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Applied Water Science

, 9:25 | Cite as

Landuse and surface water quality in an emerging urban city

  • Olutoyin Adeola FashaeEmail author
  • Hannah Abiola Ayorinde
  • Adeyemi Oludapo Olusola
  • Rotimi Oluseyi Obateru
Open Access
Original Article
First Online:
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Abstract

The study analyzed the impact of landuse types on surface water quality in an emerging urban city. The objectives were to classify the existing landuse types, examine the variation in water quality across different landuse types, examine the quality of surface water using the water quality index, and compare the water quality parameters with the World Health Organization (WHO) standards. Samples drawn from surface waters were analyzed based on in situ and ex situ analysis according to standard methods. Three landuse types were identified namely residential, vegetated and commercial. The vegetated landuse accounted for the highest landuse type with 74% of land coverage. One-way analysis of variance was used to determine the variation in water quality parameters within each landuse type. There was a significant variation in total solids (F = 8.677, P < 0.05), total dissolved solids (F = 7.836, P < 0.05), and total suspended solids (F = 10.365, P < 0.05). Using the water quality index calculator 1.0, a value of 41 was obtained thereby indicating poor quality. Water quality parameters were compared with World Health Organization (WHO) standards, and it was observed that electrical conductivity, nitrate, phosphate, sulfate, chloride were below WHO permissible limit while total dissolved solids, bacterial load and total solids were above the limit set by WHO. Therefore, there should be a continual intensive water quality monitoring program of surface waters across the area and its immediate environs to maintain healthy lifestyle of the populace and ensure ecosystem balance.

Keywords

Landuse Water quality Urban city WHO ANOVA 

Introduction

Water is an essential natural resource which forms the chief constituent of the ecosystem, and it is very vital for sustenance and livelihood of all life forms. Central to human existence is the adequacy of water in terms of quality and quantity (Olusola et al. 2017). Over the years, the impact of landuse changes on water recourses was considerably neglected or seen as a by-product of development (Scanlon et al. 2005; Schilling et al. 2008). Four major direct consequences of landuse on the hydrological cycle have been identified, and these include flood and flood potential; drought; changes in river and groundwater regimes; and water quality in an area (Rogers 2000; Fashae et al. 2017a, b). Appreciating the relationships between landuse and water quality is very important for identifying major threats to water quality (Tiwari et al. 2015; Jiao et al. 2015; Faiilagi 2015) and the achievement of sustainable development goals. The relationship becomes very relevant in targeting intensive landuse areas and to institute necessary measure to mitigate pollution loading (Faiilagi 2015). Taking into cognizance the intricate spatial and temporal variation in water quality, two prominent types of information required for an effective management of river water quality have been identified. These are spatial and temporal characteristics of the pollutants and information that relates to the propelling factors influencing the water quality (Ifabiyi 2000; Liu et al. 2016; Fashae et al. 2017a; Olusola et al. 2017). In recent years, there has been a rapid declining availability of usable fresh water in terms of water quality and quantity due to unsustainable landuse practices (Ngoye and Machiwa 2004).Water quality has variously been related to landuse in catchment (Levin 2012; Faiilagi 2015; Henderson et al. 2014; Li et al. 2008; Ayeni et al. 2006; Pullanikkatil et al. 2015; Wagner et al. 2013; Olusola et al. 2018), and studies have been focusing on their relationships with water quality variables such as dissolved salts, suspended solid, and nutrients (Mallin 2008; Mathuthu et al. 1997; Elbag 2006; Keshtkar et al. 2010; Olusola et al. 2017). However, Pullanikkatil et al. (2015) argued that there are correlations between systems of landuse and water quality, and emphasized the use of water quality index (WQI) in identifying pollution hot spots. Rapid socioeconomic advancement drives landuse change (Wagner et al. 2013) as evident in cases where deforestation, agricultural activities and urbanization, individually or collectively, modify land surface characteristics, alter runoff volume, change water temperature, generate pollution, increase algal production and decrease concentration of dissolved oxygen in water bodies (Irenosen et al. 2012; Jiao et al. 2015; Adediji and Fashae 2014; Olusola et al. 2018). Similarly, according to Faiilagi (2015), surface water can be contaminated by various sources ranging from agricultural runoff (nutrients and chemicals); urban areas (sewage contamination); mixed pollutants discharge from storm water; and likely chemical discharges from commercial and industrial activities. About 80% of all diseases in the third world countries are directly related to poor drinking water and unsanitary conditions (WHO 2003). Industrial units located at the outskirt and within cities, intensive agricultural practices, indiscriminate disposal of domestic and municipal wastes, and road construction are the sources of surface water and groundwater pollution. Osogbo has witnessed remarkable expansion, growth, and developmental activities such as increase in the number of buildings, road construction, deforestation, and many other anthropogenic activities, since its emergence as the capital of Osun State in 1991 (Taiwo et al. 2014). These have resulted in the emergence of landuse types within the state that reflect uncontrolled planning and management. Due to urbanization, there is rapid population growth in Osogbo Osun state. Rapid population growth drives other urban systems such as urbanization, pollution accumulation, increased technology leading to increase in E-waste, poor housing and sanitary conditions (e.g., open defecation) and waste generation (Taiwo et al. 2014). More importantly, landuse act and policies governing building codes and zonation are not enforced mostly when it comes to urban development, especially across developing countries in Africa (AUC-ECA-AfDB Consortium 2011). The physical transformation in landuse and population density which is characteristic of Osogbo as an emerging urban center exercise significantly impacts on the hydrology and water quality of the neighboring rivers. In other words, the removal of natural vegetation and the subsequent transition into urban landscape increase runoff and sediment loads, thereby increasing the deposition of pollutants from land to surface water bodies (Arnold and Gibbons 1996, Olusola and Fashae 2018, Olusola et al. 2018). This is further supported by the Institute of Water Resources (IWR) (1997) whose findings revealed that different landuse types are characterized by great potential of introducing contaminants into aquatic ecosystems largely due to the diversity of associated human activities. In the purview of the aforementioned, this study therefore assesses the status of dominant landuse types within this emerging urban center and the associated impact on surface water quality. As an emerging urban city in southwestern Nigeria, Osogbo experiences increase in urban population growth which invariably challenges access to safe water and healthy sanitation lifestyle (WHO/UNICEF JMP 2010). A key aspect of this challenge is the access to safe water either through surface or underground water sources. In most developing tropical countries, the use of surface water for domestic, industrial, and agricultural purposes is clearly evident and as such it calls for the need to carefully appraise the quality of surface water in order to ensure human satisfaction and well-being as well as maintain a safe and healthy environment. This becomes necessary as per capita water demand is increasing while the accessibility to drinking water is declining in Nigeria (Ayeni et al. 2006). The knowledge will assist landuse management and improve landuse planning to control and palliate any adverse impacts on adequate provision of ecosystem services (Falkenmark et al. 2009) which is very key to achieving sustainable development goals (SDG 15) (DeClerck et al. 2016).

