OR/17/056 Groundwater quality studies in urban areas

Introduction

Urbanisation processes are the cause of extensive but essentially diffuse pollution of groundwater by nitrogen and sulphur compounds, salinity as well as pathogenic bacteria, protozoa and viruses (Morris et al., 2003). Household attitudes to hazards posed by drinking water can enhance quality problems with poor water treatment, types of drinking water vessels, hand washing practices, perceptions of safe water quality using only visual parameters (normally clarity of the water), and knowledge on waste disposal practices (Kioko et al., 2012).

This section firstly reviews a wide range of water quality studies in urban and peri-urban settings in SSA (n=44) assessing the impacts of urbanisation on groundwater quality, these are summarised in Table 7.1. Summary statistics, across all 44 studies for more commonly analysed water quality indicators have been summarised by in Figure 7.1. The second section then reviews case studies (n=16) focussed on the impact of on-site sanitation, principally pit latrines, on groundwater quality, these are summarised in Table 7.2. Targeted studies focused on non-sanitary sources of contamination such as industries, historical mining legacy and waste dumps/landfills are shown in Table 7.3. Key details and conclusions from these studies are summarised and critically reviewed. A hand full of case studies include both specific assessments of impacts of pit latrines as well as broader environmental hygiene considerations in spring catchments and well capture zones and are included in both Table 7.1 and 7.2.

Overall, compared to other regions globally there have been relatively few studies carried out in SSA. The review draws on studies published in books, research articles and reports, and it is recognised that these have been published for a range of purposes. With this in mind, the studies can be categorised into two broad groups. The first being short case-studies presenting data from a limited number of groundwater sites (n<20), limited temporal resolution as a single survey or use only basic chemical indicators (n<3). The second being examples which either draw from larger data sets or include both chemical and microbiological indicators or have greater temporal resolution.

File:OR17056fig7.1.jpg

Figure 7.1    Summary statistics for common water quality indicators across 44 studies included in Table 7.1. Units for values on x-axis (log scale): a=counts/100 mL, b=μs.cm^-1 and c=mg/L.

Table 7.1    Groundwater quality surveys in Sub-Saharan urban and peri-urban areas (n=44).

