Dataset Information

Association of groundwater constituents with topography and distance to unconventional gas wells in NE Pennsylvania.

ABSTRACT: Recently we reported an association of certain diseases with unconventional gas development (UGD). The purpose of this study is to examine UGD's possible impacts on groundwater quality in northeastern Pennsylvania. In this study, we compared our groundwater data (Columbia 58 samples) with those published data from Cabot (1701 samples) and Duke University (150 samples). For each dataset, proportions of samples with elevated levels of dissolved constituents were compared among four groups, identified as upland far (i.e. ?1km to the nearest UGD gas well), upland near (<1km), valley far (?1km), and valley near (<1km) groups. The Columbia data do not show statistically significant differences among the 4 groups, probably due to the limited number of samples. In Duke samples, Ca and CI levels are significantly higher in the valley near group than in the valley far group. In the Cabot dataset, methane, Na, and Mn levels are significantly higher in valley far samples than in upland far samples. In valley samples, Ca, Cl, SO4, and Fe are significantly higher in the near group (i.e. <1km) than in the far group. The association of these constituents in valley groundwater with distance is observed for the first time using a large industry dataset. The increase may be caused by enhanced mixing of shallow and deep groundwater in valley, possibly triggered by UGD process. If persistent, these changes indicate potential for further impact on groundwater quality. Therefore, there is an urgent need to conduct more studies to investigate effects of UGD on water quality and possible health outcomes.

SUBMITTER: Yan B

PROVIDER: S-EPMC5116425 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Association of groundwater constituents with topography and distance to unconventional gas wells in NE Pennsylvania.

Yan Beizhan B Stute Martin M Panettieri Reynold A RA Ross James J Mailloux Brian B Neidell Matthew J MJ Soares Lissa L Howarth Marilyn M Liu Xinhua X Saberi Pouné P Chillrud Steven N SN

The Science of the total environment 20161104

Recently we reported an association of certain diseases with unconventional gas development (UGD). The purpose of this study is to examine UGD's possible impacts on groundwater quality in northeastern Pennsylvania. In this study, we compared our groundwater data (Columbia 58 samples) with those published data from Cabot (1701 samples) and Duke University (150 samples). For each dataset, proportions of samples with elevated levels of dissolved constituents were compared among four groups, identif ...[more]

PMID: 27817928

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Project description:Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development. IMPORTANCE The environmental implications of unconventional oil and gas extraction are only recently starting to be systematically recorded. Our research shows the utility of microbial communities paired with geochemical markers to build strong predictive random forest models of unconventional oil and gas activity and the identification of key biomarkers. Microbial communities, their transcribed genes, and key biomarkers can be used as sentinels of environmental changes. Slight changes in microbial function and composition can be detected before chemical markers of contamination. Potential contamination, specifically from biocides, is especially concerning due to its potential to promote antibiotic resistance in the environment. Additionally, as microbial communities facilitate the bulk of nutrient cycling in the environment, small changes may have long-term repercussions. Supervised random forest models can be used to identify changes in those communities, greatly enhance our understanding of what such impacts entail, and inform environmental management decisions.

Project description:BackgroundLittle is known about the environmental and public health impact of unconventional natural gas extraction activities, including hydraulic fracturing, that occur near residential areas.ObjectivesOur aim was to assess the relationship between household proximity to natural gas wells and reported health symptoms.MethodsWe conducted a hypothesis-generating health symptom survey of 492 persons in 180 randomly selected households with ground-fed wells in an area of active natural gas drilling. Gas well proximity for each household was compared with the prevalence and frequency of reported dermal, respiratory, gastrointestinal, cardiovascular, and neurological symptoms.ResultsThe number of reported health symptoms per person was higher among residents living < 1 km (mean ± SD, 3.27 ± 3.72) compared with > 2 km from the nearest gas well (mean ± SD, 1.60 ± 2.14; p = 0.0002). In a model that adjusted for age, sex, household education, smoking, awareness of environmental risk, work type, and animals in house, reported skin conditions were more common in households < 1 km compared with > 2 km from the nearest gas well (odds ratio = 4.1; 95% CI: 1.4, 12.3; p = 0.01). Upper respiratory symptoms were also more frequently reported in persons living in households < 1 km from gas wells (39%) compared with households 1-2 km or > 2 km from the nearest well (31 and 18%, respectively) (p = 0.004). No equivalent correlation was found between well proximity and other reported groups of respiratory, neurological, cardiovascular, or gastrointestinal conditions.ConclusionAlthough these results should be viewed as hypothesis generating, and the population studied was limited to households with a ground-fed water supply, proximity of natural gas wells may be associated with the prevalence of health symptoms including dermal and respiratory conditions in residents living near natural gas extraction activities. Further study of these associations, including the role of specific air and water exposures, is warranted.

Dataset Information

Association of groundwater constituents with topography and distance to unconventional gas wells in NE Pennsylvania.

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Association of groundwater constituents with topography and distance to unconventional gas wells in NE Pennsylvania.

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