water pollution related research paper

REVIEW article

Effects of water pollution on human health and disease heterogeneity: a review.

• 1 Research Center for Economy of Upper Reaches of the Yangtse River/School of Economics, Chongqing Technology and Business University, Chongqing, China
• 2 School of Economics and Management, Huzhou University, Huzhou, China

Background: More than 80% of sewage generated by human activities is discharged into rivers and oceans without any treatment, which results in environmental pollution and more than 50 diseases. 80% of diseases and 50% of child deaths worldwide are related to poor water quality.

Methods: This paper selected 85 relevant papers finally based on the keywords of water pollution, water quality, health, cancer, and so on.

Results: The impact of water pollution on human health is significant, although there may be regional, age, gender, and other differences in degree. The most common disease caused by water pollution is diarrhea, which is mainly transmitted by enteroviruses in the aquatic environment.

Discussion: Governments should strengthen water intervention management and carry out intervention measures to improve water quality and reduce water pollution’s impact on human health.

Introduction

Water is an essential resource for human survival. According to the 2021 World Water Development Report released by UNESCO, the global use of freshwater has increased six-fold in the past 100 years and has been growing by about 1% per year since the 1980s. With the increase of water consumption, water quality is facing severe challenges. Industrialization, agricultural production, and urban life have resulted in the degradation and pollution of the environment, adversely affecting the water bodies (rivers and oceans) necessary for life, ultimately affecting human health and sustainable social development ( Xu et al., 2022a ). Globally, an estimated 80% of industrial and municipal wastewater is discharged into the environment without any prior treatment, with adverse effects on human health and ecosystems. This proportion is higher in the least developed countries, where sanitation and wastewater treatment facilities are severely lacking.

Sources of Water Pollution

Water pollution are mainly concentrated in industrialization, agricultural activities, natural factors, and insufficient water supply and sewage treatment facilities. First, industry is the main cause of water pollution, these industries include distillery industry, tannery industry, pulp and paper industry, textile industry, food industry, iron and steel industry, nuclear industry and so on. Various toxic chemicals, organic and inorganic substances, toxic solvents and volatile organic chemicals may be released in industrial production. If these wastes are released into aquatic ecosystems without adequate treatment, they will cause water pollution ( Chowdhary et al., 2020 ). Arsenic, cadmium, and chromium are vital pollutants discharged in wastewater, and the industrial sector is a significant contributor to harmful pollutants ( Chen et al., 2019 ). With the acceleration of urbanization, wastewater from industrial production has gradually increased. ( Wu et al., 2020 ). In addition, water pollution caused by industrialization is also greatly affected by foreign direct investment. Industrial water pollution in less developed countries is positively correlated with foreign direct investment ( Jorgenson, 2009 ). Second, water pollution is closely related to agriculture. Pesticides, nitrogen fertilizers and organic farm wastes from agriculture are significant causes of water pollution (RCEP, 1979). Agricultural activities will contaminate the water with nitrates, phosphorus, pesticides, soil sediments, salts and pathogens ( Parris, 2011 ). Furthermore, agriculture has severely damaged all freshwater systems in their pristine state ( Moss, 2008 ). Untreated or partially treated wastewater is widely used for irrigation in water-scarce regions of developing countries, including China and India, and the presence of pollutants in sewage poses risks to the environment and health. Taking China as an example, the imbalance in the quantity and quality of surface water resources has led to the long-term use of wastewater irrigation in some areas in developing countries to meet the water demand of agricultural production, resulting in serious agricultural land and food pollution, pesticide residues and heavy metal pollution threatening food safety and Human Health ( Lu et al., 2015 ). Pesticides have an adverse impact on health through drinking water. Comparing pesticide use with health life Expectancy Longitudinal Survey data, it was found that a 10% increase in pesticide use resulted in a 1% increase in the medical disability index over 65 years of age ( Lai, 2017 ). The case of the Musi River in India shows a higher incidence of morbidity in wastewater-irrigated villages than normal-water households. Third, water pollution is related to natural factors. Taking Child Loess Plateau as an example, the concentration of trace elements in water quality is higher than the average world level, and trace elements come from natural weathering and manufacture causes. Poor river water quality is associated with high sodium and salinity hazards ( Xiao et al., 2019 ). The most typical water pollution in the middle part of the loess Plateau is hexavalent chromium pollution, which is caused by the natural environment and human activities. Loess and mudstone are the main sources, and groundwater with high concentrations of hexavalent chromium is also an important factor in surface water pollution (He et al., 2020). Finally, water supply and sewage treatment facilities are also important factors affecting drinking water quality, especially in developing countries. In parallel with China rapid economic growth, industrialization and urbanization, underinvestment in basic water supply and treatment facilities has led to water pollution, increased incidence of infectious and parasitic diseases, and increased exposure to industrial chemicals, heavy metals and algal toxins ( Wu et al., 1999 ). An econometric model predicts the impact of water purification equipment on water quality and therefore human health. When the proportion of household water treated with water purification equipment is reduced from 100% to 90%, the expected health benefits are reduced by up to 96%.. When the risk of pretreatment water quality is high, the decline is even more significant ( Brown and Clasen, 2012 ).

