The process performance of simultaneous anaerobic sulfide and nitrate removal was studied. The results showed that the process held a high sulfide and nitrate removal loading rate of 3.73kg/(m3 x d) and 0.80kg/(m3 x d), respectively, under steady state. It was capable of tolerating high influent substrate concentration (580mg/L and 110mg/L) with the optimum substrate concentration of 280mg/L and 67.5mg/L. It was capable of tolerating short hydraulic retention time (HRT) with the optimum HRT of 0.13d. Careful operation was needed when HRT was shortened because the process performance was deteriorated abruptly.
Anaerobiosis ; Bioreactors ; Nitrates ; isolation & purification ; Sulfides ; isolation & purification ; Time Factors ; Waste Disposal, Fluid ; methods ; Water Pollutants, Chemical ; isolation & purification ; Water Purification ; methods

Water hyacinth (Eichhornia crassipes (Mart.) Solms) is a prolific free floating aquatic macrohpyte found in tropical and subtropical parts of the earth. The effects of pollutants from textile wastewater on the anatomy of the plant were studied. Water hyacinth exhibits hydrophytic adaptations which include reduced epidermis cells lacking cuticle in most cases, presence of large air spaces (7 approximately 50 microm), reduced vascular tissue and absorbing structures. Textile waste significantly affected the size of root cells. The presence of raphide crystals was noted in parenchyma cells of various organs in treated plants.
Eichhornia ; anatomy & histology ; growth & development ; Industrial Waste ; Plant Roots ; anatomy & histology ; Rhizome ; anatomy & histology ; Textile Industry ; Water Pollutants, Chemical

Heavy metals, such as cadmium, copper, lead, chromium and mercury, are important environmental pollutants, particularly in areas with high anthropogenic pressure. Their presence in the atmosphere, soil and water, even in traces can cause serious problems to all organisms, and heavy metal bioaccumulation in the food chain especially can be highly dangerous to human health. Heavy metals enter the human body mainly through two routes namely: inhalation and ingestion, ingestion being the main route of exposure to these elements in human population. Heavy metals intake by human populations through food chain has been reported in many countries. Soil threshold for heavy metal toxicity is an important factor affecting soil environmental capacity of heavy metal and determines heavy metal cumulative loading limits. For soil-plant system, heavy metal toxicity threshold is the highest permissible content in the soil (total or bioavailable concentration) that does not pose any phytotoxic effects or heavy metals in the edible parts of the crops does not exceed food hygiene standards. Factors affecting the thresholds of dietary toxicity of heavy metal in soil-crop system include: soil type which includes soil pH, organic matter content, clay mineral and other soil chemical and biochemical properties; and crop species or cultivars regulated by genetic basis for heavy metal transport and accumulation in plants. In addition, the interactions of soil-plant root-microbes play important roles in regulating heavy metal movement from soil to the edible parts of crops. Agronomic practices such as fertilizer and water managements as well as crop rotation system can affect bioavailability and crop accumulation of heavy metals, thus influencing the thresholds for assessing dietary toxicity of heavy metals in the food chain. This paper reviews the phytotoxic effects and bioaccumulation of heavy metals in vegetables and food crops and assesses soil heavy metal thresholds for potential dietary toxicity.
Biological Availability ; Biological Transport, Active ; Food Contamination ; analysis ; prevention & control ; Humans ; Metals, Heavy ; analysis ; pharmacokinetics ; toxicity ; Plants, Edible ; drug effects ; growth & development ; metabolism ; toxicity ; Soil Pollutants ; analysis ; pharmacokinetics ; toxicity ; Vegetables ; drug effects ; growth & development ; metabolism ; toxicity

OBJECTIVEDuring present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.

METHODSFive uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.

RESULTSWithin the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner.

CONCLUSIONApart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.

Bioreactors ; Neural Networks (Computer) ; Oxidation-Reduction ; Sulfates ; chemistry ; Sulfides ; chemistry ; Time Factors ; Waste Disposal, Fluid ; methods