Monthly Variations of Physico-Chemical Properties from a Man-Made River in Saudi Arabia
Ahmed A. Al- Othman
This study which was carried between June 2009 and May 2010 highlights the results of periodic monitoring of surface water in a man-made river in Saudi Arabia. The samples were analyzed for pH, electric conductivity, total dissolved solids, alkalinity, metals and inorganic ions. The water quality varied depending on the time of the year and the site studied. In some sites, it exhibited poor water quality as the result of effluent discharge from small-scale industries. The study results is likely to serve as a baseline/benchmark for future evaluation of surface water pollution in similar streams in Saudi Arabia.
Received: April 21, 2015;
Accepted: April 27, 2015;
Published: May 08, 2015
Water is an essential requirement of life (Bharati et al., 2011). The quality of water needed for each individual varies as well as the criteria used to assess its quality. Typically, water quality is determined by comparing the physical and chemical characteristics of a water sample with already established water quality guidelines or standards. Water quality is neither a static condition of a system, nor can it be defined by the measurement of only one parameter (Ayers and Westcot, 1985). Rather, it varies in both time and space and requires routine monitoring to detect spatial patterns and changes over time. A wide range of chemical, physical and biological components that affect water quality can be examined which can provide a general indication of water pollution, whereas others enable the direct tracking of pollution sources (Abulude et al., 2007; Adejare et al., 2011).
Sources of chemical pollution include industrial, domestic and storm-related wastes. Polluted water is responsible for the spread of a variety of diseases (Bharati et al., 2011) making it necessary for monitoring water quality at various locations along a water course. Furthermore, if necessary, to treat the available polluted water to make it safe for human consumption. The assessment of water resources requires knowledge of water quality (Harmanciogammalu et al., 1999; UNEPGEMS., 2006) and as a result, it is important that a well-designed water quality monitoring network be put in place (Khalil et al., 2011). Normally, the required information about water quality is derived from water quality data analysis and the term water quality describes the chemical, physical and biological characteristics of water with respect to its suitability for a particular use (Chapman, 1996). Water quality therefore depends on a number of factors, such as the quality of recharge water and the nature of inputs from various sources (Domenico, 1972; EPA., 1974; Schuh et al., 1997).
In Saudi Arabia, water needs are markedly increasing due to the rapid growth in population and agricultural activities (Al-Ahmadi, 2005a), making it even more essential that the suitability of any water supplied to the city be evaluated in terms of both quality and quantity relative to its different uses (George, 2004).
One of the man-made rivers in Saudi Arabia is located in Wadi Hanifa, an area of the Nejd region of central Saudi Arabia. The river runs for a length of 120 km from north to south and passes through the capital city, Riyadh. Several towns and villages lie along the river, including Al-Oyainah, Jobailah, Diriyah, Irqah and Al-Hayir (Al-Homaidan et al., 2011). The river flows southward from its source near the town of Al-Oyainah until it flows into the Wadi Sahba, a distance of about 150 km (Fig. 1). The main flood-channel is located slightly east of the center of the catchment area and flows northwest to southeast. Most of Arriyadh city (with a population of more than 4 million) is located on the course of this man-made rivers catchment area (ADA., 1994). Prior to extensive urban development, seasonal rainfall provided the major source of water in the main channel. During the last two decades however, considerable urban and agricultural development has occurred in the catchment area, leading to the disposal of large amounts of sewage effluent and agricultural drainage (Siddiqui and Al-Harbi, 1995). Previously, the river was used as a water source. Currently, it principally acts as a convenient depot of Riyadhs wastewater. Following recycling, the water course could potentially supply significant volumes of good quality water in the future (Alhamid et al., 2007).
Mirabbasi et al. (2008) reported that the suitability of water for various uses depends on the type and concentration of dissolved minerals which in turn depends on the source of a river and ground water. Several criteria for water quality requirements had been recognized which serve as guidelines for use in determining the suitability of water for various uses. In Saudi Arabia, the quality of water is currently receiving considerable attention from environmental and water scientists (Al-Redhaiman and Magid, 2002; Al-Turki and Magid, 2003; Al-Matroud, 2003; Al-Zarah, 2008; Al-Turki, 2009; Al-Hawas, 2002). Therefore, assessment of water quality is a major requirement in the planning stages of any new development. With these considerations in mind, the main aim of this study is to determine monthly variations in various important indicators of surface water quality in the man-made river stream under study. Also, the information is likely to help in future the mangers and planners to install the most appropriate wastewater treatment methods to improve the quality of such water for its safe reuse without environmental issues.