To sum up, water pollution results from both human and natural factors. Various human activities will directly affect water quality, including urbanization, population growth, industrial production, climate change, and other factors ( Halder and Islam, 2015 ) and religious activities ( Dwivedi et al., 2018 ). Improper disposal of solid waste, sand, and gravel is also one reason for decreasing water quality ( Ustaoğlua et al., 2020 ).

Impact of Water Pollution on Human Health

Unsafe water has severe implications for human health. According to UNESCO 2021 World Water Development Report , about 829,000 people die each year from diarrhea caused by unsafe drinking water, sanitation, and hand hygiene, including nearly 300,000 children under the age of five, representing 5.3 percent of all deaths in this age group. Data from Palestine suggest that people who drink municipal water directly are more likely to suffer from diseases such as diarrhea than those who use desalinated and household-filtered drinking water ( Yassin et al., 2006 ). In a comparative study of tap water, purified water, and bottled water, tap water was an essential source of gastrointestinal disease ( Payment et al., 1997 ). Lack of water and sanitation services also increases the incidence of diseases such as cholera, trachoma, schistosomiasis, and helminthiasis. Data from studies in developing countries show a clear relationship between cholera and contaminated water, and household water treatment and storage can reduce cholera ( Gundry et al., 2004 ). In addition to disease, unsafe drinking water, and poor environmental hygiene can lead to gastrointestinal illness, inhibiting nutrient absorption and malnutrition. These effects are especially pronounced for children.

Purpose of This Paper

More than two million people worldwide die each year from diarrhoeal diseases, with poor sanitation and unsafe drinking water being the leading cause of nearly 90% of deaths and affecting children the most (United Nations, 2016). More than 50 kinds of diseases are caused by poor drinking water quality, and 80% of diseases and 50% of child deaths are related to poor drinking water quality in the world. However, water pollution causes diarrhea, skin diseases, malnutrition, and even cancer and other diseases related to water pollution. Therefore, it is necessary to study the impact of water pollution on human health, especially disease heterogeneity, and clarify the importance of clean drinking water, which has important theoretical and practical significance for realizing sustainable development goals. Unfortunately, although many kinds of literature focus on water pollution and a particular disease, there is still a lack of research results that systematically analyze the impact of water pollution on human health and the heterogeneity of diseases. Based on the above background and discussion, this paper focuses on the effect of water pollution on human health and its disease heterogeneity.

Materials and Methods

Search process.

This article uses keywords such as “water,” “water pollution,” “water quality,” “health,” “diarrhea,” “skin disease,” “cancer” and “children” to search Web of Science and Google Scholar include SCI and SSCI indexed papers, research reports, and works from 1990 to 2021.

Inclusion-Exclusion Criteria and Data Extraction Process

The existing literature shows that water pollution and human health are important research topics in health economics, and scholars have conducted in-depth research. As of 30 December 2021, 104 related literatures were searched, including research papers, reviews and conference papers. Then, according to the content relevancy, 19 papers were eliminated, and 85 papers remained. The purpose of this review is to summarize the impact of water pollution on human health and its disease heterogeneity and to explore how to improve human health by improving water pollution control measures.

Information extracted from all included papers included: author, publication date, sample country, study methodology, study purpose, and key findings. All analysis results will be analyzed according to the process in Figure 1 .

FIGURE 1 . Data extraction process (PRISMA).

The relevant information of the paper is exported to the Excel database through Endnote, and the duplicates are deleted. The results were initially extracted by one researcher and then cross-checked by another researcher to ensure that all data had been filtered and reviewed. If two researchers have different opinions, the two researchers will review together until a final agreement is reached.

