扩展阴极型膜电解法处理含铜废水的数学模型

采用均匀设计法,研究离子膜扩展阴极电解法对低浓度含铜废水的处理效果,得到了反应条件与回收效果之间的线性模型,并比较了线性预测模型和BP网络的预测效果。结果表明,线性模型有较强的定性分析作用但预测能力不强,而BP网络具有很强的预测能力。两法结合,具有很强的理论和实践指导意义。模型预测结果和实验结果均表明,扩展阴极型膜电解法可带来很好的社会效益和经济效益。     

Target loads of atmospheric sulfur deposition for the protection and recovery of acid-sensitive streams in the Southern Blue Ridge Province

An important tool in the evaluation of acidification damage to aquatic and terrestrial ecosystems is the critical load (CL), which represents the steady-state level of acidic deposition below which ecological damage would not be expected to occur, according to current scientific understanding. A deposition load intended to be protective of a specified resource condition at a particular point in time is generally called a target load (TL). The CL or TL for protection of aquatic biota is generally based on maintaining surface water acid neutralizing capacity (ANC) at an acceptable level. This study included calibration and application of the watershed model MAGIC (Model of Acidification of Groundwater in Catchments) to estimate the target sulfur (S) deposition load for the protection of aquatic resources at several future points in time in 66 generally acid-sensitive watersheds in the southern Blue Ridge province of North Carolina and two adjoining states. Potential future change in nitrogen leaching is not considered. Estimated TLs for S deposition ranged from zero (ecological objective not attainable by the specified point in time) to values many times greater than current S deposition depending on the selected site, ANC endpoint, and evaluation year. For some sites, one or more of the selected target ANC critical levels (0, 20, 50, 100μeq/L) could not be achieved by the year 2100 even if S deposition was reduced to zero and maintained at that level throughout the simulation. Many of these highly sensitive streams were simulated by the model to have had preindustrial ANC below some of these target values. For other sites, the watershed soils contained sufficiently large buffering capacity that even very high sustained levels of atmospheric S deposition would not reduce stream ANC below common damage thresholds.     

On accounting for sustainable development and accounting for the environment

Economic sustainability or intergenerational equity entails maintaining social well-being by decisions about investments in different types of assets. Under certain conditions, consumption can be sustained by depleting resources, or various kinds of natural capital, while building up other kinds of capital. Theoretically, the choices involve the use of a set of accounting prices. The question becomes one of finding and implementing accounting prices that express the roles of the various capital goods in achieving the objective of the economy.Hartwick's rule holds that an economy can be sustained if the value of the total, net investment in the economy, evaluated at those accounting prices, is zero. The rule applies to a special, abstract economic model which expresses a social objective different from the discounted-utilitarian objective on which national accounting is based. Different objectives give rise to different accounting prices. Because the prices may not be right, the zero net-investment rule using available national-accounting prices cannot generate a condition for sustaining an economy.Still, environmental accounting is a tool which, used prudently, can make an important contribution to social decision-making. This paper expands upon these ideas by discussing the incorporation of natural resource and intangible environmental costs and benefits into green accounting at the firm as well as the economy level. Common techniques of mine valuation and standard corporate accounting are the bases for this extension to the valuation of and accounting for decisions concerning the environment.     

NUMERICAL MODELING OF SALTWATER INTRUSION IN THE NORTHERN GUAM LENS1

ABSTRACT This paper deals with the application of a two-dimensional, saltwater intrusion model to the aquifer in Northern Guam. The model used finite element theory and the Galerkin, weighted-residual technique as its basis. The Northern Guam lens was discretized into 299 linear, triangular elements and 189 nodes. The model was calibrated using 1978 hydrologic data. The output of the model was compared with measured water levels in six observation wells. The calibrated values of permeability and porosity were then used to verify the model using 1979 data. A calibrated and verified model can be used to make an infinite variety of management and planning studies. In this study, three applications are provided that would be considered typical management runs. Steady state runs were made to compare the four conditions of no pumping, 1978 pumping levels, twice 1978 pumping levels, and five times 1978 pumping levels. The water levels due to these conditions are shown in plan and in cross sections of the aquifer. The effect of zero recharge to the aquifer is next demonstrated for the pumping levels existing during 1978. The final run shows how long the aquifer takes to reach steady state when the pumping rate is increased from the 1978 pumping level to twice that value. The program can be used for numerous other studies for management and planning purposes.     

