长江流域污染负荷核算及来源分析

为研究长江流域污染物输出的时空变化特征和来源,将长江流域划分为114个排污单元,195个纳污河段,应用污染物“产生-排放-入河-消减-输出”模型,结合长江流域2004—2007年的社会经济、水文监测资料,利用朱坨、寸滩、宜昌、大通4个监测断面2004—2007年逐月的水量和水质数据,通过连续演算核算长江流域各河段的污染物输出过程,进行区间污染物输出平衡分析,估算出姚港断面污染物输出过程. 结果表明,各断面计算数据与实测数据相对误差不超过20%,模型及参数组合的可靠性较好. 点源对COD_Mn输出贡献较大,占56.4%;非点源对TN、TP的贡献大,分别占74.3%和92.1%. 从污染物输出分布来看,江西、四川、湖南、湖北四省占流域污染物输出总量的60%以上,贡献率较大,是流域污染物输出的重要区域.      

鸭河口水库水体总氮、总磷预测研究

根据鸭河口水库受纳点源及面源污染负荷情况，选择枯水期为预测时期，采用迪隆模型对鸭河口水库水体总氮、总磷进行预测研究，为鸭河口水库水质保持和富营养化防治提供基础数据。     

Paying for information: partial loads in central place foragers

Information about food sources can be crucial to the success of a foraging animal. We predict that this will influence foraging decisions by group-living foragers, which may sacrifice short-term foraging efficiency to collect information more frequently. This result emerges from a model of a central-place forager that can potentially receive information on newly available superior food sources at the central place. Such foragers are expected to return early from food sources, even with just partial loads, if information about the presence of sufficiently valuable food sources is likely to become available. Returning with an incomplete load implies that the forager is at that point not achieving the maximum possible food delivery rate. However, such partial loading can be more than compensated for by an earlier exploitation of a superior food source. Our model does not assume cooperative foraging and could thus be used to investigate this effect for any social central-place forager. We illustrate the approach using numerical calculations for honeybees and leafcutter ants, which do forage cooperatively. For these examples, however, our results indicate that reducing load confers minimal benefits in terms of receiving information. Moreover, the hypothesis that foragers reduce load to give information more quickly (rather than to receive it) fits empirical data from social insects better. Thus, we can conclude that in these two cases of social-insect foraging, efficient distribution of information by successful foragers may be more important than efficient collection of information by unsuccessful ones.     

多径流出口水稻田非点源污染试验研究及验证

为验证一种水稻田非点源污染原位观测方法的准确性和适应性,以北京市上庄镇某试验水稻田为例,应用原位观测方法分别在水稻田进水口附近（4#观测点）和出水口附近（2#观测点）安装水位计观测稻田水深,利用同步观测的降雨数据以及田内5个采样点定期采集的水质数据,进行非点源污染试验研究,探究基于同一块水稻田不同位置的水深观测数据是否对该方法的应用产生影响.结果表明:①晒田期间,由于4#观测点处于相对低洼的位置,其水深数据显示4 d的无水期,与实际晒田时间相符,而2#观测点水深数据显示10 d无水.②基于2#、4#观测点水深数据得到总蒸散发和渗漏损失、总径流量以及灌溉量的相对偏差分别为1.3%、1.0%、1.8%;应用原位观测方法估算出该水稻田的灌溉量在2 620~2 710 m3之间.③基于2#、4#观测点水深得到的TN、NH₄+-N、NO₃--N、TP、COD_Cr输出系数的相对偏差在0.8%~8.0%之间.研究显示:水位计安装位置对原位观测方法应用的影响不大,且主要体现在晒田期的水深观测,建议将水位计安装在稻田相对低洼处,以确保能够完整地监测水稻生长季的水深变化;应用该方法能准确识别水稻田的径流量和灌溉量,对具有多个径流出口的水稻田非点源污染试验具有较好的准确性和适应性.      

2016~2017年长荡湖流域河湖系统营养盐时空分布机制分析

以江苏省常州市长荡湖流域河湖系统为研究对象,基于野外监测与室内分析,探讨了水体氮、磷等营养盐的时空分布特征及其影响因素.结果表明,2016~2017年长荡湖流域河湖系统氮、磷污染突出,河流TN、NO₃^--N、NH₄⁺-N、TP和Chla平均质量浓度和高锰酸盐指数分别为（3.70±0.76）mg ·L^-1、（1.81±0.42）mg ·L^-1、（1.03±0.61）mg ·L^-1、（0.38±0.31）mg ·L^-1、（25.74±37.00）μg ·L^-1和（6.35±0.81）mg ·L^-1.河流总氮污染季节差异明显,表现为冬、春季劣于夏、秋季,河流总磷污染表现为秋、冬季劣于春、夏季,空间差异表现为北 > 西北 > 南 > 东部,河流处于中~高度富营养化状态.湖区TN、NO₃^--N、NH₄⁺-N、TP和Chla平均质量浓度和高锰酸盐指数分别为（2.25±0.94）mg ·L^-1、（0.98±0.47）mg ·L^-1、（0.19±0.14）mg ·L^-1、（0.11±0.03）mg ·L^-1、（18.71±8.76）μg ·L^-1和（4.59±1.09）mg ·L^-1,氮、磷质量浓度均超过Ⅲ类水标准,在空间上表现为从西部向东、南部降低的趋势,湖区处于轻~中度富营养状态.丹金溧漕河、通济河与薛埠河等河流是长荡湖流域河湖水系的主要污染物输送通道,丹金溧漕河干流输送氮、磷年通量最大,约为通济河和薛埠河年通量总和的10~12倍.长荡湖流域河流氮、磷污染同时受到农业面源污染物流失与城镇污水排放的影响.土地利用方式转变和大气沉降是导致长荡湖流域河湖系统氮、磷污染加剧的重要驱动因素.     

