01.Climate Change, Drinking Water Security, and Public Health
04.About the Editors and Contributors
05.Part I: Climate Change and Impact on Drinking Water and Public Health
06.1. Climate Change, Drinking Water, and Public Health: An Integrated Review
09.3 Impact of Climate Change on Water Resources
10.3.1 Climatic Weather Events
11.3.2 Water-Related Pathogens and Disease Transmission
12.3.3 The Emergence of New Pathogens and Re-emergence of Diseases
13.3.4 Algal Blooms and Cyanobacteria
14.3.5 Need for One Health-Based Thinking in Relation to Climate Change Impacts
15.4 Water Quality and Public Health
16.4.1 Microorganism and Chemical Pollutants
17.4.2 Chemical Contaminants
18.4.2.1 The Effect of Increasing Sodium Ions in Drinking Water on Public Health
19.4.2.2 The Release of Toxic Ions into Coastal Aquifers by Reduction and by the Salt Effect
20.4.3 The Effect of Increasing Temperature on the Solubility of Minerals
21.4.4 Chronic Health Effects
22.5 Mitigation and Adaptation Strategies
23.5.1 Infrastructure Improvements
24.5.2 Monitoring and Early Warning Systems
25.5.3 Sustainable Water Management
26.5.4 Public Health Initiatives
28.7 Discussion Questions
29.2. Drinking Water Status Around the World and Its Effect on Health
32.3 Human Uses and Demand for Water
34.5 Access to Safely Managed Drinking Water
35.5.1 Definition of Safely Managed Drinking Water
36.5.2 Progress Toward Access to Safely Managed Drinking Water
37.6 Effects of Climate Change on Water Access
39.8 Discussion Questions
40.3. Consumption Patterns of Drinking Water and Its Impact on Public Health
43.2.1 Global Perspective
44.2.2 The United States: Access to Water
45.2.3 Environmental Justice and Health Disparities in Access to Water
46.2.4 Human Health Impacts of Water Injustice
48.3.1 Case Study 1: Flint Water Crisis
49.3.2 Case Study 2: Colonias
50.3.3 Case Study 3: Navajo Nation
51.3.4 Case Study 4: Wake County, North Carolina
52.4 Efforts to Address Environmental Injustice and Disparities in Water
55.7 Discussion Questions
56.4. Climate Change and Its Impact on Water and Food Security
59.3 Sustainable Development Goals
60.4 Water Security, Food Security, and Nutrition Security
63.4.3 Nutrition Security
65.5.1 The Modern Food System and Planetary Boundaries
66.5.2 Climate Change Impact on Water and Food Security
67.5.3 Sustainable Food Systems
68.6 Climate Change, Conflict, and Water and Food Security
69.7 Climate Change, Water, and Indigenous Food Systems
70.8 Progress on Sustainable Food Systems, and Water, Food, and Nutrition Security
71.9 Solutions for Improving Sustainable Food Systems, and Water, Food, and Nutrition Security
73.11 Discussion Questions
74.5. Climate Change Impacts on Water Resources
77.3 Materials and Methods
79.3.2 WSI Calculation Methods
80.4 Vulnerability Assessment
81.4.1 Impact of Climate Change
82.4.1.1 Progress in Combating Climate Change
83.4.2 Climate Change and Water Availability
86.4.2.3 Climate Change and Seawater Intrusion
89.4.3 Accessibility to Water Services
90.4.3.1 Groundwater Depletion
93.6 Discussion Questions
94.Part II: Drinking Water Quality, Quantity, Scarcity, and Safety
95.6. Drinking Water Quantity and Climate Change
98.3 Health Consequences of Restricted Drinking Water Quantity
100.3.2 Drinking Water Requirement Per Person
101.3.3 Water Availability and Access to Safe Drinking Water
102.3.4 Diseases Due to Lack of Safe Drinking Water (SDW)
103.4 Climate Change and Water Supply
104.4.1 Source of Drinking Water
105.4.2 Effect of Climate Change on Water Supply
106.4.2.1 Case Study: Effect of Climate Change on California’s Water Supply
107.Baseline and Projected Timeline
110.Effects of Climate Change on Water System
111.4.2.2 Other Watershed Hydrology Analysis for Water Supply
112.5 Most Affected Regions
114.7 Discussion Questions
115.7. Climate Change, Drinking Water Security, and Public Health
116.1 Learning Objectives
118.3 Climate Change and its Impact on Water Systems
119.4 How Climate Change Affects Drinking Water Quality
120.4.1 Temperature Rise and Water Contamination
121.4.2 Changes in Precipitation Patterns
122.4.3 Sea-Level Rise and Saltwater Intrusion
123.5 Extreme Weather Events
125.5.1.1 Algal Blooms in Freshwater Systems: United States
126.5.1.2 Droughts in Arid Regions: Zambia