GENERATION OF DAILY PRECIPITATION OVER AN AREA1

ABSTRACT: A model was developed of the periodic-stochastic structure of daily precipitation over an area. The model is based ona multivariate normal distribution. The square roots of daily precipitation at a point were found to approximate a sample from a univariate normal distribution that had been truncated at zero. The zero daily precipitation amounts were considered negative amounts of unknown quantity. The multivariate normal distribution was used to describe the variation of daily precipitation over an area. The periodic fluctuations of the model parameters were described with Fourier series. The model was tested using data from two areas of different precipitation characteristics. Data generated with the model contained many of the statistical characteristics observed in the historical data.     

SUSPENSOIDS IN NEW YORK CITY'S DRINKING WATER RESERVOIRS: TURBIDITY APPORTIONMENT1

ABSTRACT: The technique of i ndividual p article a nalysis conducted by s canning electron microscopy interfaced with a utomated X ‐ray microanalysis (IPA/SAX) was used to characterize suspended particulate matter in New York City's drinking water reservoirs and their tributaries. The study covered a two year period and involved analyses of more than 300 samples. The particle cross sectional area per unit volume (PAV), or area concentration, was measured to account for the observed turbidity, a representation of light scattering property of the studied medium. A simple linear model with a nearly zero intercept was able to explain more than 85 percent of the variation in the measured turbidity. Moreover, the particle assemblage was categorized into generic particle types with distinctive geochemical or geological origins. Thus, PAV compositions in terms of particle types could be apportioned into turbidity components based on the model. Inorganic tripton, dominated by aluminosilicate (clay) and silicate of nonbiological nature, was found to be the major turbidity causing constituent in most cases. With the exception of one reservoir where organic detritus was significant, the predicted inorganic particle turbidity agreed with the measured turbidity within experimental error.     

Numerical and experimental investigation of state of health of Li-ion battery

ABSTRACT

Estimation of State of Health (SoH) of Lithium-ion (Li-ion) battery is essential to predict the lifespan of batteries of an electric vehicle (EV). The efficient prediction of battery health indicates to the effective and safe operation of EV. However, delivering an effective and accurate method for the estimation of SoH in the real condition is truly a challenging task. The present study proposed a holistic procedure of combining both experimental and numerical investigations to conduct the fundamental study on coupled mechanical-electrochemical behavior of Li-ion battery. The proposed investigation highlighted the effect of stress on the capacity of the battery, considering capacity fade as an equivalent parameter to its health for real-time estimation of SoH. Finally, a simple model of Artificial Neural Network (ANN) is provided, which shows the linear dependency of stress with the SoH. The results obtained from the ANN model are validated with a Linear Regression (LR) model for a better understanding of the inspection. The predicted value of mean Square Error (MSE) and R square error in the ANN training model are found to be 0.000309 and 0.849687, respectively. Whereas for the test model, these predicted values are found to be 0.000438 and 0.819347, respectively.     

Generalized Nondimensional Depth‐Discharge Rating Curves Tested on Florida Streamflow1

Abstract: A discharge rating is a relationship between stage and discharge at a specific point in a river stream or lake outlet structure. Rating curves are useful for interpolating and perhaps extrapolating flow measurements and for use directly in storage routing models. However, rating data and stations are limited. A generalized nondimensional mathematical expression that describes the rating relation of depth and discharge has been developed and tested against observations from 46 stations in West‐Central Florida. Three approaches were tested in sequence to select the best fit. The proposed model is a log‐linear equation with zero intercept and a slope that fits more than 50% of the stations were analyzed. The model is normalized by the depth and discharge values at 10% exceedance from data published by the U.S. Geological Survey. For ungauged applications, Q₁₀ and d₁₀ were derived from a relationship shown to be reasonably well correlated to the watershed drainage area. The average relative error for this parameter set shows that for the flow range up to the Q₁₀ discharge, better than 30% agreement with the USGS rating data can be expected for about 50% of the stations. Further analysis is required to determine why so many stations exhibit such similar behavior and to identify the criteria or parameters governing the differences.     

