A Biological Assessment of Streams in the Eastern United States Using a Predictive Model for Macroinvertebrate Assemblages1

Abstract: A predictive model (RIVPACS‐type) for benthic macroinvertebrates was constructed to assess the biological condition of 1,087 streams sampled throughout the eastern United States from 1993‐2003 as part of the U.S. Geological Survey’s National Water‐Quality Assessment Program. A subset of 338 sites was designated as reference quality, 28 of which were withheld from model calibration and used to independently evaluate model precision and accuracy. The ratio of observed (O) to expected (E) taxa richness was used as a continuous measure of biological condition, and sites with O/E values <0.8 were classified as biologically degraded. Spatiotemporal variability of O/E values was evaluated with repeated annual and within‐site samples at reference sites. Values of O/E were regressed on a measure of urbanization in three regions and compared among streams in different land‐use settings. The model accurately predicted the expected taxa at validation sites with high precision (SD = 0.11). Within‐site spatial variability in O/E values was much larger than annual and among‐site variation at reference sites and was likely caused by environmental differences among sampled reaches. Values of O/E were significantly correlated with basin road density in the Boston, Massachusetts (p < 0.001), Birmingham, Alabama (p = 0.002), and Green Bay, Wisconsin (p = 0.034) metropolitan areas, but the strength of the relations varied among regions. Urban streams were more depleted of taxa than streams in other land‐use settings, but larger networks of riparian forest appeared to mediate biological degradation. Taxa that occurred less frequently than predicted by the model were those known to be generally intolerant of a variety of anthropogenic stressors.     

多因素耦合条件下硫化矿自燃神经网络动态预测模型研究

硫化矿石自燃是多种因素、多场耦合综合作用的结果,是一典型的非线性问题。笔者应用人工神经网络技术,以Matlab软件为平台,通过现场调查和理论分析,建立了矿石含硫量、通风强度、环境温度3因素与硫化矿石自燃之间的预测模型;通过数据样本学习与部分现场监测数据相结合进行模拟,研究表明预测数据与实测结果基本吻合,误差控制在10%以内,取得了较好的效果。该研究为预防硫化矿石自燃提供一个新的思路和方法,具有一定的理论意义和应用价值。     

室内安全生态学研究

从人们最基本的生存需求之一:安全性需要和室内微型生态系统的可持续发展角度出发,对人们所面临的室内环境问题进行总结归纳;在安全生态学意义下对室内环境问题的研究重新进行了分类;在室内环境问题研究方面提出加强室内安全生态学研究的建议。对室内安全生态学研究提出一些新的观点和看法;给出了室内安全生态学的架构体系;较为全面地归纳了室内各方面的环境问题;初步探讨了如何构建室内安全生态学评价的理论体系。     

CHICAGO METROPOLITAN FLOODWATER MANAGEMENT PLAN1

ABSTRACT: The Chicago Metropolitan Floodwater Management Plan is a cooperative planning program under Public Law 566 of the 83rd Congress (The Watershed Protection and Flood Prevention Act). The planning effort was jointly sponsored by the U.S. Department of Agriculture, Soil Conservation Service, and the Metropolitan Sanitary District of Greater Chicago. The project is unique in that it studies a 1260 square mile (3266 sq. kilometer) watershed, which is approximately 35 percent urbanized and contains approximately 7.5 million people. At present, approximately 4.4 percent or 330,600 people live in a floodplain. It is presently estimated that 80,000 acres (32,000 ha.) of the study area are subject to flooding with a current average annual damage estimated at approximately $10 million. The Plan which has been developed to reduce or eliminate these damages is divided into six separate watershed plans, and has been developed through extensive use of local citizen watershed steering committees. The paper discusses the planning process, public participation and implementation both at an overall river basin level and watershed case study level.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。     

The Role of Headwater Streams in Downstream Water Quality1

Abstract: Knowledge of headwater influences on the water‐quality and flow conditions of downstream waters is essential to water‐resource management at all governmental levels; this includes recent court decisions on the jurisdiction of the Federal Clean Water Act (CWA) over upland areas that contribute to larger downstream water bodies. We review current watershed research and use a water‐quality model to investigate headwater influences on downstream receiving waters. Our evaluations demonstrate the intrinsic connections of headwaters to landscape processes and downstream waters through their influence on the supply, transport, and fate of water and solutes in watersheds. Hydrological processes in headwater catchments control the recharge of subsurface water stores, flow paths, and residence times of water throughout landscapes. The dynamic coupling of hydrological and biogeochemical processes in upland streams further controls the chemical form, timing, and longitudinal distances of solute transport to downstream waters. We apply the spatially explicit, mass‐balance watershed model SPARROW to consider transport and transformations of water and nutrients throughout stream networks in the northeastern United States. We simulate fluxes of nitrogen, a primary nutrient that is a water‐quality concern for acidification of streams and lakes and eutrophication of coastal waters, and refine the model structure to include literature observations of nitrogen removal in streams and lakes. We quantify nitrogen transport from headwaters to downstream navigable waters, where headwaters are defined within the model as first‐order, perennial streams that include flow and nitrogen contributions from smaller, intermittent and ephemeral streams. We find that first‐order headwaters contribute approximately 70% of the mean‐annual water volume and 65% of the nitrogen flux in second‐order streams. Their contributions to mean water volume and nitrogen flux decline only marginally to about 55% and 40% in fourth‐ and higher‐order rivers that include navigable waters and their tributaries. These results underscore the profound influence that headwater areas have on shaping downstream water quantity and water quality. The results have relevance to water‐resource management and regulatory decisions and potentially broaden understanding of the spatial extent of Federal CWA jurisdiction in U.S. waters.     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Resolution and Analysis of Spatial Variations and Patterns in an Urban Lake with Rapid Profiling Instrumentation

Rapid response vertical profiling instrumentation was used to document spatial variability and patterns in a small urban lake, Onondaga Lake, associated with multiple drivers. Paired profiles of temperature, specific conductance (SC), turbidity (T_n), fluorometric chlorophyll a (Chl_f), and nitrate nitrogen (NO₃^?) were collected at >30 fixed locations (a “gridding”) weekly, over the spring to fall interval of several years. These gridding data are analyzed (1) to characterize phytoplankton (Chl_f) patchiness in the lake's upper waters, (2) to establish the representativeness of a single long‐term site for monitoring lake‐wide conditions, and (3) to resolve spatial patterns of multiple tracers imparted by buoyancy effects of inflows. Multiple buoyancy signatures were resolved, including overflows from less dense inflows, and interflows to metalimnetic depths and underflows to the bottom from the plunging of more dense inputs. Three different metrics had utility as tracers in depicting the buoyancy signatures as follows: (1) SC, for salinity‐enriched tributaries and the more dilute river that receives the lake's outflow, (2) T_n, for the tributaries during runoff events, and (3) NO₃^?, for the effluent of a domestic waste treatment facility and from the addition of NO₃^? solution to control methyl mercury. The plunging inflow phenomenon, which frequently prevailed, has important management implications.