Quality Assessment of the Literature

The JBI Critical Appraisal Checklist was used to evaluate the quality of each paper. The JBI (Joanna Briggs Institute) key assessment tool was developed by the JBI Scientific Committee after extensive peer review and is designed for system review. All features of the study that meet the following eight criteria are included in the final summary:1) clear purpose; 2) Complete information of sample variables; 3) Data basis; 4) the validity of data sorting; 5) ethical norms; (6); 7) Effective results; 8) Apply appropriate quantitative methods and state the results clearly. Method quality is evaluated by the Yes/No questions listed in the JBI Key Assessment List. Each analysis paper received 6 out of 8.

The quality of drinking water is an essential factor affecting human health. Poor drinking water quality has led to the occurrence of water-borne diseases. According to the World Health Organization (WHO) survey, 80% of the world’s diseases and 50% of the world’s child deaths are related to poor drinking water quality, and there are more than 50 diseases caused by poor drinking water quality. The quality of drinking water in developing countries is worrying. The negative health effects of water pollution remain the leading cause of morbidity and mortality in developing countries. Different from the existing literature review, this paper mainly studies the impact of water pollution on human health according to the heterogeneity of diseases. We focuses on diarrhea, skin diseases, cancer, child health, etc., and sorts out the main effects of water pollution on human health ( Table 1 ).

TABLE 1 . Major studies on the relationship between water pollution and health.

Water Pollution and Diarrhea

Diarrhea is a common symptom of gastrointestinal diseases and the most common disease caused by water pollution. Diarrhea is a leading cause of illness and death in young children in low-income countries. Diarrhoeal diseases account for 21% of annual deaths among children under 5 years of age in developing countries ( Waddington et al., 2009 ). Many infectious agents associated with diarrhea are directly related to contaminated water ( Ahmed and Ismail, 2018 ). Parasitic worms present in non-purifying drinking water when is consumed by human beings causes diseases ( Ansari and Akhmatov., 2020 ) . It was found that treated water from water treatment facilities was associated with a lower risk of diarrhea than untreated water for all ages ( Clasen et al., 2015 ). For example, in the southern region of Brazil, a study found that factors significantly associated with an increased risk of mortality from diarrhoea included lack of plumbed water, lack of flush toilets, poor housing conditions, and overcrowded households. Households without access to piped water had a 4.8 times higher risk of infant death from diarrhea than households with access to piped water ( Victora et al., 1988 )

Enteroviruses exist in the aquatic environment. More than 100 pathogenic viruses are excreted in human and animal excreta and spread in the environment through groundwater, estuarine water, seawater, rivers, sewage treatment plants, insufficiently treated water, drinking water, and private wells ( Fong and Lipp., 2005 ). A study in Pakistan showed that coliform contamination was found in some water sources. Improper disposal of sewage and solid waste, excessive use of pesticides and fertilizers, and deteriorating pipeline networks are the main causes of drinking water pollution. The main source of water-borne diseases such as gastroenteritis, dysentery, diarrhea, and viral hepatitis in this area is the water pollution of coliform bacteria ( Khan et al., 2013 ). Therefore, the most important role of water and sanitation health interventions is to hinder the transmission of diarrheal pathogens from the environment to humans ( Waddington et al., 2009 ).

Meta-analyses are the most commonly used method for water quality and diarrhea studies. It was found that improving water supply and sanitation reduced the overall incidence of diarrhea by 26%. Among Malaysian infants, having clean water and sanitation was associated with an 82% reduction in infant mortality, especially among infants who were not breastfed ( Esrey et al., 1991 ). All water quality and sanitation interventions significantly reduced the risk of diarrhoeal disease, and water quality interventions were found to be more effective than previously thought. Multiple interventions (including water, sanitation, and sanitation measures) were not more effective than single-focus interventions ( Fewtrell and Colford., 2005 ). Water quality interventions reduced the risk of diarrhoea in children and reduced the risk of E. coli contamination of stored water ( Arnold and Colford., 2007 ). Interventions to improve water quality are generally effective in preventing diarrhoea in children of all ages and under 5. However, some trials showed significant heterogeneity, which may be due to the research methods and their conditions ( Clasen et al., 2007 ).

Water Pollution and Skin Diseases

Contrary to common sense that swimming is good for health, studies as early as the 1950s found that the overall disease incidence in the swimming group was significantly higher than that in the non-swimming group. The survey shows that the incidence of the disease in people under the age of 10 is about 100% higher than that of people over 10 years old. Skin diseases account for a certain proportion ( Stevenson, 1953 ). A prospective epidemiological study of beach water pollution was conducted in Hong Kong in the summer of 1986–1987. The study found that swimmers on Hong Kong’s coastal beaches were more likely than non-swimmers to complain of systemic ailments such as skin and eyes. And swimming in more polluted beach waters has a much higher risk of contracting skin diseases and other diseases. Swimming-related disease symptom rates correlated with beach cleanliness ( Cheung et al., 1990 ).