Understanding of groundwater salinity using statistical modeling in a small tropical island,East Malaysia

This paper presents an understanding of groundwater salinity by identification of effective factors using chemometric methods (cluster analysis and multiple linear regressions) in Manukan Island, Sabah. Local groundwater and environmental properties were used to explore the effective factors of groundwater salinity. Cluster analysis showed salinity and chloride illustrated the highest similarities. Electrical conductivity and total dissolved solids were also grouped in the same cluster. Seawater is the only chloride source in groundwater of Manukan Island demonstrated an indication of seawater mixing in freshwater. It is an effect of upward movement of the seawater by pumping activities. Precipitation and evapotranspiration (environmental condition) with hydraulic heads were clustered together to show that they also influence salinity concentration in groundwater. Multiple linear regressions showed descending order of the factors from chloride (the largest contribution) to evapotranspiration (the smallest contribution) and illustrated the contribution to groundwater salinity in Manukan Island. The integrated results using chemometric methods have provided a way to identify the effective factors on groundwater salinity. This similar approach and resulting equation can be applied in other small tropical islands with alike hydrogeological condition and limited information available for a better understanding of its groundwater salinity.     

Estimated nationwide effects of pesticide spray drift on terrestrial habitats in the Netherlands

This study estimated the potential effects of pesticide drift on terrestrial ecosystems outside target areas, for the Dutch situation. A series of field trials was conducted to estimate the effects of drift on different species groups at different distances from a treated plot for different categories of pesticide: herbicides, fungicides and insecticides. Measurements of the pesticide drift deposition resulting from standard agricultural practice were used to model deposition outside the treated area. These data were then combined with national statistics on cropland and pesticide use to assess the ecological effects of pesticide drift for the Netherlands as a whole. Three scenarios were considered: the recent past (1998), the present (2005) and an optimised scenario based on 'best available practice' (2010). In the recent past the impact of herbicide drift on sensitive life stages non-target vascular plants is estimated to have exceeded the 50% effect level on 59% of adjacent linear landscape elements such as ditch banks and hedgerows. For the impact of insecticides and fungicides on non-target insects and fungi this 50% effect figure was 29% and 28% of linear elements, respectively. In the present situation, with (narrow) unsprayed buffer zones and other measures in place, these percentages are down to 41% for herbicides, 21% for insecticides and 14% for fungicides. In the optimised scenario, with a greater buffer width of 2.25m for potatoes (compared to 1.50m in 2005) and 1m for other crops (compared to 0.25 and 0.5m in 2005) and 'best available practice', these percentages can be cut to zero. In natural areas located within farming regions the 10% effect level can be reduced from 31% of such areas (1998) to 0% under conditions of 'best available practice'.     

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.     

Estimating plant-available nitrogen release from manures, composts, and specialty products

Recent adoption of national rules for organic crop production have stimulated greater interest in meeting crop N needs using manures, composts, and other organic materials. This study was designed to provide data to support Extension recommendations for organic amendments. Specifically, our objectives were to (i) measure decomposition and N released from fresh and composted amendments and (ii) evaluate the performance of the model DECOMPOSITION, a relatively simple N mineralization/immobilization model, as a predictor of N availability. Amendment samples were aerobically incubated in moist soil in the laboratory at 22 degrees C for 70 d to determine decomposition and plant-available nitrogen (PAN) (n = 44), and they were applied preplant to a sweet corn crop to determine PAN via fertilizer N equivalency (n = 37). Well-composted materials (n = 14) had a single decomposition rate, averaging 0.003 d(-1). For uncomposted materials, decomposition was rapid (>0.01 d(-1)) for the first 10 to 30 d. The laboratory incubation and the full-season PAN determination in the field gave similar estimates of PAN across amendments. The linear regression equation for lab PAN vs. field PAN had a slope not different from one and a y-intercept not different than zero. Much of the PAN released from amendments was recovered in the first 30 d. Field and laboratory measurements of PAN were strongly related to PAN estimated by DECOMPOSITION (r(2) > 0.7). Modeled PAN values were typically higher than observed PAN, particularly for amendments exhibiting high initial NH(4)-N concentrations or rapid decomposition. Based on our findings, we recommend that guidance publications for manure and compost utilization include short-term (28-d) decomposition and PAN estimates that can be useful to both modelers and growers.     

