Carbon dioxide emissions,output, and energy consumption categories in Algeria

This study examines the relation between CO₂ emissions, income, non-renewable, and renewable energy consumption in Algeria during the period extending from 1980 to 2011. Our work gives particular attention to the validity of environmental Kuznets curve (EKC) hypothesis. The autoregressive distributed lag (ARDL) with break point method outcome demonstrates the positive effect of non-renewable type of energy on CO₂ emissions consumption. On the contrary, the results reveal an insignificant effect of renewable energy on environment improvement. Moreover, the results accept the existence of EKC hypothesis but the highest gross domestic product value in logarithm scale of our data is inferior to the estimated turning point. Consequently, policy-makers in Algeria should expand the ratio of renewable energy and should decrease the quota of non-renewable energy consumption.     

Chemical water quality gradients in the Mongolian sub-catchments of the Selenga River basin

Even though the Selenga is the main tributary to Lake Baikal in Russia, the largest part of the Selenga River basin is located in Mongolia. It covers a region that is highly diverse, ranging from almost virgin mountain zones to densely urbanized areas and mining zones. These contrasts have a strong impact on rivers and their ecosystems. Based on two sampling campaigns (summer 2014, spring 2015), we investigated the longitudinal water quality pattern along the Selenga and its tributaries in Mongolia. While headwater regions typically had a very good water quality status, wastewater from urban areas and impacts from mining were found to be main pollution sources in the tributaries. The highest nutrient concentrations in the catchment were found in Tuul River, and severely elevated concentrations of trace elements (As, Cd, Cu, Cr, Fe, Mn, Ni, Pb, Zn), nutrients (NH₄ ⁺, NO₂ ^?, NO₃ ^?, PO₄ ^3?), and selected major ions (SO₄ ^2?) were found in main tributaries of Selenga River. Moreover, trace element concentrations during spring 2015 (a time when many mines had not yet started operation) were markedly lower than in summer 2014, indicating that the additional metal loads measured in summer 2014 were related to mining activities. Nevertheless, all taken water samples in 2014 and 2015 from the main channel of the Mongolian Selenga River complied with the Mongolian standard (MNS 1998) for the investigated parameters.     

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 μg/m³, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 μg/m³) in comparison with 2013 (23.3 μg/m³). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m³, 2014 26.9 μg/m³) and urban (2013 27.7 μg/m³, 2014 31.3 μg/m³) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m³, 2014 24.5 μg/m³) and urban-background (2013 23.0 μg/m³, 2014 26.1 μg/m³) areas, respectively.     

Optimal Policy Mechanism Design for Cross-Sector and Multi-Stage Pollution Control with a Bilevel Model: Application to SO<Subscript>2</Subscript> Emission in China

In this paper, we construct a multi-stage coordinated programming model under tax system to control SO₂ emission. The model is based on an explicitly formulated SO₂ abatement cost function created under Chinese condition. Analysis of the effectiveness and impact on the economy of the model is carried out with consideration of game theory. By solving the model, theoretical results show that the volume-based multi-stage SO₂ tax system has two properties: effectiveness and equal-rate. Based on these theoretical results, empirical study is also performed using Chinese historical data. Compared with yearly single-stage programming model, the tax rate generated by the coordinated multi-stage programming model is time-invariant and rather moderate in scale. The total abatement cost among planning years in our model is 21.03 % less than the actual number and 6.68 % less than that in the single-stage situation. The tax payment suggested by our model is 10.62 % less than by the single-stage model. In general, a coordinated multi-stage programming model helps reduce the overall costs of environmental protection while achieving the same emission control target with less burden added to the economy.     

The measurement of water scarcity: Defining a meaningful indicator

Metrics of water scarcity and stress have evolved over the last three decades from simple threshold indicators to holistic measures characterising human environments and freshwater sustainability. Metrics commonly estimate renewable freshwater resources using mean annual river runoff, which masks hydrological variability, and quantify subjectively socio-economic conditions characterising adaptive capacity. There is a marked absence of research evaluating whether these metrics of water scarcity are meaningful. We argue that measurement of water scarcity (1) be redefined physically in terms of the freshwater storage required to address imbalances in intra- and inter-annual fluxes of freshwater supply and demand; (2) abandons subjective quantifications of human environments and (3) be used to inform participatory decision-making processes that explore a wide range of options for addressing freshwater storage requirements beyond dams that include use of renewable groundwater, soil water and trading in virtual water. Further, we outline a conceptual framework redefining water scarcity in terms of freshwater storage.     

