Water is the difference between living and non-living and water for drinking should be pollutant free. Thus, in supplies for urban and rural consumption, water quality is one of the most critical parameters to verify. Well and/or open-water systems are easily liable to anthropogenic contaminations, the source of most water-borne epidemics especially in developing countries like Sierra Leone. This study analyses 10 representative well-water systems for 18 water quality parameters in Kakua Chiefdom of Bo District, Sierra Leone. The study notes that well-water quality parameters such as total dissolved solids (TDS), turbidity, electrical conductivity, coliform and nitrate (NO3) are fairly high above safe drinking water standard. The incidence of coliform in the well waters is highest in April and that of iron (Fe2+) and nitrate is highest in May. The Dipha Street well is amongst the most contaminated and has the highest scores for TDS, non-faecal coliform and fluoride (F). Correlation analysis shows an interesting bond among the water quality parameters, ranging from strongly positive (R = 1.0) to strongly negative (R = −1.0). Fe2+ is strongly positively correlated with most of the well-water quality parameters. Irrespectively, the use of contaminated water in domestic and/or agro-industrial sectors could pose various health risks and epidemic outbreaks of different intensities.

INTRODUCTION

It is critical that water, the basic element of life, is contaminant free for domestic and agricultural use (Dawson & Sartory 2000; Gopalakrishnan et al. 2012). Most of the health issues in Africa are related to poor drinking water quality (Gerba & Smith 2005; Liu et al. 2012). Water quality is the measure of the condition of water relative to the requirements of one or more biotic species and/or human needs (Johnson et al. 1997). Several conferences in 1976‒1978 mapped out strategies for improved living conditions, including safe drinking water supply (Al-Khatib et al. 2003). A United Nations (UN) conference in New York (NYK) decided on 1981‒1990 as the decade for international supply of safe drinking water (UN 1977; Gopalakrishnan et al. 2012), a decade that is known as the first water decade. Also the World Health Organization (WHO) and UN International Children's Emergency Fund (UNICEF) jointly sponsored conference on ‘safe drinking water supply’ adopted primary health care as the most effective way to meet national goals (SALWACO 2008; WHO/UNICEF 2012), a strategy especially emphasized for African communities.

In a report entitled ‘bringing water to Africa's poor’, Dovi (2007) noted that most African communities have no access to clean drinking water. For example, typical Malagasy households had to trek on a daily basis for over 2 km to get water for drinking, cooking, washing and feeding farm animals (Dovi 2007). Statistics show that health standards are lowest on the African continent (WHO/UNICEF 2012). This is generally related to the lack of good quality drinking water in African communities (Shamsu 2007; UNDP 2009), which is especially true for Third-world Sierra Leone.

In 2009, UN Development Programme (UNDP) ranked Sierra Leone as the second poorest nation in the world (UNDP 2009). Poverty affects every aspect of life, including the availability and access to drinkable water. Two-thirds of Sierra Leoneans live over 1 km away from the nearest sources of drinking water that are hardly entirely pathogen free (WHO/UNICEF 2012).

Water as a universal solvent dissolves more compounds than any other liquid. One of the purest forms of water is obtained by distillation. Although pure water is not always needed for drinking, highly contaminated water is also hazardous to health. The composition of water naturally varies with geographical region and the geologic formation that bears it (Miller et al. 2016). Water composition can also be influenced by anthropogenic activity (Schlesinger 2009). Water that is contaminated could have limited use in domestic, industrial and environmental processes. In addition, use of contaminated water could enhance disease incidence and limit life expectancy (WHO 2012a).

In the decade-long civil/rebel war, a good fraction of the population of Sierra Leone was forced to drink from well and stream waters across the country. Over a decade after the war, safe water availability and supply is still a monumental challenge in the country. Shamsu (2007) earlier projected that although 80% of Bo Town population could have access to safe water supply by 2010, some 20% will continue to rely on supplies from shallow wells. Thus, this study determines the state of water quality in Sierra Leone using Kakua Chiefdom as a case study. The study analyses selected water quality parameters pertinent to health and epidemic diseases.

Most public water systems in Sierra Leone lack basic technical information like equipment type, management strategy, available logistic, public awareness, etc. Water services are required to provide safe drinking water at low cost, which can only be achieved by building a comprehensive database via regular monitoring, rational operation and functional maintenance services. Hence this study analyses key biogeochemical quality parameters of water in 10 sample wells in Kakua Chiefdom, Bo District. The study appraises the performance of public wells in Sierra Leone and provides critical information on the state of drinking water in the country. This is taking measures to prevent water-borne diseases from developing into unmanageable national epidemics.

Study area

Sierra Leone lies on the west coast of Africa between latitudes 6.91‒10.08 °N and longitudes 10.21‒13.32 °W and has a total area of 71,740 km2. It is bordered on the west by the Atlantic Ocean, on the north and northeast by Guinea and on the southeast by Liberia (Figure 1). Sierra Leone has a population of about 6 million people (Squire 2001). About 71,620 km2 (99.8%) of the 71,740 km2 area of the country is land and 120 km2 (0.2%) is water (Barnett et al. 2000).
Figure 1

Map of Sierra Leone (inset plate on the left) depicting Bo District (black and white stripes) and map of Bo District (inset plate on the right) depicting Kakua Chiefdom (black and white strips), and then an expanded map of Kakua Chiefdom showing Bo Town where the well-water data were collected.

Figure 1

Map of Sierra Leone (inset plate on the left) depicting Bo District (black and white stripes) and map of Bo District (inset plate on the right) depicting Kakua Chiefdom (black and white strips), and then an expanded map of Kakua Chiefdom showing Bo Town where the well-water data were collected.

Sierra Leone has a tropical monsoon climate, with two distinct seasons – the wet season and dry season. The annual average temperature is 26 °C and dry season night-time temperatures can be as low as 16 °C (NRDS 2009). Tropical rainfall, the dominant form of precipitation, ranges from 5,000 mm in the coastal regions to 2,000 mm in the hinterlands. The seven main rivers of the country are fed by dense networks of tributaries which carry abundant flows throughout the year (USAID 2012).

