour removal as presented in the graph.
Percentage removal of COD against the adsorbent dosage is shown in Figure 3(b). It was observed that the highest percentage removal for both natural zeolite and CSMZ to remove COD were 51% and 59%, achieved at adsorbent dosage 3.5 g and 2.0 g respectively.
The variations in removal of turbidity of peat water at various system pH are shown in Figure 3(c). The removal rate of turbidity was highest at the adsorbent dosage of 0.5 g with 70% and 93% removal efficiency for respective natural zeolite and CSMZ. The removal rate showed a smooth downward trend with the increase in adsorbent dosage. Concurrently, the adsorption capacity gradually decreased with the increasing adsorbent dosage. The least efficient removal of turbidity was noted at dosage 5.0 g with percentage removal recorded for natural zeolite and CSMZ only 57% and 70% respectively.
Figure 3(d) demonstrates the percentage iron ion removal of natural zeolite and CSMZ with respect to their dosage. The result shows that there was a significant difference trend in iron ion adsorption efficiencies between natural zeolite and CSMZ. For natural zeolite, it was shown that the removal percentage of iron ion had increased until it reached 1.0g of dosage with 72% of removal efficiency. On the other hands, CSMZ was only able to remove about 63% of iron ion when its dosage was increased to 2.5 g. The lowest percentage removals were 47% and 57% recognized at the adsorbent dosage 5.0 g for respective natural zeolite and CSMZ.
Figure 4. Percentage of color (a), COD (b), turbidity (c), and Fe (d) removal against dosage for GAC
The result illustrated in Figure 4(a) shows the maximum removal percentage of colour for GAC at 2.5 g dosage was 62%. Moderate increment in colour removal was identified along with the addition dosage of 2.5 g whilst abatement of removal efficiency began subsequently at adsorbent dosage of 3.0 g to 4.0 g.
The results from Figure 4(b) indicated that increasing the GAC dosage would increase the efficiency in removing COD respectively. The optimum dosage was recorded at 3.0 g with 72% of removal efficiency. Meanwhile, increasing the dosage above 3.0 g exhibited a slight decrease in removal efficiency with 67% to 61% for COD removal. A better result in removing COD was also shown by GAC compared to the natural zeolite and CSMZ.
The percentage of turbidity removed by GAC in different dosages is described in Figure 4(c). The highest removal was indicated at adsorbent dosage 2.5 g with removal efficiency of 70% while the minimum removal was 52% recorded at the adsorbent dosage 0.01 g. However, starting from adsorbent dosage of 3.0 to 4.0 g, removal efficiency began to decrease to 68%, 67%, and 69% respectively.
The result of percentage removal of iron ion by GAC in peat water is presented in Figure 4(d). It was found that the rate of removal was rapid in the initial dosage between 0.01 g to 3.0 g at which the removal efficiency increased from 28% to 71% accordingly. Subsequently, a few significant changes in the rate of removal were observed. Possibly, at the beginning, the solute molecules were absorbed by the exterior surface of adsorbent particles, so the adsorption rate was rapid. However, after the optimum dose was reached, the adsorption of the exterior surface becomes saturated and thereby the molecules will need to diffuse through the pores of the adsorbent into the interior surface of the particle (Ahmad & Hameed, 2009).
3.4 Batch Column Experiment
On the first running, the column was packed with natural zeolite (1st layer), limestone (2nd layer), and GAC (3rd layer) as shown in Figure 5(a). Removal efficiency for colour, COD, turbidity, and iron ion was recognized to be increased when the contact time was increased. At the time interval 1 hour to 6 hours, however, the increment was not so significant. The removal efficiency at 1 hour treatment was 39%, 21%, 54%, 36% while at 6 hours treatment was 77%, 65%, 73%, 60% recorded for respective colour, COD, turbidity, and iron ion. Poor removal efficiency at 1 hour treatment indicated that the required time to remove all parameters were insufficient. It is evident that if the adsorption process is allowed to run for 24 hours on the column, the removal efficiency shows notable removal. Percentage removals of colour, COD, turbidity, and iron ion at 24 hours were 83%, 72%, 76%, 65% respectively. Furthermore, the highest removal for respective colour, COD, turbidity, and iron ion were obtained at 48 hours treatment with 87%, 81%, 86%, and 79% of removal efficiency.
Figure 5. Percentage removal of color, COD, turbidity, and Fe for 1st run(a), 2nd run(b), and 3rd run (c) at flowrate 30 ml/min
On the second running, the column was packed with CSMZ (1st layer), limestone (2nd layer), and GAC (3rd layer) as presented in Figure 5(b). The removal percentages of colour, COD, turbidity, and iron ion were noticed after 1 hour to be 52%, 49%, 71%, and 30% respectively. The time of contact between adsorbate and adsorbent is proven to play an important role during the uptake of pollutants from peat water samples by adsorption process. In addition, the development of charge on the adsorbent surface was governed by contact time and hence the efficiency and feasibility of an adsorbent for its use in water pollution control can also be predicted by the time taken to attain its equilibrium (Sharma, 2003). Removal efficiency of 90% for colour, 81% for COD, 91% for turbidity, and 57% for iron ion were obtained at 24 hours of contact time.
On the third running, the column was packed with a difference sequence of CSMZ (1st layer), GAC (2nd layer), and limestone (3rd layer) demonstrated in Figure 5(c). It can be seen that the adsorption of these four parameters were slightly rapid at time interval 1 hour to 6 hours treatment. Further gradual increment with the prolongation of contact time form 24 hours to 48 hours has also occurred. Observation at 1 hour treatment recorded the removal efficiency of 62%, 58%, 87%, and 48% for respective colour, COD, turbidity, and iron ion. Whereby, 6 hours treatment had yielded higher removal percentage removal of 75%, 77%, 93%, and 58% respectively for colour, COD, turbidity, and iron ion. Further removal of colour, COD, turbidity, and iron ion was recorded when the treatment was run for 24 hours which exhibited 92%, 91%, 98%, 77% of removal efficiency respectively. Prolonged time to 48 hours indeed showed better removal of colour, COD, turbidity, iron ion with percentage removal of 95%, 100%, 99%, and 89% respectively. It can be seen that the arrangement of CSMZ, GAC, and limestone has the highest removal efficiency for all parameters at the flow rate influent of 30 ml/min.
