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The widespread and unjust drinking water and clean water crisis in the United States

J. tom mueller.

1 Department of Sociology, Social Work, and Anthropology, Utah State University, Logan, UT USA

Stephen Gasteyer

2 Department of Sociology, Michigan State University, East Lansing, MI USA

Associated Data

The raw and geolocated datasets are publicly available on the Open Science Framework project for this study at 10.17605/OSF.IO/ZPQR9 ( https://osf.io/zpqr9/ ).

Analysis code is available on the Open Science Framework project for this study at 10.17605/OSF.IO/ZPQR9 ( https://osf.io/zpqr9/ ). As the raw data was not geolocated using a code-based operation, code for this portion of the analysis is not available. However, the raw data is posted, and should researchers wish they will be able to use our description provided here to replicate geolocation using the GIS software of their choice. All other elements of the analysis are easily replicated via our provided code. As the both the raw and geolocated datasets are provided, replication of our analysis should be straightforward.

Many households in the United States face issues of incomplete plumbing and poor water quality. Prior scholarship on this issue has focused on one dimension of water hardship at a time, leaving the full picture incomplete. Here we begin to complete this picture by documenting incomplete plumbing and poor drinking water quality for the entire United States, as well as poor wastewater quality for the 39 states and territories where data is reliable. In doing so, we find evidence of a regionally-clustered, socially unequal household water crisis. Using data from the American Community Survey and the Environmental Protection Agency, we show there are 489,836 households lacking complete plumbing, 1,165 community water systems in Safe Drinking Water Act Serious Violation, and 9,457 Clean Water Act permittees in Significant Noncompliance. Further, elevated levels of water hardship are associated with rurality, poverty, indigeneity, education, and age—representing a nationwide environmental injustice.

Proper water and sanitation access remains an issue for many in the United States. Here the authors estimate and map the full scope of water hardship, including both incomplete plumbing and water quality across the country.

Introduction

Both in and out of the country, most presume that residents of the United States live with close to universal access to potable water and sanitation. The United Nations Sustainable Development Goals Tracker, which tracks progress toward meeting Sustainable Development Goal Number 6—calling for universal access to potable water and sanitation for all by 2030—estimates that 99.2% of the US population has continuous access to potable water and 88.9% has access to sanitation 1 . By percentages and the lived experience of most Americans, this appears accurate. The American Community Survey shows that from 2014 to 2018 only an estimated 0.41% of occupied US households lacked access to complete plumbing—meaning access to hot and cold water, a sink with a faucet, and a bath or shower. Although this relative percentage may be low, this 0.41% corresponds to 489,836 households spread unevenly across the country, making the absolute number quite troubling. These numbers become even more dramatic when we broaden our scope to poor household water quality, where the estimates we provide in this paper show the issue affects a far greater share of the population (Table  1 ).

Estimates of water hardship in the United States.

Note: total number of counties varies due to some counties having no reporting utilities and the dropping of 13 states with Clean Water Act data issues.

This study builds on a growing body of evidence showing access to plumbing, water quality, and basic sanitation are lacking for a disturbingly large number of US residents by providing a definitive picture of the ongoing household water crisis in the United States. Water and sanitation issues have been a growing concern in the United States, particularly among policy organizations, for the past 20 years 2 – 10 . For example, the now-dated Still Living without the Basics report used Census data from 2000 to show that more than 670,000 households (0.64% of households and 1.7 million people) lacked access to complete plumbing facilities 7 . Further, the Water Infrastructure Network published a report in 2004 citing a gap of $23 billion between available funding and needed water and sanitation infrastructure investments 6 . In line with this, the American Society of Civil Engineers has repeatedly given the United States a “D” grade for water infrastructure, and “D-” for wastewater infrastructure in their annual “Infrastructure Report Card” 11 . Although water hardship in the United States has experienced some academic attention, much of the work has become dated and has generally focused on a single dimension of the issue at a time—for example, recent scholarship has focused on exclusively incomplete plumbing 3 , 4 , 9 , water quality 5 , 10 , or on only urban parts of the country 2 . This has left our understanding of the scope of the issue incomplete. In this paper, we estimate and map the full scope of water hardship for the dimensions of incomplete plumbing and poor drinking water quality across the entire United States, while also estimating and mapping the scope of poor wastewater quality for the 39 states where EPA data is reliable, in order to complete this picture.

Prior work from academics and policy groups on dimensions of water hardship has found water access issues pattern along common social inequalities in the United States. The Natural Resources Defense Council released a report demonstrating the disproportionate impact on people of color posed by Safe Drinking Water and Clean Water Act regulatory burdens 12 , which built on similar peer reviewed findings 13 , 14 . Furthermore, both policy papers and peer reviewed studies have analyzed Census data to estimate the population lacking access to complete plumbing facilities and clean water 2 – 10 , 12 . The studies suggest low-income and non-White people—particularly indigenous populations who continue to face injustices related to legacies of settler colonialism 15 —are significantly more likely to have incomplete plumbing and unclean water 3 , 12 . Further, it appears incomplete plumbing may be a disproportionately rural issue, while poor water quality may be a disproportionately urban issue 5 , 9 . Direct comparisons, as we perform here, are needed to fully establish the variability of this inequality between dimensions of water hardship.

The prior scholarship on the inequitable distribution of plumbing and pollution speaks to the well-documented environmental injustices found throughout the United States. Environmental injustice, meaning the absence of “fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies” (p. 558) 16 , has been documented in the United States along the social dimensions of income 17 , 18 , poverty 19 , race and ethnicity 20 , 21 , age 22 , education 22 , 23 , and rurality 22 , 24 , 25 . Based on the evidence of prior work on water hardship, it is clear household water access represents an ongoing environmental injustice in the United States 5 . However, the specific dimensions of this injustice, and how they vary between type of water hardship remain largely unknown. To address this gap, we estimate models of water injustice for the previously identified social dimensions at the county level for elevated levels of both incomplete plumbing and poor water quality.

Level of water hardship in the United States

Based upon the most recent available data reported by both the United States Census Bureau via the American Community Survey and the Environmental Protection Agency via Enforcement and Compliance History Online, we find that incomplete plumbing and poor water quality affects millions of Americans as of 2014–2018 and August 2020, respectively (Table  1 ) 26 , 27 . A total of 0.41% of households, or 489,836 households, lacked complete plumbing from 2014–2018 in the United States. Further, 509 counties, representing over 13 million Americans, have an elevated level of the issue where >1% of household do not have complete indoor plumbing (Table  2 ). Thus, even if individuals are not experiencing the issue themselves, they may live in a community where incomplete plumbing is a serious issue.

Estimates of elevated levels of water hardship in the United States.

The portion of the population affected by poor water quality is much greater than that of incomplete plumbing. Poor water quality in our analysis is indicated in two ways, (1) Safe Drinking Water Act Serious Violators and (2) Clean Water Act Significant Noncompliance. For the first, community water systems are regulated under the Safe Drinking Water Act and are scored based on their violation and compliance history, those community water systems that are the most problematic are recorded as Serious Violators by the Environmental Protection Agency 27 . Second, any facility that discharges directly into waters in the United States is issued a Clean Water Act permit. Those which “hold a more severe level of environmental threat” are ruled as being in Significant Noncompliance 27 . Importantly, although data on Safe Drinking Water Act Serious Violators is available nationwide, the Clean Water Act data reported by the EPA is known to be inaccurate for 13 states. Thus, although we can draw national conclusions for incomplete plumbing and Safe Drinking Water Act violations, our understanding of Clean Water Act violations is limited to the 39 states and territories for which data are available and reliable.

Using these two measures of poor water quality, we find 2.44% of community water systems, a total of 1165, were Safe Drinking Water Act Serious Violators and 3.37% of Clean Water Act permittees in the 39 states and territories with accurate data (see Methods for more details), a total of 9457, were in Significant Noncompliance as of 18 August 2020. At the county level, this corresponds to an average of 2.86% of county community water systems being listed as Safe Drinking Water Act Significant Violators and an average of 6.23% of county Clean Water Act permittees being listed as Significant Noncompliers. Due to limitations in the data, we are unable to determine exactly how many individuals are linked to each problematic community water system or Clean Water Act permittee, however, we do find that over 81 million Americans live in counties where >1% of community water systems are listed as Significant Violators, and more than 153 million Americans in the 39 reliable states and territories live in counties where greater than one percent of Clean Water Act permittees are Significant Noncompliers. Thus, although the number of individuals impacted by these issues is certainly far smaller than these totals, a vast number of Americans live in communities where issues of water quality are elevated.

Due to our conservative approach of removing all states with Clean Water Act data issues, we test the sensitivity of our estimates by also calculating supplemental estimates of Clean Water Act Significant Noncompliance under two counterfactual scenarios. In the first, we include the data as-is from the EPA for all counties in the 50 states, DC, and Puerto Rico, and in the second, we duplicate the counties in the top and bottom 20% of Significant Noncompliance in states without data issues—with the rationale being that the 945 counties removed due to poor data represented roughly 40% of the total counties remaining when problems states were removed. Thus, this attempts to simulate total counts if those removed were balanced between very high and very low levels of noncompliance. Results using all EPA data increase national estimates of Significant Noncompliance (Tables ​ (Tables3 3 and ​ and4), 4 ), with the total percent of permittees in this status jumping from 3.37% to 6.01%. While the duplication test does raise our estimates, it is not nearly as dramatic, with the percent of permittees in Significant Noncompliance only rising to 3.87%. These results make sense given that the most common reason for data issues was an overreporting of noncompliance within states.

When looking at the issue spatially, we can see that while water hardship affects all parts of the country to some degree, the issues are clustered in space (Figs.  1 – 3 ). Importantly, the clustering varies between each water issue. Incomplete plumbing is clustered in the Four Corners, Alaska, Puerto Rico, the borderlands of Texas, and parts of Appalachia (Fig.  1 ); Safe Drinking Water Act Serious Violators are clustered in Appalachia, New Mexico, Alaska, Puerto Rico, and the Northern Intermountain West (Fig.  2 ); and Clean Water Act Significant Noncompliance clearly follows state boundaries—likely speaking to variable monitoring by state. Although spatial representation is limited by the absence of 13 states with inaccurate EPA data, we can still see that Clean Water Act Significant Noncompliance is clustered in the Intermountain West, the Upper Midwest, Appalachia, and the lower Mississippi (Fig. ​ (Fig.3). 3 ). These regional clusters persist when we include the problem states, which is visible in the map included in the Supplemental Information (Supplementary Figure 1 ).

Households are determined to have incomplete plumbing if they do not have access to hot and cold water, a sink with a faucet, a bath or shower, and—up until 2016—a flush toilet.

Safe Drinking Water Act Serious Violators are those community water systems regarded by the Environmental Protection Agency as the most problematic due to violation and compliance history.

All facilities that discharge directly into water of the United States are issued a Clean Water Act permit, those who represent a more severe level of environmental threat due to violations and noncompliance are considered in Significant Noncompliance.

Water injustice modeling

Although we can easily see clustering by space in Figs.  1 through ​ through3, 3 , the maps do not tell us whether or not incomplete plumbing and poor water quality are also clustered by social dimensions, which would represent an environmental injustice. To assess this social clustering, we estimate linear probability models of elevated levels of incomplete plumbing and poor water quality with the previously identified environmental justice dimensions of age, income, poverty, race, ethnicity, education, and rurality as our independent variables. We include these independent variables due to their prevalence within prior work on environmental injustice in both rural and urban areas 17 – 25 . Further, although there is not a one-to-one overlap, these variables conceptually map onto the dimensions of the Center for Disease Control Social Vulnerability Index: Socioeconomic Status (i.e. income, poverty, education), Household Composition & Disability (i.e. age), Minority Status & Language (i.e. race and ethnicity), and Housing & Transportation (i.e. rurality) 28 .

For each outcome, we first estimate purely descriptive models with only one dimension of injustice included at a time, and then estimate a full model with all dimensions included. The outcomes are dichotomous measures of whether or not a county had >1% of households with incomplete plumbing, >1% of community water systems listed as Serious Violators, or >1% of Clean Water Act permittees in Significant Noncompliance. All descriptive statistics for the dichotomous outcomes are presented in Table ​ Table2. 2 . Descriptive statistics for the continuous independent variables are presented in Supplementary Information (Supplementary Table  1 ). Here we present the outcomes of the purely descriptive models visually in Fig.  4 and discuss the full models in the narrative. Full regression results, including exact 95% confidence intervals and p -values, for all models are available in Supplementary Information (Supplementary Tables  2 , 3 and 4 ).

Different colors for plotted coefficients represent separate blocks of variables. Models are linear probability models with state fixed effects and cluster-robust standard errors at the state level. All tests two-tailed. Dots indicate point estimates and lines represent 95% confidence intervals. Models predicted elevated levels of each dimension of water hardship. For incomplete plumbing this is indicated by >1% of households in a county having incomplete plumbing ( N  = 3219). For Safe Drinking Water Act (SDWA) Serious Violation this is indicated by >1% of active community water systems being considered Serious Violators ( N  = 3143). For Clean Water Act (CWA) Significant Non-Compliance this is indicated by >1% of Clean Water Act permittees being considered in Significant Non-Compliance ( N  = 2261). Full model results, confidence intervals, and exact p -values available in SI.

