Complex Metrics Research Articles

A note on the admissibility of complex BTZ metrics

We perform a nontrivial check of Witten’s recently proposed admissibility criterion for complex metrics. We consider the ‘quasi-Euclidean’ metrics obtained from continuing the BTZ class of metrics to imaginary time. Of special interest are the overspinning metrics, which are smooth in this three-dimensional context. Their inclusion as saddle points in the gravitational path integral would lead to puzzling results in conflict with those obtained using other methods. It is therefore encouraging that the admissibility criterion discards them. For completeness, we perform an analysis of smoothness and admissibility for the family of quasi-Euclidean BTZ metrics at all values of the mass and angular momentum.

Allowable complex scalars from Kaluza-Klein compactifications and metric rescalings

Recently there have been discussions about which complex metrics should be allowable in quantum gravity. These discussions assumed that the matter fields were real valued. We make the observation that for compactified solutions it makes sense to demand convergence of the theory's path integral in the higher-dimensional parent theory. Upon compactification this allows for more general matter configurations in the lower-dimensional theory, in particular it allows for complex scalar fields, with a bound on their imaginary parts. Similar considerations apply to metric rescalings in the presence of higher curvature corrections. We illustrate this effect with the example of the no--boundary proposal, in which scalar fields are typically required to take complex values. We find that complex no-boundary solutions exist, and satisfy the derived bound, if the potential is sufficiently flat. For instance, for a compactification from $D$ dimensions, the bound on the imaginary part $\textrm{Im}\,(\phi)$ of the internal volume modulus reads $V_{,\phi}/V < \sqrt{\frac{D-4}{D-2}}/3\sqrt{2}.$ This leads to a mild tension with swampland conjectures.

Linking host plants to damage types in the fossil record of insect herbivory

AbstractStudies of insect herbivory on fossilized leaves tend to focus on a few, relatively simple metrics that are agnostic to the distribution of insect damage types among host plants. More complex metrics that link particular damage types to particular host plants have the potential to address additional ecological questions, but such metrics can be biased by sampling incompleteness due to the difficulty of distinguishing the true absence of a particular interaction from the failure to detect it—a challenge that has been raised in the ecological literature. We evaluate a range of methods for characterizing the relationships between damage types and host plants by performing resampling and subsampling exercises on a variety of datasets. We found that the components of beta diversity provide a more valid, reliable, and interpretable method for comparing component communities than do bipartite network metrics and that the rarefaction of interactions represent a valid, reliable, and interpretable method for comparing compound communities. Both beta diversity and rarefaction of interactions avoid the potential pitfalls of multiple comparisons. Finally, we found that the host specificity of individual damage types is challenging to assess. Whereas bipartite network metrics are sufficiently biased by sampling incompleteness to be inappropriate for fossil herbivory data, alternatives exist that are perfectly suitable for fossil datasets with sufficient sample coverage.

Analysing individual 3D tree structure using the R package ITSMe

Abstract Detailed 3D quantification of tree structure plays a crucial role in understanding tree‐ and plot‐level biophysical processes. Light detection and ranging (LiDAR) has led to a revolution in tree structural measurements and its 3D data are increasingly becoming publicly available. Yet, calculating structural metrics from LiDAR data can often be complex and time‐consuming and potentially requires expert knowledge. We present the R package Individual Tree Structural Metrics (ITSMe), a toolbox that works with LiDAR tree point clouds and quantitative structure models (QSMs) derived from LiDAR point clouds to obtain individual tree structural metrics. It serves as a robust synthesis framework for researchers who want to readily obtain structural information from 3D data of individual trees. The package includes functions to determine basic structural metrics (tree height, diameter at breast height, diameter above buttresses, projected crown area, 3D alpha crown volume) from individual tree point clouds, as well as more complex structural metrics (individual tree component volumes, branch angle‐, radius‐ and length‐related metrics) from QSMs. The ITSMe package is an open‐source package hosted on GitHub that will make the use of 3D data more straightforward and transparent for a range of end‐users interested in exploiting tree structure information.

