Recent scholarship has highlighted the significant environmental impact of the rapidly expanding hydraulic fracturing industry, which is projected to grow from USD 15.31 billion in 2021 to USD 28.93 billion in 2028 at a Compound Annual Growth Rate (CAGR) of 9.5%. Recognizing the need for comprehensive, national-scale evaluations, this review of the literature investigates contamination and induced seismicity associated with shale gas hydraulic fracturing in the United States. Employing systematic reviews of the literature and federal reports up until July 2023, this study reveals multiple areas of concern, including water and soil contamination, seismic activity, and air pollution. A notable finding is the average use of 2.4 million gallons of water per well in hydraulic fracturing, of which only 15–35% is typically retrieved. However, ongoing studies are actively exploring remediation strategies, including advancements in monitoring and treatment technologies, as well as the potential of reusing wastewater for hydraulic fracturing, as exemplified by the Garfield County region in Colorado; they utilized 100% wastewater to mitigate the impact of contamination. These findings underscore the need for stringent regulations, sustained research, and effective management practices. This work emphasizes the importance of a collaborative approach that leverages field studies, experimental investigations, and computational advancements to ensure the responsible development of shale gas resources.

The purposes of this study are to analyze the groundwater quality of the Ogallala Aquifer and evaluate the hydrological characteristics in the southern High Plains region of the Permian Basin, Texas. Levels of chloride, fluoride, nitrate, selenium, pH, and total dissolved solids (TDS) were analyzed for the period 1990–2016. Data concerning a total of 133 wells were collected from the Texas Water Development Board (TWDB), which is an open database provided by the US government. The average levels of contaminants were compared to their respective Maximum Contaminant Levels (MCL) stipulated by the Environmental Protection Agency (EPA). The study area experienced high concentrations of most parameters including chloride, fluoride, nitrate, selenium, and TDS, within the contaminants’ respective MCLs. Borden and Dawson counties experienced the highest overall amounts of groundwater pollutants. Possible sources of each contaminant are discussed, with oil and gas activities, agricultural practices, and other human actions impacting the conditions. This research provides important information about groundwater quality of the Ogallala Aquifer and contributes to understanding the response to development in the Permian Basin, Texas.

Recently, deep learning-based image super-resolution (SR) has emerged as an effective and cost-efficient solution for obtaining images that surpass the physical limitation of sensors. However, the training datasets commonly used for SR tasks—particularly in remote sensing—are constrained by limited scale and spatial resolution, as they are often developed for different purposes. To address these issues, we propose LACAS2K, a large-scale dataset of aerial and satellite images specifically tailored for image super-resolution of very high-resolution (VHR) satellite imagery. The dataset is constructed with a clearly defined target input spatial resolution of approximately 50 cm, aligning with the typical resolution of panchromatic (PAN) bands in VHR satellite imagery. Moreover, it provides high-quality high-resolution (HR) images organized into 2K-resolution patches for scale factors of 2 and 4, offering richer spatial features. The experimental results demonstrate that LACAS2K effectively supports the training of data-intensive models, such as transformer-based SR networks. The LACAS2K dataset will be made publicly available through IEEE DataPort, providing a valuable resource for advancing SR research in the remote sensing domain.