A study of arsenic-affected villages in the southern Sindh province of Pakistan emphasized that skin diseases were caused by excessive water quality. By studying the relationship between excessive arsenic in drinking water caused by water pollution and skin diseases (mainly melanosis and keratosis), it was found that compared with people who consumed urban low-arsenic drinking water, the hair of people who consumed high-arsenic drinking water arsenic concentration increased significantly. The level of arsenic in drinking water directly affects the health of local residents, and skin disease is the most common clinical complication of arsenic poisoning. There is a correlation between arsenic concentrations in biological samples (hair and blood) from patients with skin diseases and intake of arsenic-contaminated drinking water ( Kazi et al., 2009 ). Another Bangladesh study showed that many people suffer from scabies due to river pollution ( Hanif et al., 2020 ). Not only that, but water pollution from industry can also cause skin cancer ( Arif et al., 2020 ).

Studies using meta-analysis have shown that exposure to polluted Marine recreational waters can have adverse consequences, including frequent skin discomfort (such as rash or itching). Skin diseases in swimmers may be caused by a variety of pathogenic microorganisms ( Yau et al., 2009 ). People (swimmers and non-swimmers) exposed to waters above threshold levels of bacteria had a higher relative risk of developing skin disease, and levels of bacteria in seawater were highly correlated with skin symptoms.

Studies have also suggested that swimmers are 3.5 times more likely to report skin diseases than non-swimmers. This difference may be a “risk perception bias” at work on swimmers, who are generally aware that such exposure may lead to health effects and are more likely to detect and report skin disorders. It is also possible that swimmers exaggerated their symptoms, reporting conditions that others would not classify as true skin disorders ( Fleisher and Kay. 2006 ).

Water Pollution and Cancer

According to WHO statistics, the number of cancer patients diagnosed in 2020 reached 19.3 million, while the number of deaths from cancer increased to 10 million. Currently, one-fifth of all global fevers will develop cancer during their lifetime. The types and amounts of carcinogens present in drinking water will vary depending on where they enter: contamination of the water source, water treatment processes, or when the water is delivered to users ( Morris, 1995 ).

From the perspective of water sources, arsenic, nitrate, chromium, etc. are highly associated with cancer. Ingestion of arsenic from drinking water can cause skin cancer and kidney and bladder cancer ( Marmot et al., 2007 ). The risk of cancer in the population from arsenic in the United States water supply may be comparable to the risk from tobacco smoke and radon in the home environment. However, individual susceptibility to the carcinogenic effects of arsenic varies ( Smith et al., 1992 ). A high association of arsenic in drinking water with lung cancer was demonstrated in a northern Chilean controlled study involving patients diagnosed with lung cancer and a frequency-matched hospital between 1994 and 1996. Studies have also shown a synergistic effect of smoking and arsenic intake in drinking water in causing lung cancer ( Ferreccio et al., 2000 ). Exposure to high arsenic levels in drinking water was also associated with the development of liver cancer, but this effect was not significant at exposure levels below 0.64 mg/L ( Lin et al., 2013 ).

Nitrates are a broader contaminant that is more closely associated with human cancers, especially colorectal cancer. A study in East Azerbaijan confirmed a significant association between colorectal cancer and nitrate in men, but not in women (Maleki et al., 2021). The carcinogenic risk of nitrates is concentration-dependent. The risk increases significantly when drinking water levels exceed 3.87 mg/L, well below the current drinking water standard of 50 mg/L. Drinking water with nitrate concentrations lower than current drinking water standards also increases the risk of colorectal cancer ( Schullehner et al., 2018 ).

Drinking water with high chromium content will bring high carcinogenicity caused by hexavalent chromium to residents. Drinking water intake of hexavalent chromium experiments showed that hexavalent chromium has the potential to cause human respiratory cancer. ( Zhitkovich, 2011 ). A case from Changhua County, Taiwan also showed that high levels of chromium pollution were associated with gastric cancer incidence ( Tseng et al., 2018 ).

There is a correlation between trihalomethane (THM) levels in drinking water and cancer mortality. Bladder and brain cancers in both men and women and non-Hodgkin’s lymphoma and kidney cancer in men were positively correlated with THM levels, and bladder cancer mortality had the strongest and most consistent association with THM exposure index ( Cantor et al., 1978 ).

