The landscape-scale relationship between lake sediment geochemistry and catchment bedrock composition from the Temagami and Gowganda areas of Northeastern Ontario,Canada

Lake sediments are integrators of watershed wide environmental information that includes bedrock geology, glacial overburden, vegetation, hydrology and land use. Lake sediments were collected from an area covering approximately 13,600 km² from the Gowganda and Temagami regions to determine the geochemical background conditions representing different bedrock types. For the entire dataset, the median value for arsenic in lake sediment was 2.6 mg/kg, well within the prescribed limits indicating that the Canadian national objective for arsenic in sediments for the protection of aquatic life (17 mg/kg). Overall, the vast majority of lake sediment samples (97.2%) were below the 17 mg/kg Canadian objective for the protection of aquatic life, however, catchments with Nipissing Diabase had the highest background levels for arsenic (10 mg/kg) with some lakes ranging up to 30 mg/kg. The major geological controls influencing changes in lake sediment geochemistry were determined using random forest classification and principal component analysis (PCA). Random forest classification was able to identify which geological province the samples were derived from and the dominant rock types in the sample catchment with respectable accuracy. PCA revealed strong spatial relationships between lake sediment geochemistry and bedrock geology, particularly a strong relationship between Nipissing Diabase in the watershed of the lake and the cobalt-type mineralization indicator elements in the lake sediment including naturally higher levels of arsenic. Within the samples that exceeded regulatory standards, over half the samples that exceeded the regulatory objectives were located in watersheds that contained Nipissing Diabase in their catchment. This study demonstrates that unique geochemical assemblages can be associated with specific geological areas which is of interest to exploration geologists, for target generation and prospecting, and to environmental policy makers, for site-specific restoration targets.     

Combined particle swarm optimization and linear discriminant analysis for landslide image classification: application to a case study in Taiwan

In Taiwan many reservoirs are constructed in mountain areas. Unfortunately, several earthquakes shook the soil, and typhoons brought a huge amount of water to the reservoir zone. In the past studies, remote-sensing image data were used to effectively monitor the landslide near reservoirs. In recent years, linear discriminant analysis (LDA) has become a well-known method for image classification. However, there are few studies to optimize the linear classification function. While the ancillary information has been adopted easily by new methodologies, the ancillary information must be examined by a landslide image classification system. To explore the effects of optimization on the LDA equations, three approaches were compared: (a) conventional LDA; (b) combined discrete rough sets and LDA (DRS + LDA), which identify the core factors and the corresponding thresholds of landslide occurrence; and (c) combined particle swam optimization algorithm and LDA (PSO + LDA), which optimizes the parameters of LDA equation to attain the best classification outcomes. The above methods were applied to a reservoir region in Taiwan, and the following classification results were obtained. The application of DRS + LDA in our case study reduced the number of ancillary attributes from 14 to 5, and resulted in an accuracy rate of 0.83. On the other hand, the application of PSO + LDA resulted in the same accuracy rate as that of DRS + LDA, whereas the accuracy rate of conventional LDA was found to be 0.78.     

Identifying key hydrochemical processes in a confined aquifer of an arid basin using multivariate statistical analysis and inverse modeling

Tertiary fractured permeable confined aquifer, which covered about 70 % of the studying area, played an important role in alleviating drinking water shortages. However, about 58 and 79 % of the groundwater samples exceeded the desirable limits for fluoride (1.5 mg/L) and TDS (1,000 mg/L). Two multivariate statistical methods, hierarchical cluster analysis (HCA) and principal components analysis (PCA), were applied to a subgroup of the dataset in terms of their usefulness for groundwater classification, as well as to identify the key processes controlling groundwater geochemistry. In the PCA, two principal factors have been extracted, which could explain 73 % of the total data variability. Among them, factor 1 revealed the source of groundwater salinity and factor 2 explained the elevated fluoride. Two major groups were classified by HCA and Group 1 was near the groundwater recharge zone and Group 2 was mainly distributed over the groundwater discharge zone. Inverse modeling (NETPATH) results indicated that the hydrochemical evolution was primarily controlled by (1) the dissolution of mirabilite, gypsum and halite for the source of groundwater salinity; (2) the release of the adsorbed fluoride through desorption or through competition with HCO₃ ^? under alkalinity condition for the elevated fluoride in the groundwater.     