Area	Geology	Sample sites (n)	Selected results for TTC, TC, FC, (mg/L) for inorganic chemistry*			Sampling time frame	Conclusion and sources of contamination	Reference
Dakar, Senegal	Quaternary	Wells (56)	NO3 0–122			July–October 1997	Nitrate contamination from point-source seepage in urban areas	Cissé Faye et al. (2004)
Conakry, Guinea	Volcanic rocks, fissured	Wells (69)	Fountains TC 0–39 FC 0–28 FS 0–2 NO30–2.5 NH40–0.05 Cl 1.2–1.8 F 0–0.05 Turb. 0.9–6.1	Mod.wells TC n.d FC 370–1x105 FS 90–9x103 NO3 2–46 NH4 0.06–7 Cl 17–130 F 0–0.16 Turb. 1–70	Trad. wells TC n.d FC 50–2 x105 FS 150–2 x104 NO37–51 NH4 0.01–8 Cl 8–284 F 0.0.38 Turb. 1–63	Dry season April–May 1994	Widespread contamination by nitrate and FC linked to poor sanitation and well construction	Gélinas et al. (1996b)
Various. Ivory coast	Basement	Boreholes (230)	NO3 mean 69 Cl mean 24			1981 and 1982	High nitrate (up to 200 mg/L) linked to domestic pollution and deforestation	Faillat (1990)
Bolama City, Guinea Bissau	Sandy soils and Cenozoic — Modern sediments	Wells (28)	Turbidity 1–26, mean 6.5 SEC 27–326, mean 136 TC 0–23000, mean 2306 FC 0–5000, mean 410 Fecal Enterococci 0–850, mean 74 NO3 0.9–55.3, mean 16.6 NH4 0.01–1.37, mean 0.11 NO2 0.03–0.13, mean 0.04 Cu, Fe, Cr, As			July 2006	80% of wells contaminated with FC linked to widespread use of PL	Bordalo and Savva-Bordalo (2007)
Cotonou, Benin	Quaternary to mid Pleistocene sandstone	Dug wells in upper aquifer in densely populated area (379)	SEC 320–1045 Mn 0.06–0.19 NO3 10.4–118 PO4 <0.05–21.6 SO4 3.14–86.3			May 1991, August 1991 and April 1992	High P and K concentrations in upper aquifers linked to anthropogenic pollution	Boukari et al. (1996)
Kumasi, Ghana	Precambrian Basement	Hand-dug wells (10)	TDS 6–230, mean 113 NO3 0–0.968, mean 0.16 PO4 0.67–15, mean 7.8 TH 8–103, mean 54 TC and EC <20			N/A	Water quality survey showed that water quality parameters were within WHO drinking water guideline values	Nkansah et al. (2010)
Kumasi, Ghana	Precambrian Basement	Borehole and wells in peri-urban communities (9)	Fe 0.001–0.955 Mn 0.018–0.238 Pb 0.005–0.074 TC 3–16.8×106 FC 1.5–4.37×104 Enterococci 1.3–53.5			Monthly between Dec 2000 and Jan 2001	Poor quality overall, contamination linked to proximity to PL and refuse tips as well as livestock	Obiri-Danso et al. (2009)
Niamey, Niger	Basement	Wells (20)	NO3 1.4–162, mean 28 δ15N >+12‰			Monthly between July 1989 and April 1990	Contamination found even in deeper sites up to 10 mg/L. Source of loading (δ15N) linked to rapid urbanisation (latrines) and deforestation.	Girard and Hillaire-Marcel (1997)
Ilesha, Nigeria	Basement	Wells (86)	Mean results: NO3 35 Cl 34 SO4 2.8			Single survey	Evidence of anthropogenic impact on water quality degradation	Malomo et al. (1990)
Benin City, Nigeria	Quaternary to mid Pleistocene sandstone	Boreholes and open wells (6)	Pb 0.03–0.25 Zn 0.98–7.19 Cr 0.02–1.1 Cd Nd–0.23 FC 4600–240000 FS 600–35000			Single survey	Elevated Pb, Cr, Cd and Zn attributed to indiscriminate waste disposal and FC occurrence linked to PL, soak-always and septic tanks	Erah and Akujieze (2002)
Calabar, Nigeria	Tertiary to recent sands and gravels	Existing wells (20)	BOD 0.06–4.09, mean 1.72 N 0.09–3.5, mean 2.15 Cl 0.1–1, mean 0.45 FC 0.75–4.32, mean 1.86			N/A	FC, nitrate and Cl had a positive correlation with urbanisation	Eni et al. (2011)
Ibadan, Nigeria	Basement, banded gneiss and schist	Existing wells (N/A)	TSS 159–186.6, mean 174 Cl 1.1–10, mean 5 TC 2300–9200, mean 5120			Dry season	Gross pollution of groundwater attributed to poor well construction, PL and waste management	Ochieng et al. (2011)
Lagos, Nigeria	Alluvium over sedimentary	Urban wells (18)	TDS 79–1343, mean 514 TH 24–289, mean 110 (as CaCO3?) Na 8–274, mean 79 NO3 0.05–1.51, mean 0.4 Pb 0–1.9, mean 1.6 Zn 0–4.2 mean 0.3			Survey August to October 2004	Sources of contamination included sanitation, textiles, pharmaceuticals, food, tanneries, motor industry	Yusuf (2007)
Ibogun, Pakoto, Ifo, Ogun State, Nigeria	Cambrian basement geology and weathered regolith	Dug wells, communities of 5000–20 000 people (20)	TDS 100–2200 TH 6–246 NO3 0.8–88 TC 0–0.6 (cfu x105) FC 0–0.2 (cfu x105) FS 0–0.7 (cfu x105)			July-August 2009	Water quality standards for nitrate, FC, FS not met for significant proportion of wells	Adelekan (2010)
Surulere, Lagos, Nigeria	Alluvium over sedimentary	Wells and boreholes in a middle class area (49)	Al 1–99 µg/L Cd 1–98 µg/L Pb 1–24 µg/L			July 2009	Pb and Cd above WHO drinking water standards in >30% of sites	Momodu and Anyakora (2010)
Abeokuta, Nigeria	Basement igneous and metamorphic	Shallow wells including sanitary survey (40)	All bacterial count>20 Maximum 800 EC+PA+SAL			December 2005	Shallow groundwater is highly contaminated with bacteria. Sources include pit latrines, livestock and solid waste	Olabisi et al. (2008)
Urban & peri-urban area, Abeokuta, Nigeria	Basement igneous and metamorphic	Shallow wells (76)	Urban TDS 402 TH 30.3 NO3 12.02 PO4 0.21 Pb 0.25 Zn 0.12 TC 10500	Peri-urban TDS 263 TH 31.7 NO3 10.7 PO4 0.03 Pb 0.19 Zn 0.09 TC 10000		Dry season	Mean values for Pb, nitrate EC and TC > WHO standards. Trading, textiles, transport, cottage industries, pit latrines Generally higher in dry season	Orebiyi et al. (2010)
Peri-urban area, Abeokuta, Nigeria	Basement igneous and metamorphic	Hand-dug wells (25)	TDS 50–270, mean 163 NO3 2.97–40.7, mean 17.6 NH4 0–0.59, mean 0.11 PO4 12–86 µg/L, mean 46 TH 12–210, mean 106 (as CaCO3?)			Rainy season 2008	Direct surface run off into wells is suggested as possible contamination source	Taiwo et al. (2011)
Warri River plain, Delta, Nigeria	Alluvial Benin formation	Boreholes near WW treatment plant	TDS 16–81 COD 0.4–44.4 NO3 0.3–1.2 Fe 0.05–0.15			2 year sampling campaign	River infiltration, municipal wastewater, agriculture, oil industry	Ibe and Agbamu (1999)
Warri River plain, Delta, Nigeria	Quaternary and older sedimentary sequences	Dug wells	Fe 0.32–2.75 Pb 0.058–0.443 Ni 0.008–0.188 V 0–4 Cr 0–9 Cd 0.75–8.5 Zn 0–1.8			N/A	Pb, Ni exceed WHO standards. Sources include Warri River, settlement,refinery. Highest values in village 3 km from refinery	Aremu et al. (2002)
Calabar, Nigeria	Lacustrine deposits overlying gravels	Boreholes in low income area (8)	SEC 52–321, mean 149 µS/cm NO3 0.23–10.63, mean 2.3 NO2 0.001–0.027, mean 0.008 NH4 0–5.18, mean 0.88 PO4 0.007–0.122, mean 0.012 TC 0–7, mean 2.1			Wet and dry season July–Dec 1999	Pollution in low income area linked with pit latrines and shallow waste dump	Ugbaja and Edet (2004)
Masaka, Nigeria	Cretaceous sandstone and clay	Dug wells, high density (12)	TDS 528–935 NO3 44.5–92.5 Alk 67–179 Cl 41–118 Fe 0.085–0.199 Cr 0.005–0.0126 Total bacteria 25900–78400			Samples taken in wet season but not during rainfall events to avoid contamina-tion from surface runoff	WHO standards exceeded for a range of contaminants including nitrate, TDS, Cr, Cd and TC. High density settlement with shallow water table	Alhassan and Ujoh (2011)
Yaounde, Cameroon	Basement	Springs and wells in high density area (>40)	For groundwater SEC 18.2–430, mean 87 FC 60% >100 FS 5%>100			-	Groundwater’s in high density zones show significant degradation (chemical and microbiological), linked to PL	Ewodo et al. (2009)
