Streamflow Forecasting Using Singular Value Decomposition and Support Vector Machine for the Upper Rio Grande River Basin

The current study improves streamflow forecast lead‐time by coupling climate information in a data‐driven modeling framework. The spatial–temporal correlation between streamflow and oceanic–atmospheric variability represented by sea surface temperature (SST), 500‐mbar geopotential height (Z₅₀₀), 500‐mbar specific humidity (SH₅₀₀), and 500‐mbar east–west wind (U₅₀₀) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). SVD significant regions are weighted using a nonparametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed model for the period of 1965–2014. The April–August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1–13 months. SVD results showed the streamflow variability was better explained by SST and U₅₀₀ as compared to Z₅₀₀ and SH₅₀₀. The SVM model showed satisfactory forecasting ability with best results achieved using a one‐month lead to forecast the following four‐month period. Overall, the SVM results showed excellent predictive ability with average correlation coefficient of 0.89 and Nash–Sutcliffe efficiency of 0.79. This study contributes toward identifying new SVD significant regions and improving streamflow forecast lead‐time of the URGRB.     

Utilization of anaerobically treated distillery spent wash for production of cellulases under solid-state fermentation

Pollution caused by distillery spent wash on one hand has stimulated the need to develop new technologies to treat the waste and on the other, forced us to reevaluate the efficient utilization of its nutritive potential for production of various high value compounds. In this study, anaerobically treated distillery spent wash was used for the production of cellulases by Aspergillus ellipticus under solid-state fermentation using wheat straw as a substrate. The interactions between distillery effluent concentration, initial pH, moisture content and inoculum size were investigated and modeled using response surface methodology (RSM) involving Box–Behnken design (BBD). Under optimized conditions, filter paper activity, β-glucosidase and endo-β-1,4-glucanase activities were found to be 13.38, 26.68 and 130.92 U/g of substrate respectively. Characterization of endo-β-1,4-glucanase and β-glucosidase was done after partial purification by ammonium sulfate fractionation followed by desalting. The partially purified endo-β-1,4-glucanase and β-glucosidase showed maximum activity at 60 °C. Saccharification studies performed with different lignocellulosic substrates showed that wheat bran was most susceptible to enzymatic hydrolysis. The study suggests that anaerobically treated distillery spent wash can be used as a viable nutrient source for cellulase production under solid-state fermentation by A. ellipticus.     

Pulling force prediction using neural networks

Purpose. In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recently, multiple regression methods have gained importance for task-relevant inputs in predicting PF. Artificial neural networks (ANNs) also play a vital role in fitting the data; however, their use in work-related biomechanics and ergonomics is inadequate. Therefore, the current research aimed to accomplish comparative investigation of ANN and regression models by assessing their capacity to predict PF values. Methods. Multipositional PF data were acquired from 200 subjects at three different handle heights and body locations. ANN and regression models were formed using a random sample of three subsets (75% training, 15% selection, 10% validation) for proving the outcomes. Results. The comparison of ANN and regression models shows that the predictions of ANN models had a profoundly explained variance and lower root mean square difference values for the PF data at three handle heights. Conclusions. These outcomes advise that ANNs offer a precise and robust substitute for regression methods, and should be considered a useful method in biomechanics and ergonomics task assessments.     

Non-powered hand tool improvement research for prevention of work-related problems: a review

Purpose. In lower-middle-income countries, most of the work is performed manually using non-ergonomic hand tools which results in work-related health problems. Using hand tools designed in line with ergonomic principles may play an important role in reducing work-related health concerns significantly. Methods. Scientific databases (PubMed, EBSCOhost) and e-publishers were searched for articles from 1985 to 2015 using the following keywords: ‘hand tool’, ‘ergonomics’, ‘usability’ and ‘design’. After applying selection criteria to 614 articles, 58 articles related to the physical design of hand tools were selected. Results. Seventeen articles were related to hand tool improvement in the manufacturing sector. Musculoskeletal disorders were found to be the most frequently occurring work-related health problems. Most of the articles focused on product and qualitative variables for improvement in hand tools, while few articles considered human and task variables. Conclusions. Literature shows that hand tool improvement studies have been given less importance in low-income and lower-middle-income countries. However, some work of significance is reported in the agriculture sectors of these countries. Hence, it is concluded that ergonomic intervention in hand tools is much needed for those industries which employ traditional methods of working.     

