USING GIS AND GEOSTATISTICS TO DEVELOP HAZARD AND RISK MAPS OF ARSENIC IN ...

210 Pages·2010·5.38 MB·English

USING GIS AND GEOSTATISTICS TO DEVELOP
HAZARD AND RISK MAPS OF ARSENIC IN
SHALLOW GROUNDWATERS OF CAMBODIA

A dissertation submitted to the University of Manchester for
the degree of Master of Philosophy in the Faculty of
Engineering and Physical Sciences

2010

Chansopheaktra SOVANN

School of Earth, Atmospheric and Environmental
Sciences
TABLE OF CONTENTS

List of figures 8
List of tables 11
Abstract 12
Declaration 13
Copyright statement 14
Acknowledgements 15

Chapter 1
Introduction 16
1.1 Introduction 16
1.2 Statement of problems 17
1.3 Rationale of the study 18
1.4 Objectives 18
1.5 Scope and limitation 19

Chapter 2
Cambodian geographic overview 20
2.1 Physical environment overviews 20
2.1.1 Geography and topography 20
2.1.2 Soil type 21
2.1.3 Land use 23
2.1.4 Climate 25
2.1.5 Geology 27
2.1.6 Hydrology 28
2.1.7 Hydrogeological structures and units 29
a. Depth of basement rocks 29
b. Hydrological units 29
c. Sedimentary profile 30
2.1.8 Groundwater resources 31
a. Specific capacity of aquifers 31
b. Groundwater level 32
c. Water balance analysis. 32
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2.1.9 Groundwater quality in Cambodia 33
a. Bacteriological contaminant 33
b. Arsenic (As) 34
c. Iron (Fe) 36
d. Salinity 37
e. pH 38
f. Manganese (Mn) 38
2.2 Socio-economic overviews 38
2.2.1 Population 38
2.2.2 Economic 39

Chapter 3
Literature review 40
3.1 Risk assessment overviews 40
3.1.1 The Concept of risk assessment 40
3.1.2 Overview of risk assessment methods 41
3.2 Arsenic overviews 44
3.2.1 What is arsenic? 44
3.2.2 Arsenic mobilizing mechanisms in groundwater 44
3.2.3 Factors to elevate arsenic concentration to groundwater 45
3.2.4 Impact of arsenic on human health 46
3.3 Mapping of arsenic contamination in groundwater of Cambodia 47
3.4 Spatial interpolation methods 50
3.4.1 Introduction to spatial interpolation methods 50
3.4.2 Types of interpolators 50
a. Non-geostatistical interpolators 51
a.1 Linear interpolation 51
a.2 Thiessen polygon (voronoi polygon) 52
a.3 Triangular irregular network 53
a.4 Inverse distance weighting 53
a.5 Regression models 54
b. Non-geostatistical interpolators 55
b.1 Introduction of regionalized variable and kriging 55
b.2 Kriging overviews 58
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b.3 Ordinary kriging 59
c. Combined procedures 60
c.1 Regression kriging 60
c.2 Trend surface analysis combined with kriging 61
3.4.3 Geostatistical analysis procedure 61
3.4.4 Statistical method for comparing performance of spatial interpolation methods 62
3.4.5 Some factors affecting performance of spatial interpolation methods 64

Chapter 4
Research methodology 65
4.1 Research framework 65
4.2 Study area selection 66
4.3 Secondary data acquisition 66
4.3.1 Arsenic dataset 66
4.3.2 Environmental covariates 67
a. Digital elevation model 67
b. Soil map 67
c. Geological map 67
4.3.3 Demographic database 68
4.4 Primary data obtained 68
4.4.1 Sampling site and sample size 68
4.4.2 Sampling method 69
4.4.3 Sample analysis technique 69
4.4.4 Water parameter output correction 70
4.5 Data preparation and analysis 71
4.5.1 Analysis statistic and interpreting primary data points 71
4.5.2 Arsenic data point preparation 71
4.5.3 Environmental covariates 74
a. Digital elevation model 74
b. Soil map 75
c. Geological map 75
d. Gender fraction and population density map 76
4.6 Model execution 78
4.6.1 Arsenic predication maps 78
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4.6.2 Arsenic risk maps 79
a. Mapping of prevalence ratio and incidence rate of arsenic induced diseases 79
b. Estimations of cases of arsenic induced diseases 82
4.7 Model validations and evaluations 82
4.7.1 Arsenic prediction maps 82
4.7.2 Arsenic health risk maps 82

