A Comprehensive Analysis of Groundwater Quality in The Barnett Shale Region

Hildenbrand, Zacariah L.; Carlton, Doug D.; Fontenot, Brian E.; Meik, Jesse M.; Walton, Jayme L.; Taylor, Josh T.; Thacker, Jonathan B.; Korlie, Stephanie; Shelor, C. Phillip; Henderson, Drew; Kadjo, Akinde F.; Roelke, Corey E.; Hudak, Paul F.; Burton, Taylour G.; Rifai, Hanadi S.; Schug, Kevin A.

doi:10.1021/acs.est.5b01526

Cited by 111 publications

(71 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Samples collected for organic ion analysis were preserved with chromatography grade chloroform to prevent microbial degradation. Samples collected for the analysis of volatile organic and semi-volatile organic compounds, total organic carbon (TOC), and total nitrogen were untreated [10,11,27,29]. Select samples for dissolved hydrocarbons gases (methane, ethane, and propane) were collected using Isoflask containers as per Molofsky et al [30].…”

Section: Water Well Samplingmentioning

confidence: 99%

Attitudes, Perceptions, and Geospatial Analysis of Water Quality and Individual Health Status in a High-Fracking Region

Granados

Hildenbrand²,

Mata

et al. 2019

Water

View full text Add to dashboard Cite

The expansion of unconventional oil and gas development (UD) across the US continues to be at the center of debates regarding safety to health and the environment. This descriptive study evaluated the water quality of private water wells in the Eagle Ford Shale as well as community members' perceptions of their water. Community members (n = 75) were surveyed about their health status and perceptions of drinking water quality. Water samples from respondent volunteers (n = 19) were collected from private wells and tested for a variety of water quality parameters. Of the private wells sampled, eight had exceedances of maximum contaminant limits (MCLs) for drinking water standards. Geospatial descriptive analysis illustrates the distributions of the well exceedance as well as the well owners' overall health status. Point-biserial correlational analysis of the haversine distance between respondents and well exceedances revealed four statistically significant relationships {Well 11, Well 12, Well 13, Well 14} with correlations of {0.47, 53, 0.50, 0.48} and p-values of {0.04, 0.02, 0.03, 0.04}, respectively. These correlations suggest that as distance from these northwestern wells increase, there is a higher likelihood of exceedances. Those relying on municipal water or purchased water assessed that it was less safe to drink than those relying on private wells for drinking (p < 0.001, Odds Ratio, OR = 44.32, 95% CI = {5.8, 2003.5}) and cooking (p < 0.003, OR = 13.20, 95% CI = {1.8, 589.9}. Tests of proportional differences between self-reported conditions and provider-reported conditions revealed statistical significance in most cases, perhaps indicating that residents believed they have illnesses for which they are not yet diagnosed (including cancer). In many cases, there are statistically significant differences between self-reported, provider undiagnosed conditions and self-reported, provider diagnosed conditions.

show abstract

Section: Water Well Samplingmentioning

confidence: 99%

Attitudes, Perceptions, and Geospatial Analysis of Water Quality and Individual Health Status in a High-Fracking Region

Granados

Hildenbrand²,

Mata

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…Cyclic changes in groundwater may also be brought about by hydrological and anthropogenic components [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Application of ANN and MLR Models on Groundwater Quality Using CWQI at Lawspet, Puducherry in India

Nathan¹,

Saravanane²,

Sundararajan³

2017

GEP

View full text Add to dashboard Cite

With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH,, Mg 2+ and K + . As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physiochemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 < 0.05) showed that there are significant changes in the average values of CWQI among the three clusters, thereby confirming the formation of clusters due to anthropogenic activities. The CWQI simulation was performed using MLR and ANN models for all three clusters. Totally, 1 MLR and 9 ANN models were considered for simulation. Further the performances of ten models were compared using R 2 , RMSE and MAE (quantitative indicators). The analyses of the results revealed that both MLR and ANN models were fairly good in pre- 100dicting the CWQI in Clusters 1 and 2 with high R 2 , low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.

show abstract

“…In the Barnett Shale area, some authors (Fontenot et al ; Hildenbrand et al ; Burton et al ) suggested that unconventional gas production had a weak impact on water quality. Fontenot et al () sampled approximately 100, mostly domestic, wells in the entire Barnett Shale footprint but did not provide their approximate locations and did not analyze dissolved methane.…”

Section: Introductionmentioning

confidence: 99%

“…They concluded that concentrations of some trace elements (As, Se, Sr, and Ba) were related to the distance to the nearest gas well. Hildenbrand et al () presented results of 550 water‐well samples taken in or in the vicinity of the Barnett Shale footprint, focusing on organics, with ambiguous conclusions on the contribution of hydraulic fracturing activities on water quality. Burton et al () combined the approximately 600 water samples of the two previously described studies and hundreds of historical samples from the State database with pressure and completion data from a gas well database.…”

Section: Introductionmentioning

confidence: 99%

Trace Element Behavior in Methane‐Rich and Methane‐Free Groundwater in North and East Texas

et al. 2017

View full text Add to dashboard Cite

There is concern about adverse impacts of natural gas (primarily methane) production on groundwater quality; however, data on trace element concentrations are limited. The objective of this study was to compare the distribution of trace elements in groundwater samples with and without dissolved methane in aquifers overlying the Barnett Shale (Hood and Parker counties, 207 samples) and the Haynesville Shale (Panola County, 42 samples). Both shales have been subjected to intensive hydraulic fracturing for gas production. Well clusters with high dissolved methane were previously found in these counties and are thought to be of natural origin. Overall, groundwater in these counties is of excellent quality with typically low elemental concentrations. Several statistical analyses strongly suggest that most trace element concentrations, generally at low background levels, are no higher and even reduced when dissolved methane is present. In addition, trace element concentrations are not correlated with distance to gas wells. The reduction in trace element concentrations is attributed to anaerobic microbial degradation of methane, is associated with a higher pH (>8.5), and, likely, with precipitation of carbonates and pyrite and formation of clays. Trace and other elements are likely incorporated within the precipitating mineral crystalline network or sorbed. High pH values are found throughout these high-methane clusters (e.g., Parker-Hood cluster), even in subregions where methane is not present, which is consistent with a pervasive natural origin of dissolved methane rather than a limited gas well source.

show abstract

A Comprehensive Analysis of Groundwater Quality in The Barnett Shale Region

Cited by 111 publications

References 26 publications

Attitudes, Perceptions, and Geospatial Analysis of Water Quality and Individual Health Status in a High-Fracking Region

Attitudes, Perceptions, and Geospatial Analysis of Water Quality and Individual Health Status in a High-Fracking Region

Application of ANN and MLR Models on Groundwater Quality Using CWQI at Lawspet, Puducherry in India

Trace Element Behavior in Methane‐Rich and Methane‐Free Groundwater in North and East Texas

Contact Info

Product

Resources

About