Dynamical Signature: Complex Manifolds, Gauge Fields and Non-Flat Tangent Space

Theoretical possibilities of models of gravity with dynamical signature are discussed. The different scenarios of the signature change are proposed in the framework of Einstein-Cartan gravity. We consider, subsequently, the dynamical signature in the model of the complex manifold with complex coordinates and complex metrics are introduced, a complexification of the manifold and coordinates through new gauge fields, an additional gauge symmetry for the Einstein-Cartan vierbein fields, and non-flat tangent space for the metric in the Einstein-Cartan gravity. A new small parameter, which characterizes a degree of the deviation of the signature from the background one, is introduced in all models. The zero value of this parameter corresponds to the signature of an initial background metric. In turn, in the models with gauge fields present, this parameter represents a coupling constant of the gauge symmetry group. The mechanism of metric determination through induced gauge fields with defined signatures in the corresponding models is considered. The ways of the signature change through the gauge field dynamics are reviewed, and the consequences and applications of the proposed ideas are discussed as well.

Uses of complex metrics in cosmology

Complex metrics are a double-edged sword: they allow one to replace singular spacetimes, such as those containing a big bang, with regular metrics, yet they can also describe unphysical solutions in which quantum transitions may be more probable than ordinary classical evolution. In the cosmological context, we investigate a criterion proposed by Witten (based on works of Kontsevich & Segal and of Louko & Sorkin) to decide whether a complex metric is allowable or not. Because of the freedom to deform complex metrics using Cauchy’s theorem, deciding whether a metric is allowable in general requires solving a complicated optimisation problem. We describe a method that allows one to quickly determine the allowability of minisuperspace metrics. This enables us to study the off-shell structure of minisuperspace path integrals, which we investigate for various boundary conditions. Classical transitions always reside on the boundary of the domain of allowable metrics, and care must be taken in defining appropriate integration contours for the corresponding gravitational path integral. Perhaps more surprisingly, we find that proposed quantum (‘tunnelling’) transitions from a contracting to an expanding universe violate the allowability criterion and may thus be unphysical. No-boundary solutions, by contrast, are found to be allowable, and moreover we demonstrate that with an initial momentum condition an integration contour over allowable metrics may be explicitly described in arbitrary spacetime dimensions.

Feynman’s iϵ prescription, almost real spacetimes, and acceptable complex spacetimes

Feynman’s iϵ prescription for quantum field theoretic propagators has a quite natural reinterpretation in terms of a slight complex deformation of the Minkowski space-time metric. Though originally a strictly flat-space result, once reinterpreted in this way, these ideas can be naturally extended first to semi-classical curved-spacetime QFT on a fixed background geometry and then, (with more work), to fluctuating spacetime geometries. There are intimate connections with variants of the weak energy condition. We shall take the Lorentzian signature metric as primary, but note that allowing the complex deformation to become large leads to a variant of Wick rotation, and more importantly leads to physically motivated constraints on the configuration space of acceptable off-shell geometries to include in Feynman’s functional integral when attempting to quantize gravity. Ultimately this observation allows one to connect the discussion back to recent ideas on “acceptable” complex metrics, in the Louko-Sorkin and Kontsevich-Segal-Witten sense, with Lorentzian signature spacetimes occurring exactly on the boundary of the set of “acceptable” complex metrics. By adopting the tetrad formalism we explicitly construct the most general set of acceptable complex metrics satisfying the 0-form, 1-form, and 2-form acceptability conditions.

