Issue 3

Water balance studies in the Okavango Delta indicate that more than 90% of inflow into the Delta is lost through evaporation. This coupled with high climatic variability threatens the ecohydrology of the Delta. Trends indicate decreasing rainfall amounts and increasing temperature at the area of the Delta. The main aim of this study was therefore to investigate long term trends and variability in rain onset, cessation, number of rainy days and their impact on the dryness index at the Delta. The impact of the above variables is expressed through the standardized precipitation and evaporation index (SPEI) quantified by aggregating the climate water balance and fitting monthly series to a generalized logistic distribution using L-Moments. The SPEI, determined at windows of different time scales of one, three and twelve months, provided an extensive evaluation of dryness severity and its impact on this sensitive ecosystem. Rain onset and cessation dates were generated from cumulative pentad rainfall–evapotranspiration relationships. Analysis of climatic data showed mean rain onset occurring in November and ceding in March with average of 44 rainy days between 1970/71 and 2013/14. The results revealed a decrease in the number of rainy days at a rate of 0.16 days/yr and of the duration of the rainy season at 0.25 days/yr with high variability. Annual rainfall was found to decrease at the rate of 1.60 mm/yr with 6.8% probability of failure in rainfall onset. Analysis further revealed that both extreme dryness and wetness are rare phenomena with probabilities of less than 1% and near normal conditions for 67% of the time for all SPEI time scales. Although gradual increase in dryness in the Delta is attributed to high climatic variability, simulations undertaken using Artificial Neural Networks did not predict any major changes in the next five years. However, vulnerability to severe droughts is not completely ruled out because of the high variability in rainfall and of the location of the Delta in a semi-arid zone. 

In the present study, monthly rainfall, maximum temperature and minimum time series models were developed for Jorhat (Assam) situated in northeast India using monthly rainfall, maximum temperature and minimum temperature data from the year 1965 to 2000. A trend free time series of rainfall and temperature was obtained by eliminating the trend component in the original time series, and then was used in identifying the periodic component. Fourier series analysis was used to identify periodic component. First five harmonics explained total variance of 79.4, 72.6 and 73.7% for monthly rainfall, maximum temperature and minimum temperature respectively. In the stochastic dependent component modelling, Autoregressive (AR) models of order 12 were found suitable on the basis of minimum value of AICC and BIC statistics. Portmanteau test formulated by McLeod and Li was carried out for checking the independence of stochastic dependent component which indicated that series consist of independent and identically distributed variables. Independent stochastic components were further modelled using normal distribution function.  Nash-Sutcliffe coefficient also indicated high degree of models fitness to the observed data. Developed time series models were validated using eight years (2001-2008) data. Using the developed time series models, monthly rainfall, maximum temperature and minimum temperature were forecasted up to the year 2050. Assessment of changes in monthly rainfall, maximum temperature and minimum temperature in generated series (2009 to 2050) were predicted using linear regression which indicated no significant trend, i.e., the climate at Jorhat (Assam) in next four decades will remain more or less stable. 

Biogas production through anaerobic co-digestion of a mixture of cattle manure and citrus waste using an experimental facility for testing the biochemical methane potential (BMP) was investigated. No buffer solution is added to the mixture in order to use the buffer capacity from cattle manure. Regular measurements of pH, alkalinity, chemical oxygen demand, methane and biogas net production were performed. Three substrate inoculum ratios (SIR) 1:1, 2:1 and 3:1 (g COD/g VSS) were evaluated. Maximum COD removals of 56.4 %, 51.3 % and 48.0 % for the SIRs 1:1, 2:1 and 3:1 were obtained. For all SIR, pH was on the range of 6.5 to 7.5, while the maximum VFA concentration was 4250 mg CH₃COOH l^-1. Alkalinity ranged between 2250 to 4500 mg CaCO₃ l^-1. Both maximum methane production rate (MMPR) and percentage of anaerobic biodegradability were established. BMP of 94.3 to 146.6 mL_STP-CH4/gVSS were calculated for the ratios 1:1 and 3:1, respectively. The highest feasibility for biogas production and methane was established for SIR 3:1.

In the present study, adsorption potential of potassium permanganate (Hypermangan) modified zeolite was investigated for the removal of nitrate from synthetic wastewater. Effects of the most significant parameters (pH, adsorbent dose, nitrate concentration and contact time) were initially evaluated based on the percentage of nitrate removed from the water solutions. Over 90 % removal of 150 mg L^-1 nitrate was achieved at an optimum pH of 5, adsorbent dose of 2g L^-1 after a 60 min contact time. The chemical and morphological characterizations of Hypermangan modified zeolite (HMZ) were carried out by using scanning electron microscopy (SEM), fourier transform infrared (FT-IR), x-ray diffraction (XRD) and x-ray fluorescence (XRF) analysis methods. Kinetic evaluation indicated that the nitrate adsorption onto HMZ followed the pseudo-first-order model. The equilibrium data assessment illustrated the removal of nitrate by HMZ follows a Langmuir model which attains the maximum adsorption capacity 6.7 mg g^-1.The mean free energy of adsorption was 0.15 kJ mol^-1, which indicates the adsorption of nitrate onto HMZ, occurs through a physical mechanism.