We find elevated levels of incomplete plumbing at the county level were significantly ( p  < 0.05) associated with older populations, lower income, higher poverty, greater portions of indigenous people (American Indian, Alaska Natives, Native Hawaiian, and Other Pacific Islanders), lower levels of education, and more rural counties (Fig.  4 ). A great deal of these associations persisted in a full model with all dimensions of injustice (Supplementary Table  2 ). The only differences between the full model and the series of purely descriptive models were that income, percent with at least a bachelor’s degree, and non-metropolitan metropolitan adjacency were no longer significantly associated with elevated levels of incomplete plumbing. This indicates that the inequalities in plumbing access along the dimensions of age, poverty, indigeneity, low education, and extreme rurality persist at the county level, even when accounting for the other dimensions of environmental injustice.

The models for elevated levels of Safe Drinking Water Act Serious Violators indicated less social inequality than the models for incomplete plumbing. The purely descriptive models found elevated levels of Serious Violators were associated with higher income, higher poverty, and metropolitan counties (Fig.  4 ). The full model had minor variation, with median household income no longer being significant in the model (Supplementary Table  3 ). Thus, the full model shows that the association between elevated levels of Serious Violators and higher poverty and metropolitan status persists even when considering other social dimensions.

We see the fewest indicators of water injustice for elevated levels of Clean Water Act Significant Noncompliance—which only include counties within the 39 states and territories with accurate data. In the purely descriptive models, we find older populations, more Latino/a counties, less educated counties, and remote rural counties were significant less likely to have elevated levels of noncompliance (Fig. ​ (Fig.4). 4 ). In the full model, the association for education is no longer significant but age, Latino/a, and rurality remain (Supplementary Table 4 ). Similar to our national estimates, we also conducted model sensitivity tests using the same scenarios described above. As shown in Fig. ​ Fig.5, 5 , neither scenario substantively changes our conclusions, with the only changes in significance being for percent Latino/a and percent without a high school diploma—both of which were only marginally significant in our primary models ( p  > 0.01).

Descriptive regression model results. Different colors for plotted coefficients represent separate blocks of variables. Models are linear probability models with state fixed effects and Huber/White/Sandwich cluster-robust standard errors at the state level. All tests are two-tailed. Dots indicate point estimates and lines represent 95% confidence intervals. Models predicted whether or not there were greater than 1% of Clean Water Act permittees being considered in Significant Noncompliance in the county. First model excludes counties in states with CWA data issues ( N  = 2261), second model includes all counties reported by the EPA ( N  = 3206), third model duplicates counties in the top and bottom 10% of CWA Significant Noncompliance within states without data issues ( N  = 3151). Full model results, confidence intervals, and exact p values available in SI.

Our findings demonstrate that the problem of water hardship in the United States is hidden, but not rare. Indeed, millions live in counties where more than 1 out of 100 occupied households lack complete plumbing. Millions more live in places with chronic Safe Drinking Water Act violations and Clean Water Act noncompliance. We present this paper to help sound the alarm of this significant household water crisis in the United States. Although the relative share of Americans experiencing this problem is low, the absolute number of people dealing with incomplete plumbing—a total of 489,836 households—and poor water quality—1165 community water systems nationwide and 9457 Clean Water Act permittees in the 39 accurate states and territories—remains quite high. Further, given the water infrastructure of the United States, consistently deemed as poor by experts 6 , 11 , if action is not taken the situation may only get worse.

These findings are even more concerning when considering that water hardship is spread unevenly across both space and society, reflecting the spatial patterning of social inequality due to settler colonialism, racism, and economic inequality in the United States. Figures  1 , ​ ,2, 2 , and ​ and3 3 document the clear regional clustering of these issues and our models of environmental injustice demonstrate the social inequalities found for this form of hardship. Particularly in the case of incomplete plumbing, we find significant environmental injustice at the county level along the social dimensions of age, income, poverty, indigeneity, education, and rurality. These associations certainly stem from multiple causal pathways—for example associations with indigeneity likely stem from legacies of injustice as well as ongoing policies placing limitations on land use and infrastructure development on American Indian reservations 15 . Remedying these injustices will require careful attention to the root causes of the problem. It is important to note that the signs of injustice for poor water quality were less clear than for incomplete plumbing, with far fewer significant associations. Further, the minimal support for injustice in the case of Clean Water Act Significant Noncompliance was evident in all three specifications of counties in our sensitivity tests. Suggesting that the removal of the states with data issues did little to impact coefficient estimates. These differences between dimensions of water hardship highlight the nuance between each of these specific forms of water hardship, and suggest a one-size-fits-all approach to remedying this crisis is unlikely to be effective. This need for place-based policy is made stark when we view the obvious state level differences in Clean Water Act Significant Noncompliance in Fig. ​ Fig.3. 3 . A clear direction for future work is to investigate the cause of these notable state-level differences.

The household water access and quality crisis we have identified here is solvable. Policy is needed to specifically address these issues and bring this problem into the spotlight. However, as indicated by the persistently high levels of Safe Drinking Water Act Serious Violation and Clean Water Act Significant Noncompliance, any policy put in place must be enforceable and strong. As it currently stands, counties with elevated levels of incomplete plumbing and poor water quality in America—which are variously likely to be more indigenous, less educated, older, and poorer—are continuing to slip through the cracks.

Data sources

Data for this analysis were extracted from the American Community Survey (ACS) 5-year estimates for 2014–2018 via Integrated Public Use Microdata Series – National Historic Geographic information System (IPUMS-NHGIS) 26 , and from the Environmental Protection Agency’s (EPA) Enforcement and Compliance History Online (ECHO) Exporter 27 . Data were extracted at the county level for all 50 states, Washington DC, and Puerto Rico–the two non-state entities with available data. The ACS is an ongoing survey of the United States which documents a wide variety of social statistics ranging from simple population counts to housing characteristics. Due to the staggered sampling structure of the ACS, it takes 5 years for every county to be sampled. Because of this, researchers must use 5-year intervals to ensure complete data coverage. The data from these 5 years are projected into estimates for all counties in the United States for the 5-year period in question. As of this study, 2014–2018 was the most recently available data.

ECHO collates data from EPA-regulated facilities across the United States of America to report compliance, violation, and penalty information for all facilities for the most recent 5-year interval. ECHO data is updated weekly and the data for this paper was extracted on 18 August 2020. This means that the data in our analysis represents the status of each community water system or Clean Water Act permittee, as reported by the EPA, as of 18 August 2020. Only those community water systems or Clean Water Act permittees listed as Active by ECHO were included in this analysis. As ECHO data is at the level of the water system, permittee, or utility, we aggregated data up to the county level.

Safe Drinking Water Act data was geolocated using QGIS 3.10 based upon latitude and longitude. This was done because other geographic identifiers for the Safe Drinking Water Act data were often missing. In line with prior work 4 , 5 , 7 , 8 , and in order to facilitate a cleaner dataset, we only focus on those water systems labeled community water systems for our analysis. Community water systems were geolocated based upon the county in which their latitude and longitude were located, if a community water system had latitude and longitude over water, a nearest neighbor join was used. In total, 1334 out of 49,479 community water systems were dropped because of there being no reported latitude or longitude. Of these, a total of 4.0%, or 54 community waters systems, were reported as in serious violation. It should be noted that the EPA is aware of a small number of water systems in Washington for which ECHO data may be inaccurate. However, since this is a small number and it is not listed as a ‘Primary Data Alert,’ we retain all states in this portion of the analysis. Finally, the EPA is generally aware that there are “inaccuracies and underreporting of some data in this system,” which is listed as a Primary Data Alert 27 . However, due to the lack of specifics, we cannot exclude inaccurate cases. Thus, our analysis should be viewed as reflecting drinking water quality is as reported by the EPA in August of 2020, which may reflect some level of inaccuracy.

Active Clean Water Act permittees were first identified by listed county. This was done because 345,176 out of 350,476 permittees had a county reported. Those without a county reported were located using latitude and longitude in the same manner as community water systems. There were 10 permittees without latitude and longitude or county listed which were excluded from our analysis. Of these, seven were in significant noncompliance and three were not. Due to some Clean Water Act permittees having latitude and longitude placements far away from the United States, those over 100 km from their nearest county were excluded from analysis. Unfortunately, ECHO data for the Clean Water Act data during the study period is inaccurate for 13 states. Although the nature of the inaccuracy varies from state to state, these issues generally stem from difficulties in transferring state data into the federal system. Due to this, these states appear to have far more permittees in Significant Noncompliance than are actually in violation. To address this issue, we removed all counties within these states from our Clean Water Act analysis. The impacted states include Iowa, Kansas, Michigan, Missouri, Nebraska, North Carolina, Ohio, Pennsylvania, Vermont, Washington, West Virginia, Wisconsin, and Wyoming 29 . Finally, for community water systems and Clean Water Act permittees, some counties (76 for community water systems and 5 for Clean Water Act permittees) had no reported cases. Those counties were treated as zeroes for cartography and as missing for modeling purposes.

Similar to prior work in this area 4 , 5 , 8 , we restrict our analysis to the scale of the county for reasons related to data limitations and resulting conceptual validity. Although counties are arguably larger in geographic area than ideal for an environmental injustice analysis, if we were to use a smaller unit for which data is available such as the census tract, the conceptual validity of the analysis would be limited due to the apolitical nature of these units. As outlined above, ECHO data is messy and missing many geographic identifiers. What is provided is generally either the county or latitude and longitude. If only the county is provided, then we are constrained to using the county regardless of conceptual validity. However, even when latitude and longitude are provided—which is the case for many observations—the provided point location says nothing about which households the water system or permittee serves or impacts. Due to this, whatever geographic unit we use carries the assumption that those in the unit could be plausibly impacted by the water system or permittee. Given that counties are often responsible for both regulating drinking water, as well as maintaining and providing water infrastructure 30 , we were comfortable with this assumption between point location and presumed spatial impact when using the scale of the county. However, we believe this assumption would have been invalid and untestable for smaller apolitical units for which demographic data is available such as census tracts.

Beyond the issues presented by ECHO data, the county is also the appropriate scale of analysis for this study due to the estimate-based nature of the ACS. ACS estimates are based on a rolling 5-year sample structure and often have very large margins of error. At the census tract level, these standard errors can be massive, especially in rural areas 31 – 33 . Due to this variation, and the need to include all rural areas in this analysis, the county, where the margins of error are considerably smaller, is the appropriate unit for this study. All of this said, the county is, in fact, a larger unit than often desired or used in environmental justice studies. Studies focused on exclusively urban areas with clearer pathways of impact can and should use smaller units such as census tracts. It will be imperative for future scholarship focused on water hardship across the rural-urban continuum to gain access to reliable data on sub-county political units, as well as data linking water systems to users, to continue documenting and pushing for water justice.

Dependent variables

The dependent variables for this analysis were assessed in both a continuous and dichotomous format. For descriptive results and mapping, continuous measures were used. For models of water injustice, a dichotomous measure which classified counties as either having low levels of the specific water issue or elevated levels of the specific water issue, was used due to the low relative frequency of water access and quality issues relative to the whole United States population. For all three outcomes, we benchmark an elevated level of the issue as what would be viewed as an unacceptable level under United Nations Sustainable Development Goal 6.1, which states, “by 2030 achieve universal and equitable access to safe and affordable drinking water for all” 1 . As this goal focuses on ensuring all people have safe water, we deem a county as having an elevated level of the issue if >1% of households, community water systems, or permittees had incomplete plumbing, were in Significant Violation, or Significant Noncompliance, respectively. Although we could have used an even stricter threshold given the SDG’s emphasis on ensuring access for all people, we use 1% as our cut-off due to its nominal value and ease of interpretation.

For water access, the continuous measure was the percent of households in a county with incomplete household plumbing as reported by the ACS. The ACS currently asks respondents if they have access to hot and cold water, a sink with a faucet, and a bath or shower. Up until 2016, the question also included a flush toilet 34 . As we must use the most recent 2014–2018 5-year estimates to establish full coverage of all counties, this means that incomplete plumbing in this item may, or may not include a flush toilet depending on when the specific county was sampled. The dichotomous version of this variable benchmarked elevated levels of incomplete plumbing as whether or not 1% or more of households in a county had incomplete plumbing.

Water quality was assessed via both community water systems from the Safe Drinking Water Act, and from permit data via the Clean Water Act. For Safe Drinking Water Act data, the continuous measure was the percent of community water systems within a county classified as a Safe Drinking Water Act Serious Violator at time of data extraction. The EPA assigns point values of either 1, 5, or 10 based upon the severity of violations of the Safe Drinking Water Act. A Serious Violator is one who has “an aggregate score of at least eleven points as a result of some combination of: unresolved more serious violations (such as maximum contaminant level violations related to acute contaminants), multiple violations (health-based, monitoring and reporting, public notification and/or other violations), and/or continuing violations” 27 . The dichotomous measure benchmarked elevated rates of Safe Drinking Water Act Significant Violation as whether or not >1% of county community water systems were classified as Serious Violators.

