考虑“水—土—能—碳”关联的我国工农业碳排放效率及减排潜力研究

考虑“水—土—能—碳”关联,本文将水土资源要素纳入投入变量,构建了我国工农业碳排放效率投入产出测度指标,运用考虑非期望产出的SBM-undesirable模型计算我国29个省份2004—2017年农业、工业部门碳排放效率,利用乘法逆转法计算碳减排潜力并对影响碳排放的投入产出因素进行分析。结果表明：研究期内我国整体农业、工业碳排放效率均呈波动下降趋势,各年的农业碳排放效率均高于工业碳排放效率,江苏、山东等7省份农业碳排放效率以及北京、天津工业碳排放效率最优;各省份农业、工业减排潜力和规模具有显著差异,山西、甘肃的农业、工业碳减排均具有较大潜力;我国绝大多数省份均存在农业、工业的资源能源投入冗余和非期望产出冗余,土地资源投入过剩是影响农业碳排放效率的最重要因素,水资源投入过剩是影响工业碳排放效率的最重要因素。碳排放效率较低省份应积极开展技术创新,发展低碳技术,提高水土资源和能源利用效率,减少碳排放。     

CONTROLLING NONPOINT EXTERNALITIES WITH INPUT RESTRICTIONS IN AN IRRIGATED RIVER BASIN1

ABSTRACT: Irrigated agriculture is a major nonpoint source of surface water quality degradation. Nonpoint source discharges can be controlled by either output taxes or restrictions, or input taxes or restrictions. The economic theory of externality control with taxes or restrictions on input use is developed. The effectiveness of alternative input control policies in improving surface water quality is demonstrated with a mathematical model of the agriculture and water quality in Washington State's Yakima River Basin. Water quality parameters considered were river nitrogen concentration, water temperature, and cropland soil losses. Producer and social abatement costs were the most important measures of policy effectiveness.     

WATER QUALITY MODEL CALIBRATION: A COMPARISON OF INPUT AND OUTPUT ERROR CRITERIA1

ABSTRACT: A common framework for the analysis of water resources systems is the input-parameter-output representation. The system, described by its parameters, is driven by inputs and responds with outputs. To calibrate (estimate the parameters) models of these systems requires data on both inputs and outputs, both of which are subject to random errors. When one is uncertain as to whether the predominant source of error is associated with inputs or outputs, uncertainty also exists as to the correct specification of a calibration criterion. This paper develops and analyzes two alternative least squares criteria for calibrating a numerical water quality model. The first criterion assumes that errors are associated with inputs while the second assumes output errors. Statistical properties of the resulting estimators are examined under conditions of pure input or output error and mixed error conditions from a theoretical perspective and then using simulated results from a series of Monte Carlo experiments.     

OPTIMUM DESIGN OF BORDER IRRIGATION SYSTEM1

ABSTRACT: Border irrigation systems like most of the other surface irrigation systems, do not need too much energy and special equipment. Thus, many farmers have used this system for a long time. On the other hand, design of surface irrigation systems including border irrigation requires many input parameters, and need intensive engineering calculations. The burden of these requirements probably led the users to experimental design of the systems with low efficiencies. However, accurate design and high quality optimization of the border irrigation system that can result in a highly efficient system is possible. In this study, an optimization model for border irrigation system is presented. The Hook Jeev's pattern search optimization method in conjunction with a general mathematical model of border irrigation is used to maximize the irrigation application efficiency. The border irrigation storage and distribution efficiencies, border slope and length, inflow rate, cutoff time, and the Manning's roughness coefficient are selected as constraints. The model is applied to field-measured data. The results show that it is possible to select a suitable combination of the border system's parameters (border's length, inflow rate, and cutoff time) to obtain a maximum application efficiency.     

