Environmental impact assessment on water quality deterioration caused by the decreased Ganges outflow and saline water intrusion in south-western Bangladesh

 This paper deals with an environmental impact assessment of low water flow in the river Ganges during a dry period at the Khulna and Mongla port areas in south-western Bangladesh. Large-scale surface water withdrawal in India after commissioning the Farakka Barrage causes a drastic fall in the Ganges low-flow condition within the Bangladesh territory during every dry period. The average lowest discharge in the Ganges is 552 m³/s, which is about 73% less than that in the pre-Farakka time. This has caused the deterioration of both surface and groundwater quality of the study area. Salinity is the principal cause of water quality degradation in the area. Present observation shows that the surface water of the area is sulphate-chloride dominated, which signifies high salinity whereas the groundwater is categorized as of medium to high salinity. To maintain the Rupsa River's maximum salinity below 1000 μS/cm the discharge in the Ganges should be ∼1500 m³/s, whereas that at Garai basin is ∼10 m³/s. If this present situation continues it will be a crippling blow to the environment of the area in the long term. An integrated multidisciplinary approach to hydrogeological research is urgently required to salvage the area from further deterioration. Received: 9 August 1999 · Accepted: 8 March 2000     

Lake and reservoir water quality affected by metals leaching from tropical soils, Bangladesh

 The release of metals during weathering has been studied in order to assess its geochemical controls and possible effects on environmental health in Bangladesh. A total of 27 soil samples and 7 surface water samples were collected from four locations covering three major regions in the country. Results show that weathering effects are a strong function of climatic conditions. Surface waters are typically enriched in Al, Mg, Ca, Na, K, As, Ba, Cr, Cu, Ni, Pb and Zn. The solubility of metal ions, organometallic complexes, co-precipitation or co-existence with the colloidal clay fraction are the main processes that lead to metal enrichment in lake and reservoir water. Aluminium concentrations exceed World Health Organization (WHO) drinking-water standards in all samples, and in two regions, arsenic concentrations also significantly exceed WHO standards. The elevated levels of As indicate that arsenic contamination of water supplies in Bangladesh is not confined to groundwater. Received: 4 June 1999 · Accepted: 17 August 1999     

High temporal and spatial resolution observations of meso-scale features of pre- and mature summer monsoon cloud systems over Bangladesh

Meso-scale characteristics of disturbances that bring about atmospheric disasters in pre- and mature monsoon seasons in Bangladesh are analyzed. Several types of meteorological instruments capable of observations with high temporal and spatial resolutions were introduced for the first time in this area to capture the meso-scale structure of rainfall systems. We installed an automatic weather station (AWS) and several automatic raingauges (ARGs) and utilized the weather radar of Bangladesh Meteorological Department (BMD). From the radar image in the summer of 2001 (16–18 July), a striking feature of the systematic diurnal variation in this area was elucidated. In these 3 days, the diurnal evolutions of convective activity were remarkably similar to each other, implying that this pattern can be understood as a typical response of local cloud systems to the diurnal variation of insolation under some summer monsoon situations. The ARG data show the difference in characteristics of rainfall between pre- and mature monsoon seasons. The short intense downpour tends to occur more frequently in the pre-monsoon season than in the mature monsoon season. The pre-monsoon rainfall also has clear diurnal variation with a peak that is more strongly concentrated in time. In the northern part the rainfall peak is found in between midnight and early morning, while it is observed in the daytime in central to western parts of the country. Two disaster cases caused by meso-scale disturbances are analyzed. Although they occurred in the same season, the structures of the cloud systems were largely different from each other. The disturbance brought about tornadoes on 14 April 2004, consisting of many spherical cloud systems of approximately 20 km size. On the other hand, another one that caused the tragic river water transport accident on 23 May 2004 had meso-scale rain band structure. The latter case was captured by the AWS located at Dhaka. Sudden changes in temperature, wind and pressure were observed clearly, showing the typical structure of convective rain bands.     

Flood risk management in Central Viet Nam: challenges and potentials

This article explores the impacts of floods on the economy, environment, and society and tries to clarify the rural community’s coping mechanism to flood disasters in Central Viet Nam. It focuses on the social aspects of flood risk perception that shapes the responses to floods. The research findings revealed that flooding is an essential element for a coastal population, whose livelihood depend on productive functions of cyclical floods. The findings also revealed that floods, causing losses and damages, often inhibited economic development. The surveyed communities appeared to have evolved coping mechanisms to reduce the negative impacts of the floods, yet these coping mechanisms are under pressure due to environmental degradation. Integrated flood risk management is considered as a suitable paradigm for coping with flood disasters.

