SPATIAL DEPENDENCY OF BURULI ULCER ON POTENTIAL … · Buruli Ulcer (BU) is an endemic prevalent...

Jan. 2014. Vol. 3, No.5 ISSN 2307-2083 International Journal of Research In Medical and Health Sciences © 2013-2014 IJRMHS & K.A.J. All rights reserved http://www.ijsk.org/ijrmhs.html

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SPATIAL DEPENDENCY OF BURULI ULCER ON POTENTIAL

SURFACE RUNOFF AND POTENTIAL MAXIMUM SOIL WATER

RETENTION

Saviour Mantey

1, Richard K. Amankwah

2 and Alfred Allan Duker

3

1Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana 2Department of Mineral Engineering, University of Mines and Technology, Tarkwa, Ghana

3Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Email addresses: [email protected],

ABSTRACT

Buruli Ulcer (BU) is an endemic prevalent disease in Ghana and other West African countries including; Cote

d´Ivoire, Benin and Togo. Despite recent upsurge of research in Buruli Ulcer, the natural reservoir and mode of

transmission of Mycobacterium ulcerans (MU) have not yet been determined. However, all major foci are found

in wetlands of tropical and subtropical countries. In this study, a landscape spatial hydrological modeling

approach based on Potential Maximum Soil Water Retention (PMSWR), Soil Conservation Service Curve

Number Grid (SCS CNGrid) and empirical evidence from field research were applied to understand their

relationship with BU disease in two districts of Ghana.

Landuse data, Hydrological Soil Groups (HSGs), Landsat images and Digital Elevation Models (DEMs) were

used to generate the SCS CNGrid and the PMSWR of the study areas. The results of the SCS CNGrid and

PMSWR maps linked BU endemic areas to low to moderate surface runoff potential and high to moderate

PMSWR. BU endemic communities in the two districts were also mostly enclaved by galamsey (illegal) mining

activities and farms. This study proved that the PMSWR and SCS CNGrid values are important hydrological

parameters to determine surface runoff potential and thus delineate BU disease prone areas.

Key words: BU, SCS CNGrid, PMSWR, Potential Surface Runoff, DEM, Ghana

INTRODUCTION

BU is an endemic disease which destroys the skin,

underlying tissues, muscles and bones when not

treated early. It is caused by MU. The disease has

been known in endemic communities in Ghana for

years with an overall prevalence rate of 20.7 per

100 000 [1]. But in recent years, cases have

increased, prompting renewed interest in BU

research especially in parts of West Africa (Cote

d´Ivoire, Ghana, Guinea, Liberia, Benin, Burkina

Faso and Togo) [2-7]. Despite the renewed research

interest in BU disease, the natural reservoir and

mode of transmission of MU is still unclear.

However, research has pointed to wetlands of

tropical and subtropical areas as potential reservoirs

of the MU [3, 7-13]. It is also widely believed that

BU disease mostly occurs in people who live and

work close to stagnant bodies of water [14].

Additionally, research has shown that new cases of

BU have been reported after flood events [5, 7, 13,

15-18]. For example, Bainsdale in Australia,

recorded the first case of BU in 1939 after the worst

floods in 1935 [19-21]. BU cases were also

reported after flood events along the settlements of

Sepik and Kumusi Rivers in Papua New Guinea in

1957 [22]. This flooding was in concert with the

explosive eruption of Mt. Lamington in 1951 [23,

24].

Case and control studies have linked landscape

disturbances to BU disease [16, 25]. Furthermore,

surface runoff from landscape disturbances also

lead to solubilisation of exposed reactive minerals

causing pollution of rivers, streams and soils by

sulphuric acid and harmful elements such as

arsenic, cadmium and mercury [26, 27].

Pollution of rivers, streams and soils usually occurs

when mined materials from uncontrolled and

mailto:[email protected]


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unscientific mining (e.g. galamsey) activities are

exposed to oxygen and water [28]. For example,

spatial analysis performed by [9], detected that

some 250 cases of BU were within a 1000 m buffer

zone around mine sites, compared to 62 cases

elsewhere within his study area. Likewise, Kumi-

Boateng et al. [29], observed that improper mining

methods by galamsey operators and lack of post

mining treatment and management further alters the

already fragile ecosystem. The gravity of the

impact, nonetheless, depends on the techniques

used and the size of the Mine [30]. A study by

Salvarredy-Aranguren et al. [31], in the Milluni

Valley, Bolivia, further revealed that potentially

harmful elements (e.g. Cd, Zn, As, Cu, Ni, Pb and

Sn) exceeded the recommended World Health

Organization (WHO), limits in surface waters

downstream of galamsey mining sites.