Note on complex metrics, complex time, and periodic universes

Motivated on the one hand by recent results on isochronous dynamical systems, and on the other by quantum gravity applications of complex metrics, we show that, if such enlarged class of metrics is considered, one can easily obtain periodic or bouncing complex solutions of Einstein's equations. It is found that, for any given solution $g_{\mu\nu}$ of the Einstein's equations, by means of a complex change of time, one can construct infinitely many periodic or bouncing complex solutions $\hat g_{\mu\nu}$ that are physically indistinguishable from $g_{\mu\nu}$ over an arbitrarily long time interval. These results, that are based on the use of complex diffeomorphisms, point out an unacceptable arbitrariness in the theory. As we will show, a condition on the class of physically meaningful complex metrics proposed in [M. Kontsevich and G. B. Segal, Q. J. Math. 72, 673 (2021)] and discussed in [E. Witten, arXiv:2111.06514] solves this problem, restricting the family of admissible complex diffeomorphisms. We conclude arguing that this condition can be viewed as a quantum-gravity generalization of the equivalence principle to complex space-times.

Dynamic tracking of scaphoid, lunate, and capitate carpal bones using four-dimensional MRI.

A preliminary exploration of technical methodology for dynamic analysis of scaphoid, capitate, and lunate during unconstrained movements is performed in this study. A heavily accelerated and fat-saturated 3D Cartesian MRI acquisition was used to capture temporal frames of the unconstrained moving wrist of 5 healthy subjects. A slab-to-volume point-cloud based registration was then utilized to register the moving volumes to a high-resolution image volume collected at a neutral resting position. Comprehensive in-silico error analyses for different acquisition parameter settings were performed to evaluate the performance limits of several dynamic metrics derived from the registration parameters. Computational analysis suggested that sufficient volume coverage for the dynamic acquisitions was reached when collecting 12 slice-encodes at 2.5mm resolution, which yielded a temporal resolution of and 2.6 seconds per volumetric frame. These acquisition parameters resulted in total in-silico errors of 1.9°±1.8° and 3°±4.6° in derived principal rotation angles within ulnar-radial deviation and flexion-extension motion, respectively. Rotation components of the carpal bones in the radius coordinate system were calculated and found to be consistent with earlier 4D-CT studies. Temporal metric profiles derived from ulnar-radial deviation motion demonstrated better performance than those derived from flexion/extension movements. Future work will continue to explore the use of these methods in deriving more complex dynamic metrics and their application to subjects with symptomatic carpal dysfunction.

