hypothesis on water quality

An Introduction to Water Quality Analysis

• January 2019
• 6(1):201-205

• National Institute of Technology, Agartala

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Anjali Patel
• Sonal Choubey
• Md Shahad Hasan

• Md Mahedi Hasan Moon

• Ikhwanussafa Sadidan

• Nur Ridha Amethysia
• Norhidayatul Hikmee Mahzan
• Muhammad Faiz Amrie Ibrahim
• Ahmad Jamalludin

• Manjusha Nambiar PV
• Dr. M. Saravanaraja
• Dr. A. Senthil Kumar
• Dr. P.S. Aruna Devi
• K. Sampathkumar
• Alfonso Miguel Atienza
• Jan Florenz Padilla
• Nanette Santos

• Safira Firdaus Mujiyanti
• Putri Yeni Aisyah
• Muhammad Tsabit Algifary

• R. M. Brown
• N. I. McClelland
• R. A. Deininger
• R. G. Tozer
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Montana Science Partnership

• How to Navigate MSP Modules
• What Are Maps?
• How Are Maps Used in Science?
• Schoolyard Mapping Activity
• Mapping With Google Earth
• Extension Activities
• Mapping Glossary
• Sample Field Map Sketches
• Mapping Resources
• The Shape of Landscapes
• Tectonic Extension
• Transcurrent Tectonics
• Compressional Tectonics
• Weathering & Erosion
• Tectonics, Weathering & Erosion
• Time & Landscape
• Landform Examples
• Landscape Glossary
• Landscape Resources
• What Is Living in the Soil?
• Soil Profiles
• Understanding Soils
• The Tree of Life: Part 2
• The Tree of Life: Part 3
• Microbes & Biofilms
• Colonizing & Culturing Microbes
• Isolating a Colony from a Soil Sample
• Wrapping Petri Dishes in Parafilm
• Soil Glossary
• Soil Resources
• An Introduction to Water
• Polar Molecules
• Polarity of Water
• Conductivity: Pure Water + NaCl
• Conductivity: Pure Water + CaCO3
• Conductivity: Pure Water + Acetic Acid
• Conductivity: Pure Water + Sulfuric Acid
• An Introduction to Water Quality
• The Water Cycle
• Water Quality Parameters: pH
• Water Quality Parameters: Turbidity
• Water Quality Parameters: Temperature
• Water Quality Parameters: Dissolved Oxygen
• Water Quality Parameters: Conductivity & Hardness
• Water Quality Impacts: Point Source
• Water Quality Impacts: Nonpoint Source
• Step 1: Observation & Research
• Step 2: Formulate a Hypothesis & Make Predictions
• Step 3: Observe, Collect Data & Evaluate Results
• Step 4: Analyze & Communicate Your Data
• Water Glossary
• Water Resources
• Snowflake Shapes
• Snowflakes: Symmetry
• Water Phase Change
• Earth’s Energy Budget
• Absorption & Reflection of Energy
• Snow as an Insulator: Snow Density
• Heat Flow in Water
• Heat Flux Through the Snow Cover
• Calculating Heat Flux
• Calculating Data with Excel
• The Impact of Global Climate Change on Snow & Ice
• The Science of Ice Freeze-up & Break-up
• Snow Glossary
• Snow Resources
• What Is a Bird?
• Bird Classification
• From the Outside In
• Bird Legs & Feet
• Bird Locomotion
• How Do Birds Fly?
• Heart Rate & Breathing
• Diet & Beak Shape
• Bird Vocalizations
• Nests & Eggs
• Parental Care
• Orientation & Navigation
• The Evolution of Birds
• Creating Good Backyard Bird Habitat
• Bird Legends & Indigenous People
• Birds & Climate Change
• Bird Extremes
• Birds Glossary
• Birds Resources
• Life History
• Photosynthesis
• Respiration
• Stems & Vascular Tissue
• Flower Parts
• Flower Types
• Fruit Types
• Types of Plants
• Plant Diseases & Pests
• Community Assembly
• Unique Adaptations of Plants
• Parts of a Flower
• General Pollination Syndromes
• Specialized Pollination Syndromes
• Mutualisms & Cheaters
• Pollination Constancy Exercise
• Vegetation Assessment Activity
• Plants & Pollen Glossary
• Plants & Pollen Resources
• What is an Insect?
• Body Segment: The Head
• Body Segment: The Thorax
• Body Segment: The Abdomen
• Courtship & Mating
• Larva ID Tips: Dragonflies & Damselflies
• Larva ID Tips: Mayflies
• Larva ID Tips: Stoneflies
• Larva ID Tips: Caddisflies
• Larva ID Tips: Beetles
• Larva ID Tips: Flies
• Larva ID Tips: Moths
• Food Webs & Trophic Levels
• Functional Feeding Groups: Shredders
• Functional Feeding Groups: Collectors
• Functional Feeding Groups: Scrapers
• Functional Feeding Groups: Piercers
• Functional Feeding Groups: Predators
• Primary Defenses: Reducing Encounters with Predators
• Insect Vision: The Compound Eye
• Insects Glossary
• Insects Resources
• Impacts on Landscape
• Impacts on Soils
• Impacts on Bacteria
• Impacts on Water Quality
• Impacts on Insects
• Impacts on Snow
• Impacts on Birds
• Impacts on Plants & Pollen
• Mapping Human Impacts
• The Process of Science
• Human Impacts Resources

What do you want to find out about your study site’s water quality, how will I measure it and what are your predictions?

Check Your Thinking: Scenario: There is an abandoned mine dump within 5 meters of your study site stream. How might contaminants in the mine waste be impacting your stream? When would be the best time of year/day to collect water monitoring data that could help answer this question? What tests should you conduct?

Using your recorded observations and information compiled in the first step, the next step is to come up with a testable question. You can use the previously mentioned question (Based on what I know about the pH, DO, temperature and turbidity of my site, is the water of a good enough quality to support aquatic life?) as it relates to the limitations of the World Water Monitoring Day kit, or come up with one of your own.

What results do you predict? For example, your hypothesis may be “I believe the pH, DO, temperature and turbidity of the water at my study site are of good enough quality to support aquatic life because there are no visible impacts to water quality upstream or on the site.” Once you’ve formulated your question, begin planning the experiment or, in this case, the water monitoring you will conduct .

Leave A Comment

Name (required)

Mail (will not be published) (required)

XHTML: You can use these tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

The MSP project is funded by an ESEA, Title II Part B Mathematics and Science Partnership Grant through the Montana Office of Public Instruction. MSP was developed by the Clark Fork Watershed Education Program and faculty from Montana Tech of The University of Montana and Montana State University , with support from other Montana University System Faculty.

• Entries RSS
• Comments RSS

Recent Comments

• jt beatty on Wrapping Petri Dishes in Parafilm
• admin on What Are Maps?
• A Teacher on What Are Maps?
• A Teacher on What is the Montana Science Partnership?

Explore MSP

Copyright © 2024 · All Rights Reserved · Montana Science Partnership

Design by Red Mountain Communications · RSS Feed · Log in

8   Hypothesis Testing

This section introduces some statistical approaches commonly used in out projects. For an in depth discussion and examples of statistical approaches commonly employed across surface water quality studies, the reader is highly encouraged to review Helsel et al. ( 2020 ) .

8.1 Hypothesis Tests

Hypothesis tests are an approach for testing for differences between groups of data. Typically, we are interested in differences in the mean, geometric mean, or median of two or more different groups of data. It is useful to become familiar with several terms prior to conducting a hypothesis test:

Null hypothesis : or \(H_0\) is what is assumed true about a system prior to testing and collecting data. It usually states there is no difference between groups or no relationship between variables. Differences or correlations in groups should be unlikely unless presented with evidence to reject the null.

Alternative hypothesis : or \(H_1\) is assumed true if the data show strong evidence to reject the null. \(H_1\) is stated as a negation of \(H_0\) .

\(\alpha\) -value : or significance level, is the probability of incorrectly rejecting the null hypothesis. While this is traditionally set at 0.05 (5%) or 0.01 (1%), other values can be chosen based on the acceptable risk of rejecting the null hypothesis when in fact the null is true (also called a Type I error ).

\(\beta\) -value : the probability of failing to reject the null hypothesis when is is in fact false (also called a Type II error ).

Power : Is the probability of rejecting the null when is is in fact false. This is equivalent to \(1-\beta\) .

The first step for an analysis is to establish the acceptable \(\alpha\) value. Next, we want to minimize the possibility of a Type II error or \(\beta\) by (1) choosing the test with the greatest power for the type of data being analyzed; and/or, (2) increasing the sample size.

With an infinite sample size we can detect nearly any difference or correlation in two groups of data. The increase in sample size comes at a financial and human resource cost. So it is important to identify what magnitude difference needs to be detected for relevance to the system being detected 1 . After establishing \(H_0\) , \(H_1\) , and the acceptable \(\alpha\) -value, choose the test and sample size needed to reach the desired power.

1  See Helsel et al. ( 2020 ) (Chapter 13) and Schramm ( 2021 ) for more about power calculations.

The probability of obtaining the calculated test statistic when the null is true the p -value. The smaller the p -value the less likely the test statistic value would be obtained if the null hypothesis were true. We reject the null hypothesis when the p -value is less than or equal to our predetermined \(\alpha\) -value. When the p -value is greater than the \(\alpha\) -value, we do no reject the null (we also do no accept the null).

8.2 Choice of test

Maximize statistical power by choosing the hypothesis test appropriate for the characteristics of the data you are analyzing. Table  8.1 provides an overview of potential tests covered in Helsel et al. ( 2020 ) . There are many more tests and methods available than are covered here, but these cover the most likely scenarios.

Comparison types:

Two independent groups: Testing for differences between two different datasets. For example, water quality at two different sites or water quality at one site before and after treatment.

Matched pairs: Testing differences in matched pairs of data. For example, water quality between watersheds or sites when the data are collected on the same days, or comparing before and after measurements of many sites.

Three of more groups: Testing differences in data collected at three or more groups. For example, comparing runoff at 3 treatment plots and one control plot.

Two-factor group comparison: Testing for difference in observations between groups when more than one factor might influence results. For example, testing for difference in water quality at an upstream and downstream site and before and after an intervention.

Correlation: Looking for linear or monotonic correlations between two independent and continuous variables. For example, testing the relationship between two simulatanesouly measured water quality parameters.

We also select test by the characteristics of the data. Non-skewed and normally distributed data can be assessed using parametric tests. Data following other distributions or that are skewed can be assessed with non-parametric tests. Often, we transform skewed data and apply parametric tests. This is appropriate but the test no longer tell us if there are differences in means, instead it tells us if there is a difference in geometric means. Similarly, nonparametric test tell us if there is shift in the distribution of the data, not if there is a difference in the means. Finally, we can utilize permutation tests to apply parametric test procedures to skewed datasets without loss of statistical power.

8.2.1 Plot your data

Data should, at minimum, be plotted using histograms and probability (Q-Q) plots to assess distributions and characteristics. If your data includes treatment blocks or levels, the data should be subset to explore each block and the overall distribution. The information from these plots will assist in chosing the correct type of tests described above.

8.3 Two independent groups

This set of tests compares two independent groups of samples. The data should be formatted as either two vectors of numeric data of any length, or as one vector of numeric data and a second vector of the same length indicating which group each data observation is in (also called long or tidy format). The example below shows random data drawn from the normal distribution using the rnorm() function. The first sample was drawn from a normal distribution with mean ( \(\mu\) )=0.5 and standard deviation ( \(\sigma\) )= 0.25. The second sample is drawn from a normal distribution with \(\mu\) =1.0 and \(\sigma\) = 0.5.

In the example above sample_1 and sample_2 are numeric vectors with the observations of interest. These can be stored in long or tidy format. The advantage to storing in long format, is that plotting and data exploration is much easier:

8.3.1 Two sample t-test

A test for the difference in the means is conducted using the t.test() function:

For the t -test, the null hypothesis ( \(H_0\) ) is that the difference in means is equal to zero, the alternative hypothesis ( \(H_1\) ) is that the difference in means is not equal to zero. By default t.test() prints some information about your test results, including the t-statistic, degrees of freedom for the t-statistic calculation, p-value, and confidence intervals 2 .

2  By assigning the output of t.test() to the results object it is easier to obtain or store the values printed in the console. For example, results$p.value returns the \(p\) -value. This is useful for for plotting or exporting results to other files. See the output of str(results) for a list of values.

The example above uses “formula” notation. In formula notation, y ~ x , the left hand side of ~ represents the response variable or column and the right hand side represents the grouping variable. The same thing can be achieved with:

In this example, we do not have the evidence to reject \(H_0\) at an \(\alpha\) = 0.05 (t-stat = -0.887, \(p\) = 0.387).

Since this example uses randomly drawn data, we can examine what happens when sample size is increased to \(n\) = 100:

Now we have evidence to reject \(H_0\) due to the larger sample size which increased the statistical power for detecting a smaller effect size at a cost of increasing the risk of detecting an effect that is not actually there or is not environmentally relevant and of course increased monitoring costs if this were an actual water quality monitoring project.

The t -test assumes underlying data is normally distributed. However, hydrology and water quality data is often skewed and log-normally distributed. While, a simple log-transformation in the data can correct this, it is suggested to use a non-parametric or permutation test instead.

8.3.2 Rank-Sum test

The Wilcoxon Rank Sum (also called Mann-Whitney) tests can be considered a non-parametric versions of the two-sample t -test. This example uses the bacteria data first shown in Chapter 6 . The heavily skewed data observed in fecal indicator bacteria are well suited for non-parametric statistical analysis. The Wilcoxon test is conducted using the wilcox.test() function.

8.3.3 Two-sample permutation test

Chapter 5.2 in Helsel et al. ( 2020 ) provide an excellent explanation of permutation tests. The permutation test works by resampling the data for all (or thousands of) possible permutations. Assuming the null hypothesis is true, we can draw a population distribution of the null statistic all the resampled combinations. The proportion of permutation results that equal or exceed the difference calculated in the original data is the permutation p -value.

The coin package provides functions for using the permutation test approach with different statistical tests.

We get roughly similar results with the permutation test and the Wilcoxon. However, the Wilcoxon tells us about the medians, while the permutation test tells us about the means.

8.4 Matched pairs

Matched pairs test evalute the differences in matched pairs of data. Typically, this might include watersheds in which you measured water quality before and after an intervention or event; paired upstream and downstream data; or looking at pre- and post-evaluation scores at an extension event. Since data has to be matched, the data format is typically two vectors with observed numeric data. The examples below use mean annual streamflow measurements from 2021 and 2022 at a random subset of stream gages in Travis County obtained from the dataRetrieval package.

8.4.1 Paired t -test

The paired t-test assumes a normal distribution, so the observations are log transformed in this example. The resulting difference are differences in log-means.