Figure 6. Percentage removal of color, COD, turbidity, and Fe against contact time for 2nd run(a) at flow rate 60 mL/min and at flowrate 90 mL/min (b)
The experimental adsorption behaviour was further seen for its adsorption capacity during 60 ml/min and 90 ml/min flow rate. In addition, the flow rate adjustment had also resulted in differences in surface loading rate in which the sample going through the surface area of adsorbent bed (horizontal surface area, A= 22.9 cm2) for 30 ml/min equals to 1.31 cm/min while the flow rate of 60ml/min equals to 2.62 cm/min, and the flow rate of 90 ml/min equals to 3.93 cm/min. The percentage removal for both flow rate adjustments of CSMZ, GAC, and limestone arrangement were exhibited in Figure 6 (a) and Figure 6 (b). Based on these Figures, lower removal efficiencies were indicated at 1 hour time interval of 6 hours of contact time. The percentage removals for both 60 ml/min and 90 ml/min flow rate at 1 hour were 57%, 56%, 80%, 38% and 49%, 58%, 61%, 35% for colour, COD, turbidity, and iron ion respectively. Subsequently, when the contact time was at 6 hours, the removal percentage were 70%, 79%, 88%, 56%, and 60%, 77%, 70%, 47%. However, the maximum removal efficiency at 48 hours for both flow rates was not much different from the 30ml/min flow rate.
3.5 Adsorption Isotherm
In the present investigation, the experimental data were tested with respect to both Freundlich and Langmuir isotherms. Based on the linearized Freundlich isotherm models for natural zeolite, CSMZ, GAC in terms of adsorptive capacity to remove colour, COD, turbidity, and iron ion, the majority of them exhibited fits for all adsorbate with regression value (R2) above 0.6, except for iron ion and turbidity for respective CSMZ, and GAC. On the other hand, the linearized Langmuir isotherm models for natural zeolite, CSMZ, GAC in terms of adsorptive capacity to remove colour, COD, turbidity, and iron ion, had exhibited fits for all adsorbate with regression value (R2) was at range of 0.242 to 0.912. The Langmuir isotherm model for all adsorption mechanisms were identified to have smaller R2 values compared to the Freundlich isotherm model. Thereby, it can be concluded that the Freundlich isotherm model was more applicable in determining the adsorption mechanisms for this study.
3.6 Peat Water Quality Post Column Treatment
Peat water treatment in column with serial sequence of natural zeolite, CSMZ, and limestone had exhibited the highest removal with percentage removal at 48 hours at 95%, 100%, 99%, and 89% for colour, COD, turbidity, and iron ion respectively. Final readings at 48 hours treatment on pH, TDS, DO, conductivity, salinity, colour, turbidity, COD, and iron ion were 7.78, 74 mg/l, 4.03 mg/l, 137 uS/cm, 0.05 ppt, 12 TCU, 0.23 NTU, 0 mg/l, and 0.11 mg/l respectively (see Table 3). These findings, on the other hand, have indicated that peat water treatment had successfully produced water which satisfied the standard drinking water quality.
Table 3. The characteristics of results of peat water treatment from Beriah Peat Swamp Forest
Note: 1. *)Malaysian standard for drinking water quality;2. NA = Not analyzed.
From the results presented in this paper, the following conclusions can be drawn:
1) The optimum removal of colour, COD, and turbidity for all adsorbents were observed to occur during acidic conditions at pH range 2 – 4 whereas for iron ion, the maximum removal was noted at pH range 6 – 8.
2) At pH 2, CSMZ yielded the highest removal for colour and turbidity with removal efficiency of 90% and 98% respectively. Meanwhile, GAC has the highest percentage removal of COD at pH 4 with removal efficiency obtained about 61% while at pH 6, GAC exhibited the best removal of iron ion with percentage removal around 80%.
3) CSMZ revealed stronger adsorptive capacity for colour, COD, and turbidity compared to natural zeolite.
4) The optimal removal was achieved for the serial sequence of CSMZ (1st layer), GAC (2nd layer), and Limestone (3rd layer) with the adsorbent media at 30 ml/min of flow rate.
5) Freundlich isotherm was more reliable to describe the adsorption mechanisms of colour, COD, turbidity, and iron ion for natural zeolite, CSMZ, and GAC.
The authors wish to acknowledge the financial support from the School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia and Universiti Sains Malaysia (Short Term Grant No. 304/PAWAM/60312015).
Ahmad, A. A., & Hameed, B. H. (2009). Reduction of COD and colour of dyeing effluent from a cotton textile mill by adsorption onto bamboo-based activated carbon. Journal of Hazardous Materials, 172, 1538-1543. http://dx.doi.org/10.1016/j.jhazmat.2009.08.025
American Public Health Association (APHA), AWWA, WPCF. (1992). Standard Methods for Examination of Water and Wastewater (16th ed.). Washington.
Bhatmagar, A., & Minocha, A. K. (2006). Conventional and non-conventional adsorbents for removal of pollutant from water – A review. In Indian Journal of Chemical Technology, 13, 203-217
Botero, W. G., Oliveira, L. C., Rocha, J. C., Rosa, H. R., & Santos, A. D. (2010). Peat humic substances enriched with nutrients for agricultural applications: competition between nutrients and non-essential meals present in tropical soils. Journal of Hazardous Materials, 177, 307-311.