Chapter 5
Results 83
5.1 Primary dataset analysis result and interpretation 83
5.1.1 The quality assurance of arsenic analysis in samples 83
5.1.2 Descriptive result of arsenic concentration in samples 84
5.1.3 Correlation of arsenic with other chemical elements in samples 84
5.1.4 Piper diagrams of samples 85
5.1.5 Bicarbonate concentration in samples 86
5.1.6 Chemical quality of drinking water assessment 87
5.2 Exploring input data for model 89
5.2.1 Arsenic dataset 89
a. Training dataset transformation 90
b. Well depths attached with datasets 91
c. Elevation attached with datasets 91
d. Slope attached with datasets 92
e. Soil types attached with datasets 92
f. Geological types attached with datasets 92
g. Relations of arsenic concentration and depth of wells 93
5.2.2 Explanatory analysis focusing on the feature space 94
5.2.3 Regression modelling 95
a. Exploring qualitative relations between continuous predictors and arsenic
concentration attached with training dataset 95
b. Exploring qualitative relations between discrete predictors and arsenic
concentration attached with training dataset 96
5.3 Fitting the model 100
5.3.1 Variogram model for OK 100
5.3.2 Variogram model for RK 101
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a. Principal component analysis of environmental covariates 101
b. Stepwise regression of principal components 102
c. Fitting variogram model for RK 102
5.4 Arsenic prediction mapping 103
5.4.1 Arsenic prediction map by IDW model 103
5.4.2 Arsenic prediction map by OK model 104
5.4.3 Arsenic prediction map by RK model 106
5.5 Arsenic model validation and evaluation 107
5.5.1 IDW model 107
5.5.2 OK model 109
5.5.3 RK model 109
5.6 Arsenic risk maps 113
5.6.1 Prediction maps of fraction of hyperpigmentation and keratosis 113
5.6.2 Prediction map of fraction of arsenicosis 113
5.6.3 Prediction map of fraction of skin cancer 114
5.6.4 Prediction map of incidence rate of liver cancer 115
5.6.5 Prediction map of incidence rate of lung cancer 116
5.6.6 Prediction map of incidence rate of bladder cancer 117
5.6.7 Estimation of numbers of cases of arsenic induced diseases 118

Chapter 6
Discussions 120
6.1 Piper diagrams of the groundwater samples 120
6.2 Bicarbonate contamination in samples 120
6.3 Chemical quality of drinking water assessment 120
6.4 Arsenic concentrations and depth of the wells 121
6.5 Relations between soil, geological type and arsenic concentrations 121
6.6 Arsenic prediction models 122
6.6.1 Comparison between IDW and OK models 122
6.6.2 Comparison between OK and RK models 122
6.6.3 Factors to decrease accuracy of arsenic prediction of RK model 122
6.7 Comparison of the RK model to previous study models 123
6.7.1 Comparison to Winkel et al. (2008) model 123
6.7.2 Comparison to Lado et al. (2008) model 124
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6.8 Cross-checking results of arsenic risk maps 124
6.8.1 Cross-checking results of arsenicosis estimated by the models 124
6.8.2 Cross-checking results of arsenic induced cancers estimated by model 125
6.8.3 The uncertainty of arsenic risk maps 126

Chapter 7
Conclusions and recommendations 127
7.1 Conclusions 127
7.2 Recommendations 129

References 131

Appendix A 135
Appendix B 174
Appendix C 176
Appendix D 179
Appendix E 198
Appendix F 209

