Site amplification investigation in Dhaka, Bangladesh, using H/V ratio of microtremor

The degree of damage during earthquakes strongly depends on dynamic characteristics of buildings as well as amplification of seismic waves in soils. Among the other approaches, microtremor is, perhaps, the easiest and cheapest way to understand the dynamic characteristics of soil. Non-reference microtremor measurements have been carried out in 45 locations in and around the capital Dhaka city of Bangladesh. Subsoil investigations (Standard Penetration Test and Shear Wave Velocity) have also been executed in those locations. Soil model has been developed for those locations for site response analysis by means of the program SHAKE. Among those 45 locations, predominant frequency of microtremor observation varies from 0.48 to 3.65 Hz. Out of those 45, for 35 locations Transfer function obtained from the program SHAKE have higher frequency compared to microtremor H/V ratio and for one location it has lower predominant frequency. For six locations, frequencies obtained from two methods are identical. For three other locations, there are no similarities between predominant frequency obtained from microtremor and transfer function. The seismic Vulnerability Index (Kg) for 45 sites varies between 0.45 and 31.85. Ten sites have been identified as having moderate vulnerability of soil layers to deform.     

Effectiveness of Mg–Al-layered double hydroxide for heavy metal removal from mine wastewater and sludge volume reduction

Health hazards from heavy metal pollution in water systems are a global environmental problem. Of similar concern is sludge that results from wastewater treatment due to unsatisfactory sludge management technology. Therefore, the effectiveness of using Mg–Al-layered double hydroxide in the removal of heavy metals from mine wastewater was tested and compared with that of calcium hydroxide [Ca(OH)₂], which is a common treatment method for heavy metal removal. Initially, the mine wastewater contained cations of the heavy metals iron (Fe), zinc (Zn), copper (Cu), and lead (Pb). The Mg–Al-layered double hydroxides were able to remove 371, 7.2, 121, and 0.4 mg/L of these pollutants, respectively, using the co-precipitation method. The removal of these metals is most effective using 0.5 g Mg–Al-layered double hydroxide (Mg/Al molar ratio 4) and 20 min of shaking. Zn was removed by the formation of Zn(NO₃)(OH)·H₂O and Zn₅(NO₃)₂(OH)₈ when LDH, Mg/Al molar ratios of 4 and 2, respectively, were used. Similarly, Fe, Cu, and Pb were removed by the formation of Fe–Al-layered double hydroxide, Cu₂(OH)₃·NO₃ and Pb₄(OH)₄(NO₃)₄, respectively. While Ca(OH)₂ is also capable of reducing the heavy metal concentrations below the Japanese recommended values, this analysis shows that using 0.5 g Mg–Al-layered double hydroxide is a better treatment condition for mine wastewater, because it generates lower sludge volumes than 0.1 g of Ca(OH)₂. The measured sludge volume was 1.5 mL for Mg–Al-layered double hydroxide and 2.5 mL for Ca(OH)₂, a nearly twofold further reduction.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Fuzzy neural network models for liquefaction prediction

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.     

Levels of organochlorine pesticide residues in some organs of the Ganges perch, Lates calcarifer, from the Ganges-Brahmaputra-Meghna Estuary, Bangladesh

Levels of organochlorine pesticide residues (p,p′ DDT, DDD, DDE, Aldrin, Dieldrin, Lindane, Heptachlor and BHC) were analysed in the dry and wet seasons in four organs (muscle, liver, gut and egg samples) of Ganges Perch, Lates calcarifer, collected during October–November–December, 1996 and May–June–July, 1997 from the Ganges–Brahmaputtra–Meghna estuary. The residues were analysed by using gas-chromatography (GC) in electron capture detector (ECD) mode and were verified by thin layer chromatography (TLC). Among the four organs analysed, the residues were found in the order egg > gut > muscle > liver. The pesticide residues were found in the order ∑DDT > Heptachlor >Dieldrin > Aldrin. Higher levels of residues were found during the dry season due to high lipid content in fishes. A positive correlation was observed between the pesticide residues (∑DDT and ∑OCs) and lipid contents of fish, and the correlation was found to be linear. The concentrations of pesticide residues in muscle, liver and gut were below the FAO/WHO (1993) recommended permissible limit except in eggs.     

Geospatial and geostatistical approach for groundwater potential zone delineation

Over the past few decades, groundwater has become an essential commodity owing to increased demand as a result of growing population, industrialization, urbanization and so on. The water supply situation is expected to become more severe in the future because of continued unsustainable water use and projected change in hydrometeorological parameters due to climate change. This study is based on the integrated approach of remote sensing, geographical information system and multicriteria decision‐making techniques to determine the most important contributing factors that affect the groundwater resources and to delineate the groundwater potential zones. Ten thematic layers, namely, geomorphology, geology, soil, topographic elevation (digital elevation model), land use/land cover, drainage density, lineament density, proximity of surface water bodies, surface temperature and post‐monsoon groundwater depth, were considered for the present study. These thematic layers were selected for groundwater prospecting based on the literature; discussion with the experts of the Central Ground Water Board, Government of India; field observations; geophysical investigation; and multivariate techniques. The thematic layers and their features were assigned suitable weights on Saaty's scale according to their relative significance for groundwater occurrence. The assigned weights of the layers and their features were normalized by using the analytic hierarchy process and eigenvector method. Finally, the selected thematic maps were integrated using a weighted linear combination method to create the final groundwater potential zone map. The final output map shows different zones of groundwater potential, namely, very good (16%), good (35%), moderate (28%) low (17%) and very low (2.1%). The groundwater potential zone map was finally validated using the discharge and groundwater depth data from 28 and 98 pumping wells, respectively, which showed good correlation. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamics of hourly sea level at Hillarys Boat Harbour,Western Australia: a chaos theory perspective

Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model.