Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation – Arizona, New Mexico, and Utah, USA

3.1. Study area

The Navajo Nation encompasses >70,000 km² in the Four Corners area of Utah, Arizona, and New Mexico in the southwest United States (Fig. 3A). Groundwater is the primary drinking water source for the Navajo Nation, which is supplied by three multi-part aquifer systems (Cooley et al., 1964). As much as 30% of the population lacks household access to a public water supply and may rely on unregulated water sources. While some residents do haul water from public water supplies, which are regulated by national drinking water laws, there remain families who use unregulated sources for domestic water (NN DWR, 2011).

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Fig. 3

Overview map (3A) of the Navajo Nation in the southwest United States and locations of unregulated water sources classified into Water Quality Cluster 1, 2, or 3. In panel B, C and D the larger dots are unregulated sources classified into the WQC of interest and the smaller black dots are other tested unregulated sources with different WQC membership. The dashed polygons around subsets of water sources indicate a significant spatial cluster.

3.2. Water quality data

A water quality dataset for unregulated sources located on the Navajo Nation was queried for this investigation. The collection and analytical procedures used to generate these data are described in detail by Hoover et al. (2017) and the sample collection and laboratory procedures are summarized briefly here. Federal, Tribal, and state agencies (Murphy et al., 2009; US EPA, 2000; US EPA, 2006; US EPA Region IX, 2008; US EPA Region IX, 2010; US EPA Region IX, 2011), and university and non-profit organizations (Hund et al., 2015) worked with Navajo communities to identify and collect water samples from unregulated sources between 1998 and 2010 using similar sample collection methods. The sampled water sources were unregulated and tested, because of community concern about safety for human consumption. Documented use of the unregulated sources was limited to one study (Hund et al., 2015), but the other studies identified and sampled sources thought to be used by residents or in response to residents' concerns. Each source was flushed for 1–2 min before a sample was collected. This collection procedure was designed to match how Navajo Nation residents collect water from unregulated sources (US EPA Region IX, 2008). The samples were subsequently filtered and acidified prior to analysis by a certified drinking water laboratory using Inductively Coupled Plasma Optical Emissions Spectroscopy (USEPA analysis method 200.7/6010B) or Inductively Coupled Plasma Mass Spectroscopy (USEPA analysis method 200.8). For a subset of samples, uranium activity (measured in picocuries per liter), was determined using US EPA method 907.0 or HASL 300 U-02-RC; for this analysis uranium activity was converted to mass using the assumption that 0.67 picocurie uranium was equivalent to 1 μg uranium (Weiner, 2013).

A contaminant was included in this analysis if it was measured in >75% of tested water sources. This threshold was selected so that a set of contaminants measured in unregulated sources dispersed throughout the Navajo Nation was used. Aluminum, arsenic, barium, beryllium, cadmium, chromium, copper, iron, manganese, nickel, lead, selenium, uranium, and zinc met the selection criterion. Other contaminants like antimony, mercury, molybdenum, thallium, and vanadium were measured, but excluded as they measured in fewer than 75% of tested sources and were not available throughout the Navajo Nation. Different laboratories analyzed the water samples so there were different reporting limits for each contaminant. The highest reporting limit for each contaminant is provided in Table 1. A full list of reporting limits for each element is provided in Supplemental Table 1. Please see Hoover et al. (2017) for more information about the data compilation, quality control, and storage processes.

3.3. Profile regression

Bayesian Profile Regression (BPR), a non-parametric method that uses a Dirichlet process mixture model, was employed to identify subgroups (clusters) of unregulated water sources with similar patterns of co-occurring metals and metalloids. BPR was selected for this analysis due to the large number of correlated variables, no prior information about the optimal number of clusters, and to the need to understand what contaminants might be driving clustering. This method has been used previously for environmental epidemiology research (Coker et al., 2017; Coker et al., 2016; Papathomas et al., 2010; Pirani et al., 2015) as a result of the advantages offered by BPR for clustering analyses.