Alzheimer's Biomarker Findings Cast Doubt on Dietary Supplement

Genetic Engineering & Biotechnology NewsVol. 42, No. 6 News & AnalysisFree AccessAlzheimer's Biomarker Findings Cast Doubt on Dietary SupplementPublished Online:8 Jun 2022https://doi.org/10.1089/gen.42.06.04AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail KATERYNA KON/SCIENCE PHOTO LIBRARY/Getty ImagesScientists are still trying to understand how Alzheimer's disease (AD) is related to serine, which is known to modulate synaptic plasticity, and phosphoglycerate dehydrogenase (PHGDH), which is required for serine's synthesis. Although some researchers have suggested that a form of serine could serve as an AD therapeutic, other researchers have expressed doubts.The scientists with doubts include the researchers at the University of California, San Diego, led by neuroscientist Xu Chen and bioengineer Sheng Zhong. For these researchers, a key issue is whether PHGDH and serine expression levels rise or fall in AD. Two years ago, in Current Biology, the researchers reported that they had analyzed blood samples of older adults and found a steep increase in PHGDH gene expression in AD patients, and that the increase was evident in healthy individuals approximately two years before they were diagnosed with the disease.The results were promising, indicating that high blood levels of RNA produced by the PHGDH gene could serve as a blood biomarker for early detection of AD. To follow up on these results, the researchers investigated whether increases in PHGDH gene expression could be linked back to the brain. They measured PHGDH mRNA and protein levels in two mouse models of AD and in four human cohorts in AD brains compared to ageand sex-matched control brains.The researchers also compared the subjects' PHGDH expression levels with their scores on two different clinical assessments: the Dementia Rating Scale (which rates a person's memory and cognitive ability) and Braak Staging (which rates the severity of AD based on the brain's pathology).What did the scientists find? The answers appeared in Cell Metabolism, in an article titled, “PHGDH expression increases with progression of Alzheimer's disease pathology and symptoms.” The increases were found in experiments with mouse models of AD and/or tauopathy, and in analyses of genetic data collected from postmortem human brains.“PHGDH mRNA and protein levels,” the scientists reported, “are progressively increased in human brains with no, early, and late AD pathology, as well as in people with no, asymptomatic, and symptomatic AD.”“It's exciting that our previous discovery of a blood biomarker is now corroborated with brain data,” said Zhong. “Now we have strong evidence that the changes we see in human blood are directly correlated to changes in the brain in AD.”“The fact that this gene's expression level directly correlates with both a person's cognitive ability and disease pathology is remarkable,” Zhong added. “Being able to quantify both of these complex metrics with a single molecular measurement could potentially make diagnosing and monitoring the progression of AD much simpler.”The scientists also noted that in their work, they were careful to control for postmortem delay (PMD). “Considering that human PHGDH protein is sensitive to protease cleavage at room temperature,” the scientists wrote, “PMD-related protein degradation may explain the lower PHGDH levels” that were reported by another research team. In Cell Metabolism, the other team wrote, “Our findings … suggest oral L-serine as a ready-to-use therapy for AD.” In contrast, the team led by Chen and Zhong declared that oral L-serine “warrants precaution.”FiguresReferencesRelatedDetails Volume 42Issue 6Jun 2022 Information© 2022 by GEN PublishingTo cite this article:Alzheimer's Biomarker Findings Cast Doubt on Dietary Supplement.Genetic Engineering & Biotechnology News.Jun 2022.12-12.http://doi.org/10.1089/gen.42.06.04Published in Volume: 42 Issue 6: June 8, 2022PDF download

Mapping terrestrial ecosystem health in drylands: comparison of field-based information with remotely sensed data at watershed level

ContextCombining field-based assessments with remote-sensing proxies of landscape patterns provides the opportunity to monitor terrestrial ecosystem health status in support of sustainable development goals (SDG).ObjectivesLinking qualitative field data with quantitative remote-sensing imagery to map terrestrial ecosystem health (SDG15.3.1 “land degradation neutrality”).MethodsA field-based approach using the Interpreting Indicators of Rangeland-Health (IIRH) protocol was applied to classify terrestrial ecosystem health status at the watershed level as “healthy”, “at-risk”, and “unhealthy”. Quantitative complex landscape metrics derived from Landsat spaceborne data were used to explore whether similar health statuses can be retrieved on a broader scale. The assignment of terrestrial ecosystem health classes based on field and the remotely sensed metrics were tested using multivariate and cluster analysis methods.ResultsAccording to the IIRH assessments, soil surface loss, plant mortality, and invasive species were identified as important indicators of health. According to the quantitative landscape metrics, “healthy” sites had lower amounts of spectral heterogeneity, edge density, and resource leakage. We found a high agreement between health clusters based on field and remote-sensing data (NMI = 0.91) when using a combined approach of DBSCAN and k-means clustering together with non-metric multi-dimensional scaling (NMDS).ConclusionsWe provide an exemplary workflow on how to combine qualitative field data and quantitative remote-sensing data to assess SDGs indicators related to terrestrial ecosystem health. As we used a standardized method for field assessments together with publicly available satellite data, there is potential to test the generalizability and context-dependency of our approach in other arid and semi-arid rangelands.