For Clean Water Act permit data, the continuous measure was the percent of permit holders listed as in Significant Noncompliance at the time of data extraction. Significant Noncompliance in the Clean Water Act refers to those permit holders who may pose a “more severe level of environmental threat” and is based upon both pollution levels and reporting compliance 27 . The dichotomous measure again set the threshold for elevated levels of poor water quality at whether or not >1% of Clean Water Act permittees in a county were listed as in Significant Noncompliance at time of data extraction.

Independent variables

The independent variables we include in models of water injustice are those frequently shown to be related to environmental injustice in the United States. These include age, income, poverty, race, ethnicity, education, and rurality 17 – 25 . Age was included as median age. Income was included as median household income. Poverty was the poverty rate of the county as determined by the official poverty measure of the United States 35 . Race and ethnicity was included as percent non-Latino/a Black, percent non-Latino/a indigenous, and percent Latino/a. Because the focus was on indigeneity, percent American Indian or Alaska Native was collapsed with Native Hawaiian or Other Pacific Islander. We did not include percent non-Latino/a white due to issues of multicollinearity. Finally, rurality was included as a three-category county indicator of metropolitan, non-metropolitan metropolitan-adjacent, and non-metropolitan remote, as determined by the Office of Management and Budget in 2010 36 . The OMB determines a county is metropolitan if it has a core urban area of 50,000 or more people, or is connected to a core metropolitan county by a 25% or greater share of commuting 36 . A non-metropolitan county is simply any county not classified as metropolitan. Non-metropolitan metropolitan adjacent counties are those which immediately border a metropolitan county, and non-metropolitan remote counties are those that do not.

Water injustice modeling approach

Water injustice was assessed by estimating linear probability models for the three dichotomous outcome variables with state fixed effects to control for the visible state level heterogeneity and differences in policy, reporting, and enforcement (e.g. the clear state boundary effects in Fig.  3 ). We employ the conventional Huber/White/Sandwich cluster-robust standard errors at the state level—which account for heteroskedasticity while also producing a consistent standard error estimate in-light of the lack of independence found between counties in the same state. All modeling was performed in Stata 16.0 and mapping was performed in QGIS 3.10. We assessed all full models for multicollinearity via condition index and VIF values and the independent variables had an acceptable condition index of 5.48 for incomplete plumbing and Safe Drinking Water Act models and 5.63 for Clean Water Act models, well below the conservative cut-off of 15, as well as VIF values of <10. We initially included percent non-Latino/a white as an independent variable, but removed the item due to unacceptably high condition index levels (>20). All indications of statistical significance are at the p  < 0.05 level and 95% confidence intervals and exact p -values of all estimates are provided in Supplementary Information. Each dependent variable was analyzed through a series of six models. First, we estimated separate purely descriptive models, where the only independent variables included were those associated with that specific dimension and the state fixed effects, for all five dimensions of environmental injustice. After estimating these five models, we estimated a full model including all social dimensions at once.

The reason for this approach was to ensure that we provided a robust descriptive understanding of the on-the-ground social patterns of water hardship, in addition to a full model showing the strongest social correlates of this issue. For example, if when we only included income variables we found that incomplete plumbing is less likely in counties with higher median incomes, but this effect goes away when we include other social variables, this does not remove the fact that there is an unequal distribution of incomplete plumbing by income on-the-ground. All that it means is that this income effect does not persist over and above the other social dimensions of environmental injustice. It may be that once other dimensions such as structural racism, captured by race and ethnicity variables, are considered, income is no longer a significant predictor. However, at a pure associational level, incomplete plumbing would still be unequally distributed by income on-the-ground. In fact, this is exactly what we find for incomplete plumbing (Supplementary Table  2 ). Due to this, both the pure descriptive and full models are needed for full understanding. Complete tables of all results are presented in the Supplementary Information File (Supplementary Tables  1 through 4 ).

Sensitivity tests

Due to our conservative approach to remove all problem states from the Clean Water Act portion of our analysis, we conducted a series of sensitivity tests wherein we generated national estimates of Significant Noncompliance, as well as models of elevated Significant Noncompliance under two scenarios (Supplementary Tables 5 and 6 ). In the first scenario we include all data reported by the EPA, meaning that we use all data for the 50 states, DC, and Puerto Rico, regardless of any EPA data flags. In the second scenario, we replaced the data lost when dropping states by duplicating the counties in the top and bottom 20% of significant violations in the remaining counties. The top and bottom 20% was chosen because the 945 counties removed when the 13 states were dropped was roughly equal to 40% of the remaining 2262 counties. This counterfactual allows us to get closer to a plausible estimate of the absolute scope of CWA Significant Noncompliance by adopting a scenario where the counties dropped in problem states were either very high, or very low in terms of Significant Noncompliance. Functionally, duplicating the bottom 20% posed a challenge because the bottom 30% of counties had zero permittees in Significant Noncompliance. This zero-bias is one of the primary reasons why our outcome variable was dichotomized. To address this, we randomly selected two-thirds of these counties for duplication using a seeded pseudorandom number generator in Stata. Following duplication of cases, all estimates and models were generated in the same manner as the primary models of this study.

Reporting summary

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

Supplementary information

Acknowledgements.

The authors would like to acknowledge Tom Dietz, Lauren Mullenbach, Matthew Brooks, and Jan Beecher for their feedback on this manuscript. They would also like to thank Colleen Keltz at the Washington State Department of Ecology for alerting us to the issues with Clean Water Act data for Washington and other states.

Author contributions

Conceptualization: J.T.M. and S.G.; methodology: J.T.M.; formal analysis: J.T.M.; data curation: J.T.M.; writing- original draft preparation: J.T.M. and S.G.; writing – review and editing: J.T.M. and S.G.; visualization: J.T.M.

Data availability

Code availability, competing interests.

The authors declare no competing interests.

Peer review information Nature Communications thanks Benjamin Rachunok and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

The online version contains supplementary material available at 10.1038/s41467-021-23898-z.

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Open Access

Improve water quality through meaningful, not just any, citizen science

* E-mail: [email protected]

Affiliation Rathenau Instituut, Royal Netherlands Academy of Arts and Sciences, The Hague, The Netherlands

Affiliation HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

• Anne-Floor M. Schölvinck, 
• Wout Scholten, 
• Paul J. M. Diederen

Published: December 7, 2022

• https://doi.org/10.1371/journal.pwat.0000065
• Reader Comments

Citation: Schölvinck A-FM, Scholten W, Diederen PJM (2022) Improve water quality through meaningful, not just any, citizen science. PLOS Water 1(12): e0000065. https://doi.org/10.1371/journal.pwat.0000065

Editor: Debora Walker, PLOS: Public Library of Science, UNITED STATES

Copyright: © 2022 Schölvinck et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Water pollution is an urgent and complex problem worldwide, with many dire consequences for ecosystems, human health and economic development. Although policy measures in OECD countries have helped to reduce point source pollution, the situation is set to worsen: population growth and climate change are placing increasing pressures on the ability of water bodies to process wastewater, nutrients and contaminants [ 1 ].

For future generations to maintain a sufficient supply of clean drinking water and to retain a vital level of biodiversity, it is critical to involve the general public in dealing with the problems of water quality and water pollution. One specifically important and increasingly prominent way for the general public to get acquainted with water quality issues is through participation in research projects. All around the world numerous citizen science (CS) projects take place in the field of (drinking) water quality, hydrology, groundwater levels, and water biology [ 2 ]. In most cases these projects are motivated by the enormous potential volunteering citizens have to increase the temporal and spatial data availability. We argue that the value of many CS projects lies beyond data availability, in the broader societal benefits that these projects aspire or claim to achieve. In turn, these benefits could improve the way we approach water quality issues. The list of claimed and potential benefits is long: raising awareness, democratisation of science, development of mutual trust, confidence, and respect between scientists, authorities and the public, increased knowledge and scientific literacy, social learning, incorporation of local, traditional and indigenous knowledge, increased social capital, citizen empowerment, behavioural change, improved environment, health and livelihoods, and finally motivational benefits [ 3 ].

Many of these broader societal benefits of public engagement with water research are especially important to battle water related issues worldwide. Increased ‘water awareness’ among the public is needed to encourage a general sense of urgency and hence support for research investments and policy measures. In the Netherlands, like in many other countries, many citizens take safe and clean (drinking) water for granted [ 4 ]. Therefore, people are not sufficiently aware what investments are needed to provide safe tap water and what they themselves should do to reduce domestic water pollution. To truly counter the dangers of deteriorating water quality, water science and policy must be organised more inclusively and democratically.

The potential societal effect of CS in the water quality sector is substantial. In the Netherlands alone, more than 100,000 citizens volunteer as ‘sensors’ or observers in the numerous nature oriented research projects, in which they, for example, count aquatic animals or measure the chemical composition of river water. These projects are generally low-threshold, because the research tasks are relatively simple and adapted to the limited expertise and research skills of the participants. The large-scale and long-term monitoring done by volunteers would be unaffordable if carried out by professionals [ 5 ]. In other CS projects, though smaller in quantity, citizens have a larger degree of control. This is a gradual difference, typically divided in four categories, ranging from contributory (lowest level of control) to collaborative, co-creative and finally collegial [ 6 ]. Alternatively, these levels have been designated crowdsourcing, distributed intelligence, participatory science and extreme citizen science [ 7 ]. We consider all these levels of control as participating in research, even when the volunteers merely function as observers.

Although the potential benefits of citizen involvement with research projects are numerous and the potential societal impact is high, there are two main obstacles that must be overcome. First, the actual effects of these types of projects, other than the well-reported scientific benefits, remain largely unknown [ 3 , 8 , 9 ]. Do participants have an increased understanding of the concerns of water quality researchers? Do they flush fewer medicines down the toilet? Do they avoid using pesticides in their gardens? Moreover, in order to truly raise public awareness and support for policies addressing water quality, it is important to not only get people involved who are already interested in nature, water quality and/or scientific research. The challenge is to have a diverse group of participants and to involve hard-to-reach groups [ 10 ].

Second, the dominant picture of CS projects, in our own Dutch based study as well as all across the world [ 3 ], is that most citizens participate in the collection of research data. Recalling Shirk et al.’s typology of involvement [ 6 ], this can be considered the lowest level of control and participation. Researchers, policy makers and interest groups hope that this type of involvement will generate public support for more scientific research and more effective policy measures to improve water quality, but citizens performing more significant roles in the research process is still uncommon.

From our analysis, we draw three recommendations to overcome these obstacles and to move beyond CS in water research for the sake of research only, in order to make it more meaningful in a broader, societal sense. For a start, we recommend to thoroughly evaluate the effect of citizen science on the attitudes , behaviour and knowledge of participants and on the system as a whole . As mentioned above, and also pointed out by Somerwill & Wehn [ 9 ], ‘the exact impacts of citizen science are still to be fully and comprehensively understood, while up to date impact assessment methods and frameworks are not yet fully integrated in practice’. Since the potential and claimed benefits are substantial, there is a considerable responsibility to prove these effects and to improve CS project designs to stimulate the occurrence of these benefits. Recent work provides the necessary tools to guide professional researchers and citizens to build the right project designs [ 11 , 12 ], integrate working evaluations [ 9 ], and consider several factors for successful CS projects [ 2 ]. It also needs to be established how to include diverse groups of participants, including the ones with a low interest in nature and environmental issues.

Secondly, we recommend to involve participants more intensively in agenda setting and research design . Currently, the threshold to participate in CS projects tends to be fairly low, but so is the level of control and participation. Tasks of citizen scientists are typically limited and so is their sense of project ownership, although the likelihood of actual effects taking place increases with an increased degree of control for participants [ 3 ]. For instance, a number of projects report a rise or restoration of trust in local authorities and research institutions ‘due to the co-production process and the appreciation of local knowledge’ [ 3 , 13 ].

There is ample potential to increase participation to more shared decision-making on the purpose and design of the research. An important step would be to open up the drafting of research agendas to diverse groups of citizens and societal actors. This type of citizen involvement is already common practice in other fields of research. One might look at some research fields within health and healthcare studies as good practices. ‘Nothing about us without us’ has become a guiding principle, also within health research (see one of our other studies, on public engagement in psychiatry research [ 14 ]).

In the Netherlands, it is becoming common practice for experts by experience (current patients, recovered patients, patient associations) to have a seat at the table when funding decisions are made. Funding agencies increasingly demand applicants to demonstrate how they included patients or other experts by experience in the development of their research proposal. Funding agencies also include patient associations in the development of their research and funding agendas. These practices show that more shared-decision making processes are possible. We consider three conditions that are crucial for meaningful involvement: A) leadership and management of funding agencies to actively value and endorse public engagement leading to changes in their modus operandi; B) training and support for participating citizens, experts by experience and other societal stakeholders; C) researchers who do not regard public engagement as just another box to tick, but who truly integrate public engagement in their research design. This also means these researchers should be incentivised to integrate public engagement in their research, which points to necessary changes in the way they are recognised and rewarded [ 15 ].