Douala, Cameroon	Alluvium over Pliocene sand and gravel	Springs, wells and boreholes (72)	SEC 25–362 NO3 0.21–94.3 FC 0–2311			-	High levels of FS indicative of contamination from PL, related to age and density of settlement	Takem et al. (2010)
Kinshasa, DR Congo	Alluvial and sedimentary sequences	Wells including sanitary survey	Dry season TDS 180–450 NO3 76–118 PO4 0.53–4.6 TH 110–149 Pb 0.04–0.09 Cd 0.13–0.20	Wet season TDS 200–710 NO3 97–198 PO4 3.6–14.6 TH 17–52.5		-	Latrines, metal works, solid waste dump	Vala et al. (2011)
Mekelle, Ethiopia	Mesozoic sediments	Wells, springs and boreholes (100)	SEC 542–5300 TDS 330–3454 NH4 0.01–2.38 NO3 0.21–336 Cl 5.76–298 F 0–1.27 PO4 0.001–0.58			N/A	Highly variable water quality indicative of a range of redox zones and sources of contamination	Berhane and Walraevens (2013)
Bahir Dar, Ethiopia	Weathered and fractured Alkaline Basalt	Dug wells and protected pumps in inner, middle and outer zones (8)	Middle and inner city TDS 20–600 NO3 0.18–57.2 NH4 0–12 Cl 46–270 FC 93% of sites, mean 1.5 log cfu EC 80% sites mean 1.4 log cfu	Outer city TDS 20–70 NO3 0.08–8.8 NH4 0–12 Cl 0–40		Sampling over a 5 month period 2006/2007	Groundwater contamination linked to population density and urbanisation. All dug wells and boreholes had microbiological contamination in excess of WHO/EU standards. Dug wells had significantly higher FC.	Goshu and Akoma (2011) Goshu et al. (2010)
Addis Ababa, Ethiopia	Volcanics	Boreholes and springs (9)	Alk 8–41 NO3 0.72–35 NO2 <0.01 COD 6.8–41 Cl 6.8-28 PO4 <0.03–0.1 F 0.22–0.72 As 0.2–0.63 Pb 4.6–25 SEC 300–1200 TC 0–34000			Various	The authors made a link between the surface water quality and groundwater quality. Major sources of contamination identified were domestic waste, and industrial pollution from textile industry and petrol stations	Abiye (2008)
Addis Ababa, Ethiopia	Volcanics	Springs and boreholes (10)	Zn 0.87–146 Ni 0.31–0.98 Cu 0.44–1.82 Pb 4.3–56.2 Cd <0.1–0.2 Co <0.1–0.12			2002	Geogenic sources of heavy metals is the likely sources of groundwater contamination in this setting due to high heavy metal concentrations in soils and rocks	Alemayehu (2006)
Addis Ababa, Ethiopia	Volcanics	Springs and wells (63)	Ni 2–152 µg/L Pb <1 Co 0.5–165 As <3 Zn <20–2100 Cu 1.5–164 Cd 0.3–12.3 Cr 18.2–214			Februrary–March 2004, July to September 2005	Urban area, leaching from polluted soils.	Demlie and Wohnlich (2006)
Timbuktu, Mali and Lichinga, Mozambique	Quaternary/Basement gneiss-granite complex	Hand dug wells: Timbuktu (31), Lichinga (159)	Timbuktu SEC 221–2010 NO3–N 35 med Cl 500	Lichinga SEC 220 med NO3 5.6 med Cl 13.5		Timbuktu September 2002 to May 2003 Lichinga, April 2002–August 2004	Contamination of groundwater sources from on site sanitation traced using N:Cl	Cronin et al. (2007)
Kampala, Uganda	Weathered Basement	Wells and springs	High density NO3 mean 67 Cl mean 59 TC mean 14	Low density NO3 mean 22 Cl mean 21 TC mean 544		Contrasting hydrological conditions	Significantly higher contamination in high density regions compared to low density	Barrett et al. (1998)
Kampala, Uganda	Weathered Basement	Springs (25)	TtC (FC) FS BLD–23000			Monthly between September 1998–March 1999	Evidence of rapid recharge to springs following rainfall. Local environment hygiene and improved sanitary completion shown to be more important than on-site sanitation for spring protection	Howard et al. (2003)
Kampala, Uganda	Weathered Basement	Monitoring wells (16)	Dry season SEC 272–345 P BDL–0.11 N BDL–5.5 NO3 24–144 Cl 31–50.5 TC 0-131 FC 0–35	Wet Season SEC 280–372 P BDL0.04 N BDL–263 NO3 24–692 Cl 28–192 TC 29–10000 FC 6–8300		2003: weekly March–May and September in dry season, and June to August, wet season.	High population density with pit latrines and livestock sources identified. Microbiological water quality deterioration after heavy rainfall	Kulabako et al. (2007)
Lusaka, Zambia	Dolomite	Wells and streams in intensely urbanised area (9)	Broad hydrochemistry suite including key water quality variables: SEC 200–710 NO3 <0.1–43 NH4 <0.25–3.5 Cl 4.6–36 PO4 <0.1–4 B <1–10 As <0.2–0.49 Pb 0.14–0.67 Hg <0.4–13			July 2001	Values for nitrate and Hg were in excess of WHO standards on some occasions. Poor sanitation and solid waste disposal implicated.	Cidu et al. (2003)
Lusaka province, Zambia	Dolomite	Boreholes and wells (28)	Limited inorganic and organic suit, no microbiology			N/A	Nitrate concentrations in GW significantly higher than surface water and compared to copperbelt. Poor sanitation implicated.	Nachiyunde, Kabunga et al. (2013)
Lusaka, Zambia	Dolomite	Private and public boreholes (N/A)	Alk 124–564 NO3 0.03–39 NO2 0.002–42 NH4 0.08–60 Cl 42–102 TC 1–TNTC FC 21–TNTC BOD 2–69 COD 9–320			Various: 1995–2000	Hydrochem, microbiology and incidence of cholera outbreaks compiled to show the rapid deterioration of GW sources associated with poor sanitation	Nkhuwa (2003)
South Lunzu, Blantyre, Malawi	Precambrian Basement	Borehole, springs and dug well (9)	Dry season SEC 210–330 Cl 21–35 Fe 0.1–0.8 FC 0–5200 FS 0–640	Wet season SEC 306–383 Cl 14–29 Fe 0.4–0.7 FC 0–11 000 FS 0–7000		Wet and dry season on two occasions	Groundwaters highly contaminated due to poor sanitation and domestic waste disposal. 58% of residence use traditional PL	Palamuleni (2002)
Southern Malawi	Weathered basement	Shallow wells	NO3 0–2 NH4 detectable most samples TC None 0, 50% >50 cfu			Wet and dry season (26)	Contamination levels higher during wet season	Pritchard et al. (2008)
Tamatave and Foulpointe, Madagascar	Weathered basement and unconsolidated sediments	Boreholes (53)	FC 73%>0, 55% 0–10, 54%>10 NO3 4.4–35, mean 23 Pb 1–215, mean ca.5			Single study	Widespread drinking water contaminated with FC and concerns over Pb from pump materials	MacCarthy et al. (2013)
Dodomo, Tanzania	Basement	Wells, boreholes and springs	NO3 0.01–331 mean 41 Cl 5.4–1104 mean 238 SO4 0.01–748 mean 83 SEC 301–4740			Single study	High nitrate (>100 mg/L)common in groundwater. Likely sources identified as sewage effluents	Nkotagu (1996)
Bagamaoyo, Tanzania	Unconsolidated sediments	Shallow wells and boreholes	Entovirus detected in 1% Rotavirus 4% E. Coli virulence genes 42% Human specific Bacteriodales marker 4% Turbidity			Single study	The occurrence of E. coli VG in source water was important for transmission. Significant correlation with turbidity	Mattioli et al. (2013)
Epworth, Harare, Zimbabwe	Granite	Wells and boreholes, transect of formal and informal zones (18)	NO3 0–30, mean 11 PO4 0–27.2, mean 3.03 FC 0–2, mean 0.75 (cfu x104)			Survey carried out with duplicate sampling	Pit latrines, faecal coliforms in older and informal trading areas, urban agriculture, home industries and commercial areas	Zingoni et al. (2005)ref name="Zingoni 2005">ZINGONI, E, LOVE, D, MAGADZA, C, MOYCE, W, and MUSIWA, K. 2005. Effects of a semi-formal urban settlement on groundwater quality: Epworth (Zimbabwe): Case study and groundwater quality zoning. Physics and Chemistry of the Earth, Parts A/B/C, Vol. 30, 680–688. </ref>
Bulawayo, Zimbabwe	Granite and greenstone	Boreholes, residential, commercial and industrial (32)	Dry season SEC 610–1210 NO3 6–8.2 PO4 1.7–4.6	Wet season SEC 180–1450 NO3 0.1–1 PO4 0.5–1.8		Seasonal study	Municipal landfill. N highest in residential, P in commercial and SEC in industrial and residential	Mangore and Taigbenu (2004)