The land cover mainly includes lowland deciduous forests, inland valley swamps, coastal mangrove swamps, bolilands and wooded savannah. The main cultivated crops include coffee, cocoa, oil palm, rice, cassava, groundnut, coconut, citrus, maize and cashew (USAID 2012). Rainfed agriculture is the main mode of cultivation, directly employing some 75% of the population (Barnett et al. 2000).

Sierra Leone is divided into 149 chiefdoms, 12 districts, three provinces plus the western area. Bo District, which is in the southern province, has a total area of 1,400 km2 and population of 463,668 people. It is comprised of 15 chiefdoms, with Kakua Chiefdom (the study area), centrally located in the district. The district headquarter (Bo Town, also the second largest city in Sierra Leone) is also located in the Kakua Chiefdom study area. A total of 10 wells, nearly evenly distributed across Bo Town, are investigated for water quality levels in this study. The Bo Town study area is slightly undulating and densely dotted with inland swamps.

MATERIALS AND METHODS

To a large extent, the accuracy of water sample analysis is a function of the collection, handling and preservation methods used. In this study, well-water quality data were collected through interviews, observations and on-site/laboratory analysis (colorimetric tests). Out of 50 catalogued wells in the Kakua Chiefdom study area, 10 (20%) were used in the study. The criteria for well selection included well use, well location, community impact, etc. An inclusive detail of the well selection criteria are summarized in Table 1. In addition, the full range of water quality parameters tested in the study (in the field and laboratory) is listed in Table 2.

Table 1

Details of the selected 10 wells for sample collection and water quality analysis in the Kakua Chiefdom study area in Bo District, Sierra Leone

Well site (code)UseImpactDepth (m)Safety
SAS Multi-use High 37.4 Covered & fenced 
SBO Domestic High 18.6 Covered & unfenced 
CHE Domestic High 58.1 Covered & fenced 
JQS Domestic High 10.3 Covered & fenced 
KTN Domestic High 13.7 Covered & fenced 
NYK Domestic Moderate 8.4 Covered & fenced 
SRP Multi-use High 59.2 Covered & fenced 
WST Domestic Moderate 7.5 Covered & unfenced 
DST Domestic Moderate 8.9 Covered & fenced 
SRD Domestic High 11.2 Covered & unfenced 
Well site (code)UseImpactDepth (m)Safety
SAS Multi-use High 37.4 Covered & fenced 
SBO Domestic High 18.6 Covered & unfenced 
CHE Domestic High 58.1 Covered & fenced 
JQS Domestic High 10.3 Covered & fenced 
KTN Domestic High 13.7 Covered & fenced 
NYK Domestic Moderate 8.4 Covered & fenced 
SRP Multi-use High 59.2 Covered & fenced 
WST Domestic Moderate 7.5 Covered & unfenced 
DST Domestic Moderate 8.9 Covered & fenced 
SRD Domestic High 11.2 Covered & unfenced 

About 80% of the investigated wells are for domestic use and 20% for multi-purpose use (domestic, agricultural and industrial). While all the wells have metal sheet covers, 70% are fenced with concrete, iron rod, wood or combinations of such materials.

Table 2

Mean values and the ±standard deviations of measured water quality parameters at each well site in the Kakua Chiefdom study area in Bo District, Sierra Leone