8.4.2 Signed rank test

Instead of the t-test, the sign rank test is more appropriate for skewed datasets. Keep in mind this does not report a difference in means because it is a test on the ranked values.

8.4.3 Paired permutation test

The permutation test is appropriate for skewed datasets where there is not a desire to transform data. The function below evaluates the mean difference in the paired data, then randomly reshuffles observations between 2021 and 2022 to create a distribution of mean differences under null conditions. The observed mean and the null distribution are compared to derive a \(p\) -value or the probability of obtaining the observed value if the null were true.

8.5 Three of more independent groups

8.5.1 anova, 8.5.2 kruskal-walis, 8.5.3 one-way permutation, 8.6 two-factor group comparisons, 8.6.1 two-factor anova.

When you have two-(non-nested)factors that may simultaneously influence observations, the factorial ANOVA and non-parametric alternatives can be used.

In 2011, an artificial wetland was completed to treat wastewater effluent discharged between stations 13079 (upstream side) and 13074 (downstream side). The first factor is station location, either upstream or downstream of the effluent discharge. We expect the upstream station to have “better” water quality than the downstream station. The second factor is before and after the wetland was completed. We expect the downstream station to have better water quality after the wetland than before, but no impact on the upstream water quality.

The aov() function fits the ANOVA model using the formula notation. The formula notation is of form response ~ factor where factor is a series of factors specified in the model. The specification factor1 + factor2 indicates all the factors are taken together, while factor1:factor2 indicates the interactions. The notation factor1*factor2 is equivalent to factor1 + factor2 + factor1:factor2 .

Here we fit the ANOVA to log-transformed ammonia values. The results indicate a difference in geometric means (because we used the log values in the ANOVA) between upstream and downstream location and a difference in the interaction terms.

We follow up the ANOVA with a multiple comparisons test (Tukey’s Honest Significant Difference, or Tukey’s HSD) on the factor(s) of interest.

The TukeyHSD() function takes the output from aov() and optionally the factor you are interested in evaluating the difference in means. The output provide the estimate difference in means between each level of the factor, the 95% confidence interval and the multiple comparisons adjusted p-value. Figure  8.3 is an example of how the data can be plotted for easier interpretation.

8.6.2 Two-factor Brunner-Dette-Munk

The non-parametric version of the ANOVA model is the two-factor Brunner-Dette-Munk (BDM) test. The BDM test is implemented in the asbio package using the BDM.2way() function:

The BMD output indicates there is evidence to reject the null hypothesis (no difference in concentration) for each factor and the interaction. We can conduct a multiple comparisons test following the BDM test using the Wilcoxon rank-sum test on all possible pairs and use the Benjamini and Hochberg correction to account for multiple comparisons.

Since we are interested in the impact of the wetland specifically, group the data by location (upstream, downstream) and subtract the median of each group from the observed values. Subtraction of the median values defined by the location factor adjusts for difference attributed to location. The pairwise.wilcox.test() function provides the pairwise compairson with corrections for multiple comparisons:

8.6.3 Two-factor permutation test

In Section 8.6.1 we identified a significant differencs in ammonia geometric means for each factor and interaction. If the interest is to identify difference in means, a permutation test can be used. The perm.fact.test() function from the asbio package can be used:

8.7 Correlation between two independent variables

8.7.1 pearson’s r.

Using the estuary water quality example data from #sec-plotclean we will explore correlations between two independent variables:

Pearson’s r is the linear correlation coefficient that measures the linear association between two variables. Values of r range from -1 to 1 (indicate perfectly positive or negative linear relationships). Use the cor.test() function to return Pearson’s r and associated p-value:

The results indicate we have strong evidence to reject the null hypothesis of no correlation between Temperature and dissolved oxygen (Pearson’s r = -0.83, p < 0.001).

8.7.2 Spearman’s p

Spearman’s p is a non-parametric correlation test using the ranked values. The following example looks at the correlation between TSS and TN concentrations.

The cor.test() function is also used to calculate Spearman’s p , but the method argument must be specified:

Using Spearman’s p there isn’t evidence to reject the null hypothesis at \(\alpha = 0.05\) .

8.7.3 Permutation test for Pearson’s r

If you want to use a permutation approach for Pearson’s r we need to write a function to calculate r for the observed data, then calculate r for the permutation resamples. The following function does that and provides the outputs along with the permutation results so we can plot them:

Now, use the function permutate_cor() to conduct Pearson’s r on the observed data and resamples:

Using the permutation approach, we don’t have evidence to reject the null hypothesis at \(\alpha = 0.05\) .

The following code produces a plot of the mull distribution of test statistic values and the test statistic value for the observed data:

A Primer on Water Quality

What is in the water.

What do we mean by "water quality"?

Water quality can be thought of as a measure of the suitability of water for a particular use based on selected physical, chemical, and biological characteristics. To determine water quality, scientists first measure and analyze characteristics of the water such as temperature, dissolved mineral content, and number of bacteria. Selected characteristics are then compared to numeric standards and guidelines to decide if the water is suitable for a particular use.

Why do we have water-quality standards and guidelines?

Standards and guidelines are established to protect water for designated uses such as drinking, recreation, agricultural irrigation, or protection and maintenance of aquatic life. Standards for drinking-water quality ensure that public drinking-water supplies are as safe as possible. The U.S. Environmental Protection Agency (USEPA) and the States are responsible for establishing the standards for constituents in water that have been shown to pose a risk to human health. Other standards protect aquatic life, including fish, and fish-eating wildlife such as birds.

How do natural processes affect water quality?

Natural water quality varies from place to place, with the seasons, with climate, and with the types of soils and rocks through which water moves. When water from rain or snow moves over the land and through the ground, the water may dissolve minerals in rocks and soil, percolate through organic material such as roots and leaves, and react with algae, bacteria, and other microscopic organisms. Water may also carry plant debris and sand, silt, and clay to rivers and streams making the water appear “muddy” or turbid . When water evaporates from lakes and streams, dissolved minerals are more concentrated in the water that remains. Each of these natural processes changes the water quality and potentially the water use.

How do human activities affect water quality?

What about bacteria, viruses, and other pathogens in water?

The quality of water for drinking cannot be assured by chemical analyses alone. The presence of bacteria in water, which are normally found in the intestinal tracts of humans and animals, signal that disease-causing pathogens may be present. Giardia and cryptosporidium are pathogens that have been found occasionally in public-water supplies and have caused illness in a large number of people in a few locations. Pathogens can enter our water from leaking septic tanks, wastewater-treatment discharge, and animal wastes.

How can I find out more about my water quality?

Contact your local water supplier and ask for information on the water quality in your area. The USEPA requires public-water suppliers to provide water-quality data to the public on an annual basis in an understandable format. State agencies that deal with health, environmental quality, or water resources also can provide information on the quality of your water. Additional resources can be found on the Internet at: http://water.usgs.gov/nawqa http://www.epa.gov/safewater

– Gail E. Cordy

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Analysis of the water quality status and its historical evolution trend in the mainstream and major tributaries of the yellow river basin.

1. Introduction

2. materials and methods, 2.1. study area, 2.2. methodology, 3.1. water quality in the yellow river basin, 3.1.1. water usage, wastewater discharge, and pollutant emissions in the yellow river basin, 3.1.2. classification of overall water quality in the yellow river basin, 3.2. water quality in the yellow river mainstream, 3.2.1. classification of overall water quality in the yellow river mainstream, 3.2.2. pollutant concentrations in the yellow river mainstream, 3.3. water quality of major tributaries in the yellow river, 3.3.1. classification of overall water quality in the yellow river tributaries, 3.3.2. pollutant concentrations in the yellow river tributaries, 3.4. variations in water quality in the huayuankou section, 4. discussion, 4.1. water quality in the yellow river basin, mainstream, and tributaries, 4.2. water quality in the huayuankou section, 5. conclusions, supplementary materials, author contributions, data availability statement, conflicts of interest.

• Xia, J.; Jiang, Q.; Deng, S.; Zhou, M.; Cheng, Y.; Li, Z.; Wang, Z. Morphological characteristics and evolution processes of sharp bends in the Lower Yellow River. Catena 2022 , 210 , 105936. [ Google Scholar ] [ CrossRef ]
• Chen, Y.P.; Fu, B.J.; Zhao, Y.; Wang, K.B.; Zhao, M.M.; Ma, J.F.; Wu, J.H.; Xu, C.; Liu, W.G.; Wang, H. Sustainable development in the Yellow River Basin: Issues and strategies. J. Clean. Prod. 2020 , 263 , 121223. [ Google Scholar ] [ CrossRef ]
• Yin, L.; Feng, X.; Fu, B.; Wang, S.; Wang, X.; Chen, Y.; Tao, F.; Hu, J. A coupled human-natural system analysis of water yield in the Yellow River basin, China. Sci. Total Environ. 2021 , 762 , 143141. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Zhang, J.; Jin, J.; Weinberg, J.; Bao, Z.; Liu, C.; Liu, Y.; Yan, X.; Song, X.; Zhai, R. Impacts of climate change on water resources in the Yellow River basin and identification of global adaptation strategies. Mitig. Adapt. Strateg. Glob. Change 2017 , 22 , 67–83. [ Google Scholar ] [ CrossRef ]
• Feng, Y.; Zhu, A. Spatiotemporal differentiation and driving patterns of water utilization intensity in Yellow River Basin of China: Comprehensive perspective on the water quantity and quality. J. Clean. Prod. 2022 , 369 , 133395. [ Google Scholar ] [ CrossRef ]
• Chang, J.; Li, Y.; Yuan, M.; Wang, Y. Efficiency evaluation of hydropower station operation: A case study of Longyangxia station in the Yellow River, China. Energy 2017 , 135 , 23–31. [ Google Scholar ] [ CrossRef ]
• Si, Y.; Li, X.; Yin, D.; Liu, R.; Wei, J.; Huang, Y.; Li, T.; Liu, J.; Gu, S.; Wang, G.; et al. Evaluating and optimizing the operation of the hydropower system in the Upper Yellow River: A general LINGO-based integrated framework. PLoS ONE 2018 , 13 , e0191483. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, W.; Liang, W.; Gao, X.; Li, J.; Zhao, X. Trajectory in water scarcity and potential water savings benefits in the Yellow River basin. J. Hydrol. 2024 , 633 , 130998. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Zhao, Z.; Fu, B.; Ma, R.; Yang, Y.; Lü, Y.; Wu, X. Identifying ecological security patterns based on the supply, demand and sensitivity of ecosystem service: A case study in the Yellow River Basin, China. J. Environ. Manag. 2022 , 315 , 115158. [ Google Scholar ] [ CrossRef ]
• Yu, S.-Y.; Li, W.-J.; Zhou, L.; Yu, X.; Zhang, Q.; Shen, Z. Human Disturbances Dominated the Unprecedentedly High Frequency of Yellow River Flood over the Last Millennium. Sci. Adv. 2023 , 9 , eadf8576. [ Google Scholar ] [ CrossRef ]
• Quan, J.; Xu, Y.; Ma, T.; Wilson, J.P.; Zhao, N.; Ni, Y. Improving surface water quality of the Yellow River Basin due to anthropogenic changes. Sci. Total Environ. 2022 , 836 , 155607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jiang, L.; Zuo, Q.; Ma, J.; Zhang, Z. Evaluation and prediction of the level of high-quality development: A case study of the Yellow River Basin, China. Ecol. Indic. 2021 , 129 , 107994. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Wang, X.; Liu, C.; Lu, J.; Qin, Y.; Mo, Y.; Xiao, P.; Liu, Y. Identification of the heavy metal pollution sources in the rhizosphere soil of farmland irrigated by the Yellow River using PMF analysis combined with multiple analysis methods—Using Zhongwei city, Ningxia, as an example. Environ. Sci. Pollut. Res. 2020 , 27 , 16203–16214. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wu, N.; Liu, S.M.; Zhang, G.L.; Zhang, H.M. Anthropogenic impacts on nutrient variability in the lower Yellow River. Sci. Total Environ. 2021 , 755 , 142488. [ Google Scholar ] [ CrossRef ]
• Ministry of Ecology and Environment of People’s Republic of China, Action Plan for Prevention and Control of Water Pollution. Available online: https://english.mee.gov.cn/News_service/news_release/201504/t20150427_299595.shtml#:~:text=The%20action%20plan%20proposed%20that,level%2C%20the%20overexploitation%20of%20groundwater (accessed on 5 June 2024).
• Zhou, Y.; Ma, J.; Zhang, Y.; Qin, B.; Jeppesen, E.; Shi, K.; Brookes, J.D.; Spencer, R.G.M.; Zhu, G.; Gao, G. Improving water quality in China: Environmental investment pays dividends. Water Res. 2017 , 118 , 152–159. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Qiu, Y.; Fu, R.; Liu, Y.; Suo, C. Identifying the water quality variation characteristics and their main driving factors from 2008 to 2020 in the Yellow River Basin, China. Environ. Sci. Pollut. Res. 2023 , 30 , 66753–66766. [ Google Scholar ] [ CrossRef ]
• Sun, X.; Zhou, Z.; Wang, Y. Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model. Environ. Sci. Pollut. Res. 2023 , 30 , 22743–22759. [ Google Scholar ] [ CrossRef ]
• Liu, T.; Wang, F.; Michalski, G.; Xia, X.; Liu, S. Using 15N, 17O, and 18O to determine nitrate sources in the Yellow River, China. Environ. Sci. Technol. 2013 , 47 , 13412–13421. [ Google Scholar ] [ CrossRef ]
• Li, B.; Feng, Q.; Wang, X.; Li, Z.; Wang, F.; Zhao, C.; Yu, T.; Chen, W. Formation of the upper reaches of the Yellow River: Provenance evidence from the strata of the Yellow River sedimentary basin. Glob. Planet. Change 2023 , 229 , 104224. [ Google Scholar ] [ CrossRef ]
• Wu, Z.; Tian, G.; Xia, Q.; Hu, H.; Li, J. Connotation, calculation and influencing factors of the water-use rights benchmark price: A case study of agricultural water use in the Ningxia Yellow River irrigation area. Agric. Water Manag. 2023 , 283 , 108300. [ Google Scholar ] [ CrossRef ]
• Xu, Z.; Liu, M.; Guan, G.; Guo, X. Emergency intervention modes for ice jam events in large-scale water diversion projects. Hydrol. Res. 2023 , 54 , 1134–1151. [ Google Scholar ] [ CrossRef ]
• Wu, X.; Wei, Y.; Fu, B.; Wang, S.; Zhao, Y.; Moran, E.F. Evolution and Effects of the Social-Ecological System over a Millennium in China’s Loess Plateau. 2020. Available online: https://www.science.org/doi/full/10.1126/sciadv.abc0276 (accessed on 8 June 2024).
• Pan, B.; Hu, Z.; Wang, J.; Vandenberghe, J.; Hu, X.; Wen, Y.; Li, Q.; Cao, B. The approximate age of the planation surface and the incision of the Yellow River. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2012 , 356–357 , 54–61. [ Google Scholar ] [ CrossRef ]
• Kong, D.; Miao, C.; Li, J.; Zheng, H. Full-stream erosion in the lower Yellow River: Feasibility, sustainability and opportunity. Sci. Total Environ. 2022 , 807 , 150810. [ Google Scholar ] [ CrossRef ]
• Zhou, Y.; Huang, H.Q.; Ran, L.; Shi, C.; Su, T. Hydrological controls on the evolution of the Yellow River Delta: An evaluation of the relationship since the Xiaolangdi Reservoir became fully operational. Hydrol. Process. 2018 , 32 , 3633–3649. [ Google Scholar ] [ CrossRef ]
• He, H.; Yu, Q.; Zhou, J.; Tian, Y.Q.; Chen, R.F. Modelling complex flood flow evolution in the middle Yellow River basin, China. J. Hydrol. 2008 , 353 , 76–92. [ Google Scholar ] [ CrossRef ]
• Gippel, C.; Jiang, X.; Zhang, D.; Cooling, M.; Kerr, G.; Close, P.; Jin, S.; Li, L.; Wang, Z.; Ma, Z.; et al. Environmental Flows Assessment for the Lower Yellow River. 2012. Available online: www.watercentre.org (accessed on 15 May 2024).
• Liu, W.; Shi, C.; Zhou, Y. Trends and attribution of runoff changes in the upper and middle reaches of the Yellow River in China. J. Hydro-Environ. Res. 2021 , 37 , 57–66. [ Google Scholar ] [ CrossRef ]
• Galler, J.J. Medium-and Long-Term Changes in Fluvial Discharge to the Sea: Medium-and Long-Term Changes in Fluvial Discharge to the Sea: The Yellow River Case Study the Yellow River Case Study. Master’s Thesis, William & Mary, Williamsburg, VA, USA, 1999. [ Google Scholar ] [ CrossRef ]
• Dai, Y.; Lu, F.; Ruan, B.; Song, X.; Du, Y.; Xu, Y. Decomposition of contribution to runoff changes and spatial differences of major tributaries in the middle reaches of the Yellow River based on the Budyko framework. Hydrol. Res. 2023 , 54 , 435–450. [ Google Scholar ] [ CrossRef ]
• Zhao, M.M.; Wang, S.M.; Chen, Y.P.; Wu, J.H.; Xue, L.G.; Fan, T.T. Pollution status of the Yellow River tributaries in middle and lower reaches. Sci. Total Environ. 2020 , 722 , 137861. [ Google Scholar ] [ CrossRef ]
• Wang, Z.C.; Zhang, S.X.; Mao, H.T.; Shen, J.W.; Guo, W.D. Analysis of Water and Sediment Characteristics of the Yellow River and Their Correlations. Pol. J. Environ. Stud. 2023 , 32 , 2909–2923. [ Google Scholar ] [ CrossRef ]
• Jin, F.; Yang, W.; Fu, J.; Li, Z. Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China. Sci. Total Environ. 2021 , 773 , 145514. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Chao, Y.; Fan, R.; Ren, F.; Qi, B.; Ji, K.; Xu, B. Spatial-temporal trends of rainfall erosivity and its implication for sustainable agriculture in the Wei River Basin of China. Agric. Water Manag. 2021 , 245 , 106557. [ Google Scholar ] [ CrossRef ]
• Storozum, M.J.; Mo, D.; Wang, H.; Ren, X.; Zhang, Y.; Kidder, T.R. Anthropogenic origins of a late Holocene, basin-wide unconformity in the middle reaches of the Yellow River, the Luoyang Basin, Henan Province, China. Quat. Res. 2017 , 87 , 423–441. [ Google Scholar ] [ CrossRef ]
• Lv, W.; Yang, H. Distribution of Phosphorus and Iron Species in the Sediments along the Path of Yellow River and Correlation of Contents of the Two Species. Source: J. Coast. Res. 2020 , 104 , 813–817. [ Google Scholar ] [ CrossRef ]
• van Maren, D.S.; Yang, M.; Wang, Z.B. Predicting the Morphodynamic Response of Silt-Laden Rivers to Water and Sediment Release from Reservoirs: Lower Yellow River, China. J. Hydraul. Eng. 2011 , 137 , 90–99. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Ping, J.; Mei, X.; Leng, W.; Li, H.; Zhao, J.; Liu, J. The trend of groundwater recharge in the secondary perched reaches of the Yellow River in the past 50 years. Front. Environ. Sci. 2023 , 11 , 1310181. [ Google Scholar ] [ CrossRef ]
• China National Environmental Monitoring Centre, National Surface Water Quality Automatic Monitoring Real-Time Database. Available online: https://szzdjc.cnemc.cn:8070/GJZ/Business/Publish/Main.html (accessed on 1 April 2024).
• GB 3838-2002 ; Environmental Quality Standards for Surface Water. Ministry of Ecology and Environment of People’s Republic of China: Beijing, China, 2002.
• Xu, Y.; Liu, S.; Wang, J. Impact of environmental regulation intensity on green innovation efficiency in the Yellow River Basin, China. J. Clean. Prod. 2022 , 373 , 133789. [ Google Scholar ] [ CrossRef ]
• Lu, C.; Ji, W.; Hou, M.; Ma, T.; Mao, J. Evaluation of efficiency and resilience of agricultural water resources system in the Yellow River Basin, China. Agric. Water Manag. 2022 , 266 , 107605. [ Google Scholar ] [ CrossRef ]
• Yu, J. Coordinated development of urban economy and total amount control of water environmental pollutants in the Yellow River basin. Arab. J. Geosci. 2021 , 14 , 658. [ Google Scholar ] [ CrossRef ]
• Du, H.; Ji, X.; Chuai, X. Spatial Differentiation and Influencing Factors of Water Pollution-Intensive Industries in the Yellow River Basin, China. Int. J. Environ. Res. Public Health 2022 , 19 , 497. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Sheng, Y.; Liu, Q.; Li, Z. Suspended particulate matter affects the distribution and migration of heavy metals in the Yellow River. Sci. Total Environ. 2024 , 912 , 169537. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Yu, Z.; Sun, X.; Yan, L.; Yu, S.; Li, Y.; Jin, H. Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin. Water 2024 , 16 , 2413. https://doi.org/10.3390/w16172413