Bowman, R. S. (2003). Applications of surfactant-modified zeolites to environmental remediation. Microporous Mesoporous Materials, 61, 43-56. http://dx.doi.org/10.1016/S1387-1811(03)00354-8
Chao, H. P., & Chen, S. H. (2012). Adsorption characteristics of both cationic and oxyanionic metal ions on hexadecyltrimethylammonium bromide-modified NaY zeolite. Chemical Engineering Journal, 193-194, 283-289. http://dx.doi.org/10.1016/j.cej.2012.04.059
Ćurković, L., Cerjan-Stefanović, Š., & Filipan, T. (1997). Metal ion exchange by natural and modified zeolites,
Water Research, 31(6), 1379-1382. http://dx.doi.org/10.1016/S0043-1354(96)00411-3
Eltekova, N. A., Berek, D., Novak, I., & Belliardo, F. (2000). Adsorption of Organic Compounds on Porous Carbon Sorbents. Carbon, 38, 373-377. http://dx.doi.org/10.1016/S0008-6223(99)00113-X
Franceschi, M., Girou, A., Carro-Diaz, A. M., Maurette, M. T., & Puech-Coste, E. (2002), Optimisation of the coagulation–flocculation process of raw water by optimal design method. Water Research, 36(14), 3561-72.
Fu, F., & Wang, Q. (2011). Removal of heavy metal ions from wastewaters: A review. Journal of Environmental Management, 92, 407-418. http://dx.doi.org/10.1016/j.jenvman.2010.11.011
Hidaka, T., Hiroshi, T., & Kishimoto, N. (2003). Advanced treatment of sewage by pre-coagulation and biological filtration process. Water Research, 37(17), 4259-4269. http://dx.doi.org/10.1016/S0043-1354(03)00353-1
Huling, S. G., Robert, G. A., Raymond, A. S., & Matthew, R. M. (2001). Influence of Peat on Fenton Oxidation.
Water Research, 35(7), 1687-1694. http://dx.doi.org/10.1016/S0043-1354(00)00443-7
Jamil, T. S., Ibrahim, H. S., Abd El-Maksoud, I. H., & El-Wakeel, S. T. (2010). Application of zeolite prepared from Egyptian kaolin for removal of heavy metals: I. Optimum conditions. Desalination, 258, 34-40. http://dx.doi.org/10.1016/j.desal.2010.03.052
Karadag, D., Akgul, E., Tok, S., Erturk, F., Arif Kaya, M., & Turan, M., (2007). Basic and reactive dye removal using natural and modified zeolite. Journal of Chemical Engineering Data, 52, 2436-2441. http://dx.doi.org/10.1021/je7003726
Liu, T., Chen, Zh. L., Yu, W. Z., Shen, J. M., & Gregory, J. (2011). Effect of two-stage coagulant addition on coagulation-ultrafiltration process for treatment of humic-rich water. Water Research, 45(14), 4260-4268. http://dx.doi.org/10.1016/j.watres.2011.05.037
Li, Zh. H., & Bowman, R. S. (2001). Regeneration of surfactant-modified zeolite after saturation with chromate and perchloroethylene. Water Research, 35(1), 322-326. http://dx.doi.org/10.1016/S0043-1354(00)00258-X
Li, Z. H., Jones, H. K., Robert, S., Bowman, & Helferich, H. (1999). Enhanced Reduction of Chromate and PCE by Pelletized Surfactant Modified Zeolite/Zerovalent Iron. Environmental Science and Technology, 33, 4326-4330. http://dx.doi.org/10.1021/es990334s
Li, Z., Roy, S. J., Zou, Y., & Bowman, R. S. (1998). Long Term Chemical and Biological Stability of Surfactant Modified Zeolite. Environmental Science Technology, 32, 2628-2632. http://dx.doi.org/10.1021/es970841e
Loo, S. L., Fane, A. G., Krantz, W. B., & Lim, T. T. (2012). Emergency water supply: A review of potential technologies and selection criteria. Water Research, 46(10), 3125-51.
Paune, F., Caixach, J., Espadaler, I., Om, J., & Riveraet, J. (1998). Assessment on the removal of organic chemicals from raw and drinking water at a Llobregat river water works plant using GAC. Water Research, 32(11), 3313-3324. http://dx.doi.org/10.1016/S0043-1354(98)00108-0
Rieley, J. O. (1992). The ecology of tropical peatswamp forest ± a South-east Asian perspective. In Tropical Peat, Proceedings of International Symposium on Tropical Peatland, Kuching, Sarawak, Malaysia, 6±10 May 1991
(B.Y. Aminuddin, ed.) pp. 244±54. Kuching, Malaysia: Malaysia Agricultural Research Development Institute & Department of Agriculture, Sarawak, Malaysia
Syafalni, S., Abustan, I., Dahlan, I., & Wah, C. K. (2012b). Treatment of Dye wastewater Using Granular Activated Carbon and Zeolite Filter. Modern Applied Science, 6(2), 37-51. http://dx.doi.org/10.5539/mas.v6n2p37
Syafalni, S., Abustan, I., Zakaria, S. N. F., & Zawawi, M. H. (2012a). Raw water treatment using bentonite-chitosan as a coagulant. Water Science & Technology: Water Supply, 12(4), 480-488. http://dx.doi.org/10.2166/ws.2012.016
Torresdey, J. L., Tang, L., & Salvador, J. M. (1996). Copper adsorption by esterified and unesterified fractions of sphagnum peat moss and its different humic substances. Journal of Hazardous Materials, 48, 191-206. http://dx.doi.org/10.1016/0304-3894(95)00156-5
World Wildlife Fund (WWF) Malaysia. (2004). The importance of rivers.
