过硫酸盐去除石灰土介质中汽油BTEX的效果及乙醇的影响

为了认识岩溶区石灰土含水介质中化学氧化联合生物降解去除燃油苯系物的效果,以汽油中的苯、甲苯、乙苯和二甲苯(BTEX)作为污染物,通过批实验研究过硫酸盐(PS)联合硝酸盐去除汽油BTEX的效果,并认识不同浓度乙醇(EtOH)存在时带来的影响及化学氧化与反硝化联合修复的可能性.结果表明,在35 d内各组的BTEX去除率均达到91.00%以上,PS化学氧化能有效地去除BTEX.在不含EtOH及含EtOH初始浓度为500和5000 mg·L^-1的情况下,BTEX浓度在第65 d时分别为未检出、0.226 mg·L^-1、0.243 mg·L^-1,去除率分别为99.98%、99.00%、98.70%,其中,苯的去除率分别为99.98%、97.01%、93.32%.随着EtOH浓度的增加,苯的去除受到抑制,EtOH对BTEX的化学氧化具有阻碍作用.石灰土介质中高铁含量对PS具有活化能力,高含量有机质能促进PS分解,并导致pH值回升,出现反硝化作用和硫酸盐还原作用,有利于生物降解作用的恢复.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

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.     

污泥含炭吸附剂对挥发性有机废气吸附实验研究

研究了污泥含炭吸附剂对挥发性有机污染物的吸附特性。结果表明,污泥含炭吸附剂对苯系物的吸附为典型的物理吸附,其吸附甲苯等温线的类型系优惠型吸附等温线,表明具有良好的吸附能力;在吸附反应温度为20℃,气体流量为500 mL/m in(停留时间为0.424 s),甲苯浓度为2 700 mg/m3时,甲苯的饱和吸附容量为150.0 mg/g;同时,研究表明污泥含炭吸附剂对苯系物的饱和吸附容量和吸附强弱次序为二甲苯甲苯苯。结果表明污泥含炭吸附剂适合对中低浓度有机废气的吸附净化。     

A NEW LOOK AT FLOOD RISK DETERMINATION1

ABSTRACT: The probability distributions of annual peak flows used in flood risk analysis quantify the risk that a design flood will be exceeded. But the parameters of these distributions are themselves to a degree uncertain and this uncertainty increases the risk that the flood protection provided will in fact prove to be inadequate. The increase in flood risk due to parameter uncertainty is small when a fairly long record of data is available and the annual flood peaks are serially independent, which is the standard assumption in flood frequency analysis. But standard tests for serial independence are insensitive to the type of grouping of high and low values in a time series, which is measured by the Hurst coefficient. This grouping increases the parameter uncertainty considerably. A study of 49 annual peak flow series for Canadian rivers shows that many have a high Hurst coefficient. The corresponding increase in flood risk due to parameter uncertainty is shown to be substantial even for rivers with a long record, and therefore should not be neglected. The paper presents a method of rationally combining parameter uncertainty due to serial correlation, and the stochastic variability of peak flows in a single risk assessment. In addition, a relatively simple time series model that is capable of reproducing the observed serial correlation of flood peaks is presented.     

ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

MFAM模型在河流水质污染模拟及预测中的应用

文中以时间序列分析为基础，介绍了均值生成函数这一崭新概念，并且经成份因子提取分析推导建立了模拟序列的数字模型（简记为MFAM），经对黄河下游花园口断面的1988-1989年实测水质污染指标溶解氧（DO），氨氧，化学耗氧量（COD），五日生化需氧量（BOD5）等序列模拟，结果表明MFAM模型能较好地模拟河流水质污染指标的变化趋势，拟合平均误差只有5.2-6.4%，MFAM模型应用于预测1990-1991年水质污染指标变化，结果表明预测精度达85%以上，文中最后得出结论：MFAM模型应用于河流污染模拟和预测，是完全可行且十分方便。     

Evaluation of tidal marsh restoration: Comparison of selected macroinvertebrate populations on a restored impounded valley marsh and an unimpounded valley marsh within the same salt marsh system in Connecticut,USA

Macroinvertebrates were examined on an impounded valley marsh in Stonington, Connecticut, that has changed from aTypha-dominated system to one with typical salt-marsh vegetation during 13 years following the reintroduction of tidal exchange. Animal populations on this restored impounded marsh were evaluated by comparing them with populations on a nearby unimpounded valley marsh of roughly the same size. Populations of the high marsh snail,Melampus bidentatus Say, were quantitatively sampled along transects that extended from the water-marsh edge to the upland; those of the ribbed mussel,Geukensia demissa Dillwyn, were sampled in low marsh areas on transects along the banks of creeks and mosquito ditches. The occurrence of other marsh invertebrates also was documented, but their abundance was not measured. The mean density ofMelampus was 332±39.6 SE/m² on the restored impounded marsh and 712±56.0 SE/m² on the unimpounded marsh. However, since snails were larger on the restored impounded marsh, the difference in snail biomass was less pronounced than the difference in snail density. MeanMelampus biomass was 4.96±0.52 SE g dry wt/m² on the restored impounded marsh and 6.96±0.52 SE g dry wt/m² on the unimpounded marsh. On the two marshes, snail density and biomass varied in relation to plant cover and other factors. The density and biomass ofGeukensia at the edge of the marsh were comparable on the restored impounded and unimpounded marshes. Mean mussel densities ranged from 80 to 240/m² and mean mussel biomass varied from 24.8–64.8 g dry wt/m² in different low marsh areas. In contrast, below the impoundment dike, meanGeukensia density was 1100±96.4 SE/m² and meanGeukensia biomass was 303.6±33.28 SE g dry wt/m². A consideration of all available evidence leads to the conclusion that the impounded marsh is in an advanced phase of restoration.     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY1

ABSTRACT: Fractional differencing is a tool for modeling time series which have long-term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box-Jenkins ARIMA(p, d, q) models to take nonintegral values; they permit the simultaneous modeling of the long-term and short-term behavior of an observed time series. This paper discusses the usefulness of fractional differencing to time-series modeling, with emphasis on hydrologic applications. A methodology for fitting fractionally differenced ARIMA models is described, and examples are presented.