Accuracy of self-reported distance to nearest unconventional oil and gas well in Pennsylvania, Ohio, and West Virginia residents and implications for exposure assessment.

Self-reported distances to industrial sources have been used in epidemiology as proxies for exposure to environmental hazards and indicators of awareness and perception of sources. Unconventional oil and gas development (UOG) emits pollutants and has been associated with adverse health outcomes. We compared self-reported distance to the nearest UOG well to the geographic information system-calculated distance for 303 Pennsylvania, Ohio, and West Virginia residents using Cohen's Weighted Kappa. Agreement was low (Kappa = 0.18), and self-reports by Ohioans (39% accuracy) were more accurate than West Virginians (22%) or Pennsylvanians (13%, both p < 0.05). Of the demographic characteristics studied, only educational attainment was related to reporting accuracy; residents with 12-16 years of education were more accurate (31.3% of group) than those with <12 or >16 years (both 16.7%). Understanding differences between objective and subjective measures of UOG proximity could inform studies of perceived exposures or risks and may also be relevant to adverse health effects. IMPACT: We compared objective and self-reported measures of distance to the nearest UOG well for 303 Appalachian Basin residents. We found that residents' self-reported distance to the nearest UOG well had limited agreement with the true calculated distance category. Our results can be used to inform the collection and contextualize the use of self-reported data in communities exposed to UOGD. Self-reported metrics can be used in conjunction with objective assessments and can be informative regarding how potentially exposed populations perceive environmental exposures or risks and could provide insights into awareness of distance-related policies, such as setbacks.

Predictors of early life residential mobility in urban and rural Pennsylvania children with acute lymphoblastic leukemia and implications for environmental exposure assessment.

BACKGROUND: Residential mobility can introduce exposure misclassification in pediatric epidemiology studies using birth address only. OBJECTIVE: We examined whether residential mobility varies by sociodemographic factors and urbanicity/rurality among children with cancer. METHODS: Our study included 400 children born in Pennsylvania during 2002-2015 and diagnosed with leukemia at ages 2-7 years. Addresses were obtained from state registries at birth and diagnosis. We considered three aspects of mobility between birth and diagnosis: whether a child moved, whether a mover changed census tract, and distance moved. We evaluated predictors of these aspects in urban- and rural-born children using chi-square, t-tests, and regression analyses. RESULTS: Overall, 58% of children moved between birth and diagnosis; suburban/rural-born children were more likely to move than urban-born children (67% versus 57%). The mean distance moved was 16.7 km in suburban/rural-born and 14.8 km in urban-born movers. In urban-born children, moving between birth and diagnosis was associated with race, education, participation in the Nutrition Program for Women, Infants and Children (WIC), and census tract-level income (all χ2 p < 0.01). Urban-born movers tended to be born in a census tract with a higher Social Vulnerability Index than non-movers (t-test p < 0.01). No factors were statistically significantly associated with any of the residential mobility metrics in suburban/rural-born children, although the sample size was small. IMPACT STATEMENT: In this study of a vulnerable population of children with cancer, we found that rural-born children were more likely to move than urban-born children, however, the frequency of movers changing census tracts was equivalent. Mobility in urban-born children, but not rural-born, was associated with several social factors, although the sample size for rural-born children was small. Mobility could be an important source of misclassification depending on the spatial heterogeneity and resolution of the exposure data and whether the social factors are related to exposures or health outcomes. Our results highlight the importance of considering differences in mobility between urban and rural populations in spatial research.

Social Vulnerability and Groundwater Vulnerability to Contamination From Unconventional Hydrocarbon Extraction in the Appalachian Basin.

Unconventional oil and gas (UOG) development, made possible by horizontal drilling and high-volume hydraulic fracturing, has been fraught with controversy since the industry's rapid expansion in the early 2000's. Concerns about environmental contamination and public health risks persist in many rural communities that depend on groundwater resources for drinking and other daily needs. Spatial disparities in UOG risks can pose distributive environmental injustice if such risks are disproportionately borne by marginalized communities. In this paper, we analyzed groundwater vulnerability to contamination from UOG as a physically based measure of risk in conjunction with census tract level sociodemographic characteristics describing social vulnerability in the northern Appalachian Basin. We found significant associations between elevated groundwater vulnerability and lower population density, consistent with UOG development occurring in less densely populated rural areas. We also found associations between elevated groundwater vulnerability and lower income, higher proportions of elderly populations, and higher proportion of mobile homes, suggesting a disproportionate risk burden on these socially vulnerable groups. We did not find a statistically significant association between elevated groundwater vulnerability and populations of racial/ethnic minorities in our study region. Household surveys provided empirical support for a relationship between sociodemographic characteristics and capacity to assess and mitigate exposures to potentially contaminated water. Further research is needed to probe if the observed disparities translate to differences in chemical exposure and adverse health outcomes.