127.5.1.3 Saltwater Intrusion in Coastal Cities: Italy
128.5.1.4 Flooding and Water Contamination: Thailand
129.6 Impacts on Public Health
130.6.1 Waterborne Diseases
131.7 Addressing the Challenges: Solutions and Adaptations
132.7.1 Improved Water Treatment Systems
133.8 International Agreements and Standards
134.8.1 World Health Organization Drinking Water Guidelines
136.10 Discussion Questions
137.8. A Model for the Release of Toxic Ions into Coastal Aquifers: The Release of Arsenic into Bangladesh’s Drinking Well Water by Reduction and by the Salt Effect
138.1 Learning Objectives
140.3 Sea Level Rise and the Release of Inorganic Ions into Coastal Drinking Water Aquifers by Reduction
141.3.1 A Review of Oxidation and Reduction
142.3.2 Case Study: Sea Level Rise and the Release of Arsenic into Bangladesh’s Coastal Drinking Water Aquifer by Reduction
143.3.3 Global Implications
144.4 Sea Level Rise and the Release of Inorganic Ions into Coastal Drinking Water Aquifers by the Salt Effect
145.4.1 A Review of the Salt Effect
146.4.2 Case Study: Sea Level Rise and the Release of Arsenic into Bangladesh’s Coastal Drinking Water Aquifer by the Salt Effect
147.4.3 Global Implications
149.6 Discussion Questions
150.9. Evaluating the Physical, Chemical, and Bacteriological Aspects of Water Quality Related to Climate Change
151.1 Learning Objectives
154.2.2 Water Quality Parameters
155.2.3 Climate Change Impacts on Water Quality
156.3 Physical, Chemical, and Bacteriological Aspects of Water Quality Related to Climate Change
157.3.1 Physical Aspects of Water Quality Related to Climate Change
158.3.2 Chemical Aspects of Water Quality Related to Climate Change
159.3.3 Radiological Aspects of Water Quality Related to Climate Change
160.3.4 Bacteriological Aspects of Water Quality Related to Climate Change
162.4.1 Case Study: Effects of Drought on Drinking Water Contaminants in California (Sum 2024)
163.4.2 Case Study: Depletion of Groundwater and Declining Surface Water Quality (Lall et al. 2018)
164.4.3 Case Study Algae in Water (USEPA 2024e)
166.6 Discussion Questions
167.Part III: Cost of Climate Change and Drinking Water
168.10. Climate Change Vulnerability Assessment, Risk Management, and Risk Assessment
169.1 Learning Objectives
172.3.1 Challenges in Climatic Change Assessments
173.3.2 Impacts of Climate Change on Water Quality, Health, and Environment
174.3.3 Various Climatic Change Risk Assessment Studies
175.3.4 Various Climate Change Vulnerability Assessment Studies
176.4 Comparison and Evaluation of Various Methodologies
178.6 Future Research Needs
180.8 Discussion Questions
181.11. On the Application of the Statistical and Fuzzy Systems Regression and Clustering to Analyze the Multivariate Chemical Composition of Groundwater: Case Study of Arsenic-Contaminated Groundwater in Bangladesh
182.1 Learning Objectives
184.3 Groundwater Sampling
185.4 Quality Assurance and Quality Control and Statistical Analysis Using Correlation, and Hierarchical Clustering
186.4.1 Quality Assurance and Quality Control of Analytical Data for Statistical Analysis
187.4.2 Pairwise Correlation Analysis
188.4.3 Determining an Optimal Number of Clusters
189.4.4 Principal Component Analysis
190.4.5 Hierarchical Clustering on Principal Components
191.4.6 Summary of the Pros and Cons of Traditional Statistical Methods
192.5 Fuzzy Systems Modeling Approach
193.5.1 Concepts of Fuzzy System Modeling
194.5.2 Software for Calculations
198.7 Discussion Questions
200.12. The Burden of Disease: The Economics of Drinking Water
201.1 Learning Objectives
202.2 Introduction: The Economic Impact of Climate Change via Global Water Resources
203.2.1 Costs of Climate Disasters
204.2.2 Production Disruption Due to Climate Change
205.3 Economic Evaluation of Drinking Water
206.3.1 Cost-Effectiveness Analysis
207.3.2 Cost-Utility Analysis
208.4 Breaking Down the Economics of Drinking Water
209.4.1 Economic Costs of Providing Clean Drinking Water
210.4.2 Economic Costs of Contaminated Drinking Water
211.4.3 Economic Benefits of Safe Drinking Water
212.5 Safe, Secure, and Affordable Drinking Water
215.8 Discussion Questions
216.13. Climate Change and the Economics of Accessing Safe Drinking Water: Contexts of the Physiographic and Climatic Vulnerable Areas of the Bengal Basin