BPR implements a Dirichlet process mixture model (DPMM) that allows the data to determine the number of subgroups (clusters), hence the number of clusters need not be defined ahead of time (Molitor and Papathomas, 2010). BPR sets the DPMM in a Bayesian framework with Markov chain Monte Carlo (MCMC) estimation that appropriately propagates uncertainty in cluster assignment and the number of clusters and simultaneously links a profile of exposures to a cluster at each iteration. The post-processing of the BPR includes a dissimilarity matrix for assigning optimal cluster membership (or “best clustering”). Uncertainty in cluster membership is ascertained with Bayesian model averaging rather than hard cluster allocation or probabilistic allocation to a cluster. This approach provides greater flexibility than hard cluster assignment and enhances interpretation compared to purely probabilistic clustering methods. Variable selection may also be employed to identify which variables support clustering (Liverani et al., 2015). Additional information about BPR can be found in other recent publications (Hastie et al., 2013; Liverani et al., 2015; Papathomas et al., 2012).

Briefly, BPR uses a Dirichlet process (DP) as the prior for the mixing distribution, defined as P~DP(P_θ0,α) where α is a scale parameter and P_ϴ0 is the base probability distribution (Liverani et al., 2015). For each unregulated water source i, a water quality profile was defined as x_i = (x₁, …x_iP) where i is the number of unregulated water sources and P is the number of metals and metalloids included in the analysis.

The BPR output produces discreet subgroup (cluster) membership for each water source, referred to as Water Quality Clusters (WQCs). Measured contaminant concentrations were converted to quantiles for clustering and measurements below the limit detection (LOD) were assigned the LOD value before conversion to a quantile value. Similar to Coker et al. (2016), clustering was fit without an outcome and the ‘best’ clustering was used for characterizing and analyzing each cluster and for ease of interpretation. BPR was implemented using the PRe-MiuM package (v 3.1.3) (Liverani et al., 2015) for R (v 3.3.1).

At each iteration the average subgroup probability was calculated and the collection of these averages represents the posterior distribution of cluster specific parameters. Bayesian model averaging accounts for uncertainty by averaging the posterior distributions of cluster parameters across all iterations (Molitor and Papathomas, 2010). If an observation is consistently placed in the same cluster then the credible interval of cluster specific parameters will be narrower, indicating a higher certainty. Conversely, if an individual observation is placed in different clusters then the credible interval will be relatively wider indicating more uncertainty. In this way uncertainty is accounted for in the ‘best’ clustering assignment. Cluster assignment uncertainty was evaluated by reviewing the MCMC output, as recommended by Molitor and Papathomas (2010).

The probability that each contaminant contributed to the clustering output was evaluated using a variable selection method. The PReMiuM package generates a latent selection weight that ranges between 0 and 1. Values closer to 1 suggest a greater probability of supporting clustering while values closer to 0 suggest less cluster support. There is no threshold for inclusion/exclusion based on the variable selection weight. This variable is generated to inform interpretation of which covariates might be driving clustering. As suggested by the authors of the PReMiuM package, contaminants were also considered to lack cluster support if the credible intervals for a contaminant did not exhibit significantly higher or lower levels for at least one cluster.

3.4. Summary statistics

Summary statistics were calculated for each WQC using the NADA Package (v 1.5.6) for R (v 3.3.1), because there were observations less than the limit of detection, also known as left-censored data (Antweiler and Taylor, 2008). Left-censored statistical methods are used when a dataset includes observations that are not precisely measured. Environmental datasets commonly contain censored observations that are less than a limit of detection but the precise value cannot be determined (Antweiler and Taylor, 2008; Field, 2011; Helsel, 1990; Helsel, 2006). Robust Regression on Order (ROS) statistics, a left-censored statistical method, was employed for this analysis (Helsel, 2012; Lee, 2013; Lee and Helsel, 2005).

Values for contaminant measurements that were less than the LOD were imputed using the robust ROS method and were not assumed to be zero. The robust ROS method determines the Weibull plotting position of each uncensored and censored (observations less than the limit of detection) observation. Then the plotting position and normal score of the uncensored observations are used to create a linear regression model. Estimates of the censored values are then calculated based on the Weibull plotting position using the regression equation. Lastly, the estimated censored observations are combined with the uncensored observations and summary statistics are calculated using all observations. This method has been used previously in other water quality studies (Helsel, 2005; Levitan et al., 2014) and is appropriate for this dataset because it handles multiple limits of detection, which is present in this dataset.