Application of evolutionary algorithms to develop a rule set for assessing the rehabilitation status of asbestos mines in South Africa

Asbestos mining has left a legacy of pollution in former mining areas that continues to negatively affect both the environment and local communities. In 2007, the Rehabilitation Prioritisation Index was developed as a scientific tool to indicate the preferred sequence for mine site rehabilitation and served as a departure point for the present investigation in which a database for the rehabilitation success of asbestos sites was developed. Broad-based quantitative and qualitative data, typically used for monitoring rehabilitation success, including amongst others, soil cover depth, physical and chemical soil properties, microbial activity, vegetation properties and small mammal abundance were analysed using multivariate statistics, specifically a redundancy analysis. The most representative model was subsequently selected for the classification of the rehabilitated sites. The multivariate analysis revealed those factors typically associated with rehabilitation success or failure, as well as essentials to be addressed. The feasibility of development of a rule set for rehabilitated site classification was firstly investigated using neural networks which also assisted in the selection of significant parameters. Results from the neural network approach were then used to guide parameter selection for the evolutionary algorithm software. The coordinate scores for the first two axes of the redundancy analysis served as targets for the evolutionary algorithms. Overall, a targeting match of 71 % for the first axis coordinates and 38 % for the second axis coordinates were obtained. Contributing parameters for the rule set included: Cl, K, pH, percentage organic carbon, Zn, NH₄ and SO₄ content of the sites.     

PCA and SVM as geo-computational methods for geological mapping in the southern of Tunisia,using ASTER remote sensing data set

The purpose of this study was to examine the efficiency of Advanced Space Borne Thermal Emission and Reflection Radiometer (ASTER) data in the discrimination of geological formations and the generation of geological map in the northern margin of the Tunisian desert. The nine ASTER bands covering the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) spectral regions (wavelength range of 400–2500 nm) have been treated and analyzed. As a first step of data processing, crosstalk correction, resampling, orthorectification, atmospheric correction, and radiometric normalization have been applied to the ASTER radiance data. Then, to decrease the redundancy information in highly correlated bands, the principal component analysis (PCA) has been applied on the nine ASTER bands. The results of PCA allow the validation and the rectification of the lithological boundaries already published on the geologic map, and gives a new information for identifying new lithological units corresponding to superficial formations previously undiscovered. The application of a supervised classification on the principal components image using a support vector machine (SVM) algorithm shows good correlation with the reference geologic map. The overall classification accuracy is 73 % and the kappa coefficient equals to 0.71. The processing of ASTER remote sensing data set by PCA and SVM can be employed as an effective tool for geological mapping in arid regions.     

应用MapGIS空间分析模块进行土地质量地球化学评估的探讨

利用农业地质调查资料,探讨了如何应用MapGIS空间分析模块进行土地质量地球化学评估。以内江市沱江乡土地整理区为例,选取土壤养份指标、土壤环境指标、水、大气环境指标以及大宗农作物安全性指标为主要评价指标,通过MapGIS数字地面模型,绘制出单因素单指标分级图,并应用区对区的空间分析模块进行多指标、多因素的叠加分析,最终绘制成土地质量地球化学分等定级图。采用MapGIS空间分析模块进行土地质量地球化学评估,可以提高土地质量地球化学评估后期成图的效率,其方法简单实用,评估结果与工作区实际情况相符。     

The distribution of soil erosion as a function of slope aspect and parent material in Ciskei,Southern Africa

The paper investigates the relationship between geology, soils and slope aspects in four Ciskei catchments. The primary aim of the study was to determine whether any simple bivariate relationship exists between these independent variables and soil erosion. The secondary aim of the study was to determine whether combining the three independent variables and using a multivariate analysis could produce a more lucid picture of spatial variations in soil erosion. The results obtained from the study show that all the three independent variables (geology, soil type and slope aspects) can be used to account for spatial variations in soil erosion in the study catchments. A parent material classification (based on geology and soil type) and a parent material plus slope aspect classification provide more detailed explanation for variations in the extent of soil erosion in the study area. The paper discusses possible reasons for the observed relationships and the need for more detailed multivariate investigations.     

Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

Using chemical analysis and modeling to enhance the understanding of soil solution of some calcareous soils

The chemistry of soil solutions can be altered by human activities, due to the intense agricultural and husbandry, leading to leaching of nutrients and subsequently elevating ground water levels. Multivariate statistical and inverse geochemical modeling techniques were used to determine the main factors controlling soil solution chemistry of calcareous soils. In this research, a total of 21 calcareous soils was characterized and assessed for soil solution using soil column. The major cations in the studied soil solutions were in the decreasing order as Ca²⁺ > Mg²⁺ > Na⁺ > K⁺. The anions were also arranged in decreasing order as HCO $ _{3}^{ - } $  > Cl $ ^{ - } $  > SO $ _{4}^{2 - } $  > NO $ _{3}^{ - } $ . Concentrations of NO $ _{3}^{ - } $ , P, and K⁺ in soil solutions were in the range of 6.8–307.5 mg l^?1 (mean 63.2 mg l^?1), 5.0–10.4 mg l^?1 (mean 5.9 mg l^?1), and 2.8–54.6 mg l^?1 (mean 11.3 mg l^?1), respectively. Results suggest that the concentration of P in the soil solutions could be primarily controlled by the solubility of dicalcium phosphate dihydrate and dicalcium phosphate. Interactions between soil properties and observed solubility of nutrients were described, and put into empirical multivariate formulations. Obtained equations contained electrical conductivity (EC) as a key factor in determining nutrients solubility. Inverse geochemical modeling of soil solution using PHREEQC indicates the dissolution of calcite, anhydrite, halite, CO₂ (g), N₂ (g), and hydroxyapatite, and precipitation of sulfur. Cation exchange between Ca²⁺, Mg²⁺, K⁺ and Na⁺ occurred with Mg²⁺ and K⁺ into the solution, and Ca²⁺ and Na⁺ out of the solution. Determination of soil solution will improve soil management in the area, and preventing groundwater deterioration.     

Evaluating the effects of ground motion parameters on response spectra in Uttarakhand Himalayas,India

Uttarakhand, a state of India, is located in seismically active Himalayan region and in the proximity of plate boundaries. The effects of important ground motion parameters like magnitude, distance, and local geology and site conditions on acceleration response spectra are examined in Uttarakhand Himalayas in this work. A total of 447 strong ground motion histories (horizontal and vertical) from 42 earthquakes were selected. The results show that the shape of the acceleration response spectra is influenced by the local site conditions and regional geology. The studies are carried out for two categories of sites, i.e., rock sites and soft soil sites. The maximum average horizontal spectral amplification for rock sites is 2.7 at 0.1 s, while for soft soil sites, it is found to be 3.2 at 0.2 s. In the same way, the maximum average vertical spectral amplification for rock is found to be 2.7 at 0.1 s, while for soft soil, it is found to be 2.95 at 0.1 s. The average spectral amplification in vertical component also shifts from low period (rock) to high period (soft soil). The level of spectra increases with decrease in distance for rock sites as well as soft soil sites. When comparing different magnitude earthquakes in different geological conditions, the response spectra are found to follow each other up to 0.04 s, while for period greater than 0.04 s, the spectra of higher magnitude earthquake is observed on the higher side. For soft soil sites, spectra from different magnitude earthquakes are observed to follow each other up to 0.1 s, beyond which they get separated.     