217.1 Learning Objectives
219.2.1 The Major Sources and Technologies of Water Supply in Bangladesh
220.3 How Much Do People in Different Countries, Regions, and Development Levels Pay for Drinking Water Supply?
221.4 How Much Do People Pay for Water Treatment?
222.5 What Is the Interaction Between the Economic Burden of Safe Drinking Water Supply and Human Health?
223.6 How Will This Change with Climate Change?
224.6.1 Water Supply Practices in the Climatic and Physiographic Vulnerable Areas of Bangladesh
226.8 Discussion Questions
227.14. Climate Justice: Access to Safe Drinking Water and Health—A Legal Discourse
228.1 Learning Objectives
230.3 Climate Change, Water and Health
232.3.2 Increase in Extreme Weather Events
233.3.3 Problems Associated with Climate Change
234.3.4 Impacts of Climate Change on Water
235.4 Climate Change, Water Quality, and Environmental Justice
236.4.1 United States Examples
237.4.2 Asia and Africa Examples
238.4.3 Susceptible Groups
239.5 Climate Change Discourse and Policies
242.8 Discussion Questions
243.Part IV: A Global Agenda with Community-Based Solutions
244.15. Impact of Climate Change and War on Water, the Environment, and Health in Ukraine
245.1 Learning Objectives
247.3 Impact of Climate Change on Water Resources in Ukraine
248.4 Impact of War in Ukraine on Water Resources
249.5 Effects of War on Human and Environmental Health and Well-Being
251.5.1.1 Medical Facilities
254.5.1.4 Chemical and Radiological Contamination of Water, Land, and Air
255.5.1.5 Kakhovka Dam Destruction Impact on Human Health
256.5.1.6 Nuclear Power Plant (NPP): Potential Release of Radiological Materials Due to War Actions
257.5.2 Environmental Health
258.5.2.1 Kakhovka Dam Destruction: Environmental Impacts
259.5.2.2 Water Sector Impacts (Shumilova et al. 2023)
260.5.2.3 National and International Impacts Due to the Water/Irrigation/Agriculture/Food Nexus
262.5.2.5 Potential Extinction of Endangered Species (Stone 2024)
263.6 On the Need for Restoration of the Kakhovka Reservoir
264.7 Prospects for Using the Danube Water in Southern Ukraine
266.9 Discussion Questions
267.16. Waterless and Low-Water Sanitation Technologies that Improve Quality of Life and Conserve Water Resources
268.1 Learning Objectives
269.2 Large-Scale Sanitation Infrastructure and Its Impacts
270.2.1 The Impact of Human Waste on the Nitrogen Cycle
271.2.2 Modern Sanitation Infrastructure Uses Treated Water and Disrupts the Nitrogen Cycle
272.2.3 Modern Centralized Sanitation Infrastructure Cannot Meet Demand
273.3 Compost Toilets as a Waterless Sanitation Solution
274.3.1 Separating and Processing Liquids and Solids in Compost Systems
275.3.2 Uses for Composted Feces and Urine
276.3.3 Cost and Materials
277.3.4 Strategies for Uptake and Acceptance of Compost Systems
278.4 Vermifiltration Systems as a Water-Recycling Sanitation Solution
279.4.1 Components of an Effective Vermifiltration System
280.4.2 Effectiveness of Water Treatment in Vermifiltration Systems
281.4.3 Strategies for Uptake and Acceptance of Vermifiltration Systems
282.5 Using Participatory Design and Development to Promote Community Buy-In
284.7 Discussion Questions
285.17. Climate Change and Its Effect on Bangladesh’s Drinking Water: Status, Projections, and Community-Based Integrated Solutions
286.1 Learning Objectives
288.3 Status of Drinking Water in Bangladesh
289.3.1 Water Resources Overview
291.3.2.1 Arsenic Contamination
292.3.2.2 Salinity Intrusion
293.3.2.3 Flooding and Waterlogging
294.4 Projections Under Climate Change
295.4.1 Impact on Water Availability
296.4.1.1 Increased Frequency of Extreme Weather
298.4.2 Impact on Water Quality
299.4.2.1 Salinity Increase
300.4.2.2 Contamination Risks
301.4.3 Socioeconomic Implications
303.4.3.2 Economic Impact
304.4.3.3 Innovation at the Grassroots Level
305.4.4 Impact on Public Health
306.5 Community-Based Integrated Solutions
307.5.1 Enhanced Water Management Practices
308.5.1.1 Rainwater Harvesting
309.5.2 Alternative Water Sources
311.5.2.2 Deep Tube Wells and Piped Water Systems
312.5.3 Community Engagement and Education
313.5.3.1 Awareness Programs
314.5.3.2 Community Radio
315.5.3.3 Local Water Management Committees
316.5.4 Government and Non-Governmental Organization (NGO) Collaboration
317.5.4.1 Integrated Water Resource Management
318.5.4.2 Nature-Based Solution Strategies
319.5.4.3 Funding and Research
321.7 Discussion Questions
322.18. Climate Change Mitigation Strategies for Drinking Water
323.1 Learning Objectives
326.3.1 Impacts and Mitigations of Climate Change on Water Quality and Supply
327.3.2 Impacts and Mitigations of Climate Change on Temperature and Environment
328.3.3 Impacts and Mitigations of Climate Change on Groundwater
329.3.4 Impacts and Mitigations of Climate Change on Livestock
332.5 Future Research Needs
334.7 Discussion Questions
335.19. Case Studies of Waterborne Disease Outbreaks Related to Climate Change
336.1 Learning Objectives
338.3 Types of Agents of Waterborne Outbreaks
339.3.1 Bacterial Pathogens
340.3.2 Protozoan Pathogens
342.3.4 Harmful Algal Blooms
345.5 Discussion Questions
346.20. Harnessing Artificial Intelligence to Transform Drinking Water Safety
347.1 Learning Objectives
348.2 Introduction: The Universal Quest for Safe Water
349.3 Journey through Time: From Manually Pumped Wells to Smart Technical Systems
350.3.1 Historical Context
351.3.2 Present Challenges
353.3.4 AI’s Role in the Journey
354.4 Redefining Risk Assessment: AI-Powered Solutions for Arsenic Contamination in Groundwater
355.4.1 Introduction: A Global Challenge
356.4.2 Reimagining Historical Data
357.4.3 Navigating a Dynamic Landscape
358.4.4 Enhancing Detection at the Frontlines
359.4.5 A Holistic Vision for Risk Assessment
360.4.6 Conclusion: Toward a Safer Future
