A GIS-based modeling of environmental health risks in populated areas of Port-au-prince, Haiti

A GIS-BASED MODELING OF ENVIRONMENTAL HEALTH RISKS

IN POPULATED AREAS OF PORT-AU-PRINCE, HAITI

By

Myrtho Joseph

________________________________

A Thesis submitted to the Faculty of the

SCHOOL OF NATURAL RESSOURCES

In Partial Fulfillment of the Requirements

For the Degree of

MASTER OF SCIENCE

WITH A MAJOR IN RENEWABLE NATURAL RESOURCES STUDIES

In the Graduate College

THE UNIVERSITY OF ARIZONA

2007

STATEMENT BY AUTHOR

This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: ____________________________

APPROVAL BY THESIS COMMITTEE

This thesis has been approved on the date shown below:

_____________________________ ________________

D. Phillip Guertin Date

Associate Professor of Watershed Management

_____________________________ ____________________

Craig Wissler Date

Assistant Professor Landscape Studies

_____________________________ ____________________

Gary Christopherson Date

Director, Center for Applied Spatial Analysis

ACKNOWLEDGEMENTS

Without any doubt the success of a study like this relies on a well-thought methodology, an excellent design, a good theoretical background, reliable data and tools, and a sound analysis. However, without the support of people who are expert in specific domains, this study would be more challenging and might not be made possible. A popular and wise biblical verse says: «People die for lacking knowledge». I would not physically die without access to some precious information released by many of those who helped, but I would be dying slowly with impatience, discouragement, sense of defeat, lack of inspiration, and frustration.

I want to take advantage of this occasion to thanks D. Phil Guertin who has been my advisor not only for the thesis but during the complete course of my studies at the School of Natural Resources. His support has gone beyond academics, and covered a large range of assistance that is not possible to list without missing some. I am more than certain Dr Guertin will continue to guide me even after the completion of my master's study. I want to express my gratitude to Dr Christopherson who opened the CASA Lab for me during the tedious digitization process and had provided me profitable guidance for the generation of Port-au-Prince's DEM. My appreciation goes to Wissler for sporadic but precious intervention when I was struggling with some ArcMap processes. Mickey Reed was irreplaceable for specific advice and access to fine-point tools and processes. Thanks to all the Advanced Resource Technology's staff for unconditional support and flexibility. I am grateful to Kareen Thermil, who granted me access to some precious information and data, and Juvenel Joseph, my brother who did any necessary arrangement to facilitate acquisition of much of the data needed and available from Haiti. I want to thank either the group of Haitian professionals and students who accepted to participate in the EOW survey. Finally, the best for last, I want to thank my wife who accepted heartedly to sacrifice our time and invest it in the accomplishment of the thesis. Her devotion and support were priceless for the completion of my study.

DEDICATION

This thesis is dedicated to my mother who would not have the opportunity to witness the fulfillment of my dream; to my wife whose unconditional support helped me to be at the same time a father, a spouse, and a fulltime master's student; finally to my country which unfortunately is the inspiration of this topic.

