2.1. Environmental justice

The concept of water justice is developed within the broader framework of environmental justice, understood as the equitable treatment in access, distribution, and decision-making regarding environmental benefits and burdens, regardless of race, ethnicity, or social class [9,10].

Environmental injustice, in turn, acknowledges that historically marginalized communities have disproportionately borne the burden of pollution and environmental degradation [11].

In this context [12], proposed a multidimensional approach to the study of environmental injustice, incorporating distributive justice, recognition, procedural justice, and capabilities, emphasizing the need to move beyond mere material redistribution to include effective participation and respect for cultural identities. This aligns with the work of [13] and [14], who link justice to recognition and representation within deliberative contexts.

In applied terms [15], connected environmental justice with scientific evidence of environmental harm and health inequality, highlighting the role of cities and wealthy countries in generating global impacts that disproportionately affect poorer populations. Similarly [16], argue that the classical pillars of environmental justice, distribution, participation, and recognition, should be complemented by a restorative perspective, adapted to both local and global scales.

2.2. Water justice

In the specific domain of water, studies such as [17] and [18] frame water justice as a distinct form of environmental injustice, where social, economic, and political factors perpetuate inequalities in access, management, and perceptions of water quality. These contributions have been instrumental in consolidating water justice as a theoretical and political tool to understand and address the unequal distribution of water resources.

Building on this theoretical foundation, recent studies have emphasized the multiscalar nature of water injustice, examining connections between socio-technical systems and historical infrastructure disparities [19]. Illustrate how historical injustices in water distribution continue to impact vulnerable communities, particularly at the intersection of water, energy, and food systems.

From a sociopolitical perspective, researchers have explored how exclusionary dynamics affect citizens’ perceptions of water and their capacity to participate in decision-making processes [17] introduce the concept of “hydraulic citizenship,” explaining how policies addressing environmental crises often exacerbate existing inequalities, further marginalizing vulnerable populations. This pattern aligns with structural barriers identified by [20] who highlight the inefficacy of regulations in ensuring equitable water distribution.

These exclusionary dynamics become even more pronounced in the context of large-scale hydraulic projects [18] and [21], analyze how these initiatives create tensions between communities and states, marginalizing less powerful groups in water governance. These dynamics affect not only resource distribution but also public perceptions of water quality, particularly in areas with high environmental contamination.

Moreover, structural exclusion also impacts both rural and urban poor communities [22]. Underscore the similar conditions of water marginalization faced by these groups, underscoring the need for equitable policies that prioritize the most disadvantaged populations.

In response to these challenges, intersectionality has emerged as a key theoretical tool to address inequalities in water management [23]. Advocate for active community participation to promote more inclusive and sustainable solutions, while [24] identify barriers to integrating water justice into policies addressing extreme events such as urban flooding.

2.3. Manifestations of water injustice: international evidence

Water injustice in Latin America appears as a deep structural issue affecting both urban and rural areas. It is shaped by geographic location, socioeconomic status, and public policy. The 2017–2018 water rationing crisis in Brasília illustrates how centralized state-private governance models reinforce inequality, disproportionately impacting low-income, less-educated, and peripheral communities through institutional violence and water alienation [7].

This pattern is evident across the region. In Peru’s Ica Valley, agro-export companies, only 0.1% of users, consume 36% of available water, leaving small farmers with dry wells and no means of livelihood. In Mexico, the 2013 energy reform enabled the mining-energy sector to accumulate water rights, leading to legal and economic dispossession in Indigenous communal lands. Extractivism, as a dominant model, has intensified socio-environmental conflicts and deepened water inequities [25].

Similarly, extractive industries like mining in Cerro de San Pedro have turned ecological reserves into toxic waste sites, while hydroelectric megaprojects such as the Chixoy dam in Guatemala displaced Indigenous communities through structural violence. In urban contexts, rural to urban water transfers, such as those in Lima, strip highland communities of customary rights. Water service privatization in countries like Peru, India, and Tanzania has raised costs and reduced service quality, prompting remunicipalization [21].