361.5 Understanding and Addressing Co-Exposures: Leveraging AI to Navigate Complexity
362.5.1 Introduction: The Challenge of Co-Exposures
363.5.2 Unraveling the Double-Edged Nature of Co-Exposures
364.5.2.1 Compounding Toxicities
365.5.2.2 Balancing Effects
366.5.2.3 Emerging Challenges
367.5.3 Mapping Co-Exposure Hotspots
369.5.3.2 The AI Solution
371.5.4 Modeling Biogeochemical Interactions
372.5.4.1 Modeling Approach and Challenges
373.5.4.2 The AI Solutions
375.5.5 Real-Time Monitoring with AI-Enhanced Sensors
377.5.5.2 The AI Solution
379.5.6 Predictive Modeling for Future Risks
381.5.6.2 The AI Solution
383.5.7 Conclusion: Charting a Path Forward
384.6 Toward Global Standards for Groundwater Safety
385.6.1 Introduction: Bridging the Gap in Groundwater Guidelines
386.6.2 The Relatively New Reliance on Groundwater
387.6.3 The Case for Clear Guidelines
388.6.4 The Importance of Groundwater Safety Guidelines
389.7 Conclusion: The Path Forward for AI to Establish Drinking Water Safety
390.8 Discussion Questions
736.Fig. 1.1: The links between climate change, water quantity and quality, and human exposure to water-related illness. (Reproduced from the United States Global Change Research Program 2016)
737.Fig. 1.2: The pathways for climate related exposures and potential non-communicable and chronic disease effects. (Kjellstrom et al. 2010) (Reproduced from Kjellstrom et al., Public health impact of global heating due to climate change: potential effects on chronic non-communicable diseases, Int J Public Health, Vol 55, p. 99, Figure 1, 2010, Springer Nature, https://doi.org/10.1007/s00038-009-0090-2)
738.Fig. 2.1: Water withdrawals per capita in 2015. (Ritchie and Roser 2024) (https://ourworldindata.org/water-use-stress, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
739.Fig. 2.2: Freshwater withdrawals by country in 2020. (Ritchie and Roser 2024) (https://ourworldindata.org/water-use-stress, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
740.Fig. 2.3: Variables used by the United States Geological Service to assess water availability, which is driven by both supply and demand. (USGS 2023)
741.Fig. 2.4: The Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) service ladder for drinking water availability. (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 25, p. 28. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission)
742.Fig. 2.5: Percentage risk of fecal contamination in drinking water sources measured in 33 countries, from selected surveys 2014–2020. (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 37, p. 39. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission.)
743.Fig. 2.6: Percentage of population using safely managed drinking water services in 2020, as defined by SDG Indicator 6.1.1. (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 4, p. 8. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission.)
744.Fig. 2.7: Death rate by country from diseases related to the consumption of untreated water in 2021. (Ritchie et al. 2024) (https://ourworldindata.org/clean-water, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
745.Fig. 2.8: Percentage of rural and urban populations using improved sources available on premises, available when needed, and free from contamination, in 50 countries in 2020. (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 33, p. 35. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission.)
746.Fig. 2.9: Stress on freshwater withdrawal by country in 2021. (Ritchie and Roser 2024) (https://ourworldindata.org/water-use-stress, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
747.Fig. 2.10: Renewable freshwater resources per capita in selected countries and worldwide. (Ritchie and Roser 2024) (https://ourworldindata.org/water-use-stress, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
748.Fig. 3.1: Causes and impacts of water and sanitation insecurities. Source: Zheng et al. (2022) (Reproduced from Zheng et al. 2022, graphical abstract. Minor modification was made. https://doi.org/10.2166/wh.2022.085, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
749.Fig. 4.1: Hidden cost drivers (of the agrifood system) along the environmental, social, and health pathways and their impact domains (FAO 2024a) (Reproduced from FAO 2024a, p. 7. https://doi.org/10.4060/cd2616en, licensed under the terms of the Creative Commons Attribution- 4.0 International licence (CC BY 4.0: https://creativecommons.org/licenses/by/4.0/legalcode.en))
750.Fig. 4.2: Examples of food system failure due to climate change (Bolster et al. 2023) (Adapted from Chodur, G.M., Zhao, X., Biehl, E. et al. 2018, Figure 3. Assessing food system vulnerabilities: a fault tree modeling approach. BMC Public Health 18, 817. https://doi.org/10.1186/s12889-018-5563-x, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
751.Fig. 4.3: Sustainable food system framework (Source: HLPE. 2020. Food security and nutrition: Building a global narrative towards 2030, Rome. https://openknowledge.fao.org/server/api/core/bitstreams/8357b6eb-8010-4254-814a-1493faaf4a93/content. Reproduced with permission)
752.Fig. 4.4: Water usage in Ukraine (Source: author Nechyporenko, based on calculations of data from the State Statistics Service of Ukraine (2023) https://www.ukrstat.gov.ua/)
753.Fig. 5.1: Location map depicting climate stations, monitoring wells, farms, planted forests, dams, and the grid of the water budget model (adapted from Sherif et al. 2021) (Reproduced from Sherif et al. 2021, Figure 1. https://doi.org/10.3390/w13060864, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
754.Fig. 5.2: The top graph illustrates the variation of annual rainfall from 1975 to 2024, while the bottom graph displays the average yearly rainfall departures from the usual rainfall during the same period (Modified from Sherif et al., 2021) (Adapted from Sherif et al. 2021, Figure 2. Some modifications were made. https://doi.org/10.3390/w13060864, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