If fewer than 30% of sources had detectable concentrations then summary statistics were not calculated for that metal or metalloid. Previous research demonstrated that measures of central tendency were biased when fewer than 30% of observations had detectable concentrations (Helsel, 2012).

3.5. Potential for toxicity

The Benchmark Index was employed to assess potential for human health impact (Toccalino et al., 2012), limited to arsenic, cadmium, chromium, lead, nickel, selenium, and uranium. These contaminants have established human-health based thresholds and are regulated in drinking water by the US EPA. Additionally, the Health-Based Screening Level (HBSL) for manganese was selected because of evidence suggesting manganese exposure is associated with negative human health outcomes (Wasserman et al., 2016); manganese is not regulated as a primary drinking water contaminant by the US EPA. The Benchmark Index assumes dose additivity, meaning the overall potential for toxicity of the mixture is a summation of the individual contaminants (Toccalino et al., 2012). There are limitations to the Benchmark Index, such as assuming dose additivity, no synergistic effects, and it can be applied only to contaminants with an established human health benchmark.

For each contaminant and each WQC, a Benchmark Quotient score was calculated by dividing the representative concentration by the respective health-based threshold, as proposed by Toccalino et al. (2012). Subsequently, the individual Benchmark Quotients scores were summed to determine the Benchmark Index for each WQC. An index score >1.0 suggests greater potential for toxicity. The Benchmark Quotient scores were also evaluated and WQCs with two or more scores exceeding 0.1 were identified. This threshold was selected because the US Agency For Toxic Substances and Disease Registry suggested that mixtures should be considered toxicological study when two or more contaminants are present at concentrations exceeding 10% of the human health based threshold (ATSDR, 2004). Benchmark Quotient and Index scores were calculated for the 50th and 75^thpercentile concentrations of each metal and metalloid in each WQC. These percentile concentrations were selected to illustrate the potential toxicity for contaminant concentrations that Navajo Nation residents are likely to encounter when using unregulated water sources. Higher percentiles, such as the 90th or 95th, would be encountered by residents at few water sources on the Navajo Nation and likely do not represent common exposures.

4.2. Water quality clusters

Seven water quality clusters (WQC) were identified using Bayesian Profile Regression. Initial clustering runs indicated a lower probability (<0.5) that barium and beryllium were driving clustering. Other contaminants had a greater probability of influencing cluster membership (>0.80). Zinc had had a moderate influence on clustering output (probability 0.67). Upon review of the MCMC post processing visualization (Supplemental Fig. 1), a strong clustering signal was observed with relatively narrow credible intervals, giving confidence in the ‘best clustering’ allocation. The probability of contaminant concentration categories (quantile scores) varied among the clusters. Some WQCs showed a higher probability for the third or fourth quantile contaminant concentrations (suggesting higher concentrations). Other WQCs had a higher probability for the first or second quantile contaminant concentrations (suggesting lower concentrations).

For WQC 1 (N = 53) and WQC 3 (N = 123) the median concentration of most tested contaminants was similar to or lower than the median for all tested sources in the study area (Table 1, NFig. 2). Meanwhile the median concentrations of arsenic and uranium in WQC 3 was similar to the median concentrations for the whole study area. Additionally, exceedance of health-based thresholds for arsenic and uranium were similar to the study area as a whole. For WQC 4 (= 43), the median manganese concentration exceeded the median value for the Navajo Nation; the median concentrations of copper, nickel, selenium and uranium were similar to their respective medians for the Navajo Nation. Approximately 20% of unregulated sources in WQC 4 exceeded the manganese health based screening level of 300 μg/L (Table 2).

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Fig. 2

Boxplot of select water contaminants, partitioned into clusters for comparison. The value below each boxplot indicates the percentage of sources with contaminant measurements below the limit of detection. Black horizontal lines for arsenic (As), manganese (Mn), selenium (Se), and uranium (U) indicate the overall median.

The median concentrations of arsenic and uranium were elevated in WQC 2 (N = 35) when compared to the respective study area medians for these contaminants. Furthermore, chromium and lead were detected more frequency in water sources grouped into this WQC, albeit infrequently; exceedance of health based thresholds was more frequent in these water sources for arsenic (~20% of sources) and uranium (~20% of sources) when compared to the study area. Additionally, median concentrations of aluminum and iron were higher.