Environmental risks, including urban flooding, also disproportionately affect vulnerable groups. In Santa Fe, Argentina, spatial analysis revealed systematic overexposure of marginalized communities to flood hazards, highlighting how planning policies neglect environmental equity [26].

These patterns are not isolated cases but part of a broader global trend in which policy and environmental strategies often reinforce water injustice instead of resolving it. For example [27], documents how mining activities in Finland have worsened water pollution, prompting local communities to advocate for equitable policy reforms. At the policy level [28], reveal how carbon neutrality strategies in Portugal, while environmentally driven, have unintentionally increased water access vulnerabilities for marginalized populations. Similarly [29], highlight spatial inequalities in rural Kenya, emphasizing the critical need for infrastructure design that is both socially just and context-sensitive. These cases illustrate how even progressive environmental initiatives may reproduce structural exclusion unless justice is placed at the core of water governance.

2.4. Factors influencing perceptions of water quality

Perceptions of water quality are shaped by a wide range of interrelated factors that go beyond objective measurements. While physicochemical indicators provide essential information, they do not fully capture how people interpret, experience, and emotionally respond to the state of water. Research has increasingly highlighted how sensory, social, cultural, psychological, and demographic elements collectively influence how water quality is perceived across diverse contexts [30,31].

In North America, a systematic review by [31] identified a set of key variables influencing perceptions among private water users. These include organoleptic attributes (such as taste, odor, and appearance), awareness of chemical and microbial contaminants, prior experiences, well infrastructure, external information sources, and demographic variables like age, gender, and income. Similarly [32], found that perceptions of drinking water safety are strongly influenced by educational level, health status, income, and social networks, often more than by objective measures.

In Latin America, these dynamics are equally evident [33]. Emphasize how socioeconomic factors, particularly education and income, directly influence perceptions of water quality and daily water-related practices, reinforcing existing inequalities [8]. Further link the lack of infrastructure in urban peripheries with heightened perceptions of water-related risk [34], meanwhile, argue that while decentralized water management solutions may offer more localized control, their effectiveness depends on context-sensitive designs to avoid reinforcing exclusion.

Previous studies have shown that environmental conditions affect the perception of water contamination. In Antofagasta, Chile, where desalinated water was introduced in 2003, [33] found that dissatisfaction with taste and concerns about quality led many households to adopt precautionary behaviors such as boiling water, even when the water met safety standards.

It is important to note that public perceptions have direct implications for the success of water policies and the adoption of sustainable practices [35] and [29]. Emphasized the importance of incorporating community perspectives into policy design to foster legitimacy, trust, and ownership. Along these lines [36], advocated for interdisciplinary educational strategies to raise awareness and promote equity in water management.

3.1. Data

The data used in this study were sourced from the Quality of Life Survey conducted by the National Administrative Department of Statistics (DANE) in 2022, and the Water Quality Risk Index for Human Consumption (IRCA).

The sampling design of the 2022 Quality of Life Survey employed a probabilistic, multi-stage, stratified, and clustered approach. Municipalities (primary sampling units) were selected proportionally to population size based on the 2018 census. Clusters (secondary sampling units) consisted of ten systematically selected contiguous households. The final sample included 77,400 households, distributed across urban and rural areas, with differentiated error margins and a 95% confidence interval. This design ensured national representativeness and result accuracy [40].

This study is based exclusively on secondary data from the 2022 National Quality of Life Survey (ECV), which are publicly available and fully anonymized through the microdata portal of Colombia’s National Administrative Department of Statistics (DANE). The use of publicly available and anonymized secondary data does not require formal ethical approval or additional informed consent.

Table 1 summarizes the distribution of respondents, who were household heads (37,028) individuals), In the departments where IRCA levels indicated moderate or high risk. The departments include Antioquia, Boyacá, Caldas, Chocó, Huila, Magdalena, Nariño, Sucre, Tolima, Valle del Cauca, Putumayo, Amazonas, Guainía, and Vaupés (Table 1).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 1. Number of Respondents by Department with High or Unsanitary Water Quality Risk Index (2022).

https://doi.org/10.1371/journal.pone.0332274.t001

3.2. Variables

The dependent variable for this study was the perception of water contamination, assessed through the question:

“In the past 12 months, how often have the following problems occurred in the area where your home is located: contamination in rivers, canals, lakes, and reservoirs?”

