Effects of spent engine oil on the growth parameters, chlorophyll and protein levels of Amaranthus hybridus L.

The susceptibility of Amaranthus hybridus L. seedlings to spent engine oil was investigated in soil supplemented with concentrations of oil ranging from 1–5 percent v/w. Parameters considered were relative growth rate (RGR), leaf area ratio (LAR), whole plant height, leaf area, leaf number, chlorophyll and protein levels. A relationship was found to exist between the inhibitory effects and the treatment concentrations. After seventy days growth in the treated soils, the mean height and leaf area of plants in soils treated with 5 percent spent engine oil were 27.0±1.25 cm and 5.63±0.36 cm². These were significantly different (at p=0.05) from the respective values of 41.4±0.8 cm and 13.44±0.22 cm² for the control plants. Levels of total chlorophyll (per gram fresh weight of leaves) and protein (per gram dry weight of whole plant) were higher in the control plants compared with those grown in oil treated soil. Results obtained from the growth analysis showed the inhibitory effects of spent engine oil on Amaranthus hybridus L.     

Variations in public and private employees’ perceptions of organizational preparedness for natural disasters

Studies have demonstrated that public and private organizations differ in many respects (e.g. funding mechanisms and risk-taking capabilities). Based on this scholarship, we expect to see differences in their disaster preparedness levels. Hence, we propose the following research question: Are there variations in public and private employees’ perceptions of organizational preparedness for natural disasters? We answer this question by employing the theories of publicness and social identity. We define publicness through the core approach arguing that public and private organizations are uniquely different based on their legal status or ownership and social identity as an individual’s feeling of oneness or belongingness to a particular group or organization. Using data gathered in 2014 from a nationally representative sample of 1634 public and private employees in the United States, we posit that employees of private organizations will report higher preparedness levels in comparison to employees of public organizations. Our proposition is based on scholarship that found a negative relationship between publicness and organizational identification and a positive relationship between organizational identification and organizational performance. Contrary to our proposition, but in line with the disaster literature, the results showed that in general, employees of public organizations reported a higher preparedness level than employees of private organizations.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.     

Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking framework

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework (R _WQI framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R _WQI framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R _WQI framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events.     

Radioecological impacts of tin mining

The tin mining activities in the suburbs of Jos, Plateau State, Nigeria, have resulted in technical enhancement of the natural background radiation as well as higher activity concentrations of primordial radionuclides in the topsoil of mining sites and their environs. Several studies have considered the radiological human health risks of the mining activity; however, to our knowledge no documented study has investigated the radiological impacts on biota. Hence, an attempt is made to assess potential hazards using published data from the literature and the ERICA Tool. This paper considers the effects of mining and milling on terrestrial organisms like shrubs, large mammals, small burrowing mammals, birds (duck), arthropods (earth worm), grasses, and herbs. The dose rates and risk quotients to these organisms are computed using conservative values for activity concentrations of natural radionuclides reported in Bitsichi and Bukuru mining areas. The results suggest that grasses, herbs, lichens, bryophytes and shrubs receive total dose rates that are of potential concern. The effects of dose rates to specific indicator species of interest are highlighted and discussed. We conclude that further investigation and proper regulations should be set in place in order to reduce the risk posed by the tin mining activity on biota. This paper also presents a brief overview of the impact of mineral mining on biota based on documented literature for other countries.     

COVID-19 fear and volatility index movements: empirical insights from ASEAN stock markets

Environmental Science and Pollution Research - This research aims to look into the effect of COVID-19 on emerging stock markets in seven of the Association of Southeast Asian Nations’...     

A rough set-based game theoretical approach for environmental decision-making: A case of offshore oil and gas operations

Environmental decision-making in offshore oil and gas (OOG) operations can be extremely complex due to conflicting objectives or criteria, availability of vague and uncertain information, and interdependency among multiple decision-makers. Most existing studies ignore conflicting preferences and strategic interactions among decision-makers. This paper presents a game theoretical approach to solve multi-criteria conflict resolution problem under constrained and uncertain environments. Uncertainties in the quantification of imprecise data are expressed using rough numbers. A multi-criteria game is developed to model a decision problem in which three groups of decision-makers (i.e., operators, regulators and service engineers) are involved. This game is solved using the generalized maximin solution concept. With the solution (i.e., optimal weights of the criteria), the rough numbers can be aggregated to an expected payoff for each alternative. Finally, the weights of upper and lower limits of a rough number are employed to transform the expected payoff into a crisp score, based on which all alternatives are ranked to identify the best one. A numerical example is outlined to demonstrate the application of the proposed method to the selection of management scenarios of drilling wastes.