755.Fig. 5.3: (a) Temperature trend over UAE from 1901 to 2021 based on CRU datasets; (b) Seasonal, decadal temperatures (adapted from Sefelnasr et al. 2022) (Adapted from Sefelnasr et al. 2022, Figure 10. Some modifications were made. https://doi.org/10.3390/w14213448, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
756.Fig. 5.4: Historical (1975–2020) and projected precipitation over UAE under CMIP6 SSPs (4.5 and 8.5)
757.Fig. 5.5: (a) The yearly mean rainfall (in millimeters) for the water year 2019–2020 in the United Arab Emirates (UAE) and (b) the deviation of the average yearly precipitation (in millimeters) in the UAE for each year from the overall annual average precipitation (Sherif et al. 2023) (Reproduced from Sherif et al. 2023, Figure 2. https://doi.org/10.3390/w15040742, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
758.Fig. 5.6: EPI performance indicators over UAE for mitigating climate change (Source: YCELP and CIESIN, 2023)
759.Fig. 5.7: (a) Groundwater salinity (mg/l) for 2020 and (b) Recharge to the Quaternary aquifer for the years 2019–2020 (m3/km2)
760.Fig. 5.8: Groundwater replenishment percentage (%) in the Quaternary aquifer. The white-colored areas represent a sandy desert where groundwater abstraction is nil and limited to natural discharge
761.Fig. 5.9: Spatial variation of the adjusted blue water scarcity index (WSI) with the consideration of only renewable water resources
762.Fig. 5.10: The spatial variability of the adjusted blue water scarcity index (WSI) when fossil groundwater reserve (TDS up to 5000 mg/l) is considered
763.Fig. 5.11: The spatial variability of the adjusted blue water scarcity index (WSI) when fossil groundwater reserve (TDS up to 10,000 mg/l) is considered
764.Fig. 5.12: The spatial variability of the amended blue water scarcity index (WSI) when fossil groundwater reserves and non-conventional water resources are considered
765.Fig. 6.1: Percent of people in a country which have access to safe drinking water (SDW) (data source: FAO 2024a) (Source: Food and Agriculture Organization of the United Nations. AQUASTAT. Total population with access to safe drinking-water (JMP). Latest update: 2022. Accessed: September 8, 2024. https://data.apps.fao.org/aquastat/?lang=en&share=f-6b6ec785-1e2a-4add-906e-5bfde8018cca)
766.Fig. 6.2: Percent access to safe drinking water as a function of GDP per capita (data sources: FAO 2024a; World Bank 2024) (Source: Food and Agriculture Organization of the United Nations. AQUASTAT. Total population with access to safe drinking-water (JMP). Latest update: 2022. Accessed: September 8, 2024. https://data.apps.fao.org/aquastat/?lang=en&share=f-6b6ec785-1e2a-4add-906e-5bfde8018cca)
767.Fig. 6.3: Countries with at least 1000 diarrheal death per year (data source: WHO 2024c) (World Health Organization (2024c) data.who.int, {Number of diarrhoea deaths from inadequate water} [{numeric}]. {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/number-of-diarrhoea-deaths-from-inadequate-water} (Accessed on September 8, 2024))
768.Fig. 6.4: Global mean annual surface temperature rise compared to average temperature in 1951–1980 and global mean sea level rise since 1993 (data source: NASA 2024)
769.Fig. 6.5: Mean season warming pattern in California (data source: DWR 2019)
770.Fig. 6.6: Hydrologic model NSE (Nash–Sutcliffe efficiency) coefficients values for different subbasins (data source: DWR 2019)
771.Fig. 6.7: Percent change in water system response in California as a function of temperature change (data source: DWR 2019)
772.Fig. 6.8: Probability of inferior performance of different water system matrix in California under mid-century climate conditions (data source: DWR 2019)
773.Fig. 6.9: 2050 water stress regions in the world (data source: Kuzma et al. 2023b) (Kuzma et al. 2023, Aqueduct40_rankings_download_Y2023M07D05.xlsx. Copyright 2023 World Resources Institute. https://doi.org/10.46830/writn.23.00061, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
774.Fig. 6.10: Mortality rate per 100,000 due to lack of adequate access to safe water, sanitation, and hygiene (WASH) (data source: WHO 2024d) (World Health Organization (2024d) data.who.int, {Mortality rate attributed to exposure to unsafe WASH services (per 100,000 population) (SDG 3.9.2)} [{numeric}]. {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/mortality-rate-attributed-to-exposure-to-unsafe-wash-services-(per-100-000-population)-(sdg-3-9-2)} (Accessed on September 15, 2024))
775.Fig. 6.11: Mortality rate as a function of percent of total population in a country with access to safe drinking water (SDW) (data sources: FAO 2024a; WHO 2024d) (Source: Food and Agriculture Organization of the United Nations. AQUASTAT. Total population with access to safe drinking-water (JMP). Latest update: 2022. Accessed: September 8, 2024. https://data.apps.fao.org/aquastat/?lang=en&share=f-6b6ec785-1e2a-4add-906e-5bfde8018cca)
776.Fig. 7.1: The loading of phosphorus (nutrient) to the central basin of Lake Erie. (USEPA 2024)
777.Fig. 7.2: The distribution of drought severities and anomalies on the continent of Africa, during the year 2023. (WMO 2024)
778.Fig. 7.3: Lake Erie Harmful Algal Bloom (Doermann 2024) (NASA Earth Observatory image by Wanmei Liang, using Landsat data from the U.S. Geological Survey)
779.Fig. 8.1: Proportional symbol maps of (a) arsenic (As) in micrograms per liter (μg/L) and (b) dissolved oxygen (DO; O2(aq)) in milligrams per liter (mg/L) in Bangladesh’s drinking well water (1000 μg/L = 1 mg/L). (Frisbie et al. 2024) (Reproduced from Frisbie et al. 2024, Figures 2 and 3. https://doi.org/10.1371/journal.pone.0295172, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