Lastly, we recommend to employ public involvement as an extra stimulus for the practical application of knowledge . For professional scientists, the participation of volunteers in research has concrete value. They use the inputs to improve data availability, improve data quality and for their publications. For participants, the benefit is less tangible. Often, their only reward is the joy of the experience itself. However, as participants contribute more, there is a risk of exploitation. We emphasise that intrinsic motivations are most important for participants, but these motivations go beyond the joy of the experience, such as learning, environmental concern, making a difference, and social aspects of participation [ 2 , 16 ]. Rewards should fit these main drivers of participants for instance by showing how their engagement makes a difference, and by public acknowledgement for their work. A stronger incentive for participation could be provided by showing how the research contributes to the improvement of the (local) natural environment, water quality and biodiversity. Therefore, researchers should provide the volunteers with feedback about the results of the study to which they contributed. Beyond this act of courtesy, they should derive inspiration from the interaction with societal actors to focus more on the societal impact of their work. Some scholars emphasise how several motivations and effects of CS projects reinforce one another to create a desired upwards spiral (e.g. more knowledge and scientific literacy → more environmental concern → intrinsic motivation to make a difference → greater participation in CS projects → more knowledge and scientific literacy) [ 2 ], [ 3 ]. Professional scientists could and should play an active role in realising these societal effects.

In all, citizen science has great potential in water quality research. In fact, numerous projects already illustrate the value of CS to improve water quality around the world. It may help fight the dire threats of water pollution, by raising water awareness, strengthening public support for research, and ultimately for better policies and changes in behaviour. Yet, to reap success with citizen science fully, it should be purposefully designed for such broader societal goals. Therefore, efforts must be made to get a better understanding of the effects of research participation on volunteers, to involve citizen scientist in research agenda setting and the design of research projects, and to listen to them for the practical application of research results.

This article is based on the Dutch report Scholten W, Schölvinck AFM, Van Ewijk S, Diederen PJM. Open science op de oever–Publieke betrokkenheid bij onderzoek naar waterkwaliteit. The Hague: Rathenau Instituut; 2020. Available from: https://www.rathenau.nl/nl/vitale-kennisecosystemen/open-science-op-de-oever [ 17 ].

• 1. OECD. Diffuse Pollution, Degraded Waters: Emerging Policy Solutions. Paris: OECD; 2017.
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• 4. OECD. Water Governance in the Netherlands: Fit for the future? Paris: OECD; 2014.
• 15. Felt U. “Response-able practices” or “new bureaucracies of virtue”: The challenges of making RRI work in academic environments. In: Asveld L, Van Dam-Mieras R, Swierstra T, Lavrijssen S, Linse K, Van den Hoven J, editors. Responsible Innovation 3: A European Agenda? Cham: Springer; 2017. pp. 49–68.

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Assessment of Drinking Water Quality Using Water Quality Index: A Review

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• Published: 30 January 2023
• Volume 8 , article number  6 , ( 2023 )

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• Atanu Manna 1 &
• Debasish Biswas   ORCID: orcid.org/0000-0001-8747-0934 2  

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Nowadays, declining water quality is a significant concern for the world because of rapid population growth, agricultural and industrial activity enhancement, global warming, and climate change influencing hydrological cycles. Assessing water quality becomes necessary by using a suitable method to reduce the risk of geochemical contaminants. Water’s physical and chemical properties are compared to a standard guideline to determine its quality. The water quality index (WQI) model is a commonly helpful technique for evaluating surface and groundwater quality. The model mainly employs aggregation techniques to diminish large amounts of data to a sole value. The WQI model has been used across the globe to assess ground and surface water using regional standards. The model has become popular for its ease of use and general structure. Typically, WQI models include five stages: (1) choosing water quality indicators, (2) generating sub-parameters for each variable, (3) calculating variable weighting numbers, (4) aggregating sub-parameters to finding the total WQI value, and (5) classification of WQI value to highlight the category of water quality. In addition, the model creates ambiguity when converting vast volumes of data into a single value. The study considered 2011–2021 blinded peer-reviewed articles and book chapters to assess WQI models and their application in evaluating drinking water quality. This study mainly concentrated on the comparison of WQI models and their applications. The study also focused on the selection of parameters and problems associated with the accuracy of the models.

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Manna, A., Biswas, D. Assessment of Drinking Water Quality Using Water Quality Index: A Review. Water Conserv Sci Eng 8 , 6 (2023). https://doi.org/10.1007/s41101-023-00185-0

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Environmental Science: Water Research & Technology

Water quality in drinking water distribution systems: research trends through the 21st century.

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This paper provides new insight into the global landscape of water quality research in drinking water distribution systems and how it has evolved over the first twenty years of the 21st century. An up-to-date bibliometric analysis of relevant literature published between 2000 and 2020 revealed how the research landscape has expanded in terms of number of publications made, variety of topics, and geographic diversity that offers an increasingly inclusive global conversation. Results showed technological, microbial and chemical needs are currently the major research streams that are concentrated on popular topics of simulations, chlorine, biofilms, intrusion and monitoring. However, there is a vast diversity of sub-disciplines related to maintaining water quality, which are highly interconnected. These changing priorities and perspectives offer opportunities for sharing of best practice, identification of research gaps, and interdisciplinary thinking as we all strive to provide consumers with high quality drinking water now and into the future.

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• Published: 21 January 2016

Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia

• Yirdaw Meride 1 &
• Bamlaku Ayenew 1  

Environmental Systems Research volume  5 , Article number:  1 ( 2016 ) Cite this article

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Water is a vital resource for human survival. Safe drinking water is a basic need for good health, and it is also a basic right of humans. The aim of this study was to analysis drinking water quality and its effect on communities residents of Wondo Genet.

The mean turbidity value obtained for Wondo Genet Campus is (0.98 NTU), and the average temperature was approximately 28.49 °C. The mean total dissolved solids concentration was found to be 118.19 mg/l, and EC value in Wondo Genet Campus was 192.14 μS/cm. The chloride mean value of this drinking water was 53.7 mg/l, and concentration of sulfate mean value was 0.33 mg/l. In the study areas magnesium ranges from 10.42–17.05 mg/l and the mean value of magnesium in water is 13.67 mg/l. The concentration of calcium ranges from 2.16–7.31 mg/l with an average value of 5.0 mg/l. In study areas, an average value of sodium was 31.23 mg/1and potassium is with an average value of 23.14 mg/1. Water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters. All the Campus drinking water sampling sites were consistent with World Health Organization standard for drinking water (WHO).

Safe drinking water is a basic need for good health, and it is also a basic right of humans. Fresh water is already a limiting resource in many parts of the world. In the next century, it will become even more limiting due to increased population, urbanization, and climate change (Jackson et al. 2001 ).

Drinking water quality is a relative term that relates the composition of water with effects of natural processes and human activities. Deterioration of drinking water quality arises from introduction of chemical compounds into the water supply system through leaks and cross connection (Napacho and Manyele 2010 ).

Access to safe drinking water and sanitation is a global concern. However, developing countries, like Ethiopia, have suffered from a lack of access to safe drinking water from improved sources and to adequate sanitation services (WHO 2006 ). As a result, people are still dependent on unprotected water sources such as rivers, streams, springs and hand dug wells. Since these sources are open, they are highly susceptible to flood and birds, animals and human contamination (Messeret 2012 ).

The quality of water is affected by an increase in anthropogenic activities and any pollution either physical or chemical causes changes to the quality of the receiving water body (Aremu et al. 2011 ). Chemical contaminants occur in drinking water throughout the world which could possibly threaten human health. In addition, most sources are found near gullies where open field defecation is common and flood-washed wastes affect the quality of water (Messeret 2012 ).

The World Health Organization estimated that up to 80 % of all sicknesses and diseases in the world are caused by inadequate sanitation, polluted water or unavailability of water (WHO 1997 ). A review of 28 studies carried out by the World Bank gives the evidence that incidence of certain water borne, water washed, and water based and water sanitation associated diseases are related to the quality and quantity of water and sanitation available to users (Abebe 1986 ).

In Ethiopia over 60 % of the communicable diseases are due to poor environmental health conditions arising from unsafe and inadequate water supply and poor hygienic and sanitation practices (MOH 2011 ). About 80 % of the rural and 20 % of urban population have no access to safe water. Three-fourth of the health problems of children in the country are communicable diseases arising from the environment, specially water and sanitation. Forty-six percent of less than 5 years mortality is due to diarrhea in which water related diseases occupy a high proportion. The Ministry of Health, Ethiopia estimated 6000 children die each day from diarrhea and dehydration (MOH 2011 ).

There is no study that was conducted to prove the quality water in Wondo Genet Campus. Therefore, this study is conducted at Wondo Genet Campus to check drinking water quality and to suggest appropriate water treated mechanism.

Results and discussions

The turbidity of water depends on the quantity of solid matter present in the suspended state. It is a measure of light emitting properties of water and the test is used to indicate the quality of waste discharge with respect to colloidal matter. The mean turbidity value obtained for Wondo Genet Campus (0.98 NTU) is lower than the WHO recommended value of 5.00 NTU.

Temperature

The average temperature of water samples of the study area was 28.49 °C and in the range of 28–29 °C. Temperature in this study was found within permissible limit of WHO (30 °C). Ezeribe et al. ( 2012 ) reports similar result (29 °C) of well water in Nigeria.

Total dissolved solids (TDS)

Water has the ability to dissolve a wide range of inorganic and some organic minerals or salts such as potassium, calcium, sodium, bicarbonates, chlorides, magnesium, sulfates etc. These minerals produced un-wanted taste and diluted color in appearance of water. This is the important parameter for the use of water. The water with high TDS value indicates that water is highly mineralized. Desirable limit for TDS is 500 mg/l and maximum limit is 1000 mg/l which prescribed for drinking purpose. The concentration of TDS in present study was observed in the range of 114.7 and 121.2 mg/l. The mean total dissolved solids concentration in Wondo Genet campus was found to be 118.19 mg/l, and it is within the limit of WHO standards. Similar value was reported by Soylak et al. ( 2001 ), drinking water of turkey. High values of TDS in ground water are generally not harmful to human beings, but high concentration of these may affect persons who are suffering from kidney and heart diseases. Water containing high solid may cause laxative or constipation effects. According to Sasikaran et al. ( 2012 ).

Electrical conductivity (EC)

Pure water is not a good conductor of electric current rather’s a good insulator. Increase in ions concentration enhances the electrical conductivity of water. Generally, the amount of dissolved solids in water determines the electrical conductivity. Electrical conductivity (EC) actually measures the ionic process of a solution that enables it to transmit current. According to WHO standards, EC value should not exceeded 400 μS/cm. The current investigation indicated that EC value was 179.3–20 μS/cm with an average value of 192.14 μS/cm. Similar value was reported by Soylak et al. ( 2001 ) drinking water of turkey. These results clearly indicate that water in the study area was not considerably ionized and has the lower level of ionic concentration activity due to small dissolve solids (Table 1 ).

PH of water

PH is an important parameter in evaluating the acid–base balance of water. It is also the indicator of acidic or alkaline condition of water status. WHO has recommended maximum permissible limit of pH from 6.5 to 8.5. The current investigation ranges were 6.52–6.83 which are in the range of WHO standards. The overall result indicates that the Wondo Genet College water source is within the desirable and suitable range. Basically, the pH is determined by the amount of dissolved carbon dioxide (CO 2 ), which forms carbonic acid in water. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ).

Chloride (Cl)

Chloride is mainly obtained from the dissolution of salts of hydrochloric acid as table salt (NaCl), NaCO 2 and added through industrial waste, sewage, sea water etc. Surface water bodies often have low concentration of chlorides as compare to ground water. It has key importance for metabolism activity in human body and other main physiological processes. High chloride concentration damages metallic pipes and structure, as well as harms growing plants. According to WHO standards, concentration of chloride should not exceed 250 mg/l. In the study areas, the chloride value ranges from 3–4.4 mg/l in Wondo Genet Campus, and the mean value of this drinking water was 3.7 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey.

Sulfate mainly is derived from the dissolution of salts of sulfuric acid and abundantly found in almost all water bodies. High concentration of sulfate may be due to oxidation of pyrite and mine drainage etc. Sulfate concentration in natural water ranges from a few to a several 100 mg/liter, but no major negative impact of sulfate on human health is reported. The WHO has established 250 mg/l as the highest desirable limit of sulfate in drinking water. In study area, concentration of sulfate ranges from 0–3 mg/l in Wondo Genet Campus, and the mean value of SO 4 was 0.33 mg/l. The results exhibit that concentration of sulfate in Wondo Genet campus was lower than the standard limit and it may not be harmful for human health.

Magnesium (Mg)

Magnesium is the 8th most abundant element on earth crust and natural constituent of water. It is an essential for proper functioning of living organisms and found in minerals like dolomite, magnetite etc. Human body contains about 25 g of magnesium (60 % in bones and 40 % in muscles and tissues). According to WHO standards, the permissible range of magnesium in water should be 50 mg/l. In the study areas magnesium was ranges from 10.42 to 17.05 mg/l in Wondo Genet Campus and the mean value of magnesium in water is 13.67 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey. The results exhibit that concentration of magnesium in Wondo Genet College was lower than the standard limit of WHO.