Duker et al. [9] also linked arsenic levels in soil and

illegal gold mining activities with increased BU

disease risk in Ghana.

Thus, landscape alteration, can lead to increased

surface runoff and contamination of surface waters

[32-35]. Landscape alteration may also increase the

risk of flooding [36-39] as a result of increased

surface runoff and subsequently induce the

outbreak of BU disease due to contamination of

surface waters [9].

Surface runoff, however, varies depending on the

nature of the soil and its management [40]. Surface

runoff and PMSWR are undoubtedly important to

the understanding of BU prevalence. Though, a

number of surface runoff models have been

developed, the SCS CNGrid method stands-out,

produces better results and is well acclaimed [41-

47]. The SCS CNGrid considers major surface

runoff watershed characteristics such as soil type,

landuse, digital elevation models and antecedent

moisture conditions (AMCs) to estimate the loss

and runoff volume [41, 48, 49], which can be

obtained from optical and microwave remotely

sensed data.

The fundamental premise of the SCS CNGrid

method is that, for a single storm, the ratio of the

actual soil retention after runoff begins to the

PMSWR is equal to the ratio of direct runoff to

available rainfall. This relationship is expressed as

[41]

{ ( )

.………………….. (1)

where is the actual direct runoff (mm); P is total

precipitation (mm); is PMSWR after runoff

begins (mm); is initial abstraction (mm) [41]. To

simplify equation one (1), Ia is fixed at = 0.2S

[41]. Thus, equation one (1) can be expressed as:

{ ( )

………………… (2)

The SCS CNGrid is then related to the PMSWR as

(

) ..…..……… (3) [41]

SCS CNGrid can be found in the Technical Release

55 (TR-55) Table or can be calculated as the

composite CNGrid [41]. Equation three (3) shows

that the CNGrid decreases as the PMSWR (S)

increases.

The objective of this research therefore is to use the

SCS CNGrid model, which thoroughly considers

physiographic heterogeneity (e.g. soil, landuse and

digital elevation models) and empirical evidence

from field research to determine the relationship

between surface runoff potential, PMSWR and BU

disease.

MATERIALS AND METHODS

Study areas: The study areas are Amansie West

District (AWD) and Upper Denkyira West District

(UDWD). These study areas were selected due to

their similarities in socio-economic activities (e.g.

illegal “galamsey” mining and farming activities)

and BU prevalence.

The AWD lies between latitudes 6°07' N and 6°35'

N and longitudes 1°42' W and 2°08' W (Fig. 1). The

district is bounded by UDWD, Amansie Central

district, Bibiani-Anwiaso-Bekwai district, Atwima

Mponua district, Atwima Nwabiagya district,

Atwima Kwanwoma district and Bekwai

Municipal. The district is a tropical rain forest area

of about 1 320 km2 with an estimated population of

134 331 (2010 Population and Housing Census). It


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Fig. 1: Map of Amansie West District

Fig. 2: Map of Upper Denkyira West District


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is approximately 60 km southwest of Kumasi, the

regional capital of Ashanti. AWD has the highest

BU prevalence rate of 150.8 per 100 000 in the

country [50-52] and the second highest reported

active cases across the country [52]. The main

occupation of the people is subsistence farming and

small scale “galamsey” mining. The AWD is

drained by the Offin and Oda rivers and their

tributaries (Fig. 1).

UDWD is the second study area and lies between

latitudes 5° 54' N and 6° 18' N and longitudes 1° 49'

W and 2° 12' W of the Greenwich Meridian (Fig.

