Kinetics of copper desorption from soils as affected by different organic ligands

Desorption of Cu and low molecular weight dissolved organics are the primary factors that impact fate and transport of Cu in soils. To improve predictions of the toxicity and threat from Cu contaminated soil, it is critical that time-dependent desorption behavior be understood. In this paper, the effect of organic ligands citrate, malate, and succinate on the kinetics of Cu desorption from contaminated soils varying widely in soil characteristics was investigated at 25° C and the soils used were referred to as clay, calcareous and sandy soils. The amount of Cu released by the used organic ligands varied greatly with physicochemical properties of the soils. The rate of Cu release by different extractants was in the order citric > malic > succinic, which was consistent with the stability constants of Cu complexes with these ligands. The modified Freundlich and the Elovich and Parabolic diffusion models were used to describe dsorption of Cu²⁺ from the three studied soils as affected by the organic ligands. All of the models fit the data well with correlation coefficients ranging from 0.83 to 1.00 (P < 0.01). Each Model has a set of assumptions for the different physical and chemical properties of the systems to which they are being applied. The uses of these equations yield different magnitudes for the calculated variable, but the relationships between the soil + organic ligands and their effect (i.e., increase or decrease) on these variables are the same. Such information is critical, since Cu is used in a variety of industrial and manufacturing processes and is one of the most common contaminants found at hazardous waste sites.     

浙江花岗岩中斜长石钾交代产生的条纹长石

对研究区内花岗岩类的60个薄片进行了岩相学研究.研究的目的是确定区内岩石中普遍存在的矿物组成及结构类型.     

Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming

Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σ_n) and anchorage length (L) and the least effective parameters are average particle size (D₅₀) and anchor base area (A_b).

     

Dedolomitization of dolocrete deposits in Kuwait,Arabian Gulf

Dedolomitization of a dolocrete profile hosted in Mio-Pleistocene siliciclastic deposits in the area of Kuwait City, Arabian Gulf was investigated. Dolocrete dolomite crystals vary considerably in size, shape and internal structure; however, they are mostly zoned. Zonation is usually due to the alteration of cloudy and clear zones. The cloudy zones, which are mostly formed of disordered metastable dolomite, are more susceptible to dedolomitization than the stable, well ordered clear zones. Two modes of dedolomitization were recognized; the first involves complete dissolution of the metastable dolomite followed by precipitation of intracrystalline cavity-filling calcite. The second is a pseudomorphic replacement of dolomite by calcite. This replacement takes place by the simultaneous dissolution of dolomite and precipitation of calcite in such a manner that the original dolomite fabrics are inherited in the dedolomite. Exposed and near-surface dolocrete profile (less than 5 m deep) are almost completely dedolomitized and altered to secondary calcrete whereas subsurface profiles are slightly dedolomitized. Dedolomitization of the sub surface dolocrete profiles may indicate the effect of flushing by fresh groundwater; which flows from west to east, whereas the alteration of the exposed dolocrete profile could be attributed to be an effect of meteoric water. A new mode of calcrete genesis by dedolomitization and/or complete calcitization of precursor dolocrete is suggested.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques

In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes.     

A new record and a new ichnospecies of Arachnostega from the Middle Miocene of Egypt

The ichnogenus Arachnostega Bertling, 1992 is recorded for the first time from a bioeroded, coral-rich, Middle Miocene limestone bed in the middle Siwa Escarpment Member (Marmarica Formation) at Siwa Oasis, northern Western Desert of Egypt. These burrowing traces are preserved on the surface of a few internal moulds of bivalves and gastropods. Until now, the ichnogenus Arachnostega included a single ichnospecies, A. gastrochaenae Bertling, 1992. In the material studied herein, two ichnospecies are identified, described, illustrated and compared with other, similar traces. One of these is a new ichnospecies of Arachnostega; this is here named A. siwaensis ichnosp. nov. It is distinguished from A. gastrochaenae in the possession of tunnels that usually branch in a V-pattern; intervals of ramification are longest in the main branch and decrease in second- and third-order branches. Arachnostega traces were produced in a warm, low-energy, shallow-marine (<10?m water depth) environment. Polychaetes are the most likely producers of marine Arachnostega, which are commonly occurred in the same studied bed. This new occurrence is significant in extending the known stratigraphical and geographical ranges of Arachnostega into the Middle Miocene of Egypt.     

Analysis and farmers’ perception of climate change in the Kashmir Valley,India

Theoretical and Applied Climatology - Analysis of climatic variables is important for the detection and attribution of climate change trends and has received considerable attention from researchers...     

Multi-factor impact analysis of agricultural production in Bangladesh with climate change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.