The use of liquid biofuels has expanded over the past decade in response to policies such as the U.S. Renewable Fuel Standard (RFS) that promote their use for transportation. One rationale is the belief that biofuels are inherently carbon neutral, meaning that only production-related greenhouse gas (GHG) emissions need to be tallied when comparing them to fossil fuels. This assumption is embedded in the lifecycle analysis (LCA) modeling used to justify and administer such policies. LCA studies have often found that crop-based biofuels such as corn ethanol and biodiesel offer at least modest net GHG reductions relative to petroleum fuels. Data over the period of RFS expansion enable empirical assessment of net CO₂ emission effects. This analysis evaluates the direct carbon exchanges (both emissions and uptake) between the atmosphere and the U.S. vehicle-fuel system (motor vehicles and the physical supply chain for motor fuels) over 2005–2013. While U.S. biofuel use rose from 0.37 to 1.34 EJ/yr over this period, additional carbon uptake on cropland was enough to offset only 37 % of the biofuel-related biogenic CO₂ emissions. This result falsifies the assumption of a full offset made by LCA and other GHG accounting methods that assume biofuel carbon neutrality. Once estimates from the literature for process emissions and displacement effects including land-use change are considered, the conclusion is that U.S. biofuel use to date is associated with a net increase rather than a net decrease in CO₂ emissions.

Identifying critical source areas (CSAs) is the first step to effectively managing nonpoint source (NPS) pollution. Increasing variability in climate can affect identification of CSAs. In this study, we identified present and future CSAs of NPS pollution in the Nakdong River watershed and examined how climate change will influence the identification of CSAs. Nine NPS pollution-related factors affecting the watershed environment and water quality were considered. These factors were rescaled through a min-max normalization to propose an index system that ranks basins based on the sensitivity of basins to climate change on identifying CSAs. For analyses, past rainfall was replaced with future rainfall under two RCP scenarios, RCP 2.6 and RCP 8.5. Results showed insignificant differences in the spatial distribution of CSAs between the present and the future and between the future scenarios. Basins that are on or adjacent to the Nakdong River mainstream were mainly identified as CSAs, in addition to many basins of the Geumho and Nam rivers. Highly ranked CSAs including the level 1 CSAs, were mainly distributed in the mid- and downstream areas of the Nakdong River, indicating high need of NPS pollution management. This study can provide a foundation for the effective management of NPS pollution in the present and the future.

The purpose of this study is to evaluate the impact of hydraulic fracturing on groundwater quality in Ector, Midland, and Martin Counties located in the Permian Basin, West Texas. Chemical fluids used in hydraulic fracturing and groundwater quality parameters (chloride, fluoride, calcium carbonate, nitrate, pH, and total dissolved solids), were statistically analyzed assuming a normal probability function distribution and through a one-way analysis of variance of the parameters. Additionally, the depth of groundwater well versus water quality analysis as well as historical analysis of groundwater quality parameters of wells were performed. The result for each county was individually examined and contrasted with the other two counties, in order to make inferences about groundwater quality and oil and gas activities for the three counties. Potential risks to human health from the abnormal levels of the groundwater quality parameters studied were also discussed based on the Environmental Protection Agency’s (EPA) standards. This research provides important information on groundwater quality in the Permian Basin and contributes on understanding the response to development in hydraulic fracturing.

The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality.

Rotary drilling for oil and natural gas uses drilling fluid for lubrication of the bit, to seal off unstable shale layers, and floating out rock cuttings. Drilling fluid is a water–clay chemical mixture. Produced water is a water–sand chemical mixture. Land farming is a common disposal technique of drilling fluid and produced water. In the land farming process, amendments of fluid are repeatedly applied to the soil surface. Plant growth and soil chemical properties may be altered by additions of drilling fluid, because of alkalinity, salinity, trace elements, and petroleum residue contained in waste. The objective of this study was to determine the change in soil pH, electrical conductivity (EC), total nitrogen and carbon, and extractable nutrient levels following the land application of drilling fluid and produced water. The study was a comparison of three plots with similar soil properties and conditions. The three study plots had various levels of drilling fluid and produced water applications. The data show a major difference from field-to-field for EC, Na, and Cl levels. The EC and salt levels increased with additional applications of drilling fluid and produced water. The percent total nitrogen values and plant available P levels were very low in all fields. High EC and salt values, coupled with low N and P levels, would be detrimental to plant growth and development. To successfully vegetate this land-farm site, application of N and P fertilizer would be required. This study help to give a better understanding of practical ways to land-farm drilling fluid and produced water in a fashion that both minimizes environmental issues and is economically feasible in Arkansas. Thus, this research will provide important information for soil contamination management and contributes on understanding of the responses of soil properties to drilling fluid and produced water in the future.