Spatial distribution of unconventional gas wells and human populations in the Marcellus Shale in the United States: Vulnerability analysis |
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| Institution: | 1. Department of Epidemiology, Shanghai Jiaotong University School of Public Health, China;2. Department of Environmental Medicine, New York University School of Medicine, Tuxedo, NY 10987, USA;3. School of Material Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;4. Biochemistry and Molecular Pharmaceutical, New York University School of Medicine, USA;5. Robert Wood Johnson Medical School Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;6. College of Science, Donghua University, Shanghai 201620, China;1. Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong Province 511430, China;2. Department of Epidemiology, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, Guangdong Province 510515, China;3. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong Province 511430, China;4. Nicholas School of the Environment, Duke Global Health Institute, Duke University, Durham, NC 27705, USA;5. Duke Kunshan University, Kunshan, Jiangsu Province 215316, China |
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| Abstract: | Modern forms of drilling and extraction have recently led to a boom in oil and gas production in the U.S. and stimulated a controversy around its economic benefits and environmental and human health impacts. Using an environmental justice paradigm this study applies Geographic Information Systems (GIS) and spatial analysis to determine whether certain vulnerable human populations are unequally exposed to pollution from unconventional gas wells in Pennsylvania, West Virginia, and Ohio. Several GIS-based approaches were used to identify exposed areas, and a t-test was used to find statistically significant differences between rural populations living close to wells and rural populations living farther away. Sociodemographic indicators include age (children and the elderly), poverty level, education level, and race at the census tract level. Local Indicators of Spatial Autocorrelation (LISA) technique was applied to find spatial clusters where both high well density and high proportions of vulnerable populations occur. The results demonstrate that the environmental injustice occurs in areas with unconventional wells in Pennsylvania with respect to the poor population. There are also localized clusters of vulnerable populations in exposed areas in all three states: Pennsylvania (for poverty and elderly population), West Virginia (for poverty, elderly population, and education level) and Ohio (for children). |
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| Keywords: | Hydraulic fracturing Environmental justice Vulnerability LISA |
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