SEC-specific electrical conductivity, TDS- total dissolved solids, TH-total hardness, BOD-biological oxygen demand, COD-chemical oxygen demand, FC-faecal coliforms,
EC-E. Coli, TC-total coliforms, FS-faecal streptococcus. Microbiological units as cfc/100 mL unless stated otherwise, TNCT-too numerous to count, BDL-below detection limit.

Table 7.2    Studies investigating groundwater contamination from pit latrines (n=16).

Region/Country (rural/urban)	Subsurface conditions	Sample sites (n)	Water quality parameters	Sampling time frame	Conclusion	Reference
Kamangira, Zimbabwe (rural)	Sandy soils	Installed test wells (17)	Ammonium, nitrate, turbidity, pH, Conductivity, TC, FC	Feb–May 2005	Low FC >5 m from PL, N conc. usually below WHO standards	Dzwairo et al. (2006)
Epworth, Zimbabwe (urban)	N/A	New and existing wells (28)	Na, Zn, Cu, Fe, phosphate, nitrite, TC, FC	N/A	Elevated N and Coliforms in most of study area	Zingoni et al. (2005)
Epworth, Zimbabwe (urban)	Fine sandy soils	Installed wells	Nitrogen, sulphate, coliforms	2–8 week intervals 1998–1999	Rapid reduction in Coliforms, S and N 5–20 m from PL	Chidavaenzi et al. (2000)
Lusaka, Zambia (urban)	Dolomite	Existing wells (?)	Nitrate, Cl, FC	November 2003, March 2004, October 2004	Greatest FC loading from PL and other waste sources in wet season and dilution of N pollution	Nkhuwa (2006)
Dakar, Senegal (urban)	Fine-course sands	Existing wells (47)	Broad hydrochemistry, FC	July and November 1989	Nitrate strongly linked to PL proximity	Tandia et al. (1999)
NW Province, South Africa (rural)	N/A	Existing wells (9)	Ammonium, nitrate, nitrite	June–July	High contamination <11 m from PL	Vinger et al. (2012)
Mbazwana, South Africa (urban)	Sands	Installed test wells (5)	FC and nitrate	Bimonthly 2000–2002	Low nitrate (<10 mg/L) and FC (<10/100 mL) >1 m from PL	Still and Nash (2002)
Bostwana, Mochudi/Ramotswa (rural)	Well-poorly drained soils	Existing wells (>60)	P, N, stable isotopes and Cl	N/A	Variable N leaching from PL	Lagerstedt et al. (1994)
Botswana (rural)	Clayey soils and fissured geology	Existing well and observation well (2)	Broad Hydrochemistry, E. coli	October–February 1977	Contamination of wells near latrine with E. Coli and nitrate	Lewis et al. (1980)
Various, Benin (rural)	N/A	Existing wells (225)	Andenovirus, rotavirus	Wet/dry season 2003–2007	Viral contamination n linked to PL proximity	Verheyen et al. (2009)
Langas, Kenya (urban)	N/A	Existing wells (35)	TC,FC	January–June 1999	97% wells positive for FC, 40% of wells >15 m from PL	Kimani-Murage and Ngindu (2007)
Kisumu, Kenya (urban)	N/A	Existing wells (191)	FC, nitrate, Cl, F	1998 to 2004	Density of PL within a 100 m radius was significantly correlated with nitrate and Cl but not FC	Wright et al. (2012)
South Lunzu, Blantyre, Malawi (urban)	Precambrian Basement	Borehole, springs and dug well (4)	SEC, Cl, Fe, FC,FS	Wet and dry season on two occasions	Groundwaters highly contaminated due to poor sanitation and domestic waste disposal. 58% of residents use traditional PL	Palamuleni (2002)
Uganda, Kampala (urban)	Karstic geology	Installed wells and spring (17)	Conductivity, pH, P, nitrate, Cl, FC and FS	March–August 2003, weekly and monthly	Widespread well contamination linked to PL and other waste sources	Kulabako et al. (2007)
Uganda, Kampala (urban)	Karstic geology	Springs (4)	FC, FS., nitrate, ammonium	Wet and dry season for 5 consecutive weeks	Widespread contamination from PL and poor animal husbandry, both protected and unprotected sources unfit for drinking	Nsubuga et al. (2004)
Uganda, Kampala (urban)	Karstic sediments	Springs (25)	FC, FS	Monthly September 1998–March 1999	Spring contamination linked to local environmental hygiene and completion rather than on-site sanitation	Howard et al. (2003)

PL = Pit latrine, FC = Fecal coliform, TC = Total coliform, FS = Fecal strep. Concentrations in mg/L unless otherwise stated.

Table 7.3    Studies focused on impacts of non-sanitary sources on groundwater quality.

Area/Country	Geology	Sample sites	Results (mg/L)		Sources	Reference
Akure, Nigeria	Basement	Boreholes in landfill vicinity	TDS 18–342 TH 136–140 NO3 30–61 Fe 0.9–1.4 Pb 0–1.21 Zn 0–2.3 Cr 0–0.25		Landfill values decrease with distance 50–100 m	Akinbile and Yusoff (2011)
Ojota, Lagos, Nigeria	Sedimentary	10 boreholes, 10 dug wells	SEC 68–3030, mean 584 µS/cm Fe 0–21.4, mean 4.23 Cu 0–33, mean 0.02 Pb 0–14.8, mean 2.4 Zn 0–0.23, mean 0.04		Industrial areas and landfill, Sites within 2 km radius of landfill affected.	Oyeku and Eludoyin (2010)
Igando, Lagos, Nigeria	Sedimentary	Wells 10–375 m from landfill	TDS 3–23, mean 9.0 NO3 17.4–60.5, mean 38.5 NH4 0.12–0.3, mean 0.22 PO4 7.07–15.12, mean 10.7		Municipal landfill	Longe and Balogun (2009)
Ibadan, Nigeria	Basement	Soil and groundwater	Cd 0.01 Cr, Pb, Co, Ni not detected		Adelekan and Alawode (2011)
Ilorin, Nigeria	Basement		Colour, turbidity over WHO limit EC 161–731 TH 37–153 TC 1600–>1800		Industrial estate	Adekunle (2009)
Dar-es-Salaam, Tanzania	Sedimentary, Sandstones	Wells up and down gradient	Dry Up gradient Mn 0.03 Fe 0.07 FC (cfux104/100 mL) 1.5 EC 5,400 SO4 76 Dry Down gradient Mn 0.02 Fe 0.12 FC (cfux104/100 mL) 3.4 EC 6,500 SO4 49	Wet Up gradient Mn 0.00 Fe 0.12 FC (cfux104/100 mL) 0.7 EC 5,000 SO4 35 Wet Down gradient Mn 0.05 Fe 0.24 FC (cfux104/100 mL) 3.7 EC 5,300 SO4 72	Solid waste disposal	Kassenga and Mbuligwe (2009)
Lokpaukwu, Lekwesi and Ishiagu, Nigeria	Shales and igneous intrusions	Springs and open dug wells	Dry season TDS 25–3150 Cl 0–30 NO3–N 0.04–0.74 SO4 0–33.6 Fe 0–3.98 mg/L Mn 0–0.21 mg/L Pb BDL Zn 0–0.06 mg/L Cd 0–0.258 mg/L	Wet season TDS 33–11 126 Cl 2.1–1155 NO3–N 0.04–0.68 SO4 1–381 Fe 0–5.07 mg/L Mn 0–0.82 mg/L Pb 0–0.24 mg/L Zn 0–1.07 mg/L Cd 0–0.196 mg/L	Mining	Ezekwe et al. (2012)
Lusaka and Copperbelt, Zambia	Sedimentary, Dolomite	Surface and groundwater	As 0–0.506, mean 0.009 Cr 0–0.089, mean 0.01 Cu 0–0.270, mean 0.012 Mn 0–10.4, mean 0.369 Ni 0–0.698, mean 0.015 Pb 0–0.094, mean 0.003 Zn 0–1.21, mean 0.75		Mining –Mn, Cu and Ni correlated	Nachiyunde, K et al. (2013)

It is clear from looking at the studies in the published literature that there have been particular concerns related to groundwater quality in southern Nigeria, which account for about 30% of the published studies. These studies are located near Lagos and Abeokuta in the south west, the Delta area in the south and Calabar, and there are additional concerns over pollution from the oil industry in parts of southern Nigeria. Other notable examples of urban areas that have a number of studies include Lusaka in Zambia, Dakar in Senegal, Addis Ababa in Ethiopia and Kampala in Uganda.