A radiometric airborne geophysical survey of the Isle of Wight

A high resolution airborne geophysical survey across the Isle of Wight and Lymington area conducted in 2008 provided the first modern radiometric survey across the geological formations that characterise much of southern England. The basic radiometric data are presented and it is evident that bedrock geology exerts a controlling influence on the broad response characteristics of the naturally occurring radioelements. A GIS-based geological classification of the data provides a quantitative assessment and reveals that a relatively high percentage of the variability of the data is explained by the Cretaceous bedrock geology but this is much reduced in the Palaeogene. The three traditional Chalk units (Lower, Middle and Upper Chalk depicted on the currently available Geological Map) provide the lowest and most distinct behaviour within the Cretaceous sequence. Mineral content within the Chalk appears to increase with increasing age. A new method of representing the baseline radiometric information from the survey in terms of the mean values of the geological classification is presented. The variation of radioelement geochemistry within individual formations is examined in two case studies from the Cretaceous Lower Greensand Group and the Palaeogene Hamstead Member (Bouldnor Formation). The Cretaceous sequences provide the higher levels of discrimination of localised variations in radioelement distributions. A more detailed case study examines the potential influences from the degree of water saturation in the soil and superficial deposits.     

Geochemistry of Ground and Groundwater Pertinent to Chemical Attacks Mitigation on Concrete Foundations; A Case Study of the City of Rasht,Northern Iran

Presence of aggressive chemicals in ground and groundwater can deteriorate concrete foundations or its reinforcements. Concrete codes require preventive measures to assure durability of concrete foundations which is based on geochemistry information of the site. Construction in the city of Rasht, in the Iranian Caspian coast, is fast growing often without sufficient geochemistry data for residential buildings. The later may pose risk of chemical attacks on concrete foundations. To resolve this shortcoming, this study aimed at investigating geochemistry of ground and groundwater from the upper 10 m which can serve as a preliminary guideline for shallow foundation constructions in the city. The database for this study included previous geochemical investigations from 50 boreholes in the city as well as boring eleven test pits at various locations along with soil and groundwater geochemistry tests. The geochemistry experiments included measurement of sulfate, chloride, organic matter, as well as pH in soil and groundwater. Geochemistry of ground and groundwater in the city mostly fell within permissible limits set by a local concrete code. Based on current study, service environment for shallow concrete foundations in the city was categorized in a moderate class; hence, durability requirements from the same local concrete code were emphasized.     

Using principal components analysis (PCA) with cluster analysis to study the organic geochemistry of sinking particles in the ocean

Principal components analysis (PCA) is a multivariate data analysis tool that can be used to recombine the variables of a large multivariate dataset in such a way that the first few variables of the reconstructed dataset account for the majority of the variance in the data. Application of PCA in marine geochemistry has become quite common in recent years. In this study, we illustrate the use of PCA through examples that arose while investigating the geochemistry of sinking particles during the MedFlux project. The examples presented do not simply repeat the analyses of the original study, but instead extend them in the context of simultaneous application of PCA and cluster analysis. Our results show that constructing a one dimensional (1D) “degradation index” using only the first principal component (PC) is in most cases oversimplified, and that constructing 2D or 3D “degradation trajectories” with the first 2 or 3 PCs is more informative. Use of the first three PCs is indicated when the variance explained by the third PC is comparable in magnitude to that explained by the second PC in the reconstructed dataset. We also discuss the use of scree plots and cluster analysis in helping decide whether the third PC is needed to capture the essential information in the dataset.     

Adsorption kinetics of chromium(III) removal from aqueous solutions using natural red earth

Laboratory-scale-simulated experiments were carried out using Cr(III) solutions to identify the Cr(III) retention behavior of natural red earth (NRE), a natural soil available in the northwestern coastal belt of Sri Lanka. The effects of solution pH, initial Cr(III) concentration and the contact time were examined. The NRE showed almost 100 % Cr(III) adsorption within the first 90 min. [initial [Cr(III)] = 0.0092–0.192 mM; initial pH 4.0–9.0]. At pH 2 (298 K), when particle size ranged from 125 to 180 μm the Cr(III) adsorption data were modeled according to Langmuir convention assuming site homogeneity. The pH-dependent Cr(III) adsorption data were quantified by diffused layer model assuming following reaction stoichiometries: $$ \begin{aligned} 2\, {>}{\text{AlOH}}_{{({\text{s}})}} + {\text{ Cr }}\left( {\text{OH}} \right)_{{ 2\,({\text{aq}})}}^{ + } \, \to \, \left( { {>}{\text{AlO}}} \right)_{ 2} {\text{Cr}}_{{({\text{s}})}}^{ + } + {\text{ 2H}}_{ 2} {\text{O}} \quad {\text{log K 15}}. 5 6\\ 2\, {>}{\text{FeOH}}_{{({\text{s}})}} + {\text{ Cr}}\left( {\text{OH}} \right)_{{ 2\,({\text{aq}})}}^{ + } \, \to \, \left( { {>}{\text{FeO}}} \right)_{ 2} {\text{Cr}}_{{({\text{s}})}}^{ + } + {\text{ 2H}}_{ 2} {\text{O}}\quad {\text{log K 5}}.0 8.\\ \end{aligned} $$ The present data showed that NRE can effectively be used to mitigate Cr(III) from aqueous solutions and this method is found to be simple, effective, economical and environmentally benign.     