2). The district is bounded to the south-east by

Upper Denkyira East district, Amansie Central

district and Obuasi Municipal. Bounded to the

south-west are Wassa Amenfi East and Amansie

West districts. The district is also bounded to the

north-west by Bibiani-Anwiaso-Bekwai and Wassa

Amenfi West districts. UDWD covers an area of

about 1 700 km2 with an estimated population of

about 132 864 (2010 Population and Housing

Census). While the Central Region has the highest

overall prevalence rate of active cases [52], UDWD

is among the most endemic districts in the Region

and has the third highest prevalence rate of 114.7

per 100 000 nationwide together with Upper

Denkyira East District after Asante Akim North

district (prevalence rate of 131.5 per 100 000) and

Amansie West district (prevalence rate of 150.8 per

100 000) [52]. Subsistence farming and small scale

“galamsey” mining are the main occupation of the

people in the district. UDWD is drained by Offin,

Dia and Subin rivers as well as their tributaries

(Fig. 2).

Data Acquisition: Data for the study include;

DEMs, Epidemiological data of BU for the two

districts, Landuse and Soil data for the two study

areas. Landsat images (path 194/rows 056 and 057)

were acquired on January 01, 1991, February 01,

2008 and May 31, 2013 for this study. The DEMs

were obtained from the Advanced Space borne

Thermal Emission and Reflection Radiometer

Global Digital Elevation Models (ASTER-GDEM).

The Epidemiological data was obtained from the

National Buruli Ulcer Control Programme and from

field investigation based on clinical case definition

of WHO [1, 53]. The Landuse data was acquired

from the Centre for Remote Sensing and GIS

(CERSGIS) at the University of Ghana. The Soil

data was also procured from the Soil Research

Institute of the Council for Scientific and Industrial

Research (CSIR-SRI). The Landsat images were

made available by the Earth Resources Observation

Systems Data Centre, United States Geological

Survey (USGS) and were rectified to the Universal

Transverse Mercator projection system.

Methods Used: BU disease prone areas were

delineated from the study areas using SCS CNGrid

and PMSWR maps. The procedure adopted to

achieve this objective is summarized in the flow

chart as shown in Fig. 3.

Fig. 3: Flow chart for Generating SCS CNGrid

and PMSWR

Selecting Soil, Impervious Surface and

Vegetation (S-I-V) Fractions

Composite CNGrid values were computed from S-

I-V fraction which was derived from normalized

spectral mixture analysis (NSMA) and linear

spectral mixture analysis (LSMA). The NSMA was

applied to estimate and normalize the percentage of

impervious surfaces by modelling a mixed


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spectrum as a linear combination of three end-

members; soil, impervious surface and vegetation.

The normalization process of NSMA was used to

reduce the within-class radiometric variations and

therefore facilitate the separation of S-I-V land

cover types. Two key steps were employed to

perform the NSMA. Firstly, the original Landsat

TM/ETM+ images were normalized to reduce

within-class radiometric variations using equation

four (4) [54].

……………… (4)

where

∑ and is the normalized

reflectance for band in a pixel, is the original

reflectance for band , is the most probable

reflectance for that pixel and n is the total number

of bands. Secondly, LSMA was applied to calculate

the fractions of the three end-members within the

normalized spectra. Clusters of each of the end-

members were identified by visual interpretation of

the Landsat images and the feature space images of

a principal component transformation. The fraction

of S-I-V end-members in a pixel was derived with a

least squares method in which the residual, is

minimized (Equation 5):

∑ ……………… (5)

where i = 1,…m (represents number of spectral

bands); = 1,… (represents number of end-

members); Ri is the normalized spectral reflectance

of band i which contains end-members; is the

proportion of end-member within the pixel;

is the known normalized spectral reflectance of

end-member within the pixel on band i; and is the residual for band i. A constrained least-

squares solution is used with the assumption that

the following two requirements are satisfied

concurrently (Equation 6):

∑ ………..(6) [55-60].

Computing Composite CNGrid Values

To calculate the composite CNGrid values, four

steps where used (Fig. 3). The first was to classify

vegetation types based on NDVI values and assign

initial CNGrid values with reference to TR-55

Table. Secondly, the S-I-V fraction images were

extracted from Landsat images of the study areas

using the NSMA and LSMA models. Thirdly,

hydrological soil groups of the study areas were

assigned initial CNGrid values with reference to the

TR-55 Table. Finally, the composite CNGrid is

calculated as the weighted average of the initial

CNGrid values of S-I-V fractions, which were then,

keyed into the Look-Up Table for generating the

final CNGrid and PMSWR maps of the study areas.

Processing DEM and Generating SCS CNGrid

The DEM used to create the SCS CNGrid was

processed by performing “fill sink”. This was

necessary to minimize sinks in the DEM which

were due to errors.