MATERIALS AND METHODSM
Study area: The area around the man-made Wadi Hanifa river stream is one of the most important natural landmarks in the central region of Saudi Arabia. A discharge of 400000-600000 m3 of ground water, rainwater, industrial waste effluent and domestic sewage water reaches the stream every day (Al-Homaidan et al., 2011). Table 1 shows the area code, sampling area and a description of region to the north and east of this sampling area.
Sampling procedure: Water samples from specified locations were collected during the months of June, July, August, September, October, November and December of 2009 and during the months of January, February and March of 2010. The samples were transferred to the laboratory of Saudi Berkefeled Filters Co., Riyadh for chemical analysis. Sample temperature in the laboratory was measured and the mean, standard deviations, maximum and minimum temperatures are shown in Table 2. All chemical analyses were conducted according to standard methods (APHA., 1992).
Data statistical analysis: Excel spread sheet was used to obtain the mean, standard deviations, maximum and minimum of all concentrations. The results of different concentrations are shown in Table 3 and 4. The water quality indicators were selected as 28 water quality variables. A correlation matrix was done using Excel. The statistical correlation matrices used were multivariate analyses which correlate the relationships between variables. A correlation matrix is always a symmetric matrix to locate the correlation for any pair of variables and to find the value in the intersection for those two variables as reported by Gawad et al. (2010).
|Table 1:|| Area code, sampling area, description of area, northing and easting of sampling area
|Table 2:|| Mean, standard deviation, maximum and minimum temperature of the laboratory (°C) and in water samples
|Table 3:||Monthly variations of pH, EC, TDS and alkalinity in surface water samples and mean, standard deviation (SD), maximum (Max) and minimum (Min) concentration values for different studies sites
|Table 4:||Variation of bicarbonate, calcium, chloride and magnesium in surface water samples with months and mean, standard deviation (SD), maximum (Max) and minimum (Min) concentration values for the different studied sites
RESULTS AND DISCUSSION
The results of the chemical analysis of surface water from the sites studied are shown in Table 3-6. The data for the results of the chemical analysis show considerable variations among months for water samples collected and the study sites.
|Table 5:||Variation of nitrate, potassium, sodium and sulphate in surface water samples with months and mean, standard deviation (SD), maximum (Max) and minimum (Min) concentration values for the different studied sites
|Table 6:||Variation of ammonium, boron, copper and iron in surface water samples with months and mean, Standard Deviation (SD), maximum (Max) and minimum (Min) concentration values for the different studied sites
pH: An important measure of water quality is its pH. The pH of surface water in the study sites ranged from 6.6-8.4 with an average of 7.5 depending on month and site. The results showed that the water samples ranged from slightly acidic to slightly alkaline (Table 3). Inspection of these values revealed that all site samples lie within the recommended pH limit of irrigation water, i.e., within a permissible range of 6.0-8.5 (Ayers and Westcot, 1985). The highest pH of 8.4 was found during February in S1 and the lowest pH of 6.6 was measured during February in sample S5. The monthly pH was measured and ranged in June (6.96-7.85), July (7.46-8.07), August (7.23-7.95), September (7.2-7.76), October (7.1-7.93), November (7.1-7.95), December (7.64-8.26), January (6.84-8.24), February (6.61-8.40) and March (7.43-8.21). The water samples which were slightly acidic in nature may be due to the presence of low amounts of Ca, Mg, Na and HCO3 (Mahmud et al., 2007).