Appraisal of groundwater quality in a crystalline aquifer: a chemometric approach

The purpose of this study is to assess the groundwater quality and identify the processes that control the groundwater chemistry in a crystalline aquifer. A total of 72 groundwater samples were collected during pre- and post-monsoon seasons in the year 2014 in a semi-arid region of Gooty Mandal, Anantapur district, Andhra Pradesh, India. The study utilized chemometric analysis like basic statistics, Pearson’s correlation coefficient (r), principal component analysis (PCA), Gibbs ratio, and index of base exchange to understand the mechanism of controlling the groundwater chemistry in the study area. The results reveal that groundwater in the study area is neutral to slightly alkaline in nature. The order of dominance of cations is Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ while for anions, it is \( {\mathrm{HCO}}_3^{-}>{\mathrm{Cl}}^{-} \)>\( {\mathrm{NO}}_3^{-} \)>\( {\mathrm{SO}}_4^{2-} \)>\( {\mathrm{CO}}_3^{2-}>{\mathrm{F}}^{-} \) in both seasons. Based on the Piper classification, most of the groundwater samples are identified as of sodium bicarbonate (\( {\mathrm{Na}}^{+}-{\mathrm{HCO}}_3^{-}\Big) \) type. According to the results of the principal component analysis (PCA), three factors and two factors were identified pre and post monsoon, respectively. The present study indicates that the groundwater chemistry is mostly controlled by geogenic processes (weathering, dissolution, and ion exchange) and some extent of anthropogenic activities.     

Database extension for digital soil mapping using artificial neural networks

Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.     

Multivariate statistical approach to identify heavy metal sources in agricultural soil around an abandoned Pb–Zn mine in Guangxi Zhuang Autonomous Region, China

A study of agricultural lands around an abandoned Pb–Zn mine in a karst region was undertaken to (1) assess the distribution of heavy metals in the agricultural environment, in both dry land and paddy field; (2) discriminate between natural and anthropogenic contributions; and (3) identify possible sources of any pollution discovered. Ninety-two samples of cultivated soils were collected around the mine and analyzed for eight heavy metals, pH, fluoride (F^?), cation exchange capacity, organic matter, and grain size. The eight heavy metals included Cd, Cr, Cu, Ni, Pb, Zn, As, and Hg. The average concentrations (mg/kg) obtained were as follows: Cd 16.76 ± 24.49, Cr 151.5 ± 18.24, Cu 54.28 ± 18.99, Ni 57.5 ± 14.43, Pb 2,576.2 ± 1,096, Zn 548.7 ± 4,112, As 29.1 ± 6.36, and Hg 1.586 ± 1.46. In a site where no impact from mining activities was detected, the mean and median of Cd, Cu, Ni, Pb, Zn, As, and Hg concentrations in investigated topsoils were higher than the mean and median of heavy metal concentrations in reference soils. An ensemble of basic and multivariate statistical analyses was performed to reduce the multidimensional space of variables and samples. Two main sets of heavy metals were revealed as indicators of natural and anthropogenic influences. The results of principal component analysis (PCA) and categorical PCA demonstrated that Cd, Cu, Pb, Zn, and Hg are indicators of anthropogenic pollution, whereas Cr, As and Ni concentrations are mainly associated with natural sources in the environment. The contamination from Pb–Zn mining operations, coupled with the special karst environment, was a key contributing factor to the spatial distribution of the eight heavy metals in the surrounding soil. The values of heavy metals in the soil samples suggested the necessity of conducting a rigorous assessment of the health and environmental risks posed by these residues and taking suitable remedial action as necessary.     