Well-site/variableUnitSRDSRPDSTKTNNYKSASWSTCHEJQSSBOAVG
Carbonate (CO32−mg/mL × 10−2 29.33 ± 7.02 8.00 ± 0.00 83.00 ± 2.65 23.67 ± 5.51 15.67 ± 1.53 14.00 ± 4.00 32.67 ± 3.06 39.33 ± 1.15 43.67 ± 17.1 13.33 ± 1.15 30.27 ± 4.32 
Hardness (Hard) mg/mL × 10−2 38.67 ± 1.15 10.67 ± 1.15 80.67 ± 1.15 10.67 ± 1.15 16.00 ± 1.00 12.67 ± 1.15 29.33 ± 1.15 40.67 ± 1.15 21.00 ± 1.00 7.33 ± 0.58 26,77 ± 1.06 
Chloride (Clmg/mL × 10−2 23.33 ± 1.15 6.67 ± 0.58 72.33 ± 11.24 16.33 ± 7.00 24.00 ± 2.00 12.67 ± 1.15 21.33 ± 2.31 28.33 ± 11.14 35.67 ± 1.53 15.33 ± 0.58 25.60 ± 3.87 
Nitrate (NO3mg/mL × 10−2 6.03 ± 0.06 11.47 ± 8.85 3.63 ± 0.15 3.70 ± 0.26 5.10 ± 0.10 4.63 ± 0.32 13.03 ± 0.93 5.50 ± 0.01 4.70 ± 0.10 4.07 ± 0.12 6.19 ± 1.09 
Sulphate (SO42−mg/mL × 10−2 1.00 ± 0.00 12.00 ± 2.00 2.00 ± 1.00 1.00 ± 0.00 1.00 ± 0.00 1.70 ± 0.05 3.00 ± 0.00 2.50 ± 0.50 2.50 ± 0.5 2.00 ± 0.00 2.87 ± 0.41 
Copper (Cu2+mg/mL × 10−2 1.09 ± 0.04 1.17 ± 0.06 0.36 ± 0.35 1.03 ± 0.11 1.72 ± 0.20 1.65 ± 0.31 1.37 ± 0.58 1.60 ± 0.58 1.22 ± 0.58 1.08 ± 0.58 1.23 ± 0.34 
Turbidity (Turb) mg/mL × 10−2 1.43 ± 0.15 1.87 ± 0.06 0.67 ± 0.12 1.00 ± 0.36 1.07 ± 0.55 0.80 ± 0.60 1.20 ± 0.30 0.67 ± 0.11 1.63 ± 0.74 1.80 ± 0.01 1.21 ± 0.30 
Fluoride (Fmg/mL × 10−2 0.36 ± 0.03 0.45 ± 0.03 0.61 ± 0.01 0.57 ± 0.01 0.31 ± 0.00 0.34 ± 0.02 0.66 ± 0.03 0.28 ± 0.02 0.36 ± 0.01 0.46 ± 0.02 0.44 ± 0.02 
Iron (Fe2+mg/mL × 10−2 0.34 ± 0.57 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.05 0.02 ± 0.00 0.00 ± 0.00 0.00 ± 0.01 0.00 ± 0.02 0.00 ± 0.03 0.00 ± 0.04 0.04 ± 0.11 
Manganese (Mn2+mg/mL × 10−2 0.04 ± 0.05 0.00 ± 0.05 0.10 ± 0.05 0.02 ± 0.05 0.01 ± 0.01 0.01 ± 0.00 0.02 ± 0.05 0.03 ± 0.05 0.10 ± 0.00 0.00 ± 0.00 0.03 ± 0.03 
Salinity (Sal) mg/mL × 10−2 0.03 ± 0.05 0.00 ± 0.00 0.07 ± 0.05 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.10 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.01 
Phosphate (PO42−mg/mL × 10−2 0.00 ± 0.08 0.00 ± 0.07 0.00 ± 0.06 0.00 ± 0.05 0.07 ± 0.04 0.00 ± 0.03 0.00 ± 0.02 0.00 ± 0.01 0.00 ± 0.00 0.00 ± 0.01 0.01 ± 0.00 
TDS ppm 155.67 ± 0.12 41.80 ± 1.57 233.33 ± 2.31 37.30 ± 0.79 34.70 ± 0.79 122.00 ± 2.65 58.53 ± 3.87 35.67 ± 11.93 09.03 ± 14.44 36.27 ± 0.97 96.43 ± 3.91 
EC μS/cm 30.10 ± 3.61 84.63 ± 3.91 64.10 ± 8.54 75.63 ± 3.40 69.63 ± 0.98 24.40 ± 5.30 26.00 ± 5.57 21.87 ± 7.09 45.43 ± 6.70 57.50 ± 2.31 48.23 ± 4.74 
Temperature (Temp) °C 27.97 ± 0.37 27.80 ± 0.28 27.83 ± 0.23 27.90 ± 0.50 28.13 ± 0.43 28.00 ± 0.42 27.90 ± 0.43 27.93 ± 0.47 27.80 ± 0.28 28.03 ± 0.42 27.93 ± 0.40 
FC um/mL × 10−2 25.00 ± 5.00 20.00 ± 5.00 33.33 ± 7.64 5.00 ± 5.00 0.00 ± 0.00 0.00 ± 0.29 26.67 ± 7.64 0.00 ± 0.00 0.67 ± 11.55 0.00 ± 0.00 11.074.21 
NFC um/mL × 10−2 8.33 ± 7.64 8.33 ± 2.89 15.00 ± 10.00 6.67 ± 5.77 1.67 ± 2.89 6.83 ± 5.49 13.33 ± 2.89 8.33 ± 7.64 15.00 ± 10.00 10.00 ± 0.00 9.35 ± 5.25 
pH – 6.73 ± 0.58 6.70 ± 0.01 6.47 ± 0.58 6.77 ± 0.58 6.70 ± 0.08 6.80 ± 0.00 6.53 ± 0.58 6.70 ± 0.08 6.60 ± 0.08 6.67 ± 0.06 6.67 ± 0.26 
Well-site/variableUnitSRDSRPDSTKTNNYKSASWSTCHEJQSSBOAVG
Carbonate (CO32−mg/mL × 10−2 29.33 ± 7.02 8.00 ± 0.00 83.00 ± 2.65 23.67 ± 5.51 15.67 ± 1.53 14.00 ± 4.00 32.67 ± 3.06 39.33 ± 1.15 43.67 ± 17.1 13.33 ± 1.15 30.27 ± 4.32 
Hardness (Hard) mg/mL × 10−2 38.67 ± 1.15 10.67 ± 1.15 80.67 ± 1.15 10.67 ± 1.15 16.00 ± 1.00 12.67 ± 1.15 29.33 ± 1.15 40.67 ± 1.15 21.00 ± 1.00 7.33 ± 0.58 26,77 ± 1.06 
Chloride (Clmg/mL × 10−2 23.33 ± 1.15 6.67 ± 0.58 72.33 ± 11.24 16.33 ± 7.00 24.00 ± 2.00 12.67 ± 1.15 21.33 ± 2.31 28.33 ± 11.14 35.67 ± 1.53 15.33 ± 0.58 25.60 ± 3.87 
Nitrate (NO3mg/mL × 10−2 6.03 ± 0.06 11.47 ± 8.85 3.63 ± 0.15 3.70 ± 0.26 5.10 ± 0.10 4.63 ± 0.32 13.03 ± 0.93 5.50 ± 0.01 4.70 ± 0.10 4.07 ± 0.12 6.19 ± 1.09 
Sulphate (SO42−mg/mL × 10−2 1.00 ± 0.00 12.00 ± 2.00 2.00 ± 1.00 1.00 ± 0.00 1.00 ± 0.00 1.70 ± 0.05 3.00 ± 0.00 2.50 ± 0.50 2.50 ± 0.5 2.00 ± 0.00 2.87 ± 0.41 
Copper (Cu2+mg/mL × 10−2 1.09 ± 0.04 1.17 ± 0.06 0.36 ± 0.35 1.03 ± 0.11 1.72 ± 0.20 1.65 ± 0.31 1.37 ± 0.58 1.60 ± 0.58 1.22 ± 0.58 1.08 ± 0.58 1.23 ± 0.34 
Turbidity (Turb) mg/mL × 10−2 1.43 ± 0.15 1.87 ± 0.06 0.67 ± 0.12 1.00 ± 0.36 1.07 ± 0.55 0.80 ± 0.60 1.20 ± 0.30 0.67 ± 0.11 1.63 ± 0.74 1.80 ± 0.01 1.21 ± 0.30 
Fluoride (Fmg/mL × 10−2 0.36 ± 0.03 0.45 ± 0.03 0.61 ± 0.01 0.57 ± 0.01 0.31 ± 0.00 0.34 ± 0.02 0.66 ± 0.03 0.28 ± 0.02 0.36 ± 0.01 0.46 ± 0.02 0.44 ± 0.02 
Iron (Fe2+mg/mL × 10−2 0.34 ± 0.57 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.05 0.02 ± 0.00 0.00 ± 0.00 0.00 ± 0.01 0.00 ± 0.02 0.00 ± 0.03 0.00 ± 0.04 0.04 ± 0.11 
Manganese (Mn2+mg/mL × 10−2 0.04 ± 0.05 0.00 ± 0.05 0.10 ± 0.05 0.02 ± 0.05 0.01 ± 0.01 0.01 ± 0.00 0.02 ± 0.05 0.03 ± 0.05 0.10 ± 0.00 0.00 ± 0.00 0.03 ± 0.03 
Salinity (Sal) mg/mL × 10−2 0.03 ± 0.05 0.00 ± 0.00 0.07 ± 0.05 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.01 ± 0.00 0.10 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.03 ± 0.01 
Phosphate (PO42−mg/mL × 10−2 0.00 ± 0.08 0.00 ± 0.07 0.00 ± 0.06 0.00 ± 0.05 0.07 ± 0.04 0.00 ± 0.03 0.00 ± 0.02 0.00 ± 0.01 0.00 ± 0.00 0.00 ± 0.01 0.01 ± 0.00 
TDS ppm 155.67 ± 0.12 41.80 ± 1.57 233.33 ± 2.31 37.30 ± 0.79 34.70 ± 0.79 122.00 ± 2.65 58.53 ± 3.87 35.67 ± 11.93 09.03 ± 14.44 36.27 ± 0.97 96.43 ± 3.91 
EC μS/cm 30.10 ± 3.61 84.63 ± 3.91 64.10 ± 8.54 75.63 ± 3.40 69.63 ± 0.98 24.40 ± 5.30 26.00 ± 5.57 21.87 ± 7.09 45.43 ± 6.70 57.50 ± 2.31 48.23 ± 4.74 
Temperature (Temp) °C 27.97 ± 0.37 27.80 ± 0.28 27.83 ± 0.23 27.90 ± 0.50 28.13 ± 0.43 28.00 ± 0.42 27.90 ± 0.43 27.93 ± 0.47 27.80 ± 0.28 28.03 ± 0.42 27.93 ± 0.40 
FC um/mL × 10−2 25.00 ± 5.00 20.00 ± 5.00 33.33 ± 7.64 5.00 ± 5.00 0.00 ± 0.00 0.00 ± 0.29 26.67 ± 7.64 0.00 ± 0.00 0.67 ± 11.55 0.00 ± 0.00 11.074.21 
NFC um/mL × 10−2 8.33 ± 7.64 8.33 ± 2.89 15.00 ± 10.00 6.67 ± 5.77 1.67 ± 2.89 6.83 ± 5.49 13.33 ± 2.89 8.33 ± 7.64 15.00 ± 10.00 10.00 ± 0.00 9.35 ± 5.25 
pH – 6.73 ± 0.58 6.70 ± 0.01 6.47 ± 0.58 6.77 ± 0.58 6.70 ± 0.08 6.80 ± 0.00 6.53 ± 0.58 6.70 ± 0.08 6.60 ± 0.08 6.67 ± 0.06 6.67 ± 0.26 