Water quality clusters 5, 6, and 7 demonstrated elevated concentrations of metals and metalloids and more frequency exceeded health-based thresholds when compared to the entire study area. WQC 5 (N = 39) had the highest median concentrations of arsenic. Unregulated sources in this WQC also had higher median concentrations of lead, manganese, nickel, aluminum, iron, and zinc compared to the Navajo Nation. Nearly 44% of sources in WQC5 exceeded the arsenic drinking water standard, 17% exceeded the uranium or manganese standards, and 15% exceeded the lead action level. Sources grouped into WQC 6 (N = 81) had elevated concentrations of arsenic, uranium, and aluminum, but lower concentrations of iron when compared to WQC 5. Lastly, WQC 7 (N = 79) had the highest median concentration of uranium, and median concentrations of arsenic, manganese, aluminum, and iron were greater than the Navajo Nation medians.

4.3. Potential for human health impacts

When the 50th percentile concentration for each contaminant was evaluated two combinations were present that may suggest potential for human health risk: arsenic and uranium (WQCs 2, 3, 6 and 7); and arsenic, uranium, and lead (WQC 5) (Table 3).

When the 75th percentile contaminant concentrations were evaluated five contaminant combinations were present at concentrations that may suggest a need for additional toxicological study: arsenic and uranium (WQC 3); arsenic, uranium, and selenium (WQC 6); arsenic, uranium, and manganese (WQC 7); arsenic, uranium, selenium, and lead (WCQ 2); arsenic, uranium, selenium, lead, and manganese (WQC 5).

5.1. Utility of Bayesian profile regression

In this investigation Bayesian Profile Regression was applied to identify metal and metalloid mixtures in unregulated water sources. BPR is designed to identify subgroups with similar joint distributions of a combination of variables in a manner that is mostly data-driven. Unlike other forms of clustering, it does not require pre-specification in the number of subgroups and incorporates uncertainty into clustering. Moreover, BPR can be used to understand associations between joint levels of covariates and an outcome (Molitor and Papathomas, 2010). Alternatively, as done in the present study, BPR may be combined with other analytic approaches to elucidate important relationships (e.g., spatial patterns of clusters or contaminant combinations with potential for human health toxicity).

The use of variable selection enabled identification of contaminants that supported a clustering pattern. Initial runs indicated a lower probability that barium and beryllium supported clustering. Barium may have had limited clustering support because it was weakly correlated with other contaminants, due to no commonly shared contaminant source, as illustrated in Fig. 1. A sensitivity analysis was conducted (results not shown) by including and excluding barium and in cluster assignment and spatial clustering were assessed. Only 2% of unregulated sources (N = 12) changed cluster membership, 7 WQCs were identified, and the spatial analysis results were not meaningfully different; spatial clusters of the same WQCs were found in the same parts of the study area.

Similarly, the BPR clustering results were not very sensitive to the inclusion of beryllium, which may be because this contaminant was detected in only 5% of unregulated water sources. A sensitivity analysis indicated that when beryllium was included 7% of unregulated water sources changed WQC membership. There remained seven WQCs in the optimal output despite a small number of sources changing WQC membership. This provides evidence that the number of clusters was stable even without the inclusion or exclusion of barium and beryllium.

BPR also enabled evaluation of cluster assignment uncertainty, which is important because even a noisy dataset will produce a ‘best’ partition. Ignoring assignment uncertainty could lead to over interpretation of the resulting clusters. The cluster uncertainty for this dataset was relatively low because the best partition, illustrated in Supplemental Fig. 1, demonstrated some clusters with high probabilities for different contaminant categories and very little overlap of credible intervals with expected average probability (e.g., 0.25). If the cluster uncertainty was high then the cluster specific probabilities for many or all categories would not be different than the overall category probability for each cluster (illustrated as a green shaded boxplot). The strong clustering signal may be due to the shared sources of contaminants within clusters.