Yu Z, Sun X, Yan L, Yu S, Li Y, Jin H. Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin. Water . 2024; 16(17):2413. https://doi.org/10.3390/w16172413

Yu, Zhenzhen, Xiaojuan Sun, Li Yan, Shengde Yu, Yong Li, and Huijiao Jin. 2024. "Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin" Water 16, no. 17: 2413. https://doi.org/10.3390/w16172413

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 144 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

share this!

August 26, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Public trust in drinking water safety is low globally: Study finds association with perceptions of public corruption

by Northwestern University

A new study finds more than half of adults surveyed worldwide expect to be seriously harmed by their water within the next two years. Led by global health experts at Northwestern University and the University of North Carolina at Chapel Hill, the study sought to understand public perceptions of drinking water safety. The study "Self-reported anticipated harm from drinking water across 141 countries" is published in the journal Nature Communications .

Because perceptions shape attitudes and behaviors, distrust in water quality has a negative impact on people's health, nutrition, psychological and economic well-being—even when the water meets safety standards.

"If we think our water is unsafe, we will avoid using it," said Sera Young, professor of anthropology and global health at Northwestern and senior author of the new study.

"When we mistrust our tap water, we buy packaged water, which is wildly expensive and hard on the environment; drink soda or other sugar-sweetened beverages , which is hard on the teeth and the waistline; and consume highly processed prepared foods or go to restaurants to avoid cooking at home, which is less healthy and more expensive," Young said. "Individuals exposed to unsafe water also experience greater psychological stress and are at greater risk of depression."

Young is a Morton O. Schapiro Faculty Fellow at the Institute for Policy Research, a faculty fellow at the Paula M. Trienens Institute for Sustainability and Energy, and co-lead of the Making Water Insecurity Visible Working Group at the Buffett Institute for Global Affairs.

Using nationally representative data from 148,585 adults in 141 countries from the 2019 Lloyd's Register Foundation World Risk Poll, the authors found a high prevalence of anticipated harm from water supply, with the highest in Zambia, the lowest in Singapore and an overall mean of 52.3%.

They also identified key characteristics of those who thought they would be harmed by their drinking water. Women, city dwellers , individuals with more education, and those struggling on their current income were more likely to anticipate being harmed by their drinking water.

The researchers found that, surprisingly, higher corruption perception index scores were the strongest predictor of anticipated harm from drinking water, more so than factors like infrastructure and Gross Domestic Product.

Further, even within countries with consistent access to basic drinking water services, doubts about the safety of water were widespread. This includes the U.S. where 39% of those polled anticipated serious harm from drinking water in the short term.

"Our research highlights that it is imperative both to deliver safe drinking water and to make sure that people have confidence in their water source," said Joshua Miller, a doctoral student at the UNC Gillings School of Global Public Health and the study's first author.

The researchers note that it is difficult for consumers to judge the hazards and safety of their water supply because many contaminants are invisible, odorless and tasteless. Without adequate information, many are left to evaluate the safety of their water based on prior experiences, media reports, and personal values and beliefs.

"It's also possible that people correctly judge the safety of their water," Young said. "The good people of Flint didn't trust their water and they were spot on."

The co-authors suggest actions officials can take to improve public trust around drinking water, including efforts to make testing more readily available, translate test results, replace lead pipes and provide at-home water filters when contaminants are detected, as well as provide improved access to safe drinking water.

"This is the kind of work that can catalyze greater attention and political will to prioritize these services in national development plans and strategies, and get us closer to achieving universal access to safe drinking water ," said Aaron Salzberg, director of the Water Institute at the UNC Gillings School of Global Public Health.

Salzberg previously served as the special coordinator for water resources in the U.S. Department of State, where he was responsible for managing the development and implementation of U.S. foreign policy on drinking water and sanitation, water resources management and transboundary water issues.

Journal information: Nature Communications

Provided by Northwestern University

Explore further

Feedback to editors

Doughnut-shaped region found inside Earth's core deepens understanding of planet's magnetic field

5 hours ago

Study combines data and molecular simulations to accelerate drug discovery

Biodiversity loss: Many students of environment-related subjects are partly unaware of the causes

6 hours ago

How stressed are you? Nanoparticles pave the way for home stress testing

7 hours ago

Researchers identify genes for low glycemic index and high protein in rice

8 hours ago

New discoveries about how mosquitoes mate may help the fight against malaria

New study highlights expansion of drylands amidst impact of climate change

9 hours ago

Novel chemical tool aims to streamline drug-making process

Heat waves impair bumblebees' ability to detect floral scents, study finds

Higher-order topological simulation unlocks new potential in quantum computers

Relevant physicsforums posts, the secrets of prof. verschure's rosetta stones.

23 hours ago

Alaska - Pedersen Glacier: Landslide Triggered Tsunami

Aug 23, 2024

Iceland warming up again - quakes swarming

Shiveluch volcano erupts on kamchatka peninsula.

Aug 18, 2024

A very puzzling rock or a pallasite / mesmosiderite or a nothing burger

Aug 14, 2024

M6.8 and M6.3 east of Mindanao, Philippines

Aug 13, 2024

More from Earth Sciences

Related Stories

Computer simulations suggest more than half of people on Earth have limited access to safe drinking water

Aug 16, 2024

Parents from lower-income families less likely to say child's water supply is safe

Feb 17, 2020

Breakthrough technology purifies water using the power of sunlight

Aug 10, 2020

Drinking water turned off in all Detroit's public schools

Aug 31, 2018

Public concern about water safety driven more by severe weather than climate change, finds poll

Feb 15, 2023

Children drink sweet drinks when they lack clean drinking water

Jul 23, 2019

Recommended for you

How a salt giant radically reshaped Mediterranean marine biodiversity

Aug 29, 2024

Land-sea 'tag-team' devastated ocean life millions of years ago, reveal scientists

Study reveals crucial role of mixing Atlantic and Arctic waters in global ocean circulation

Labor day crowds temporarily impact local streams, research shows

Global timber supply threatened as climate change pushes cropland northwards

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

• Share full article

Advertisement

Supported by

Drinking Water Is at Risk in Parts of Long Island, Study Finds

Decades of pumping have allowed saltwater to threaten the aquifers that supply many communities, including Long Beach and Great Neck.

By Christopher Flavelle

The supply of drinking water for parts of Long Island is under threat, according to a new federal report .

The report found that the groundwater in some coastal areas of Nassau County, a major suburb of New York City, is increasingly turning salty. That shift, called saltwater intrusion, is the result of decades of pumping fresh water out of wells for homes and irrigation, creating space for saltwater from the ocean to seep into the underground aquifers once filled with freshwater.

The change could take generations to reverse, even if pumping stopped altogether, according to the report. And it could force coastal areas — including Long Beach, Great Neck and Oyster Bay — to look for new supplies of drinking water, possibly by digging wells further inland, which could put new pressure on those places as well.

Those places “are at that point of the spear,” said Frederick Stumm, a research hydrologist at the U.S. Geological Survey and the report’s lead author. “They’re the most vulnerable communities right now to intrusion.”

The findings in Long Island come as the United States faces a groundwater crisis. In an investigation last year, The New York Times examined data for tens of thousands of wells around the country. In almost half those sites, the amount of groundwater had declined significantly over the past 40 years.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 25 August 2024

Self-reported anticipated harm from drinking water across 141 countries

• Joshua D. Miller   ORCID: orcid.org/0000-0002-2171-856X 1 ,
• Chad Staddon   ORCID: orcid.org/0000-0002-2063-8525 2 ,
• Aaron Salzberg 3 ,
• Julius B. Lucks   ORCID: orcid.org/0000-0002-0619-6505 4 , 5 , 6 ,
• Wändi Bruine de Bruin 7 &
• Sera L. Young   ORCID: orcid.org/0000-0002-1763-1218 5 , 6 , 8  

Nature Communications volume  15 , Article number:  7320 ( 2024 ) Cite this article

1158 Accesses

332 Altmetric

Metrics details

• Environmental social sciences
• Risk factors
• Water resources

Perceptions of drinking water safety shape numerous health-related behaviors and attitudes, including water use and valuation, but they are not typically measured. We therefore characterize self-reported anticipated harm from drinking water in 141 countries using nationally representative survey data from the World Risk Poll ( n  = 148,585 individuals) and identify national- and individual-level predictors. We find that more than half (52.3%) of adults across sampled countries anticipate serious harm from drinking water in the next two years. The prevalence of self-reported anticipated harm is higher among women (relative to men), urban (relative to rural) residents, individuals with self-reported financial difficulties (relative to those getting by on their present income), and individuals with more years of education. In a country-level multivariable model, the percentage of the population reporting recent harm from drinking water, percentage of deaths attributable to unsafe water, and perceptions of public-sector corruption are associated with the prevalence of self-reported anticipated harm. Consideration of users’ perspectives, particularly with respect to trust in the safety and governance of water services, is critical for promoting effective water resource management and ensuring the use, safety, and sustainability of water services.