Wosten, J. H. M., Clymans, E., Page, S. E., Rieley, J. O., & Limin, S. H. (2008). Peat- Water interrelationships in a Tropical Peatland Ecosystem in Southeast Asia. Catena, 73, 212-224. http://dx.doi.org/10.1016/j.catena.2007.07.010
Zhang, P., Tao, X., Li, Z., & Bowman, R. S. (2002). Enhanced Perchloroethylene Reduction in Column Systems Using Surfactant Modified Zeolite/zero-valent Iron Pellets. Environmental Science and Technology, 36, 3597-3603. http://dx.doi.org/10.1021/es015816u
Modern Applied Science; Vol. 7, No. 2; 2013
ISSN 1913-1844 E-ISSN 1913-1852
Published by Canadian Center of Science and Education
S. Syafalni1, Ismail Abustan1, Aderiza Brahmana1, Siti Nor Farhana Zakaria1 & Rohana Abdullah1
1 School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia. Correspondence: S. Syafalni, School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia,
Nibong Tebal 14300, Penang, Malaysia. E-mail: firstname.lastname@example.org
Received: December 3, 2012 Accepted: January 14, 2013 Online Published: January 22, 2013 doi:10.5539/mas.v7n2p39 URL: http://dx.doi.org/10.5539/mas.v7n2p39
Shared via Creative Commons Attribution 3.0 Unported license
Study of Physico-Chemical Characteristics of Wastewater in an Urban Agglomeration in Romania – MyronLMeters.com
TweetStudy of Physico-Chemical Characteristics of Wastewater in an Urban Agglomeration in Romania Abstract This study investigates the level of wastewater pollution by analyzing its chemical characteristics at five wastewater collectors. Samples are collected before they discharge into the Danube during a monitoring campaign of two weeks. Organic and inorganic compounds, heavy metals, and biogenic compounds […]
Study of Physico-Chemical Characteristics of Wastewater in an Urban Agglomeration in Romania
This study investigates the level of wastewater pollution by analyzing its chemical characteristics at five wastewater collectors. Samples are collected before they discharge into the Danube during a monitoring campaign of two weeks. Organic and inorganic compounds, heavy metals, and biogenic compounds have been analyzed using potentiometric and spectrophotometric methods. Experimental results show that the quality of wastewater varies from site to site and it greatly depends on the origin of the wastewater. Correlation analysis was used in order to identify possible relationships between concentrations of various analyzed parameters, which could be used in selecting the appropriate method for wastewater treatment to be implemented at wastewater plants.
Sources of wastewater in the selected area are microindustries (like laundries, hotels, hospitals, etc.), macroindustries (industrial wastewater) and household activities (domestic wastewater). Wastewater is collected through sewage systems (underground sewage pipes) to one or more centralized Sewage Treatment Plants (STPs), where, ideally, the sewage water is treated. However, in cities and towns with old sewage systems treatment stations sometimes simply do not exist or, if they exist, they might not be properly equipped for an efficient treatment. Even when all establishments are connected to the sewage system, the designed capacities are often exceeded, resulting in a less efficient sewage system and occasional leaks.
Studies of water quality in various effluents revealed that anthropogenic activities have an important negative impact on water quality in the downstream sections of the major rivers. This is a result of cumulative effects from upstream development but also from inadequate wastewater treatment facilities. Water quality decay, characterized by important modifications of chemical oxygen demand (COD), total suspended solids (TSSs), total nitrogen (TN), total phosphorous (TP), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), lead (Pb), and so forth  are the result of wastewater discharge in rivers. Water-related environmental quality has been shown to be far from adequate due to unknown characteristics of wastewater . Thus an important element in preventing and controlling river pollution by an effective management of STP is the existence of reliable and accurate information about the concentrations of pollutants in wastewater. Studies of wastewater in Danube basins can be found, for instance, in central and eastern European countries, but we are not aware of extensive studies of wastewater quality at regional/national level in Romania.
This paper analyses the chemical composition of wastewater at several collectors/stations in an important Romanian city, Galati, before being discharged into natural receptors, which in this case are the Danube and Siret Rivers. No sewage treatment existed when the monitoring campaign took place, except the mechanical separation. The study presented here is part of a larger project aiming at establishing the best treatment technology of wastewater at each station. Presently this project is in the implementation stage at all stations. Possible relationships between concentrations of various chemical residues in wastewater and with pollution sources are also investigated. The study is based on daily measurements of chemical parameters at five city collectors in Galati, Romania, during a two-week campaign in February 2010.
2. Experimental Analysis
2.1. Location of Sampling Sites
Galati-Braila area is the second urban agglomeration in Romania after Bucharest, which is located in Romania at the confluence of three major rivers: Danube, Siret, and Prut. The wastewater average flow is about 100000 m3/day . The drainage system covers an area of 2300 ha, serving approximately 99% of the population (approximately 300000 habitants). The basic drainage system is very old, dating back to the end of the 19th century, and was extended along with the expansion of the city due to demographic and industrial evolution. There are several collectors that collect wastewater and rainwater from various areas with very different characteristics, according to the existing water-pipe drainage system. There is no treatment at any station, except for simple mechanical separation. However, industrial wastewater is pretreated before being discharged in the city system. The five wastewater collectors are denoted in the following as S 1 , S 2 , … , S 5. Four of them discharge in the Danube River and the fifth discharges in the Siret River (which is an affluent of Danube River). Figure 1 shows the distribution of the monitoring sites and highlights the type of collecting area (domestic, industrial, or mixed). For the sake of brevity, these stations will be named in the present paper as “domestic,” “mixed,” and “industrial” stations, according to the type of collected wastewater. The mixture between domestic and industrial water at the two mixed collectors is the result of changes in city planning and various transformations of small/medium enterprises.
Figure 1: Monitoring sampling sites of wastewater from Galati city.