Agricultural GHG emission and calorie intake nexus among different socioeconomic households of rural eastern India

A study was conducted to examine the interrelationships among socioeconomic factors, household consumption patterns, calorie intake and greenhouse gas emissions factors in rural eastern India based on household survey data. Findings indicated that higher monthly per capita incomes (12.1–80.1$) were associated with greater average calorie intakes (2021–2525 kcal d^?1). As estimated by the FEEDME model, in total 17.2% of the population was calorie malnourished with a regional disparity of 29.4–18.2% malnourishment. Greenhouse gas (GHG) emissions were calculated only on the basis of crop and livestock production and consumption. Rice accounted for the highest share of total GHG emissions, on average 82.6% on a production basis, which varied from 58.1% to 94.9% in regional basis. Rice contributed the greatest share (~?65% and 66.2%) in terms of both calories and GHG emissions (CO₂ eq y^?1), respectively, on a consumption basis. We conclude that extensive rice farming and increasing animal product consumption are dominant factors in the higher carbon footprint in this region and are likely to further increase with increase in per capita income. This study provides useful information to help for better crop planning and for fine-tuning food access policy, to reduce carbon footprint and calorie malnutrition.

     

Catalytic advancements in carbonaceous materials for bio-energy generation in microbial fuel cells: a review

Environmental Science and Pollution Research - Microbial fuel cells (MFCs) are a sustainable alternative for wastewater treatment and clean energy generation. The efficiency of the technology is...     

Bioleaching of heavy metals from sewage sludge: A review

During the treatment of sewage, a huge volume of sludge is generated, which is disposed of on land as soil fertilizer/conditioner due to the presence of nitrogen, phosphorus, potassium and other nutrients. However, the presence of toxic heavy metals and other toxic compounds in the sludge restricts its use as a fertilizer. Over the years, bioleaching has been developed as an environmentally friendly and cost-effective technology for the removal of heavy metals from the sludge. The present paper gives an overview of the various bioleaching studies carried out in different modes of operation. The various important aspects such as pathogen destruction, odor reduction and metal recovery from acidic leachate also have been discussed. Further, a detailed discussion was made on the various technical problems associated with the bioleaching process, which need to be addressed while developing the process on a larger scale.     

Dynamics of traffic noise in a tropical city Varanasi and its abatement through vegetation

Noise level monitoring and its reduction with different width and height of vegetation belt were studied in the Varanasi city. Noise level monitoring of the Varanasi city revealed the fact that area category A (without vegetation) was highly polluted as compare to area category B (with vegetation) having less fluctuation of traffic load. Four plant species Putranjeva roxburghi, Cestrum nocturnum, Hibiscus rosasinensis and Murraya peniculata were tested for noise reduction study at different frequencies. Experiment revealed the fact that H. rosasinensis reduced noise highest at both low and high frequencies (100-500 Hz, 22 dB and 2.5-6.3 KHz 26 dB), followed by M. peniculata (100-500 Hz, 18 dB and 2.5-6.3 KHz 20 dB), P. roxburghi (100-500 Hz 15 dB and 2.5-6.3 KHz 17 dB) and C. nocturnum (100-500 Hz 9 dB and 2.5-6.3 KHz 14 dB). Significance of vegetation belt in noise reduction was established with multiple regression models.     

Preparation of Alginic Acid and Metal Alginate from Algae and their Comparative Study

Alginic acid and metal alginates are prepared from fresh algae using extraction method. A FTIR spectrum indicates that alginic acid is converted into the metal alginate. Comparing calcium and cobalt alginates, asymmetric stretching of free carboxyl group of calcium alginate at 1630 cm⁻¹ is shifted to 1585 cm⁻¹ in cobalt alginate, due to the change of charge density, radius and atomic weight of the cation, creating a new environment around the carbonyl group. The strong exothermic peak of alginic acid in DSC thermogram indicates the decomposition of biopolymer, whereas strong exothermic peak of metal alginate in DSC thermogram attributed to the decomposition of biopolymer and formation of respective carbonate. Based on DSC study, the decomposition of cobalt alginate occurs at higher temperature comparing to those of sodium and calcium alginate, which may conclude into the higher stability of cobalt alginate. TGA results reveal that, cobalt alginate is more stable than calcium and sodium alginate at 300 °C temperature. Surface morphology (at same magnification), as well as porosity (%) and pore size distribution results change with metals present in different metal alginates.     

Heavy Metal Accumulation in Lichens from the Hetauda Industrial Area Narayani Zone Makwanpur District, Nepal

Lichen samples collected in and around Hetauda Industrial area,(HIA) Narayani zone, Makwanpur district, Nepal, were analyzedfor Cr, Ni, Pb, Zn, Ca, Mn, Fe, Si, and Al. The samples fromthe location inside the industrial area have higher levels ofmetal than the outside areas. Pyxine meissnerina growinginside the industrial area accumulated higher levels of all themetals analyzed.