TABLE OF CONTENTS

LIST OF TABLES 8

LIST OF FIGURES 9

ABSTRACT 13

1. INTRODUCTION 15

2. LITERATURE REVIEW 18

2.1 RISK 18

2.2 HAZARDS 19

2.3 VULNERABILITY 19

2.4 RISK ASSESSMENT 20

2.5 HAZARD IDENTIFICATION OR DELINEATION 22

2.6 ENVIRONMENTAL HEALTH FACTORS 23

2.7 APPROACHES TO VULNERABILITY ASSESSMENT 25

2.8 MULTI-CRITERIA EVALUATION (MCE) AND WEIGHTED LINEAR COMBINATION (WLC) 28

2.9 CLASSIFICATION METHODS 30

3. METHODS 31

3.1 STUDY AREA 31

3.2 DATA COLLECTION 33

3.3 DATA LIMITATION 34

3.4 CONSTRUCTION OF THE MODEL 35

3.4.1 Process Overview 35

3.4.2 Environmental Health Factors 36

3.4.2.1 Air pollution from traffic 36

3.4.2.2 Waste Pollution 39

3.4.2.3 Public formal and informal market places 42

3.4.2.4 Hospitals and the main cemetery 43

3.4.2.5 Housing density 44

3.4.2.6 Pollution from water bodies 47

3.4.2.7 Proximity to the sea 48

3.4.2.8 Proximity to high voltage power line 49

3.4.3 Linear Combination of the Variables 50

3.4.4 Classification Schemes 51

3.5 MODEL'S SUMMARY 52

4. RESULTS AND DISCUSSION 53

4.1 RESULTS BY LINEAR COMBINATION SCHEMES 54

4.1.1 Expert Opinion Survey, Equal Influence (Equal Weight), and Personalized Weightings 54

4.1.2 The Maximum Weighting Scheme 57

4.2 COMPARISON OF THE CLASSIFICATION TECHNIQUES 59

4.3 NEIGHBORHOODS EXPOSED AT HIGH RISKS 61

4.4 ENVIRONMENTAL HEALTH HAZARDS 63

4.4.1 Traffic Pollution 65

4.4.2 Waste Pollution 66

4.4.3 Housing Density 67

4.4.4 Pollution from Market Places 68

4.4.5 Pollution water bodies 69

4.4.6 Pollution from the coast 71

4.4.7 Pollution from high voltage electric power 72

4.4.8 Pollution from the hospitals 73

4.4.9 Pollution from the cemetery 74

4.5 SENSITIVITY ANALYSIS 75

4.5.1 Traffic Pollution Influence 75

4.5.2 Waste Pollution Influence 76

4.5.3 Proportional Spatial Incidence of the factors 77

5. CONCLUSIONS 77

APPENDIX A - TABLES 81

APPENDIX B - FIGURES 89

APPENDIX C - MODEL'S OUTLINE 99

APPENDIX D - MODEL'S EXECUTION SCRIPT 100

REFERENCES 117

LIST OF TABLES

Table 1: Pollution from Traffic - Risk Thresholds 39

Table 2: Pollution from Waste - Risk Levels 42

Table 3: Pollution from Market Places and Risk Levels 43

Table 4: Housing Density classification in the original grid 46

Table 5: Housing Density and Risk Levels 47

Table 6: Pollution from water bodies - Risk Levels 48

Table 7: Distance to the sea and Risk Levels 49

Table 8: Distance to High Voltage Power Lines and Vulnerability level 50

Table 9: Percent of areas per vulnerability level - Average score for the four classification techniques 55

Table 10: Percent of areas by risk level and aggregation scheme using a standardized classification 55

Table 11: Increase in traffic pollution weight compared to EOW 76

Table 12: Increase in waste pollution weight compared to EOW 76

Table 13: Comparison of EOW and Proportional Incidence Weighting Results 77

Table 14: Risk of Air pollution from traffic - Vulnerability scales 81

Table 15: EOW Results 82

Table 16: Results for different combination and classification schemes 83

Table 17: Summary results for the classification schemes 84

Table 18: Summary Results by Health Hazard and Risk Level 85

Table 19: Weighting schemes and Results Ranking 86

Table 20: Regression of EOW on Percent of High and Very High Risks 86

Table 21: Regression of Own Weight on Percent of High and Very High Risks 87

Table 22: Regression of EOW on Average of Area Covered (%) 87

Table 23: Regression of Own Weighting on Percent of Area (%) 88

LIST OF FIGURES

Figure 1: left: Base map of Port-au-Prince and the study area; right: Port-au-Prince's view from the southeast hills. 33

Figure 2: Housing Density as classified in the original grid 0.5x0.5 km 46

Figure 3: Housing Density after reclassification (grid size: 0.3x0.3 km) 46

Figure 4: Environmental Health Risks - 56

Figure 5: Environmental Health Risks in Port-au-Prince - EOW classified with the geometric interval technique 57

Figure 6: Environmental Health Risks in Port-au-Prince - Maximum combination technique using the Geometric Interval classification method 58

Figure 7: Environmental Health Risks in Port-au-Prince - Percent of area at-risk by classification technique 60

Figure 8: Environmental Health Risks - Own weighting scheme using the quantile technique: greater proportion of high/very high risks 61

Figure 9: Environmental Health Risks - Own weighting scheme using the geometric interval technique: smaller proportion of high/very high risks 61

Figure 10: Environmental Health Risks in Port-au-Prince: Percent of areas at-risk using the Own weighting scheme and the natural breaks classification method 62