Impacts of urbanisation

Chemical and physical indicators of groundwater quality degradation

Physical indicators

Total dissolved solids (TDS) or specific electrical conductivity (SEC) are the most commonly applied physical water quality indicators in groundwater studies and are often used in combination with more specific indicators such as dissolved chemistry or microbiology (see Table 7.1). They have a major advantage of being field methods, which are easy to use and versatile, enabling the user to carry out a crude assessment of water quality rapidly and with minimal cost. The baseline quality of groundwater, with relatively low total dissolved solids (TDS), makes TDS a good indicator of contaminant loading. Many studies also use hardness or field alkalinity in the same manner, as these are also indicative of contaminant loading and have many of the benefits outlined above. Geophysical methods such as electrical resistivity tomography (ERT) are also valuable for characterising subsurface contamination from landfills and sites with historical industrial pollution as well as saline intrusion induced by abstraction (Loke et al., 2013; Martínez et al., 2009). However, these are costly systems to deploy there are only a limited number of studies that have used these techniques for assessing groundwater contamination in urban SSA, with the majority of the studies being from South Africa and Nigeria (Akankpo, 2011; Ehirim et al., 2009; Silliman et al., 2010).

Nitrate and chloride

Nitrate and chloride are the most widely used water quality indicators of anthropogenic pollution. Nitrate data has been reported in over 80% of the groundwater studies summarised in Table 7.1. Relatively simple sample preservation and analysis required makes these parameters attractive for initial water quality screening. Nitrate concentrations ranged from Below Detection Level (BDL) to >500 mg/L (as NO₃), although typical maximum concentrations were generally below 150 mg/L. The WHO guideline value for nitrate is 50 mg/L as NO₃. The WHO has not published a health-based guideline for chloride, but suggests that concentrations over 250 mg/l can give rise to a detectable taste.

Both tracers have been used in a broad range of geologic and climate zones to investigate pollution from on-site sanitation, waste dumps, as well as urban agriculture (Table 7.3). Nitrate concentrations show a high degree of variability both within studies and between studies that have been reviewed. Two principle factors that affect nitrate occurrence are firstly the prevailing redox conditions in groundwater, and secondly the residence time and vulnerability of the groundwater body. There are several examples of low nitrate groundwater in Table 7.1 which show evidence of faecal contamination (Gélinas et al., 1996a; Mwendera et al., 2003; Nkhuwa, 2003) which has implications for the potential for denitrification in shallow groundwaters. Nitrate has been used successfully to characterise urban loading to groundwater from a range of sources including pit latrines (Cissé Faye et al., 2004), landfills (Ugbaja and Edet, 2004; Vala et al., 2011) and applied to look at impacts on groundwater quality across different population densities (Goshu and Akoma, 2011; Goshu et al., 2010; Orebiyi et al., 2010). There are other sources of N loading to groundwater in growing urban areas including the impact of deforestations, and these have been delineated using N:Cl ratios and in a few examples by using δ¹⁵N analysis (Faillat, 1990).

A series of geochemical transformations can occur in water with a high carbon loading with a progressive decline in redox potential, leading sequentially to the removal of nitrate by denitrification, the mobilisation of manganese and iron and the reduction of sulphate (Gooddy et al., 1999). Borehole mixing processes can cause dilution and overall low nitrate concentrations while still having significant microbiological contamination. Lagerstedt et al. (1994) and Cronin et al. (2007) have successfully used NO₃:Cl to fingerprint different sources of urban and peri-urban pollution in groundwaters in SSA. This has a certain appeal due to its simplicity; however, prevailing redox conditions and mixing processes need to be considered when using this approach. Many studies have effectively used nitrate in combination with other basic physical indicators such as SEC or TDS and turbidity to assess contamination and map areas of high and low pollution.

Ammonium and phosphate

It is evident from the literature that only a minority of case studies (ca. 20%) contain data for NH₄ and close to 30% contain data for PO₄. In part this is due to the more involved analytical procedures for NH₄, the high detection limits for PO₄ by ion chromatography and the fact that these parameters need to be analysed rapidly after sampling to ensure valid results. The WHO have not published health-based guidelines for ammonium and phosphate, however P is often the limiting nutrient in the aquatic environmental and therefore concentrations >20 mg/L are considered high in surface water bodies.

Both species are closely associated with contamination from pit latrines and leaking sewer systems. Examples of ammonium and phosphate contamination from the cities of Lusaka, Abeokuta, Calabar and Makelle are shown in Table 7.1. (Berhane and Walraevens, 2013; Cidu et al., 2003; Taiwo et al., 2011; Ugbaja and Edet, 2004). Ammonium concentrations in urban groundwater range from BDL-60 mg/L, although most case studies had maximum concentrations below 10 mg/L. The highest concentrations were reported in Lusaka, Zambia where karstic limestone aquifer which underlies much of the city and very rapid transport times in the groundwater are implicated. Both indicators do not behave conservatively in soils and groundwater, NH₄ is positively charged and therefore has a strong affinity for negatively charged surfaces such as clays, for this reason, as well as microbiological processing, attenuation is particularly high in the soil zone.

Phosphate concentrations range from BLD-86 mg/L, although very few studies report values >20 mg/L. Phosphate has very limited mobility in the subsurface and has a strong affinity to iron oxy-hydroxides as well as carbonates, background concentrations are usually low, e.g. <0.2 mg/L, concentrations in urban groundwater are also usually low unless there is either a very high loading or very rapid groundwater flow for example in fractured basement or karstic limestone (Cidu et al., 2003; Nkansah et al., 2010; Zingoni et al., 2005).

Trace elements

Overall relatively few studies have characterised trace element contamination in urban groundwater, and studies published to date usually report results for only a handful of elements (e.g. Fe, Mn, Pb, Zn, Ni, V, Cr, Cd). This is in part due to the cost and access to suitable analytical facilities in SSA for multi-element analysis by ICP-MS or AES, and the relatively poor detection limits for some single element methods.

Studies investigating trace element concentrations have tended to be focused on non-sanitary sources, either the effect of mining (Ikenaka et al., 2010; Nachiyunde, Kabunga et al., 2013; Von der Heyden and New, 2004) or waste dumps (Momodu and Anyakora, 2010; Yusuf, 2007). The main findings from these studies are summarised in Table 7.1 and there are further examples in Table 7.3. Low concentrations of Cd (0.13–0.2 mg/L) and Pb (0.04-0.09 mg/L) were reported by Vala et al. (2011) in a study in Kinshasa, DRC, where groundwater was contaminated from waste dumps. Aremu et al. (2002) report high Pb (0.4 mg/L) and moderate Cr (9 mg/L) and Cd (8.5 mg/L) in groundwaters from the Warri River plain in Nigeria, contamination from industry is cited as the source of contamination. Von der Heyden and New (2004) reported elevated concentrations of Co, Ni and Zn down flow gradient of tailings, however concentrations were found to be below WHO drinking water quality standards in all cases, this is likely due to sulphide precipitation and natural attenuation processes.

There have been three notable studies in Addis Ababa, Ethiopia, that have considered natural geogenic contamination within an urban context (Abiye, 2008; Alemayehu, 2006; Demlie and Wohnlich, 2006). These studies report elevated concentrations of Zn, Ni, Pb, Cd, Co and Cr all above WHO drinking water quality standards.

Microbiological indicators of groundwater quality degradation

The importance of concentrating efforts on the control of microbial contamination of drinking water rather than chemical contamination is expressed in the WHO Guidelines for Drinking-Water Quality (WHO, 2011):

‘The most common and widespread health risk associated with drinking water is microbial contamination, the consequences of which mean that its control must always be of paramount importance.’