Simultaneous adsorption of Remazol brilliant blue and Disperse orange dyes on red mud and isotherms for the mixed dye system

The paper presents the adsorption of Remazol brilliant blue (RBB) and Disperse orange 25 (DO25) dyes from aqueous solution of the mixture of dyes onto concentrated sulphuric acid-treated red mud (ATRM). First-order derivative spectrophotometric method was developed for the analysis of RBB and DO25 in mixed dye aqueous solution to overcome the limitations arising due to interference in the zero-order spectral method. The optimum conditions to maximize RBB adsorption favoured the adsorption of RBB, and those for DO25 favoured DO25 adsorption from the mixed dye aqueous solutions. Presence of a second dye always inhibited the adsorption of a target dye. The uptake and percentage adsorption of each of the dyes onto ATRM from the aqueous solution of the mixture of dyes decreased considerably with increasing concentrations of the other dye showing the antagonistic effect. Monocomponent Langmuir isotherm fitted the mixed dye adsorption equilibrium data better than the monocomponent Freundlich isotherm. However, monocomponent models are suitable for the fixed concentration of the other dye. Modified Langmuir isotherm model adequately predicted the multi-component adsorption equilibrium data for RBB-DO25-ATRM adsorption system with a good accuracy and is more generic from the application point of view.     

Assessment of tap water quality and corrosion scales from the selected distribution systems in northern Pakistan

Corrosion deposits formed within drinking water distribution systems deteriorate drinking water quality and resultantly cause public health consequences. In the present study, an attempt was made to investigate the concurrent conditions of corrosion scales and the drinking water quality in selected water supply schemes (WSS) in districts Chitral, Peshawar, and Abbottabad, northern Pakistan. Characterization analyses of the corrosion by-products revealed the presence of α-FeOOH, γ-FeOOH, Fe₃O₄, and SiO₂ as major constituents with different proportions. The constituents of all the representative XRD peaks of Peshawar WSS were found insignificant as compared to other WSS, and the reason could be the variation of source water quality. Well-crystallized particles in SEM images indicated the formation of dense oxide layer on corrosion by-products. A wider asymmetric vibration peak of SiO₂ appeared only in Chitral and Abbottabad WSS, which demonstrated higher siltation in the water source. One-way ANOVA analysis showed significant variations in pH, turbidity, TDS, K, Mg, PO₄, Cl, and SO₄ values, which revealed that these parameters differently contributed to the source water quality. Findings from this study suggested the implementation of proper corrosion prevention measures and the establishment of international collaboration for best corrosion practices, expertise, and developing standards.     

Evaluating climatic and non-climatic stresses for declining surface water quality in Bagmati River of Nepal

Both climatic and non-climatic factors affect surface water quality. Similar to its effect across various sectors and areas, climate change has potential to affect surface water quality directly and indirectly. On the one hand, the rise in temperature enhances the microbial activity and decomposition of organic matter in the river system and changes in rainfall alter discharge and water flow in the river ultimately affecting pollution dilution level. On the other hand, the disposal of organic waste and channelizing municipal sewage into the rivers seriously worsen water quality. This study attempts to relate hydro-climatology, water quality, and impact of climatic and non-climatic stresses in affecting river water quality in the upper Bagmati basin in Central Nepal. The results showed that the key water quality indicators such as dissolved oxygen and chemical oxygen demand are getting worse in recent years. No significant relationships were found between the key water quality indicators and changes in key climatic variables. However, the water quality indicators correlated with the increase in urban population and per capita waste production in the city. The findings of this study indicate that dealing with non-climatic stressors such as reducing direct disposal of sewerage and other wastes in the river rather than emphasizing on working with the effects from climate change would largely help to improve water quality in the river flowing from highly populated urban areas.     

Fluctuating asymmetry rather than oxidative stress in <Emphasis Type="Italic">Bufo raddei</Emphasis> can be an accurate indicator of environmental pollution induced by heavy metals

Oxidative stress (OS) and fluctuating asymmetry (FA) as risk markers for environmental stress are widely used to predict changes in the health and fitness of many animals exposed to pollutants. However, from the perspective of protecting declining amphibians, it remains to be verified which one would be a reliable indicator for amphibians exposed to long-term heavy metal pollution under natural conditions. In this study, the OS and FA of Bufo raddei exposed to natural heavy metal pollution were analyzed to determine which marker is more accurate for indicating heavy metal-induced stress. Three years of data were collected during the breeding season of B. raddei from Baiyin (BY), which has been mainly contaminated with Cu, Zn, Pb, and Cd compounds for a long period, and from Liujiaxia (LJX), which is a relatively unpolluted area. Unexpectedly, although significant accumulation of the four heavy metals was found in the kidney and liver of B. raddei from BY, the levels of superoxide dismutase, glutathione peroxidase, and malondialdehyde in these two organs were found to be irregular, with low repeatability in both BY and LJX. However, significant differences in the levels of FA were observed in B. raddei populations from these two areas over the past 3 years (P < 0.01). The degrees of FA in B. raddei populations from BY and LJX were assessed as degree 4 and 1, respectively. In short, this study suggested that FA was a more reliable and effective indicator than OS to monitor and predict long-term environmental stress on anuran amphibians.     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.