Training and Predicting Visual Error for Real-Time Applications

Visual error metrics play a fundamental role in the quantification of perceived image similarity. Most recently, use cases for them in real-time applications have emerged, such as content-adaptive shading and shading reuse to increase performance and improve efficiency. A wide range of different metrics has been established, with the most sophisticated being capable of capturing the perceptual characteristics of the human visual system. However, their complexity, computational expense, and reliance on reference images to compare against prevent their generalized use in real-time, restricting such applications to using only the simplest available metrics. In this work, we explore the abilities of convolutional neural networks to predict a variety of visual metrics without requiring either reference or rendered images. Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates. The resulting models account for 70%-90% of the variance while achieving up to an order of magnitude faster computation times. Our solution combines image-space information that is readily available in most state-of-the-art deferred shading pipelines with reprojection from previous frames to enable an adequate estimate of visual errors, even in previously unseen regions. We describe a suitable convolutional network architecture and considerations for data preparation for training. We demonstrate the capability of our network to predict complex error metrics at interactive rates in a real-time application that implements content-adaptive shading in a deferred pipeline. Depending on the portion of unseen image regions, our approach can achieve up to 2x performance compared to state-of-the-art methods.

Metrics and mechanisms: Measuring the unmeasurable in the science of science

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.

Evaluating an Application Aware Distributed Dijkstra Shortest Path Algorithm in Hybrid Cloud/Edge Environments

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra&#x0027;s routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

Cost analysis and cost-effectiveness of hand-scored and automated approaches to writing screening

Although researchers have investigated technical adequacy and usability of written-expression curriculum-based measures (WE-CBM), the economic implications of different scoring approaches have largely been ignored. The absence of such knowledge can undermine the effective allocation of resources and lead to the adoption of suboptimal measures for the identification of students at risk for poor writing outcomes. Therefore, we used the Ingredients Method to compare implementation costs and cost-effectiveness of hand-calculated and automated scoring approaches. Data analyses were conducted on secondary data from a study that evaluated predictive validity and diagnostic accuracy of quantitative approaches for scoring WE-CBM samples. Findings showed that automated approaches offered more economic solutions than hand-calculated methods; for automated scores, the effects were stronger when the free writeAlizer R package was employed, whereas for hand-calculated scores, simpler WE-CBM metrics were less costly than more complex metrics. Sensitivity analyses confirmed the relative advantage of automated scores when the number of classrooms, students, and assessment occasions per school year increased; again, writeAlizer was less sensitive to the changes in the ingredients than the other approaches. Finally, the visualization of the cost-effectiveness ratio illustrated that writeAlizer offered the optimal balance between implementation costs and diagnostic accuracy, followed by complex hand-calculated metrics and a proprietary automated program. Implications for the use of hand-calculated and automated scores for the universal screening of written expression with elementary students are discussed.

Autoethnography on Researcher Profile Cultivation

Information Communication Technology (ICT) and social networks have significant impact on everyday life. One the one hand, Internet users enjoy promoting themselves and feel free to disseminate information about themselves through websites and social networks, but on the other hand, people feel forced to reveal information about them on the Internet. Web technologies enable self-promotion for many reasons, i.e., social relations development, acquiring a new job, or research career support. This paper concerns autoethnography application for social science researcher profile cultivation. Autoethnography belongs to qualitative methods and focuses on deep analysis of experiences and competencies in a narrative way. In this study, autoethnography is self-reflection for personal development strategy. This study methodology includes the literature survey and case study. The Literature Survey (LS) on autoethnographic research is included to answer the question for what purposes autoethnography is applied. In the case study, the author proposes to expand autoethnography and presents that beyond stories, statistical data can be used to reveal researcher’s experiences and personality, and data anonymization is a solution for privacy protection in autoethnographic research. The results indicate that perception of individual profile is significantly influenced by ICT, Internet services, and social networks platforms and portals. Contemporary researchers are evaluated by Web statistical measures. The researcher’s profiling is much more complex and statistical measures and metrics provide a general view of the researcher. Application of statistical measures leads to concluding on general competencies of the researcher and precludes a deep focus on local scientific specificity of the researcher. This paper has added value because of presenting the academic community integration with the Internet social networks, e.g., Facebook, LinkedIn, or SciVal. The paper emphasizes transparency and visibility of researchers’ profiles, as well as the necessity to analyze their activities and publications in academic community context and in comparisons with others.