Electric Conductivity (EC): The EC is an indicator of water quality and is used to determine the concentration of contaminants and thereby determine the purity of a water sample. Electrical conductance of water sample is a function of the types and quantities of dissolved substances in water (Radtke et al., 1998). Distilled water should typically have an EC of less than 0.3 μS cm-1. For groundwater, EC values >500 μS cm-1 indicate a polluted water, although values as high as 2000 μS cm-1 may be acceptable for irrigation water. The EC values in stream water averaged around 300 μS cm-1 (www.gsf.fi/publ/foregsatlas/text/EC.pdf Last Time Access on This Date 2015-04-21 ). EC values of samples ranged from 1895-5782 μS cm-1 (Table 3), indicating moderate salinity (Al-Ahmadi, 2005b). Regarding EC values, water samples from sites SW1C and SW 14 ranged from 4223-5782 μS cm-1 (Table 3). The EC of water samples showed the monthly variations as in June (1963-5782), July (2160-5014), August (1998-4790), September (2063-4492), October (2135-4732), November (1927-5100), December (1960-4544), January (1895-4660), February (1953-4740) and March (2114-5168).
Total Dissolved Solid (TDS): The TDS ranged from 1396-4624 mg L-1 with an average value of 2231 mg L-1. Water containing TDS <1000 mg L-1 is considered to be fresh (Raghunath, 1987). Accordingly, all water samples were rated as not fresh. It is clear from Table 3 that the TDS of all water samples exceeded the WHO (1993) values for drinking water (1000 mg L-1). The monthly TDS (mg L-1) of water samples were as follows: June (1426-4624), July (1579-3965), August (1451-3743), September (1504-3518), October (1560-3724), November (1396-4039), December (1423-3560), January (1320-3662), February (1416-3730) and March (1543-4090).
Alkalinity (Alk): Alkalinity provides the acid-neutralizing capacity of water and is primarily a function of carbonate, bicarbonate and hydroxide content. Excessive alkalinity levels may cause scale formation (Khanfar, 2008). Most natural waters have an alkalinity range of 10-500 mg L-1 (Khanfar, 2008). However, the typical alkalinity range of average surface waters is 20-200 mg L-1, while the alkalinity in surface water of regions with alkaline soils is within 100-500 mg L-1 (SWRP., 2011). It is clear from Table 3 that alkalinity values of all water samples were in acceptable range (20-200 mg L-1). Alkalinity values for water samples from sites SW1C and SW8C ranged from 150-200 mg L-1 and from 80-120 mg L-1, respectively (Table 3). The alkalinity (mg L-1) of the water samples showed low monthly variations: June (80-190), July (90-200), August (120-160), September (100-180), October (100-180), November (100-180), December (110-180), January (80-190), February (80-180) and March (110-190).
Bicarbonate (HCO3): The bicarbonate of surface water ranged from 96-240 mg L-1 with an average value of 154 mg L-1 (Table 4) and all samples were within the regulations set by WHO (1996) which is 250 mg L-1 for surface water. Bicarbonate values for the water samples showed a low monthly variation with the highest mean value recorded in July (240 mg L-1) and the lowest values in June, January and February (96 mg L-1). Bicarbonate concentrations in water collected from site SW1C showed higher values than other sites.
Calcium (Ca): Calcium concentration in the watercourse ranged from 128-700 mg L-1 with a mean value of 287 mg L-1, well outside the recommended limit of 100 mg L-1 in surface water (WHO., 2003) (Table 4). However, it exhibited high monthly variations with the highest mean value of 700 mg L-1 recorded in June and lowest mean value of 128 mg L-1 in July. It showed the highest value at site SW1C compared to other sites.
Chloride (Cl): Chloride ranged from 170-879 mg L-1 with a mean value of 426 mg L-1 which is above the recommended limit of 250 mg L-1 (WHO., 1996) (Table 4). The chloride content of the water samples showed low monthly variations with the highest mean value in June (879 mg L-1) and the lowest in December (170 mg L-1). The sites SW1C and SW14 exhibited the highest range of chloride (519-879 mg L-1) than other sites.
Magnesium (Mg): Magnesium concentration in water samples ranged from 26-127 mg L-1 with a mean value of 71 mg L-1 and was above the recommended limit of 50 mg L-1 (WHO., 1996) (Table 4). However, sites SW8, SW23 and SW10b had acceptable overall mean magnesium concentrations (41-49 mg L-1). The magnesium concentration of water samples showed low monthly variations with the highest mean value in March (127 mg L-1) and the lowest mean value in June and July (26 mg L-1) (Table 4). The site SW14 showed the highest range of magnesium concentrations (108-127 mg L-1) and site SW14 had the lowest (26-58 mg L-1) concentrations compared with other sites.
