Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin

Batch and continuous removal of heavy metals from industrial effluents using microbial consortia

Bio-removal of heavy metals, using microbial biomass, increasingly attracting scientific attention due to their significant role in purification of different types of wastewaters making it reusable. Heavy metals were reported to have a significant hazardous effect on human health, and while the conventional methods of removal were found to be insufficient; microbial biosorption was found to be the most suitable alternative. In this work, an immobilized microbial consortium was generated using Statistical Design of Experiment (DOE) as a robust method to screen the efficiency of the microbial isolates in heavy metal removal process. This is the first report of applying Statistical DOE to screen the efficacy of microbial isolates to remove heavy metals instead of screening normal variables. A mixture of bacterial biomass and fungal spores was used both in batch and continuous modes to remove Chromium and Iron ions from industrial effluents. Bakery yeast was applied as a positive control, and all the obtained biosorbent isolates showed more significant efficiency in heavy metal removal. In batch mode, the immobilized biomass was enclosed in a hanged tea bag-like cellulose membrane to facilitate the separation of the biosorbent from the treated solutions, which is one of the main challenges in applying microbial biosorption at large scale. The continuous flow removal was performed using fixed bed mini-bioreactor, and the process was optimized in terms of pH (6) and flow rates (1 ml/min) using Response Surface Methodology. The most potential biosorbent microbes were identified and characterized. The generated microbial consortia and process succeeded in the total removal of Chromium ions and more than half of Iron ions both from standard solutions and industrial effluents