Similar content being viewed by others

The determinants of household water consumption: A review and assessment framework for research and practice

Socio-psychological determinants of Iranian rural households' adoption of water consumption curtailment behaviors

Climate change to exacerbate the burden of water collection on women’s welfare globally

Introduction.

Water crises are endemic in much of the world, and are increasing both in scope and severity 1 , 2 . Suboptimal water availability and accessibility are widespread issues 3 , 4 that have been shown to negatively impact agricultural productivity 5 , economic development 6 , conflict and regional stability 7 , and human well-being 8 , 9 , 10 . The breadth of water quality issues and associated social and health consequences have been less well characterized 11 . This knowledge gap has been identified by the World Health Organization (WHO), the United Nations Children’s Fund (UNICEF), and the World Bank as a barrier to generating global water safety estimates 12 . In the absence of reliable access to trusted information about water safety, individuals primarily make decisions about water use based on their perceptions and past experiences 13 . Importantly, though, individuals’ lived experiences with water issues and perceptions about water hazards are not typically measured.

Drinking water service availability is the primary water indicator monitored by national statistical offices and global health agencies. The WHO and UNICEF track progress toward Sustainable Development Goal Target 6.1, “the proportion of the population using safely managed drinking water services”, by estimating the proportion of households using “safely managed”, “basic”, “limited”, or “unimproved” water services, which are classified based on the types of sources used, associated travel time to those sources, and the presence of priority biological and chemical contaminants 4 . Water quality data needed to measure progress toward Sustainable Development Goal 6.1, however, are available for less than half of the global population, such that access to at-least basic drinking water services is often reported and used for cross-country comparisons 4 . Further, even safely managed drinking water sources can have measurable levels of contaminants 14 , and there is a growing number of emerging contaminants (e.g., microplastics, per- and polyfluoroalkyl substances) of public health concern that are not consistently monitored 15 . Water from safely managed sources can also become contaminated during transportation or storage, rendering it unsafe for consumption 16 . For example, jerrycans and other containers used to collect water can be a source of contamination if not regularly cleaned and maintained according to public health guidelines 17 , 18 . As such, current global water indicators do not capture all drinking water risks that may be of concern to consumers.

Data on water users’ perspectives have the potential to complement and expand upon current water service indicators by capturing attitudes and beliefs that ultimately influence people’s willingness to use, maintain, and pay for drinking water services 13 . For example, the United States is classified as having nearly universal safely managed drinking water services according to Sustainable Development Goal 6.1 19 , yet millions of residents avoid tap water given well-documented cases of water system failure, such as lead contamination in Flint, Michigan 20 , 21 and water quality violations throughout Texas 22 , 23 . As a result, millions of individuals preferentially consume bottled water, which is more expensive and potentially of worse quality, long after acute water crises are resolved 24 , 25 . The perceived healthfulness of bottled water and its ability to confer higher social status in some contexts may also motivate bottled water use 13 . Additional documented determinants of individuals’ water safety perceptions include organoleptic properties (e.g., taste, smell); (dis)trust of institutions; knowledge about water management and treatment practices; access to media; risk awareness and tolerance; and personal values and beliefs 13 , 26 . It is thus evident that factors beyond objective quality shape people’s perceptions of water safety.

Believing that one’s drinking water is harmful has substantial behavioral and health implications. Individuals who self-report exposure to unsafe water experience greater psychological stress (e.g., worry about ensuring sufficient water supplies for all household uses, anger at perceived governmental failure) 27 and are at greater risk of depression than those who do not 28 . Further, individuals who perceive their water to be of suboptimal quality are more likely to avoid or not pay for piped water, to consume bottled water, and to substitute sugar-sweetened beverages for water, compared to those who believe their water is safe 29 , 30 , 31 , 32 . These behaviors have negative impacts on the sustainability of public water services and human well-being, ranging from the pollution generated by production of packaged water and added financial stress from its purchase 33 , to elevated risk of dental caries and other diseases (e.g., type 2 diabetes) associated with higher sugar-sweetened beverage intake 9 . Tools that capture individuals’ subjective evaluations of their drinking water therefore have high utility for predicting water-related behaviors and assessing if the four pillars of water security—whether water is physically available, accessible, useable (i.e., whether individuals perceive water to be adequate for diverse needs), and reliable across time for all domestic uses 34 —are met. Indeed, quantification of perceptions has recently been demonstrated to predict a range of subsequent behaviors 35 . Measures of self-reported experiences can also be used to identify inequalities by urbanicity, gender, and other characteristics 36 , 37 to better target resources and develop tailored interventions that address the needs of consumers. Despite their value, data about perceived water safety and hazards have not been systematically collected.

We therefore sought to provide insights into the prevalence and predictors of self-reported drinking water risks across diverse settings. We used data from the Lloyd’s Register Foundation 2019 World Risk Poll, which collected nationally representative data about perspectives on contemporary threats to human well-being. Specifically, we analyzed self-reports of harm attributed to drinking water in the prior year as well as harm anticipated to be experienced from drinking water in the forthcoming two years among non-institutionalized individuals aged 15 years and older. We aimed to (1) estimate the prevalence of self-reported harm from drinking water and concern about it, (2) assess country-level predictors that explained variation in the national prevalence of self-reported anticipated harm from drinking water, and (3) identify which individuals are most likely to perceive their water to be unsafe.

Prevalence of self-reported and anticipated harm from drinking water

In nationally representative survey data from 142 countries collected through the Lloyd’s Register Foundation World Risk Poll, 14.3% (95% CI: 13.6%, 15.0%) of individuals reported that they had personally experienced or knew someone who had experienced serious harm from drinking water in the prior two years (Fig.  1A ). The prevalence of self-reported harm from drinking water ranged from 0.9% in Singapore to 54.3% in Zambia (Supplementary Data  1 ). We estimated that more than half of individuals across 141 sampled countries (52.3%; 95% CI: 51.2%, 53.4%) anticipate experiencing serious harm from drinking water in the next two years (Fig.  1B ). The prevalence of anticipated harm from drinking water ranged from 8.0% in Sweden to 78.3% in Lebanon. Data about anticipated harm from drinking water were missing for Kuwait.

Percentage of the population in countries that ( A ) reported personally experiencing or knowing someone who experienced harm from drinking water in the prior two years ( N  = 142 countries) and ( B ) anticipated experiencing serious harm from drinking water in the next two years ( N  = 141 countries), based on data from the Lloyd’s Register Foundation 2019 World Risk Poll. Point estimates provided in Supplementary Data  1 .

Country-level predictors of anticipated drinking water harm

To assess whether perception-based indicators offer insights into water insecurity, we first explored if the prevalence of self-reported anticipated harm from drinking water was associated with traditional supply-side indicators aggregated at the country level. National water availability (m 3 renewable freshwater resources per capita) was not statistically significantly associated with prevalence of self-reported anticipated harm from drinking water in a bivariate weighted least squares regression with robust standard errors [Fig.  2A , Table  1 ; β (95% CI): 0.2 (−1.5, 1.9); p  = 0.819]. Prevalence of self-reported anticipated drinking water harm was neither consistently high in countries where water is physically scarce, such as Saudi Arabia (72.5 m 3 /capita, 37.9% anticipated harm), nor low where water is abundant, as in Venezuela (27,389.9 m 3 /capita, 73.4% anticipated harm).

Percentage of the population in each country that anticipated harm from their drinking water in the next two years in 2019 by A water availability (log renewable freshwater resources, m 3 /capita) in 2017 ( N  = 136 countries) 72 , B water infrastructure (percentage of population with at-least a basic drinking water service level) in 2019 ( N  = 135 countries) 4 , C percentage of wastewater that was treated in 2015 ( N  = 137 countries) 73 , D self-reported experienced drinking water harm in 2019 ( N  = 141 countries), E water-related mortality (percentage of deaths in the country attributable to water; shaded region represents countries with ≥1% of deaths attributable to unsafe water) in 2019 ( N  = 138 countries) 74 , F economic development (log gross domestic product per capita, USD) in 2019 ( N  = 137 countries) 75 , and G quality of public governance (Corruption Perceptions Index score) in 2019 ( N  = 140 countries) 41 . Model coefficients and 95% confidence intervals are in Table  1 . Data are presented as observed values (circles) and predicted values (black line) with associated 95% confidence intervals (gray area) based on fitted weighted least squares regressions with robust standard errors. Tests were two-tailed.

The percentage of national coverage of at-least basic drinking water services explained 23.4% of the variation in prevalence of self-reported anticipated harm from drinking water (Table  1 ). The relationship was non-linear [β quadratic term (95% CI): −0.02 (−0.04, −0.01); p  < 0.001], although in general, prevalence of self-reported anticipated harm from drinking water was lower at the highest levels of basic drinking water service coverage (Fig.  2B ). There was heterogeneity among the 76 countries with greater than 95% access to basic drinking water services. For instance, the entire populations of Finland and Greece were estimated to have access to at-least basic drinking water services, yet a much greater percentage of respondents in Greece anticipated harm from their drinking water (58.9%) than those in Finland (9.1%).

Wastewater treatment is a process that protects surface water and groundwater from contaminants, thereby shaping drinking water quality 38 . Primary wastewater treatment removes large solids, secondary treatment involves use of microorganisms to remove dissolved and suspended organic matter, and tertiary treatment reduces the concentration of inorganic compounds 39 . Among World Risk Poll respondents across 141 countries, the percentage of domestic and manufacturing wastewater that was treated nationally (aggregated across the three treatment forms) was, in general, negatively associated with prevalence of self-reported anticipated drinking water harm [β quadratic term (95% CI): −0.004 (−0.006, −0.001); p  = 0.003], explaining 36.6% of variation in responses (Fig.  2C , Table  1 ).

To explore the association between indicators of water quality and perceptions of drinking water harm, we used available data about the percentage of drinking water sources estimated to be contaminated with Escherichia coli , an indicator organism for pathogenic water contamination that contributes substantially to the global burden of diarrheal disease 40 . In the 23 countries in the World Risk Poll for which nationally representative data on the presence of Escherichia coli in a household’s primary water source were available, there was no statistically significant association between percentage of the population using contaminated drinking water and prevalence of self-reported anticipated drinking water harm [Fig.  3 ; β (95% CI): −0.1 (−0.3, 0.1); p  = 0.215].

Percentage of the population in 23 countries who anticipated harm from their drinking water in the next two years (measured in 2019), by the percentage of the population estimated to be using drinking water contaminated with Escherichia coli at the point of use (measured between 2012 and 2019). Data are presented as observed values (circles) and predicted values (black line) with associated 95% confidence intervals (gray area) based on a fitted weighted least squares regression with robust standard errors. The statistical test was two-tailed.

Prevalence of self-reported experienced harm to self or others from drinking water in the prior two years explained 52.9% of the variation in the prevalence of anticipated future harm from drinking water (Table  1 ). The relationship was curvilinear [β quadratic term (95% CI): −0.03 (−0.04, −0.02); p  < 0.001]: percentage of individuals reporting prior harm from drinking water was generally positively associated with percentage of individuals anticipating serious future harm from drinking water, but among countries with greater than 40% of the population reporting prior harm from drinking water, the inverse was observed (Fig.  2D ).

The percentage of deaths attributable to unsafe water was positively associated with prevalence of self-reported anticipated drinking water harm (Fig.  2E ). On average, a greater percentage of respondents in countries with 1% or more of annual deaths attributable to unsafe water anticipated harm from drinking water compared to those with fewer relative water-related deaths [β (95% CI): 12.6 (7.8, 17.3); p  < 0.001]. Nevertheless, it is notable that a high percentage of respondents from countries with less than 1% of annual deaths attributable to unsafe water anticipated future harm. For example, ~0.1% of deaths in Lebanon were attributable to water, but 78.3% of respondents from that country anticipated future drinking water harm.

Greater logged per capita gross domestic product (GDP) was associated with a lower percentage of the population reporting anticipated harm from drinking water [β quadratic term (95% CI): −2.7 (−3.8, −1.6); p  < 0.001], explaining 47.3% of variation in the outcome (Fig.  2F , Table  1 ). Within high-income countries, 37.2% of respondents anticipated harm from drinking water in the next two years; the percentage was similarly high across upper middle- (54.8%), middle- (56.4%), and low-income (57.1%) countries.

Finally, we sought to understand whether prevalence of self-reported anticipated harm from drinking water was correlated with the perceived quality of public governance. Lower corruption (i.e., higher Corruption Perceptions Index scores 41 ) was associated with lower self-reported anticipated harm from drinking water [Fig.  2G , Table  1 ; β quadratic term (95% CI): −0.01 (−0.02, −0.01); p  < 0.001] and independently explained the greatest variation in prevalence of anticipated harm from drinking water (53.8%) relative to other examined factors. For example, in Yemen, the country with the greatest perceived corruption, 52.3% of individuals anticipated future harm from drinking water compared to only 11.4% in Denmark, assessed as the least corrupt country. While public sector corruption explained a large amount of variation in self-reported anticipated harm, other factors clearly shape the conceptualization of drinking water risks. For instance, 62.3% (95% CI: 60.9%, 63.7%) of individuals who anticipated harm from their drinking water in the next two years also affirmed that their government did a “good job” ensuring safe drinking water.

Country-level characteristics were included in a multivariable regression to identify the most salient predictors of the prevalence of self-reported anticipated harm from drinking water. Prevalence of self-reported experienced harm to self or others from drinking water in the prior two years [β quadratic term (95% CI): −0.02 (−0.03, −0.01); p  = 0.001], having 1% or more of annual deaths attributable to unsafe water [β (95% CI): −5.9 (−11.5, −0.02); p  = 0.042], and public sector corruption score [β quadratic term (95% CI): −0.01 (−0.02, −0.01); p  < 0.001] were associated with prevalence of self-reported anticipated harm (Table  2 ). Model results suggest that a country with a Corruption Perceptions Index score of 80 (range: 0–100, with higher scores indicating lower perceived corruption) would be expected to have a 16.6-percentage-point lower (95% CI: −27.5, −5.3; p  = 0.003) prevalence of self-reported anticipated future harm from drinking water than a country with a score of 60, with all other characteristics being identical. Interestingly, the direction of association between percentage of annual deaths attributable to unsafe water and the prevalence of self-reported anticipated harm changed when adjusting for all covariates. The multivariable model explained 74.8% of the variation in prevalence of self-reported anticipated harm from drinking water.

