Trace Element Contaminants in Sediments From the Noaa National Status and Trends Programme Compared to Data From Throughout the World

The National Oceanic and Atmospheric Administration's (NOAA) National Status and Trends (NS&T) Programme has analyzed samples of surface sediment collected at almost 300 coastal and estuarine sites throughout the United States since 1984. the overall distributions for concentrations of each element are approximately lognormal allowing a definition of geometric means and of “high” concentrations as those exceeding the mean plus one standard deviation of the lognormal distribution. Those mean and “high” concentrations should be useful for comparing the NS&T data set and with other reports on sediment contamination. A world-wide data set, constructed from data in the literature, was summarized in an identical manner. It was found, after excluding locally extreme values, that the NS&T and world-wide data sets yielded very similar mean and “high” concentrations for Cu, Ni, Pb and Zn. Chromium was the sole element showing higher summary statistics in the NS&T data set, a result attributed to NS&T sampling in areas of naturally (not contaminated) elevated Cr content. It remains undetermined, however, why the world-wide data set, even with exclusion of “hot spots”, yielded higher means and “highs” for Cd, Hg, As, and Ag.     

SYNTHESIS OF FLOW-DURATION CURVES FOR UNREGULATED STREAMS IN NEW HAMPSHIRE1

ABSTRACT: Flow-duration curves are concise pictures of flow variability at a point on a stream, and provide essential information for all water-resource planning. In New Hampshire, useful estimates of flow-duration curves for ungaged points on unregulated streams can be made using only information readily available from contour maps: 1) area of the basin above the point of interest; and 2) either the measured mean basin elevation or the elevations of the highest and lowest points in the basin. Measured or estimated mean basin elevation is then used in regression equations to estimate mean flow QC and the flow exceeded 95% of the time, Q₉₅. QC is assumed to occur at the 27% exceedance frequency. Q₀₂, Q₀₅, and Q₃₀ are estimated as multiples of QC. Equations are provided for calculating 95% confidence intervals for future estimates using the method. The dependence of mean flow on elevation is due to positive vertical precipitation gradients and negative vertical evapotranspiration gradients. The dependence of Q₉₅ on elevation appears to be due largely to the fact that it rains more often, that snowmelt takes longer, and that evapotranspiration is reduced at higher elevations.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

城市水源地水质指标年内分配的不均匀变化

以昆明市主要供水水源（松华坝水库）的总氮、总磷、氮氨、高锰酸盐指数为研究对象,借鉴水文界经验,应用集中度、不均匀系数和调节系数概念,计算并分析比较了集中度、不均匀系数和调节系数在水质指标年内分配规律中的适用性。研究表明：集中度、不均匀系数和调节系数可从不同侧面反映了水质变化的年内分布特征,且3个指标在不同时间尺度间具有明显的一致性,因此,在水质监测中只需要应用每个季度的监测数据就能反映出水质变化的年内分配不均匀性。     

不同后处理器对DMCC发动机PM排放的影响

在一台整车排放达到国Ⅲ标准的YC4D140电控单体泵增压中冷柴油机上加装甲醇喷射系统,改成柴油甲醇组合燃烧(DMCC)发动机.使用AVL 415S烟度计和DMS 500快速颗粒物光谱仪研究不同后处理器对DMCC发动机颗粒物(PM)排放的影响.试验结果表明,各工况下单独CDPF、DOC+CDPF基本可以消除发动机产生的干炭烟.DOC+CDPF后处理方式与单独CDPF相比,颗粒物捕集效率明显提高,且前者对颗粒物中核态部分的捕集效果更加突出.甲醇掺烧后,炭烟和颗粒物几何平均直径降低.经CDPF和DOC+CDPF处理,415S烟度计测得的炭烟进一步降低;DMS颗粒物光谱仪显示的颗粒物粒径,因其中核态部分大部被氧化捕集而仅余较大几何平均直径的部分.小负荷时经CDPF和DOC+CDPF处理,颗粒物的几何平均直径会增大,大负荷时则相反.各工况下相比于CDPF,DOC+CDPF对核态颗粒物捕集更有效.     