Methodology

Study area

Osogbo, the capital of Osun state, is located on latitude 7°46′N and 4°34′E with a land area of about 47 km2  (Fig. 1). The geology consists of Basement Complex which outcrop considerable parts of the area. Rocks of the basement complex in Osogbo are schist and synite which are associated with quartzite ridges. The landscape of Osogbo is situated on a raised land, which is well over 500 m above the sea level and is drained by River Osun and its tributaries. The climate is humid tropical type and characterized by wet and dry seasons. The wet season begins toward the end of March and ends in October while the dry season begins with the onset of tropical continental air mass commonly referred to as Harmattan. This wind is usually predominant between the months of November and February (Olaniran 2000). Osogbo falls into the southern savanna zone. This zone is a transition between the high forest in the southern part of the country and the north with woodland properties. Agricultural activities such as fish farming, poultry, market gardening, poultry, and cultivation of vegetables, yam and maize are predominant in Osogbo.
Fig. 1

Map of Osun state showing Osogbo the study area

Materials and method

Thirty water samples from different sampling points in Osogbo metropolis (nine within residential, twelve within commercial and nine within vegetated landuse types) were collected in October 2014. The distribution was determined based on the assumption that commercial landuse is expected to contribute more pollutants ahead of residential and vegetated. At each sampling point, Global Positioning System (GPS) was used to determine the coordinates and elevation. The water samples were collected using composite sampling method in 75 cl transparent plastic bottles. Samples for biochemical oxygen demand (BOD) were collected in dark glass bottles for incubation while samples for bacteriological analysis were collected in sterilized plain glass bottles. Before sampling, the bottles were rinsed at least thrice with the water to be sampled. The water samples were kept in an ice chest and transported to the laboratory within three hours of sampling. The samples were refrigerated upon receipt in the laboratory to avoid external contamination or deterioration till the time of analysis. Each sample was analyzed for the following parameters: pH, electrical conductivity (EC), temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), sulfate (SO4), phosphate (PO4), nitrate (NO3), chlorides (Cl), total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), and bacterial load (Fecal Coliform, FC) using standard procedure (APHA 1995).

Laboratory analysis

Heavy metals were determined using flame atomic absorption spectrophotometer. Anions assessed include nitrate (NO3), phosphate (PO4), and sulfate (SO4), and this was done using HI83200 multispectral bench photometer at a wavelength of 525 mm. Temperature was measured in situ using WET-PRO field kit. The electrical conductivity and total dissolved solids were measured using a JENWAY 3540 Bench combined pH/conductivity/TDS meter (UK). The Winkler’s titration method was used to determine the dissolved oxygen. CFU (colony forming units) per ml was calculated using the method of Cheesbrough (2002).

