Modeling 4-Chlorophenol Removal from Aqueous Solutions by Granular Activated Carbon

Since phenols and phenolic compounds in many industrial wastewaters are toxic organic contaminants for humans and aquatic life, to remove these compounds via the most efficient way is very important for environmental remediation treatment. In this context, almost all of the isotherm models (Freundlich, Langmuir, Temkin, Redlich–Peterson, Sips, and Khan) for adsorption in the literature were applied to explain the adsorption mechanism of 4-chlorophenol on activated carbon in this study. Also theoretical modeling data were obtained using model equations; interpolation and analysis of variance were made to compare data by using statistics software. In addition, the thermodynamic and kinetic studies for adsorption mechanism were included in the article. The adsorption of 4-chlorophenol on activated carbon fits well to the pseudo-first-order kinetic model than the pseudo-second-order, intraparticular diffusion and Bangham models. It is also indicated that 4-chlorophenol adsorption by granular activated carbon would be attributed to a type of transition between physical and chemical adsorption rather than a pure physical or chemical adsorption process. As a result, an environmental remediation problem and the adsorption mechanism on activated carbon that can be regarded as a solution to this problem are described and explained using the mathematical models and calculations in this study.     

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the “Enter” method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685–3.65% and 0.9988–0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.     

Soil properties and root biomass responses to prescribed burning in young Corsican pine (Pinus nigra Arn.) stands

Fire is an important tool in the management of forest ecosystems. Although both prescribed and wildland fires are common in Turkey, few studies have addressed the influence of such disturbances on soil properties and root biomass dynamics. In this study, soil properties and root biomass responses to prescribed fire were investigated in 25-year-old corsican pine (Pinus nigra Arn.) stands in Kastamonu, Turkey. The stands were established by planting and were subjected to prescribed burning in July 2003. Soil respiration rates were determined every two months using soda-lime method over a two-year period. Fine (0-2 mm diameter) and small root (2-5 mm diameter) biomass were sampled approximately bimonthly using sequential coring method. Mean daily soil respiration ranged from 0.65 to 2.19 g Cm(-2) d(-1) among all sites. Soil respiration rates were significantly higher in burned sites than in controls. Soil respiration rates were correlated significantly with soil moisture and soil temperature. Fine root biomass was significantly lower in burned sites than in control sites. Mean fine root biomass values were 4940 kg ha(-1) for burned and 5450 kg ha(-1) for control sites. Soil pH was significantly higher in burned sites than in control sites in 15-35 cm soil depth. Soil organic matter content did not differ significantly between control and burned sites. Our results indicate that, depending on site conditions, fire could be used successfully as a tool in the management of forest stands in the study area.     

The dynamic analysis of renewable energy’s contribution to the dimensions of sustainable development and energy security

In sustainable development, energy is critical in human activities and shapes a sustainable future. Thus, it is an unignorable element in human development. This paper analyzes the contributions of renewable energy sources (RES)’s to the economic, environmental, and social dimensions of sustainable development. Moreover, we add energy security as a possible fourth dimension into the analysis. For the sample size, we limit the countries members of the OECD and run generalized methods of moments for the period from 1995 to 2015. This method can produce efficient estimators under the problems of endogeneity, omitted-variable bias, measurement errors, and heteroscedastic residuals. According to the results, RES has a small reducing effect (?.007%) on output in the Cobb-Douglas production function for the economic dimension. We found that RES has a positive contribution to the environmental dimension and abates the level of carbon emission (?.093%). RES also confirms the inverted-U shape of environmental Kuznets curve. In the social dimension, RES improves human development and a 1% increase in RES consumption causes to .0045% increase in human development. In the last contribution, RES has a positive effect on sustainable energy supply security in the context of electricity generation (.032%). Although the effects of RES on the environment, social, and energy security are significant, they are limited. These limitations point to barriers that can be overcome over time. Our conclusions recommend that these effects might flourish with technical developments and political support in the long run. Furthermore, public awareness, rising income level, and economies of scale are also beneficial in this process. As a result, RES might be an excellent source for a sustainable future and development. Especially, RES might have remarkable contributions to the 7th, 11th, 12th, and 13th goals of sustainable development.

