Thermal venting to recover less-volatile hydrocarbons from the unsaturated zone, 1. Theory

Thermal venting is a remediation technique suitable to the liquid unsaturated zone to enhance recovery of less-volatile residual hydrocarbon contaminants. Thermal venting is different to traditional soil venting because heated air instead of air at ambient conditions is applied to the contaminated zone. The vapor pressure of a less-volatile contaminants is typically increased by temperature causing the gas-phase concentrations to increase by three- to five-fold over a temperature increase of 20–30°C. The work described in this first paper provides the theoretical framework of analysis related to thermal venting. The analysis included nonisothermal gas flow, thermal energy transport and multicomponent mass transport in a multiphase porous medium. The transient gas flow analysis included the effect of temperature on fluid properties and gas compressibility. The heat energy transport analysis was performed under the thermodynamic equilibrium condition with phase-summed effective thermal properties. Multi-component mass transport was performed under local equilibrium for partitioning between phases. Model verification was performed to the extend possible using analytical and available experimental data for different physical processes. The second paper of this two-part series will demonstrate the applicability of thermal venting technique through numerical simulations of hypothetical laboratory and field-scale scenarios.     

Used lubricating oil recycling using hydrocarbon solvents

A solvent extraction process using new hydrocarbon solvents was employed to treat used lubricant oil. The solvents used were liquefied petroleum gas (LPG) condensate and stabilized condensate. A demulsifier was used to enhance the treatment process. The extraction process using stabilized condensate demonstrated characteristics that make it competitive with existing used oil treatment technologies. The process is able to reduce the asphaltene content of the treated lubricating oil to 0.106% (w/w), the ash content to 0.108%, and the carbon residue to 0.315% with very low levels of contaminant metals. The overall yield of oil is 79%. The treated used oil can be recycled as base lubricating oil. The major disadvantage of this work is the high temperature of solvent recovery. Experimental work and results are presented in detail.     

Potential contributions of clay minerals and organic matter to pentachlorophenol retention in soils

Sorption of pentachlorophenol (PCP) by pure minerals and humic acids were measured to obtain additional perspective on the potential contributions of both clay minerals and soil organic matter (SOM) to contaminants retention in soils. Four types of common soil minerals and two kinds of humic acids (HAs) were tested. The sorption affinity for PCP conformed to an order of HAs > K-montmorillonite > Ca-montmorillonite > goethite > kaolinite. Such a difference in sorption capacity could be attributed to the crucial control of HAs. Clay minerals also had their contribution, especially K-montmorillonite, which played an important, if not dominant, role in the controlling process of PCP sorption. By removing 80% (on average) of the organic carbon from the soils with H(2)O(2), the sorption decreased by an average of 50%. The sorption reversibility had been greatly favored as well. Considering the uncharged mineral fractions in soil before and after H(2)O(2)-treated, the main variation in sorption behavior of the soil might thus be related to the removed organic carbon and the reduced pH. This testified rightly the interactive effect of SOM and clay minerals on PCP sorption as a function of pH.     

Managing mangroves in Bangladesh: A strategy analysis

Bangladesh, favoured by a tropical climate, houses the world’s largest stretch of mangroves forests (Sundarbans Reserved Forest) and plantations. Around half of the forests of the country occur in the coastal zone. People extract various goods and services from the mangroves. Nevertheless the mangrove forests are depleting. Although the extent of the Sundarbans forest has not changed much, its decline is of a qualitative nature. Mangrove plantations are increasing in area but they are losing growing stock. To arrest this, Bangladesh has adopted several strategies. The ‘Sustainable Ecosystem Management’ strategy has now been adopted instead of the ‘Sustained Yield Principle’. Biodiversity conservation and enhancement has been taken as a key management goal. A zoning system is being developed for both production and protection purposes. The government facilitates alternative income for the local people by generating activities for the communities which are dependent on the forest. Different non-governmental organizations collaborate with the government in reducing the local people’s dependence on the forest. Coastal plantations are erected to protect people from cyclones and to make the land more suitable for habitation. Through this greening of the coastal belt tree plantation is encouraged in coastal villages. Coastal embankments are being planted and leased to poor settlers in exchange for routine maintenance of the embankments. Plantations on newly accreted mud flats help in stabilizing the land, which can later on be settled by victims of erosion elsewhere. These adopted management measures do not only contribute to forestry resource management but also to the social, environmental and economic wellbeing of the coastal communities. These efforts are at present being integrated into an Integrated Coastal Zone Management (ICZM) project.     

Reverse logistics in the Chinese auto-parts firms: implementation framework development through multiple case studies

In recent years, the implementation of reverse logistics (RL) in automobile manufacturing has drawn extensive interest from the Chinese government that has launched relevant take-back and environmental regulations and pilot projects. This study focuses on RL implementation issues in Chinese auto-parts manufacturing sector. Based on comprehensive literature review and multiple case studies in the Chinese auto-parts firms, this paper proposes a conceptual RL framework and highlights the major implementation issues. The purpose of this paper is to draw an overall picture of RL in the Chinese context, highlighting implementation issues in the auto-parts sector. Our study suggests that Chinese RL implementation requires close government monitoring mechanism and incentives, top management commitment within firms, technology and human capabilities support.     

Assessing the nexus between COVID-19 pandemic–driven economic crisis and economic policy: lesson learned and challenges

Environmental Science and Pollution Research - This study examines China’s budgetary policy during the COVID-19 pandemic as a result of China’s insufficient ability to deal with a new...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.