Assessment of treated wastewater infiltration in Bright Angel Wash and the potential for contaminants of emerging concern influencing spring water quality along the South Rim of the Grand Canyon in Grand Canyon National Park, Arizona

Nicholas V. Paretti; Kimberly R. Beisner; Sarah J.R. Shepherd

doi:10.3133/sir20255095

Home/Archive/USGS

USGS · sir20255095

Assessment of treated wastewater infiltration in Bright Angel Wash and the potential for contaminants of emerging concern influencing spring water quality along the South Rim of the Grand Canyon in Grand Canyon National Park, Arizona

Nicholas V. Paretti·Kimberly R. Beisner·Sarah J.R. Shepherd

View original source ↗Read source document ↗Export metadata ↓

Abstract

In April 2021, a synoptic study conducted by the U.S. Geological Survey (USGS) and National Park Service (NPS) identified wastewater-related contaminants of emerging concern (CECs) in springs along the South Rim of the Grand Canyon. These springs are located north of Bright Angel Wash, an ephemeral channel that receives treated effluent from the South Rim Wastewater Treatment Plant (SRWTP). Although water flows southwest and away from the canyon, there is evidence that treated wastewater is finding a flow path along fractures associated with the Bright Angel Fault back to water sources along the South Rim. The CECs identified during the April 2021 sampling included several per- and polyfluoroalkyl substances (PFAS) and pharmaceutical compounds. The PFAS compounds detected only consisted of perfluoroalkyl acids, and these were only detected at Bright Angel Wash (treated wastewater), Monument Spring, and upper Horn Bedrock Spring. The other five sampled springs (the Salt Creek, Horn East Alluvium, Garden, Pumphouse, and Pipe Springs) had no detections of PFAS compounds. The five perfluoroalkyl acids detected at Monument Spring (in descending order of concentration) were perfluorobutanesulfonic acid (PFBS), perfluoropentanoic acid, perfluorooctanoic acid, perfluorohexanoic acid, and perfluorooctanesulfonic acid. Only the short-chained PFBS and perfluorobutanoic acid compounds were detected at the upper Horn Bedrock Spring. All the same perfluoroalkyl acids were found at Bright Angel Wash, except for PFBS. Monument Spring was the only spring to have detections of pharmaceuticals. The two pharmaceuticals detected at the highest concentrations at Monument Spring were diphenhydramine (antihistamine) and carbamazepine (anticonvulsant or anti-epileptic drug). The other detected pharmaceuticals included (in descending order of concentration) fluconazole (antifungal), sulfamethoxazole (antibiotic), metformin (diabetes drug), tramadol (opioid analgesic), and venlafaxine (antidepressant and nerve pain medication). The same pharmaceuticals were detected in the wastewater at Bright Angel Wash but in greater concentrations. No CEC concentrations measured at Monument Spring exceeded any drinking water standards or human health benchmarks; however, most of the compounds detected have no regulatory standards. Studies of the ecological effects of these compounds show that some of the compounds detected can have endocrine and physiological effects, but generally, effects were observed at concentrations multiple orders of magnitude greater than what was measured during the April 2021 study. Data from 1980 through 2022 retrieved from the Water Quality Portal were combined with data from the one-time synoptic sampling in April 2021 to assess the usefulness of other analytes for identifying a wastewater connection to South Rim springs. Most of the historical water chemistry data showed a statistical difference between samples collected within and east of the Garden Creek watershed and samples collected from locations in watersheds to the west of Garden Creek, including the Horn Creek, Salt Creek, Monument Creek, and Hermit Creek watersheds, which roughly align with the Bright Angel Fault. Most of the historical analytes were inconclusive as potential wastewater tracers, but nitrate, chloride, and gadolinium data possibly support the historical contribution of wastewater to Monument Spring.

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 35.94693133579284° to 36.1° latitude; -112.25° to -112° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Nicholas V. Paretti, Kimberly R. Beisner, Sarah J.R. Shepherd. 2025-12-29. Assessment of treated wastewater infiltration in Bright Angel Wash and the potential for contaminants of emerging concern influencing spring water quality along the South Rim of the Grand Canyon in Grand Canyon National Park, Arizona. https://doi.org/10.3133/sir20255095

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

Scientific Investigations Report↗

USGS2026

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗

USGS2026

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