The well code SRD is for Sewa Road, SRP for Sewa River Plant, DST for Dipha Street, KTM for Kebbie Town, NYK for New York, SAS for St Andrews, WST for Water Street, CHE for Cheshire Home, JQS for J-Quarters, SBO for SALWACO Bo and AVG for the average of the individual parameters for all the wells. Also note that the values are averages over the months of March, April and May 2010.

To ensure quality control during the sample collection, disposable or sterilized reusable equipment were used. Reused equipment was sterilized in 10% hydrochloric acid (HCL) solution, triple rinsed in deionized water and triple rinsed in water from sampling wells prior to sample collection (Conrad et al. 1999; Wang et al. 2012). The 0.45 µ filters were used at each site for the parameters (e.g., coliform bacteria algae and particulates that could contaminate or easily absorb water elements) determinable by filtration and reusable filter apparatus sterilized as well. Also field meter probes and other accessories were rinsed with deionized water prior to and after use. To preserve the representativeness of water samples of the originating well conditions, extra care was taken to prevent accidental contaminations during sampling, delivery and storage.

Collected water samples were stored in ice-cooled hard-foam boxes for delivery to the laboratory. Sample containers were labelled with sampling numbers, which were then related to site name, sample collection date, preservation method and required analysis. At least one duplicate/backup sample was submitted along with each batch of samples (Carey & Stickney 2001; Ho et al. 2012). As the study involved large diameter wells, a case-by-case purging was done to ensure that fresh formation waters were sampled (Shortt et al. 2003). Wells without pumps were purged manually, preferably using bailers already installed in the wells. Wells with pumps were run at high flow for at least 20 min to purge the lines/walls of stagnant water before sampling. The mouth of the pump was then sterilized with alcohol or 10% HCL and the pump run for another 2 min before collecting water samples (CHEMetrics 2000).

Sophisticated biomedical (e.g. streptococci and coliforms), biological (e.g. chlorophyll as an indicator for eutrophication) and physico-chemical (e.g. electrical conductivity (EC), pH, hardness, nitrate, chloride and phosphorus) methods could be used to determine pollutant loads in well waters (Carey & Stickney 2001; Rajasooriyar 2003). In this study, CO150 conductivity meter was used to determine EC, salinity and total dissolved solids (TDS) of well waters. The MV pH-meter was used to measure well-water pH and temperature. Temperature, EC and pH were measured on-site using the calibrated portable meters and results recorded in field log books (O'Dell et al. 2004; Clasen et al. 2009). Other physical analyses such as chemical oxygen demand were also done on-site in the field. Hygrometer was used to measure the depth to water and depth of water column in the wells via a double alarm system – the first at well-water surface and the other at well bed. For wells without pumps, sterilized, weighed bottles with string and stone were used. The bottles were carefully lowered into the wells right to the bottom without touching well walls, pulled out and stoppered afterwards.