Responses were categorized on an ordinal scale:

1. Frequently or always
2. Never or occasionally

This indicator captures respondents’ perceptions of the frequency of issues related to the contamination of water bodies in their residential areas.

Table 2 lists the predictors used in the analysis of water quality perception, grouped into sociodemographic, environmental, health, pro-environmental practices, and potable water access categories. Each category includes specific survey questions, their respective response scales, and notation codes, facilitating variable identification within the dataset.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 2. Predictors of Water Quality Perception in Colombia (2022).

https://doi.org/10.1371/journal.pone.0332274.t002

1. 1. Economic and Sociodemographic Factors: This category includes predictors such as socioeconomic status, age, gender, and ethnic self-identification to analyze differences between population groups.

Sociodemographic factors play a critical role in shaping how individuals perceive water quality, as they influence not only access to information but also lived experiences of environmental inequities. Education level, for instance, is closely linked to environmental awareness. Individuals with higher educational attainment tend to possess greater knowledge of water-related risks and standards, which in turn affects how they evaluate water safety [32].

Socioeconomic status, particularly income and housing conditions, also profoundly influences perceptions. As [33] show, low-income households frequently report poorer water quality, not necessarily because the water is chemically unsafe, but because infrastructure deficits degrade service reliability and aesthetic quality. These conditions are often compounded by geographic marginalization, such as living in urban peripheries or rural areas where public investment is limited.

1. 2. Environmental Perceptions: This dimension captures perceived issues such as foul odors, garbage accumulation, and air pollution to assess the broader environmental impacts on quality of life. These perceptions are essential not only for understanding environmental discomfort but also for revealing how external environmental cues shape attitudes toward other public services, including water.

Perceptions of water quality are strongly influenced by broader environmental concerns such as air pollution, waste, flooding, and climate change, forming a cognitive and emotional ecosystem in which accumulated environmental experiences affect trust in water resources. Studies in both rural and urban contexts [26,41] show that visual and symbolic factors, such as visible waste or extreme weather events, intensify perceptions of vulnerability and distrust, even when technical indicators of water quality are adequate.

Furthermore, exposure to negative environmental messages in media or social networks can amplify these perceptions [38].

1. 3. Subjective Health Perception: Assesses satisfaction with general health on a scale from 0 to 10, reflecting individual well-being. At this point, it is important to highlight that when individuals perceive themselves as healthy, they may tend to assume that the water they consume is safe, without necessarily considering the actual levels of contaminants or linking their health issues to potential water contamination. As demonstrated by the studies of [42,43].
2. 4. Pro-environmental Practices: This category includes variables such as water reuse, rainwater collection, use of low-flush toilets, and unplugging electronic devices to measure sustainable household habits. Although a broader range of practices was initially considered, such as waste separation, use of energy-saving light bulbs, turning off lights, efficient ironing or avoiding ironing, and installation of water-saving devices in showers and faucets, some of these did not show statistical significance in preliminary models and were therefore excluded from the analysis. Only the variables with empirical relevance were retained, as they more clearly captured sustainable behavior at the household level.

Environmental awareness plays a crucial role in shaping perceptions of water quality, as it enhances individuals’ ability to identify ecological risks and relate them to their immediate surroundings. In this context, the study by [44], based on Eurobarometer data and the WISE system of the European Environment Agency, demonstrated that perceived water-related risks are significantly influenced by environmental knowledge, participation in pro-environmental activities, and conscious consumption practices. These factors increase sensitivity to actual environmental changes, aligning individual perceptions with the objective condition of the water environment.

