The Importance of the Chosen Technique to Estimate Diffuse Solar Radiation by Means of Regression

The Ordinary Least Squares (OLS) is one of the widely used methods which is used for estimating the diffuse solar radiation. However, in order to use the OLS method in the estimation, the dataset must provide certain assumptions. In this study, alternative robust methods have been described and they were compared with the OLS method, which is used for estimating diffuse radiation frequently in an application. At the end of the analysis, the R² value obtained by the OLS method is less than the values obtained by M regression models. In other words, the explanation of the dependent value is weak when the OLS method is used. Finally, it can be said that the most appropriate method is Andrews for estimating the diffuse solar radiation.     

New molecular method to detect denitrifying anaerobic methane oxidation bacteria from different environmental niches

The denitrifying anaerobic methane oxidation is an ecologically important process for reducing the potential methane emission into the atmosphere. The responsible bacterium for this process was Candidatus Methylomirabilis oxyfera belonging to the bacterial phylum of NC10. In this study, a new pair of primers targeting all the five groups of NC10 bacteria was designed to amplify NC10 bacteria from different environmental niches. The results showed that the group A was the dominant NC10 phylum bacteria from the sludges and food waste digestate while in paddy soil samples, group A and group B had nearly the same proportion. Our results also indicated that NC10 bacteria could exist in a high pH environment (pH 9.24) from the food waste treatment facility. The Pearson relationship analysis showed that the pH had a significant positive relationship with the NC10 bacterial diversity (p < 0.05). The redundancy analysis further revealed that the pH, volatile solid and nitrite nitrogen were the most important factors in shaping the NC10 bacterial structure (p = 0.01) based on the variation inflation factors selection and Monte Carlo test (999 times). Results of this study extended the existing molecular tools for studying the NC10 bacterial community structures and provided new information on the ecological distributions of NC10 bacteria.     

Fostering coastal resilience to climate change vulnerability in Bangladesh,Brazil, Cameroon and Uruguay: a cross-country comparison

This paper describes a comparative study of four different cases on vulnerability, hazards and adaptive capacity to climate threats in coastal areas and communities in four developing countries: Bangladesh, Brazil, Cameroon and Uruguay. Coastal areas are vulnerable to sea-level rise (SLR), storm surges and flooding due to their (i) exposure, (ii) concentration of settlements, many of which occupied by less advantaged groups and (iii) the concentration of assets and services seen in these areas. The objective of the paper is twofold: (i) to evaluate current evidence of coastal vulnerability and adaptive capacity and (ii) to compare adaptation strategies being implemented in a sample of developing countries, focusing on successful ones. The followed approach for the case evaluation is based on (i) documenting observed threats and damages, (ii) using indicators of physical and socioeconomic vulnerability and adaptive capacity status and (iii) selecting examples of successful responses. Major conclusions based on cross-case comparison are (a) the studied countries show different vulnerability, adaptive capacity and implementation of responses, (b) innovative community-based (CBA) and ecosystem-based adaptation (EbA) and (c) early warning systems are key approaches and tools to foster climate resilience. A recommendation to foster the resilience of coastal communities and services is that efforts in innovative adaptation strategies to sea-level rise should be intensified and integrated with climate risk management within the national adaption plans (NAPAs) in order to reduce the impacts of hazards.     

Vanadium Concentrations in Settled Outdoor Dust Particles

Atmospheric dustfall is an important aspect of urban dust studies. Vanadium is considered as the marker element of air pollution emitted from residual oil and coal combustion. In this study, vanadium levels of outdoor dust particles are determined. The studied area covers the six sites located in Adapazarí (Turkey), which represents an earthquake-hit environment. The mass deposition rate was calculated for each sampling plate over the 30-days collection periods. The deposition rates for the six places in Adapazarí ranged from 20.5 to 84.9 μg/cm²/day. The arithmetic mean deposition rate for all places was 45.3 μg/cm²/day. Total dust deposition and vanadium loadings typically increased in magnitude according to the area order: Kampus > Serdivan > Çark C. > Ozanlar > Erenler > Ye?iltepe and Kampus > Serdivan > Çark C. > Ozanlar > Erenler > Ye?iltepe, respectively. The results suggested that vanadium may be useful for assessing the level of environmental pollution.     

