Isotopic and geochemical characterization of recharge and salinity in a shallow floodplain aquifer near El Paso, Texas

The shallow alluvial aquifer of the Rio Grande floodplain near El Paso, Texas is a dynamic intermediary of recharge, discharge, and salinization processes between the deeper Hueco Bolson Aquifer and the overlying Rio Grande. Environmental tracer analyses suggest that 1) shallow aquifer groundwater is primarily of either Modern-Day Rio Grande origin or from older, less evaporated Pre-Dam Rio Grande waters, with modern local meteoric waters contributing a dominant fraction generally only near mountain fronts and 2) salinization of the Rio Grande and shallow aquifer in the southeastern portion of the Hueco Bolson is due to both near-surface agricultural impacts as well as the influx of naturally saline deep groundwater. With leakage from the shallow aquifer providing significant recharge to the deep Hueco Bolson Aquifer, any future development which may potentially decrease river infiltration must be carefully considered. Additionally, efforts to increase local irrigation efficiencies may have limited effects on water quality given the presence of natural solute sources.hydrology collectio

Origins of Sulfate in Groundwater and Surface Water of the Rio Grande Floodplain, Texas, USA and Chihuahua, Mexico

Sulfate isotopes (δ34S, δ18OSO4) interpreted in conjunction with sulfate concentrations show that sulfate of both agricultural and geologic sources is present in groundwater and surface water in the Rio Grande flood plain within the Hueco Bolsón. From previous studies, water isotopes (δ2H, δ18O) in the study area indicate groundwater age relative to dam construction upstream. Surface water entering the Hueco Bolsón contains a mixture of soil-amendment sulfate and sulfate from deep-basin groundwater seeps at the terminus of Mesilla Valley. In the shallow Rio Grande alluvial aquifer within the Hueco Bolsón, ranges of δ34S in pre-dam (+2 to +9‰) and post-dam (0 to +6‰) groundwater overlap; the range for post-dam water coincides with common high-sulfate soil amendments used in the area. Most post-dam groundwater, including discharge into agricultural drains, has higher sulfate than pre-dam groundwater. In surface water downstream of Fabens, high-δ34S (>+10‰) sulfate, resembling Middle Permian gypsum, mixes with sulfate from upstream sources and agriculture. The high- δ34S sulfate probably represents discharge from the regional Hueco Bolsón aquifer. In surface water downstream of Fort Hancock, soil-amendment sulfate predominates, probably representing discharge from the Rio Grande alluvial aquifer near the basin terminus. The δ18OSO4 dataset is consistent with sulfate origins determined from the larger δ34S dataset

Origins of Sulfate in Groundwater and Surface Water of the Rio Grande Floodplain, Texas, USA and Chihuahua, Mexico

Sulfate isotopes (δ34S, δ18OSO4) interpreted in conjunction with sulfate concentrations show that sulfate of both agricultural and geologic sources is present in groundwater and surface water in the Rio Grande flood plain within the Hueco Bolsón. From previous studies, water isotopes (δ2H, δ18O) in the study area indicate groundwater age relative to dam construction upstream. Surface water entering the Hueco Bolsón contains a mixture of soil-amendment sulfate and sulfate from deep-basin groundwater seeps at the terminus of Mesilla Valley. In the shallow Rio Grande alluvial aquifer within the Hueco Bolsón, ranges of δ34S in pre-dam (+2 to +9‰) and post-dam (0 to +6‰) groundwater overlap; the range for post-dam water coincides with common high-sulfate soil amendments used in the area. Most post-dam groundwater, including discharge into agricultural drains, has higher sulfate than pre-dam groundwater. In surface water downstream of Fabens, high-δ34S (>+10‰) sulfate, resembling Middle Permian gypsum, mixes with sulfate from upstream sources and agriculture. The high- δ34S sulfate probably represents discharge from the regional Hueco Bolsón aquifer. In surface water downstream of Fort Hancock, soil-amendment sulfate predominates, probably representing discharge from the Rio Grande alluvial aquifer near the basin terminus. The δ18OSO4 dataset is consistent with sulfate origins determined from the larger δ34S dataset

Geochemical Triggers of Arsenic Mobilization during Managed Aquifer Recharge

Mobilization of arsenic and other trace metal contaminants during managed aquifer recharge (MAR) poses a challenge to maintaining local groundwater quality and to ensuring the viability of aquifer storage and recovery techniques. Arsenic release from sediments into solution has occurred during purified recycled water recharge of shallow aquifers within Orange County, CA. Accordingly, we examine the geochemical processes controlling As desorption and mobilization from shallow, aerated sediments underlying MAR infiltration basins. Further, we conducted a series of batch and column experiments to evaluate recharge water chemistries that minimize the propensity of As desorption from the aquifer sediments. Within the shallow Orange County Groundwater Basin sediments, the divalent cations Ca²⁺ and Mg²⁺ are critical for limiting arsenic desorption; they promote As (as arsenate) adsorption to the phyllosilicate clay minerals of the aquifer. While native groundwater contains adequate concentrations of dissolved Ca²⁺ and Mg²⁺, these cations are not present at sufficient concentrations during recharge of highly purified recycled water. Subsequently, the absence of dissolved Ca²⁺ and Mg²⁺ displaces As from the sediments into solution. Increasing the dosages of common water treatment amendments including quicklime (Ca­(OH)₂) and dolomitic lime (CaO·MgO) provides recharge water with higher concentrations of Ca²⁺ and Mg²⁺ ions and subsequently decreases the release of As during infiltration

OC San simplified flow diagrams.