Geospatial analysis

The study area map was derived from quick bird satellite imagery. The coordinates of the sampling points obtained in the field using GPS were entered in Microsoft Excel (2007) and imported into ArcGIS 9.3 environment to identify the total extent of the study area from which relevant features like road networks, water bodies, built-up areas and vegetation cover were digitized for the purpose of the study (Fig. 2).
Fig. 2

Map of Osogbo metropolis showing the sampling points

Landuse classification and its metrics were carried out using Landsat 2014. The analysis was carried out in Idrissi where major landuse types based on supervised classification were identified. Supervised classification was preferred as the study relies on quantitative analysis. Since the area is well known, geometric correction and validation were further carried out. The mean of the physicochemical and biological parameters were determined. Analysis of variance (one-way ANOVA) was used to determine whether there is variation in characteristics of surface water across different landuse types. The choice of ANOVA was based largely on the structure of the data. The mean value of the physiochemical parameters were compared with the standard set by World Health Organization (WHO 2003) using t test. Water quality index was used to provide a single number that expresses overall water quality within the study area. The descriptive statistics, analysis of variance and t test were done using SPSS 15.0 (statistical product and service solutions). Water quality index was calculated using online National Sanitation Calculator System software also known as NSF information software (Eq. 1). The mean value of each parameter recorded was transferred to a weighting curve chart, where numerical values of Q were obtained.

$${\text{WQI}} = \varSigma Q_{\text{n}} W_{\text{n}} /\varSigma W_{\text{n}}$$
(1)
where WQI is the water quality index; Qn quality rating for the nth water quality parameter; and Wn unit weight for nth parameter.
The weight factor was derived based on the test’s importance to water quality. The index was derived by multiplying the Q by weight factor of each water quality parameter. The index was calculated for each landuse type in the study area, and a general index was calculated combining all the landuse types together. The rating of index ranges from 0 to 100 (Table 1) with 0 indicating very bad water quality and 100 indicating excellent water quality condition.
Table 1

NSF water quality index rating

Range

Quality

90–100

Excellent water quality

70–90

Good water quality

50–70

Medium water quality

25–50

Bad water quality

0–25

Very bad water quality

Results and discussion

Landuse classification

Three landuse types were identified in the study area: residential, commercial, and vegetated (Fig. 3). The percentage area coverage of each is shown in Table 2. The vegetated landuse type accounts for the highest percentage. The residential landuse type is associated with cluster of buildings which supports large human population based on its affiliation to the colonial railway lines and cultural heritage (Adedotun 2015). The commercial landuse type is characterized by primary economic, secondary economic and tertiary economic activities as well as the existence of financial establishments. The vegetated landuse partly consists of secondary vegetation, dominated by grasses, shrubs, and trees, which are becoming degraded due to pressure from urbanization (Taiwo et al. 2014; Gasu 2016). Agricultural activities, mostly involving the cultivation of vegetables and arable crops for subsistence consumption, are also typical of this landuse type, especially on floodplains.
Fig. 3

Map of Landuse types in Osogbo metropolis

Table 2

Areal extent of landuse types in Osogbo metropolis.

Source: Author’s fieldwork

Landuse

Areal extent (Km2)

Percentage (%) coverage

Commercial

24.2

8.5

Residential

49.8

17.4

Vegetated

211.5

74.1

Physical parameters of surface water quality across landuse types

The mean values of pH across the landuse types ranged between 6.0 and 7.9, while temperature mean values were almost the same across the landuse types (Fig. 4). The mean values of electrical conductivity (EC) (Fig. 5) for all the landuse types that is, commercial (766.0 µS cm−1), residential (444.4 µS cm−1), and vegetated (347.3 µS cm−1), were all within the SON (2007) acceptable limit of 1000 μScm. High magnitude of EC within the commercial and residential landuse types is closely tied to the secretion of acidic substances such as sulfate, phosphate, and nitrate, contained in solid wastes entering into the water bodies as well as effluent discharged from industries into water ways. Increasing levels of conductivity and cations are the products of decomposition and mineralization of organic materials (Abida 2008). Conductivity level increases with the existence of inorganic suspended solids in runoff as well as the presence of chloride and nitrate from sewage systems which by extension reduces the purity of the surface water (Elberg 2016).
Fig. 4

Variation in temperature

Fig. 5

Variation in electrical conductivity

There was a distinct variation in the mean values of total solids (TS) across the three landuse types. Total solids (TS) values in the residential landuse type (Fig. 6) were considerably high (1262.3 mg/l), while the commercial and vegetated landuse types recorded similar values of 534.2 mg/l and 541.3 mg/l, respectively. Similar to TS, TDS showed a marked variation across the landuse types, with the residential landuse having the highest mean value of 1039.0 mg/l, while commercial and vegetated landuse recorded 462.3 mg/l and 444.2 mg/l, respectively (Fig. 6). Also, total suspended solids (TSS) presented relatively high values across the landuse types with residential landuse type recording the highest with mean value of 223.3 mg/l, followed by vegetated (97.0 mg/l) and commercial (71.8 mg/l) landuse types. The mean values recorded across the three landuse types violated the permissible limit as set by WHO (2003). The three parameters (TS, TDS, and TSS) to a greater extent evaluate the quality of a drinking water, and their violation limits the aesthetic value of the water by increasing its turbidity which in a way becomes a public health issue (Mendie 2005; Ayeni et al. 2006). Some studies have indicated high turbidity in urban areas (Pullanikkatil et al. 2015) and in areas where sewage is discharged into waterways (Mendie 2005). Furthermore, the high values of TS and TDS across the landuse could be attributed to various sociocultural factors such as indecent disposal of household water into water channels, excessive use of fertilizers from farming activities on floodplains and unchecked effluents discharged directly into water bodies.
Fig. 6