Technical details about each collector/station can be found in Table 1. The first station, S1, collects 10% of the total quantity of wastewater. A high percentage of the water collected at this station comes from domestic sources from the south part of the city (more than 96%). Station S2 collects 64% of the total daily flow of wastewater, out of which 30% comes from domestic sources and the rest (70%) is industrial. Most of the industrial sources in this area are food-production units (milk, braid, wine) while the domestic sources include 20 schools, 4 hospitals, and important social objectives. Station S3 is located in the old part of the city and collects 5% of the total wastewater and has domestic sources. At the fourth station, S4, 11% of the quantity of wastewater is collected from domestic (70%) and industrial (30%) sources. The last collector, S5, collects wastewater from the industrial area of the city, where the most important objectives are a shipyard, metallurgical, and mechanical plants and transport stations.
Table 1: Characteristics of collectors S 1 , … , S 5.
2.2. Physico-Chemical Parameters and Methods of Analysis
The physico-chemical parameters which were measured are the following:(i)pH;(ii)chemical oxygen demand (COD) and dissolved oxygen (DO);(iii)nutrients such as nitrate (N-NO3) and phosphate (P-PO4) (these were included due to their impact on the eutrophication phenomenon);(iv)metals such as aluminum (Al+3), soluble iron (Fe+2), and cadmium (Cd+2).
The pH and DO were determined in situ using a portable multiparameter analyzer. Other chemical parameters such as COD, metals and nutrients were determined according to the standard analytical methods for the examination of water and wastewater .
The COD values reflect the organic and inorganic compounds oxidized by dichromate with the following exceptions: some heterocyclic compounds (e.g., pyridine), quaternary nitrogen compounds, and readily volatile hydrocarbons. The concentration of metals (Al+3, Cd+2, Fe+2) was determined as a result of their toxicity.
The value of pH was analyzed according to the Romanian Standard using a portable multiparameter analyzer, Consort C932.
COD parameter was measured using COD Vials (COD 25–1500 mg/L, Merck, Germany). The digestion process of 3 mL aliquots was carried out in the COD Vials for 2 h at 148°C. The absorbance level of the digested samples was then measured with a spectrophotometer at λ = 605 nm (Spectroquant NOVA 60, Merck, Germany), the method being analogous to EPA methods , US Standard Methods, and Romanian Standard Methods.
The DO parameter was analyzed according to Romanian Standard using a portable multiparameter analyzer, Consort C932.
Aluminum ions (Al+3) were determined using Al Vials (Aluminum Test 0.020–1.20 mg/L, Merck, Germany) in a way analogous to US Standard Methods. The absorbance levels of the samples were then measured with a spectrophotometer (Spectroquant NOVA 60; Merck, Germany) at λ = 550 nm. The method was based on reaction between aluminum ions and Chromazurol S, in weakly acidic-acetate buffered solution, to form a blue-violet compound that is determined spectrophotometrically. The pH of the sample must be within range 3–10. Where necessary, the pH will be adjusted with sodium hydroxide solution or sulphuric acid.
Iron concentration (Fe+2) was determined using Iron Vials (Iron Test 0.005–5.00 mg/L, Merck, Germany) and their absorbance levels were then measured using a spectrophotometer (Spectroquant NOVA 60; Merck, Germany) at λ = 565 nm. The method was based on reducing all iron ions (Fe+3) to iron ions (Fe+2). In a thioglycolate-buffered medium, these react with a triazine derivative to form a red-violet complex which is spectrophotometrically determined. The pH must be within range 3–11. Where necessary the pH was adjusted with sodium hydroxide solution or sulphuric acid.
Cadmium ions (Cd+2) were determined using Cadmium Vials (Cadmium Test 0.005–5.00 mg/L, Merck, Germany), their absorbance levels being measured with a spectrophotometer (Spectroquant NOVA 60; Merck, Germany) at λ = 525 nm. The method was based on the reaction of cadmium ions with a cadion derivative (cadion-trivial name for 1-(4-nitrophenyl)-3-(4-phenylazophenyl)triazene), in alkaline solution, to form a red complex that is determined spectrophotometrically. The pH must be within the range 3–11, and, if not, the pH will be adjusted with sodium hydroxide solution or sulphuric acid.
Nitrogen content was determined using Nitrate Vials (Nitrate Cell test in seawater 0.10–3.00 mg/L NO3-N or 0.4–13.3 mg/L N O3 −, Merck, Germany). The method being based on the reaction of nitrate ions with resorcinol, in the presence of chloride, in a strongly sulphuric acid solution, to form a red-violet indophenols dye that is determined spectrophotometrically. The absorbance levels of the samples were then measured with a spectrophotometer (Spectroquant NOVA 60; Merck, Germany) at λ = 500 nm.
Phosphorous content was determined using Phosphate Vials (Phosphate Cell Test 0.5–25.0 mg/L PO4-P or 1.5–76.7 mg/L P O4 − 3, Merck, Germany) with a method that was analogous to the US Standard Methods . The method was based on the reaction of orthophosphate anions, in a sulphuric solution, with ammonium vanadate and ammonium heptamolybdate to form orange-yellow molybdo-vanado-phosphoric acid that is determined spectrophotometrically (“VM” method). The absorbance levels of the samples were then measured with a spectrophotometer (Spectroquant NOVA 60; Merck, Germany) at λ = 410 nm.
All results were compared with standardized levels for wastewater quality found in accordance with European Commission Directive  and Romanian law .
3. Results and Discussion
3.1. The Acidity (pH)
The results for pH for all the investigated five collectors are shown in Figure 2.
Figure 2: Daily variation of pH at all sites.
Generally, the wastewater collected at the monitored sites is slightly alkaline. The pH varies between 6.8 and 8.3—average value 7.82—thus the pH values are within the accepted range for Danube River according to the Romanian law, which is between 6.5 and 9.0. The pH variation is relatively similar at collectors S1–S4 (domestic and/or mixed domestic-industrial contribution). Lower pH values are observed at S5, which is dominated by industrial wastewater, originating from major enterprises and heavy industry. However, these values are not too low, since usually pH values for industrial wastewater are smaller than 6.5.