Soil responses near Delhi ridge and adjacent regions in Greater Delhi during incidence of a local earthquake

In this study, soil response was carried out for the Greater Delhi region. A folded Proterozoic formation was identified as Delhi ridge, passes through its central part along SSW–NNE direction, and appears to be a main geomorphic feature for the study area. The Delhi ridge is an exposed quartzite rock of about 10–100 m wide and ~25 km long with gentler dipping both toward east and west. We have considered the exposed part as an outcrop side near the ridge axis and the dipping area as rigid base away from the ridge axis for ground motion study during the occurrence of the 25 November 2007 earthquake with magnitude M _L 4.3 (Richter scale) that occurred at Delhi–Haryana State boundary. The degree of shaking was very strong and reported major cracks in the buildings near the epicenter area. We have studied the soil response parameters at the surface level, considering horizontally stratified soil layers above rigid base. The equivalent linear method was used for soil response analysis at 25 sites in Greater Delhi area. The peak amplification factors vary from 3.2 to 5.9 and peak resonance frequency varies from 1.2 to 5.3 Hz. The correlation among the peak amplification factor (A) and frequency (f) was empirically established as A = 0.36f + 3.60. Increasing peak amplification factor was found at sites with increasingly thicker alluvium deposit with lower frequency contains ground motion and vice versa. Seismic zoning map was also reconstructed for peak amplification factors and predominant periods for the study area for the mitigation purposes of earthquake damage. The average shear wave velocity up to 30 m soil depth is also obtained for site classification. The average velocity to 30 m [ $ \overline{{V_{\text{s}} }} (30) $ ] is a widely used parameter for classifying sites for predicting their potentiality to amplify seismic shaking. A lower value [ $ \overline{{V_{\text{s}} }} (30) $ ] thus yields a more conservative estimate of ground motion, which generally increases as $ \overline{{V_{\text{s}} }} (30) $ decreases. Present estimate of $ \overline{{V_{\text{s}} }} (30) $ varies from 315 to 419 m/s. In this study, we have identified two site classes C and D, as per National Earthquake Hazard Reduction Program. The city planner or engineers can directly use these data for site-specific assessment during retrofitting of the existing structure, demolition of the old buildings and design a new structure to avoid major destruction of the buildings due to future earthquake.     

Nonlinear kinetic analysis of phenol adsorption onto peat soil

Phenolic compounds are considered as a serious organic pollutant containing in many industrial effluents particularly vulnerable when the plant discharge is disposed on land. In the present study, the phenol removal potential of peat soil as adsorption media was investigated as the adsorption process are gaining popular for polishing treatment of toxic materials in industrial wastewater. Batch experiments were performed in the laboratory to determine the adsorption isotherms of initial concentrations for 5, 8, 10, 15, and 20 mg/L and predetermined quantity of peat soil with size ranges between 425 and 200 μm poured into different containers. The effects of various parameters like initial phenol concentration, adsorbent quantity, pH, and contact time were also investigated. From experimental results, it was found that 42 % of phenol removal took place with optimized initial phenol concentration of 10 mg/L, adsorbent dose of 200 g/L, solution pH 6.0 for the equilibrium contact time of 6 h. The result exhibits that pseudo-first-order (R ² = 0.99) and Langmuir isotherm models are fitted reasonably (R ² = 0.91). Adams–Bohart, Thomas, Yoon–Nelson, and Wolborska models were also investigated to the column experimental data of different bed heights to predict the breakthrough curves and to determine the kinetic coefficient of the models using nonlinear regression analysis. It was found that the Thomas model is the best fitted model to predict the experimental breakthrough curves with the highest coefficient of determination, R ² = 0.99 and lowest root mean square error and mean absolute performance error values.