Variation in TS, TDS and TSS

Chemical parameters of surface water across landuse types

Nitrate (NO3) concentration across the three landuse types was within the WHO (2003) permissible limit of 50 mg/l. However, its mean value within the commercial landuse type (2.3 mg/l) is higher compared to the other two landuse types, residential and vegetated landuse types (1.7 mg/l and 1.6 mg/l, respectively) (Fig. 7). The higher mean value observed within the commercial landuse is closely tied to the slaughter houses located near the water bodies and uncontrolled human activities especially open defecation into water ways (Olusola et al. 2018) which is a common practice as witnessed during data collection. Mean value of phosphate (PO4) concentration was higher in commercial and vegetated landuse (2.3 mg/l and 2.2 mg/l, respectively) as against 1.7 mg/l in residential landuse (Fig. 7). Although regulation against PO4 is not listed in the drinking water quality standard in Nigeria (NSDWQ 2007), WHO (1980) recommended a value ≤ 0.05 mg/l for rivers flowing into a lake or reservoir essentially for controlling eutrophication. The studied river flows into a reservoir downstream; hence, the values as observed across the three landuse types are way beyond the permissible limit. Anthropogenic activities are the main contributors of excessive PO4 into river channels; sewage discharge, runoff from agricultural sites, and the release of detergent during domestic washing especially along river channel sections are common practices (Saksena et al. 2008; Pullanikkatil et al. 2015). The load contribution of detergents raises the level of phosphate in water channels due to the weight of inorganic condensed phosphates (Usharani et al. 2010). As illustrated in Fig. 8, a considerably higher mean value concentration of sulphate (SO4) was observed in the commercial landuse (4.4 mg/l) as against residential and vegetated landuse types (3.8 mg/l and 3.5 mg/l, respectively) (Fig. 7). This can be attributed to domestic and sewage waste which most often than not results in irreversible harmful effects through direct consumption of food supplies in contact with the surface water. However, the presented values are well within the permissible limits; hence, they pose no threat to human system (NSDWQ, 2007). The chloride concentration recorded the highest in the commercial landuse with a mean value of 36.3 mg/l, and this could be as a result of wastewater from factories such as metal plating, automobile repairs, and industrial scraps (Paul 2000). Chloride concentration mean value of 19.9 mg/l and 17.7 mg/l were observed in the residential and vegetated landuse, respectively (Fig. 8). The observed mean values in chloride are well within the permissible limit (WHO 2003; NSDWQ 2007). The highest mean value (4.1 mg/l) for dissolved oxygen (DO) was recorded in the vegetated landuse largely due to productivity of aquatic plants (Fig. 9). The commercial and residential landuse had a mean value of 4.0 mg/l and 3.7 mg/l, respectively. Adequate DO is necessary for good water quality, and it is needed in order to provide for aerobic life forms (WHO 2006). The observed level of dissolved oxygen is within permissible limit of surface water. Chemical oxygen demand (COD) mean value as observed within the commercial landuse (3.0 mg/l) is slightly different from the residential (2.4 mg/l) and vegetated (2.6 mg/l) landuse (Fig. 9), and its presence is an indicator of organic pollution in surface water (Hamner et al. 2006; Nwenya 2006). Biochemical oxygen demand (BOD) mean values of 1.7 mg/l and 1.8 mg/l were observed within the residential landuse and vegetated landuse, respectively, and the highest mean value of 2.0 mg/l was recorded at the commercial landuse. The high occurrence BOD within commercial landuse type may be attributed to discharge of organic wastes such as refuse, human and animal excreta and detergents ejected directly into the water which subsequently resulted into the uptake of oxygen and in the oxidation of these wastes. BOD increases due to biodegradation of inorganic materials which exerts oxygen tension in water bodies (Abida 2008).
Fig. 7

Variation of sulfates, phosphates and nitrates

Fig. 8

Variation of chloride

Fig. 9

Variation in DO, BOD and COD

Bacterial load (fecal coliform)

The concentration of fecal coliform (FC) was high in the commercial landuse type (Fig. 10) with a mean value of 5616.7 Cfu. The presence of FC is an indication of sewage contamination (household septic tanks discharging into river channels, abattoirs abutting river channels, agricultural waste from open fields) of the medium and the possible presence of other pathogenic organisms. Fecal coliform bacteria are the most common microbiological contaminants of natural waters. Fecal coliform lives in the digestive tracks of warm-blooded animals including humans (Ho et al. 2003; Wolf-Rainer 2011; Louwanda and William 2013).
Fig. 10