A significant decrease in the pH value was observed during the 8th day of the analyzed period at each station. Interestingly, a heavy snowfall took place at that particular time, thus the decrease could be attributed to the mixing between wastewater and a high quantity of low pH water, resulted from the melting of snow . One could speculate that the snowfall, which has an acidic character, might have affected the pH of the wastewater through “run off” phenomena.
No other snowfall took place during the monitoring campaign, thus no definite conclusion can be drawn for a possible relationship between pH and snowfalls.
3.2. Results for Chemical Oxygen Demand (COD)
Detection of COD values in each sampling site of wastewater is presented in Figure 3.
Figure 3: Daily variation of COD at all sites.
All COD values are higher than the maximum accepted values (125 mg O2/L) of the Romanian Law . Both organic and inorganic compounds have an effect on urban wastewater’s oxidability since COD represents not only oxidation of organic compounds, but also the oxidation of reductive inorganic compounds. That means some inorganic compounds interfere with COD determination through the consumption of C r2O7 − 2. Two different behaviors can be observed, which are associated with the type of the collected wastewater as follows.(i)The first group consists of stations S2, S4 and S5 where the wastewater has an important industrial component. At these stations, COD values are approximately between 150 and 300 mg O2/L, smaller, for instance, than COD values found by in the raw wastewater produced by an industrial coffee plant where COD values were between 4000 and 4600 mg O2/L. Also, the temporal variation of COD values at all three stations is similar with no significant deviations from the average value, which is about 250 mg O2/L. Interestingly, the lowest COD level can be seen, on the average, at S5, which has the highest percentage of industrial wastewater. The second group comprises the “domestic” stations S1 and S3. The COD levels are higher, with values of 500 mg O2/L or more. Also, the variability is clearly higher than at the industrial-type stations. No clear association between the variations at the two sites can be seen. A peak in COD was measured in the 14th day of the study at site S1 (1160 mg O2/L). Since S1 is a domestic type station, it is unlikely that some major discharge led to such a high variation of COD. Unfortunately, no other information exists that might indicate a possible cause for this increase.
3.3. Results for Dissolved Oxygen (DO)
The amount of DO, which represents the concentration of chemical or biological compounds that can be oxidized and that might have pollution potential, can affect a sum of processes that include re-aeration, transport, photosynthesis, respiration, nitrification, and decay of organic matter. Low DO concentrations can lead to impaired fish development and maturation, increased fish mortality, and underwater habitat degradation . No standards are given by Romanian or European Law for DO in wastewater. The DO values for the analyzed wastewater at all five sites are shown in Figure 4.
Figure 4: Daily variation of DO at all sites.
Concentration of DO varies at all sampling sites and has values between 0.96 (at S2) and 11.33 (at S4) mg O2/L with a mean value of 6.39 mg O2/L. These are clearly higher than DO values measured, for instance, in surface natural waters in China, where the Taihu watershed had the lowest DO level (2.70 mg/L), while in other rivers DO varied from 3.14 to 3.36 mg O2/L . On the other hand, such high values of DO (9.0 mg O2/L) could be found, for instance, in the Santa Cruz River , who argued that discharging industry and domestic wastewater induced serious organic pollution in rivers, since the decrease of DO was mainly caused by the decomposition of organic compounds. Extremely low DO content (DO < 2 mg O2/L) usually indicates the degradation of an aquatic system .
The DO levels vary similarly for all selected sampling sites. The DO levels cover a wide range, with a minimum value of 1.0 mg O2/L at S1 and S3 and a maximum value of 11.33 mg O2/L at S4. There is a drop in DO at all stations, observed is in the 8th day of the monitoring interval, which coincides with the day when a similar decrease in pH took place. The lowest values of DO are observed for S1, one of the two “domestic” stations. It is interesting to note that DO at S5 is low although the wastewater here comes only from industry sources.
The variation of Al+3, Fe+2, and Cd+2 concentrations in wastewater are shown in Figures 5, 6, and 7. Al+3 concentrations (Figure 5) were mostly within the 0.05–0.20 mg/L range at all the sampling sites. However, during the beginning and the end of the monitoring campaign, Al+3 concentration at station S2 is high (reaching even 0.65 mg/L), nonetheless below the limit imposed by the Romanian law, which is 5 mg/L . The fact that in the beginning of the time interval, the concentration of Al+3 is high at two neighboring stations (S1 and S2) suggests that some localized discharge affecting both runaway and waste water, might have happened in the southern part of the city, which led to the increase of Al+3concentration in the collected wastewater. This is supported by the fact that the concentration gradually decreases at S2.
Figure 5: Daily variation of Al at all sites.
Figure 6: Daily variation of Fe at all sites.
Figure 7: Daily variation of Cd at all sites.
The variation of Fe+2 concentrations is shown in Figure 6. Fe+2 concentration is within the 0.07–0.4 mg/L interval, below 5.0 mg/L, which is the maximum accepted value of the Romanian law . Two higher values were observed at S2 and S4 (both with industrial component) during the third and fourth days of the monitoring campaign.
Besides Al+3 and Fe+2, concentrations of Cd+2 were determined and the variations at the five stations are shown in Figure 7. Cd+2 is a rare pollutant, originating from heavy industry. Leakages in the sewage systems can also lead to Cd+2. Except for two days, Cd+2 varies between 0.005 and 0.04 mg/L. The two high values of 0.11 mg/L were observed in the first and fourth days at S5, which collects industrial wastewater. However, Cd+2 concentrations do not exceed the maximum accepted values of the Romanian law  for the monitoring interval which is 0.2 mg/L.