     

Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques

The Harran Plain is located in the southeastern part of Turkey and has recently been developed for irrigation agriculture. It already faces soil salinity problems causing major yield losses. Management of the problem is hindered by the lack of information on the extent and geography of the salinization problem. A survey was carried out to delineate the spatial distribution of salt-affected areas by randomly selecting 140 locations that were sampled at two depths (0 to 30 and 30 to 60 cm) and analyzed for soil salinity variables: soil electrical conductivity (EC), soluble cations (Ca^2+, Mg²⁺, Na⁺, and K⁺), soluble anions (SO ₄ ^2? , Cl^?), exchangeable Na⁺ (me 100 g^?1) and exchangeable sodium percentage. Terrain attributes (slope, topographical wetness index) were extracted from the digital elevation model of the study area. Variogram analyses after log transformation and ordinary kriging (OK) were applied to map spatial patterns of soil salinity variables. Multivariate geostatistical methods—regression kriging (RK) and kriging with external drift (KED)—were used using elevation and soil electrical conductivity data as covariates. Performances of the three estimation methods (OK, RK, and KED) were compared using independent validation samples randomly selected from the main dataset. Soils were categorized into salinity classes using disjunctive kriging (DK) and ArcGIS, and classification accuracy was tested using the kappa statistic. Results showed that soil salinity variables all have skewed distribution and are poorly correlated with terrain indices but have strong correlations among each other. Up to 65 % improvement was obtained in the estimations of soil salinity variables using hybrid methods over OK with the best estimations obtained with RK using EC_0–30 as covariate. DK–ArcGIS successfully classified soil samples into different salinity groups with overall accuracy of 75 % and kappa of 0.55 (p?<?0.001).     

Estimation of transport parameters of phenolic compounds and inorganic contaminants through composite landfill liners using one-dimensional mass transport model

One-dimensional (1D) advection–dispersion transport modeling was conducted as a conceptual approach for the estimation of the transport parameters of fourteen different phenolic compounds (phenol, 2-CP, 2-MP, 3-MP, 4-MP, 2-NP, 4-NP, 2,4-DNP, 2,4-DCP, 2,6-DCP, 2,4,5-TCP, 2,4,6-TCP, 2,3,4,6-TeCP, PCP) and three different inorganic contaminants (Cu, Zn, Fe) migrating downward through the several liner systems. Four identical pilot-scale landfill reactors (0.25 m³) with different composite liners (R1: 0.10 + 0.10 m of compacted clay liner (CCL), L_e = 0.20 m, k_e = 1 × 10⁻⁸ m/s, R2: 0.002-m-thick damaged high-density polyethylene (HDPE) geomembrane overlying 0.10 + 0.10 m of CCL, L_e = 0.20 m, k_e = 1 × 10⁻⁸ m/s, R3: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick bentonite layer encapsulated between 0.10 + 0.10 m CCL, L_e = 0.22 m, k_e = 1 × 10⁻⁸ m/s, R4: 0.002-m-thick damaged HDPE geomembrane overlying a 0.02-m-thick zeolite layer encapsulated between 0.10 + 0.10 m CCL, L_e = 0.22 m, k_e = 4.24 × 10⁻⁷ m/s) were simultaneously run for a period of about 540 days to investigate the nature of diffusive and advective transport of the selected organic and inorganic contaminants. The results of 1D transport model showed that the highest molecular diffusion coefficients, ranging from 4.77 × 10⁻¹⁰ to 10.67 × 10⁻¹⁰ m²/s, were estimated for phenol (R4), 2-MP (R1), 2,4-DNP (R2), 2,4-DCP (R1), 2,6-DCP (R2), 2,4,5-TCP (R2) and 2,3,4,6-TeCP (R1). For all reactors, dispersion coefficients of Cu, ranging from 3.47 × 10⁻⁶ m²/s to 5.37 × 10⁻² m²/s, was determined to be higher than others obtained for Zn and Fe. Average molecular diffusion coefficients of phenolic compounds were estimated to be about 5.64 × 10⁻¹⁰ m²/s, 5.37 × 10⁻¹⁰ m²/s, 2.69 × 10⁻¹⁰ m²/s and 3.29 × 10⁻¹⁰ m²/s for R1, R2, R3 and R4 systems, respectively. The findings of this study clearly indicated that about 35–50% of transport of phenolic compounds to the groundwater is believed to be prevented with the use of zeolite and bentonite materials in landfill liner systems.     