Nitrate (NO3): Inorganic nitrogen in the aquatic environment occurs in four forms: Ammonia (NH3), nitrate (NO3) nitrite (NO2) and the ammonium ion (NH4+) according to Rouse et al. (1999). Nitrate is listed as the worlds second greatest chemical threat to surface and ground water (Khanfar, 2010) and many water resources faced with problems related to high nitrate concentrations. The maximum limit of nitrate in drinking water is 45 mg L-1 (Almadini, 2010). Guidelines for the use of water with known nitrate content (Khanfar, 2010) are shown in Table 5. In this study, nitrate concentration was in the range of 5-190 mg L-1 with a mean value of 41 mg L-1 (Table 5). The nitrate concentrations of water samples showed the highest variations with site SW14 having the highest range (124-190 mg L-1) and the site SW8C with the lowest value (5-9 mg L-1). The mean value of highest and lowest nitrate concentration occurred in June with 190 and 5 mg L-1, respectively.
Potassium (K): Potassium was in the range of 18-40 mg L-1 with a mean value of 24 mg L-1 above the recommended limits of 12 mg L-1 (Khan et al., 1999) as presented in Table 5. Potassium range in site SW1C was 18-40 mg L-1. The potassium concentration showed very low monthly variations with the highest mean value of 40 mg L-1 was observed in June and the lowest of 18 mg L-1 in August (Table 5). The high potassium levels obtained from the samples may have originated from the discharges of the industrial zones located near the area.
Sodium (Na): Concentration of sodium in water above 50 mg L-1 is defined as unsuitable for domestic use (Alexander, 2008). Sodium concentration ranged from 112-720 mg L-1 with a mean value of 312 mg L-1 as shown in Table 5. At site SW1C it ranged from 417-720 mg L-1. The sodium concentration in the water samples showed moderate monthly variations with the highest mean value observed in June (720 mg L-1) and the lowest in December (112 mg L-1) (Table 5). In many natural waters, the concentration of potassium is commonly less than one-tenth the concentration of sodium (Davis and de Wiest, 1970). In the water studied here, the ratio is 0.08.
Sulphate (SO4): Sulphate concentrations in the water samples ranged between 501-1810 mg L-1 with an overall mean value of 907 mg L-1, still outside WHO (1996) level of 250 mg L-1 (Table 5). High concentrations of sulphate may cause problems such as respiratory illness and a drinking water sulphate concentration >1000-1200 mg L-1 can cause diarrhea, dehydration and weight abatement (Savari, 2006; Pirzada et al., 2011). The existence of sulphate in water may be due to natural or anthropogenic sources such as atmospheric precipitation and industrial wastes (Mazloomi et al., 2009). The concentration of sulphate in water samples showed low monthly variation with the highest mean observed in June. While, the site SW1C had a value of 1810 mg L-1 (Table 5).
Ammonium (Al): Ammonium is found in surface water sources at low levels (up to 1 mg L-1 as the ion) and may be due to the biological breakdown of organic nitrogen compounds. Surface water sources can also be contaminated with ammonium from septic systems, animal feed lot runoff, or agricultural runoff from fields fertilized with ammonia or urea. Ammonium is prevalent in municipal waste facilities with levels up to 20 mg L-1 as the ion in the effluent, the result of high levels of organic nitrogen compounds and the biological activity (http://www.water-chemistry.in/2008/08/ammonium-nh4/). In this study, ammonium was found to be in the range of 0.19-6.26 mg L-1 with a mean value of 1.68 mg L-1 (Table 6). The highest value was observed in October (6.26 mg L-1) at site SW8C and the lowest value was in November (0.19 mg L-1) at site SW12a.
Boron (B): Boron concentrations in surface water depend on the amount of boron present in the soils of the drainage area. Surface waters can also accumulate boron from effluent discharges, both from industrial processes and from municipal sewage treatment. Boron concentrations in surface water range widely, from 0.001 mg L-1 to as much as 360 mg L-1, although average boron concentrations are typically well below 0.6 mg L-1 as suggested by IPCS (1998) and given in Table 7. In the present study, the overall average boron concentration was 0.77 mg L-1 (Table 6). During December, the mean boron was highest as compared to other months (0.82 mg L-1). This mean (0.77 mg L-1) is allowable in irrigation water for boron-sensitive plants (Rowe and Abdel-Magid, 1995).