5.2. Implications for public health and exposure

Despite the well-established co-occurrence of contaminants in unregulated water supplies (Bacquart et al., 2015; DeSimone et al., 2009; Toccalino et al., 2012) there remain few applications of multivariate techniques to identify and characterize mixtures in water sources. On the Navajo Nation for example, previous research focused on the individual occurrence of arsenic or uranium, and few studies addressed co-occurrence of these contaminants (Murphy et al., 2009).

Due to the regional focus of this study different contaminant mixtures were expected when compared to national studies. Nationally in the United States, arsenic co-occurs with contaminants such as manganese, molybdenum, strontium, radon, and nitrates (Toccalino et al., 2012); and uranium co-occurs more frequently with manganese and radon. These study results differ from national studies because the list of analyzed contaminants was more limited. Thus, the generalizability of the specific combinations observed in this study may be limited to other water sources in the southwestern United States. Previous water quality research in the study area investigated the presence of arsenic and uranium in unregulated sources (Corlin et al., 2016; Hoover et al., 2017). The work presented here suggests that arsenic, uranium, and other contaminants jointly drive potential toxicity in tested unregulated sources.

For this study, the identified contaminant mixtures are likely representative of unregulated water sources throughout the Navajo Nation, limited to the 14 metals and metalloids included in the analysis. Data included in this analysis came from water samples collected in a similar manner to how Navajo Nation residents collect water from these sources. The water quality data are likely representative of the conditions encountered by Navajo Nation residents who use these unregulated sources. Additionally, the 14 contaminants evaluated in this paper were measured in sources dispersed throughout most of the Navajo Nation. This includes unregulated water sources both close to and far from abandoned uranium mines and other features that might be contaminant sources. However, as noted by Toccalino et al. (2012) characterization of contaminant mixtures is limited by the availability of occurrence data. For the Navajo Nation there remains a need to test for other contaminants (e.g., pesticides, radionuclides, microbial, etc. …) that were not available for this analysis.

The spatial clustering analysis indicated specific areas of the Navajo Nation that had higher occurrences of certain WQCs. Small differences between the Kernel Density visualizations and SaTScan results were observed, likely due to how results are visualized by each method. Kernel density plots present a single smoothed surface that represents the density of all points classified as part of the WQC of interest. SaTScan presents the best collection of spatial clusters that are statistically significant. SaTScan results indicate only areas of significant spatial clustering, and not the full geographic extent of members of certain WQCs in the study area. There are some smaller groups of unregulated water sources with the same WQC membership that were not represented due to lack of significance. Overall, the areas with the greatest density of unregulated sources with the same WQC membership overlapped with the areas of statistically significant spatial clustering. These spatially clustered unregulated water sources may be pumping from water bearing geologic units with similar geochemistry or mineral composition. While the geochemistry data to evaluate the drivers of the spatial clustering is unavailable, this observation still has public health relevance. Decision makers may be able to use this information to inform policy choices and focus resources on areas with higher occurrences of deleterious metals or metalloids.

The spatial analysis results also indicated that 55% of sources were not part of a spatial cluster. Unregulated water sources from each WQCs occurred outside of a spatial cluster, including all sources classified into WQC 5. This could be an illustration of the complex hydrogeology of the aquifers underlying the Navajo Nation. The C aquifer and N aquifers, named for the Coconino sandstone and Navajo sandstone respectively, generally produce high quality water (Brown and Macy, 2012). The C aquifer is found throughout much of the Navajo Nation, while the N aquifer is found primarily in the central area of Navajo near the Hopi Indian Reservation (Macy et al., 2012). Both aquifers are comprised of multiple water-bearing formations that occur at different depths below surface level (Cooley et al., 1964). The D aquifer is another important water supply, named for Dakota Sandstone, but it produces water with total dissolved solids concentrations exceeding 1000 mg per liter (Truini and Macy, 2006). There are also many small perched and alluvial aquifers that also supply water for domestic, livestock or agricultural uses(NN DWR, 2011). As can be observed in Figs. 3 and ​and4,4, unregulated sources with different WQC membership occurred throughout the Navajo Nation, and this may result from water sources tapping different water bearing units at different depths, despite proximity at surface level. For many unregulated water sources on the Navajo Nation there is limited information available about the producing formation, depth to formation, well type or construction materials, or geochemistry for the tested unregulated water sources. Therefore, a link between water quality profiles and specific water-bearing geologic formations could not be made; this remains an opportunity for future research.