Regional Scale Assessment of Shallow Groundwater Vulnerability to Contamination from Unconventional Hydrocarbon Extraction.

Concerns over unconventional oil and gas (UOG) development persist, especially in rural communities that rely on shallow groundwater for drinking and other domestic purposes. Given the continued expansion of the industry, regional (vs local scale) models are needed to characterize groundwater contamination risks faced by the increasing proportion of the population residing in areas that accommodate UOG extraction. In this paper, we evaluate groundwater vulnerability to contamination from surface spills and shallow subsurface leakage of UOG wells within a 104,000 km2 region in the Appalachian Basin, northeastern USA. We test a computationally efficient ensemble approach for simulating groundwater flow and contaminant transport processes to quantify vulnerability with high resolution. We also examine metamodels, or machine learning models trained to emulate physically based models, and investigate their spatial transferability. We identify predictors describing proximity to UOG, hydrology, and topography that are important for metamodels to make accurate vulnerability predictions outside their training regions. Using our approach, we estimate that 21,000-30,000 individuals in our study area are dependent on domestic water wells that are vulnerable to contamination from UOG activities. Our novel modeling framework could be used to guide groundwater monitoring, provide information for public health studies, and assess environmental justice issues.

Community concern and government response: Identifying socio-economic and demographic predictors of oil and gas complaints and drinking water impairments in Pennsylvania.

Oil and gas development has led to environmental hazards and community concerns, particularly in relation to water supply issues. Filing complaints with state agencies enables citizens to register concerns and seek investigations. We evaluated associations between county-level socio-economic and demographic factors, oil and gas drilling, and three outcomes in Pennsylvania between 2004-2016: number of oil and gas complaints filed, and both the number and proportion of state investigations of water supply complaints yielding a confirmed water supply impairment (i.e., "positive determination"). We used hierarchical Bayesian Poisson and binomial regression analyses. From 2004-2016, 9,404 oil and gas-related complaints were filed, of which 4,099 were water supply complaints. Of those, 3,906 received investigations, and 215 yielded positive determinations. We observed a 47% increase in complaints filed per $10,000 increase in annual median household income (MHI) (Rate Ratio [RR]: 1.47, 95% credible interval [CI]: 1.09-1.96) and an 18% increase per 1% increase in educational attainment (RR: 1.18, 95% CI: 1.11-1.26). While the number of complaints filed did not vary by race/ethnicity, the odds of a complaint yielding a positive determination were 0.81 times lower in counties with a higher proportion of marginalized populations (Odds Ratio [OR]: 0.81 per 1% increase in percent Black, Asian, and Native American populations combined, 95% CI: 0.64-0.99). The odds of positive determinations were also lower in areas with higher income (OR per $10,000 increase in MHI: 0.35, 95% CI: 0.09-0.96). Our results suggest these relationships are complex and may indicate potential environmental and procedural inequities, warranting further investigation.

Box-Counting Dimension Revisited: Presenting an Efficient Method of Minimizing Quantization Error and an Assessment of the Self-Similarity of Structural Root Systems.

Fractal dimension (FD), estimated by box-counting, is a metric used to characterize plant anatomical complexity or space-filling characteristic for a variety of purposes. The vast majority of published studies fail to evaluate the assumption of statistical self-similarity, which underpins the validity of the procedure. The box-counting procedure is also subject to error arising from arbitrary grid placement, known as quantization error (QE), which is strictly positive and varies as a function of scale, making it problematic for the procedure's slope estimation step. Previous studies either ignore QE or employ inefficient brute-force grid translations to reduce it. The goals of this study were to characterize the effect of QE due to translation and rotation on FD estimates, to provide an efficient method of reducing QE, and to evaluate the assumption of statistical self-similarity of coarse root datasets typical of those used in recent trait studies. Coarse root systems of 36 shrubs were digitized in 3D and subjected to box-counts. A pattern search algorithm was used to minimize QE by optimizing grid placement and its efficiency was compared to the brute force method. The degree of statistical self-similarity was evaluated using linear regression residuals and local slope estimates. QE, due to both grid position and orientation, was a significant source of error in FD estimates, but pattern search provided an efficient means of minimizing it. Pattern search had higher initial computational cost but converged on lower error values more efficiently than the commonly employed brute force method. Our representations of coarse root system digitizations did not exhibit details over a sufficient range of scales to be considered statistically self-similar and informatively approximated as fractals, suggesting a lack of sufficient ramification of the coarse root systems for reiteration to be thought of as a dominant force in their development. FD estimates did not characterize the scaling of our digitizations well: the scaling exponent was a function of scale. Our findings serve as a caution against applying FD under the assumption of statistical self-similarity without rigorously evaluating it first.