Calcium (Ca)

Calcium is 5th most abundant element on the earth crust and is very important for human cell physiology and bones. About 95 % of calcium in human body stored in bones and teeth. The high deficiency of calcium in humans may caused rickets, poor blood clotting, bones fracture etc. and the exceeding limit of calcium produced cardiovascular diseases. According to WHO ( 2011 ) standards, its permissible range in drinking water is 75 mg/l. In the study areas, results show that the concentration of calcium ranges from 2.16 to 7.31 mg/l in Wondo Genet campus with an average value of 5.08 mg/l.

Sodium (Na)

Sodium is a silver white metallic element and found in less quantity in water. Proper quantity of sodium in human body prevents many fatal diseases like kidney damages, hypertension, headache etc. In most of the countries, majority of water supply bears less than 20 mg/l, while in some countries the sodium quantity in water exceeded from 250 mg/l (WHO 1984 ). According to WHO standards, concentration of sodium in drinking water is 200 mg/1. In the study areas, the finding shows that sodium concentration ranges from 28.54 to 34.19 mg/1 at Wondo Genet campus with an average value of 31.23.

Potassium (k)

Potassium is silver white alkali which is highly reactive with water. Potassium is necessary for living organism functioning hence found in all human and animal tissues particularly in plants cells. The total potassium amount in human body lies between 110 and 140 g. It is vital for human body functions like heart protection, regulation of blood pressure, protein dissolution, muscle contraction, nerve stimulus etc. Potassium is deficient in rare but may led to depression, muscle weakness, heart rhythm disorder etc. According to WHO standards the permissible limit of potassium is 12 mg/1. Results show that the concentration of potassium in study areas ranges from 20.83 to 27.51 mg/1. Wondo Genet College with an average value of 23.14 mg/1. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ). These results did not meet the WHO standards and may become diseases associated from potassium extreme surpassed.

Nitrate (NO 3 )

Nitrate one of the most important diseases causing parameters of water quality particularly blue baby syndrome in infants. The sources of nitrate are nitrogen cycle, industrial waste, nitrogenous fertilizers etc. The WHO allows maximum permissible limit of nitrate 5 mg/l in drinking water. In study areas, results more clear that the concentration of nitrate ranges from 1.42 to 4.97 mg/l in Wondo Genet campus with an average value of 2.67 mg/l. These results indicate that the quantity of nitrate in the study site is acceptable in Wondo Genet campus (Table 2 ).

Bacterial contamination

The total coliform group has been selected as the primary indicator bacteria for the presence of disease causing organisms in drinking water. It is a primary indicator of suitability of water for consumption. If large numbers of coliforms are found in water, there is a high probability that other pathogenic bacteria or organisms exist. The WHO and Ethiopian drinking water guidelines require the absence of total coliform in public drinking water supplies.

In this study, all sampling sites were not detected of faecal coliform bacteria. Figure  1 shows the mean values of total coliform bacteria in drinking water collected from the study area. All drinking water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml. In Wondo Genet College, the starting point of drinking water sources (Dam1), the second (Dam2) and Dam3 samples showed the presence of total coliform bacteria (Fig.  1 ). According to WHO ( 2011 ) risk associated in Wondo Genet campus drinking water is low risk (1–10 count/100 ml).

The mean values of total coliform bacteria in drinking water

According to the study all water sampling sites in Wondo Genet campus were meet world health organization standards and Ethiopia drinking water guideline. Figure  2 indicated that mean value of the study sites were under the limit of WHO standards.

Comparison of water quality parameters of drinking water of Wondo Genet campus with WHO and Ethiopia standards

Effect of water quality for residence health’s

Diseases related to contamination of drinking-water constitute a major burden on human health. Interventions to improve the quality of drinking-water provide significant benefits to health. Water is essential to sustain life, and a satisfactory (adequate, safe and accessible) supply must be available to all (Ayenew 2004 ).

Improving access to safe drinking-water can result in tangible benefits to health. Every effort should be made to achieve a drinking-water quality as safe as practicable. The great majority of evident water-related health problems are the result of microbial (bacteriological, viral, protozoan or other biological) contamination (Ayenew 2004 ).

Excessive amount of physical, chemical and biological parameters accumulated in drinking water sources, leads to affect human health. As discussed in the result, all Wondo Genet drinking water sources are under limit of WHO and Ethiopian guideline standards. Therefore, the present study was found the drinking water safe and no residence health impacts.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters in all the College drinking water sampling sites, and they were consistent with World Health Organization standard for drinking water (WHO). The samples were analyzed for intended water quality parameters following internationally recognized and well established analytical techniques.

It is evident that all the values of sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), SO 4 , and NO 3 fall under the permissible limit and there were no toxicity problem. Water samples showed no extreme variations in the concentrations of cations and anions. In addition, bacteriological determination of water from College drinking water sources was carried out to be sure if the water was safe for drinking and other domestic application. The study revealed that all the College water sampling sites were not contained fecal coliforms except the three water sampling sites had total coliforms.

The study was conducted in Wondo Genet College of Forestry and Natural Resources campus, which is located in north eastern direction from the town of Hawassa and about 263 km south of Addis Ababa (Fig.  3 ). It lies between 38°37′ and 38°42′ East longitude and 7°02′ and 7°07′ north latitude. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level.

Map of study area

The study area is categorized under Dega (cold) agro-ecological zone at the upper part and Woina Dega (temperate) agro-ecological zone at the lower part of the area. The rainfall distribution of the study area is bi-modal, where short rain falls during spring and the major rain comes in summer and stays for the first two months of the autumn season. The annual temperature and rainfall range from 17 to 19 °C and from 700 to 1400 mm, respectively (Wondo Genet office of Agriculture 2011).

Methodology

Water samples were taken at ten locations of Wondo Genet campus drinking water sources. Three water samples were taken at each water caching locations. Ten (10) water samples were collected from different locations of the Wondo Genet campus. Sampling sites for water were selected purposely which represents the entire water bodies.

Instead of this study small dam indicates the starting point of Wondo Genet campus drinking water sources rather than large dams constructed for other purpose. Taps were operated or run for at least 5 min prior to sampling to ensure collection of a representative sample (temperature and electrical conductivity were monitored to verify this). Each sample’s physico–chemical properties of water were measured in the field using portable meters (electrical conductivity, pH and temperature) at the time of sampling. Water samples were placed in clean containers provided by the analytical laboratory (glass and acid-washed polyethylene for heavy metals) and immediately placed on ice. Nitric acid was used to preserve samples for metals analysis.

Analysis of water samples

Determination of ph.

The pH of the water samples was determined using the Hanna microprocessor pH meter. It was standardized with a buffer solution of pH range between 4 and 9.

Measurement of temperature

This was carried out at the site of sample collection using a mobile thermometer. This was done by dipping the thermometer into the sample and recording the stable reading.

Determination of conductivity

This was done using a Jenway conductivity meter. The probe was dipped into the container of the samples until a stable reading will be obtained and recorded.

Determination of total dissolved solids (TDS)

This was measured using Gravimetric Method: A portion of water was filtered out and 10 ml of the filtrate measured into a pre-weighed evaporating dish. Filtrate water samples were dried in an oven at a temperature of 103 to 105 °C for \(2\frac{1}{2}\)  h. The dish was transferred into a desiccators and allowed cool to room temperature and were weighed.

In this formula, A stands for the weight of the evaporating dish + filtrate, and B stands for the weight of the evaporating dish on its own Mahmud et al. ( 2014 ).

Chemical analysis

Chloride concentration was determined using titrimetric methods. The chloride content was determined by argentometric method. The samples were titrated with standard silver nitrate using potassium chromate indicator. Calcium ions concentrations were determined using EDTA titrimetric method. Sulphate ions concentration was determined using colorimetric method.

Microorganism analysis

In the membrane filtration method, a 100 ml water sample was vacuumed through a filter using a small hand pump. After filtration, the bacteria remain on the filter paper was placed in a Petri dish with a nutrient solution (also known as culture media, broth or agar). The Petri dishes were placed in an incubator at a specific temperature and time which can vary according the type of indicator bacteria and culture media (e.g. total coliforms were incubated at 35 °C and fecal coliforms were incubated at 44.5 °C with some types of culture media). After incubation, the bacteria colonies were seen with the naked eye or using a magnifying glass. The size and color of the colonies depends on the type of bacteria and culture media were used.

Statically analysis

All data generated was analyzed statistically by calculating the mean and compare the mean value with the acceptable standards. Data collected was statistically analyzed using Statistical Package for Social Sciences (SPSS 20).

Abbreviations

ethylene dinitrilo tetra acetic acid

Minstor of Health

nephelometric turbidity units

total dissolved solid

World Health Organization

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Authors’ contributions

YM: participated in designing the research idea, field data collection, data analysis, interpretation and report writing; BA: participated in field data collection, interpretation and report writing. Both authors read and approved the final manuscript.

Authors’ information

Yirdaw Meride: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on solid waste, carbon sequestration and water quality. He has published three articles mainly in international journals. Bamlaku Ayenew: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on Natural Resource Economics. He has published three article with previous author and other colleagues.

Acknowledgements

Hawassa University, Wondo Genet College of Forestry and Natural Resources provided financial support for field data collection and water laboratory analysis. The authors thank anonymous reviewers for constructive comments.

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Meride, Y., Ayenew, B. Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia. Environ Syst Res 5 , 1 (2016). https://doi.org/10.1186/s40068-016-0053-6

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• Published: 18 August 2020

A framework for monitoring the safety of water services: from measurements to security

• Katrina J. Charles   ORCID: orcid.org/0000-0002-2236-7589 1 ,
• Saskia Nowicki   ORCID: orcid.org/0000-0001-9011-6901 1 &
• Jamie K. Bartram   ORCID: orcid.org/0000-0002-6542-6315 2 , 3  

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The sustainable developments goals (SDGs) introduced monitoring of drinking water quality to the international development agenda. At present, Escherichia coli are the primary measure by which we evaluate the safety of drinking water from an infectious disease perspective. Here, we propose and apply a framework to reflect on the purposes of and approaches to monitoring drinking water safety. To deliver SDG 6.1, universal access to safe drinking water, a new approach to monitoring is needed. At present, we rely heavily on single measures of E. coli contamination to meet a normative definition of safety. Achieving and sustaining universal access to safe drinking water will require monitoring that can inform decision making on whether services are managed to ensure safety and security of access.

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Introduction.

Access to affordable, safe drinking water is critical for securing health gains from development. Significant gains were made in water access during the millennium development goal (MDG) period (1990–2015); however, the approach to drinking water safety relied on a binary improved/unimproved categorisation of water source types, approximating a crude sanitary inspection, which inadequately addresses water safety 1 . Building on the achievements of the MDG period, the sustainable development goals (SDGs) include a target for safe drinking water. The associated indicator for this target is based on water quality analysis for a one-off cross-sectional survey of a nationally representative sample of households and the primary water source that they use.

The term ‘safe’ was used in the MDGs (target 7c—‘halve the proportion of people without sustainable access to safe drinking water and basic sanitation’) and again in the SDGs (target 6.1—‘achieve universal and equitable access to safe and affordable drinking water for all’) to emphasise the importance that drinking water should not propagate disease; however, measurement of ‘safety’ has been an ongoing challenge. The intent and operationalisation of the SDGs—through the wording of targets and indicators—was developed through international participatory processes and built on the successes of the MDGs. The negotiations concerning targets and indicators demanded that these be supported by meaningful baseline data. These restrictions constrained the monitoring approach options, in this case to safety as defined by the Joint Monitoring Programme as Escherichia coli and a few selected ‘priority chemical contaminants’. In the coming decade, as we continue to achieve gains in access to water, we need to ensure our monitoring approaches move beyond quality to monitor the safety, and ongoing security, of drinking water services.

In this Special Collection on Monitoring drinking water quality for the Sustainable Development Goals , we reflect on the purposes of monitoring, considering the tools used and their limitations in guiding achievement of that purpose and of progress towards universal use of safe drinking water. Our reflections are framed around the prevention of transmission of infectious disease through drinking water, in both endemic and outbreak forms, and we focus on E. coli as the most common indicator used in drinking water safety monitoring. In developing a framework for monitoring the safety of drinking water safety, we recognise limitations in approaches to delivering safe drinking water, consider the historical pathways that have led us here, and explore opportunities to reimagine monitoring for safe drinking water.

A framework for monitoring drinking water safety

With reference to SDG target 6.1, the aim of drinking water monitoring is to track and advance progress towards universal access to safe drinking water. This indicates two important components of purpose—one concerns the intention to determine levels of coverage and compare them to the goal of universality; the second is the reference to safety as distinct from quality, which requires that the water be judged as to its fitness for human consumption.

The word ‘monitoring’ is defined by the notion of keeping track of something. Scientific dictionaries normally refine this to include two concepts: the ongoing nature of the activity, and the taking of periodic and programmed observations or measurements. Whether implicit or explicit, the definitions convey an understanding that monitoring ought to be designed with reference to a declared purpose, with the resulting data fit for the intended use. Monitoring for different purposes, to inform different decisions, will require different approaches and measures. For example, compliance monitoring tracks performance against a regulated standard such as a chemical or microbiological parameter, whereas operational monitoring tracks performance against process indicator limits such as for turbidity or residual chlorine 2 , 3 .