不同载荷下轮式车辆运输振动数据预测模型研究

目的为便于执行GJB 150A进行车辆运输振动试验,研究了不同载荷下运输振动数据预测模型。方法对模型的特点和适用范围进行了分析;运用该模型分析了不同载荷下运输振动试验的试验数据规律。结果随着载荷的增加,运输振动逐渐减小,能够反映轮式车辆不同载荷下的运输振动规律,给出了运用该模型对不同载荷下车辆运输振动数据的预测方法。结论该模型简单实用,在执行GJB 150A进行运输振动试验过程中,若直接引用标准中的数据,可以采用该模型对不同载荷下的运输振动试验数据进行预测。     

Assessment of the risk of excess sulfur input into terrestrial ecosystems of the Kola Peninsula

The tolerance of terrestrial ecosystems of the Kola Peninsula to atmospheric deposition of acid-forming sulfur compounds and the risk of their excess input into these ecosystems have been assessed on the basis of the critical load concept. The most sensitive ecosystems (critical sulfur load <400 equiv/ha per year) occupy 58% of the total area of the peninsula. These are mainly pine, spruce, and birch forests (including open and crooked birch forests) growing on podzols formed on sandy boulder-pebble glacial deposits. The zones of ecological risk cover the northwestern and central parts of the peninsula near the Pechenganikel and Severonikel combined works (20% of the total area).     

A national critical loads framework for atmospheric deposition effects assessment: IV. Model selection,applications, and critical loads mapping

The critical loads approach is emerging as an attractive means for evaluating the effects of atmospheric deposition on sensitive terrestrial and aquatic ecosystems. Various approaches are available for modeling ecosystem responses to deposition and for estimating critical load values. These approaches include empirical and statistical relationships, steady-state and simple process models, and integrated-effects models. For any given ecosystem, the most technically sophisticated approach will not necessarily be the most appropriate for all applications; identification of the most useful approach depends upon the degree of accuracy needed and upon data and computational requirements, biogeochemical processes being modeled, approaches used for representing model results on regional bases, and desired degree of spatial and temporal resolution. Different approaches are characterized by different levels of uncertainty. If the limitations of individual approaches are known, the user can determine whether an approach provides a reasonable basis for decision making. Several options, including point maps, grid maps, and ecoregional maps, are available for presenting model results in a regional context. These are discussed using hypothetical examples for choosing populations and damage limits. The research described in this article has been funded by the US Environmental Protection Agency. This document has been prepared at the EPA Environmental Research Laboratory in Corvallis, Oregon, through contract #68-C8-0006 with ManTech Environmental Technology, Inc., and Interagency Agreement #1824-B014-A7 with the U.S. Department of Energy and at Oak Ridge National Laboratory managed by Martin Marietta Energy Systems, Inc., under Contract DE-AC05-84OR21400 with the US Department of Energy. Environmental Sciences Division Publication No. 3904. It has been subjected to the agency’s peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

A national critical loads framework for atmospheric deposition effects assessment: II. Defining assessment end points,indicators, and functional subregions

The United States Environmental Protection Agency, with support from the US Department of Energy and the National Oceanographic and Atmospheric Administration, has been evaluating the feasibility of an effects-based (critical loads) approach to atmospheric pollutant regulation and abatement. The rationale used to develop three of the six steps in a flexible assessment framework (Strickland and others, 1992) is presented along with a discussion of a variety of implementation approaches and their ramifications. The rationale proposes that it is necessary to provide an explicit statement of the condition of the resource that is considered valuable (assessment end point) because: (1) individual ecosystem components may be more or less sensitive to deposition, (2) it is necessary to select indicators of ecosystem condition that can be objectively measured and that reflect changes in the quality of the assessment end point, and (3) acceptable status (i.e., value of indicator and quality of assessment end point at critical load) must be defined. The rationale also stresses the importance of defining the assessment regions and subregions to improve the analysis and understanding of the indicator response to deposition. Subregional definition can be based on a variety of criteria, including informed judgment or quantitative procedures. It also depends on the geographic scale at which exposure and effects models are accurate and on data availability, resolution, and quality. The research described in this article has been funded by the US Environmental Protection Agency. This document has been prepared at the EPA Environmental Research Laboratory in Corvallis, Oregon, through contract #68-C8-0006 with ManTech Environmental Technology, Inc., and Interagency Agreement #1824-B014-A7 with the US Department of Energy and at Oak Ridge National Laboratory managed by Martin Marietta Energy Systems, Inc., under Contract DE-AC05-84OR21400 with the US Department of Energy. Environmental Sciences Division Publication No. 3903. It has been subjected to the agency’s peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

ANALYSIS OF WASTE LOAD ALLOCATION PROCEDURES1

A study was made to analyze and modify procedures used for stream assimilation capacity and point source wasteload allocation calculations. This paper describes the sources and types of information collected and the analysis of alternative computation methods developed during the study. The calculation of stream assimilation capacity or Total Maximum Daily Load (TMDL), will depend upon assumed stream flows, quality standards, reaction rates, and modeling procedures. The “critical conditions” selected for TMDL calculations usually are low flows and warm temperatures. The complexity of water quality models used for TMDL and allocation calculations can range from simple, complete mixing to calibrated and verified mathematical models. A list of 20 wasteload allocation (WLA) methods was developed. Five of these WLA's were applied to an example stream to permit comparisons based on cost, equity, efficient use of stream assimilation capacity, and sensitivity to fundamental stream quality data. Based on insensitivity to data errors and current use by several states, the WLA method of “equal percent treatment” was preferable in the example stream.