ON THE USE OF SEEPAGE METERS TO ESTIMATE GROUNDWATER NUTRIENT LOADING TO LAKES1

Data from a study on East Lake Tohopekaliga, Florida, indicate that the seepage meter measurement method may often overestimate nutrient contributions to lakes. Nutrient loading data from this method and a method employing lakeside piezometer nutrient data and seepage meter flows were not comparable. Seepage nutrient loading from the meter and piezometer methods comprised 39 and 18 percent of the nitrogen budget and 38 and 9 percent of the phosphorus budget, respectively, for East Lake Tohopekaliga. In terms of water, groundwater seepage accounted for only 14 percent of the total input to the lake. It is felt that some of the past studies using the seepage meter method to estimate nutrient loading may be in error due to reasons related to the enclosure of lake sediments by the meter and the accompanying anaerobic conditions which quickly result.     

THE FLEXIBLE TOLERANCE METHOD FOR ESTIMATING HYDROLOGIC PARAMETERS IN THE ROOT ZONE1

In this study, a constrained minimization method, the flexible tolerance method, was used to solve the optimization problems for determining hydrologic parameters in the root zone: water uptake rate, spatial root distribution, infiltration rate, and evaporation. Synthetic soil moisture data were first generated using the Richards' equation and its associated initial and boundary conditions, and these data were then used for the inverse analyses. The results of inverse simulation indicate the following. If the soil moisture data contain no noise, the rate of estimated water uptake and spatial root distribution parameters are equal to the true values without using constraints. If there is noise in the observed data, constraints must be used to improve the quality of the estimate results. In the estimation of rainfall infiltration and surface evaporation, interpolation methods should be used to reduce the number of unknowns. A fewer number of variables can improve the quality of inversely estimated parameters. Simultaneous estimation of spatial root distribution and water uptake rate or estimation of evaporation and water uptake rate is possible. The method was used to estimate the water uptake rate, spatial root distribution, infiltration rate, and evaporation using long‐term soil moisture data collected from Nebraska's Sand Hills.     

DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1

ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model.     

A HYBRID OPTIMIZATION APPROACH TO THE ESTIMATION OF DISTRIBUTED PARAMETERS IN TWO-DIMENSIONAL CONFINED AQUIFERS1

ABSTRACT: In using non-linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated-Newton search technique to estimate groundwater parameters for a confined steady-state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near-correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near-global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated-Newton search technique.     

WATER QUALITY MODELING BY MONTE CARLO SIMULATION1

ABSTRACT: The applicability of the Monte Carlo simulation technique to water quality modeling is demonstrated with the aid of a simple Streeter-Phelps model. The model accounts for the stochasticity of the input parameters. Triangular probability density functions are shown to be useful in case insufficient information is available to define meaningful frequency distributions of input parameters. The model output is presented as probability distributions of stream quality parameters.     

Material flow analysis of paper in Korea. Part I. Data calculation model from the flow relationships between paper products

The flows of paper are analyzed throughout the papermaking processes, with the year 2007 and Korea defined as the system boundaries. In practice, the statistical data on the production, import and export of paper or pulp can be collected with relative ease from the government and industrial associations. However, the input data regarding the volumes of pulp and wastepaper used in different paper products, such as newsprint, printing papers, sanitary and household papers, specialty papers, and corrugating board base, are difficult to obtain because such information is generally kept confidential in the course of corporate operations.The production processes of paper products in Korea are modeled using information on raw materials, their compositions and production yields of products in order to identify and quantify the amounts of pulp and wastepaper used in each paper product. The material flows of paper are then analyzed based on the calculation model derived from the correlation of input and output flows between the individual processes throughout the entire paper lifecycle. Accuracy analysis using both mean absolute error (MAE) and mean absolute percent error (MAPE) is conducted to verify the amounts of pulp and wastepaper calculated from the proposed model against the volumes of domestically consumed pulp and wastepaper provided in the national statistics. Although the calculated values for the past (i.e., the 1980s and 1990s) differ to some degree from the statistical values, the data for the 2000s have a relatively higher level of accuracy, with the MAPE of the total pulp and recycling volume at 5.39% and 5.30%, respectively, thus validating the adequacy of the proposed modeling method. The proposed calculation model can be effectively used in the material flow analysis (MFA) of paper to reduce the burden of data collection and obtain relatively accurate results.     

