Participatory Vulnerability Reduction (PVR): an urban community-based approach for earthquake management

Participation of local people during any disaster is enormous. They possess better knowledge and information about their own community than anyone else from the outside. This study proposes Participatory Vulnerability Reduction (PVR), a community-based approach for disaster management. The concept of PVR was applied to an urban community of Dhaka city (Ward no. 06 of Dhaka North City Corporation) which has been identified by the Comprehensive Disaster Management Programme as one of the most vulnerable areas of the city for earthquake. PVR consists three steps, and in each step, different participatory urban appraisal tools were used. In the first step, the community people assessed the earthquake vulnerability. It was found that some certain parts of the study area are highly vulnerable due to lack of accessibility to the critical facilities, inadequacy of open space, poor construction practice and unsuitable soil condition for building construction. This was followed by analyzing the root causes and effects of these problems. Structural fragility of the buildings, construction of settlements by filling the low-lying areas and development of slums beside taller buildings are the three major causes behind the above vulnerable issues. In the second step, capacity of the community was assessed in terms of resources and their organizational structure. In the final step, local people developed the strategies to overcome the vulnerability and a community-based organizational set up was proposed to coordinate the collective actions. Although developed in local context, application of PVR is not limited for earthquake and it can be replicated for other communities as well.     

Comparison of dissolved and particulate arsenic distributions in shallow aquifers of Chakdaha, India, and Araihazar, Bangladesh

Background  

The origin of the spatial variability of dissolved As concentrations in shallow aquifers of the Bengal Basin remains poorly understood. To address this, we compare here transects of simultaneously-collected groundwater and aquifer solids perpendicular to the banks of the Hooghly River in Chakdaha, India, and the Old Brahmaputra River in Araihazar, Bangladesh.     

New ophiolite occurrences in Sudan and constraint on the western boundary of the Nubian Shield: Petrographical and geochemical evidence

Important mafic–ultramafic masses have been located for the first time in the intersection area between the Keraf Shear Zone and the Nakasib Suture Zone of the Nubian Shield. The masses, comprising most of the members of the ophiolite suite, are Sotrebab and Qurun complexes east of the Nile, and Fadllab complex west of the Nile. The new mafic–ultramafic masses are located on the same trend of the ophiolitic masses decorating the Nakasib Suture. A typical complete ophiolite sequence has not been observed in these complexes, nevertheless, the mafic–ultramafic rocks comprise basal unit of serpentinite and talc chlorite schists overlain by a thick cumulate facies of peridotites, pyroxenites and layered gabbros overlain by basaltic pillow lavas with dolerite dykes and screens of massive gabbros. Associated with pillow lavas are thin layers of carbonates and chert. The best section of cumulate mafic–ultramafic units has been observed in Jebel Qurun and El Fadlab complexes, comprising peridotites, pyroxenites and layered gabbros. Dolerite dykes and screens of massive gabbros have been observed with basaltic pillow lava sections in Wadi Dar Tawaiy. The basal ultramafic units of the complexes have been fully or partly retrograded to chlorite magnetite schist and talc to talc-carbonate rocks (listowenites), especially in the Jebel Qurun and Sotrebab complexes. Petrographically, the gabbros (layered and massive) and the basaltic pillow lavas show mineral assemblages of epidote amphibolite facies. The mafic members from the three complexes show a clear tholeiitic trend and oceanic floor affinity. The pillow lavas plot in the field of oceanic floor basalt, namely in the back arc field. Primitive mantle normalized spider diagram of the pillow lavas reveals a closer correspondence to Enrich-Mid-Oceanic Ridge Basalt (E-MORB) type, which is confirmed by the flat chondrite normalized Rare Earth Elements (REE) pattern. Field, petrographical and geochemical evidence supports ophiolitic origin of the three complexes. The newly discovered ophiolitic complexes mark the western continuation of the Nakasib Suture Zone.     

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.