MODEL DEVELOPMENT FOR CONJUNCTIVE USE STUDY OF THE SAN JACINTO BASIN,CALIFORNIA1

ABSTRACT: A two-layered confined-unconfined numerical model for flow and mass transport is developed for the San Jacinto Basin. The model structure is determined by the geological structure of the Basin and model parameters are calibrated using 20 years of historical records. The total number of historical head observations used for the flow model calibration is 1,117 and the total number of the estimated parameters is 91. The two-layered transport model is also calibrated using historical water quality records. Sensitivity analysis of the flow model shows that only 68 parameters (out of a total of 91) are relatively sensitive and reliable. However, the unreliable parameters (23 of them) are found to be insensitive and thus not significant to the prediction and management of conjunctive use of surface water and ground water. The developed flow model has been used to study the two proposed artificial recharge scenarios for the San Jacinto Basin. We have found that during a relatively dry condition, an artificial recharge rate of 80 acre-ft/day can be achieved during the recharge period October through January. However, for a relatively wet condition, only 80 percent of the proposed rate can be effectively stored in the Basin during these months.     

Integrated model for renewable energy planning in Turkey

All economic sectors are associated with energy use; therefore, government organizations aim to supply sustainable energy for human needs and economic growth. In particular, increased environmental concerns of the public in Turkey have impacted policymaking for renewable energy (RE) management in Turkey. The primary objective for RE sources of the Turkish Ministry of Energy is to ensure that 30% of the share of electricity production is from RE resources in 2023. In this paper, the integrated multi-objective, multi-period linear programming model is presented to determine effective allocation of RE supply for seven different geographical regions in Turkey for the period of 2017 to 2024. The integrated model consists of two different stages. The first stage involves qualitative evaluations of RE sources for seven geographical regions. Analytical Hierarchy Process (AHP) is applied to determine criteria priorities and overall ratings of geographical regions across determined criteria for RE sources are computed. The second stage of the integrated model consists of a multi-objective, multi-period linear programming model. The proposed multi-objective linear programming model is coded in MPL (Mathematical Programming Language) and solved using the GUROBI 5.1.0 solver. The output of the integrated model presents the total supply amount of RE sources for geographical regions in planning period. The ε-constraints method is applied to compute the total supply amount of RE from geographical regions for the period of 2017 to 2024. In this study, a systematic decision-making model is generated to allocate renewable energy sources to the geographical regions. The presented model integrates qualitative evaluations and quantitative parameters of different geographical regions to determine the optimal supply amount of RE. The obtained results are consistent with the potential quantities of RE alternatives in geographical regions, regional specifications, and social requirements.     

Health effects from the nuclear fuel cycle and other sources in perspective

The data base for each process of the nuclear fuel cycle has been updated as a part of the Committee on Nuclear and Alternative Energy Systems (CONAES) at Brookhaven National Laboratory (BNL). The BNL Energy System Network Simulator (ESNS) was modified to accommodate the new data, and methodology was developed for estimating population dose and health effects resulting from atmosphere releases of radioactive materials from the nuclear fuel cycle.Estimates of population dose and health effects were made using these new CONAES emission data and the new model for three scenarios out to the year 2000: (1) no reprocessing; (2) reprocessing, 1-year cooling; and (3) reprocessing, 5-year cooling. Results indicate that radon emissions from mining and milling of uranium bearing ores will have greater impacts than any other component in the open nuclear fuel cycle. The estimated number of health effects will depend, to a large extent, on the lung model mechanism assumed to induce cancer; i.e., either the smeared or the unsmeared model. The smear model and the linear relationship predict for scenario 1, 630; for scenario 2, 949; and for scenario 3, 854 lung cancers, respectively, using the new CONAES data.Epidemiologic data from six United States counties were correlated using a new statistical model (described in the text) in order to test the validity of the lung model and the linear relationship. Results do not support the high lung cancer correlations expected from the unsmear model and the linear relationship; therefore, it is concluded that low-dose mechanisms may be different from those developed from high-dose data. The best place to look for effects of low-dose radiation may be the less developed countries because of a reduction in the noise level caused by chemical pollutants.     