780.Fig. 8.2: The solubility of barium sulfate (BaSO4) in moles per liter (M) versus moles per liter of added sodium chloride (NaCl; the salt effect) and moles per liter of added sodium sulfate (Na2SO4; the common ion effect). (Kielland 1937) (Adapted with permission from Kielland 1937. Individual activity coefficients of ions in aqueous solutions. Table II. J Am Chem Soc (59)9:1675–1678. https://doi.org/10.1021/ja01288a032. Copyright 1937 American Chemical Society)
781.Fig. 8.3: The mole fraction of (a) arsenic acid (H3As(V)O4) and (b) arsenous acid (H3As(III)O3) species as functions of pH. The dotted vertical lines at pH = 3.90 and pH = 7.96 demarcate the range of pH values observed in our study. (Frisbie et al. 2024) (Reproduced from Frisbie et al. 2024, Figures 8 and 9. https://doi.org/10.1371/journal.pone.0295172, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
782.Fig. 8.4: Proportional symbol maps of (a) arsenic (As) in micrograms per liter (μg/L) and (c) specific conductance (SC) in microsiemens per centimeter (μS/cm) of Bangladesh’s drinking well water. (Frisbie et al. 2024) (Reproduced from Frisbie et al. 2024, Figures 2 and 10. https://doi.org/10.1371/journal.pone.0295172, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
783.Fig. 9.1: The water or hydrologic cycle. This image provides details on the hydrologic cycle including precipitation, flow of water onto the surface and into the groundwater, and evaporation. (Reproduced from USGS 2019)
784.Fig. 11.1: A flowchart illustrating the methodology of combining traditional statistical analysis with fuzzy systems analysis
785.Fig. 11.2: Map of Bangladesh showing the locations of collecting groundwater samples from drinking water wells. The color legend illustrates the ground surface elevation (Frisbie et al. 2024) [Reproduced from Frisbie et al. 2024, Figure 1. https://doi.org/10.1371/journal.pone.0295172, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)]
786.Fig. 11.3: Boxplots of arsenic (As) concentration in micrograms per liter (μg/L), dissolved oxygen (DO) concentration in milligrams per liter (mg/L), oxidation-reduction potential (ORP) in millivolts (mV), specific conductance (SC) in microsiemens per centimeter (μS/cm), pH, temperature in Celsius (°C), and reported depth in meters (m)
787.Fig. 11.4: Histograms and correlation coefficients of variables calculated using the Pearson method. Table 11.6 in the Appendix provides pairwise correlation coefficients of variables
788.Fig. 11.5: The plot of the gap statistics method for evaluating an optimal number of clusters
789.Fig. 11.6: A Principal Component Analysis graph of the seven-variable dataset (Figs. 11.6 through 11.8 were plotted using the R package “FactoMineR” dedicated to multivariate exploratory data analysis: https://cran.r-project.org/web/packages/FactoMineR/FactoMineR.pdf)
790.Fig. 11.7: A two-dimensional (2D) Principal Component Analysis graph of individual water samples partitioned into six clusters (see Fig. 11.5). The numbers on this graph go from 1 to 83 and are the same as the sample numbers in Table 11.7 in the Appendix
791.Fig. 11.8: A three-dimensional (3D) Principal Component Analysis map demonstrating the connections between the clusters
792.Fig. 11.9: Hierarchical clustering tree (dendrogram) shows water sample partitioning into six clusters
793.Fig. 11.10: Pair-wise correlation plots based on the Fuzzy C-Means clustering. Colors indicate individual clusters
794.Fig. 11.11: Results of Fuzzy C-Means clustering showing the overlap between clusters. Numbers are from 1 to 83, and the correspondence to sample numbers is given in Table 11.7 in the Appendix
795.Fig. 11.12: The Fuzzy Membership Functions of arsenic concentration, dissolved oxygen concentration and oxidation-reduction potential, specific conductance and pH, temperature, and reported depth for each cluster
796.Fig. 11.13: Fuzzy regression between ln(As) and ln(SC) showing the asymmetric triangular membership functions for the intercept and the regression slope of the fuzzy regression ln(As) vs. ln(SC)
797.Fig. 11.14: Fuzzy regression between ln(As) and ln(Depth) showing the asymmetric triangular Fuzzy Membership Functions (FMFs) for the intercept of the fuzzy regression ln(As) vs. ln(Depth)
798.Fig. 11.15: The optimal number of clusters using the silhouette method is 6 to 8
799.Fig. 11.16: The optimal number of clusters using the wss (elbow) method is 7
800.Fig. 11.17: Fuzzy regression of ln(As) vs. pH and the Fuzzy Membership Functions (FMF) of the regression slope
801.Fig. 11.18: The fuzzy regression of oxidation-reduction potential (ORP) vs. pH. Oxidation-reduction potentials are in millivolts (mV)
802.Fig. 11.19: Boxplots of arsenic (As) concentration in Clusters 1–6 from fuzzy clustering results. The arsenic concentrations are in micrograms per liter (μg/L)
803.Fig. 12.1: Causal loop diagram: a systems approach to explore the relationship between water resources, public health, economics, and human security (Lyons 2024) [Reproduced from Lyons 2024, Figure 1. https://doi.org/10.5604/01.3001.0054.7399, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)]
804.Fig. 13.1: Arsenic contamination of shallow groundwater and saline water encroachment in the coastal belt of the Bengal Basin posed more financial involvement to provide alternate options for safe drinking water (BWDB 2022)
805.Fig. 13.2: Physiographic division and sub-divisions of Bangladesh (Alam et al. 1990)
806.Fig. 13.3: Photographs of different water supply technologies used in the climatic vulnerable areas as well as rural Bangladesh (Photo by: Anwar Zahid)
807.Fig. 15.1: Evapotranspiration is the sum of plant transpiration and evaporation (USGS 2018)