In the laboratory analysis, a digital titrator was used to determine chloride (Cl) levels in the collected well-water samples. Digital titrator is a high-precision dispensation device fitted with compact cartridges that contain concentrated titrants for titrimetric analyses in the laboratory, plant or field. While the transmittance photometer was used to determine phosphate (PO43−) levels, the spectrophotometer (complete with microprocessor data-loggers for field/laboratory colorimetric tests) was used to determine sulphate (SO42−), iron (Fe2+) and nitrate (NO3) levels in well-water samples. Then, the zero-model refrigerator was used to keep stored water samples at 4 °C for later chemical analysis. Boilers were used to sterilize sample kits, culture media membranes and lauryl sulphate broth.

Samples were collected once per month between 08:00‒10:00 hrs local time for a period of three months (March, April and May) in 2009. A total of about 300 mL of water was collected per well per sampling and the collected samples protected from direct sunlight using special protective containers. The mean values of the analysed parameters in this study are given in Table 2. All the samples were transferred to Sierra Leone Water Company (SALWACO) headquarters in Freetown for analysis. For appropriate preservation, the split sample method was used in the analysis. For chemical analysis, split samples were acidified in 2 mL of 10% HNO3 and stored in zero-model refrigerators at 6 °C. All chemical analyses were done within 48 hr of sample collection. Also bacteriological analyses were run within 6 hr of sample collection.

As previously discussed, some of the analyses were performed in the laboratory and some others done on-site in the field. In either case, standard procedures (in terms of instrument setup, use, sample collection/handling or water quality analysis) were observed. Such procedures have been documented by APHA (1998) and O'Dell et al. (2004) and the operation and analysis manuals of the used instruments and laboratory water quality analyses. In all this, extra care was taken to avoid errors from sampling instrument preparation through sample analysis and data recording.

RESULTS AND DISCUSSION

Monthly trend

Figure 2 plots the measured water quality parameters for the months of March, April and May, spatially averaged for the 10 investigated wells across the study area (also see Table 3). The plots in Figure 2 are grouped on the basis of the units of measurement of the parameters. The trends of each parameter are similar for the three months, which belong to the rainy summer season in the study area. Although the climatic conditions are similar for the months, the processing of the parameters in well water could be different (NRDS 2009). About 17% of the parameters are highest in March, 33% in April, 44% in May and 6% in April and May. Then 44% of the parameters are lowest in March, 17% in April, 22% in May, 11% in April and May, and 6% in March and April. Overall, the parameters are lowest for PO42− and highest for TDS (Table 3). The geological/climatic conditions, anthropogenic effects and reactions among the parameters (but also with the bearing geo-matrix) could influence the observed concentrations. Generally, however, the monthly differences in the individual parameters are small (Figure 2). Note also that the values of some of the parameters (e.g., PO42− and non-fecal coliform (NFC)) are the same for two of the three months (Table 3). Furthermore, note that the data are collected for a short period of time and therefore the temporal analyses here should be treated with caution.
Table 3

Monthly values of measured water quality parameters averaged for all the 10 wells investigated in the Kakua Chiefdom study area in Bo District, Sierra Leone

ParameterUnitMarchAprilMayMean
Phosphate (PO42−mg/mL × 10−2 0.00 0.01 0.01 0.01 
Manganese (Mn2+mg/mL × 10−2 0.03 0.04 0.03 0.03 
Salinity (Sal) mg/mL × 10−2 0.04 0.03 0.02 0.03 
Iron (Fe2+mg/mL × 10−2 0.01 0.01 0.11 0.04 
Fluoride (Fmg/mL × 10−2 0.45 0.44 0.44 0.44 
Turbidity (Turb) mg/mL × 10−2 0.94 1.19 1.51 1.21 
Copper (Cu2+mg/mL × 10−2 1.22 1.24 1.23 1.23 
Sulphate (SO42−mg/mL × 10−2 2.90 2.91 2.80 2.87 
Nitrate (NO3mg/mL × 10−2 5.14 6.42 7.00 6.19 
Chloride (Clmg/mL × 10−2 23.80 25.00 28.00 25.60 
Calcium hardness (Hard) mg/mL × 10−2 26.80 26.40 27.10 26.77 
Carbonate (CO32−mg/mL × 10−2 30.70 29.10 31.00 30.27 
Non-faecal coliform (NFC) μm/mL × 10−2 8.00 12.05 8.00 9.35 
FC μm/mL × 10−2 10.50 14.00 8.75 11.08 
pH — 6.64 6.67 6.69 6.67 
Temperature (Temp) °C 27.55 28.56 27.68 27.93 
EC μS/cm 50.86 46.08 47.75 48.23 
TDS ppm 94.74 96.9 97.65 96.43 
ParameterUnitMarchAprilMayMean
Phosphate (PO42−mg/mL × 10−2 0.00 0.01 0.01 0.01 
Manganese (Mn2+mg/mL × 10−2 0.03 0.04 0.03 0.03 
Salinity (Sal) mg/mL × 10−2 0.04 0.03 0.02 0.03 
Iron (Fe2+mg/mL × 10−2 0.01 0.01 0.11 0.04 
Fluoride (Fmg/mL × 10−2 0.45 0.44 0.44 0.44 
Turbidity (Turb) mg/mL × 10−2 0.94 1.19 1.51 1.21 
Copper (Cu2+mg/mL × 10−2 1.22 1.24 1.23 1.23 
Sulphate (SO42−mg/mL × 10−2 2.90 2.91 2.80 2.87 
Nitrate (NO3mg/mL × 10−2 5.14 6.42 7.00 6.19 
Chloride (Clmg/mL × 10−2 23.80 25.00 28.00 25.60 
Calcium hardness (Hard) mg/mL × 10−2 26.80 26.40 27.10 26.77 
Carbonate (CO32−mg/mL × 10−2 30.70 29.10 31.00 30.27 
Non-faecal coliform (NFC) μm/mL × 10−2 8.00 12.05 8.00 9.35 
FC μm/mL × 10−2 10.50 14.00 8.75 11.08 
pH — 6.64 6.67 6.69 6.67 
Temperature (Temp) °C 27.55 28.56 27.68 27.93 
EC μS/cm 50.86 46.08 47.75 48.23 
TDS ppm 94.74 96.9 97.65 96.43 
Figure 2

Trends in measured well-water quality parameters for the months of March, April and May averaged spatially for the 10 investigated wells across the Kakua Chiefdom study area in Bo District, Sierra Leone. Note that phosphate (PO42−), iron (Fe2+), manganese (Mn2+), salinity (Sal), fluoride (F), turbidity (Turb), copper (Cu2+), sulphate (SO42−), nitrate (NO3), chloride (Cl), hardness (Hard), carbonate (CO32−), EC, NFC and FC. Also note that the separate plots are desirable because the units of measurement of the parameters are different.