In Fig. 1 , we present a framework for monitoring drinking water safety. Monitoring (as it relates to prevention of infectious disease transmission through drinking water) is framed in sequential domains of concern, moving from taking single measurements of indicators or contaminants, to interpreting health hazard, tracking safety of services, and finally monitoring the prospective security of safe services. For the purposes of our discussion here, we use the term ‘safe’ to imply potable water, i.e. that which is fit for human consumption. The framework enables us to interrogate the role of indicators in monitoring drinking water safety. Further, we use it to illustrate the constraints, benefits, and interrelationships of different outlooks—in terms of both conceptualising drinking water safety and interpreting the findings of associated monitoring activities.

A framework for monitoring drinking water safety.

Sample: measuring water quality parameters

Water quality is measured to assess potential contamination. The most common measure used to determine microbial drinking water quality is E. coli . Used as an index of certain pathogens or as an indicator of faecal contamination, the presence of E. coli informs on the likelihood that pathogens are present 4 . E. coli are one of a methodologically defined group of indicators, referred to as total coliforms, that includes members of the genera Escherichia , Klebsiella , Enterobacter , Citrobacter and Serratia . Colony count approaches for coliform bacteria have been formally used to manage water quality in the UK since Report 71 5 was published 4 . E. coli were identified in the 1880s 4 , and were suggested as an indicator of water quality in 1892 6 . As ideas on what was required of an indicator organism advanced in the 1960s and 1970s 7 , 8 , E. coli became the preferred indicator of faecal contamination 9 . E. coli were recognised as ‘the more precise indicator of faecal pollution’ 10 because at the time they were thought to originate exclusively from human and warm-blooded animal faeces, in which they are always present in high quantities. Whereas, other coliform organisms were already known to originate from non-faecal sources as well as from faeces, making them less likely to be reliably associated with the presence of human pathogens 9 . Recognition of this, alongside advancements in methods, resulted in inclusion of E. coli as a preferred indicator of faecal contamination in the second edition of WHO’s Guidelines for Drinking Water Quality (GDWQ) in 1993 11 , and in the EU Drinking Water Directive in 1998 12 . In addition to E. coli , a less specific methodologically defined faecal indicator organism group, thermotolerant coliforms, is also recognised as useful in the GDWQ. Thermotolerant coliforms are a sub-group of coliforms, inclusive of E. coli , that grow at 44.5 °C. Use of this elevated temperature is intended to inhibit the growth of non- Escherichia coliforms, but mostly Citrobacter and Enterobacter are reduced, and even then, not all strains of those genera 13 . While fixed ratios of E. coli to thermotolerant coliforms are sometimes reported, this relationship varies widely 14 —including by climate 15 and water type 16 , and by the enumeration method 14 . Thermotolerant coliforms are sometimes referred to as ‘faecal coliforms’, a misnomer as they do not all originate from faeces 17 . An analytical result of zero (or more correctly <1) thermotolerant coliforms in a water sample would imply zero E. coli , but might be more difficult to achieve, whereas a result positive for thermotolerant coliforms does not confirm the presence of E. coli .

Measurement in our framework is the test that can help to assess if a ‘glass’ of water is contaminated, either by direct measurement of contaminants or using indicators. It is worth noting that the volume used in analyses is typically 100 mL, a volume equated with a ‘glass of water’ 18 . The first GDWQ recommended 100 mL sample sizes while also recognising that it would be ‘statistically more meaningful to examine larger samples, possibly 200, 500, or 1000 ml’ 19 .

Quality: interpreting measurements

Measurement results are often interpreted in terms of the hazard they represent. This is too often used to define the quality of water as ‘safe’ or not. For example, water is considered safe with respect to measured parameters if it does not exceed relevant guidelines or standards. This positivist, normative definition assumes that all potential hazards are known, are measurable, and have been considered. Its limitations are exemplified by widespread occurrence of arsenic in groundwater that had been previously declared ‘safe’ in Bangladesh in the 1990s 20 , 21 .

Drinking water may contain numerous potential health threats for which guidelines have not been established due to insufficient or inconsistent evidence, the low-priority given to threats deemed to be ‘only’ locally significant in few settings, and as yet unrecognised hazards. Guidelines are revisited as new evidence emerges. But guidelines (and therefore our understanding of safe water by normative definitions) are constrained: firstly, by practical considerations, such as the limits of readily available detection methods (for which arsenic is again an example 22 ) and treatment technologies; and secondly, by political considerations such as trade-offs among competing hazards as is the case of disinfectants versus disinfection by-products.

Consequently, judgments of safety are assisted by objective definitions. The GDWQ 23 defines safe drinking-water as that which “does not represent any significant risk to health over a lifetime of consumption, including different sensitivities that may occur between life stages”, where significant risk is defined in terms of the tolerable burden of disease of 10 −6 disability adjusted life years per person per year. The UK also uses an objective definition in their water regulations: the term ‘wholesome’ is applied to water that ‘does not contain any micro-organism… or parasite or any substance …at a concentration or value which would constitute a potential danger to human health’ whether or not a standard has been set 24 .

When E. coli is detected, it is interpreted as a health hazard—in keeping with the notion that it is a ‘faecal indicator bacteria’ (FIB), a long-established notion in sanitary microbiology. Several authors have proposed necessary and desirable characteristics of the ideal faecal indicator 25 , 26 , 27 , and reviewed the degree of fit of candidate organisms against them. Here, we selectively use the criteria that are included in the GDWQ. It is important to revisit these criteria to reflect on the role that E. coli performs as the most common measure to assess progress against SDG 6.1. E. coli align with several of the GDWQ criteria: 23 they are (generally) not pathogens themselves; they are universally present in faeces of humans and animals in large numbers; they are present in higher numbers than faecal pathogens; and they are ‘readily detected by simple, inexpensive culture methods’ (p148). However: while the majority of detected E. coli are not pathogens, a significant subset are pathogenic; and while methods may be simple and inexpensive compared to tests for specific pathogens, they remain sufficiently expensive in most settings that they do not meet the intent of this criterion articulated by Medema et al. 9 : that indicator tests should be inexpensive ‘thereby permitting numerous samples to be taken’.

While field tests have been developed, for regulatory purposes, most E. coli tests are undertaken in a laboratory. This requires that a cold chain be maintained during sample transport and that samples be processed within 6 h, which can be logistically and financially problematic, limiting the validity of results due to changes in the sample composition during storage. For example, in Colombia an estimated 30% of rural water samples would require storage for more than 6 h en route to the laboratory 28 .

Three of the WHO FIB criteria are not met by E. coli

Firstly, some E. coli multiply in natural waters. Such growth has been demonstrated in soils 29 , 30 , sediments 31 , and water columns 32 including in drinking water reservoirs 33 . It has also been shown in the biofilms of distribution systems 34 and in handpumps 35 . Conditions for growth require temperatures over 15 °C, assimilable carbon availability, and absence of disinfectant residuals 36 . While these conditions are uncommon in large utility systems in temperate countries, they are common in many other water systems; for example, shallow groundwaters are over 15 °C in much of the world 37 and contain high loads of organic carbon 38 .

Secondly, E. coli are less robust than many pathogens, so they neither persist in water nor respond to treatment processes in a similar fashion to faecal pathogens. E. coli die-off quicker than many viral or protozoan pathogens in surface water and groundwater 39 , 40 , 41 and during treatment 23 . E. coli are larger and have different surface charge characteristics than viruses and, therefore, are more readily trapped in filters and soil matrices 42 . The different behaviour of pathogens and E. coli illustrates why there is no direct correlation between concentrations of indicators and pathogens 43 . This lack of correlation is a limitation of quantitative microbial risk assessment approaches, since health risk from pathogens is extrapolated from E. coli measurements 44 .

Since the presence of E. coli is interpreted as indicating a health hazard, ‘immediate investigative action’ 23 is recommended when E. coli are detected in drinking water. However, because of the limitations described above, presence of E. coli indicates that, at the time the test was taken, there had either been recent faecal contamination, or a large faecal contamination event less recently, or environmental conditions were appropriate for growth of E. coli . Conversely, the absence of E. coli does not definitively confirm the absence of faecal pathogens. To interpret the results of E. coli tests, it becomes necessary to have more information available. Throughout the water safety literature, it is emphasised that E. coli (or FIB) are most useful as a component of a programme of measurements, not as a single test result 5 , 9 , 45 , 46 . We expand on this in the next section.

A single (or infrequent) test of water for E. coli , and subsequent interpretation of the health hazard, is widely understood as water quality ‘monitoring’, but is not able to advance water safety. E. coli , the most common measure of progress towards universal safe water, has strengths and limitations when we try to use it to infer health hazard. If we use an objective definition of safe drinking water, even within the context of infectious diseases, one test is insufficient to identify and manage threats. For E. coli , when the interpretation of the result is also unclear, a gap emerges between measurement and the stated aim of safety. With reliance on E. coli , and in the absence of other information, results are subject to confirmation bias: If E. coli are not detected in a second test then it is often assumed that the first was wrong and there is no health hazard, rather than considering variability in occurrence and detection. Or if an outbreak occurs, detection of E. coli is assumed to confirm that the water represents a health hazard, however, E. coli may be present for reasons other than recent faecal contamination, and without validation testing it is not possible to ascribe the source of the outbreak.

Safety: tracking safety of services

The preceding domain of our framework, focusing on interpreting measurements, deals simplistically with risk in terms of the likelihood of experiencing a hazard (e.g. of contracting a disease) given a specific exposure. Here we consider safety, which is not simply the inverse of hazard. Effective disease prevention demands consistent hazard-free status. A water supply is not ‘safe’ if it produces one glass of hazard-free water, nor if it delivers pathogen-laden water, briefly, once a year. Empirically, even in highly compliant water supply systems, Setty et al. 47 show that disease (diarrhoea) prevalence increased following changes in water quality due to rainfall. The importance of consistency is further illustrated by the modelling work of Hunter et al. 48 , which suggests an increased risk of over 10% in the probability of annual infection from enteropathogenic E. coli (12.7%), Cryptosporidium (18%) and rotavirus (12%) associated with switching from treated to untreated drinking water for 1 day.

Because sampling and analysis provide a snapshot of quality at the moment of sampling; and because samples of water necessarily represent a negligible fraction of the volume and time of water supplied, assessing on-going safety demands that we move from making and interpreting single measurements to planning sequences of measurements i.e. ‘monitoring’.

There is abundant evidence that E. coli concentrations in water vary rapidly and across orders of magnitude. This is true within natural waters due to non-random distribution 49 , from hazardous events 47 or failures in control measures, which routine sampling regimes do not readily capture as they are limited by logistics. For example, samples are disproportionately taken in mornings and on days earlier in the working week to facilitate transport, analysis and reporting during normal work hours 18 . Here, online measures, such as turbidity or chlorine residual, can improve understanding of temporal variability 47 , 50 and interpretation of other measurements.

Measurements only provide evidence of what the quality was, so management approaches that integrate understanding of system performance into planning are needed to oversee water safety on an ongoing basis. Prospective management of safety requires and builds on the knowledge of historical measurements. It combines evidence that a system has reliably delivered potable water, based on a programmed series of measurements, with knowledge that controls are in place to ensure that perturbations do not compromise quality. At this level of the framework, we have moved from a focus on measurements, which inform on the water system safety yesterday (which is what E. coli tests currently help us understand), to consideration of safety for tomorrow. This prospective safety perspective and its emphasis on ensuring adequate conditions is exemplified by sanitary inspection and water safety plans (WSPs).

Sanitary inspection originated as an adjunct, to water quality measurement. Victorian hygiene literature is replete with examples, and almost a century ago, Prescott and Winslow 51 stated that ‘the first attempt of the expert called in to pronounce upon the character of a potable water should be to make a thorough sanitary inspection’. This illustrates understanding that, even in the absence of contamination at a moment of sampling, a system that is vulnerable to contamination is not safe. As bacteriological methods developed, and their limitations were recognised, these preventative approaches continued to be valued. For example, the first edition of the GDWQ stated that, for non-piped systems, ‘considerable reliance must be placed on sanitary inspection and not exclusively on the results of bacteriological examination’ 19 . Equally, it advised managers of untreated water for piped supplies to include in their assessment of safety both frequent bacteriological results showing the absence of faecal coliforms and information on whether ‘sanitary inspection has shown the catchment area and storage conditions to be satisfactory’ 19 .

In the sense used here, a sanitary inspection is a visual inspection of a piece of water system infrastructure, with the objective of identifying physical factors that could facilitate contamination. It is exhaustive in the sense that all observable faults are considered, but not comprehensive in the sense that not all faults are detectable by visual inspection. Sanitary inspection, reviewed and explored substantively in the paper by Kelly and Bartram 52 , is widely used by those working on rural water systems, where it is frequently applied to community water sources such as boreholes with handpumps.

Sanitary inspection was one of the tools that inspired the development of the concept of WSPs, along with the hazard analysis and critical control points (HACCP) approach, failure mode analysis, quality management, and multi-barrier approaches (the importance of these approaches is highlighted in Kelly and Bartram’s 52 results). Indeed, sanitary inspection is a key component of WSPs, which extend the principles of sanitary inspection to the whole system (‘catchment-to-consumer’).