1. 5. Potable Water Access and Infrastructure: Examines water supply, primary sources for drinking and cooking, and sufficiency of services. Water access infrastructure is a key factor in shaping perceptions of water quality. As demonstrated by [31] in their systematic review of private water users in Canada and the United States, the structural conditions of water collection systems, such as their age, maintenance, and the type of technology used, directly influence how users perceive the safety of the water they consume.

3.3. Algorithms

3.3.1. Penalized Logistic Regression (LASSO).

The LASSO method, introduced by [45], combines logistic regression with penalization for variable selection and regularization. The loss function is defined as:

(1)

Where:

• 𝑝𝑖: Predicted probability for observation iii,
• yi: Binary dependent variable (0 or 1),
• βj: Estimated coefficients,
• λ: Regularization parameter controlling penalization.

The optimal λ\lambdaλ value was selected using kkk-fold cross-validation in Stata.

3.3.2. Double Machine Learning (DML).

Double Machine Learning estimates causal effects by separating covariate modeling from treatment effects [46]. The approach comprises two stages:

1. Stage One: Models dependent variables and controls using random forests and logistic regression to adjust non-parametric effects.
2. Stage Two: Uses residuals from the first stage to estimate causal parameters with adjusted linear or logistic regression.

The DML framework for causal estimation is defined as:

(2)

Where:

• yi: Observed outcome (dependent variable), representing the perception of water quality relative to IRCA values.
• Di: Binary treatment variable (ccc), set to 1 if contam_valor≥65, and 0 otherwise.
• Xi: Covariates, including demographic, socioeconomic, environmental, and pro-environmental factors (see Table 1).
• m(Xi) and k(Xi): Adjusted predictions of non-parametric functions

4.1. Water injustice in Colombia

Table 3 illustrates how respondents perceive the frequency of contamination in rivers, canals, lakes, and reservoirs across various Colombian departments over the past 12 months. These perceptions are presented alongside the Water Quality Risk Index for Human Consumption (IRCA) and its corresponding classifications, providing a way to compare the community’s subjective experience with objective water quality measurements.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 3. Perception of water quality and the water quality risk index for human consumption (IRCA) by Colombian departments (2022).

https://doi.org/10.1371/journal.pone.0332274.t003

The data in Table 3 illustrate a significant disconnect between objective IRCA measurements and public perception of contamination in water bodies. Departments with IRCA ratings classified as high risk, such as Nariño (IRCA = 46), Guanía (IRCA = 37), and Vaupés (IRCA = 51), show that over 72 percent of respondents perceive contamination as occurring “never” or “sometimes,” significantly underestimating the objective risk.

In Colombia, access to potable water is deeply inequitable, with disparities tied to socioeconomic strata. Higher- and middle-income strata (3, 4, 5, 6) rely predominantly on public water systems, with between 88 and 93 percent of households connected to treated and regulated sources ensuring access to treated and safe water. In contrast, lower-income groups (strata 0, 1, and 2) have much lower access to public water networks, ranging from 14.9 to 61,7 percent, and are more dependent on precarious sources such as wells, cisterns, and rivers, which account for up to 57 percent of supply in stratum 0, and 33 percent in stratum 1, thereby increasing exposure to health risks. Communal water systems are also more common in lower strata, reaching up to 25 percent in stratum 1, but remain marginal in higher strata, where their use falls below ten percent (see Table 4).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. Distribution of population access to water sources by socioeconomic stratum in departments with high or sanitarily unviable IRCA (2022).

https://doi.org/10.1371/journal.pone.0332274.t004

These disparities not only reflect unequal access to water infrastructure but also perpetuate structural marginalization that reinforces socioeconomic inequities.

The findings highlight the presence of structural water injustice in Colombia. The gap between public perception and technical water quality indicators, particularly in departments classified as high risk, reveals a critical information void that obscures the real dangers faced by the most vulnerable communities.

This disconnection is further deepened by stark inequalities in access to safe drinking water, shaped by socioeconomic stratification. While wealthier households benefit from formal and regulated water systems, low-income families rely on unsafe sources such as untreated wells and rivers, which heightens their exposure to health risks and limits their ability to demand fair environmental protections.