A long-term trend in precipitation of different spatial regions of Bangladesh and its teleconnections with El Niño/Southern Oscillation and Indian Ocean Dipole

A long-term (1948 to 2012) trend of precipitation (annual, pre-monsoon, monsoon, and post-monsoon seasons) in Bangladesh was analyzed in different regions using both parametric and nonparametric approaches. Moreover, the possible teleconnections of precipitation (annual and monsoon) variability with El Niño/Southern Oscillation (ENSO) episode and Indian Ocean Dipole (IOD) were investigated using both average and individual (both positive and negative) values of ENSO index and IOD. Our findings suggested that for annual precipitation, a significant increasing monotonic trend was found in whole Bangladesh (4.87 mm/year), its western region (5.82 mm/year) including Rangpur (9.41 mm/year) and Khulna (4.95 mm/year), and Sylhet (10.12 mm/year) and Barisal (6.94 mm/year) from eastern region. In pre-monsoon, only Rangpur (2.88 mm/year) showed significant increasing trend, while in monsoon, whole Bangladesh (3.04 mm/year), Sylhet (7.17 mm/year), and Barisal (6.94 mm/year) showed similar trend. In post-monsoon, there was no significant trend. Our results also revealed that the precipitation (annual or monsoon) of whole Bangladesh and almost all of the spatial regions did not show any significant correlation with ENSO events, whereas the average IOD values showed significant correlation only in monsoon precipitation of western region. The individual positive IODs showed significant correlation in whole Bangladesh, western region, and its two divisions (Rajshahi and Khulna). So, in the context of Bangladesh climate, IOD has the more teleconnection to precipitation than that of ENSO. Our findings indicate that the co-occurrence of ENSO and IOD events may suppress their influence on each other.

     

Heavy metal biosorption potential of a Malaysian Rhodophyte (<Emphasis Type="Italic">Eucheuma denticulatum</Emphasis>) from aqueous solutions

Biosorption is a promising technology for the removal of heavy metals from industrial wastes and effluents. In the present study, biosorption of Pb²⁺, Cu²⁺, Fe²⁺ and Zn²⁺ onto the dried biomass of Eucheuma denticulatum (Rhodophyte) was investigated as a function of solution pH, contact time, temperature and initial metal ion concentration. The experimental data were evaluated by Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherm models. The sorption isotherm data followed Langmuir and Freundlich models, and the maximum Langmuir monolayer biosorption capacity was found as 81.97, 66.23, 51.02 and 43.48 mg g^?1 for Pb²⁺, Cu²⁺, Fe²⁺ and Zn²⁺, respectively. The sorption kinetic data followed pseudo-second-order and intraparticle diffusion models. Thermodynamic study revealed feasible, spontaneous and endothermic nature of the sorption process. Fourier transform infrared analysis showed the presence of amine, aliphatic, carboxylate, carboxyl, sulfonate and ether groups in the cell wall matrix involved in metal biosorption process. A total of nine error functions were applied in order to evaluate the best-fitting models. We strongly suggest the analysis of error functions for evaluating the fitness of the isotherm and kinetic models. The present work shows that E. denticulatum can be a promising low-cost biosorbent for removal of the experimental heavy metals from aqueous solutions. Further study is warranted to evaluate its potential for the removal of heavy metals from the real environment.     

Multiple step ahead river flow modelling for south east tasmanian aquaculture

We develop multiple step ahead prediction models of river flow for locations in Tasmania (Australia) for decision support in aquaculture. In predicting river flows for multiple days ahead, we first statistically determine the maximum input lags of rainfall and river flow. We then use machine learning techniques in building models. In multiple step ahead prediction, we consider both static and dynamic approaches. In dynamic approach, one day prediction is served as input to two days ahead prediction. The experimental results demonstrate that, in general, a dynamic approach provides better accuracy in multiple day’s ahead prediction. For Duck Bay location using dynamic approach, support vector regression performs best over linear regression, M5P and multilayer perceptron. However, at Montagu Bay location, we find that M5P performs best over methods. We find that multiple step ahead prediction of river flow for each location requires modelling of lags with associated machine learning techniques.