Until relatively recently there has been a widely held misconception that groundwater was safe to drink with the minimum of treatment. The overlying soil layers were believed to provide a barrier to the transport of surface contamination to the groundwater, and that pathogenic microorganisms in particular would be removed by filtration or inactivated by sunlight, desiccation, and predation. Although this assumption may be accurate in many cases, it cannot be universally applied: it is particularly dangerous assumption when managing shallow groundwater sources of the type widely used in SSA. Hand-dug wells are widespread in urban and rural areas of SSA and are frequently poorly constructed, inadequately protected and badly maintained, making them vulnerable to contamination from pollution sources on the surface and underground. The microbiological quality of the water in these sources is often poor due to contamination by human and animal excreta.

Although the contamination of water by faeces can be measured by a number of different chemical parameters, it is a common and widespread practice to use bacterial indicators of faecal contamination as the best representative of the presence of pathogens. Of the 44 studies listed in Table 7.1, 38 have included one or more microbiological indicators in the suite of parameters that have been tested. The most widely used group of indicator bacteria in these studies is the coliform group, which includes E.coli, but some authors, for example Gelinas et al (1996) and Bordalo and Savva-Bordalo (2007) have also included faecal streptococci as an alternative indicator of faecal contamination. This section will briefly describe the ideas behind the use of faecal indicator bacteria, and then list the main characteristics of the two groups of indicators used in the 44 studies listed above.

Indicator concept

The main source of pathogen contamination in water is faeces. Human and animal faeces contains a high microbial load with a very diverse range of species (Leclerc et al, 2001) that are derived from the normal flora of the gut. Many of the bacteria in the gut are either very difficult to culture, or cannot be cultured with the techniques currently available, so estimating the bacterial load in faeces is extremely difficult. Using methods that detect genetic material, O'Hara and Shanahan (2006) have estimated between 1011 and 1012 bacteria in one gram of colonic content (60% of the faecal mass). Most of these bacteria are harmless saprophytes that colonise the gut and aid digestion, but faeces also contains pathogenic microorganisms, and it is these that are of concern when they get into water. Table 7.4 shows the range of pathogens that have been isolated from groundwater under the different categories of bacteria, viruses and protozoa, and the diseases that they are known to cause.

Table 7.4    Pathogenic microorganisms of concern in groundwater (from: Pedley et al., 2006).

Organism	Disease
Viruses
Coxsackievirus	Fever; pharyngitis; rash; respiratory disease; diarrhoea; haemorrhagic conjunctivitis; myocarditis; pericarditis; aseptic meningitis; encephalitis; reactive insulin-dependent diabetes; hand, foot and mouth disease
Echovirus	Respiratory disease; aseptic meningitis; rash; fever
Norovirus	Gastroenteritis
Hepatitis A	Fever; nausea; jaundice; liver failure
Hepatitis E	Fever; nausea; jaundice; death
Rotavirus A and C	Gastroenteritis
Enteric adenovirus	Respiratory disease; haemorrhagic conjunctivitis; gastroenteritis
Calicivirus	Gastroenteritis
Astrovirus	Gastroenteritis
Bacteria
Escherichia coli	Gastroenteritis; haemolytic Uremic Syndrome (HUS, enterotoxic E.coli)
Salmonella spp.	Enterocolitis; endocarditis; meningitis; pericarditis; reactive arthritis; pneumonia; typhoid fever; paratyphoid fever
Shigella spp.	Gastroenteritis; dysentery; reactive arthritis
Campylobacter jejuni	Gastroenteritis; Guillain-Barrè syndrome
Yersinia spp.	Diarrhoea; reactive arthritis
Vibrio cholera	Cholera
Protozoa
Cryptosporidium spp.	Diarrhoea
Giardia lamblia	Chronic diarrhoea

Ideally, drinking water quality monitoring strategies would include a screen for the major pathogens causing disease, but currently there are methodological problems that make this approach impractical. As shown in Table 7.4 there are a lot of different pathogens and each one has its own method of analysis, which can be slow and expensive. Furthermore, many of the pathogens in the three main categories cannot be cultivated using standard laboratory methods. New methods of analysis that detect nucleic acids, and which can be applied to the detection of any organism, are being introduced, but they are not yet widely available and are expensive and technically demanding. A further constraint on the direct detection of pathogens for routine monitoring is that their occurrence in water is often seasonal, with their presence coinciding with the pathogen circulating in the population (Payment and Locas, 2011).

To overcome the current limitations of testing directly for pathogens in water, microbiologists have identified a small number of enteric microorganisms, universally present in faeces, that are not themselves pathogens but which can be used to indicate the potential risk of pathogens being present in the water. These microorganisms are referred to as faecal indicator organisms. The characteristics of an ideal indicator are (Payment and Locas, 2011):

• Should be absent in unpolluted water and present when the source of pathogenic microorganisms of concern is present.
• Should not multiply in the environment.
• Should be present in greater numbers than the pathogenic microorganisms.
• Should respond to natural environmental conditions and water treatment processes in a manner similar to the pathogens of concern.
• Should be easy to isolate, identify, and enumerate.
• Should be inexpensive to test thereby permitting numerous samples to be taken.
• Should not be pathogenic microorganisms (to minimise the health risk to the analyst).

The coliform group of bacteria, which includes E.coli, and faecal streptococci (enterococci), are the most widely used groups of faecal indicator bacteria; however, none of them fulfil all of the characteristics of an ideal indicator and there are widely reported limitations to their use. Despite the negative publicity faced by coliforms and other faecal indicators, they are likely to continue being used in water quality monitoring programmes for the foreseeable future. This is reflected in the conclusions of a recent review of faecal indicator studies by Payment and Locas (2011) who state:

‘Fecal indicators are the best predictor of potential risk, but their concentrations rarely correlate perfectly with those of pathogens. Thus, bacterial indicators can predict the probable presence of pathogens in water, but they cannot predict precisely the level of occurrence.’

In the following sections we describe the basic characteristic of the coliform group of faecal indicators, and faecal streptococci. More comprehensive reviews of faecal indicators have been published (Ashbolt et al., 2001; Gleeson and Gray, 1997; Leclerc et al., 2001; Mesquita et al., 2013; Tallon et al., 2005).

Total coliforms

This is a diverse group of bacteria that share several biochemical and physiological properties. They are all Gram negative (a simple staining procedure that divides bacteria into two groups — Gram positive and Gram negative — that is determined by the cell wall structure), rod-shaped organisms. They will grow in the presence of bile salts (chemicals excreted into the gut by the gall bladder) and utilise lactose as a sugar source, producing acid and gas as metabolic by-products. They are oxidase negative and do not produce spores or other sub-cellular structures that are highly resistant to environmental stress. More recently they have been grouped according to having the enzyme B-galactosidase, which is involved in the metabolism of lactose and can be detected using a colour producing substrate. They grow at 37°C and are facultative anaerobes, which mean that they can grow in the presence and absence of oxygen using two different metabolic systems.

The total coliforms include several genera of bacteria. From early on it was discovered that several of these genera contain bacterial species that can grow in the environment in the absence of faecal contamination. Thus the isolation of total coliforms from water did not necessarily mean that the water was contaminated by faeces. In a an analysis of over 1000 strains of Enterobacteriacae from drinking water, 51% were not of faecal origin (Gavini et al., 1985). Their role as faecal indicators, and their potential significance as a measure of health risk, was thus undermined and recently many countries have reduced their significance in the list of water quality indicators (Stevens et al., 2003 ).

Tables 7.1 to 7.3 above list several studies that have included total coliforms amongst the suite of microbiological parameters that have been used to measure the quality of water from a variety of sources. But given the widespread occurrence in the environment of species belonging to the total coliform group (Leclerc et al., 2001), the significance of their presence in shallow groundwater in peri-urban areas is questionable.