Individual-level predictors of anticipated drinking water harm

Self-reported anticipated harm from drinking water varied by demographic characteristics within national income strata (Tables  3 and 4 ) and across countries (Supplementary Figs.  1 – 4 ). Across sites in bivariate models, a greater percentage of women [prevalence difference (PD) (95% CI): 4.9 percentage points (pp) (3.4pp, 6.3pp); p  < 0.001], individuals reporting difficulty getting by on their income [PD (95% CI): 4.1pp (2.5pp, 5.6pp); p  < 0.001], urban residents [PD (95% CI): 5.1pp (3.1pp, 7.1pp); p  < 0.001], and college-educated individuals [PD (95% CI): 10.9pp (8.4pp, 13.5pp); p  < 0.001] reported anticipating harm from drinking water in the next two years compared to men, individuals reporting no difficulty getting by on their income, rural residents, and individuals with less than a high school education, respectively (Table  3 ). In a multivariable model, all factors were similarly associated with self-reported anticipated harm from drinking water (Table  3 ).

Associations between individual-level characteristics and self-reported anticipated harm from drinking water were modified by national income level (Supplementary Tables  1 – 4 ). For instance, there were no statistically significant differences in anticipated harm by gender in low- [PD (95% CI): −1.0 (−3.2, 1.3); p  = 0.399] and lower middle-income countries [PD (95% CI): −1.0 (−3.4, 1.4); p  = 0.412], but a greater percentage of women were estimated to anticipate harm from drinking water in upper middle- [PD (95% CI): 8.5pp (5.8pp, 11.2pp); p  < 0.001] and high-income countries [PD (95% CI): 10.5pp (8.2pp, 12.8pp); p  < 0.001] (Table  4 , Fig.  4 ). Importantly, observed differences were heterogenous across countries (Supplementary Fig.  1 ). For instance, the percentage of women anticipating harm from drinking water was 19.2-pp higher than that of men in Moldova, but 11.6-pp lower in Nigeria.

Estimated percentage point (pp) differences in prevalence of anticipated drinking water harm among individuals in 141 countries, by gender, difficulty getting by on income, urbanicity, and years of education, across national income strata ( n  = 147,555 individuals). Model coefficients and 95% confidence intervals are in Table  4 . Data are presented as point estimates (circles) and associated 95% confidence intervals (capped lines) based on generalized linear models with binomial distributions and the identity link function. Tests were two-tailed.

A greater percentage of individuals reporting difficulty getting by on their income were estimated to anticipate future drinking water harm in low- [PD (95% CI): 5.4pp (2.8pp, 7.9pp); p  < 0.001], upper middle- [PD (95% CI): 5.4pp (2.7pp, 8.0pp); p  < 0.001], and high-income countries [PD (95% CI): 13.7pp (10.4pp, 17.1pp); p  < 0.001] compared to their counterparts who were able to get by on their income (Table  4 , Fig.  4 ). The prevalence of anticipated harm from drinking water differed by urbanicity in lower [PD (95% CI): 6.4pp (3.0pp, 9.9pp); p  < 0.001] and upper middle-income countries [PD (95% CI): 6.5pp (2.7pp, 10.2pp); p  = 0.001], but not in low- [PD (95% CI): 0.7pp (−2.4pp, 3.7pp); p  = 0.664] or high-income countries (PD (95% CI): 1.7pp (−0.8pp, 4.2pp); p  = 0.178]. In contrast, greater education was consistently associated with higher prevalence of self-reported anticipated harm across income strata, except in high-income countries. The magnitude of association was high. In upper middle-income countries, for instance, the prevalence of anticipated harm was estimated to be 16.5-pp higher (95% CI: 12.1pp, 20.9pp; p  < 0.001) among individuals with four or more years of education beyond high school compared to those with 8 or fewer years of education. In multivariable models, most factors were similarly associated with self-reported anticipated harm from drinking water (Supplementary Table  5 ).

In a multilevel mixed-effects model, individual- and country-level factors were associated with anticipated harm from drinking water in similar directions as observed in the separate multivariable models, except percentage of deaths within a country attributable to unsafe water (Supplementary Table  6 ). For instance, the odds of anticipating harm from drinking water was 1.25 times higher (95% CI: 1.22, 1.28; p  < 0.001) among women compared to men. Further, individuals who reported experiencing or personally knowing someone who experienced serious harm from drinking water in the prior two years had 4.23 times the odds (95% CI: 4.07, 4.39; p  < 0.001) of anticipating harm from drinking water compared to those who did not.

These nationally representative data, drawn from a survey on public perceptions of drinking water, indicate that an estimated 52.3% of individuals from 141 countries believe that they are likely to be harmed by their drinking water in the next two years. This estimate is higher and more geographically heterogenous than would be expected if risk perceptions tracked objective global water quality or income data 4 . The high prevalence of self-reported anticipated harm is consistent with emergent experiential evidence that issues with water availability, accessibility, use, and stability are common in both high- 42 and low- and middle-income settings 3 , 43 . Widespread self-reported anticipated harm from drinking water is likely multi-factorial and may be, in part, a response to projected threats to water quality and the sustainability of water services, including the ability to keep water sources safe from microbial and chemical contamination under conditions of worsening climate change 44 , although such data were not collected in this study.

Significant country-level predictors of greater anticipated harm from drinking water included higher prevalence of self-reported harm attributed to poor drinking water, more deaths attributable to unsafe water, and greater perceived public sector corruption. These findings align with national or sub-national studies that have found that drinking water appraisal is largely driven by experiences, perceptions, and attitudes 13 , 26 . Indeed, prior research has demonstrated that individuals who have experienced adverse consequences of an environmental hazard (e.g., flooding) are more likely to have stronger risk perceptions of that hazard 45 , 46 .

The association between self-reported anticipated harm and perceived public corruption may be partially explained by a decades-long decline in general trust in public institutions which, tellingly, is tracked in some locations by changes in bottled water consumption 47 . Relatedly, trust in the capabilities and will of political institutions and leaders may influence risk perceptions, which are strongly and consistently associated with each other 48 . For instance, a study in Australia found that political outlook influenced support for a local potable water recycling scheme 49 . Similarly, a study in the Netherlands found that generalized political trust was the strongest predictor of trust in water managers 50 . Importantly, we found that nearly two-thirds of individuals who anticipated harm from their drinking water in the next two years also affirmed that their government did a “good job” ensuring safe drinking water. This suggests that individuals may believe that their governments are setting appropriate regulations but distrust their implementation, enforcement, and uptake by water utilities, which may be managed by private businesses.

Improving public trust in the safety of drinking water will require better data, appropriate messaging, and programs that acknowledge and effectively respond to widespread safety concerns. These measures include improvements in transparency about and actions to address issues with water management and the presence of contaminants of concern, as well as relationship building across utilities, national and local governments, public health agencies, and water users to improve water system trustworthiness 51 , 52 . For example, 20% of participants enrolled in a study in the Netherlands reported that there was insufficient information provided about tap water quality, which contributed to feelings of distrust and increased bottled water use 53 . Numerous projects have used the Integrated Water Resources Management framework, which emphasizes public engagement and participation, to expand access to critical water services 54 . Although procedures for implementing the Integrated Water Resources Management framework have been criticized for being poorly defined 55 , the process has been demonstrated to improve trust in water resources and democratize water management if local communities are meaningfully involved at all stages of development 56 , 57 . Large-scale reforms beyond the water sector, including reduction in economic disparities and greater accountability for corrupt actors, may also increase trust and should be tested 47 . To help develop and evaluate programs and policies that accomplish these aims, objective data on water quality as well as information about experiences with and anticipated harm from drinking water should be collected concurrently 43 . Such information has been found to be useful for predicting public acceptance of new water schemes, such as use of recycled water 58 , as well as trust in community water sources following disease outbreaks 59 .

Interventions tailored to individuals anticipating greater harm are also needed. For example, women reported similar or greater perceived future drinking water risk than men across income strata; this may be due to gendered disparities in access 60 or greater awareness about a household’s water risk situation, as women are typically the managers of domestic water 61 . Urban residents anticipated greater drinking water harm, although they typically have greater access to improved water sources than rural households 4 . This seemingly paradoxical relationship may be explained by the fact that urban households have greater access to information about water, may be closer to industrial contamination sites, and may be more severely impacted by poorly managed waste systems 62 . Moreover, they may have less access to alternative water supplies (e.g., rainwater capture, private wells) than rural residents. Programs that address drinking water disparities by, for example, equitably expanding access to and information about safe and trusted water could be beneficial 63 , as could interventions that mitigate exposure to environmental contaminants via other pathways (e.g., poor hygiene, air pollution) that individuals may attribute to unsafe drinking water. For example, in the United States, where drinking water service disparities are largely a product of environmental racism and discriminatory housing policies 64 , replacement of lead pipes and the provision of at-home water filters may decrease harm from water and bolster trust in water services. Given that country income level modified the strength of the relationships between each demographic characteristic and anticipated harm, a uniform approach cannot be taken when addressing concerns over drinking water.

The ability to compare perceptions about the potential for serious harm from drinking water across diverse settings expands our understanding of the global water crisis and consumer concerns. Self-reported anticipated drinking water harm does not, however, always translate to future hazard occurrence—it is unlikely that all who anticipate harm will experience harm. A further limitation of these data is that self-reported experienced or anticipated harm from drinking water may be attributable to numerous causes beyond water, including other environmental conditions (e.g., air pollution, food contamination). Despite this, the fact that individuals believe that they have been or may be harmed by drinking water is meaningful because these perceptions shape attitudes and health-related behaviors 26 , 65 , 66 . Importantly, as with all surveys, there is potential for self-selection bias. Gallup, which conducted the World Risk Poll on behalf of the Lloyd’s Register Foundation, addresses these issues through rigorous sampling protocols and post-stratification weighting, but such methods may not fully resolve this concern. In addition, these data provide only one snapshot in time. We therefore encourage researchers to examine these trends longitudinally to identify causal relationships and understand how consumers’ perspectives are shifting, especially in the context of climate change and in relation to diverse objective measures of water quality. More rigorous analyses at the country and sub-national levels are also needed to understand context-specific drivers of risk perceptions about water harms and other hazards queried about in the World Risk Poll (e.g., food safety, severe weather events). Finally, the Corruption Perceptions Index only assesses perceived public sector corruption. Future work should examine how perceived corruption of private sector actors, particularly those involved with the management and provision of water, influences drinking water risk assessments.

Taken together, these findings suggest that the prevalence of anticipated harm from drinking water is high across diverse populations, geographies, and water service levels. Anticipated harm from drinking water is an underappreciated aspect of the global water crisis that may have myriad negative implications for health and well-being. There is clear need to consider users’ perspectives to promote water security and ensure the safety, use, and sustainability of water services.

Inclusion and ethics

This research complies with all relevant ethical regulations. We used deidentified data from Lloyd’s Register Foundation World Risk Poll for these analyses. These data were collected by Gallup, and survey procedures were approved by Gallup’s ethics committee and relevant governing bodies as required in each country prior to data collection. All participants provided verbal informed consent, and all local laws and restrictions were followed by Gallup while conducting interviews with 15–17-year-olds, including obtaining parental consent where required. No compensation was provided. The Gallup World Poll included local in-country researchers and study staff throughout the research process, including in the survey design and implementation.

Study sample

Data about anticipated harm were drawn from the publicly available Lloyd’s Register Foundation World Risk Poll, which was funded by the Lloyd’s Register Foundation and implemented in 2019 by Gallup. A full methodology report describing how surveys were developed and implemented is available from the Lloyd’s Register Foundation 67 . Briefly, a national probability-based sample of ~1000 non-institutionalized individuals (i.e., those not in living in institutions such as prisons or nursing homes) aged 15 years and older in each of 142 countries were surveyed by phone or face-to-face. Simple random sampling was used in countries where Gallup conducted phone surveys. A multi-stage sampling procedure was used in countries where Gallup conducted face-to-face interviews, with stratification of administrative units to determine primary sampling units and implementation of a random-route procedure within these units. Exceptions were made for areas that posed safety threats to interviewers, and scarcely populated areas that could only be accessed by foot, animal, or small boat. Ultimately, 154,195 participants ( n  = 82,568 women, 71,627 men) were recruited, consented, and interviewed by trained study staff. Due to their relative population sizes, samples were larger in some countries, such as China ( N  = 3709 individuals) and India ( N  = 3377 individuals), and smaller in others, such as Jamaica ( N  = 501 individuals). Data on self-reported anticipated harm from drinking water were unavailable for Kuwait.

Data collection

Phone surveys were conducted via mobile and landline phones using sample frames purchased from vendors in countries where telephone coverage was at-least 80% or where phone interviews were customary. Otherwise, face-to-face interviews were used. As such, individuals without access to a landline or mobile phone may have been under-represented, but they comprise a small percentage of the population in most countries in which surveys were done using telephone. To address potential under-coverage and increase representativeness, Gallup applied post-stratification sampling weights. Full details on response rates, ranging from 6% in Northern America to 80% in Central and Western Africa, are available in the methodology report 67 . A prior study using these data found no evidence that face-to-face interviews and phone interviews yield differential findings 68 . Interview mode was not adjusted for because it only varied in upper middle-income countries and high-income countries, and was therefore confounded by country income category.

Gallup implemented numerous quality assurance strategies to measure perceived risks and related constructs in the most reliable, valid, and equivalent way 67 , 69 . First, cognitive interviews and pilot tests were used to ensure that survey topics and wording made sense to and were understood equivalently among target participants 69 . Second, consistency across languages was achieved through one of two translation strategies: two independent translations occurred and harmonized by a third party, or the document was translated by one contractor, back-translated by another, and then edited by a third. Third, each survey included a definition of risk that was read aloud to participants for shared understanding: “Risk refers to something that may be dangerous or that could cause harm or the loss of something. Risk could also result in a reward or something good.” Fourth, Gallup used best data collection practices, including the development of standardized survey guides and multi-day training for interviewers, to ensure high implementation fidelity and comparability across applications. Fifth, to ensure that responses were reliably recorded, at-least 30% of surveys conducted face-to-face (through accompanied interviews or re-contacts) and 15% of those conducted via telephone (by listening to live or recorded interviews) were assessed for accuracy 67 . Sixth, to reduce the potential for psychological priming, individuals were first asked to report how worried they were about experiencing harm from each risk, then rated how likely they would experience harm from each factor in the next two years, and concluded by reporting whether they experienced harm from each factor in the prior two years. Test-retest reliability was not evaluated, such that the consistency and reproducibility of results is not known.

