ABSTRACT
In this research, the effect of using vermicompost on growth rate, fertility and characteristics of tomatoes has been studied. Four vermicompost: soil mixture were supplied with ratios of 1:1, 1:2, 1:3, and 1:4 and also four different beds were provided. Total of 24 small globe tomato plants were tested and in each bed combination, six tomato plants were embedded. Rate of growth and yielding of plants grown in each of four beds were investigated in two periods of 40 days and 90 days after planting. The results showed a significant rise in growth of tomato plants by increasing ratio of vermicompost combined with soil. Obviously, the plant was mostly appeared in the main stem of the plant and there was no significant enhancement in the number of leaves. The main stem diameter, height, the number of leaves per plant, and yielding of tomato plants obtained the highest rate in four tested beds after 40 days in vermicompost to soil ratios of 1:3, 1:1, 1:3, and 1:2, respectively. In aforementioned observations some changes were made after 90 days of testing and maximum yielding and height of tomato plants were obtained in 1:1 ratio. Vitamin C and total sugar content in tomatoes increase with using vermicompost. The maximum amount of vitamin C and total sugar, soluble solids, insoluble solids and total nitrites of fresh tomato were observed in ratios of 4:1, 4:1, 3:1, 2:1 and 3:1, correspondingly
ABSTRACT
Forecasting of municipal waste generation is a critical challenge for decision making and planning, because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste [MSW] streams analysis and accurate predictions of solid waste quantities generated. Due to dynamic and complexity of solid waste management system, models by artificial intelligence can be a useful solution of this problem. In this paper, a novel method of Forecasting MSW generation has been proposed. Here, support vector machine [SVM] as an intelligence tool combined with partial least square [PLS] as a feature selection tool was used to weekly prediction of MSW generated in Tehran, Iran. Weekly MSW generated in the period of 2008 to 2011 was used as input data for model learning. Moreover, Monte Carlo method was used to analyze uncertainty of the model results. Model performance evaluated and compared by statistical indices of Relative Mean Errors, Root Mean Squared Errors, Mean Absolute Relative Error and coefficient of determination. Comparison of SVM and PLS-SVM model showed PLS-SVM is superior to SVM model in predictive ability and calculation time saving. Also, results demonstrate which PLS could successfully identify the complex nonlinearity and correlations among input variables and minimize them. The uncertainty analysis also verified that the PLS-SVM model had more robustness than SVM and had a lower sensitivity to change of input variables
Subject(s)
Forecasting , Waste Management/methods , Decision MakingABSTRACT
Present study determines not only the total amounts of metals [Cr, Cu and Pb] in superficial agricultural soil of Sistan area in Eastern Iran, but also the chemical partitioning of these elements in seven statistically selected cases. The analysis was run for local soil, soil treated by non-contaminated organic, compost and chemical fertilizers as well as soil treated by metal-contaminated fertilizers. The sampling campaign was done in Zabol University research farm in 2009. The grab samples were taken from seven different cases, the chemical partitioning analysis was performed and metallic concentrations were detected using FAAS. It may be concluded that the bioaccessibility of metals Cu and Cr would be increased in case of imposed contamination where the soil is treated with all three kinds of fertilizers. Although a relatively similar distribution pattern is seen between anthropogenic and geopogenic portions of bulk concentration in all three kinds of fertilizers, chemical fertilizer seems to manifest a more risky condition. According to the results achieved by cluster analysis, a close correlation exists between Cu and Cr behavior which may be attributed to the geological texture of the study area. In accordance with the results gained by partitioning analysis, IpOLL index values also show contaminated chemical fertilizer as the most risky case for all three metals in comparison with others
ABSTRACT
Tabriz petrochemical complex is located in the northwest of Iran. Major products of this industry include raw plastics like, polyethylene, polystyrene, acrylonitrile, butadiene, styrene, etc. Sources of waste generation include service units, health and cure units, water, power, steam and industrial processes units. In this study, different types of solid waste including hazardous and non hazardous solid wastes were investigated separately. The aim of the study was to focus on the management of the industrial wastes in order to minimize the adverse environmental impacts. In the first stage, locating map and dispersion limits were prepared. Then, the types and amounts of industrial waste generated in were evaluated by an inventory and inspection. Wastes were classified according to Environmental Protection Agency and Basel Standards and subsequently hazards of different types were investigated. The waste management of TPC is quite complex because of the different types of waste and their pollution. In some cases recycling/reuse of waste is the best option, but treatment and disposal are also necessary tools. In this study, using different sources and references, generally petrochemical sources, various solid waste management practices were investigated and the best options were selected. Some wastes should be treated before land filling and some of them should be reused or recycled. In the case of solid waste optimization, source reduction ways were recommended as well as prior incineration system was modified