Quantitative evaluation of precautions on chemical tanker operations

Large quantities of liquid chemicals are carried by chemical tankers all over seas. Chemical cargoes have different properties and chemical tankers are complex ships that are designed to carry different types of chemical cargoes. Carriage of chemical cargoes contains different hazards both for human life and marine environment. There are several cargo operations that are regularly done on chemical tankers such as loading, discharging, inerting, washing tanks, sampling, and freeing gas. These operations constitute their own risks. Therefore, risk assessment has become a critical issue in maritime industry. The present investigation of this study is attempting to examine the priorities of precautions that are taken by chemical tankers before, during, and after cargo operations. Analytic hierarchy process (AHP) is used for prioritizing the precautions in order to clarify the risk assesment option that will be used for pro-active approach to prevent marine casualties. The main aim of this study is to identify an appropriate management tool to increase the level of safety for chemical tankers during cargo operations at a terminal by using the results of AHP application.     

The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂?=?400 mg/L, Fe⁺²?=?40 mg/L, pH?=?3, irradiation time?=?150 min, and temperature?=?30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R ²?=?0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.     

Investigating motivating factors for sound hospital waste management

Sustainable management of hospital waste requires an active involvement of all key players. This study aims to test the hypothesis that three motivating factors, namely, Reputation, Liability, and Expense, influence hospital waste management. The survey for this study was conducted in two phases, with the pilot study used for exploratory factor analysis and the subsequent main survey used for cross-validation using confirmatory factor analysis. The hypotheses were validated through one-sample t tests. Correlations were established between the three motivating factors and organizational characteristics of hospital type, location, category, and size. The hypotheses were validated, and it was found that the factors of Liability and Expense varied considerably with respect to location and size of a hospital. The factor of Reputation, however, did not exhibit significant variation. In conclusion, concerns about the reputation of a facility and an apprehension of liability act as incentives for sound hospital waste management, whereas concerns about financial costs and perceived overburden on staff act as disincentives.

Implications: This paper identifies the non economic motivating factors that can be used to encourage behavioral changes regarding waste management at hospitals in resource constrained environments. This study discovered that organizational characteristics such as hospital size and location cause the responses to vary among the subjects. Hence a policy maker must take into account the institutional setting before introducing a change geared towards better waste management outcomes across hospitals. This study covers a topic that has hitherto been neglected in resource constrained countries. Thus it can be used as one of the first steps to highlight and tackle the issue.     

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0–30cm, 30–60cm, 60–90cm and 90–120cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.     

Application of Tabu Search Algorithm With a Coupled AnnAGNPS‐CCHE1D Model to Optimize Agricultural Land Use1

Abstract: A principal contributor to soil erosion and nonpoint source pollution, agricultural activities have a major influence on the environmental quality of a watershed. Impact of agricultural activities on the quality of water resources can be minimized by implementing suitable agriculture land‐use types. Currently, land uses are designed (location, type, and operational schedule) based on field study results, and do not involve a science‐based approach to ensure their efficiency under particular regional, climatic, geological, and economical conditions. At present, there is a real need for new methodologies that can optimize the selection, design, and operation of agricultural land uses at the watershed scale by taking into account environmental, technical, and economical considerations, based on realistic simulations of watershed response. In this respect, the present study proposes a new approach, which integrates computational modeling of watershed processes, fluvial processes in the drainage network, and modern heuristic optimization techniques to design cost effective land‐use plans. The watershed model AnnAGNPS and the channel network model CCHE1D are linked together to simulate the sediment and pollutant transport processes. Based on the computational results, a multi‐objective function is set up to minimize soil losses, nutrient yields, and total associated costs, while the production profits from agriculture are maximized. The selected iterative optimization algorithm uses adaptive Tabu Search heuristic to flip (switching from one alternative to another) land‐change variables. USDA’s Goodwin Creek experimental watershed, located in Northern Mississippi, is used to demonstrate the capabilities of the proposed approach. The results show that the optimized land‐use design with BMPs using an integrated approach at the watershed level can provide efficient and cost‐effective conservation of the environmental quality by taking into account both productivity and profitability.     

Synthesis of magnetite-based nanocomposites for effective removal of brilliant green dye from wastewater

Environmental Science and Pollution Research - The present study aims at evaluating the batch scale potential of cotton shell powder (CSP), Moringa oleifera leaves (ML), and magnetite-assisted...