Variation in bacterial load (fecal coliform)

Implication of variation in water quality within the landuse types

Analysis of variance test (one-way ANOVA) was used to ascertain the variation in physical parameters (temperature, pH, dissolved oxygen, conductivity, TS, TDS, and TSS) across the selected three landuse types within the study area (Table 3). The result revealed that there were significant variations in the following parameters: TS (F2,27 = 8.677, P < 0.05); TDS (F2,27 = 7.836, P < 0.05); and TSS (F2,27 = 10.365, P < 0.05) as shown in Table 3. Possible explanations for these differences may be as a result of the volume of waste from the different landuse types and the type of waste generated.
Table 3

Variation in the physical parameters of surface water along the landuse types

Parameter

Landuse

df

Sig.

Temperature

Residential

2

0.9

Commercial

27

Vegetated

29

Total

 

pH

Residential

2

0.6

Commercial

27

Vegetated

29

Total

 

Dissolved oxygen

Residential

2

0.6

Commercial

27

Vegetated

29

Total

 

Conductivity

Residential

2

0.1

Commercial

27

Vegetated

29

Total

 

TS

Residential

2

0.0

Commercial

27

Vegetated

29

Total

 

TDS

Residential

2

0.0

Commercial

27

Vegetated

29

Total

 

TSS

Residential

2

0.0

Commercial

27

Vegetated

29

Total

 
Furthermore, one-way analysis of variance test (ANOVA) was used to determine the variation in BOD, COD, SO4, PO4, NO3, Cl, and FC load among the three different landuse types (Table 4). It was revealed that none of these parameters showed a significant variation across the landuse types. However, the non-significant variation in mean values across the landuse as regards biophysical parameters implies that their concentration are within the permissible limits of surface water based on the global and local standards of WHO (2003) and NSDWQ (2007), respectively.
Table 4

Variation in the chemical parameters of surface water and the landuse types.

Source: Author’s fieldwork

Chemical Parameter

Landuse

N

df

Sig.

BOD

Residential

9

2

0.6

Commercial

12

27

Vegetated

9

29

Total

30

 

COD

Residential

9

2

0.6

Commercial

12

27

Vegetated

9

29

Total

30

 

Sulfates

Residential

9

2

0.2

Commercial

12

27

Vegetated

9

29

Total

30

 

Phosphates

Residential

9

2

0.3

Commercial

12

27

Vegetated

9

29

Total

30

 

Nitrates

Residential

9

2

0.1

Commercial

12

27

 

Vegetated

9

29

Total

30

 

Chlorides

Residential

9

2

0.2

Commercial

12

27

Vegetated

9

29

Total

30

 

Bacteria

Residential

9

2

0.6

Commercial

12

27

Vegetated

9

29

Total

30

 
To account for the difference between standards and observed values, a one-sample t test (Table 5) was conducted to ascertain whether EC, TDS, FC, NO3, PO4, SO4, Cl, and TS were significantly different in value from the standard set by WHO (2003). The result revealed that EC, bacterial load (fecal coliform), nitrate, phosphate, sulfate, chloride, and TS were significantly different from WHO limits (P < 0.05).
Table 5

Water quality factors and weight

Parameters

Weight

Dissolved oxygen

0.17

Fecal coliform

0.16

pH

0.11

BOD

0.11

Temperature

0.10

Phosphates

0.10

Nitrates

0.10

Total solids

0.07

Assessment of surface water quality across various landuse types using the water quality index

Water quality index was further used to assess the overall quality of the surface water across the various landuse types. The index gave a general idea of the quality of surface water based on selected parameters (Table 6).
Table 6

Water quality index across the various landuse types.

Source: Author’s fieldwork

Parameters

Water quality index

Residential landuse

Commercial landuse

Vegetated landuse

Temperature

14

13

14

DO

4

4

4

Fecal coliform

16

10

14

BOD

88

80

85

Phosphates

29

25

26

Nitrates

95

94

95

Total solids

20

20

20

pH

92

92

93

Based on the eight parameters, the water quality index for residential, commercial, and vegetated landuse types were 42, 39 and 41, respectively (Table 7), hence an indication of poor water quality across the study area.
Table 7

General water quality index for all the landuse types

Factors

Water quality index

Temperature

14

DO

4

Bacteria

14

BOD

84

Phosphate

27

Nitrate

95

TS

20

pH

92

Similarly, based on the eight parameters, the general water quality index was 41 (Table 8), an indication of a poor water quality (Table 1). The results obtained here are almost at par with those observed by Pullanikkatil et al. (2015) downstream of urban areas of Likangala catchment, southern Malawi. This was further supported by Keshtkar et al. 2010, who linked water quality problem in urban areas to the increasing intensity of anthropogenic activities. Occurrence of pollutants in urban waters as presented in this study clearly reveals the impact of man as he modifies his environment. However, recent studies on urban waters globally established the fact that of great concern are the state of urban waters and the quality of waste being received. Aside metal load and biological contamination as presented in this study, the presence of contaminants of emerging concerns (CECs) such as pharmaceutical and personal care products (PPCPs) and hospital wastes (Barber et al. 2006; Ellis 2006; Fakoya et al. 2018 and Bai et al. 2018) in urban waters coupled with established load of metals and salts in quantities calls for effective monitoring of urban waters and sensitization of urban dwellers.
Table 8

Differences between the parameters and World Health Organization (WHO) limits.