Figure 2

Trends in measured well-water quality parameters for the months of March, April and May averaged spatially for the 10 investigated wells across the Kakua Chiefdom study area in Bo District, Sierra Leone. Note that phosphate (PO42−), iron (Fe2+), manganese (Mn2+), salinity (Sal), fluoride (F), turbidity (Turb), copper (Cu2+), sulphate (SO42−), nitrate (NO3), chloride (Cl), hardness (Hard), carbonate (CO32−), EC, NFC and FC. Also note that the separate plots are desirable because the units of measurement of the parameters are different.

Well-water temperature can be influenced by a range of factors, including moderation effect of nearby surface water, top-down solar heat flow, bottom-up earth mantle heat flow or heat flow from within due to microbial activity and/or pollutant load. As the wells lie in similar biogeophysical and hydroclimatic conditions, the differences in well temperature could be due to generated heat from within by microbial or pollutant reactions. Generally, the occurrence of coliforms could indicate the availability of nutrients in the well waters. Nutrients such as NO3 and PO42− originating from point/non-point sources and the bearing geo-matrix could end up in the well waters. Also chemicals from farmlands and light industries near the wells can generate significant nutrients which enter and support microbial growth in the wells. The slightly acidic conditions of the well waters also favour microbial growth. This increases health risks for people that depend on the wells for various uses.

Mean temporal trend

The last column of Table 3 gives the values of the measured well-water quality parameters averaged temporally for the months of March through May and spatially for the 10 investigated wells across the Kakua Chiefdom study area in Bo District, Sierra Leone. In the table, the average values of the water quality parameters are arranged from the lowest to the highest. On this basis and that of the units of measurement of the parameters, the lowest value is for PO42+ (0.01 mg/mL × 10−2) and the highest for CO32− (30.27 mg/mL × 10−2). Also the count of NFC (9.35 um/mL × 10−2) is lower than that of fecal coliform (FC) (11.08 um/mL × 10−2). Although temperature is highest in April, about 39% of the parameters show an increasing trend from March to April. Temperature pH, EC, TDS and coliform have different units of measurement and therefore are given at the bottom end of the Table 3 as it is not sensible enough to rank with the other parameters.

The average concentration of NO3 (6.19 mg/mL × 10−2) is also higher than that of SO42− (2.87 mg/mL × 10−2), while Fe2+ concentration is generally low (0.04 mg/mL × 10−2). Numerous studies show positive correlation between temperature and nitrogen processing (Starry et al. 2005; Schaefer & Alber 2007). Why this could be influenced by temperature, the role of other factors such as point (e.g., septic tanks for FC) and non-point (e.g., farmlands for NO3) source pollutions cannot be entirely ignored. Fe2+ and some other substances could originate from the geologic formations, but also biochemical reactions in the water wells. Although the measured values of the water quality parameters are generally low, prolonged non-monitoring could wreck health havoc and trigger widespread epidemic diseases among the population that are dependent on the water wells for water supply.

Mean spatial trend

The well-by-well values of the analysed water quality parameters averaged temporally for the three months are presented in Table 2. Based on the average values in the last column of Table 2, well NYK has the lowest and Dipha Street (DST) the highest values. This suggests that NYK is the least polluted well and DST the highest polluted wells in the study area. The NYK community where well NYK is located is a new emerging section of Bo Town with a sparse, affluent and literate population. Well NYK is also covered with metal sheet, fenced with concrete and has low use intensity due the low population in the community; thus the least contamination. On the contrary, well DST along Dipha Street is in a crowded community that is largely poor and illiterate. The well also has a high use intensity, minimally protected and thus has high contamination.

For the period from March to May, average temperature is highest for well St Andrews (SAS), followed by SALWACO Bo (SBO) and NYK. It is lowest for well Sewa River Plant (SRP) and J-Quarters (JQS). The wells with high temperatures are in the relatively sparsely populated areas like the outskirts of the city and often far from open water bodies like rivers. Although some low temperature sites are also on the outskirts of the city, they are generally in proximity with surface river systems, and could be influenced by temperature-moderation effects of surface water on the surrounding micro-environments (Moiwo et al. 2010).

TDS is highest (233.33 ppm) for well DST and lowest (34.70 ppm) for well NYK. FC is highest (33.33 μm/ mL × 10‒2) for well DST and lowest (0.00 μm/mL × 10‒2) for wells NYK, Cheshire Home (CHE) and SBO. Also NFC is highest (15.00 μm/mL × 10‒2) for wells DST and JQS and lowest (1.67 μm/mL × 10‒2) for well NYK. The highest NO3 (13.03 mg/mL × 10‒2) is for well Water Street (WST) and the lowest (3.63 mg/mL × 10‒2) for well DST. Equally, the highest (12.00 mg/mL × 10‒2) and lowest (1.00 mg/mL × 10‒2) SO42− are for wells SRP and Sewa Road (SRD), Kebbie Town (KTN) and NYK, respectively. Cl is highest (72.33 mg/mL × 10‒2) for well DST and lowest (6.67 mg/mL × 10‒2) for well SRP (Table 3).