In order to generate the SCS CNGrid, the corrected

DEMs, reclassified landuse and HSGs were

merged. The original landuse data of the study

areas were reclassified into four major landuse

categories namely; water, medium residential,

forest and agricultural. The reclassification was

based on Anderson’s land use classification scheme

[61] and empirical field evidence (groundtruthing).

The reclassification was to normalize the different

categories of the dataset for analyses. The

reclassified landuse data were then converted to

polygons.

The diverse characteristics of the soil data such as

rate of infiltration of the study areas were used to

generate the HSGs for all polygons in the study

areas. The three HSGs identified in the study areas

indicated the amount of infiltration rates as follows

[45]: A-soil with high infiltration rates (lowest

CNGrid values); B-soil with moderate infiltration

rates (moderate runoff values) and C-soil with slow

infiltration rates (high runoff values).

The percentage of each soil group in a particular

polygon was also calculated. After processing the

HSGs, the soil and landuse were then merged to

create polygons. A Look-Up Table was also created

to contain SCS CNGrid for all landuse and soil

groups.

The merged HSGs and the landuse data, was

converted to polygons to represent the merged

HSGs and landuse. Composite SCS CNGrid values

for each reclassified landuse were assigned in the

Look-Up Table [62]. HEC-GeoHMS, a SCS

CNGrid generating tool in ArcGIS was used to


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generate the SCS CNGrid maps using the HSG

map, the landuse map, the corrected DEM and the

Look-Up Table. The PMSWR maps were generated

using equation six (6) from the SCS CNGrid.

(

)……………….(6)

The PMSWR maps are related to the soils and land

cover conditions of the study areas through SCS

CNGrid, which is dependent on landuse, HSGs and

DEMs. The landscape parametres from the SCS

CNGrid and PMSWR maps were extracted using

kriging method of gridding. Kriging method was

chosen because it yielded the best correlation

coefficients for all the data sets.

Hydrological Soil Groups (HSGs): The HSGs

found in the study areas include A, B and C. Soils

are classified into HSG’s to indicate the rate of

infiltration obtained for soils after prolonged

wetting. Group “A” soils have low runoff potential

due to high infiltration rates (7.62–11.43 cm/h).

Group “B” soils have moderate runoff potential due

to moderate infiltration rates (3.81–7.62 cm/h) and

group “C” soils have a moderate to high runoff

potential due to slow infiltration rates (1.27–3.83

cm/h) [63]. The initial SCS CNGrid values for each

HSGs and corresponding landuse classes adopted

from the description of HSGs for Ghana [45, 62]

are presented in Table 1.

Table 1: CNGrid Look-Up Table for Landuse and

HSGs of Study Area

(Source: Anon., 1986; McCuen, 1982)

RESULTS AND DISCUSSION

HSG “A” leads to low CNGrid values while the

HSGs “B” and “C” lead to moderate and moderate

to high CNGrid values respectively in the study

areas. Maps showing the SCS CNGrid are

presented in Fig. 6 to 7b. The SCS CNGrid values

ranging from 80-100 have the highest runoff

potential while SCS CNGrid values from 30-49.99

and 50-79.99 have low to moderate runoff

potentials respectively. The landuse types in the

SCS CNGrid values from 30 to 79.99 are mostly

forest, agricultural and galamsey activities with

HSGs of mainly A and B. The landscape

parameters extracted from communities in the two

districts and their respective BU cases are shown in

Tables 2 and 3 and Fig. 4a to 5b. Correlation

coefficients (R) between CNGrid, PMSWR and BU

cases were determined to examine their statistical

relationships. There was a strong positive

correlation between CNGrid and BU cases (R =

0.83) as well as a strong negative correlation

between PMSWR and BU cases (R = -0.80) for

AWD (Fig. 4a and 4b). Correlation coefficients

between CNGrid and BU cases (R = 0.74) as well

as PMSWR and BU cases (R = -0.67) for UDWD

were also strong (Fig. 5a and 5b). The positive

correlation between CNGrid and BU cases link low

to moderate potential surface runoff areas to

increasing BU cases whilst the negative

correlations between PMSWR and BU cases show

that, as PMSWR decreases, BU cases increase. BU

cases generally decline with CNGrid values of

eighty (80) and above. CNGrid values from 80 to

100 represent high surface runoff potential areas.