Source: Author’s fieldwork

Parameters

N

Mean

SD

t

df

Sig.

Test value (WHO)

Conductivity

30

543.9

476.6

− 5.2

29

0.0

1000

TDS

30

629.9

449.7

1.6

29

0.1

500

Bacteria load

30

4733.4

4898.8

5.2

29

0.0

100

Nitrates

30

1.9

0.8

− 56.8

29

0.0

10

Phosphates

30

2.1

0.9

− 17.7

29

0.0

5

Sulfates

30

4.0

1.1

− 5.0

29

0.0

5

Chlorides

30

25.8

25.5

− 15.9

29

0.0

100

Cyanides

30

0.0

0.0

− 93.5

29

0.0

0.05

TS

30

754.8

540.3

2.6

29

0.0

500

Conclusion and recommendation

From the study, it was revealed that quality of surface water in the study area and its environs is very poor. The presence of fecal contamination alone is an indication that a potential health risk exists for individuals exposed to this water, especially downstream. Furthermore, the significant difference between the WHO standard (2003) and some measured physicochemical parameters (total solids, total dissolved solids, total suspended solids, electrical conductivity, phosphate, and sulfate) implied the presence of human waste and other allied materials within the surface water channels. Based on the results of this study, there should be a continual intensive water quality monitoring program of surface waters across the area and its immediate environs. It becomes important to monitor water quality spatio-temporally to understand changes in water quality that occur under different conditions. Therefore, to obtain an adequate collection of baseline data, it will be necessary to monitor water quality over both the wet and dry seasons at a variety of spatial scales. Also, mass education of the local populace on environmental friendliness and its importance are very crucial in maintaining water quality standards. Through such programs, valuable information might be obtained on how to conserve our water ways locally and ensure sustainable and healthy ecosystem.