The influence of biomass energy consumption on CO<Subscript>2</Subscript> emissions: a wavelet coherence approach

In terms of today, one may argue, throughout observations from energy literature papers, that (i) one of the main contributors of the global warming is carbon dioxide emissions, (ii) the fossil fuel energy usage greatly contributes to the carbon dioxide emissions, and (iii) the simulations from energy models attract the attention of policy makers to renewable energy as alternative energy source to mitigate the carbon dioxide emissions. Although there appears to be intensive renewable energy works in the related literature regarding renewables’ efficiency/impact on environmental quality, a researcher might still need to follow further studies to review the significance of renewables in the environment since (i) the existing seminal papers employ time series models and/or panel data models or some other statistical observation to detect the role of renewables in the environment and (ii) existing papers consider mostly aggregated renewable energy source rather than examining the major component(s) of aggregated renewables. This paper attempted to examine clearly the impact of biomass on carbon dioxide emissions in detail through time series and frequency analyses. Hence, the paper follows wavelet coherence analyses. The data covers the US monthly observations ranging from 1984:1 to 2015 for the variables of total energy carbon dioxide emissions, biomass energy consumption, coal consumption, petroleum consumption, and natural gas consumption. The paper thus, throughout wavelet coherence and wavelet partial coherence analyses, observes frequency properties as well as time series properties of relevant variables to reveal the possible significant influence of biomass usage on the emissions in the USA in both the short-term and the long-term cycles. The paper also reveals, finally, that the biomass consumption mitigates CO₂ emissions in the long run cycles after the year 2005 in the USA.     

Modeling seasonal variations of long-term soil CO<Subscript>2</Subscript> emissions in an orchard plantation in a semiarid area,SE Turkey

Emissions of soil CO₂ under different management systems have a significant effect on the carbon balance in the atmosphere. Soil CO₂ emissions were measured from an apricot orchard at two different locations: under the crown of trees (CO₂-UC) and between tree rows (CO₂-BR). For comparison, one other measurement was performed on bare soil (CO₂-BS) located next to the orchard field. Analytical data were obtained weekly during 8 years from April 2008 to December 2016. Various environmental parameters such as air temperature, soil temperature at different depths, soil moisture, rainfall, and relative humidity were used for modeling and estimating the long-term seasonal variations in soil CO₂ emissions using two different methods: generalized linear model (GLM) and artificial neural network (ANN). Before modeling, data were randomly split into two parts, one for calibration and the second for validation, with a varying number of samples in each part. Performances of the models were compared and evaluated using means absolute of estimations (MAE), square root of mean of prediction (RMSEP), and coefficient of determination (R²) values. CO₂-UC, CO₂-BR, and CO₂-BS values ranged from 11 to 3985, from 9 to 2365, and from 8 to 1722 kg ha^?1 week^?1, respectively. Soil CO₂ emissions were significantly correlated (p?<?0.05) with some environmental variables. The results showed that GLM and ANN models provided similar accuracies in modeling and estimating soil CO₂ emissions, as the number of samples in the validation data set increased. The ANN was more advantageous than GLM models by providing a better fit between actual observations and predictions and lower RMSEP and MAE values. The results suggested that the success of environmental variables for estimations of CO₂ emissions using the two methods was moderate.     

The hardrock minerals industry and the landscape architect

The landscape architecture profession is a source of creative thinking and planning which is relatively untapped within the mining industry in the United States of America. The landscape architect has been active in European operations for years as indicated by the four examples presented. The landscape architect can provide an important interface with mine design and engineering and environmental affairs. To initiate this interface within the United States, and increase its effectiveness, six objectives are presented and discussed.