From the perspective of water treatment process, carcinogens may be introduced during chlorine treatment, and drinking water is associated with all cancers, urinary cancers and gastrointestinal cancers ( Page et al., 1976 ). Chlorinated byproducts from the use of chlorine in water treatment are associated with an increased risk of bladder and rectal cancer, with perhaps 5,000 cases of bladder and 8,000 cases of rectal cancer occurring each year in the United States (Morris, 1995).

The impact of drinking water pollutants on cancer is complex. Epidemiological studies have shown that drinking water contaminants, such as chlorinated by-products, nitrates, arsenic, and radionuclides, are associated with cancer in humans ( Cantor, 1997 ). Pb, U, F- and no3- are the main groundwater pollutants and one of the potential causes of cancer ( Kaur et al., 2021 ). In addition, many other water pollutants are also considered carcinogenic, including herbicides and pesticides, and fertilizers that contain and release nitrates ( Marmot et al., 2007 ). A case from Hebei, China showed that the contamination of nitrogen compounds in well water was closely related to the use of nitrogen fertilizers in agriculture, and the levels of three nitrogen compounds in well water were significantly positively correlated with esophageal cancer mortality ( Zhang et al., 2003 ).

In addition, due to the time-lag effect, the impact of watershed water pollution on cancer is spatially heterogeneous. The mortality rate of esophageal cancer caused by water pollution is significantly higher downstream than in other regions due to the impact of historical water pollution ( Xu et al., 2019 ). A study based on changes in water quality in the watershed showed that a grade 6 deterioration in water quality resulted in a 9.3% increase in deaths from digestive cancer. ( Ebenstein, 2012 ).

Water Pollution and Child Health

Diarrhea is a common disease in children. Diarrhoeal diseases (including cholera) kill 1.8 million people each year, 90 per cent of them children under the age of five, mostly in developing countries. 88% of diarrhoeal diseases are caused by inadequate water supply, sanitation and hygiene (Team, 2004). A large proportion of these are caused by exposure to microbially infected water and food, and diarrhea in infants and young children can lead to malnutrition and reduced immune resistance, thereby increasing the likelihood of prolonged and recurrent diarrhea ( Marino, 2007 ). Pollution exposure experienced by children during critical periods of development is associated with height loss in adulthood ( Zaveri et al., 2020 ). Diseases directly related to water and sanitation, combined with malnutrition, also lead to other causes of death, such as measles and pneumonia. Child malnutrition and stunting due to inadequate water and sanitation will continue to affect more than one-third of children in the world ( Bartlett, 2003 ). A study from rural India showed that children living in households with tap water had significantly lower disease prevalence and duration ( Jalan and Ravallion, 2003 ).

In conclusion, water pollution is a significant cause of childhood diseases. Air, water, and soil pollution together killed 940,000 children worldwide in 2016, two-thirds of whom were under the age of 5, and the vast majority occurred in low- and middle-income countries ( Landrigan et al., 2018 ). The intensity of industrial organic water pollution is positively correlated with infant mortality and child mortality in less developed countries, and industrial water pollution is an important cause of infant and child mortality in less developed countries ( Jorgenson, 2009 ). In addition, arsenic in drinking water is a potential carcinogenic risk in children (García-Rico et al., 2018). Nitrate contamination in drinking water may cause goiter in children ( Vladeva et al.., 2000 ).

Discussions

This paper reviews the environmental science, health, and medical literature, with a particular focus on epidemiological studies linking water quality, water pollution, and human disease, as well as studies on water-related disease morbidity and mortality. At the same time, special attention is paid to publications from the United Nations and the World Health Organization on water and sanitation health research. The purpose of this paper is to clarify the relationship between water pollution and human health, including: The relationship between water pollution and diarrhea, the mechanism of action, and the research situation of meta-analysis; The relationship between water pollution and skin diseases, pathogenic factors, and meta-analysis research; The relationship between water pollution and cancer, carcinogenic factors, and types of cancer; The relationship between water pollution and Child health, and the major childhood diseases caused.