Notes

References

  1. Abida B, Harikrishna (2008) Study on the quality of water in some streams of calvary river. J Chem 5(2):37–384. ISSN 0973-4945Google Scholar
  2. Adedotun SB (2015) A study of urban transportation system in Osogbo, Osun State, Nigeria. Eur J Sustainable Dev 4(3):93–101Google Scholar
  3. Adediji A, Fashae OA (2014) Sediment dynamics in a small, 2nd order urban River Awba catchment, Ibadan, Nigeria. J Environ Geogr 7(1–2):23–28CrossRefGoogle Scholar
  4. APHA (1995) WPCF, Standard methods for the examination of water and wastewater. American Public Health Association, Washington, DCGoogle Scholar
  5. Arnold CL, Gibbons CJ (1996) Impervious surface coverage: the emergence of a key environmental indicator. J Am Plan Assoc 62:243–258CrossRefGoogle Scholar
  6. AUC-ECA – AFDB Consortium (2011) Law policy in Africa: West Africa Regional Assessment. ECA Publications and Conference Management Section (PCMS), p 123Google Scholar
  7. Ayeni AO, Balogun II, Adeaga OA (2006) Impact of selected landuse types on surface water quality downstream of ASA Dam in Kwara State, Nigeria. J Environ Syst 32(3):221–238CrossRefGoogle Scholar
  8. Bai X, Lutz A, Carroll R, Ketele K, Dahlin K, Murphy M, Nguyen D (2018) Occurrence, distribution, and seasonality of emerging contaminants in urban watersheds. Chemosphere 200:133–142CrossRefGoogle Scholar
  9. Barber LB, Murphy SF, Verplanck PL, Sandstrom MW, Taylor HE, Furlong ET (2006) Chemical loading into surface water along a hydrological, biogeochemical, and land use gradient: a holistic approach. Environ Sci Technol 40(2):475–486CrossRefGoogle Scholar
  10. Cheesbrough M (2002) District laboratory practice in tropical countries, 2nd edn. Cambrige University Press, Cambrige, pp 29–30Google Scholar
  11. DeClerck FAJ, Jones SK, Attwood S, Bossio D, Girvetz E, Chaplin-Kramer B, Enfors E, Fremier AK, Gordon LJ, Kizito F, Lopez Noriega I, Matthews N, McCartney M, Meacham M, Noble A, Quintero M, Remans R, Soppe R, Willemen L, Wood SLR, Zhang W (2016) Agricultural ecosystems and their services: the vanguard of sustainability? Curr Opin Environ Sustain 23:92–99CrossRefGoogle Scholar
  12. Elbag MA (2006) Impact of surrounding landuses on surface water quality. M.Sc. Thesis in Environmental Engineering Worcester Polytechnic InstituteGoogle Scholar
  13. Ellis JB (2006) Pharmaceutical and personal care products (PPCPs) in urban receiving waters. Environ Pollut 144:184–189CrossRefGoogle Scholar
  14. Faiilagi SA (2015) Assessing the impacts of landuse patterns on river water quality at catchment level: a case study of Fuluasou River Catchment in Samoa (Doctoral dissertation, Massey University)Google Scholar
  15. Fakoya OT, Oluyemi EA, Olabanji IO, Eludoyin AO, Makinde OW, Oyinloye JA (2018) Seasonal variation of heavy metal speciation in soil and stream sediments from hospital waste dumpsite in Ilesa, Southwestern Nigeria. Afr J Environ Sci Technol 12(9):312–322CrossRefGoogle Scholar
  16. Falkenmark M, Rockstrom J, Karlberg L (2009) Present and future water requirements for feeding humanity. Food Secur 1(1):59–69CrossRefGoogle Scholar
  17. Fashae OA, Olusola AO (2017) Landuse types within channel corridor and river channel morphology of RiverOna, Ibadan, Nigeria. Indones J Geogr 49(2):111–117CrossRefGoogle Scholar
  18. Fashae OA, Ayomanor R, Orimoogunje OOI (2017) Landuse dynamics and surface water quality in a typical Urban Centre of South-Western, Nigeria. Analele Universităţii din Oradea, Seria Geografie 27(1):98–107Google Scholar
  19. Gasu MB (2016) Geospatial analysis of land use dynamics in Osogbo between 1986 and 2012. Abuja J Geogr Dev 4:59–68Google Scholar
  20. Hamner S, Tripathi A, Mishra RK, Bouskill N, Broadaway SC, Pyle BH, Ford TE (2006) The role of water use patterns and sewage pollution in incidence of water-borne/enteric diseases along the Ganges River in Varanasi, India. Int J Environ Health Res 16:113–132CrossRefGoogle Scholar
  21. Henderson L, Mahoney C, McClelland C, Amber M (2014) The effect of Landuse and Land Cover on Water Quality in Urban Environments. Natural Resources and Environmental Sciences (NRES), Kansas State UniversityGoogle Scholar
  22. Ho KC, Chow YL, Yau JT (2003) Chemical and microbiological qualities of the East River (Dongjiang) water, with particular reference to drinking water supply in Hong Kong. Chemosphere 52:1441–1450CrossRefGoogle Scholar
  23. Ifabiyi IP (2000) (eds) Contemporary issue in environmental studies. Haytee Press and Publishing, IlorinGoogle Scholar
  24. Institute of Water Resources (IWR) (1997) Landuse effects on Water Quality. Michigan State University, MichiganGoogle Scholar
  25. Irenosen OG, Festus AA, Coolborn AF (2012) Water quality assessment of the Owena multi-purpose dam, Ondo State, south-western Nigeria. J Environ Prot 3:14–25CrossRefGoogle Scholar
  26. Jiao D, Yuan J, Lan F, Qi L, Qiuzhi P, Muyi K (2015) Impacts of landuse on surface water quality in a subtropical river Basin: A case study of the Dongjiang River Basin, Southeastern China. Water. www.mdpi.com/journal/water
  27. Keshtkar AR, Mahdavi M, Salajegheh A, Ahmadi H, Sadoddin A, Ghermezcheshmeh B (2010) Exploring the relationship between landuse and surface water quality using multivariate statistics in arid and semi-arid regions. Desert 16:33–38Google Scholar
  28. Levin KR (2012) Linking land use and water quality: guiding development surrounding durham county’s drinking watershed. Master’s Thesis, Duke University, DurhamGoogle Scholar