WSPs and similar systematic risk-based approaches have demonstrated benefits for reducing temporal variation in water quality 47 , as well as reducing the health burden 53 . One of the key attributes of a WSP 54 is that there is evidence that a water supply system can achieve safe water through validation and verification procedures. In validation, evidence is gathered that a water system can effectively meet water quality targets; this may use a variety of tools, including challenging a water system with different conditions and organisms. Verification provides ongoing evidence that a water system is delivering water of the desired quality, for which regular E. coli measurements provides a useful tool. With the system performance characterised through validation and verification, an individual E. coli measurement can be meaningfully interpreted.

Monitoring water safety requires frequent data collection, underpinned by knowledge of system performance and maintenance. Furthermore, to be effective, monitoring data must be available and useful for decision makers, and should support stakeholder cooperation rather than threaten it 55 .

Security: ensuring safe services are sustained

As the SDG target of universal access is progressively achieved, the importance of the future sustainability of safe drinking water supply becomes increasingly apparent, i.e. attention turns towards the risk that those with access might experience a reduction of the level of service, or a loss of service 56 . While for the purpose of this article we focus on safety of drinking water for human consumption, the level of service we aim to secure includes the broader aspects of the human right to water on which the SDG indicators are based 57 . Securing appropriate levels of quantity, reliability, accessibility, and affordability of water that is fit for purpose, are essential to achieving the health-based targets that WSPs are designed to meet 58 .

Terms describing sustainability and sustaining services are interpreted inconsistently. While our preceding reflections concern monitoring for sustained achievement of safety and its implied continuation, here we use the term security in a prospective manner similar to that of ‘sustainable development’ i.e. in a sense that differentiates management of the day to day and familiar (safety) from adequacy for the long term and against the unfamiliar (security). We use three factors to illustrate the potential for attained access to be systematically undermined: demographic change, climate change, and increasing water pollution. Although we focus on these three types of systemic change, we recognise that there are others, such as economic volatility and armed conflict, that are important at this final level of the framework.

Demographic trends warn us of impending risks to availability of sufficient quantities of water because of increasing population, and accelerating demand for water as populations change lifestyles with urbanisation and increasing affluence 59 , 60 . In Kenya, for example, water is scarce (647 m 3 per capita in 2006 61 ) and by 2030, the population is projected to increase by over 80%, with a 50% increase in the proportion of urban dwellers. Increases in water demand will coincide for domestic supply, agriculture and industry (supplying both domestic and international markets 62 ), and good regulation will be necessary to prevent damaging shortages.

Further to demographic pressure, climate change will have substantial consequences for water access. Shortages in Cape Town and São Paulo have highlighted the vulnerability of water supplies to climatic events, and the importance of appropriate management 63 . With climate change, more frequent higher intensity rainfall events will increase the risk of infrastructure damage 64 . Rising temperatures and increasing evapotranspiration rates will reduce available water and increase competing water demand for irrigation 65 . And water quality will deteriorate due to heavier and more erratic rainfall 47 , 66 , increasing release of glacial flows with associated geochemical hazards 67 , and increasing salinity from rising sea levels and expanding irrigation.

In addition to climate change impacts, the problem of water quality deterioration is compounded by pollution. Pollution threatens health directly (through contamination of drinking water that treatment processes do not remove) or indirectly (if chemicals make water unpalatable). Thus, increasing pollution threatens to reduce access to safe drinking water; for example, substantial investments are needed in Dhaka, Bangladesh, to ensure continued access where industrial growth has resulted in the need to pump water from over 30 kms away to avoid local pollution 68 . Environmental water pollution is addressed through SDG 6.3: to ‘improve water quality by reducing pollution…’. Realisation of this goal will require combatting pollution from domestic, agricultural and industrial sources.

To ensure we are working towards sustainable access to safe water, we need tools that can measure and track this progress. Without appropriate indicators, we will continue to focus on access rather than sustainability, potentially misdirecting resources.

New monitoring tools are needed to assess security of safe water supplies

The MDGs, and now SDGs, are based on the notion of provision: increasing the proportion of people with reliable access to affordable, safe drinking water. There remain challenges to achieving the SDG target of universal access, especially for difficult-to-reach populations. There are also practical limits of measuring universality. As we approach the target of universality, however, we need to consider how to shift to a security perspective that encompasses sustainability of high levels of service. Thanks to the achievements of the MDGs and SDGs, 71% of the global population have access to safely managed water services 69 . But because of this success, it is now possible and necessary to adopt a security perspective and to target prospective, inter-generational access to safe water in our changing world.

The participatory, political nature of the processes that define global targets and indicators creates a conservative environment that hinders innovation and diffusion-adoption of improved approaches. We argue that now is the time to start planning for 2030 and beyond, to support a change in focus from access to security of sustainable, safe water supply services and to ensure we can provide the level of evidence needed for adoption of better indicators by the United Nations committees and processes. We recall that the initial Rapid Assessment of Drinking Water Quality (RADWQ) research, done in 2004/2005 15 , created a platform for change that advanced a recognition of drinking water quality in the MDGs, and then monitoring of quality in the SDGs. In 2020, 5 years into the SDGs, where are the new tools we will need for 2030 and beyond?

Through our framework for monitoring drinking water safety we have deconstructed ‘drinking water quality monitoring’, reflecting on its purpose and component parts. Measurement of drinking water quality alone will not deliver the changes needed for safe water. We have become highly reliant on E. coli as a means to assess drinking water quality. It is critical that we remember, however, that the goal of safe water supply is fitness for human consumption, not absence of E. coli . There are a range of measurement tools available that can facilitate regular checks to track direct or indirect changes in water quality or system performance. These tools are important, but cheaper, quicker methods to measure water quality parameters are not enough. The usefulness of measures, such as E. coli , in communicating the problem of water quality has to be tempered by the risk that we lose sight of their purpose and neglect the range of tools needed to achieve safe water and to sustain improvements.

To assess and manage the safety and security of drinking water services, we need monitoring that includes more than direct water quality measures. Through our framework we advocate for this next step. For water safety, there has been consistent emphasis in the GDWQ on the importance of frequent water testing being complemented by knowledge of risks from sanitary inspections and accompanied by systematic management approaches like WSPs. Increased regulation of water services can be anticipated to increase data availability 57 and create an opportunity to focus on management indicators for monitoring the application, oversight and audits of WSPs and sanitary inspections.

These practices, supported by routine monitoring, are essential for safety, and they contribute to ensuring security of drinking water services in the face of threats from demographic and climate change, and pollution. To fully progress from ‘safety’ to ‘security’, however, will require innovations in monitoring that go beyond current practices. There is an opportunity to act smartly, invest strategically, and accelerate progress by incorporating a security perspective. This perspective should enable us to account for prospective long-term drivers that threaten the ongoing sustainability of access to safe water.

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No datasets were generated or analysed during the current study.

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Acknowledgements

K.C. and S.N. are supported by the REACH programme funded by UK Aid from the UK Department for International Development (DFID) for the benefit of developing countries (Aries Code 201880). However, the views expressed and information contained in it are not necessarily those of or endorsed by DFID, which can accept no responsibility for such views or information or for any reliance placed on them. The authors thank the health-related water community for all the discussions that have informed this work.

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Charles, K.J., Nowicki, S. & Bartram, J.K. A framework for monitoring the safety of water services: from measurements to security. npj Clean Water 3 , 36 (2020). https://doi.org/10.1038/s41545-020-00083-1

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Ensuring the availability of safe drinking water remains a critical challenge in developing countries, including Ethiopia. Therefore, this paper aimed to investigate the prevalence of fecal coliform and E. coli bacteria and, geographical, children availability, and seasonal exposure assessment through a meta-analysis.

Two independent review groups extensively searched internet databases for English-language research articles published between 2013 and 2023. This systematic review and meta-analysis followed PRISMA guidelines. The methodological quality of each included study was evaluated using the STROBE guidelines. Publication bias was assessed by visual inspection of a funnel plot and then tested by the Egger regression test, and meta-analysis was performed using DerSimonian and Laird random-effects models with inverse variance weighting. Subgroup analyses were also conducted to explore heterogeneity.

Out of 48 potentially relevant studies, only 21 fulfilled the inclusion criteria and were considered for meta-analysis. The pooled prevalence of fecal coliform and E. coli was 64% (95% CI: 56.0–71.0%, I 2  = 95.8%) and 54% (95% CI: 45.7–62.3%, I 2  = 94.2%), respectively. Subgroup analysis revealed that the prevalence of fecal coliform bacteria increased during the wet season (70%) compared to the dry season (60%), particularly in households with under-five children (74%) compared to all households (61%), in rural (68%) versus urban (66%) areas, and in regions with high prevalence such as Amhara (71%), Gambela (71%), and Oromia (70%). Similarly, the prevalence of E. coli was higher in households with under-five children (66%) than in all households (46%).

Conclusions

The analysis highlights the higher prevalence of fecal coliform and E. coli within households drinking water, indicating that these bacteria are a significant public health concern. Moreover, these findings emphasize the critical need for targeted interventions aimed at improving drinking water quality to reduce the risk of fecal contamination and enhance public health outcomes for susceptible groups, including households with under-five children, in particular geographical areas such as the Amhara, Gambela, and Oromia regions, as well as rural areas, at point-of-use, and during the rainy season.

Registration

This review was registered on PROSPERO (registration ID - CRD42023448812).

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Introduction

An essential requirement for the health and well-being of people is access to safe drinking water. However, most of the world’s population lacks access to adequate, sustainable, and safe water [ 1 , 2 ]. Considering this, in 2015, the United Nations ratified different developmental goals, including the Sustainable Development Goal (SDG) 6.1, which aspires to achieve universal and equitable access to safe and affordable drinking water for all by 2030 [ 3 ]. This goal emphasizes having access to safe drinking water for every household [ 4 ]. To monitor this phenomenon, most countries, including Ethiopia, have adopted the World Health Organization’s (WHO) guidelines for drinking water quality [ 1 ].

Worldwide, water-related diseases account for approximately 80% of all illnesses and diseases and, in turn, cause an estimated 505,000 diarrheal deaths each year [ 5 ]. Children are more susceptible to microbiological pollutants and develop an illness due to their immature immune systems [ 6 ]. As a result, waterborne diseases continue to be major health problems worldwide. Particularly in most developing nations where access to potable water is scarce, water-borne diseases are a serious public health concern as a result of bacterial contamination of drinking water [ 7 ]. Water-borne pathogenic bacteria could infect or harm humans by secreting toxins that could harm human tissue, living as parasites within human cells, or colonizing within the body to interfere with regular bodily processes. Numerous harmful bacteria, such as fecal coliforms, Escherichia coli , Salmonella typhi , and Vibrio cholerae , have been identified in water [ 8 ]. These bacteria can lead to various waterborne diseases, including cholera, typhoid, and diarrhea [ 5 ].

In developing countries, the main causes of diarrheal diseases are bacteria, protozoa, viruses, and helminths [ 9 ]. Specifically, in rural areas of most developing countries, where water sources are communally shared and exposed to several fecal-oral transmission channels within their local boundaries, fecal contamination of drinking water is a primary cause of water-borne diseases, including fatal diarrhea [ 10 ]. This could be detected by examining the presence of potential indicator organisms such as fecal coliforms [ 11 , 12 ].

Several pathogenic bacteria can be transmitted via polluted drinking water [ 13 , 14 ]. Drinking water can be polluted at the source, distribution line, and/or household level, and such polluted water can be a vehicle for several pathogens [ 2 , 15 ]. In Ethiopia, poor environmental health conditions resulting from subpar water quality and inadequate hygiene and sanitation standards are responsible for more than 60% of infectious diseases [ 16 ]. Studies conducted in Ethiopia revealed that the prevalence of fecal contamination in drinking water, including Escherichia coli ( E. coli ), total coliforms (TC), and fecal coliforms, have been extremely high [ 17 , 18 , 19 , 20 , 21 , 22 ].

This could be due to many reasons, as water safety depends on various factors, from the quality of the source water to its storage and handling practices in the home [ 23 ]. Even if the source is clean, the process of collecting, transporting, storing, and drawing water in the household can all lead to fecal contamination [ 17 ]. In addition, pollutants in drinking water sources include human excreta, animal waste, effluent agricultural practices, and floods, as well as a lack of knowledge among end-users about hygiene and environmental cleanliness [ 17 , 24 ]. Due to inadequate access and frequent interruptions in the piped water supply [ 25 ], drinking water is commonly stored, often for considerable lengths of time, resulting in gross contamination [ 26 ].

Therefore, understanding the extent and epidemiological variation of bacterial contamination in household drinking water is vital for policymakers and public health officials to allocate resources efficiently and target interventions effectively to reduce the burden of waterborne illnesses in Ethiopia. Despite individual studies on contamination levels, there is a notable research gap due to the lack of a national systematic review and meta-analysis. Existing research does not fully examine how contamination varies with factors such as children’s availability, geographic regions, water sources, and seasonal changes. This study aims to address this gap by offering a comprehensive review and meta-analysis, providing essential insights for policymakers to effectively allocate resources and target interventions to reduce waterborne illnesses and support vulnerable groups.