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LIST OF FIGURES

Figure 1: Geographical map of Cambodia 21
Figure 2: Soil type map of Cambodia 23
Figure 3: Landuse map of Cambodia for 2002 24
Figure 4: The distribution of yearly average rainfall (1981-2004), yearly average
temperature, and dry duration 26
Figure 5: Cross-section of sedimentary profile in quaternary geology from
the Mekong River to wetlands in Kien Svay district, Kandal province,
Cambodia 31
Figure 6: The distribution of different concentration of arsenic responding
to geology in Cambodia 35
Figure 7: Distribution of low (<10 µg/L as), medium (10-50 µg/L as) and
high (>50 µg/L as) arsenic wells from four communes in Kean Svay
district, Kandal province 36
Figure 8: Interpolated iron concentration in groundwater contaminated along
the lower Mekong River in Cambodia. The maps were drawn using
a nearest neighbor algorithm, a standard geostatistical technique (n= 352) 37
Figure 9: Principle steps of risk assessment 42
Figure 10: The elements of risk assessment 43
Figure 11: Winkel et al.’s probability map of arsenic concentration exceeding
10 µg/L (left) and Lado et al.’s regression kriging map of arsenic
concentration estimation (ppb) of 16-100 m depth groundwater (right) 49
Figure 12: The cross section of arsenic concentration variation from the wetlands
to the Mekong River in Kean Svay district, Kandal province 50
Figure 13: Delauney triangulation and corresponding thiessen polygon network
for a set of scatter points 52
Figure 14: The different types of variogram models: spherical model (top left),
exponential model (top right), linear model (bottom left), gaussian model
(bottom right) 58
Figure 15: The research framework 65
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Figure 16: Groundwater sampling sites around Tonle Sap Lake and coastal
provinces of Cambodia. Hydrological layers from MRC (2003).
Based map from NIS (2008) 69
Figure 17: Map of arsenic data point location before filtering (top) and after filtering 73
Figure 18: DEM after geoprocessing (left) and slope analysis in 500 m cell grid
of DEM (right) 74
Figure 19: Soil map before (left) and after reclassification (right) 75
Figure 20: Geological map before (left) and after reclassification (right) 76
Figure 21: The preparation steps of arsenic dataset and environmental covariates for
arsenic prediction model 77
Figure 22: The flow diagram of the modelling of arsenic health risk maps 81
Figure 23: The percentage variations of different standards (top) and the percentage
variations of three samples’ triplications (bottom) 83
Figure 24: The concentration of arsenic in the samples from both Tonle Sap Lake
region and coastal provincial region compared to Cambodia’s and WHO
standards for arsenic concentration in drinking water 84
Figure 25: The correlation matrix of all chemical elements of samples from
the Tonle Sap regions (left) and coastal provincial regions (right) 85
Figure 26: The piper diagrams of samples from the Tonle Sap region (left)
and the coastal region (right) 86
Figure 27: The correlation between [DIC] and [HCO -] (left) and the comparison
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of [HCO -] analysis by instrument and by titration methods in both
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Tonle Sap and coastal regions (right) 87
Figure 28: The stem-and-leaf plot (top) and the histogram of training dataset (bottom) 90
Figure 29: The histogram of arsenic training data after transformation 91
Figure 30: The scatter plot of arsenic concentration against depth of wells 94
Figure 31: Back to back histograms of elevation (left) and slope (right)
between training samples (977 locations) and raster map (all raster nodes) 95
Figure 32: The smooth scatter plot of arsenic concentration against elevation (left)
and against slope (right) 96
Figure 33: The box plot of arsenic concentration and soil types (right) and the mean
plots of arsenic in different soil types with 95 % CI of mean (left) 97
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Figure 34: The box plot of arsenic concentration and geological types (right)
and the mean plots of arsenic in different geological type with 95 % CI
of mean (left) 98
Figure 35: The OK variogram fitted by rule of thumb (left) and autofit method (right) 101
Figure 36: The variance of the computed principle components 102
Figure 37: The RK variogram fitted by rule of thumb (left) and autofit method (right) 103
Figure 38: The prediction map of arsenic concentration in Cambodian groundwater
by the IDW model, the prediction of arsenic concentrations below 10 ppb
and 50 ppb maps by the IDW model (bottom) 104
Figure 39: The prediction map of arsenic concentration in Cambodian groundwater
by OK model 105
Figure 40: The prediction map of arsenic concentration in Cambodian groundwater
by RK model 107
Figure 41: The plot of IDW model’s residual 108
Figure 42: The prediction variance map of OK model (top) and RK model (bottom) 111
Figure 43: The prediction maps of fractions of hyperpigmentation (left) and keratosis
(right) in Cambodia 113
Figure 44: The prediction map of fraction of arsenicosis in Cambodia 114
Figure 45: The prediction map of fraction of skin cancer in Cambodia 115
Figure 46: The prediction map of incidence rate of liver cancer in Cambodia 116
Figure 47: The prediction map of incidence rate of lung cancer in Cambodia 117
Figure 48: The prediction map of incidence rate of bladder cancer in Cambodia 118

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