Experienced and anticipated harm

Self-reported experienced harm was assessed by asking participants whether they had experienced or personally knew someone who “experienced serious harm from drinking water in the past two years” (dichotomous: yes or no). Self-reported anticipated harm was assessed with a question that asked participants how likely it was that they would experience “serious harm in the next two years” from their drinking water. Response options were “not at all likely”, “somewhat likely”, and “very likely”. We created a binary variable of anticipated drinking water harm by combining “somewhat likely” and “very likely” because few individuals affirmed “very likely” in some countries (less than 5% in 21 countries) (Supplementary Data  2 ). Further, collapsing categories can improve cross-country comparability given known cultural variations in the affirmation of extreme categories (e.g., “very likely”) for questions with Likert-type response formats 70 .

The term “serious harm” was kept intentionally broad to account for the diverse ways by which suboptimal drinking water can manifest and in turn differentially impact well-being. Subjective interpretation of “serious harm” is central to this analysis given that idiosyncratic perceptions of risk ultimately influence behavior. The phrase was not identified as being poorly or even differentially understood in cognitive interviews or the pilot tests conducted in low-, middle-, and high-income countries 67 , 69 .

Perceived anticipated harm from drinking water in the next two years was selected as the outcome of interest given our study aims and shortcomings in the other water-related item included in the World Risk Poll. Along with reporting likelihood of serious harm from drinking water in the next two years, individuals were asked to share “how worried are you that the water you drink could cause you serious harm?”. While this item provides interesting insights into perceptions of water hazards, it does not comprehensively capture individuals’ assessments of whether such risks are likely to produce harm. In some contexts, there may be other risks that pose greater threats to well-being or water issues may be normalized due to their frequency and pervasiveness, such that individuals believe they are likely to be harmed by their water but not worry it.

A single item was used for self-reported anticipated harm from drinking water to reduce survey costs and respondent burden. Single-item measures of risk perception are often highly correlated with multi-item measures of risk, showing comparable validity in terms of correlations with relevant outcomes 71 . Reliability of our single-item measure of self-reported anticipated harm is demonstrated through positive correlations with a yes/no question that asked about whether participants worried that their drinking water could cause them harm, both in aggregate (r = 0.59) and across countries (r: 0.17–0.83). In addition, in a generalized linear model with a binomial distribution, identity link function, and country membership included as a fixed effect, the prevalence of self-reported anticipated from drinking water was estimated to be 57.2 percentage points higher (95% CI: 55.9pp, 58.5pp; p  < 0.001) among individuals who reported worrying about drinking water compared to those who did not, suggesting item reliability.

Country-level variables

To explore whether risk perceptions were associated with more commonly used water indicators, we leveraged other publicly available datasets. Estimates of national renewable freshwater resources (m 3 per capita, log transformed to reduce skewness) in 2017—the latest round of data currently available—were drawn from AQUASTAT, the Food and Agriculture Organization’s global information system on water and agriculture. Data were available for 136 of the 141 countries in the World Risk Poll with data on self-reported anticipated harm 72 . We used nationally representative data about the percentage of households in each country with at-least basic drinking water service levels (data for access to safely managed drinking water services were only available for 96 of the 141 countries), estimated by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation for 2019, as a proxy for water quality and access ( N  = 135/141 countries) 4 .

Objective water quality data sufficient for generating cross-culturally comparable estimates of the percentage of the population using contaminated drinking water were collected in 29 countries between 2012 and 2019 19 . For this, contamination was measured by the presence of Escherichia coli at the point of use; Escherichia coli is an organism indicative of pathogenic water contamination that can cause diarrheal disease 40 . Twenty-three of these countries were in the World Risk Poll.

A publicly available dataset on wastewater production and use was used to estimate the percentage of wastewater from domestic and manufacturing processes that was treated in 2015 ( N  = 137/141 countries) 73 . The percentage of deaths in a country attributable to unsafe water was estimated using data from the 2019 Global Burden of Disease Study ( N  = 138/141 countries) 74 . Estimates of per capita GDP in 2019 (USD, log transformed to reduce skewness) ( N  = 137/141 countries), as well as classifications of country income levels (low, lower middle, upper middle, and high) ( N  = 141/141 countries), were retrieved from the World Bank database 75 . In addition, the 2019 Corruption Perceptions Index was used to estimate the perceived level of corruption in the public sector by key stakeholders, including business and political experts, within each country. The Corruption Perceptions Index is calculated by aggregating and averaging data from 13 sources (e.g., the African Development Bank Country Policy and Institutional Assessment, World Economic Forum Executive Opinion Survey), with potential scores ranging from 0 to 100; 0 represents the highest level of perceived corruption and 100 represents the lowest level of perceived corruption. Corruption Perceptions Index scores are only assigned to countries or territories with assessments from 3 or more sources ( N  = 140/141 countries) 41 . Finally, the percentage of the population that reported experiencing serious harm from drinking water in the prior two years was estimated using data from the 2019 Lloyd’s Register Foundation World Risk Poll ( N  = 141/141 countries) 76 .

Individual-level variables

We explored how self-reported anticipated harm varied by individual-level demographic characteristics to identify populations that are disproportionately impacted by this negative experience. Demographic characteristics included gender (determined by the interviewer as man or woman), ability to get by on present income (dichotomous: “finding it difficult” or “getting by on present income”), household location (dichotomous: “rural area, small town, or village” or “large city or suburb of a large city”), and education (categorical: ≤8 years or basic education, 9–15 years of education, completed four years of education beyond high school).

Statistical analysis

Base sampling weights for each country were developed by Gallup to account for the probability of being selected into the sample. Base sampling weights were then adjusted for non-response as well as national distributions of gender, age, and (if reliable data were available) education and socioeconomic status. First, to assess the prevalence of self-reported harm from drinking water and concern about it, we applied these post-stratification weights to generate nationally representative estimates of self-reported experienced and anticipated harm within each country. When pooling across sites for regional and aggregate estimates, we applied projection weights that accounted for each country’s adult population size.

Second, to evaluate country-level predictors that explained variation in the national prevalence of self-reported anticipated harm from drinking water, we visualized trends between estimates of self-reported anticipated drinking water harm and country-level covariates using lowess curves to qualitatively assess their relationships. We then fitted weighted least squares regressions (observations weighted by the inverse of the standard errors of the dependent variable estimates for each country) with robust standard errors—which account for uncertainty in the estimated outcome, design effects, and heteroskedasticity 77 —to assess the relationship between the percentage of the population reporting anticipated harm from drinking water and predictors of interest, exploring linear and quadratic functional forms of each country-level covariate, except for percentage of deaths in a country attributable to unsafe water. The lowess curve for this variable in relation to the prevalence of self-reported anticipated harm from drinking water substantially changed at 1% of deaths attributable to unsafe water. As such, we dichotomized the variable to reflect less than ( n  = 90 countries) or greater than or equal to 1% of deaths attributable to unsafe water ( n  = 48 countries). The Akaike information criterion (AIC) was used to determine which variable form provided better model fit; lower AIC values indicate better fit (Supplementary Table  7 ). A multivariable model with all country-level covariates was developed to identify the most salient predictors of self-reported anticipated harm. Models met statistical assumptions, except the one with per capita renewable freshwater resources as the main predictor, which had non-normally distributed residuals.

Third, to identify which individuals are most likely to perceive their water to be unsafe, we built generalized linear models with binomial distributions and used the identity link function to estimate the prevalence difference in self-reported anticipated harm from drinking water by demographic characteristics (gender, education, household location, and difficulty getting by on present income). Each demographic characteristic was interacted with World Bank country income classification to assess for potential effect measure modification. Analyses were then stratified by World Bank country income classification given statistically significant interactions. To identify the most salient predictors of self-reported anticipated harm from drinking water, we included all demographic characteristics in a multivariable model. To account for clustering of observations within each country, we used a fixed effects approach (i.e., country membership was included as a predictor in each model); model assumptions of independence were thus satisfied.

As a sensitivity analysis, we developed a multilevel mixed effects logistic regression to understand how individual- and country-level factors may concurrently influence perceptions of potential harm from drinking water. Country membership was treated as a random effect and all other predictors were treated as fixed.

Analyses were two-tailed tests ( ɑ  = 0.05) and completed using Stata 17.0 (StataCorp).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The datasets used for these analyses are publicly available. They include data on perceived water risks from the Lloyd’s Register Foundation World Risk Poll ( https://wrp.lrfoundation.org.uk/data-resources/ ) 76 , data on water availability from AQUASTAT 72 , data on household drinking water services from the Joint Monitoring Programme for Water Supply ( https://washdata.org/data/household#!/ ) 4 , data on water quality from UNICEF and WHO 73 , data on wastewater from Pangea ( https://doi.org/10.1594/PANGAEA.918731 ) 73 , data on deaths attributable to unsafe water from the Global Burden of Disease Study ( http://ghdx.healthdata.org/gbd-2019 ) 74 , country income data from the World Bank ( https://data.worldbank.org/ ) 75 , and data on perceived public sector corruption from Transparency International ( https://www.transparency.org/en/cpi/2019/index/nzl ) 41 .

Code availability

Analytic code is available through an open-access repository ( https://doi.org/10.21985/n2-0n23-hn72 ) 78 .

Mekonnen, M. M. & Hoekstra, A. Y. Four billion people facing severe water scarcity. Sci. Adv. 2 , e1500323 (2016).

Article   ADS   PubMed   PubMed Central   Google Scholar  

IPCC. et al. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (2020).

Young, S. L., Bethancourt, H. J., Ritter, Z. R. & Frongillo, E. A. The Individual Water Insecurity Experiences (IWISE) Scale: reliability, equivalence and validity of an individual-level measure of water security. BMJ Glob. Health 6 , e006460 (2021).

Article   PubMed   PubMed Central   Google Scholar  

WHO & UNICEF. Progress on Household Drinking Water, Sanitation and Hygiene 2000–2020: Five Years into the SDGs (2021).

Rosegrant, M. W., Ringler, C. & Zhu, T. Water for agriculture: maintaining food security under growing scarcity. Annu Rev. Environ. Resour. 34 , 205–222 (2009).

Article   Google Scholar  

World Bank. High and Dry: Climate Change, Water, and the Economy (2016).

Abbott, M., Bazilian, M., Egel, D. & Willis, H. H. Examining the food–energy–water and conflict nexus. Curr. Opin. Chem. Eng. 18 , 55–60 (2017).

Rosinger, A. Y. & Young, S. L. The toll of household water insecurity on health and human biology: current understandings and future directions. Wiley Interdiscip. Rev. Water 7 , e1468 (2020).

Miller, J. D. et al. Water security and nutrition: current knowledge and research opportunities. Adv. Nutrition Int. Rev. J. https://doi.org/10.1093/advances/nmab075 (2021).

Young, S. L. et al. Perspective: The importance of water security for ensuring food security, good nutrition, and well-being. Adv. Nutr . https://doi.org/10.1093/advances/nmab003 (2021).

Damania, R., Desbureaux, S., Rodella, A.-S., Russ, J. & Zaveri, E. Quality Unknown: The Invisible Water Crisis (World Bank Group, Washington, 2020).

WHO, UNICEF & World Bank. State of the World’s Drinking Water: An Urgent Call to Action to Accelerate Progress on Ensuring Safe Drinking Water for All (2022).

Doria, M. & de, F. Factors influencing public perception of drinking water quality. Water Policy 12 , 1–19 (2010).

Bain, R., Johnston, R., Khan, S., Hancioglu, A. & Slaymaker, T. Monitoring drinking water quality in nationally representative household surveys in low- and middle-income countries: cross-sectional analysis of 27 multiple indicator cluster surveys 2014–2020. Environ. Health Persp. 129 , 097010 (2021).

Richardson, S. D. & Ternes, T. A. Water analysis: emerging contaminants and current issues. Anal. Chem. 90 , 398–428 (2018).

Article   CAS   PubMed   Google Scholar  

Wright, J., Gundry, S. & Conroy, R. Household drinking water in developing countries: a systematic review of microbiological contamination between source and point‐of‐use. Trop. Med. Int. Health 9 , 106–117 (2004).

Article   PubMed   Google Scholar  

Meierhofer, R., Wietlisbach, B. & Matiko, C. Influence of container cleanliness, container disinfection with chlorine, and container handling on recontamination of water collected from a water kiosk in a Kenyan slum. J. Water Health 17 , 308–317 (2019).

Reed, B. An Engineer’s Guide to Domestic Water Containers (WEDC, Loughborough University, 2011).

UNICEF & WHO. Integrating Water Quality Testing into Household Surveys: Thematic Report on Drinking Water (2020).

Pauli, B. J. The Flint water crisis. Wiley Interdiscip. Rev. Water 7 , e1420 (2020).

Heard-Garris, N. J. et al. Voices from Flint: community perceptions of the Flint water crisis. J. Urban Heal 94 , 776–779 (2017).

Allaire, M., Wu, H. & Lall, U. National trends in drinking water quality violations. Proc. Natl Acad. Sci. USA 115 , 2078–2083 (2018).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Araya, F., Singer, R., Charnitski, J. & Faust, K. M. Beyond the pipes: how user perceptions influence their water and wastewater infrastructure interactions in texas colonias. J. Sustain Water. Built Environ. 9 , 04022015 (2023).

Hu, Z., Morton, L. W. & Mahler, R. L. Bottled water: united states consumers and their perceptions of water quality. Int. J. Environ. Res. Public Health 8 , 565–578 (2011).

Rosinger, A. Y. Using water intake dietary recall data to provide a window into US water insecurity. J. Nutr. 152 , 1263–1273 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Delpla, I., Legay, C., Proulx, F. & Rodriguez, M. J. Perception of tap water quality: Assessment of the factors modifying the links between satisfaction and water consumption behavior. Sci. Total Environ. 722 , 137786 (2020).

Kruger, D. J. et al. Toxic trauma: Household water quality experiences predict posttraumatic stress disorder symptoms during the Flint, Michigan, water crisis. J. Community Psychol. 45 , 957–962 (2017).

Cuthbertson, C. A., Newkirk, C., Ilardo, J., Loveridge, S. & Skidmore, M. Angry, scared, and unsure: mental health consequences of contaminated water in Flint, Michigan. J. Urban Heal Bull. N. Y. Acad. Med. 93 , 899–908 (2016).

Google Scholar  

Allaire, M., Mackay, T., Zheng, S. & Lall, U. Detecting community response to water quality violations using bottled water sales. Proc. Natl Acad. Sci. USA 116 , 20917–20922 (2019).

Saylor, A., Prokopy, L. S. & Amberg, S. What’s wrong with the tap? Examining perceptions of tap water and bottled water at Purdue University. Environ. Manag. 48 , 588–601 (2011).