808.Fig. 15.2: (a) Temporal trend of annual temperatures in Ukraine and the 10-year rolling average; (b) Z-score of temperature for 1901–2024. The source of the annual temperature record is the Climate Change Knowledge Portal, World Bank Group (n.d.) (https://climateknowledgeportal.worldbank.org/country/ukraine/climate-data-historical, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)). The 10-year rolling average and the Z-score calculations were made using the R code zoo (Zeileis and Grothendieck 2005)
809.Fig. 15.3: Comparing Renewable Freshwater Resources of USA, France, UK, and Ukraine (GCDL and FAO 2024) (https://ourworldindata.org/grapher/renewable-water-resources-per-capita, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
810.Fig. 15.4: Graphs showing changes over time in the annual precipitation in Ukraine from 1901 to 2024 and the 10-year rolling average of precipitation. Annual data downloaded from the Climate Change Knowledge Portal of the World Bank Group (n.d.) (https://climateknowledgeportal.worldbank.org/country/ukraine/climate-data-historical, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)) Calculations of the 10-year rolling precipitation were made using the R code zoo (Zeileis and Grothendieck 2005)
811.Fig. 16.1: The Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) sanitation ladder (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 49, p. 48. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission)
812.Fig. 16.2: Percentage of population using safely managed sanitation services in 120 countries in 2020, as defined by Sustainable Development Goal (SDG) Indicator 6.2.1 (WHO and UNICEF 2021) (Reproduced from WHO and UNICEF 2021, Figure 7, p. 9. https://data.unicef.org/wp-content/uploads/2022/01/jmp-2021-wash-households_3.pdf, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission)
813.Fig. 16.3: Construction in progress of a compost chamber for a toilet in El Salvador (Photo courtesy of the author)
814.Fig. 16.4: Concrete urine-diversion toilet seats prepared from a mold in El Salvador (Photo courtesy of the author)
815.Fig. 16.5: Example of a vermifiltration system design positioned under a latrine, which includes two drainage layers below the worm bedding layer (Snoad 2019) (The material “Tiger worm toilets design manual – C Snoad – 2019: 2 figures on pages 4 and 19” is reproduced with the permission of Oxfam, Oxfam House, John Smith Drive, Cowley, Oxford OX4 2JY, UK www.oxfam.org.uk. Oxfam does not necessarily endorse any text or activities that accompany the materials)
816.Fig. 16.6: Example of an offset vermifiltration system design employed in Myanmar (Snoad 2019) (The material “Tiger worm toilets design manual – C Snoad – 2019: 2 figures on pages 4 and 19” is reproduced with the permission of Oxfam, Oxfam House, John Smith Drive, Cowley, Oxford OX4 2JY, UK www.oxfam.org.uk. Oxfam does not necessarily endorse any text or activities that accompany the materials)
817.Fig. 16.7: Pyramid-shaped waste distributor added to a vermifiltration system in Colombia in 2023 (Photo courtesy of the author)
818.Fig. 16.8: Horizontal flow constructed wetland (Milonova 2020) (The material “WASH Handbook for Protracted Emergencies: The OXSI Experience in Myanmar – Sonya Milonova – 2020: Figure 4.3.2, p. 16 (p. 117 in the PDF)” is reproduced with the permission of Oxfam, Oxfam House, John Smith Drive, Cowley, Oxford OX4 2JY, UK www.oxfam.org.uk. Oxfam does not necessarily endorse any text or activities that accompany the materials)
819.Fig. 16.9: Constructed wetland in a household-scale vermifiltration system in Aguabonita, Colombia (Photo courtesy of the author)
820.Fig. 16.10: MIT D-Lab design cycle (MIT D-Lab 2024). Used with permission
821.Fig. 17.1: Bangladesh map showing major rivers (Central Intelligence Agency 2024) (Bangladesh map showing major cities as well as parts of surrounding countries and the Bay of Bengal. In: The World Factbook 2024. Washington, DC: Central Intelligence Agency. https://www.cia.gov/)
822.Fig. 17.2: Schematic of a rainwater harvesting system. (Reproduced from Rahman et al. 2014) (Reproduced from Rahman et al. 2014, Figure 8. https://doi.org/10.1155/2014/721357, licensed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence (https://creativecommons.org/licenses/by/3.0/))
823.Fig. 17.3: Key outputs from the DFID (Department for International Development)-funded project “Building adaptation to climate change in health in LDCs (Learning and Capacity Development) through resilient WASH (water, sanitation and hygiene).” (Reproduced from WHO 2024) (Reproduced from WHO 2024, Figure 2. https://www.who.int/publications/m/item/bangladesh-climate-change-wash, licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO) licence (https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Used with permission)
824.Fig. 20.1: Estimated percentages (< 1%, 1% to 10%, 11% to 20%, 21% to 30%, >30%) of private wells with arsenic concentrations greater than 10 micrograms per liter (μg/L). (Reproduced from The United States Geological Survey 2017)
825.Fig. 20.2: Schematic of workflow for developing a statistical model for predicting drinking water quality. (Hu et al. 2023) (Reproduced from Hu et al. 2023, Figure 1. https://doi.org/10.1007/s40572-022-00389-x, licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/))
826.Table 1.1: Types of waterborne pathogens
827.Table 1.2: Common cyanobacterial toxins
828.Table 1.3: The criteria for the spontaneous dissolution of a solid into water: ΔG = ΔH—TΔS. (Petrucci 1985)
829.Table 2.1: Estimate of global freshwater distribution. (Shiklomanov 1994)
830.Table 4.1: Sustainable Development Goals related to water and food security and climate change (Department of Economic and Social Affairs n.d.)