Thermotolerant coliforms and escherichia coli

Thermotolerant coliforms (sometimes referred to as faecal coliforms) are a subgroup of the total coliform group. They are differentiated from the total coliform group by their ability to grow at 44°C. The thermotolerant coliforms are much more indicative of faecal contamination than the total coliform group; however, this group still contains some genera of bacteria, particularly Klebsiella, Enterobacter and Citrobacter that can be isolated from environmental sources in the absence of faeces (Tallon et al 2005).

The method for quantifying thermotolerant coliforms is relatively straight forward because it only requires incubation at 44°C to select for the organisms; no further selective growth steps or confirmation steps are performed on the isolates. In many laboratories in developing countries this is the limit of the resources that are available, and several of the more well-known field test kits (for example, Potalab, Delagua and Paqualab) can only be this specific. But there is a stronger justification for using TTC as an indicator of faecal contamination in drinking water than total coliforms. E.coli is the predominant species of the TTC group and constitutes over 90% of the species isolated from faeces (Tallon et al, 2005), and as much as 99% of TTC isolated from shallow groundwater in peri-urban areas (Howard et al, 2003). It is the only species in the coliform group that is exclusively associated with a faecal source (animal and human) in temperate waters. The association is not quite so clear in tropical waters, with the results of some studies suggest that E.coli can be isolated from pristine waters (Carrillo et al., 1985; Ishii and Sadowsky, 2008) and from certain industrial discharges that have not been exposed to faeces (Gauthier and Archibald, 2001). Nevertheless, E.coli is recommended as the sole indicator bacteria of recent faecal contamination (Tallon et al, 2005).

The predominance of E.coli in the TTC group supports the assumption of faecal contamination in drinking water based on the TTC result alone. However, further confirmation of TTC isolates as E.coli proves the presence of faecal contamination. E.coli has the general characteristics of TTCs but, in addition, synthesises the enzyme β-glucuronidase, which has been used as a target for the development of simple, relatively rapid, specific and sensitive detection methods (Tallon et al, 2005). To date, these methods based on specific enzyme detection have not been used widely in developing countries due to the cost of equipment and the availability of reagents. But recent developments have created low-cost packaging for the test that removes the need for equipment (for example see: http://www.aquagenx.com/) and makes it more appropriate for use in low-income settings.

Enterococci

Faecal streptococci (sometimes referred to as enterococci or faecal enterococci) are not used as widely as the coliform group in SSA as an indicator of faecal contamination. Of the studies listed in Table 7.1, eight report the use of enterococci.

Enterococci are between ten-fold and a thousand-fold less numerous in human faeces than E.coli, (Gleeson and Gray, 1997; Stevens et al., 2003) but are generally more numerous than E.coli in faeces from herbivores. Enterococci survive longer in water than E.coli and can be used to indicate older contamination sources. Furthermore, there is no evidence to suggest that enterococci can replicate in the environment, even in tropical regions where some authors have reported the growth of E.coli.

Several major studies have been done during the last 30 years to determine the relative significance of E.coli and enterococci as an indicator of health risk, rather than as an indicator of faecal contamination. The majority of these studies have concentrated on the health effects of sea-bathing and have reached the conclusion that enterococci are a better predictor of the risk of ill-health than E.coli (for example Kay et al., 1994) and they have now been introduced as the indicator of choice in several countries. The relative significance of E.coli and enterococci as an indicator of health risk in drinking water is less well established; however, Howard et al (2003) conclude from a study of springs in Kampala, Uganda, that faecal streptococci may be more useful as an indicator relevant to public health.

Impacts from pit latrines

In-situ sanitation, largely in the form of pit latrines, is the dominant cause of microbiological contamination and a major cause of nutrient loading to water resources in SSA. This is a very well-studied area and a worldwide review has been published recently by Graham and Polizzotto (2013). The main findings from studies carried out in SSA have been collated in Table 7.2 and are summarised below along with other studies specifically targeting contamination from pit latrines.

Microbiological contaminants

Human faeces harbour a large number of microbes, including bacteria, archaea, microbial eukarya, viruses, protozoa, and helminths (Graham and Polizzotto, 2013). In the context of this review there have been no studies that have assessed protozoa or helminths, which exhibit little movement in groundwater due to their size (Lewis et al., 1982). The characteristics of microorganisms and the aquifer and soil environment that affect microbial transport and attenuation in groundwater are shown in Table 7.4.

Microorganisms have been assumed to be rapidly attenuated after excretion but recent studies with viruses suggest that water quality may be impaired for a considerable length of time. Using a mixture of routine culture methods and genetic detection methods, Charles and coworkers detected viruses over 300 days after they were introduced in simulated groundwater systems (Kay et al., 1994). A number of approaches have been used to define the quantities and transport distances of latrine-derived microbial contaminants. The majority of these have been culture-based studies of faecal bacteria; there has only been one study of viruses related to pit latrines (Verheyen et al., 2009).

Attenuation of microbes is likely to be dependent on the hydrological conditions both in terms of water levels and recharge rate and permeability of the aquifer, and is highly variable (see Microbiological indicators of groundwater quality degradation). Dzwairo et al. (2006) found faecal and total coliforms greatly reduced beyond 5 m from pit latrines in Zimbabwe, whereas Still and Nash (2002) found faecal coliforms to be attenuated to <10 cfu/100 mL after 1 metre in Maputaland, KwaZulu-Natal. In Abeokuta, Nigeria, Sangodoyin (1993) found coliform attenuation to be correlated both with distance from the source and with the depth of the groundwater well. In Epworth, Zimbabwe, groundwater contamination was higher in the dry season rather than in the wet, with coliforms detected up to 20 metres from the pit (Chidavaenzi et al., 1997). In Benin, Verheyen et al. (2009) found a positive association for detection of viruses in water sources with at least one latrine within a 50 m radius. They postulated that during the wet season viruses were transported in shallow groundwater whereas in the dry season contamination was likely to be from surface water.

In an informal settlement in Zimbabwe, Zingoni et al. (2005) found detectable total and faecal coliforms in over 2/3 of domestic boreholes and wells. In the area 75% of households used pit latrines and there were also informal trading areas. In Langas, Kenya, Kimani-Murage and Ngindu (2007) found that 50% of wells were within 30 m of a pit latrine and that all shallow wells were positive for total coliforms with 70% >1100 mpn/100 mL; however, in Kisumu Wright and co-workers failed to find a significant correlation between the levels of thermotolerant coliforms in water sampled from shallow wells and the density of pit latrines (Wright et al., 2012).

Table 7.4    Factors affecting transport and attenuation of
micro-organisms in groundwater (from Mesquita et al., 2013).