Article   ADS   Google Scholar  

Bauer, K. W., Weeks, H. M., Clayson, M. & Needham, B. Perceptions of tap water associated with low-income Michigan mothers’ and young children’s beverage intake. Public Health Nutr. 25 , 2772–2781 (2022).

Onufrak, S. J., Park, S., Sharkey, J. R. & Sherry, B. The relationship of perceptions of tap water safety with intake of sugar-sweetened beverages and plain water among US adults. Public Health Nutr. 17 , 179–185 (2014).

Parag, Y. & Roberts, J. T. A battle against the bottles: building, claiming, and regaining tap-water trustworthiness. Soc. Nat. Resour. 22 , 625–636 (2009).

Young, S. L. Viewpoint: The measurement of water access and use is key for more effective food and nutrition policy. Food Policy 104 , 102138 (2021).

Kaiser, C. & Oswald, A. J. The scientific value of numerical measures of human feelings. Proc. Natl Acad. Sci. USA 119 , e2210412119 (2022).

Octavianti, T. & Staddon, C. A review of 80 assessment tools measuring water security. Wiley Interdiscip. Rev. Water 8 , e1516 (2021).

Young, S. L., Bethancourt, H. J., Ritter, Z. R. & Frongillo, E. A. Estimating national, demographic, and socioeconomic disparities in water insecurity experiences in low-income and middle-income countries in 2020–21: a cross-sectional, observational study using nationally representative survey data. Lancet Planet Heal 6 , e880–e891 (2022).

Malik, O. A., Hsu, A., Johnson, L. A. & Sherbinin, Ade A global indicator of wastewater treatment to inform the Sustainable Development Goals (SDGs). Environ. Sci. Policy 48 , 172–185 (2015).

Riffat, R. & Husnain, T. Fundamentals of Wastewater Treatment and Engineering (2022).

Khalil, I. A. et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: the Global Burden of Disease Study 1990-2016. Lancet Infect. Dis. 18 , 1229–1240 (2018).

Transparency International. Corruption Perceptions Index 2019. https://images.transparencycdn.org/images/2019_CPI_Report_EN_200331_141425.pdf (2020).

Meehan, K. et al. Exposing the myths of household water insecurity in the global north: a critical review. Wiley Interdiscip Rev. Water 7 , e1486 (2020).

Young, S. et al. The Household Water InSecurity Experiences (HWISE) Scale: development and validation of a household water insecurity measure for low-income and middle-income countries. BMJ Glob. Health 4 , e001750 (2019).

Howard, G., Calow, R., Macdonald, A. & Bartram, J. Climate change and water and sanitation: likely impacts and emerging trends for action. Annu. Rev. Environ. Resour. 41 , 1–24 (2015).

Lujala, P., Lein, H. & Rød, J. K. Climate change, natural hazards, and risk perception: the role of proximity and personal experience. Local Environ. 20 , 489–509 (2015).

Lawrence, J., Quade, D. & Becker, J. Integrating the effects of flood experience on risk perception with responses to changing climate risk. Nat. Hazards 74 , 1773–1794 (2014).

Perry, J. Trust in Public Institutions: Trends and Implications for Economic Security . https://www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2021/08/PB_108.pdf (2021).

Siegrist, M. Trust and risk perception: a critical review of the literature. Risk Anal. 41 , 480–490 (2021).

Price, J., Fielding, K. & Leviston, Z. Supporters and opponents of potable recycled water: culture and cognition in the Toowoomba referendum. Soc. Nat. Resour. 25 , 980–995 (2012).

Voogd, R., Vries, J. Rde & Beunen, R. Understanding public trust in water managers: findings from the Netherlands. J. Environ. Manag. 300 , 113749 (2021).

Patel, A. I., Hecht, C. E., Cradock, A., Edwards, M. A. & Ritchie, L. D. Drinking water in the United States: implications of water safety, access, and consumption. Annu. Rev. Nutr. 40 , 345–373 (2020).

Wilson, N. J. et al. From “trust” to “trustworthiness”: retheorizing dynamics of trust, distrust, and water security in North America. Environ. Plan. E: Nat. Space 6 , 42–68 (2023).

Brouwer, S., Hofman-Caris, R. & Aalderen, N. van. Trust in drinking water quality: understanding the role of risk perception and transparency. Water 12 , 2608 (2020).

Pahl-Wostl, C., Möltgen, J., Ebenhoeh, E. & Holtz, G. Adaptive and Integrated Water Management, Coping with Complexity and Uncertainty 75–96 (2008).

Biswas, A. K. Integrated water resources management: is it working? Int. J. Water Resour. D. 24 , 5–22 (2008).

Dungumaro, E. W. & Madulu, N. F. Public participation in integrated water resources management: the case of Tanzania. Phys. Chem. Earth Parts B C. 28 , 1009–1014 (2003).

Delozier, J. L. & Burbach, M. E. Boundary spanning: Its role in trust development between stakeholders in integrated water resource management. Curr. Res Environ. Sustain 3 , 100027 (2021).

Ross, V. L., Fielding, K. S. & Louis, W. R. Social trust, risk perceptions and public acceptance of recycled water: testing a social-psychological model. J. Environ. Manag. 137 , 61–68 (2014).

Bratanova, B., Morrison, G., Fife‐Schaw, C., Chenoweth, J. & Mangold, M. Restoring acceptance after a contamination event. J. Appl. Soc. Psychol. 43 , 1761–1770 (2013).

Pouramin, P., Nagabhatla, N. & Miletto, M. A systematic review of water and gender interlinkages: assessing the intersection with health. Frontiers. Water 2 , 6 (2020).

Geere, J.-A. & Cortobius, M. Who carries the weight of water? Fetching water in rural and urban areas and the implications for water security. Water Alternatives 10 , 513–540 (2017).

Ge, S. & Graeme, L. B. in American Society of Agronomy, Crop Science Society of America, Soil Science Society of America 29–48 (2012).

Yang, E., Butcher, D. A., Edwards, M. A. & Faust, K. M. Exploring the relationship between public trust toward the water sector and the use of bottled water within US shrinking cities. J. Water Resour. Plan. Manag 149 , 04022071 (2023).

Mueller, J. T. & Gasteyer, S. The widespread and unjust drinking water and clean water crisis in the United States. Nat. Commun. 12 , 3544 (2021).

Grupper, M. A., Schreiber, M. E. & Sorice, M. G. How perceptions of trust, risk, tap water quality, and salience characterize drinking water choices. Hydrology 8 , 49 (2021).

Kunwar, S. B. & Bohara, A. K. Water quality avoidance behavior: bridging the gap between perception and reality. Water Econ. Policy 06 , 1950012 (2020).

Lloyd’s Register Foundation & Gallup. The Lloyd’s Register Foundation World Risk Poll, 2019 Methodology . https://wrp.lrfoundation.org.uk/LRF_WorldRiskReport_MethodologyAppendix.pdf (2021).

Bruin, W. Bde & Slovic, P. Low numeracy is associated with poor financial well-being around the world. PLoS ONE 16 , e0260378 (2021).

Lloyd’s Register Foundation. Talking Risk: Developing the Questionnaire for the World Risk Poll (2019).

Clarke, I. Extreme response style in cross-cultural research: an empirical investigation. J. Soc. Behav. Personal. 1 , 137–152 (2000).

O’Brien, E. K., Baig, S. A. & Persoskie, A. Developing and validating measures of absolute and relative e-cigarette product risk perceptions: single items can be surprisingly comprehensive. Nicotine Tob. Res. https://doi.org/10.1093/ntr/ntab156 (2021).

Article   PubMed Central   Google Scholar  

FAO. AQUASTAT Database. Preprint at http://www.fao.org/aquastat/statistics/query/index.html (2021).

Jones, E. R., Vliet, M. T. H. van, Qadir, M. & Bierkens, M. F. P. Country-level and gridded wastewater production, collection, treatment and re-use. Preprint at https://doi.org/10.1594/PANGAEA.918731 (2020).

Global Burden of Disease Consortium Network. Global Burden of Disease Study 2019. Preprint at http://ghdx.healthdata.org/gbd-results-tool (2021).

World Bank. World Bank Open Data. Preprint at https://data.worldbank.org/ (2021).

Lloyd’s Register Foundation. The Lloyd’s Register Foundation World Risk Poll: Full Report and Analysis of the 2019 Poll . https://wrp.lrfoundation.org.uk/LRF_WorldRiskReport_Book.pdf (2020).

Lewis, J. B. & Linzer, D. A. Estimating regression models in which the dependent variable is based on estimates. Polit. Anal. 13 , 345–364 (2005).

Miller, J. D. & Young, S. L. Prevalence of and variations in self-reported anticipated harm from drinking water across 141 countries: analytic code. Preprint at https://doi.org/10.21985/n2-0n23-hn72 (2024).

Download references

Acknowledgements

J.D.M. was supported by the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institute of Child Health and Human Development of the National Institutes of Health under award numbers T32DK007686 and F31HD113400-01, respectively. C.S. (Grant # R00068) and W.BdB. (Grant # TWRP100007) received support from the Lloyd’s Register Foundation, a charitable foundation helping to protect life and property by supporting engineering-related education, public engagement, and the application of research. J.B.L. was supported by Searle Funds at The Chicago Community Trust, the John Simon Guggenheim Memorial Foundation, and the National Science Foundation (Award # 2310382). J.B.L. and S.L.Y. were supported by the Buffett Institute for Global Studies at Northwestern University and the National Science Foundation (Award # 2319427). S.L.Y. was also supported by the Carnegie Foundation, a Leverhulme Trust Visiting Professorship, and the Innovative Methods and Metrics for Agriculture and Nutrition Action (IMMANA) program, led by the London School of Hygiene & Tropical Medicine (LSHTM). IMMANA was co-funded by the UK Foreign Commonwealth and Development Office (FCDO) (Grant # 300654) and by the Bill & Melinda Gates Foundation (INV-002962/OPP1211308). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. We gratefully acknowledge Gallup for their efforts to collect survey data.

Author information

Authors and affiliations.

Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Joshua D. Miller

School for Architecture and Environment, University of the West of England, Bristol, UK

Chad Staddon

Department of Environmental Science and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Aaron Salzberg

Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA

Julius B. Lucks

Center for Synthetic Biology, Northwestern University, Evanston, IL, USA

Julius B. Lucks & Sera L. Young

Center for Water Research, Northwestern University, Evanston, IL, USA

Sol Price School of Public Policy and Dornsife Department of Psychology, University of Southern California, Los Angeles, CA, USA

Wändi Bruine de Bruin

Department of Anthropology & Institute for Policy Research, Northwestern University, Evanston, IL, USA

Sera L. Young

You can also search for this author in PubMed   Google Scholar

Contributions

J.D.M., S.L.Y., and C.S. conceived the study. J.D.M. processed the data and performed all analyses. J.D.M. and S.L.Y. wrote the first draft. C.S., A.S., J.B.L., and W.B.d.B. contributed to data interpretation and provided substantive revisions on subsequent versions of the manuscript. All authors approved the final version.

Corresponding author

Correspondence to Sera L. Young .

Ethics declarations

Competing interests.

J.B.L. has a financial interest in Stemloop, Inc., which aims to commercialize rapid water quality diagnostics technologies. These interests were reviewed and managed by Northwestern University in accordance with their conflict-of-interest policies. All other authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Ulrich Kohler, Jeffrey Lees and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1, supplementary data 2, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Miller, J.D., Staddon, C., Salzberg, A. et al. Self-reported anticipated harm from drinking water across 141 countries. Nat Commun 15 , 7320 (2024). https://doi.org/10.1038/s41467-024-51528-x

Download citation

Received : 25 January 2023

Accepted : 08 August 2024

Published : 25 August 2024

DOI : https://doi.org/10.1038/s41467-024-51528-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Hydrogeochemical characterization and statistical approach to assess the quality of the spring water in the Meknes-El Hajeb region, Morocco

• Original Article
• Published: 28 August 2024

Cite this article

• Abdennabi Alitane   ORCID: orcid.org/0000-0003-4695-0338 1 , 2 ,
• Ali Essahlaoui 1 ,
• Estifanos Addisu Yimer 2 ,
• Habiba Ousmana 1 ,
• Narjisse Essahlaoui 1 ,
• Abdellah Oumou 1 ,
• Abdellah El Hmaidi 1 ,
• Said Benyoussef 3 , 4 &
• Ann Van Griensven 2 , 5  

Water resources in the Meknes-El Hajeb region are under stress because of the agricultural practices, domestic and industrial wastewater, which are the primary sources of pollution in the study area. Consuming contaminated water poses significant public health risks and is a serious problem in rural areas. This research project aims to assess water quality for drinking and irrigation purposes by using Water Quality Index (WQI) and Irrigation Water Quality Parameters (IWQP). Water samples were collected from twelve unprotected springs in the research area to evaluate their quality in two sampling campaigns during Wet and Dry seasons, 2022. The WQI was ranged from 46.13 to 128.54 during the wet season and 49.87 to 189.55 during the dry season. It is illustrates that 75% of the monitored stations exhibited good quality, while 17% showed poor quality during the wet season. On the other hand, 58% demonstrated good quality and 33% exhibited poor quality during the dry season. Additionally, 8% of the water analyzed corresponded to excellent quality for both seasons. The spring water's suitability for irrigation uses has been verified using the IWQP, which indicates that all the spring water was suitable for irrigation. Principal Component Analysis (PCA) was applied to evaluate the effective loading of the spring water. The present study revealed that the major hydrochemical facies identified were the calcium-magnesium-bicarbonate type (CaMgHCO₃) and calcium-magnesium-chloride type (CaMgCl) in the most of water springs, which explains the significant presence of the carbonate minerals in the region. According to the Wilcox classification, the springs waters were classified as good (C 3 S 1 ) to excellent (C 2 S 1 ) categories, indicating their suitability for irrigation use in both periods. This project will provide a better understanding the spring hydrochemical and suggest that the WQI and IWQP may be an effective technique for decision-makers concerned with managing the sustainability of water resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Spring water quality assessment of Anantnag district of Kashmir Himalaya: towards understanding the looming threats to spring ecosystem services

Geochemical Characterization and Controlling Factors of Chemical Composition of Spring Water in a Part of Eastern Himalaya

Hydrochemical appraisal of surface water from a subtropical urban river in southwestern Bangladesh using indices, GIS, and multivariate statistical analysis

Explore related subjects.

• Environmental Chemistry

Data availability

Data for this study will be available on request.