  29. Li S, Gu S, Liu W, Han H, Zhang Q (2008) Water quality in relation to landuse and land cover in the upper Han River Basin, China. Catena 75(2008):216–222CrossRefGoogle Scholar
  30. Liu J, Zhang X, Xia J, Wu S, She D, Zou L (2016) Characterizing and explaining spatiotemporal variation of water quality in a highly disturbed river by multi-statistical techniques. SpringerPlus 5(1):1171CrossRefGoogle Scholar
  31. Louwanda WJ, William R (2013) extension forestry and natural resources, what is focal coli form? Why is it important? Management 26:9–27Google Scholar
  32. Mallin MA (2008) Comparative impacts of stormwater runoff on water quality of an urban, a suburban, and a rural stream. Environ Monit Assess 159(1–4):475–491Google Scholar
  33. Mathuthu AS, Mwanga K, Simoro A (1997) Impact assessment of industrial and sewage effluents on water quality of the receiving Marimba River in Harare. University of Zimbabwe Publications, Harare, Zimbabwe, pp 43–52Google Scholar
  34. Mendie UE (2005) The theory and practice of clean water production for domestic and industrial use: Purified and package water. Lacto-Medal Ltd, LagosGoogle Scholar
  35. Ngoye E, Machiwa JF (2004) The influence of landuse patterns in the Ruvu river watershed on water quality in the river system. Phys Chem Earth 29:1161–1166CrossRefGoogle Scholar
  36. Nigerian Industrial Standard, NIS (2007) Nigerian standard for drinking water quality, NIS 554Google Scholar
  37. Nwenya F (2006) Water quality trends in the Eerste river. Western cape, 1990–2005. Unpublished MSc thesis, University of the Western Cape, p 41Google Scholar
  38. Olaniran JO (2000). Rainfall Anomalies in Nigeria: The contemporary understanding. 55th Inaugural lecture, University Press, IlorinGoogle Scholar
  39. Olusola A, Fashae O (2018) Urbanization and hydraulic geometry response: a model approach. Int J Water 12(2):103–115CrossRefGoogle Scholar
  40. Olusola A, Adeyeye O, Durowoju O (2017) Groundwater: quality levels and human exposure, SW Nigeria. J Environ Geogr 10(1–2):23–29CrossRefGoogle Scholar
  41. Olusola A, Onafeso O, Durowoju OS (2018) Analysis of organic matter and carbonate mineral distribution in shallow water surface sediments. Osun Geogr Rev 1(1):106–110Google Scholar
  42. Paul FH (2000) Sulphate and chloride concentration in Texas aquifer. In: Ruth EA (ed) Environmental international. Pergamon Publishing, USAGoogle Scholar
  43. Pullanikkatil D, Palamuleni LG, Ruhiiga TM (2015) Impact of landuse on water quality in the Likangala catchment, southern Malawi. Afr J Aquat Sci. 1727–9364 (Online), ISSN: 1608-5914 (Print). Journal homepage: http://www.tandfonline.com/loi/taas20
  44. Rogers P (2000) Landuse change in developing countries: comparing India and China. DEAS/HUCE, Harvard University, CambridgeGoogle Scholar
  45. Saksena DN, Garg RK, Rao RJ (2008) Water quality and pollution status of Chambal River in National Chambal Sanctuary, Madhya Pradesh. J Environ Biol 29:701–710Google Scholar
  46. Scanlon BR, Reedy RC, Stonestrom DA, Prudic DE, Dennehy KF (2005) Impact of landuse and land cover change on groundwater recharge and quality in the southwestern US. Glob Change Biol 11(10):1577–1593CrossRefGoogle Scholar
  47. Schilling KE, Jha MK, Zhang YK, Gassman PW, Wolter CF (2008) Impact of land use and land cover change on the water balance of a large agricultural watershed: historical effects and future directions. Water Resour Res 44:1–12CrossRefGoogle Scholar
  48. SON (Standard Organization of Nigeria) (2007) Nigerian Standard for drinking water quality. Nigerian Industrial StandardGoogle Scholar
  49. Taiwo OJ, Abu-Taleb KA, Ngie A, Ahmed F (2014) Effects of political dispensations on the pattern of urban expansion in the Osogbo metropolis, Osun State, Nigeria. In: Proceedings of the 10th international conference of AARSE. October, 2014Google Scholar
  50. Tiwari AK, Singh AK, Singh AK (2015) Hydrogeochemical analysis and evaluation of surface water quality of pratapgarh district, Uttar Pradesh, India. Appl Water Sci 7(4):1609–1623CrossRefGoogle Scholar
  51. Usharani K, Umarani K, Ayyasamy PM, Shanthi K, Lakshmanaperumalsamy P (2010) Physico-chemical and bacteriological characteristics of Noyyal River and ground water quality of Perur, India. J Appl Sci Environ Manage 14(2):29–35Google Scholar
  52. Wagner PD, Kumar S, Schneider K (2013) An assessment of landuse change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India. Hydrol Earth Syst Sci 17:2233–2246CrossRefGoogle Scholar
  53. WHO (2003) Guidelines for drinking-water quality, Volume 1, Recommendations (1st Addendum to 3rd ed.). Geneva: World Health Organization (Electronic version).http://www.who.int/water_sanitation_health/dwq/gdwq3rev/en/index.html
  54. Wolf-Rainer A (2011) Megacities as sources for pathogenic bacteria in rivers and their fate downstream. Int J Microbiol 2011:1–13Google Scholar
  55. World Health Organization, WHO/UNICEF Joint Water Supply and Sanitation Monitoring Programme (JMP) (2010) Progress on sanitation and drinking-water, 2010 update. World Health Organization, GenevaGoogle Scholar

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Authors and Affiliations

  • Olutoyin Adeola Fashae
    • 1
    Email author
  • Hannah Abiola Ayorinde
    • 1
  • Adeyemi Oludapo Olusola
    • 1
  • Rotimi Oluseyi Obateru
    • 1
  1. 1.Department of GeographyUniversity of IbadanIbadanNigeria

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