Characteristics of the microorganism	Aquifer/soil (environment) properties
Size	Groundwater flow velocity
Shape	Dispersion
Density	Pore size (intergranular or fracture)
Inactivation rate (die-off)	Kinematic/effective porosity
(Ir)reversible adsorption	Organic carbon content
Physical filtration	Temperature
	Chemical properties of groundwater (pH etc.)
	Mineral composition of aquifer/soil material
	Predatory microflora
	Moisture content
	Pressure

Chemical contaminants

The chemicals of greatest concern from excreta disposed in on-site sanitation systems are regarded to be nitrate, phosphate and chloride. Pin-pointing specific sources is challenging as nitrate may be derived from numerous sources including plant debris, animal manure, solid waste and fertilisers. A common approach has been to compare areas that are similar but have different latrine densities. In an informal settlement in Zimbabwe, Zingoni et al. (2005) demonstrated that the highest nitrate concentrations were associated with the highest population and pit latrine density. A similar pattern was observed in Senegal and South Africa. (Tandia et al., 1999; Vinger et al., 2012). Studies in the peri-urban areas of Kisumu, Kenya have shown that the density of latrines within a 100 m radius of the sources was significantly correlated with nitrate levels (Wright et al., 2012). In eastern Botswana the build-up of nitrogenous latrine effluent in soils and downwards leaching resulted in nitrate concentrations of above 500 mg/L (Lewis et al., 1980). The highest concentrations are found downstream of areas with high latrine use (Vinger et al., 2012). Direct measurements are sparse but Graham and Polizzotto (2013) estimate lateral travel distances of 1–25 m for pit-latrine derived nitrate. Sangodoyin (1993) found that nitrate concentrations were not related to distance from the source in Abeokuta, Nigeria.

Chloride is typically transported with minimal retention and frequently tracks nitrate (e.g. Lewis et al., 1980) unless subsurface conditions promote denitrification. Ammonium does not tend to accumulate in groundwater near latrines but can accumulate and persist in anaerobic conditions and when the water table intersects the base of the latrine pit (Ahmed et al., 2002; Dzwairo et al., 2006). Other contaminants include potassium, sulphate and DOC.

Impacts from non-sanitary sources

Table 7.3 summarises studies focused on the impact of waste dump sites, industrial activity and mining on groundwater quality. There are only a handful of case studies which have characterised the impacts of waste dumps and industry in urban areas and these are dominated by examples from Nigeria. Examples of contamination from historical mining activity come from Nigeria (Ezekwe et al., 2012) and Zambia (Nachiyunde, Kabunga et al., 2013).

Oyeku and Eludoyin (2010) investigated contamination in wells and boreholes from industrial and waste dumps in Ojota, Nigeria. Sites within a 2 km radius were found to be affected with elevated concentrations of Fe, Cu, Pb and Zn as well as high SEC. In Akure, Nigiera, boreholes in the vicinity of a landfill were found to be contaminated, with high nitrate (36–61 mg/L), Pb and Zn (Akinbile and Yusoff, 2011). In Lagos, Longe and Balogun (2009) found elevated concentrations of nitrate (mean 38.5 mg/L), phosphate (mean 10.7 mg/L) and ammonium (mean 0.2 mg/L) as well as high TDS (3–23 g/L) in wells between 10–400 m from a municipal landfill. Kassenga and Mbuligwe (2009) characterised seasonal and up/down gradient water quality in wells in the vicinity of a solid waste dump in Dar-es-Salam, Tanzania. Higher FC counts were found in sites down gradient (3.4–3.7×104) compared to up gradient (1.5–0.7×104). Higher FC counts were also found during the wet season compared to dry season down gradient of the dump site. Lower SEC as well as SO₄ and Fe were found up gradient and in the wet season, implying dilution from rainfall and recharge and contamination from the dump site.

Elevated concentrations of Pb (0.24 mg/L), Cd (0.25 mg/L) and Zn (1.07 mg/L) were found in wells in the vicinity of mining activities in Nigeria (Ezekwe et al., 2012). A study by Nachiyunde, Kabunga et al. (2013) included sites from the Copperbelt in Zambia and found overall low contamination levels in groundwater with no trace elements above WHO drinking water guideline values. Von der Heyden and New (2004) also carried out a detailed study in the Zambian Copperbelt of the impact of mine tailings on groundwater quality. They found that there was only local effect from the tailings and that concentrations of Co, Ni and Zn were below WHO drinking water quality standards.

Seasonal trends in groundwater quality

There are relatively few studies that have undertaken regular water quality monitoring or have carried out seasonal comparisons. A study by Howard et al. (2003) is one notable example where detailed seasonal monitoring of microbiological indicators was carried out over a twelve month period to characterise the risks factors for spring contamination in Kampala, Uganda. Significantly higher contamination was observed after rainfall events and there was strong evidence that rapid recharge of the shallow groundwater causes a rapid response in spring quality (Ishii and Sadowsky, 2008).

Median and maximum results for FS, SEC and NO₃ from four studies that assessed seasonal effects by carrying out sampling at the same sites in both the wet and the dry season are summarised in Figure 7.2. Higher maximum FS counts are found in the wet season compared to the dry season for studies in Uganda (Kulabako et al., 2007) and Malawi (Palamuleni, 2002). Higher maximum SEC are observed in all three case studies in Figure 7.2 during the wet season, however median values are comparable. Changes in nitrate show a mixed picture with higher maximum concentrations in two studies from Uganda and DRC (Kulabako et al., 2007; Vala et al., 2011) during the wet season, while in the case study from Zimbabwe (Mangore and Taigbenu, 2004) lower maximum values are found. Median values for nitrate are lower in the wet season for both the Uganda and Zimbabwe case studies, which may indicate a dilution effect, while the higher maximum concentrations may be explained as a result of a pulse of contaminants at the start of the rainy season and or evaporative effects during the dry season.

File:OR17056fig7.2.jpg

Figure 7.2    Comparison of groundwater quality during contrasting rainfall regimes. X- axis: a=cfu/100mL, b= μS.cm^-1, c= mg/L. Key: 1 = Kulabako et al. (2007), 2 = Palamuleni (2002), 3 = Mangore and Taigbenu (2004) and 4 = Vala et al. (2011). Some data for median and maximum values are not available from the literature.

Understanding seasonal trends in nitrate are complicated by the changes in redox conditions, particularly in low lying areas which are prone to flooding in the wet season which are not uncommon in SSA, e.g. Lusaka, Zambia. These may shift from an oxidising regime in during low water levels which retains NO₃ to a reducing regime where denitrification can take place during inundation (Sanchez-Perez and Tremolieres, 2003; Spalding and Exner, 1993).

Comparisons between difference water sources

The results from Table 7.1 show water quality data from a range of different sources. Open and unlined wells are consistently of poorer quality compared to lined or ‘improved’ wells (e.g. Godfrey et al 2005; Sorensen et al., 2015a; 2015b). In some studies springs have been found to be better quality compared to boreholes (e.g. Takem et al. 2010) and others cases the trend is reversed (e.g. Palamuleni 2002) or both sources were found to have comparable levels of contamination by FC (e.g. Abiye 2008). It is important to note that many of these studies contained very few observations for each source type and generalisations should be treated with caution. However, together they form a more compelling body of evidence.

There is some evidence that the water quality of wells may be affected by usage rates, i.e. with fewer groundwater sources being relied on towards the end of the dry season there is greater risk of contamination, e.g. from materials used for drawing water, especially for unimproved sources (Godfrey et al. 2006). For boreholes this contamination pathway is generally not a major risk factor and this supports the generally better quality found in these types of sources. The lower storage volume of shallow boreholes compared to wells may also be an important factor as this type of contamination can be more rapidly flushed out.

Improved wells do not generally exhibit the same level of gross contamination observed in traditional wells and springs. However, in the majority of studies, wells (both improved and unimproved), are found to have water with unacceptable levels of contamination with faecal coliforms by WHO standards (and typically >100 cfu/100 mL) in at least some part of the year and often throughout the year. With perhaps the exception of highly karstic settings for microbiological and nitrate the following order of water source quality (best to worst) was found as follows: boreholes >> improved wells = springs > traditional wells.