Figure 11: Factors affecting environmental health in Port-au-Prince. 65

Figure 12: Risks of traffic pollution in Port-au-Prince 66

Figure 13: Waste Pollution in Port-au-Prince 67

Figure 14: Housing Density in Port-au-prince 68

Figure 15: Pollution from market places 69

Figure 16: Pollution from Water bodies 70

Figure 17: Pollution from the sea coast 72

Figure 18: Pollution from high voltage power 73

Figure 19: Neighborhood pollution from Hospitals 74

Figure 20: Neighborhood pollution from the central cemetery 75

Figure 21: EOW - Quantile 89

Figure 22: EOW - Natural Breaks 89

Figure 23: EOW - Geometric Interval classification 90

Figure 24: EOW- Equal Interval 90

Figure 25: EOW - Defined Interval 91

Figure 26: Equal Weight - Quantile 91

Figure 27: Equal Weight - Natural Breaks 92

Figure 28: Equal Weight - Equal Interval 92

Figure 29: Equal Weight - Geometric Interval 93

Figure 30: Equal Weight - Defined Interval 93

Figure 31: Own Weight - Quantile 94

Figure 32: Own Weight - Natural Breaks 94

Figure 33: Own weight - Equal interval 95

Figure 34: Own Weight - Geometric Interval 95

Figure 35: Own weight - Defined 96

Figure 36: Maximum Output using no classification technique 96

Figure 37: Maximum Weighting - Defined 97

Figure 38: Traffic Sensitivity Analysis 97

Figure 39: Waste Sensitivity Analysis 98

Figure 40: Proportional Weighting Sensitivity Analysis 98

LIST OF ABBREVIATIONS

AHP: Analytical Hierarchy Process

ARIs: Acute Respiratory Infections

BMRC: Bureau of Meteorology Research Center

CDERA: Caribbean Disaster Emergency Response Agency

DDI: Disaster Deficit Index

DRI: Disaster Risk Index

ECVH: Enquête sur les Conditions de Vie en Haiti

EHR : Environmental Health Risks

EMMUS II: Enquête de Mortalité, Morbidité et Utilisation de Services 1994

EMMUS III : Enquête de Mortalité, Morbidité et Utilisation de Services 2000

EOW: Expert Opinion Survey

EPA: Environment Protection Agency

ESRI: Environmental System Research Institute

GDP: Gross Domestic Product

IDB: InterAmerican Development Bank

IDEA: Instituto de Estudios Ambientales

IDW: Inverse Distance Weighting

IHSI: Institut Haitien de Statistiques et d'Informatique

LDI: Local Disaster Index

MCE : Multi-Criteria Evaluation

PAHO : Panamerican Health Organization

SDE: Section D'Enumeration

SEI: Stockholm Environment Institute

SMCRS: Service Metropolitain pour la Collecte des Residus Solides

UNDP: United Nations Development program

UNDRO: United Nations Disaster Relief Organization

UNEP: United Nations Environment Program

UNISDR: International Strategy for Disaster Reduction

UTSIG/CNIGS: Unite de Teledetection et de Systeme d'Information Geographique/ Centre National de l`Information Geo-Spatiale

VIP's: Very Important Points

WHO: World Health Organization

WLC: Weighted Linear Combination

YTV: Helsinki Metropolitan Area Council

ABSTRACT

In Port-au-Prince, Haiti's capital, the increasing occurrence and casualties from landslides and floods during the last few years has focused interest toward these natural disasters. The high pressure of human settlements associated with urban migration constitutes the main trigger of these deadly events by increasing the sensitivity of the environment as well as people's vulnerability. Long term impacts of environmental degradation on health related to human settlements have not received as much attention as natural disasters. The inconspicuous nature of environmental health hazards and their related consequences may have diverted stakeholders and people's attention from them. Health hazards derived from the environment are believed to be of a greater spatial extent, cause more losses than any other hazards, and concern more than two-third of the population within the study area. The objective of this study was to identify areas where such health hazards exist and assess neighborhoods' vulnerability to these hazards using a GIS modeling approach that offers the capability of superimposing multiple parameters. Nine factors were combined with different weighting schemes including an Expert Opinion survey. Moreover, several classification techniques were tested and compared in the final process of determining the four risk levels. Finally, a sensitivity analysis was performed to assess the responsiveness of the model to changes induced in the model's parameters.

Though this study was conducted in a context of poor data availability, the results suggest that about 41% of the entire area was subjected to high risk. Pollution originated from water bodies, traffic and waste were found as the most critical, while housing density, which is simultaneously a risk and a vulnerability factor represented the main trigger of many risks encountered. This study called for a deeper investigation of the state of pollution in Port-au-Prince by taking direct field measurement in order to validate the findings. In addition, it reveals the needs for a synergistic effort of governmental and non-governmental institutions to produce and make available spatial data at fine scale and resolution in a cost-efficient manner.