Table 2: Landscape Parameters from AWD

Landuse

Description

Curve Number Grid

(CNGrid)

A B C

Water 100 100 100

Medium Residential 57 72 81

Forest 30 58 71

Agricultural 67 77 83

Community CNGrid PMSWR (mm)

BU Cases 1999 -2012

Dawusaso 68 119.53 62

Abore 71 103.75 62

Ayiem 84 48.38 35 Tontokrom 78 71.64 121

Bonsaaso 78 71.64 82 Akyekyewere 30 592.67 43

Watreso 30 592.67 45

Essienkyiem 58 183.93 55

Agoroyesum 78 71.64 120 Datano 78 71.64 76


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Fig. 4a: CNGrid and BU Cases (1999-2012) Relationships for AWD

Fig. 4b: PMSWR and BU Cases (1999-2012) Relationships for AWD

Table 3: Landscape Parameters from UDWD

WatresoAkyekyewer

eEssienkyiem Dawusaso Abore Datano Bonsaaso Tontokrom Ayiem

CNGrid 30 30 58 68 71 78 78 78 84

BU Cases 45 43 55 62 62 76 82 121 35

0

20

40

60

80

100

120

140

Watreso Akyekyewere Essienkyiem Dawusaso Abore Datano Bonsaaso Tontokrom Ayiem

BU Cases 45 43 55 62 62 76 82 121 35

PMSWR 592.67 592.67 183.93 119.53 103.75 71.64 71.64 71.64 48.38

0

100

200

300

400

500

600

700

Community CNGrid PMSWR (mm) BU Cases

1999 -2012

Diaso 79 67.52 47

Moadaso 67 125.10 30

Agona 67 125.10 35

Ntom 67 125.10 41

Adeade 67 125.10 47

Subin 67 125.10 42

Ameyaw 85 44.82 37

Ampabena 72 98.78 55

Nyinwonsu 30 592.67 22

Domenase 30 592.67 22 Ayanfuri 79 67.52 43 Nkotumso 55 207.82 30 Abora 72 98.78 56

Treposo 72 98.78 56 Adaboa 30 592.67 23

Bethlehem 67 125.10 45


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Fig. 5a: CNGrid and BU Cases (1999-2012) Relationships for UDWD

Fig. 5b: PMSWR and BU Cases (1999-2012) Relationships for UDWD


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Fig. 6: CNGrid Map of AWD

Fig. 7a: CNGrid Map of UDWD


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Fig. 7b: CNGrid Map of UDWD

Potential Maximum Soil Water Retention (PMSWR): The PMSWR were generated based on the SCS

CNGrid Method. The maps of PMSWR for the study areas are shown in Fig. 8 to 9b.


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Fig. 8: PMSWR Map of AWD

Fig. 9a: PMSWR Map of UDWD


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Fig. 9b: PMSWR Map of UDWD

CONCLUSIONS

This research presents a new approach to

delineating BU disease prone areas and will be

useful for identifying new areas where humans may

be at high risk to the disease. The study observed

that BU endemic communities were mostly

surrounded by farms and galamsey activities. The

endemic communities in the study areas correlated

with high to moderate infiltration rates as well as

moderate to high PMSWR.

Low to moderate runoff potential areas have SCS

CNGrid values which range from 30 to 79.99. The

landuse are forest, agricultural and galamsey

activities with HSGs of A and B.

CNGrid values from 80 to 100 also correlate with

high runoff potential areas with landuse of mostly,

agricultural and galamsey activities and HSG of

mainly C.

Low to moderate surface runoff potential and

moderate to high PMSWR of between 67.52 mm -

592.67 mm correlate with BU disease prone areas.

This study has demonstrated that PMSWR and SCS

CNGrid values are important hydrological

parameters for the delineation of BU disease prone

areas.

ACKNOWLEDGEMENTS

This research is part of a larger project funded by

the National Science Foundation (CNH Award

#0909447: Research and Education on Buruli ulcer,

Inundations, and Land Disturbance – ReBUild,

rebuildghana.org) involving geographers, earth

scientists, medical professionals, and education

specialists from several US and Ghanaian

universities and other research partners.

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SPATIAL DEPENDENCY OF BURULI ULCER ON POTENTIAL … · Buruli Ulcer (BU) is an endemic prevalent...

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