Water systems are very vulnerable to nitrate pollution sources like septic systems, animal waste, commercial fertilizers, and decaying organic matter . Important quantities of nutrients, which are impossible to be removed naturally, can be found in rivers and this leads to the eutrophication of natural water (like Danube River). As a result, an increase in the lifetime of pathogenic microorganisms is expected. Measurement of nutrient (different forms of nitrogen (N) or phosphorous (P)) variations in domestic wastewater is strongly needed in order to maintain the water quality of receptors . Nitrogen by nitrate (Figure 8) and phosphorous by phosphate (Figure 9) are considered as representative for nutrients.
Figure 8: Daily variation of N-NO3 at all sites.
Figure 9: Daily variation of P-PO4 at all sites.
Figure 8 shows that N-NO3 concentrations vary, on the average, between 0 and 5.0 mg/L.
At all four stations with a domestic component, S1, S2, S3 and S4, the concentration of N-NO3 is low (between 0 and 1.5 mg/L) and the daily variation is relatively similar at all sites. Noticeable drops of the N-NO3 concentration are observed at all stations in the 8th day of the monitoring interval, coinciding with pH (Figure 2) and DO strong variations (Figure 4). This supports the conclusion that the heavy snowfall recorded at that period had an important impact on wastewater quality most likely due to the runoff joining the sewage system.
The behavior of N-NO3 clearly differs at station S5, which collects only industrial wastewater. Significantly higher values of N-NO3, ranging from 2.0 to 5.0 mg/L, were detected. However, the mean concentration of N-NO3 remained below the maximum concentration given by the Romanian law . Obviously, if treatment stations have to be set up, the priority for this particular nutrient component should concentrate on stations where industrial wastewater is collected.
Another nutrient that was analyzed for our study was orthophosphate expressed by phosphorous. The P-PO4 concentration varies, on the average, between 1.0 and 6.0 mg/L (Figure 9). For this component, concentrations are higher at domestic stations, S1 and S3, than at the other three stations. P-PO4 is expected to increase in domestic wastewater because of food, more precisely meat, processing, washing, and so forth. The lowest values were observed at S5, which has a negligible domestic component. Peaks in the P-PO4 concentration are observed at S1. Interestingly enough, P-PO4 temporal variations correlated pretty well at stations S2, S4, and S5 (which collect industrial wastewater). Unlike most of the other analyzed compounds, for which the concentrations were within the accepted ranges, the maximum level of P-PO4 is exceeded at all five collectors. Both Romanian law and the European law stipulate 2.0 mg/L total phosphorous for 10000–100000 habitants, and for more than 100000 habitants (as in Galati City’s case) 1.0 mg/L total phosphorus. Interestingly, domestic stations seem to require more attention with respect to the quality of water then industrial stations.
Our results regarding the variation and levels of the analyzed parameters are grouped below as the following.(1)The values of pH are within the accepted range for Danube, and their daily variations are relatively similar for both domestic and mixed wastewater. Significantly smaller pH values were measured in the wastewater with a high industrial load. A clear minimum was observed at all sites in the 8th day of the monitoring period, when a heavy snowfall took place. One could speculate that the snowfall, which has an acidic character, might have affected the pH of the wastewater through “run off” phenomena. However, a clear connection cannot be established relying on one event only.(2)The COD level clearly depends on the type of wastewater. Higher values were observed for domestic wastewater, while “pure” industrial wastewater has the lowest COD. This might be explained by the fact that industrial wastewater benefits from some treatment before being discharged into the city sewage system. However, COD does exceed the maximum accepted values according to the Romanian law  at all sites thus additional treatment is required at all stations.(3)Concentrations of all analysed metals, Al+3, Cd+2 and Fe+2, are within the limit of the Romanian law. No association with the type of wastewater could be inferred. Isolated peaks could not be linked with any specific polluting factors, except for Cd+2, for which accidental concentration increases are observed for pure industrial wastewater.(4)The level of P-PO4, one of the two nutrients that were analyzed, was high at all stations; however, the highest concentrations are associated with domestic loads.(5)Opposingly, the N-NO3 level is the highest, by far, in wastewater with a high industrial contribution.
3.6. Possible Relationships between Various Parameters
The experimental results have shown that some parameters might be related and that their behavior greatly depends on the type of collected wastewater. Differences between the behavior of physico-chemical parameters at the domestic sites (S1 and S3), on one hand, and at the other sites, on the other, was observed. Pearson correlation coefficients have been calculated between all parameters at all the selected five sites and corresponding significances. Although most of correlations were not significant, some interesting connections between various parameters at sites with similar characteristics were revealed. Table 2 shows correlation coefficients between various parameters for all five stations. Significant correlations at different types of stations are denoted as follows: italicized fonts for domestic stations, boldface italicized fonts for the industrial station and boldface fonts for mixed stations.
Table 2: Correlation coefficients calculated for station S1 to S5. Significant correlations at each type of stations are identified as follows: boldface italicized fonts for industrial station (S5), italicized fonts for domestic stations (S1 and S3) and boldface fonts for mixed stations (S2 and S4).
An important relationship seems to exist between pH and N-NO3 at all stations except for the industrial wastewater collecting site, S5 (i.e., at all stations collecting wastewater resulting from domestic activities). Similarly, pH correlates well with DO at all stations except the industrial one.
COD correlates with two metals, Cd+2 and soluble Fe+2, which is expected , but only at S1 and S3, where the daily variations of the concentration for these two metals (Cd+2 and soluble Fe+2) were similar.
No conclusion can be drawn for the industrial wastewater collector that was analyzed, where both positive and negative correlations were observed. The lack of correlation between the two metals and COD at the industrial wastewater collectors suggests that other processes, that alter the chemical equilibrium between the two chemical compounds, must be taken into account. For example some metals are complexed by organic compounds that are present in the water and the pH values can influence these phenomena.
DO correlates with pH and N-NO3 at all four sampling stations with domestic component (S1–S4) but the relationship vanish at S5 (industrial). There is also a negative correlation between DO and Fe+2 and Cd+2 only for domestic wastewater, which is expected because of the natural oxidation of metals. The correlation vanishes at the other three stations which collect wastewater from industrial areas.