WST, SRD and DST regions all belong to the downtown part of Bo Town, which carries relatively high population density with a diverse social class. Therefore, the tendency for well contamination in the region (e.g., DST, KTN and SRD) is fairly high (Figure 3). The J-Quarter and SALWACO wells have low contamination. J-Quarter is government residential area and SALWACO is the responsible company for well-water supply in rural Sierra Leone, meaning that wells under these jurisdictions have low tendency for contamination. However, water wells in the outskirts of the city (e.g., NYK, SBO and JSQ) could be at risk of non-point source pollution from farmlands and processing industries. Well-water contamination always carries diverse health risks (including epidemic diseases) among the population (Wang et al. 2012), especially in the developing world (WHO/UNICEF 2012).
Figure 3

Trends in measured well-water quality parameters averaged for each well for the 10 investigated wells across the Kakua Chiefdom study area in Bo District, Sierra Leone. Note that the top left plot is for phosphate (PO42−), iron (Fe2+), manganese (Mn2+), salinity (Sal), fluoride (F), turbidity (Turb), copper (Cu2+), sulphate (SO42−), nitrate (NO3), chloride (Cl), hardness (Hard) and carbonate (CO32−); the top right plot is for FC and NFC; the middle left plot is for EC; the middle right plot is for TDS; the lower left plot is for temperature (°C); and lower right plot is for acidity/alkalinity (pH). The well code SRD is for Sewa Road, SRP for Sewa River Plant, DST for Dipha Street, KTM for Kebbie Town, NYK for New York, SAS for St Andrews, WST for Water Street, CHE for Cheshire Home, JQS for J-Quarters, and SBO for SALWACO Bo. Also note that the separate plots are desirable because the units of measurement of the parameters are different.

Figure 3

Trends in measured well-water quality parameters averaged for each well for the 10 investigated wells across the Kakua Chiefdom study area in Bo District, Sierra Leone. Note that the top left plot is for phosphate (PO42−), iron (Fe2+), manganese (Mn2+), salinity (Sal), fluoride (F), turbidity (Turb), copper (Cu2+), sulphate (SO42−), nitrate (NO3), chloride (Cl), hardness (Hard) and carbonate (CO32−); the top right plot is for FC and NFC; the middle left plot is for EC; the middle right plot is for TDS; the lower left plot is for temperature (°C); and lower right plot is for acidity/alkalinity (pH). The well code SRD is for Sewa Road, SRP for Sewa River Plant, DST for Dipha Street, KTM for Kebbie Town, NYK for New York, SAS for St Andrews, WST for Water Street, CHE for Cheshire Home, JQS for J-Quarters, and SBO for SALWACO Bo. Also note that the separate plots are desirable because the units of measurement of the parameters are different.

The highest FC and NFC counts are for well-sites SRP, SRD, WST and DST, different from the highest temperature wells. This further suggests that well-water temperature in the study area is influenced by a multitude of factors. Wells with the highest and lowest pH are at the SAS and WST sites, respectively. This is another indication that while well-water pH in the region is largely neutral (7.00), wells with high temperatures have relatively high pH (alkalinity) and wells with low coliforms have relatively low pH (acidic). Thus although coliform concentrations in the wells are currently low, a shift from alkaline to acidic conditions could favour bacteria growth and well contamination (UNICEF/WHO 2012). Wells with the highest and lowest EC are SRP and CHE, respectively, suggesting that high-temperature and low-coliform wells as well have low EC. Further details on the relativity of the measured water quality parameters are discussed in the next sections.

Well DST has the highest hardness, TDS, CO32−, Cl, FC and NFC. The water sample from SALWACO (SBO) has among the lowest concentrations of the water quality parameters. This suggests that on average, water quality of SBO is the best and that of DST the worst in the study area. This is not surprising because SBO is located in SALWACO compound, which is the officially responsible body for rural water supply in Sierra Leone. The ranges for temperature, pH, Fe2+, Mn2+, F, salinity and turbidity across the investigated wells are fairly similar. The wells differ mainly in TDS, EC and coliform amounts. This provides a useful guide on where local and national efforts should be directed in securing safe drinking water supply in the country.

Correlation matrix

The correlation matrix of the water quality parameters in Tables 2 and 3 is given in Table 4. The highest negative correlation in each column of Table 4 is highlighted with light grey and the highest positive correlation highlighted with dark grey. While NFC tops the list (15) in terms of the number of positive correlations, salinity tops ranks the highest (12) for the number of negative correlations. Based, however, on the number of positive correlations with R = 1.00, Fe2+ tops the list (10), followed by SO42− (6). Then the maximum number of negative correlations with R = −1.00 is 3, and that is for salinity and turbidity, Fe2+ and F, and then FC and Cl (Table 4). The correlation matrix in Table 4 further suggests that in the study area, PO42− is most positively correlated (R = 0.97) with TDS and most negatively correlated (R = 0.99) with Fe2+. There are other water quality parameters in Table 4 with strongly positive correlations (R = 1.00).

Table 4

Correlation matrix for the 18 analysed well-water quality parameters in the Kakua Chiefdom of Bo District, Sierra Leone