A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.

Allowable complex metrics in minisuperspace quantum cosmology

Kontsevich and Segal (K-S) have proposed a criterion to determine which complex metrics should be allowed, based on the requirement that quantum field theories may consistently be defined on these metrics, and Witten has recently suggested that their proposal should also apply to gravity. We explore this criterion in the context of gravitational path integrals, in simple minisuperspace models, specifically considering de Sitter (dS), no-boundary and anti--de Sitter (AdS) examples. These simple examples allow us to gain some understanding of the off-shell structure of gravitational path integrals. In all cases, we find that the saddle points of the integral lie right at the edge of the allowable domain of metrics, even when the saddle points are complex or Euclidean. Moreover the Lefschetz thimbles, in particular the steepest descent contours for the lapse integral, are cut off as they intrude into the domain of nonallowable metrics. In the AdS case, the implied restriction on the integration contour is found to have a simple physical interpretation. In the dS case, the lapse integral is forced to become asymptotically Euclidean. We also point out that the K-S criterion provides a reason, in the context of the no-boundary proposal, for why scalar fields would start their evolution at local extrema of their potential.

A Framework for the Evaluation of the Cultural Heritage Information Ontology

The intelligent management of built cultural heritage, including heritage buildings, requires common semantics in the form of standardized ontologies to achieve semantic interoperability. Foundational ontologies should be reused when building new ontologies, as they provide high-level terms; however, candidate foundational ontologies should be evaluated for quality and pitfalls. Simple metrics (e.g., number of concepts) are easy to obtain with existing tools. Complex metrics such as quality of ontology structure, functional adequacy, transferability, reliability, compatibility, maintainability, and operability, are defined in recent ontology evaluation frameworks; however, these do not evaluate interoperability features. The paper proposes an improved framework for an automated ontology evaluation based on the OQuaRE framework. Our approach improved some of the metrics of the OQuaRE framework and introduced three metrics for assessing the interoperability of the ontology in question (Externes, Composability, and Aggregability). In the experimental section, the framework is validated in an evaluation of cultural heritage information ontology (CIDOC CRM—ISO 12217:2014) with the use of new software for ontology evaluation. The detailed results reveal that the ontology is minimally acceptable and that the improved evaluation framework efficiently integrated interoperability metrics. Recommendations for the improvement of the cultural heritage information ontology are described in the Discussion and Conclusions section.

Assessing Unconventional Oil and Gas Exposure in the Appalachian Basin: Comparison of Exposure Surrogates and Residential Drinking Water Measurements.

Health studies report associations between metrics of residential proximity to unconventional oil and gas (UOG) development and adverse health endpoints. We investigated whether exposure through household groundwater is captured by existing metrics and a newly developed metric incorporating groundwater flow paths. We compared metrics with detection frequencies/concentrations of 64 organic and inorganic UOG-related chemicals/groups in residential groundwater from 255 homes (Pennsylvania n = 94 and Ohio n = 161). Twenty-seven chemicals were detected in ≥20% of water samples at concentrations generally below U.S. Environmental Protection Agency standards. In Pennsylvania, two organic chemicals/groups had reduced odds of detection with increasing distance to the nearest well: 1,2-dichloroethene and benzene (Odds Ratio [OR]: 0.46, 95% confidence interval [CI]: 0.23-0.93) and m- and p-xylene (OR: 0.28, 95% CI: 0.10-0.80); results were consistent across metrics. In Ohio, the odds of detecting toluene increased with increasing distance to the nearest well (OR: 1.48, 95% CI: 1.12-1.95), also consistent across metrics. Correlations between inorganic chemicals and metrics were limited (all |ρ| ≤ 0.28). Limited associations between metrics and chemicals may indicate that UOG-related water contamination occurs rarely/episodically, more complex metrics may be needed to capture drinking water exposure, and/or spatial metrics in health studies may better reflect exposure to other stressors.