Taken together, the empirical evidence confirms that social, economic, and territorial conditions determine not only access to water but also how its quality is perceived, perpetuating exclusionary patterns that must be addressed through a water justice perspective.

4.2. Determinants of perceived water quality in Colombia (2022)

4.2.1. Logistic regression with LASSO regularization.

For the LASSO-regularized logistic regression model, a dependent variable called “perception of water contamination” was constructed as a binary variable (1 = “often” or “always”; 0 = “never” or “sometimes”). The model demonstrated strong predictive power in assessing perceptions of water quality in Colombia, as evidenced by the Area Under the Receiver Operating Characteristic Curve (AUC). In both the training dataset (n = 19,319) and the validation dataset, the model achieved an AUC of 0.89, indicating a high level of accuracy. These results confirmed the robustness of the model in identifying the factors that shape perceptions of water quality in Colombia.

LASSO regularization made it possible to select only the most relevant variables, reduce the risk of overfitting and multicollinearity, and ensure the interpretability of the results

Among environmental predictors, variables reflecting perceived problems such as air pollution (P5661S4), foul odors (P5661S2), and visible waste (P5661S3) exhibit significant negative coefficients. This indicates that as perceptions of these issues increase, the likelihood of reporting high levels of water contamination decreases (Table 5). These findings suggest that respondents may focus on specific environmental problems, potentially reducing their awareness of others, such as water contamination. The results also highlight potential limitations in respondents’ overall environmental awareness or differences in the visibility and understanding of various environmental issues.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 5. Logistic Regression Results with LASSO Regularization.

https://doi.org/10.1371/journal.pone.0332274.t005

This trend may be explained by a selective attention effect, where individuals focus on more immediate, visible, or sensory environmental cues, thereby diminishing their awareness of less tangible threats, such as water quality [26,41] describe similar dynamics, in which environmental distress is cognitively distributed across several issues, leading to perceptual prioritization of some environmental problems over others.

The IRCA variable shows a negative coefficient (β = −0.2746), indicating that higher water risk scores are associated with lower perceptions of contamination. This result may reflect a disconnect between objective conditions and subjective perceptions, where individuals either fail to associate higher IRCA values with contamination risks or lack awareness of the significance of these indices (Table 5). In the Colombian context, this disconnect may stem from sociopolitical dynamics that perpetuate a lack of information and community participation in water governance [18,20].

In terms of pro-environmental practices, the results indicate that certain sustainable behaviors correlate with lower perceptions of water contamination. For instance, the use of low-flow toilets (1.P5012S6, coef. = −0.0932154), and water reuse (1.P5012S4, coef. = −0.0056787), display significant negative coefficients. This suggests that households adopting proactive environmental measures tend to perceive fewer water quality issues, potentially due to a higher level of awareness and confidence in their ability to mitigate environmental risks (Table 5). This supports the findings of [44], who show that environmental engagement is associated with greater ecological trust.

Conversely, individuals collecting rainwater as an alternative practice (1.P5012S5, coef. = 0.0758591) are more likely to perceive water contamination problems. This may stem from the need to supplement traditional water sources due to perceived insufficiency or poor quality. Additionally, these individuals may have greater direct exposure to environmental conditions, allowing them to identify visible signs of contamination, such as turbidity, odors, or debris in collected rainwater (Table 5).

The analysis of water service payment schemes (P792) reveals differences in perceptions based on payment methods. Households receiving free services (category 3, coef. = 0.0239593) are more likely to perceive lower water quality, potentially due to associations with inadequate infrastructure or irregular maintenance. In contrast, households that pay for water services (category 1, coef. = −0.0087834) exhibit lower contamination perceptions, possibly reflecting greater trust in formal supply systems and their quality (Table 5). Validating the observations of [31], formal and regulated access strengthens the perception of safety.

The water sources used for food preparation (P8530) reflect the diversity of access to water across the country. Households relying on public water systems (Category 1, coef. = −0.1822817) are less likely to perceive water contamination (Table 5).

