Examining the influence of urban definition when assessing
relative safety of drinking-water in Nigeria
Elizabeth
1
Christenson ,
1The
Robert
1
Bain ,
Jim
2
Wright ,
Stephen
3
Aondoakaa ,
and Jamie
1
Bartram
Water Institute at UNC, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, USA
2Geography and Environment, University of Southampton, Southampton, UK
3Geography and Environmental Management, University of Abuja, Abuja, Nigeria
4World Health Organization, Geneva, Switzerland
Objectives
(i) Examine the influence of urban extent definition on water safety
in Nigeria
Urban Definition
Urban
dataset
Globcover
(ii) Compare the frequency of thermotolerant coliform (TTC)
contamination and prevalence of sanitary risks between rural
and urban water sources of a given type
Globcover2
e-Geopolis3
(iii) Investigate differences in exposure to contaminated drinkingwater in rural and urban areas.
e-Geopolis
Results
Data
Spatial
Source
Resolution Year
10 minute grid
(~0.3 km)
10 minute grid
(~0.3 km)
~200 m
precision
MODIS
MODIS4
Introduction
 Reducing inequalities is a priority from a human rights perspective and in water
and public health initiatives.
~500 m
2006
Afripop5
– 3 minute grid
UN threshold (~0.1 km)
LandScan
2008
1. Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A. The distribution of people and the dimension of place: methodologies to improve the global estimation of urban
extents. Proc Urban Remote Sens Conf. Tempe, AZ: International Society for Photogrammetry and Remote Sensing. 2005.
2. Bicheron P, P. D, Brockmann C, Schouten L, Vancutsem C, Huc M, et al. GLOBCOVER Products Description and Validation Report. 2008 p. 47.
3. Moriconi-Ebrard F, Borne F, Cao H, Chatel C, Denis E, Douay N, et al. Africopolis Urbanization Trends 1950-2020: A Geo-statistical Approach West Africa. 2010.
– 3 minute grid
national (NAT) (~0.1 km)
LandScan8,9 - 30 minute grid
(~1km)
UN threshold
LandScan8,9 – 30 minute grid
(~1km)
national (NAT)
Figure 1: Comparing spatial extent of each urban definition
• Nigeria RADWQ study10 n=1768
2004-2005 national randomized sample survey
Water Quality
5. Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One. 2012.
Binary TTC
contamination
7. Christenson E, Elliott M, Banerjee O, Hamrick L, Bartram J. Climate-related hazards: a method for global assessment of urban and rural population exposure to
cyclone, drought, and flood. Int J Environ Res Public Health. 2014;11(2):2169–92, in press.
8. Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA. LandScan: A Global Population Database for Estimating Populations at Risk. Photogramm Eng Remote
Sensing. 2000.
9. Bright EA, Coleman PR, King AL, Rose AN, Urban ML. LandScan. 2008. Oak Ridge, TN: Oak Ridge National Laboratory SE.
10. Ince M, Bashire D, Oni OOO, Awe EO, Ogbechie V, Korve K, et al. Rapid assessment of drinking-water quality in the Federal Republic of Nigeria: country report of the
pilot project implementation in 2004-2005. Geneva: World Health Organization and UNICEF; 2010.
11. Bain R, Cronk R, Hossain R, Bonjour S, Onda K, Wright J, et al. Global assessment of exposure to fecal contamination through drinking-water based on a systematic
review. 2014 a, in press.
12. Williams M, Moran P, Nhandara C, Hove I, Charimari L, Katito C, et al. Contamination of traditional drinking water sources during a period of extreme drought in the
Zvimba Communal Lands, Zimbabwe. Cent Afr J Med. 1997;43(11):316–21.
13. Wright J a, Cronin A, Okotto-Okotto J, Yang H, Pedley S, Gundry SW. A spatial analysis of pit latrine density and groundwater source contamination. Environ Monit
Assess. 2013 May;185(5):4261–72.
night time lights
time-series satellite imagery;
Land Use Classification
System (artificial surfaces)
C5 MODIS 500m resolution
Dec. 2004 - satellite imagery by assessing
Jun 2006 the level of built area
compared to vegetative area
2006
a
AP-UN
LS-UN
AP-NAT
LS-NAT
MODIS
E-GEO
Figure 3: Risk level classification for levels of
TTC in rural and urban areas, by improved
source type using the GRUMP definition of
urban extent
2006
a
2008
2008
a
a
Sanitary Risk
Observational checklist
of 10 contamination
hazards
• Unimproved Water Source Quality
90% TTC contamination
Systematic review11,12,13
• Urban-Rural Water Source Type Coverage
2006 Core Welfare Indicator Questionnaire in
Nigeria14
GLOB
Household
container
OR (p-value)
1.02 (0.965)
1.46 (0.442)
3.50 (-)
3.00 (0.003)
0.57 (0.075)
0.82 (0.623)
1.82 (0.174)
1.11 (-)
1.98 (0.043)
0.43 (0.003)
0.56 (0.179)
1.19 (0.666)
3.50 (-)
1.98 (0.043)
0.57 (0.075)
0.82 (0.623)
1.82 (0.174)
1.11 (-)
1.98 (0.043)
0.44 (0.007)
0.73 (0.490)
1.04 (0.939)
3.50 (-)
1.77 (0.089)
0.62 (0.122)
0.70 (0.254)
0.46 (0.001)
3.50 (-)
1.36 (0.350)
1.33 (0.507)
0.67 (0.413)
1.76 (0.060)
2.67 (-)
1.64 (0.260)
0.36 (0.014)
1.50 (0.532)
0.93 (0.903)
3.50 (-)
1.91 (0.072)
Urban
definition
Utility
Piped
Boreholes
