玉米幼苗根系形态对芘污染的响应

采用盆栽试验探讨了玉米（Zea mays L.）根系形态学参数（根质量长度、根质量面积、根质量体积、根平均直径及不同根直径等级分布）对不同浓度芘污染胁迫的响应。较低浓度芘可适当地刺激玉米的生长,高浓度芘处理抑制了玉米的生长,并且抑制作用随芘处理浓度的提高而增强;芘对玉米根系的影响要大于对茎叶的影响。不同浓度芘处理下玉米根系形态学参数发生了显著的变化,与对照相比,芘处理下根直径显著降低,根质量长度、根质量面积和根质量体积显著增大,且随芘处理浓度的提高其效应更显著。     

氯丹和灭蚁灵污染场地土壤对陆生生物的毒性效应

为向污染土壤的监测、生态毒理诊断及修复提供方法和数据,以氯丹、灭蚁灵污染场地土壤为供试土壤,测定了氯丹、灭蚁灵复合污染对蚯蚓的急性毒性效应以及对小麦、小白菜、玉米和水稻4种植物种子发芽率和根伸长抑制率的影响.结果表明,蚯蚓对本场地污染响应十分敏感,处理d 3高浓度组开始出现死亡,此后死亡率随污染物浓度增大而上升,d 14部分高浓度组死亡率达到100%;同一浓度下,氯丹和灭蚁灵对4种植物种子根伸长抑制率均显著大于对种子发芽的抑制率,植物的根生长比种子发芽对有机污染物的毒性更为敏感.4种植物种子对污染场地土壤的敏感性为小麦>小白菜>水稻>玉米.氯丹和灭蚁灵对蚯蚓的毒性要大于对这几种植物的毒性,蚯蚓对氯丹和灭蚁灵的响应更加敏感.因此,蚯蚓作为指示生物,其急性毒性试验可作为氯丹和灭蚁灵污染场地的诊断指标,诊断周期以14 d为宜.     

微囊藻毒素对水稻根系生长和抗氧化系统的影响

以水稻幼苗为材料,研究了不同质量浓度(1、100、1 000、3 000μg·L-1)微囊藻毒素(MCs)在胁迫期和恢复期对水稻幼苗根系生长、吸收活力、抗氧化系统的影响以及MCs在水稻根部的积累.结果表明,胁迫处理7 d,MCs在水稻根部的积累量与MCs质量浓度呈正相关.1μg·L-1MCs处理促进了根系生长,根系活力上升,过氧化氢酶(CAT)活性上升有效维持H2O2于正常水平;100μg·L-1MCs处理下,根系生长受抑,根系活力下降,CAT无显著变化;在高质量浓度(1 000μg·L-1、3 000μg·L-1)MCs处理下,不仅根系生长受抑、根系活力下降,且CAT活性受抑,H2O2大量积累,膜质过氧化加剧.相比胁迫期,恢复7 d后各处理组水稻根系MCs的积累量均低于胁迫期.100μg·L-1MCs处理组根系各生长指标、根系活力、丙二醛(MDA)、H2O2和CAT的变幅减小,优于胁迫期,显示出一定程度的恢复,而1 000μg·L-1和3 000μg·L-1MCs处理组,根系生长与根系活力均低于胁迫期,氧化胁迫进一步加剧,表明高质量浓度(1 000μg·L-1和3 000μg·L-1)MCs对水稻根系造成的伤害不可逆.     

基于神经网络分类器的城市污水处理厂水力冲击预警系统

提出了一套基于神经网络分类器的城市污水处理厂水力负荷冲击预警系统,以期对进水水量骤增现象进行提前1天的预报,使污水处理厂可根据预报结果提前采取水力冲击防护措施,从而保证各单元的平稳运行.根据进水水量的涨幅将某污水处理厂12年日进水水量监测数据分为"常规"和"冲击"两类,重点对"冲击"数据进行提前1天的预测,并采用冲击漏报率、冲击误报率和报准率对模型的预测精度进行评价;同时,基于同样的建模方法和不同的训练、验证样本建立了N(1)、N(2)和N(3)3个平行模型,以对模型的鲁棒性和建模方法的可重复性进行考察.结果显示,3个模型对2010年、2011年和2012年3年测试样本的预测效果良好,冲击漏报率和报准率两项指标数值均较为稳定,分别在0~0.167和0.981~0.995之间浮动,冲击误报率虽然在数值上的浮动较大,最低为0.143,最高为0.500,平均为0.310,但仍在工程上的可承受范围内.该结果表明,本研究基于神经网络分类器所建立的3个神经网络模型预测精度高、鲁棒性好,显示出良好的性能,有望为污水处理厂水力冲击防护工作提供有力参考.     

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。     

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.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

水稻耐氨固氮菌施用效应研究

早、晚两季的田间试验结果表明,在电脑推荐施N水平上,耐氨固氮菌的增产效果不显著;在低N情况下(每公顷减纯N37.5kg),耐氨固氮菌的增产效果达显著水平,早、晚稻分别增产8.41％和7.21％。15N示踪盆栽试验结果表明,耐氨固氮菌对根系生长有明显的促进作用,在秧苗期明显增加秧苗对肥料15N的吸收．禾苗分蘖盛期则增强了禾苗根系的吸氮能力。