RPO42−SalMn2+Fe2+F TurbCu2+SO42−NO3pHNFCFCClHardTempCO32−EC
Sal −0.87                 
Mn2+ 0.73 −0.29                
Fe2+ 0.50 −0.87 −0.86               
F  −0.99 0.92 −0.01 −1.00              
Turb 0.83 −1.00 −0.70 0.96 0.80             
Cu2+ 0.63 −0.16 1.00 1.00 −0.47 −0.17            
SO42− −0.43 0.82 0.88 1.00 0.75 0.48 1.00           
NO3 0.95 −0.98 −0.41 1.00 0.55 0.42 0.99 1.00          
pH 0.92 −0.99 −0.54 1.00 0.38 0.98 0.74 −0.21 0.86         
NFC 0.50 0.00 0.99 0.98 0.30 0.62 0.75 −0.20 0.35 0.58        
FC 0.19 0.33 0.96 0.96 0.44 0.30 0.97 1.00 0.00 0.17 1.00       
Cl 0.72 −0.97 −0.78 1.00 0.33 −0.42 0.84 1.00 1.00 0.79 −0.68 −1.00      
Hard −0.08 −0.43 −0.95 1.00 0.34 1.00 0.75 −0.20 −0.13 0.01 0.99 −0.29 −0.80     
Temp 0.60 −0.12 1.00 1.00 0.31 −0.89 0.73 1.00 −0.12 −0.18 0.96 0.90 −0.31 −0.96    
CO32− −0.37 −0.15 −0.98 1.00 0.27 −0.95 0.37 1.00 0.72 0.87 0.41 −0.66 0.87 0.57 0.95   
EC −0.94 0.64 −0.92 1.00 0.35 0.52 1.00 0.48 0.89 0.98 1.00 −0.53 0.28 0.44 0.87 0.06  
TDS 0.97 −0.96 −0.31 1.00 −0.99 0.05 0.89 0.84 −0.29 1.00 0.98 0.97 0.90 0.66 −0.88 −0.69 −0.83 
RPO42−SalMn2+Fe2+F TurbCu2+SO42−NO3pHNFCFCClHardTempCO32−EC
Sal −0.87                 
Mn2+ 0.73 −0.29                
Fe2+ 0.50 −0.87 −0.86               
F  −0.99 0.92 −0.01 −1.00              
Turb 0.83 −1.00 −0.70 0.96 0.80             
Cu2+ 0.63 −0.16 1.00 1.00 −0.47 −0.17            
SO42− −0.43 0.82 0.88 1.00 0.75 0.48 1.00           
NO3 0.95 −0.98 −0.41 1.00 0.55 0.42 0.99 1.00          
pH 0.92 −0.99 −0.54 1.00 0.38 0.98 0.74 −0.21 0.86         
NFC 0.50 0.00 0.99 0.98 0.30 0.62 0.75 −0.20 0.35 0.58        
FC 0.19 0.33 0.96 0.96 0.44 0.30 0.97 1.00 0.00 0.17 1.00       
Cl 0.72 −0.97 −0.78 1.00 0.33 −0.42 0.84 1.00 1.00 0.79 −0.68 −1.00      
Hard −0.08 −0.43 −0.95 1.00 0.34 1.00 0.75 −0.20 −0.13 0.01 0.99 −0.29 −0.80     
Temp 0.60 −0.12 1.00 1.00 0.31 −0.89 0.73 1.00 −0.12 −0.18 0.96 0.90 −0.31 −0.96    
CO32− −0.37 −0.15 −0.98 1.00 0.27 −0.95 0.37 1.00 0.72 0.87 0.41 −0.66 0.87 0.57 0.95   
EC −0.94 0.64 −0.92 1.00 0.35 0.52 1.00 0.48 0.89 0.98 1.00 −0.53 0.28 0.44 0.87 0.06  
TDS 0.97 −0.96 −0.31 1.00 −0.99 0.05 0.89 0.84 −0.29 1.00 0.98 0.97 0.90 0.66 −0.88 −0.69 −0.83 

Note that R is correlation coefficient, PO42− is phosphate, Sal is salinity, Mn2+ is manganese, Fe2+ is iron, F is fluoride, Turb is turbidity, Cu2+ is copper, copper SO42− is copper, NO3 is nitrate, pH is acidity/alkalinity, NFC is non-faecal coliform, FC is faecal coliform, Cl is chloride, Hard is calcium hardness, Temp is temperature, CO32− is carbonate and TDS is total dissolved solids.

The trends in Table 4 suggest that the occurrence of some water quality parameters could either enhance or suppress the occurrence of some others. For instance, increasing Fe2+ concentration increases the concentrations of a number of the other parameters. Although not on the same scale as Fe2+, increasing SO42− concentration also increases the concentrations of a number of substances in the wells. Fe2+ and SO42− have a strong tendency to react with several other elements or compounds, or even facilitate the availability of nutrients in well waters. Also, high well water salinity limits the concentration of other substances in the wells. Although Table 4 shows no correlation between salinity and coliform (FC and NFC), salt is a common preservative widely used especially against microbial decay.

Thus Table 4 can be explained in terms of the effect of the occurrence of one substance on the occurrence of others in the well waters. The full chemistry of these effects is beyond the scope of this study. For safe water delivery, however, treatments for Fe2+/SO42− could limit the occurrence of other substances in well waters. Also, while salt generally limits the use of water, the negative correlation it has with most of the tested parameters suggests that salt as well limits the occurrence of pollutants in well waters.

Coliform (faecal or non-faecal), is commonly associated with various water-borne diseases that could easily develop into destabilizing epidemic conditions. Diarrhoea, typhoid, dysentery, cholera, etc., are common water-borne diseases with seasonal outbreaks (UNICEF/WHO 2009, 2012; WHO 2012b), especially in a developing country like Sierra Leone. Thus the mere occurrence of especially FC in all the investigated wells is a major health concern for the already diseased and poor population of Sierra Leone. It is therefore important that the national health sector, in collaboration with the government, takes required measures to completely eliminate health risks in all drinking water supplies across the country.

CONCLUSIONS

In this study, water samples from 10 representative wells in the Kakua Chiefdom of Bo District are analysed for 18 water quality parameters. Based on the national standards, the well waters are slightly contaminated for drinking and other uses – a source of water-borne infections and diseases in the country.

Well-water quality can be influenced by the bearing geo-matrix, anthropogenic factors, or both. The diverse occurrence of so many of the tested parameters confirms that well-water quality in the study area is a function of the bearing geo-matrix, the climatic conditions and anthropogenic factors. Irrespectively, it is important that future well-water quality studies analyse in details the climatic conditions, geologic formation, population trend and land-use activities in the areas of study in the country.

The wells are slightly acidic and with low Fe2+ concentration, which favour organic decay or minerals dissolution in the presence of NO3. Also the occurrence of NO3 in drinking water could cause methemoglobinemia (blue-baby) syndromes, especially in children and the pregnant. Domestic or agricultural use of contaminated water carries health risks like epidemic outbreaks. It is therefore critical that site geology and environment are thoroughly analysed prior to well constructions as preventive measures against well water contamination.

ACKNOWLEDGEMENTS

This study was supported by the Sierra Leone Government Scholarship (SLG) and the China Scholarship Council (CSC). We are thankful to the chemistry department of Njala University for inspiring and designing the study and to SALWACO for allowing use of its laboratory. We thank the anonymous reviewers and editor for the resourceful inputs by way of the invaluable comments during the manuscript review phase.

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