Abbasnia A, Yousefi N, Mahvi AH et al (2019) Evaluation of groundwater quality using water quality index and its suitability for assessing water for drinking and irrigation purposes: Case study of Sistan and Baluchistan province (Iran). Hum Ecol Risk Assess Int J 25:988–1005. https://doi.org/10.1080/10807039.2018.1458596

Article   CAS   Google Scholar  

Akhtar N, Ishak MIS, Ahmad MI et al (2021) Modification of the water quality index (WQI) process for simple calculation using the multi-criteria decision-making (MCDM) method: a review. Water 13:905

Alitane A, Essahlaoui A, Yimer EA, et al (2024) The combined use of GIS and water quality indices for environmental assessment of Ouislane River watershed, Morocco. Euro-Mediterr J Environ Integr. https://doi.org/10.1007/s41207-024-00550-y

Ansari MA, Deodhar A, Kumar US, Khatti VS (2015) Water quality of few springs in outer Himalayas–A study on the groundwater–bedrock interactions and hydrochemical evolution. Groundw Sustain Dev 1:59–67. https://doi.org/10.1016/j.gsd.2016.01.002

Article   Google Scholar  

Ansari MdA, Deodhar A, Kumar US (2019) Modeling of geochemical processes and multivariate statistical analysis for hydrochemical assessment of spring water of the Outer Himalaya. India Environ Earth Sci 78:665. https://doi.org/10.1007/s12665-019-8682-5

Azizi G, Layachi M, Akodad M, et al (2021) Bioaccumulation and health risk assessment of trace elements in Mytilus galloprovincialis as sea food in the Al Hoceima coasts (Morocco). https://doi.org/10.1051/e3sconf/202124001002

Bashir N, Saeed R, Afzaal M et al (2020) Water quality assessment of lower Jhelum canal in Pakistan by using geographic information system (GIS). Groundw Sustain Dev 10:100357. https://doi.org/10.1016/j.gsd.2020.100357

Batarseh M, Imreizeeq E, Tilev S et al (2021) Assessment of groundwater quality for irrigation in the arid regions using irrigation water quality index (IWQI) and GIS-Zoning maps: Case study from Abu Dhabi Emirate UAE. Groundw Sustain Dev 14:100611. https://doi.org/10.1016/j.gsd.2021.100611

Bauder TA, Waskom RM, Davis JG, Sutherland PL (2011) Irrigation water quality criteria. Colorado State University Extension, Fort Collins

Google Scholar  

Belkhiri L, Mouni L (2012) Hydrochemical analysis and evaluation of groundwater quality in El Eulma area, Algeria. Appl Water Sci 2:127–133. https://doi.org/10.1007/s13201-012-0033-6

Berhe BA, Dokuz UE, Çelik M (2017) Assessment of hydrogeochemistry and environmental isotopes of surface and groundwaters in the Kütahya Plain, Turkey. J Afr Earth Sc 134:230–240. https://doi.org/10.1016/j.jafrearsci.2017.06.015

Bouib L, Jilali A, Bba AN et al (2024) Tectono-stratigraphic modeling and hydrochemical characterization of a structurally controlled deep aquifer (Saïss basin, Morocco). J Afr Earth Sci 105235.  https://doi.org/10.1016/j.jafrearsci.2024.105235

Da Silva Rangel Neto R, Luz LD, Aguiar Junior TR (2020) Springs’ Water Quality Assessment in Areas with Different Degrees of Forest Conservation: a Study in Tropical Climate Basins. Water Air Soil Pollut 231:227. https://doi.org/10.1007/s11270-020-04564-4

Eaton FM (1950) Significance of carbonates in irrigation waters. Soil Sci 69:123–134

Egbueri JC, Mgbenu CN, Digwo DC, Nnyigide CS (2023) A multi-criteria water quality evaluation for human consumption, irrigation and industrial purposes in Umunya area, southeastern Nigeria. Int J Environ Anal Chem 103:3351–3375. https://doi.org/10.1080/03067319.2021.1907360

El Yousfi Y, Himi M, El Ouarghi H et al (2023) Assessment and Prediction of the Water Quality Index for the Groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco). Sustainability 15:402. https://doi.org/10.3390/su15010402

Etteieb S, Cherif S, Tarhouni J (2017) Hydrochemical assessment of water quality for irrigation: a case study of the Medjerda River in Tunisia. Appl Water Sci 7:469–480. https://doi.org/10.1007/s13201-015-0265-3

Ewaid SH (2018) Irrigation water quality of Al-Gharraf Canal, south of Iraq. Journal of Physics: Conference Series. IOP Publishing, p 012006

Fereres E, Goldhamer DA, Parsons LR (2003) Irrigation water management of horticultural crops. HortScience 38:1036–1042

Gazzaz NM, Yusoff MK, Aris AZ et al (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64:2409–2420

Ghalit M, Bouaissa M, Gharibi E et al (2023) Hydrogeochemical characteristics and isotopic tools used to identify the mineralization processes of bottled mineral water in Morocco. Geosciences 13:38

Gharbi A, Ali ZI, Zairi M (2019) Groundwater suitability for drinking and agriculture purposes using irrigation water quality index and multivariate analysis: case of Sidi Bouzid aquifer, central Tunisia. Environ Earth Sci 78:692. https://doi.org/10.1007/s12665-019-8733-y

Gibbs RJ (1970) Mechanisms Controlling World Water Chemistry. Science 170:1088–1090. https://doi.org/10.1126/science.170.3962.1088

Gurung A, Adhikari S, Chauhan R et al (2019) Assessment of spring water quality in the rural watersheds of western Nepal. J Geosci Environ Protect 7:39–53

Hicham G, Mustapha A, Mourad B et al (2022) Assessment of the metal contamination index in groundwater of the quaternary of the Middle Kert Basin, north-eastern Morocco. Environ Qual Manag 32:53–62. https://doi.org/10.1002/tqem.21824

Iqbal J, Su C, Rashid A et al (2021) Hydrogeochemical assessment of groundwater and suitability analysis for domestic and agricultural utility in Southern Punjab Pakistan. Water 13:3589

Kadyampakeni D, Appoh R, Barron J, Boakye-Acheampong E (2018) Analysis of water quality of selected irrigation water sources in northern Ghana. Water Sci Technol: Water Supply 18:1308–1317

CAS   Google Scholar  

Kaurish FW, Younos T (2007) Developing a Standardized Water Quality Index for Evaluating Surface Water Quality 1 . J American Water Resour Assoc 43:533–545. https://doi.org/10.1111/j.1752-1688.2007.00042.x

Kiwanuka M, Mutanda HE, Niyomukiza JB, Nakasagga E (2023) Assessment of suitability of drinking water from the springs in Urban slums of Kampala. Environmental Challenges 10:100667

Ledesma RD, Valero-Mora P, Macbeth G (2015) The scree test and the number of factors: a dynamic graphics approach. Spanish J Psychol 18:E11. https://doi.org/10.1017/sjp.2015.13

Linting M, Meulman JJ, Groenen PJ, van der Koojj AJ (2007) Nonlinear principal components analysis: introduction and application. Psychol Methods 12:336

Lipan L, Martín-Palomo MJ, Sánchez-Rodríguez L et al (2019) Almond fruit quality can be improved by means of deficit irrigation strategies. Agric Water Manag 217:236–242. https://doi.org/10.1016/j.agwat.2019.02.041

Lopushniak VI, Hrytsuliak HM (2021) The Models of the Heavy Metal Accumulation of the Multiple Grain Energy Cultures for Wasterwater Deposition on Oil-Polluted Degraded Soils. Ecol Eng Environ Technol 22:. https://doi.org/10.12912/27197050/137873

Madhav S, Ahamad A, Singh AK et al (2020) Water Pollutants: Sources and Impact on the Environment and Human Health. In: Pooja D, Kumar P, Singh P, Patil S (eds) Sensors in Water Pollutants Monitoring: Role of Material. Springer Singapore, Singapore, pp 43–62

Chapter   Google Scholar  

Meireles I, Sousa V, Bleys B, Poncelet B (2022) Domestic hot water consumption pattern: Relation with total water consumption and air temperature. Renew Sustain Energy Rev 157:112035. https://doi.org/10.1016/j.rser.2021.112035

Miche H, Saracco G, Mayer A, et al (2017) Middle Atlas Causses and the Sais basin (Morocco): implications of groundwater circulation. https://doi.org/10.1007/s10040-017-1675-0

Moeys J (2018) The soil texture wizard: R functions for plotting, classifying, transforming and exploring soil texture data. CRAN R-Project, pp 1–104

Morán-Ramírez J, Ledesma-Ruiz R, Mahlknecht J, Ramos-Leal JA (2016) Rock–water interactions and pollution processes in the volcanic aquifer system of Guadalajara, Mexico, using inverse geochemical modeling. Appl Geochem 68:79–94. https://doi.org/10.1016/j.apgeochem.2016.03.008

Mostafa MG, Uddin SMH, Haque ABMH (2017) Assessment of hydro-geochemistry and groundwater quality of Rajshahi City in Bangladesh. Appl Water Sci 7:4663–4671. https://doi.org/10.1007/s13201-017-0629-y

Moussa AB, Chahlaoui A, Rour H (2012) Évaluation de la pollution physico-chimique des eaux de l’Oued Khoumane (Moulay Idriss Zerhoun, Maroc). Int J Biol Chem Sci 6:7096–7111. https://doi.org/10.4314/ijbcs.v6i6.44

Mustapha A, Abdu A (2012) Application of principal component analysis & multiple regression models in surface water quality assessment. J Environ Earth Sci 2:16–23

Oketola AA, Adekolurejo SM, Osibanjo O (2013) Water quality assessment of River Ogun using multivariate statistical techniques. https://doi.org/10.4236/jep.2013.45055

Randolph B, Troy P (2008) Attitudes to conservation and water consumption. Environ Sci Policy 11:441–455. https://doi.org/10.1016/j.envsci.2008.03.003

Redwan M, Moneim AAA (2016) Factors controlling groundwater hydrogeochemistry in the area west of Tahta, Sohag, Upper Egypt. J Afr Earth Sc 118:328–338. https://doi.org/10.1016/j.jafrearsci.2015.10.002

Samir A (2016) Hydrodynamisme, hydrogéochimie et vulnérabilité de la nappe d’eau superficielle et leur relation avec la tectonique cassante dans la zone effondrée Timahdite–Almis Guigou (Moyen Atlas, Maroc). Université Sidi Mohammed Ben Abdellah -Fès. http://toubkal.imist.ma/handle/123456789/11035

Sandu M, Mosanu E, Gladchi V et al (2010) Study of springs water quality as sources of potable water and for irrigation in rezina district. Chem J Mold 5:84–89

Sehgal M, Garg A, Suresh R, Dagar P (2012) Heavy metal contamination in the Delhi segment of Yamuna basin. Environ Monit Assess 184:1181–1196. https://doi.org/10.1007/s10661-011-2031-9

Singh G, Kamal RK (2017) Heavy metal contamination and its indexing approach for groundwater of Goa mining region, India. Appl Water Sci 7:1479–1485. https://doi.org/10.1007/s13201-016-0430-3

Soetan KO, Olaiya CO, Oyewole OE (2010) The importance of mineral elements for humans, domestic animals and plants: A review. Afr J Food Sci 4:200–222

Sutadian AD, Muttil N, Yilmaz AG, Perera BJC (2016) Development of river water quality indices—a review. Environ Monit Assess 188:58. https://doi.org/10.1007/s10661-015-5050-0

Tirkey P, Bhattacharya T, Chakraborty S, Baraik S (2017) Assessment of groundwater quality and associated health risks: a case study of Ranchi city, Jharkhand, India. Groundw Sustain Dev 5:85–100. https://doi.org/10.1016/j.gsd.2017.05.002

Uddin MG, Nash S, Rahman A, Olbert AI (2022) A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Res 219:118532. https://doi.org/10.1016/j.watres.2022.118532

Voutsis N, Kelepertzis E, Tziritis E, Kelepertsis A (2015) Assessing the hydrogeochemistry of groundwaters in ophiolite areas of Euboea Island, Greece, using multivariate statistical methods. J Geochem Explor 159:79–92. https://doi.org/10.1016/j.gexplo.2015.08.007

Wagh VM, Panaskar DB, Muley AA et al (2016) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India. Model Earth Syst Environ 2:1–10. https://doi.org/10.1007/s40808-016-0250-3

Wilcox LV (1948) The quality of water for irrigation use. Agronomist, Division of Soil Management and Irrigation, Bureau of Plant Industry, Soils, and Agricultural Engineering, Agricultural Research Administration. Technical Bulletin No. 962. U. S. Department of Agriculture

Wilcox Lv (1955) Classification and use of irrigation waters. Water Conservation Research Branch, Agricultural Research Service. Technical Bulletin No. 969. U.S. Department of Agriculture

Yıldız S, Karakuş CB (2020) Estimation of irrigation water quality index with development of an optimum model: a case study. Environ Dev Sustain 22:4771–4786. https://doi.org/10.1007/s10668-019-00405-5

Zaman M, Shahid SA, Heng L et al (2018) Irrigation water quality. Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques 113–131. International Atomic Energy Agency. Springer Nature Switzerland AG 11, p 6330. https://doi.org/10.1007/978-3-319-96190-3

Download references

Acknowledgements

The authors would like to thank the Thematic Project 4, Integrated Water Resources Management of the institutional university cooperation, and VLIR-UOS for the financial support, equipment, and mission in Belgium.

This research received no external funding.

Author information

Authors and affiliations.

Geoengineering and Environment Laboratory, Research Group “Water Sciences and Environment Engineering”, Geology Department, Faculty of Sciences, Moulay Ismail University, Presidency, Marjane 2, Meknes, BP 298, Morocco

Abdennabi Alitane, Ali Essahlaoui, Habiba Ousmana, Narjisse Essahlaoui, Abdellah Oumou & Abdellah El Hmaidi

Water and Climate Department, Vrije Universiteit Brussels (VUB), Brussels, 1050, Belgium

Abdennabi Alitane, Estifanos Addisu Yimer & Ann Van Griensven

Biology, Environment and Health, Department of Biology, Errachidia Faculty of Science and Technology, University of Moulay Ismaïl, Meknes, Morocco

Said Benyoussef

Laboratory of Applied Sciences, National School of Applied Sciences, Abdelmalek Essaadi University, Al Hoceima, 32003, Morocco

Water Resources and Ecosystems Department, IHE Delft Institute for Water Education, Delft, Netherlands

Ann Van Griensven

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Abdennabi Alitane .

Ethics declarations

Competing interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Alitane, A., Essahlaoui, A., Yimer, E.A. et al. Hydrogeochemical characterization and statistical approach to assess the quality of the spring water in the Meknes-El Hajeb region, Morocco. Model. Earth Syst. Environ. (2024). https://doi.org/10.1007/s40808-024-02109-w

Download citation

Received : 28 June 2024

Accepted : 13 July 2024

Published : 28 August 2024

DOI : https://doi.org/10.1007/s40808-024-02109-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Hydrochemical
• Statistical analysis
• Meknes-El Hajeb region
• Springs water
• Find a journal
• Publish with us
• Track your research