The empirical results of the study support and extend existing evidence on the role of sociodemographic factors in shaping perceptions of water quality. Specifically, higher socioeconomic strata (strata 5 and 6) are less likely to perceive contamination (P8520S1A1, coef. = − 0.0720118, and coef. = −0.0250513), while lower-income households (strata 0 and 1) report higher perceptions of water contamination. These findings are consistent with [33], who argue that poor water quality perception among low-income households is not always due to chemical contamination, but rather to infrastructure deficits that degrade the reliability and aesthetic quality of water services.

Moreover, ethnic self-identification (P6080) and age (P6040) further reflect the influence of cultural and generational identities on environmental awareness. Afro-descendant individuals report higher contamination perceptions, potentially linked to historical or geographic exposure to unsafe water sources. In contrast, other ethnic groups show lower perceptions of contamination. This reinforces the view expressed by [32,33] that territorial marginalization and access to environmental resources critically shape lived environmental experiences.

Additionally, age is negatively associated with contamination perception (β = 0.0625291), suggesting that older individuals are less likely to perceive water contamination. This may be attributed to differences in perception thresholds or reduced exposure to contemporary environmental education, reflecting generational gaps in ecological awareness, again aligning with [32] arguments.

Regarding age, results show that as respondents age, the likelihood of perceiving water contamination decreases (β = −0.0427). This pattern may be associated with differences in environmental perception thresholds, lower exposure to contemporary educational campaigns, or reduced interaction with media emphasizing water quality concerns (Table 5).

Health perception (P1897 = −0.100521) also emerges as a key factor. Individuals reporting higher satisfaction with their health tend to perceive less contamination (Table 5), a finding that aligns with [42,43]. These authors argue that positive subjective health reinforces assumptions of environmental safety, potentially reducing critical engagement with actual contamination risks.

4.2.2. Double machine learning analysis.

The analysis of results obtained through the Double Machine Learning (DML) method, applied to evaluate the causal relationship between the IRCA index and the perception of water contamination, provides valuable insights into the existence and direction of this relationship.

From a technological perspective, the use of advanced tools such as Double Machine Learning (DML) and LASSO in this study reinforces findings by [37] regarding the capacity of these techniques to identify complex patterns and nonlinear relationships among demographic, socioeconomic, and environmental variables.

Logistic regression (logit) and random forest (rforest) models were used to estimate the conditional expectations of y|X and D|X, as this approach provides greater flexibility in modeling non-linear relationships. To minimize estimation bias, a cross-validation procedure with four folds (k-folds(4)) was applied.

The impact of D on Y was assessed by testing different model combinations and selecting the one with the lowest mean squared error (MSE). The estimates remained robust across various specifications, including the model with the lowest MSE and the so-called “shortstack” model, reinforcing the reliability of the findings. Furthermore, the consistent statistical significance across multiple configurations suggests that the estimated relationship is strong

The model coefficients indicate the causal relationship between the water risk level (IRCA) and the perception of contamination. The analysis identifies whether a disconnect exists between objective reality (IRCA) and subjective perceptions. In the model with the lowest MSE, the coefficient of IRCA on y was c = −0.0812918 with a standard error of 0.004. This result is statistically significant at the 1% significance level (Table 6).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 6. Shortstack double machine learning model results.

https://doi.org/10.1371/journal.pone.0332274.t006

The negative coefficient c indicates that as the IRCA index (a water quality risk indicator) increases, the likelihood of individuals perceiving water contamination decreases. This result highlights a disconnect between subjective perceptions and objective conditions, where people may not directly associate higher IRCA values with water quality issues or may lack awareness of the risks these indices represent. This reveals a structural disconnect between objective risk conditions and citizens’ subjective perceptions, which could hinder the formulation of effective public policies unless both the technical and perceptual components of the problem are simultaneously addressed. This gap has been widely discussed by scholars such as [17,21,35], who emphasize the need for participatory and contextually adapted strategies to achieve equitable water governance.