A study of more than 100 literatures found that although factors such as country, region, age, and gender may have different influences, in general, water pollution has a huge impact on human health. Water pollution is the cause of many human diseases, mainly diarrhoea, skin diseases, cancer and various childhood diseases. The impact of water pollution on different diseases is mainly reflected in the following aspects. Firstly, diarrhea is the most easily caused disease by water pollution, mainly transmitted by enterovirus existing in the aquatic environment. The transmission environment of enterovirus depends on includes groundwater, river, seawater, sewage, drinking water, etc. Therefore, it is necessary to prevent the transmission of enterovirus from the environment to people through drinking water intervention. Secondly, exposure to or use of heavily polluted water is associated with a risk of skin diseases. Excessive bacteria in seawater and heavy metals in drinking water are the main pathogenic factors of skin diseases. Thirdly, water pollution can pose health risks to humans through any of the three links: the source of water, the treatment of water, and the delivery of water. Arsenic, nitrate, chromium, and trihalomethane are major carcinogens in water sources. Carcinogens may be introduced during chlorine treatment from water treatment. The effects of drinking water pollution on cancer are complex, including chlorinated by-products, heavy metals, radionuclides, herbicides and pesticides left in water, etc., Finally, water pollution is an important cause of children’s diseases. Contact with microbiologically infected water can cause diarrhoeal disease in children. Malnutrition and weakened immunity from diarrhoeal diseases can lead to other diseases.

This study systematically analyzed the impact of water pollution on human health and the heterogeneity of diseases from the perspective of different diseases, focusing on a detailed review of the relationship, mechanism and influencing factors of water pollution and diseases. From the point of view of limitations, this paper mainly focuses on the research of environmental science and environmental management, and the research on pathology is less involved. Based on this, future research can strengthen research at medical and pathological levels.

In response to the above research conclusions, countries, especially developing countries, need to adopt corresponding water management policies to reduce the harm caused by water pollution to human health. Firstly, there is a focus on water quality at the point of use, with interventions to improve water quality, including chlorination and safe storage ( Gundry et al., 2004 ), and provision of treated and clean water ( Khan et al., 2013 ). Secondly, in order to reduce the impact of water pollution on skin diseases, countries should conduct epidemiological studies on their own in order to formulate health-friendly bathing water quality standards suitable for their specific conditions ( Cheung et al., 1990 ). Thirdly, in order to reduce the cancer caused by water pollution, the whole-process supervision of water quality should be strengthened, that is, the purity of water sources, the scientific nature of water treatment and the effectiveness of drinking water monitoring. Fourthly, each society should prevent and control source pollution from production, consumption, and transportation ( Landrigan et al., 2018 ). Fifthly, health education is widely carried out. Introduce environmental education, educate residents on sanitary water through newspapers, magazines, television, Internet and other media, and enhance public health awareness. Train farmers to avoid overuse of agricultural chemicals that contaminate drinking water.

Author Contributions

Conceptualization, XX|; methodology, LL; data curation, HY; writing and editing, LL; project administration, XX|.

This article is a phased achievement of The National Social Science Fund of China: Research on the blocking mechanism of the critical poor households returning to poverty due to illness, No: 20BJY057.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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Keywords: water pollution, human health, disease heterogeneity, water intervention, health cost

Citation: Lin L, Yang H and Xu X (2022) Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review. Front. Environ. Sci. 10:880246. doi: 10.3389/fenvs.2022.880246

Received: 21 February 2022; Accepted: 09 June 2022; Published: 30 June 2022.

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*Correspondence: Xiaocang Xu, [email protected]

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• Published: 22 June 2021

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

• J. Tom Mueller   ORCID: orcid.org/0000-0001-6223-4505 1 &
• Stephen Gasteyer 2  

Nature Communications volume  12 , Article number:  3544 ( 2021 ) Cite this article

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An Addendum to this article was published on 13 June 2023

An Author Correction to this article was published on 13 June 2023

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.

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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 ).

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 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 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 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 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.

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 3 and 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. 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 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 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 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 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. 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. 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 , and 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. 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 , 32 , 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 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 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.

Data availability

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

Code availability

Analysis code is available on the Open Science Framework project for this study at https://doi.org/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.

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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.

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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.

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Mueller, J.T., Gasteyer, S. The widespread and unjust drinking water and clean water crisis in the United States. Nat Commun 12 , 3544 (2021). https://doi.org/10.1038/s41467-021-23898-z

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• Joshua D. Miller
• Chad Staddon
• Sera L. Young

Nature Communications (2024)

Trends and disparities in concurrent tap water avoidance and household food insecurity among US children

• Asher Y. Rosinger

Nature Water (2024)

Sustainable wastewater reuse for agriculture

• Anastasis Christou
• Vasiliki G. Beretsou
• Despo Fatta-Kassinos

Nature Reviews Earth & Environment (2024)

Reused water as a source of clean water and energy

• Cecilia Tortajada

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