Density estimation using ranked-set sampling data

The ranked-set sampling (RSS) is applicable in practical problems where the variable of interest for an observed item is costly or time-consuming but the ranking of a set of items according to the variable can be easily done without actual measurement. In the context of RSS, the need for density estimation arises in certain statistical procedures. The density estimation also has its own interest. In this article, we develop a method for the density estimation using RSS data. We derive the properties of the resulted density estimate and compare it with its counterpart in simple random sampling (SRS). It is shown that the density estimate using RSS data provides a better estimate of the density than the usual density estimate using SRS data. The density estimate developed in this article can well serve various purposes in the context of RSS.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Trace Element Contaminants in Sediments From the Noaa National Status and Trends Programme Compared to Data From Throughout the World

The National Oceanic and Atmospheric Administration's (NOAA) National Status and Trends (NS&T) Programme has analyzed samples of surface sediment collected at almost 300 coastal and estuarine sites throughout the United States since 1984. the overall distributions for concentrations of each element are approximately lognormal allowing a definition of geometric means and of “high” concentrations as those exceeding the mean plus one standard deviation of the lognormal distribution. Those mean and “high” concentrations should be useful for comparing the NS&T data set and with other reports on sediment contamination. A world-wide data set, constructed from data in the literature, was summarized in an identical manner. It was found, after excluding locally extreme values, that the NS&T and world-wide data sets yielded very similar mean and “high” concentrations for Cu, Ni, Pb and Zn. Chromium was the sole element showing higher summary statistics in the NS&T data set, a result attributed to NS&T sampling in areas of naturally (not contaminated) elevated Cr content. It remains undetermined, however, why the world-wide data set, even with exclusion of “hot spots”, yielded higher means and “highs” for Cd, Hg, As, and Ag.     

Tradeoffs between forestry resource and conservation values under alternate policy regimes: A spatial analysis of the western Canadian boreal plains

An important element of resource management and conservation is an understanding of the tradeoffs between marketed products, such as timber, and measures of environmental quality, such as biodiversity. In this paper, we develop an integrated economic-ecological spatial optimization model that we then apply to evaluate alternate forest policies on a 560,000 km² study region of managed boreal forest in Alberta and British Columbia, Canada. The integrated model incorporates dynamic forest sector harvesting, current levels of oil and gas sector development, coarse-filter or habitat-based old forest indicators, a set of empirical forest bird abundance models, and statistical models of the natural and current fire regimes. Using our integrated model, economic tradeoff curves, or production possibility frontiers, are developed to illustrate the cost of achieving coarse-filter targets by a set time (50 years) within a 100-year time horizon. We found levels of ecological indicators and economic returns from the timber industry could both be increased if spatial constraints imposed by the current policy environment were relaxed; other factors being equal, this implies current policy should be revised. We explore the production possibility frontier's relationship to the range of natural variation of old forest habitat, and show how this range can be used to guide choices of preferred locations along the frontier. We also show that coarse-filter constraints on the abundance of certain habitat elements are sufficient to satisfy some fine-filter objectives, expressed as the predicted abundances of various species of songbirds.     

Examining Flood Frequency Distributions in the Midwest U.S.1

Villarini, Gabriele, James A. Smith, Mary Lynn Baeck, and Witold F. Krajewski, 2011. Examining Flood Frequency Distributions in the Midwest U.S. Journal of the American Water Resources Association (JAWRA) 47(3):447‐463. DOI: 10.1111/j.1752‐1688.2011.00540.x Abstract: Annual maximum peak discharge time series from 196 stream gage stations with a record of at least 75 years from the Midwest United States is examined to study flood peak distributions from a regional point of view. The focus of this study is to evaluate: (1) “mixtures” of flood peak distributions, (2) upper tail and scaling properties of the flood peak distributions, and (3) presence of temporal nonstationarities in the flood peak records. Warm season convective systems are responsible for some of the largest floods in the area, in particular in Nebraska, Kansas, and Iowa. Spring events associated with snowmelt and rain‐on‐snow are common in the northern part of the study domain. Nonparametric tests are used to investigate the presence of abrupt and slowly varying changes. Change‐points rather than monotonic trends are responsible for most violations of the stationarity assumption. The abrupt changes in flood peaks can be associated with anthropogenic changes, such as changes in land use/land cover, agricultural practice, and construction of dams. The trend analyses do not suggest an increase in the flood peak distribution due to anthropogenic climate change. Examination of the upper tail and scaling properties of the flood peak distributions are examined by means of the location, scale, and shape parameters of the Generalized Extreme Value distribution.