Metal distribution in incineration residues of municipal solid waste (MSW) in Japan

This study aimed to identify distribution of metals and the influential factors on metal concentrations in incineration residues. Bottom ash and fly ash were sampled from 19 stoker and seven fluidized bed incinerators, which were selected to have a variety of furnace capacity, furnace temperature, and input waste. In the results, shredded bulky waste in input waste increased the concentration of some metals, such as Cd and Pb, and the effect was confirmed by analysis of shredded bulky waste. During MSW incineration, lithophilic metals such as Fe, Cu, Cr, and Al remained mainly in the bottom ash while Cd volatilized from the furnace and condensed to the fly ash. About two thirds of Pb and Zn was found in the bottom ash despite their high volatility. Finally, based on the results obtained in this study, the amount of metal in incineration residues of MSW was calculated and the loss of metal was estimated in terms of mass and money. A considerable amount of metal was found to be lost as waste material by landfilling of incineration residues.     

EPR characterization of the catalytic activity of clays for PCE removal by gamma-radiation induced by acid and thermal treatments

Clays from tidal flat sediments showed efficient catalytic activity in the decomposition of PCE by gamma-radiation. The highest PCE removal of 98.6% was obtained with clays heated to 700 degrees C after acid treatment. The improved catalytic activity was identified by electron paramagnetic resonance (EPR) spectroscopy. The EPR spectra of clays were significantly changed by the acid and thermal treatments. The intensity of a narrow signal at g=2 (signal III) was decreased with increasing thermal treatment temperature and this increased the PCE removal efficiency. The acid treatment completely removed a broad signal at g=2 (signal II), decreased the intensity of signal III, and improved the gamma-radiation treatment of PCE.     

A dynamic compartment model for assessing the transfer of radionuclide deposited onto flooded rice-fields

A dynamic compartment model has been studied to estimate the transfer of radionuclides deposited onto flooded rice-fields after an accidental release. In the model, a surface water compartment and a direct shoot-base absorption from the surface water to the rice-plant, which are major features discriminating the present model from the existing model, has been introduced to account for the flooded condition of rice-fields. The model has been applied to the deposition experiments of 137Cs on rice-fields that were performed at three different times to simulate the deposition before transplanting (May 2) and during the growth of the rice (June 1 and August 12), respectively. In the case of the deposition of May 2, the root-uptake is the most predominant process for transferring 137Cs to the rice-body and grain. When the radionuclide is applied just after transplanting (June 1), the activity of the body is controlled by the shoot-base absorption and the activity of the grain by the root-uptake. The deposition just before ear-emergence (August 12) shows that the shoot-base absorption contributes entirely to the increase of both the activities of the body and grain. The model prediction agrees within one or two factors with the experimental results obtained for a respective deposition experiment.     

Odor and gas release from anaerobic treatment lagoons for swine manure

Odor and gas release from anaerobic lagoons for treating swine waste affect air quality in neighboring communities but rates of release are not well documented. A buoyant convective flux chamber (BCFC) was used to determine the effect of lagoon loading rate on measured odor and gas releases from two primary lagoons at a simulated wind speed of 1.0 m s(-1). Concentrations of ammonia (NH3), hydrogen sulfide (H2S), carbon dioxide (CO2), sulfur dioxide (SO2), and nitric oxide (NO) in 50-L air samples were measured. A panel of human subjects, whose sensitivity was verified with a certified reference odorant, evaluated odor concentration, intensity, and hedonic tone. Geometric mean odor concentrations of BCFC inlet and outlet samples and of downwind berm samples were 168 +/- 44 (mean +/- 95% confidence interval), 262 +/- 60, and 114 +/- 38 OU(E) m(-3) (OU(E), European odor unit, equivalent to 123 microg n-butanol), respectively. The overall geometric mean odor release was 2.3 +/- 1.5 OU(E) s(-1) m(-2) (1.5 +/- 0.9 OU s(-1) m(-2)). The live mass specific geometric mean odor release was 13.5 OU(E) s(-1) AU(-1) (animal unit = 500 kg live body mass). Overall mean NH3, H2S, CO2 and SO2 releases were 101 +/- 24, 5.7 +/- 2.0, 852 +/- 307, and 0.5 +/- 0.4 microg s(-1) m(-2), respectively. Nitric oxide was not detected. Odor concentrations were directly proportional to H2S and CO2 concentrations and odor intensity, and inversely proportional to hedonic tone and SO2 concentration (P < 0.05). Releases of NH3, H2S, and CO2 were directly proportional (P < 0.05) to volatile solids loading rate (VSLR).     

Selective leaching of valuable metals from waste printed circuit boards

This study was carried out to recover valuable metals from the printed circuit boards (PCBs) of waste computers. PCB samples were crushed to smaller than 1 mm by a shredder and initially separated into 30% conducting and 70% nonconducting materials by an electrostatic separator. The conducting materials, which contained the valuable metals, were then used as the feed material for magnetic separation, where it was found that 42% of the conducting materials were magnetic and 58% were nonmagnetic. Leaching of the nonmagnetic component using 2 M H2SO4 and 0.2 M H2O2 at 85 degrees C for 12 hr resulted in greater than 95% extraction of Cu, Fe, Zn, Ni, and Al. Au and Ag were extracted at 40 degrees C with a leaching solution of 0.2 M (NH4)2S2O3, 0.02 M CuSO4, and 0.4 M NH4OH, which resulted in recovery of more than 95% of the Au within 48 hr and 100% of the Ag within 24 hr. The residues were next reacted with a 2 M NaCl solution to leach out Pb, which took place within 2 hr at room temperature.     

Improvement of thermal hydrolysis rate of dichloroacetic acid using alcohol

Dichloroacetic acid (DCAA) is produced during the oxidation of trichloroethylene. It is also produced in drinking water treatment as a disinfection by-product. DCAA is a problem material, because of its toxicity. The objective of this research is to find the final products and the reaction pathway of the DCAA decomposition by hydrolysis, and to increase the hydrolysis rate. The removal of both chlorine atoms in DCAA structure was achieved with hydrolysis at around 75 degrees C, and the final products were oxalic acid and glycolic acid. The reaction pathway was the production of oxalic acid and glycolic acid from two glyoxylic acid molecules by Cannizzaro reaction after the glyoxylic acid production from dechlorination of DCAA with hydrolysis. The hydrolysis rate of DCAA was increased with the use of 90% ethanol solution as solvent. The activation energy of DCAA was about 80 kJ/mol in it, while it was around 105 kJ/mol in water.     

Social media and disasters: a functional framework for social media use in disaster planning,response, and research

A comprehensive review of online, official, and scientific literature was carried out in 2012–13 to develop a framework of disaster social media. This framework can be used to facilitate the creation of disaster social media tools, the formulation of disaster social media implementation processes, and the scientific study of disaster social media effects. Disaster social media users in the framework include communities, government, individuals, organisations, and media outlets. Fifteen distinct disaster social media uses were identified, ranging from preparing and receiving disaster preparedness information and warnings and signalling and detecting disasters prior to an event to (re)connecting community members following a disaster. The framework illustrates that a variety of entities may utilise and produce disaster social media content. Consequently, disaster social media use can be conceptualised as occurring at a number of levels, even within the same disaster. Suggestions are provided on how the proposed framework can inform future disaster social media development and research.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.