Copper (Cu): Levels of copper found naturally in ground water and surface water are generally very low (4 μg L-1 or less; http://dnr.wi.gov/org/water/dwg/copper.htm). The allowable copper level in effluents is 0.2 mg L-1 (Al-Motairi, 2001). In the present study, the overall average of copper was 0.004 mg L-1 (Table 6). Also, no significant variations were observed between sites and months (0.001-0.005 mg L-1).
Iron (Fe): Iron is one of the most abundant earth elements and in most waters the concentration ranges between 0.5-50 mg L-1 with the largest amounts found in ground waters and other natural sources (Mazloomi et al., 2009).
|Table 7:|| Average boron in surface water for various regions compared to present study
|Table 8:||Variations of manganse and phosphate in surface water samples with month and mean, standard deviation (SD), maximum (Max) and minimum (Min) concentration values for the different studied sites
In the studied water samples, iron concentrations ranged from 0.02-0.1 mg L-1 with a mean value of 0.05 mg L-1 (Table 6). The sites SW8, SW23 and SW10b showed acceptable overall mean iron concentrations (41-49 mg L-1). The iron concentration of the water samples showed low monthly variations and all the samples were within the acceptable range.
Manganese (Mn): Manganese concentration was in the range of 0.0001-0.0476 mg L-1 with a mean value of 0.0096 mg L-1 (Table 8). The presence of manganese in studied water samples is within the guideline value (0.05 mg L-1) as recommended by both international and Saudi standards for drinking water (Al-Otaibi and Zaki, 2009). The manganese concentration of the water samples studies here showed low monthly variations in all sites.
Phosphate (PO4): A water sample with phosphate levels <0.03 mg L-1 is generally considered to be unpolluted. Phosphate levels between 0.03-0.1 mg L-1 are sufficient to stimulate plant growth (Khanfar, 2008). In this study, phosphate was found to be within 0.18-11.92 mg L-1 with a mean value of 5.73 mg L-1 (Table 8). As such, all the water samples were polluted with phosphate. The site SW8C had a phosphate content of 7.94-11.73 mg L-1 while site SW12a had a range of 0.32-0.50 mg L-1. The highest value was in March (11.96 mg L-1) at site SW8C while the lowest value was in August (0.18 mg L-1) at site SW8A.
Correlation coefficient (r): Water quality can also be assessed by the study of correlation coefficients among the physicochemical parameters of studied sites which determines the effect and relationship between cations and anions with each other. Correlation coefficients are presented in Table 9 and showed that pH was associated with some parameters, i.e., EC, TDS, Alk, HCO3, Ca, Mg, NO3 and SO4. On the other hand, a very strong and significant correlation (r =1) was found between EC and TDS. Also, positive correlations were observed between EC and Alk, HCO3, Ca, Cl, Mg, NO3, P, Na, SO4, B and PO4. Negative (inverse) correlations were found in 56 cases.
|Table 9:|| Correlaation matrix for different water wuality parameters
|| Relationship of EC with TDS in water samples around the man-made river, Saudi Arabia
||Relationship of magnesium with sulphate in water samples around the man-made river, Saudi Arabia
||Relationship of chloride with TDS in water samples around the man-made river, Saudi Arabia
A linear relationship (Fig. 2) was observed between EC and TDS in water samples from the area around the river and this trend was also shown by Al-Matroud (2003). A linear relationship (Fig. 3) was observed between magnesium and sulphates content which confirms this trend observed by Khan et al. (1999). Lastly, a linear relationship (Fig. 4) was observed between TDS and chloride content similar to the trend observed by Khan et al. (1999).
Eighteen physical and chemical characteristics were analyzed from surface water samples collected from 10 sites in a man-made river in Saudi Arabia. The samples studied showed distinct pollution at some sites and is expected to worsen from increased industrial and human activities in the area. It is hoped that the data reported here will form a part of the baseline data-set for use in future studies. The present study may also assist future managers and planners to establish certain control measures and to suggest suitable wastewater treatment methods in the study area to maximize its reuse.
The author would like to thank ArRiyadh Development Authority (ADA) for help and cooperation for providing the surface water data of Riyadh City, Saudi Arabia.
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