Protected
dug wells
Tanker
truck
Household
container
Coeff. (pvalue)
Coeff. (pvalue)
Coeff. (pvalue)
Coeff. (pvalue)
Coeff. (pvalue)
-0.19 (0.860)
-1.04 (0.056)
0.00 (0.993)
1.70 (<0.001) -0.39 (0.487)
AP-UN
0.59 (0.097)
-0.60 (0.234)
0.60 (0.472)
-0.62 (0.282)
-0.19 (0.498)
LS-UN
0.95 (0.014)
-0.56 (0.210)
-0.77 (0.286)
-1.04 (0.056)
0.27 (0.333)
AP-NAT
0.59 (0.097)
-0.60 (0.234)
0.60 (0.472)
-0.62 (0.282)
-0.19 (0.498)
LS-NAT
0.98 (0.012)
-0.73 (0.130)
-0.73 (0.375)
-1.04 (0.056)
0.27 (0.331)
MODIS
0.79 (0.006)
-0.92 (0.040)
-1.06 (0.153)
-1.04 (0.056)
-0.13 (0.642)
E-GEO
1.59 (<0.001) -0.85 (0.029) -0.26 (0.768) -0.55 (0.331) -0.40 (0.302)
Figure 4: Sanitary risk score (the total
GLOB
number of observed contamination
0.31 (0.562) -0.59 (0.260) 0.68 (0.640) -1.04 (0.056) 0.05 (0.871)
hazards) for samples from a given improved In bold if p<=0.01. OR: odds ratio, Coef: Coefficient; A value greater than
one indicates that compliance is higher in urban areas. p-values were not
source type using the GRUMP urban
calculated for tanker trucks since these were from one single cluster.
definition
Improved and Unimproved Sources
As with Afripop but using
LandScan population density
data
Figure 5: Estimated proportion of households using
improved or unimproved water sources that contain fecal
contamination by urban definition. Based on water quality
data for improved sources from this study, for unimproved
sources from a systematic review11 and source coverage
Rural
data from a contemporary household survey14.
population estimates are based on either the 1991 census and/or the
2006 census
Tanker
truck
1.15 (0.801)
As with Afripop but using
LandScan population density
data
a Nigeria
Boreholes Protected
dug wells
Table 3: Linear regression urban versus rural sanitary risk
GRUMP
UN statistics were used to
create a national population
density threshold set so that
the percentage of urban
population matched the UN
7
estimate
A population density
threshold matches UN
6,7
estimates and contiguous
grid cells must exceed
Nigerian national defiinition
of 20,000 or more people
Utility
Piped
OR (p-value) OR (p-value) OR (p-value) OR (p-value)
Urban
Water Quality and Sanitary Risk
4. Schneider A, Friedl M a., Potere D. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on “urban ecoregions.”Remote Sens
Environ. 2010. 114(8):1733–46.
6. UN DESA Population Division. World Urbanization Prospects The 2011 Revision. New York; 2012 p. 18.
Urban
definition
General Method
Satellite imagery identifies
contiguous built up areas
a
2001 - 2008
and census identifies
satellite
whether area has more than
imagery
the global population
threshold of 10,000 people
Afripop
Afripop5
Table 2: Logistic regression of rural versus urban
differences in microbial compliance by improved source
GRUMP
 Rural dwellers are less likely to use an improved source of drinking water.
References:
Improved Sources
6
 There are periodic calls for differential national and global standards for rural and
urban areas often justified by the suggestion that safety is worse in urban areas.
 Our study discusses the implications of urban definition on microbial water
quality, however identification of urban extent and allowing for an urban-rural
gradient is important for assessing health disparities and equity in distribution of
resources within and between the urban and rural environments as well as other
environmental assessments of urban impact such as changes in precipitation
patterns, air quality and climate, deforestation and loss of biodiversity.
[email protected]
Data Sources
GRUMP1
14. WHO/UNICEF. Nigeria country file. 2013 a. Available from: www.wssinfo.org
Rifat
4
Hossain ,
AP-NAT AP-UN E-GEO GLOB GRUMP LS-NAT LS-UN MODIS
Conclusions
Improved Water
Source Types
Sampled:

BH – boreholes
or tubewell

PDW – protected
dug wells

UP – utility piped
water systems

TT – tanker truck
Figure 2: Location of improved water sources
sampled during the Nigeria RADWQ study
There is little difference between urban and rural contamination for
a given improved source type in TTC contamination and in sanitary
risk
Combining improved and unimproved source types, TTC
contamination is 1.6 – 2.3 times more likely in rural compared to
urban water sources
Rural disadvantage primarily results from the greater proportion of
rural households relying on unimproved sources (including surface
water and unprotected groundwater)
Urban-rural analyses should assess multiple definitions or indicators
of urban to assess robustness of findings and to characterize a
gradient that disaggregates the urban-rural dichotomy
Acknowledgments: We would like to express our thanks to WHO for making available the RADWQ dataset, the entire RADWQ team in Nigeria, and Oliver Schmoll for answering questions regarding survey design and implementation. The authors declare no conflicts of interest.