An in situ compost biofilter was established for the treatment of odors from biostabilization processing of municipal solid waste. The concentrations of total volatile organic compounds (VOCs) in odors and their components were measured. Biofilter media was characterized in terms of total carbon (TC), total nitrogen (TN), total phosphorus (TP), organic matter (OM), pH value and determination of bacterial colony structure. Gas chromatography–mass spectrometry (GC–MS) analysis showed that the main components of the produced gas were benzene, toluene, ethylbenzene and xylene (BTEX) along with other alkanes, alkenes, terpenes, and sulphur compounds. The compost biofilter had remarkable removal ability for alkylated benzenes (>80%), but poor removal for terpenes (~30%). Total VOC concentrations in odors during the biostabilization process period ranged from 0.7 to 87 ppmv, and the VOC removal efficiency of the biofilter varied from 20% to 95%. After about 140 days operation, TN, TC, TP and OM in compost were kept almost stable, but the dissolved N, NH4–N and NO3–N experienced an increase of 44.5%, 56.2% and 76.3%, respectively. Dissolved P decreased by 27.3%. The pH value experienced an increase in the early period and finally varied from 7.38 to 8.08. Results of bacterial colony in packing material indicated that bacteria and mold colony counts increased, but yeasts and actinomyces decreased along with biofilter operation, which were respectively, 3.7, 3.4, 0.04 and 0.07 times of their initial values. 相似文献
Characterization of the molecular properties of soluble microbial products (SMP) is critical for understanding the membrane filtration and fouling mechanisms in anaerobic and aerobic membrane bioreactors (AnMBR & MBR). In this study, the distributions of the absolute molecular weight and intrinsic viscosity of SMP polysaccharides from an AnMBR were effectively determined by a high performance size exclusion chromatography (HPSEC) that was coupled with the refractive index (RI), diode array UV (DAUV), right and low angle light scattering (LS), and viscometer (Vis) detectors. Based on the tetra-detector HPSEC determined absolute molecular weights and intrinsic viscosity, a universal calibration relationship for the SMP polysaccharides was developed and the molecular conformations, average molecular weights, and hydrodynamic sizes of the SMP polysaccharides were also explored. Two factors which can be derived from the tetra-detector HPSEC analysis were proposed for the characterization of the viscous and osmotic pressure properties of the SMP polysaccharides. In addition, it was also extrapolated how to analyze the resistance characteristics of the concentration polarization layers formed in membrane filtration based on the molecular properties determined by the tetra-detector HPSEC analysis.
Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin. 相似文献