5.3. Contaminants of concern

This investigation indicated that lead was detected at a greater frequency in unregulated sources classified into WQCs 2 and 5. The occurrence of lead in unregulated water sources is a potential public health concern; lead exposure is associated with decreased cognitive function in children, as well as cardiovascular and renal problems (ATSDR, 2007; Lanphear et al., 2005). Additionally, dissolved lead in drinking water has been associated with elevated blood lead levels (Etchevers et al., 2014). Some of the unregulated water source infrastructure was installed in the 1950s and 60 so the lead detected in select unregulated sources could leach from the pipes used to construct wells or link the water source with a storage tank. Previous research investigating lead water concentrations in homes and schools indicated that during periods of nonuse (hours to days) lead can leach from pipes into stagnant drinking water (Bryant, 2004). In some regulated water systems on the Navajo Nation, federal and tribal agencies have replaced lead pipes in some public buildings and homes; a phosphate-buffer treatment system was also installed to reduce water pH and corrosive activity. Navajo Nation residents may infrequently use some of the sampled unregulated water sources, and therefore, water may stagnate in pipes for days. There exists limited information about well construction history or frequency of use that would help better address the source of lead in these unregulated water sources. A more detailed field analysis could be conducted to better ascertain the possible sources and provide this information to people who may use these water sources.

This investigation also showed that manganese, a neurotoxicant, occurred at concentrations that regularly exceeded health-based screening levels. Consumption of manganese in the range of 0.2–0.3 mg/L (comparable to manganese concentrations in WQCs 4 and 5) has been associated with diminished attention span and lower intellectual growth in children (Wasserman et al., 2006; Wasserman et al., 2016). Unregulated water sources with membership in WQCs 4 or 5 could be further investigated to determine the manganese source and potential intervention methods for reducing human exposure. Similar to groundwater in areas such as Bangladesh and the Mekong Delta, the co-occurrence of manganese with arsenic, uranium, and other contaminants makes contaminant removal more challenging (Bacquart et al., 2015; Frisbie et al., 2009). Water filters developed to remove arsenic via oxidation, such as those developed for use in Bangladesh, may have minimal effect on other contaminants resulting from different oxidation states of contaminants (Bacquart et al., 2015; Frisbie et al., 2009). Other water chemistry factors like pH also play an important role in the removal of metals and metalloids from drinking water (Lakshmanan et al., 2010) and would likely need to be evaluated before a removal system is designed for multiple contaminants. There may also be limited financial and institutional capacity to engage in widespread point-of-extraction filtration, because of high rates of unemployment and large distances between water sources on the Navajo Nation (Navajo Access Workgroup, 2010).

Identified mixtures could be further evaluated for human toxicity or investigated for possible human health links in a population-based study. Additionally, information about common contaminant mixtures could help public health officials prioritize areas for community outreach and intervention. This approach does not lessen the need for comprehensive and regular testing of unregulated sources for a variety of contaminants as recommended by the US Centers for Disease Control and Prevention (CDC, 2009). Additionally, the Navajo Nation is working to educate residents about the health risks of drinking water from an unregulated source.

Thank you to the community and non-profit organizations that were instrumental in requesting the monitoring of unregulated water sources on the Navajo Nation. Funding for this work has been provided by the National Institute for Environmental Health Sciences, RO1 ES014565, R25 ES013208 and P30 ES-012072, a NIGMS ASERT IRACDA postdoctoral fellowship (K12 GM088021), the UNM Center for Native Environmental Health Equity Research- A Center of Excellence In Environmental Health Disparities Research- Funded jointly by grants from NIEHS & NIMHD ((1P50ES026102) & USEPA (#83615701), and the National Institute of Environmental Health Sciences Superfund Research Program (Award 1 P42 ES025589). This material was developed in part under Assistance Agreement No. 83615701 awarded by the U.S. Environmental Protection Agency to the University of New Mexico Health Sciences Center. It has not been formally reviewed by EPA. The views expressed are solely those of the speakers and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We are grateful to the UNM Center for Advanced Research Computing for computational resources.