831.Table 4.2: The six dimensions of food security
832.Table 5.1: Summary of UAE temperature projections based on CRU projection dataset (Sources: Environment Agency-Abu Dhabi, 2025; Shared Socioeconomic Pathways (SSPs), 2025)
833.Table 5.2: Types of groundwater storage available in the Quaternary aquifer for the year 2020 (km3), as computed from the Groundwater Balance Model (GWBM)
834.Table 5.3: Recharge to the Quaternary aquifer in the water year 2021–2022 (MCM)
835.Table 5.4: Generated and reused treated wastewater in the year 2022 (data obtained from MoEI Annual Statistical Book 2023)a
836.Table 5.5: Groundwater storage versus water demand
837.Table 5.6: The water scarcity/stress index was computed utilizing seven distinct methodologies
838.Table 6.1: Key National Primary Drinking Water Regulations (NPDWR) standards developed by the USEPA (2024a)
839.Table 6.2: Statistics of number of countries with greater than or below than a certain percentage of population with access to safe drinking water (data sources: FAO 2024aa; World Bank 2024)
840.Table 6.3: Worldwide available renewable water resources per capita (data source: OECD 2025)
841.Table 6.4: Top 25 countries under extreme high-water stress under different time period of climate change (data source: Kuzma et al. 2023b)a
842.Table 8.1: The relative abundances and average concentrations of elements in the rocks of the earth’s crust in milligrams per kilogram (mg/kg). (Greenwood and Earnshaw 1984)a
843.Table 8.2: The known integer oxidation states of the elements. Nonintegral values, as in B5H9, C3H8, HN3, S8+2, etc., are not included. The more common states are shown in bold. The symbol Z is the atomic number. (Greenwood and Earnshaw 1984)a
844.Table 8.3: The current (2022) World Health Organization (WHO) drinking-water guideline values (GVs), health-based values (HBVs), and aesthetic values (AVs) for inorganic contaminants in milligrams per liter (mg/L). (Mitchell and Frisbie 2023; WHO 2022)
845.Table 9.1: Climate change impacts water quality. (USEPA 2024b; APHA 2024; EEA 2024)
846.Table 9.2: Physical drinking water attributes, contaminants, and potential health effects from climate change. (Nemerow et al. 2009; USEPA 2024d)
847.Table 9.3: National Primary Drinking Water Regulations chart of select inorganic chemical contaminants and potential health effects from climate change. (USEPA 2024d)
848.Table 9.4: National Primary Drinking Water Regulations chart of select organic chemical contaminants and potential health effects from climate change (e.g., flooding, drought, temperature rise, extreme weather events). (USEPA 2024d)
849.Table 9.5: National primary drinking water regulations chart of select radionuclide contaminants and potential health effects from climate change (e.g., flooding, drought, temperature rise, and extreme weather events). (USEPA 2024d)
850.Table 9.6: Select National Primary Drinking Water bacteriological contaminants and potential health effects from climate change. (USEPA 2024d)
851.Table 11.1: The summary statistics of the quality assured and quality controlled (QA/QC-ed) chemical analysis data, which were used in calculations in Sects. 4 and 5
852.Table 11.2: Coefficients of linear relationships between the dependent variable arsenic concentration and the independent variables (DO, ORP, SC, pH, T, and Depth), which are shown as the output of calculations using the generalized linear model function glm
853.Table 11.3: The contribution of the variables’ Fuzzy Membership Functions (FMFs) to each of the six clusters
854.Table 11.4: The fuzzy regression of ln(As) vs. ln(SC) model coefficients of the fuzzy regression shown in Fig. 11.13
855.Table 11.5: The fuzzy regression of ln(As) vs. ln(Depth) model coefficients of the fuzzy regression shown in Fig. 11.14
856.Table 11.6: Summary statistics of the original dataset analyzed in this chapter
857.Table 11.7: Pairwise correlation coefficients of variables. Arsenic concentration (As in μg/L) is the dependent variable and dissolved oxygen (DO in mg/L), oxidation-reduction potential (ORP in mV), specific conductance (SC in μS/cm), pH, temperature (T in °C), and reported depth (Depth in m) are the independent variables
858.Table 11.8: A matrix containing all the eigenvalues, the percentage of variance, and the cumulative percentage of variance from the Principal Component Analysis (PAC) calculations
859.Table 11.9: A matrix of different variables’ contribution to the analyzed dataset’s principal components
860.Table 19.1: Examples of water-borne disease outbreaks linked to manifestations of climate change
861.Table 20.1: Key indicators and mitigation strategies for groundwater safety