Simplified flow diagrams illustrating secondary treatment trains and the study sampling locations. (A) Two parallel secondary treatment trains at OC San Reclamation Plant No. 1. Plant secondary clarifiers that follow TF, AS1, and AS2 have engineering differences illustrated simplistically in the diagram. (B) Trickling Filter/Solids Contactor (TF/SC) secondary treatment train at OC San Treatment Plant No. 2. (TIF)</p

Fig 2 -

Probability distributions for cultivable enteric virus concentrations obtained from raw influent and secondary effluent samples taken at OC San P1 (left) and P2 (right). Each point represents one sampling event and the solid line represents a best-fit regression. The coefficient of determination (R2 value) is also shown. Raw influent and secondary effluent cultivable enteric virus data obtained from both P1 and P2 are lognormally distributed.</p

Monte Carlo simulation.

For the Monte Carlo approach, LRVs for each microbial target were calculated using the MATLAB software (1984–2020 MathWorks, Inc., version R2020a 9.8.0.1359463), equipped with the Statistics and Machine Learning Toolbox. Microbial concentration data were imported into the MATLAB software using a simplified tab-delimited file. Once imported, a statistical model for each influent and effluent dataset for a given microbial target was generated using the maximum likelihood estimates function. Briefly, influent and effluent microbial concentrations were used to generate a statistical distribution. From these modeled influent and effluent distributions, one independent and random value was selected from each distribution and subsequently paired to calculate an LRV as shown in Eq S3: (Eq S3) Where Ceff is the concentration of the microbial target taken from the secondary effluent distribution, and Craw is the concentration of the microbial target taken from the raw wastewater distribution. This calculation was performed 10,000 times to generate a distribution of n = 10,000 LRVs. All LRVs were then sorted from low to high and assigned a rank, i, over the total number of data points, n. A cumulative probability, p, for each value was assigned as shown in Eq S4. (Eq S4) Where p is the cumulative probability, i is the rank assignment, and n represents the total number of calculated data points (10,000). The Monte Carlo simulation approach was also modified for the present study to generate only non-negative LRVs (n = 10,000+ non-negative log removal values). This was done because the standard Monte Carlo simulation approach generated a fairly large number of negative LRVs as a portion of the total 10,000 LRVs, which reduced the resulting 5th percentile LRV. To address the negative LRVs, approximately 1,000 additional LRVs were calculated for a total of 11,000 samples. Negative LRVs within the n = 11,000 dataset were removed such that the remaining number of positive LRVs were at least n = 10,000. This modified (censored) Monte Carlo approach was used to determine process-specific LRVs, imposing a condition of reality on the statistically determined outcome. A modified Monte Carlo simulation was executed to address the negative LRVs calculated for the P1 TF and P2 TF/SC distributions when using the standard Monte Carlo approach. While this only occurs a fraction of the time, these negative values are recorded as a possible LRV at the low end of the percentile distribution. The calculated negative LRVs were believed to be a mathematical artifact of the Monte Carlo’s random pairing of influent-effluent values, specifically attributed to the large overlap in concentration values observed between the raw influent distribution and OC San P1 TF effluent distribution. The modified (censored) Monte Carlo approach was used to determine process-specific LRVs, imposing a condition of reality on the statistically determined outcome. Negative LRVs generated with the modified Monte Carlo simulation were removed such that the remaining number of positive LRVs were at least n = 10,000. It is physically impossible to generate a negative LRV for an enteric virus during wastewater treatment, as virus cannot be created within primary or secondary treatment processes due to the lack of a host organism. Despite the attempt to resolve this issue by censoring data to remove negative LRVs, this estimation is not representative of the actual low-end virus log removal observed for all four treatment processes (Table 1). (DOCX)</p

OC San sampling location description.

A total of six (6) sampling locations from OC San P1 and P2 were monitored. Sampling locations for OC San P1 were raw wastewater influent, trickling filter (TF) secondary effluent, activated sludge 1 (AS1) secondary effluent, and activated sludge 2 (AS2) secondary effluent, while sampling locations from OC San P2 consisted of raw wastewater influent and trickling filter/solids contactor (TF/SC) secondary effluent. Sampling at P2 was limited to characterizing the TF/SC process and not other parallel treatment processes. Raw wastewater entering OC San P1 is treated through preliminary screening and primary clarification with chemical addition and is then diverted into one of two secondary treatment trains that operate in parallel (trickling filter process or activated sludge process). Raw wastewater from P1 was collected after primary bar-screening but before primary clarification and chemical addition. Secondary effluents generated by three parallel treatment processes at P1 were sampled in this study. The first P1 treatment train routes the primary effluent through a trickling filter (TF) process followed by secondary clarification. Treated effluent from the TF process was sampled. P1 primary effluent is also sent through two parallel trains of the activated sludge (AS) treatment trains, designated separately as AS1 and AS2. Secondary effluent samples taken from each AS process following secondary clarification was sampled. Both AS trains operate in the nitrification-partial denitrification (NDN) mode. The major difference between the AS1 and AS2 processes is that AS1 does not receive mixed liquor return, while the newer AS2 facility does receive it. Microbial concentrations of both the AS1 and AS2 effluent streams from P1 were of interest due to the operational differences between the two processes described above. (DOCX)</p