Heavy metals, Fe+2 and Cd+2 correlate only at domestic stations and no relationships can be defined to link the concentration of Al+3 with other components.
The P-PO4 variation is linked to the variation of soluble Fe+2 at the two stations that collect domestic wastewater. Interestingly, these two elements exist together in reductive domestic systems because these are dominated by proteins, lipids, degradation products. This relationship disappears at the other stations, where the industrial load is significant. The other metals, Al+3, seems to be linked with P-PO4at stations S5 and S2, which collect wastewater with the highest industrial load. No link is observed for the rest of stations and for Cd+2 which can be explained by a higher probability of iron (II) orthophosphate to form in wastewater compared to Al+3 or Cd+2 orthophosphates.
Positive correlations can also be seen between P-PO4 and COD for all sampling sites except S1, where the relationship is still positive but less significant. The other nutrient, N-NO3, is anticorrelated with COD but only at S3 and is well correlated with pH and DO at all four stations with domestic component. The only exception is station S5, which collects mostly industrial wastewater.
Concluding, positive correlations were observed between the following parameters.(1)pH and N-NO3 everywhere except “purely” industrial water.(2)COD and soluble Fe+2 at domestic stations.(3)DO and pH, on the one hand, and DO and N-NO3 at domestic stations.(4)P-PO4 and soluble Fe+2 at domestic stations.(5)P-PO4 and COD everywhere, which, taking into account the high level of P-PO4 at domestic stations, might suggest that one important contributor to water quality degradation are household discharges.(6)Al+3 and P-PO4.
In the present paper we have analyzed the daily variation of several physico-chemical parameters of the wastewater (pH, COD, DO, Al+3, Fe+2, Cd+2, N-NO3, and P-PO4) at five collectors that have been characterized as domestic, industrial and mixed, according to the type of collecting area. Different results have been obtained for domestic and industrial wastewater. Most of the chemical parameters are within accepted ranges. Nevertheless, their values as well as their behavior depend significantly on the type of collected wastewater.
The overall conclusion is that wastewater with a high domestic load has the highest negative impact on water quality in a river. On the other hand, industrial wastewater brings an important nutrient load, with potentially negative effect on the basins where it is discharged. Our results suggested that meteorological factors (snow) might modify some characteristics of wastewater, but a clear connection cannot be established relying on one event only.
Significantly smaller pH values were measured in the wastewater with a high industrial load. The COD level clearly depends on the type of wastewater. Higher values were observed for wastewater with domestic sources, while “pure” industrial wastewater has the lowest COD. This might be explained by the fact that industrial wastewater benefits from some treatment before being discharged into the city sewage system. COD does exceed the maximum accepted values according to the Romanian law at all sites thus additional treatment is required at all stations. Accidental increases of Cd+2 concentrations are observed for pure industrial wastewater. The highest concentrations of P-PO4 are associated with domestic loads. Opposing, the N-NO3 level is clearly the highest in wastewater with a high industrial contribution.
Correlation analysis has been used in order to identify possible relationships between various parameters for wastewater of similar origin.
Positive correlations between various physico-chemical parameters exist for the domestic wastewater (DO, pH and N-NO3, on the one hand, and P-PO4, COD and soluble Fe+2, on the other hand). Except for two cases, these relationships break when the industrial load is high. Some of the existing correlations are expected as discussed above, thus any removal treatment should be differentiated according to the type of collector, before discharging it into the natural receptors in order to be costly efficient. Correlations between DO and COD and nutrient load suggest that the most important threat for natural basins in the studied area, are domestic sources for the wastewater.
The different percentages of industrial and domestic collected wastewater vary at each station, which has a clear impact on concentrations of the selected chemical components. Our results show that domestic wastewater has a higher negative impact on water quality than wastewater with a high industrial load, which, surprisingly, seems to be cleaner. This might be related to the fact that most industries are forced, by law, to apply a pretreatment before discharging wastewater into the city sewage system. Industrial wastewater affects the nutrient content of natural water basins. Although the time period was relatively short, our study identified specific requirements of chemical treatment at each station. An efficient treatment plan should take into account the type of wastewater to be processed at each station. Results presented here are linked with another research topic assessing the level of water quality in the lower basin of the Danube before and after implementing the complete biochemical treatment plants.
The work of Catalin Trif was supported by Project SOP HRD-EFICIENT 61445/2009.
Copyright © 2012 Paula Popa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited – original found here: http://www.hindawi.com/journals/tswj/2012/549028/
TweetWater quality is the physical, chemical and biological characteristics of water. It is a measure of the condition of water relative to the requirements of the application. It is most frequently used by reference to a set of standards against which compliance can be assessed. The most common standards used to assess water quality relate […]
Water quality is the physical, chemical and biological characteristics of water. It is a measure of the condition of water relative to the requirements of the application. It is most frequently used by reference to a set of standards against which compliance can be assessed. The most common standards used to assess water quality relate to drinking water, safety of human contact and for the health of ecosystems.
Water quality is a very complex subject, in part because water is a complex medium intrinsically tied to the ecology of the Earth. Industrial pollution is a major cause of water pollution, as well as runoff from agricultural areas, urban stormwater runoff and discharge of treated and untreated sewage (especially in developing countries).
The complexity of water quality as a subject is reflected in the many types of measurements of water quality indicators. The list below represents some of the simple measurements that can be made on-site in direct contact with the water source in question:
- Total Dissolved Solids (TDS)
- Oxidation Reduction Potential (ORP)
- Dissolved oxygen
More complex measurements that must be made in a lab setting require a water sample to be collected, preserved, and analyzed at another location. Making these complex measurements can be expensive. Because direct measurements of water quality can be expensive, ongoing monitoring programs are typically conducted by government agencies. However, there are local volunteer programs and resources available for some general assessment. Tools available to the general public can be found here.