VALUE OF WATER IN IRRIGATED CROP PRODUCTION USING DERIVED DEMAND FUNCTIONS: A CASE STUDY OF SOUTH SASKATCHEWAN RIVER IRRIGATION DISTRICT1

ABSTRACT: Water abundance has led most North American societies to use water freely without priorizing its use. As water scarcity becomes reality in the southern part of Saskatchewan, planners and managers of water require information about the value of water in irrigation, as well as in alternative uses. In this study, the value of water to the producer in irrigation is developed both for the short and long run. The basis of this imputation is a derived demand function for water using linear programming. Water demand was bound to be inelastic at lower prices, and highly elastic at higher prices. The short-run value of water varied between $0.44 and $127.82 (1986 dollars) per acre-foot for different levels of product prices. However, the long-run value was estimated between zero and $1.59 per acre-foot of water.     

THE EFFECTS OF RISK AND RELIABILITY ON OPTIMAL RESERVOIR DESIGN1

A chance-constrained linear programming model, which utilizes multiple linear decision rules and is useful for river basin planning, is used to evaluate the effects of risk and reliability on optimal reservoir design. Streamflow forecasts or predictions can be explicitly included in the linear program. The risk associated with the predictions is included in the model through the use of cumulative distribution functions (CDF) of streamflows which are conditioned on the predictions. A multiple-purpose reservoir on the Gunpowder River in Maryland is used to illustrate the effectiveness of the model. In order to provide the decision makers with complete and useful information, trade-off curves relating minimum reservoir capacity (a surrogate for dam costs), water supply and flood control targets, and the reliability of achieving the targets are developed. The trade-off curves may enhance the decision maker's ability to select the best dam capacity, considering technological and financial constraints as well as the trade-offs between targets, risks, and costs.     

A simple method for partitioning total solar radiation into diffuse/direct components in the United States

Solar radiation is a major sustainable and clean energy resource, and use of solar radiation is expected to increase. The utilization efficiency of solar energy varies with the relative proportions of the direct and diffuse components that compose total solar radiation and with the slope and aspect of the irradiated surface. The purpose of this paper is to develop a simple method for estimating diffuse and direct solar radiation at sites with observation of only total solar radiation. An existing model for estimating diffuse radiation, i.e., a linear relationship between the diffuse fraction (the ratio of diffuse radiation to total solar radiation) and the clearness index (the ratio of total solar radiation to extraterrestrial radiation), is applied to 7 sites across the continental United States with observations of diffuse and total radiation. The linear model shows good monthly performance. The model parameters (slope and interception) show a strong seasonal pattern that exhibits small variation across the 7 sites; therefore, the average values of the two monthly parameters may be used for estimating diffuse radiation for other locations with observations of total radiation.     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.     

GROUND-WATER RECHARGE THROUGH ACTWE SAND DUNES IN NORTHWESTERN NEVADA1

ABSTRACT: Most water-resouree investigations in semiarid basins of the Great Basin in western North America conclude that ground-water recharge from direct precipitation on the valley floor is negligible. However, many of these basins contain large areas covered by unvegetated, active sand dunes that may act as conduits for ground-water recharge. The potential for this previously undocumented recharge was investigated in an area covered by sand dunes in Desert Valley, northwestern Nevada, using a deep percolation model. The model uses daily measurements of precipitation and temperature th determine energy and moisture balances, from which estimates of long-term mean annual recharge are made. For the study area, the model calculated a mean annual recharge rate of as much as 1.3 inches per year, or 17 percent of the long-term mean precipitation. Model simulations also indicate that recharge would be virtually zero if the study area were covered by vegetation rather than dunes.