Methodology

Data sources and search strategy.

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ S1 ] and the protocol of this study was registered with the International Prospective Register of Systematic Reviews (PROSPERO), with protocol registration number CRD42023448812. With a focus on English-language materials, we conducted an exhaustive search across numerous electronic databases, including PubMed, Web of Science, Google Scholar, ScienceDirect, ProQuest, Directory of Open Access Journals, POPLINE, African Journals Online, and the Cochrane Library. The search strategy included a combination of keywords and controlled vocabulary related to Ethiopia, drinking water, and specific indicators of pathogenic bacteria. Moreover, the search strategies for Google Scholar were [Microbiological OR Microorganisms OR Organisms OR Bacteriological OR pathogens] AND [“Water quality” OR “Water contamination”] AND [household OR domestic OR residential OR home] AND [“Drinking water”] AND [intitle: Ethiopia], and those for Pubmed were ((Microbiological OR Microorganisms OR Organisms OR Bacteriological OR pathogens) AND (“Water quality” OR “Water contamination”)) AND (household OR domestic OR residential OR home)) AND (“Drinking water”)) AND (Ethiopia [Title]). we have provided a detailed breakdown of the query [ S5 ].

Study selection criteria

Studies were included if they met the following criteria: (i) Original research was conducted in Ethiopia, and only peer-reviewed journal articles, as they undergo rigorous review processes and were more likely to meet high-quality standards; (ii) Focus on the prevalence of those specific indicators of pathogenic bacteria in household drinking water; (iii) Published in English; (vi) Articles with a cross-sectional study, had freely available full texts and were published between 2013 and 2023. However, articles with no clear data were excluded.

Methodological quality of the included studies

Two groups independently assessed the methodological rigor of every study included, employing the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 27 ]. Each study was then classified based on its quality: “Good” (G) if it achieved a score of at least 70% of the total points, “Fair” (F) if it scored between 50% and 69%, and “Poor” (P) if its score was below 50% [ S2 ].

Extraction and analysis

Following the selection of pertinent articles, two investigators individually screened the titles and abstracts to determine their suitability for full-text review. Subsequently, these investigators utilized a standardized data extraction template in Microsoft Office Excel 2021 to collect study characteristics, prevalence, sample sizes, season, child availability, water sources, and geographic locations. In the event of any disagreements between the two investigators, a third investigator intervened, and their decision was considered final. The data analysis was performed using STATA 16.0 software. Random effects meta-analysis models were used to investigate the pooled prevalence of indicators of pathogenic bacterial contaminants using DerSimonian and Laird’s approach with 95% confidence intervals ( CIs ) [ 28 ]. The inverse of the Freeman-Tukey double arcsine transformation was used to stabilize the variance of each study [ 29 ].

A forest plot was generated to visually assess the pooled prevalence estimates and corresponding 95% confidence intervals ( CIs ) across the included studies. For statistical heterogeneity across studies, the I 2 statistic was used [ 30 ]. Heterogeneity was considered high, moderate, or low, with I 2 values of 75, 50, and 25%, respectively. To identify potential sources of heterogeneity, subgroup analyses were conducted in under-five children’s availability, seasons, residential, water sources, and regional settings. Publication bias was assessed by visual inspection of the funnel plot and tested by the Egger regression test [ 31 , 32 ].

Search results

A total of 992 articles were identified from the nine databases, and an additional 14 articles were identified through additional manual searching. Three hundred forty-two studies were removed due to being duplicates found both within the same database and across different databases. A total of 616 studies were determined to be ineligible during title and abstract screening. At the full-text review stage, 27 articles were excluded because they did not measure microbial indicators of interest. The flow chart of the study selection process is presented in Fig.  1 , generated using the PRISMA flow diagram [ 33 ].

Flow diagram indicating how articles were included and excluded during the meta-analysis of household drinking water contamination in Ethiopia

Characteristics of the included studies

This study included 16 studies with 4,193 samples for fecal coliform [ 17 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], and eight studies with 2,594 samples for E. coli analysis [ 38 , 46 , 48 , 49 , 50 , 51 , 52 , 53 ]. The main characteristics of the selected studies are summarized in the article matrix [S2]. All the articles were cross-sectional studies and followed a random sampling procedure. The articles included in the study were conducted between 2013 and 2023 and sample sizes ranged from 42 to 538 for E. coli and from 42 to 736 for fecal coliforms.

The data were collected from seven regions of Ethiopia. Nine studies in Amhara region [ 35 , 36 , 40 , 42 , 43 , 44 , 45 , 49 , 51 ], one study each in Sidama [ 47 ], Somali [ 53 ] and Gambela [ 41 ] regions, two studies each in Oromia [ 17 , 46 ], and Tigray [ 34 , 38 ] regions, four studies in SNNPR [ 37 , 39 , 48 , 52 ], and one study in all over Ethiopia [ 50 ] were conducted. Moreover, six studies were conducted in households with under-five children only [ 41 , 42 , 44 , 49 , 51 , 53 ].

Pooled meta-analysis

The forest plot of the prevalence estimates and the corresponding 95% confidence intervals ( CIs ) of the contaminants are presented in Figs.  2 and 3 . The pooled prevalence of fecal coliform was 63.7% (95% CI: 56.0–71.0%, I 2  = 95.8%, based on 16 studies) in a total sample of 4193 households. Additionally, the pooled prevalence of E. coli was 54.0 (95% CI: 45.7–62.3%, I 2  = 94.2%, based on 8 studies) in a total sample of 2,594 households. The funnel plot [ S3 ] showed almost no publication bias, which was confirmed by the Egger regression test for both fecal coliform ( p  = 0.10) and E. coli ( p  = 0.18). Moreover, no publication bias was confirmed by the ‘Trim and Fill’ sensitivity analysis, as we did not find any hypothetical missing studies. A leave-one-out sensitivity analysis found that excluding any single study resulted in an average variation of 1% in the pooled prevalence of fecal coliforms and 1.93% for E. coli , indicating no substantial impact on the overall results.

Forest plot of the prevalence of fecal coliform bacteria in households drinking water in Ethiopia

Forest plot of the prevalence of E. coli in households drinking water in Ethiopia

Heterogeneity and subgroup analysis

The pooled prevalence of fecal coliform among the dry seasons samples was 60.1% (95% CI: 47.4–72.1), while for the wet seasons samples, it was 70.3% (95% CI: 63.8–76.3). When stratified by residence, the pooled prevalence in rural and urban areas were 68.0% [95%CI: 59.0–76.3] and 66.4% [95%CI: 49.1–81.7], respectively. Specifically, households with under-five children had a higher prevalence of fecal coliform (73.8% [95% CI: 63.9–82.7%]) than all households without any restrictions (61.0% [95% CI: 51.8–69.8]). When stratified by regions, the prevalence of fecal coliform was highest (71.4%) in Amhara, 71.2% in Gambela, and 70.1% in Oromia, compared with 58.1% in SNNP, 31.7% in Sidama, and 42.0% in Tigray. Regarding sample collection sources, the prevalence of fecal coliform is higher at the point of use (66.4% [95% CI: 57.3–74.9]) compared to the point of source (57.8% [95% CI: 42.0-72.3]). No significant publication bias was observed in any of the subgroup analyses (Table  1 ).

The pooled prevalence of E. coli (Fig.  4 ) among only households with under-five children was 65.9% (95% CI: 57.9–73.4), while for all households, it was 45.9% (95% CI: 35.2–56.9). The detailed distribution of the pooled prevalence of E. coli in household drinking water is shown in a table [ S4 ].

Forest plot of E. coli prevalence distribution in all households and only households with under-five children drinking water in Ethiopia

This systematic review and meta-analysis aimed to compile all available data reporting the prevalence and epidemiological distribution of indicators of pathogenic bacteria in households’ drinking water in Ethiopia. The study findings help enhance public health interventions in Ethiopia by identifying vulnerable groups and suggesting appropriate measures to reduce the impact of waterborne diseases. This knowledge enables more targeted interventions to mitigate the effects of such diseases effectively. The findings of this systematic review and meta-analysis revealed that the pooled prevalence of fecal coliforms in households’ drinking water in the 16 cross-sectional studies with 4193 samples was 63.7% (95% CI: 56.0, 71.0%).

This is lower than that found in a similar systematic review and meta-analysis in developing countries, where the fecal contamination of household drinking water was 75% (95% CI: 64, 84%) [ 54 ]. Differently, this is comparably high compared to the pooled prevalence in Africa (53%, 95% CI: 42, 63%); and also, significantly higher than the study conducted in South-East Asia (35%, 95% CI: 24, 45%) [ 55 ]. This variation might come from differences in how samples are collected and could also be because South-East Asia has better water quality rules and improvements compared to Ethiopia. In addition, the pooled prevalence of E. coli in households’ drinking water in the 8 cross-sectional studies with a total of 2,594 samples was 54% (95% CI: 45.7, 62.3%). This pooled prevalence is lower than a systematic review and meta-analysis conducted in Africa (2000–2021) where the pooled prevalence of E. coli in drinking water was 71.7% (95% CI: 56.2, 83.3%) [ 56 ]. Regional variations in water contamination in Ethiopia, compared to other African countries, can be influenced by factors like the types of water sources, the effectiveness of sanitation and water treatment practices, local environmental conditions, and differences in public health standards.

In this study, the prevalence of fecal coliform in households drinking water in the wet season was higher (70.3%) than in the dry season (60.1%). Similarly, a study conducted in Ghana found that the proportion of the population at risk of fecal contamination in the rainy season (41.5%) was higher compared to the dry season (33.1%) [ 57 ]. Furthermore, the pooled prevalence of fecal coliform was higher (73.8%) among households with only under-five children than in other households, this might be because of improper disposal of child feces. The pooled prevalence of fecal coliform was lower (66.4%) among urban households than rural (68%). Similarly, a systematic review and meta-analysis study on fecal contamination of drinking water globally, and in low-and-middle-income countries found that drinking water is more contaminated in rural areas than in urban areas [ 55 , 58 ]. This might be because urban areas have better infrastructure, like improved water sources and improved sanitation, which help to keep lower contamination levels.

Finally, the pooled prevalence of fecal coliform was higher at the point of use (66.4%) compared to the source point (57.8%). Similarly, a study conducted in Bangladesh found a lower contamination rate of 28% in water samples taken from the source compared to a significantly higher contamination rate of 73.96% in samples from stored household sources (point of use) [ 59 ]. The higher pooled prevalence of contamination observed in stored household water, compared to source water, is likely due to poor storage conditions, inadequate hygiene practices, and exposure to environmental contaminants. Future research should explore how Ethiopian water management practices and infrastructure impact fecal coliform and E. coli prevalence to identify effective interventions. Longitudinal studies could track changes in water quality over time. Additionally, more research in underrepresented regions is needed to understand water contamination patterns and improve policies for safer drinking water in Ethiopia.

The findings of this systematic review and meta-analysis point to a higher prevalence of E. coli and fecal coliform in Ethiopia, raising serious concerns about public health that require attention. There are variations within the country by season, residence, region, sources of sample collection and availability of under-five children. Subgroup assessments revealed an increased risk during the wet season, among only households with under-five children, at point-of-use, residing in rural areas, notably in the Amhara, Gambella, and Oromia regions. These findings emphasize the critical necessity for targeted interventions in vulnerable populations and specific geographic areas to address the risks posed by drinking water contamination and improve public health outcomes promptly.

Strengths and limitations of this study

We employed appropriate methods to stabilize variability across studies and enhance the reliability of our overall findings. The generalizability of our study might be constrained because of the restricted regional coverage within the country.

Data availability

No datasets were generated or analysed during the current study.

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EAA, AW, AMG, ZAA, TT, and AKD conceived, designed the review, and did the data collection and analysis for the study. EAA, AW, and AMG drafted the manuscript. MGW, MGS, KTT, MAK, and GKY reviewed the manuscript. EAA, AW, AMG, MT, and DAD checked the final analysis and revised the manuscript.

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Correspondence to Ermias Alemayehu Adugna .

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Adugna, E.A., Weldetinsae, A., Alemu, Z.A. et al. Prevalence and epidemiological distribution of indicators of pathogenic bacteria in households drinking water in Ethiopia: a systematic review and meta-analysis. BMC Public Health 24 , 2511 (2024). https://doi.org/10.1186/s12889-024-20067-x

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Received : 27 March 2024

Accepted : 12 September 2024

Published : 16 September 2024

DOI : https://doi.org/10.1186/s12889-024-20067-x

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Germs and chemicals in drinking water cause a variety of mild to serious health issues. Symptoms depend on the type of germ or chemical in the unsafe water.

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Meeting safety standards

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Keeping germs and chemicals out of water at home‎

Utilities monitor your water as they pipe it from its source to the treatment plant to your home. Once water enters the pipes on your property, the utility no longer monitors it. If you are having water quality problems caused by the pipes or water devices in your home, it is your (or the person who owns your home's) responsibility to fix the problem. Your health department may be able to help.

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Check with your water utility for more details about your water system's testing.

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