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.     

A disaggregated emissions inventory for Taiwan with uses in hybrid input‐output life cycle analysis (IO‐LCA)

This paper reports on a life‐cycle analysis (LCA) of Taiwan's “agriculture and forestry”, “crude petroleum, coal and natural gas extraction” and “electricity generation” sectors, revealing for the first time Taiwan's CO₂ and CH₄ emissions inventories and matching Taiwan's input‐output sectors. Integrated hybrid input‐output life cycle analysis is used to disaggregate the electricity generation sector into nuclear, hydro, gas, oil and coal, and cogeneration. Results show that the fossil‐fuel‐related electricity sub‐sectors have higher CO₂ emissions intensity than the remaining sectors in the economy and that the “paddy rice” sector is Taiwan's most CH₄‐intensive sector, making rice cultivation an important source of CH₄ emissions. This work is vital to sound policy decisions concerning power generation, coal, and agriculture and forestry at the national level.     

Estimating tree bole volume using artificial neural network models for four species in Turkey

Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors.     

Prediction of Total Sediment Load in Sand‐Bed Rivers in Korea Using Lateral Distribution Method

A new method for numerically predicting the total sediment load in a river is proposed. The method can be used to predict the total sediment load with information on channel geometry and slope, flow, and bed materials. The conventional method uses a 1D approach that assumes the channel has a wide rectangular shape. However, the proposed method computes depth‐averaged velocity over the width and predicts the total sediment load based on the flow computations. The new method, therefore, is expected to predict better if the flow changes significantly in the lateral direction. The proposed method was applied to three large sand‐bed rivers in Korea, where information is available regarding suspended sediment. Five formulas were tested of use in making total sediment load computations, namely Engelund‐Hansen's, Ackers‐White's, Yang's, Brownlie's, and Karim's formulas. The predicted total sediment loads are compared not only with measured data but also with results calculated using the 1D approach. Discrepancy ratios between the predicted and measured total sediment loads are given and the results are discussed.     

PREDICTING FECAL COLIFORM BACTERIA LEVELS IN THE CHARLES RIVER,MASSACHUSETTS, USA1

In Massachusetts, the Charles River Watershed Association conducts a regular water quality monitoring and public notification program in the Charles River Basin during the recreational season to inform users of the river's health. This program has relied on laboratory analyses of river samples for fecal coliform bacteria levels, however, results are not available until at least 24 hours after sampling. To avoid the need for laboratory analyses, ordinary least squares (OLS) and logistic regression models were developed to predict fecal coliform bacteria concentrations and the probabilities of exceeding the Massachusetts secondary contact recreation standard for bacteria based on meteorological conditions and streamflow. The OLS models resulted in adjusted R²s ranging from 50 to 60 percent. An uncertainty analysis reveals that of the total variability of fecal coliform bacteria concentrations, 45 percent is explained by the OLS regression model, 15 percent is explained by both measurement and space sampling error, and 40 percent is explained by time sampling error. Higher accuracy in future bacteria forecasting models would likely result from reductions in laboratory measurement errors and improved sampling designs.     

Traffic noise and executive functioning in urban primary school children: The moderating role of gender

The objective of this study was to examine the relationships between noise and school children's executive functioning (EF). The study included 311 children (146 boys and 165 girls) aged 7–11 years, who lived in the center of Belgrade. Teachers rated children's EF on a standard scale evaluating their ability to work independently in a focused manner to achieve an end goal as well as children's ability to follow directions carefully. Residential noise exposure was estimated in three daytime intervals, one evening interval and two nighttime intervals in the middle of the streets where children lived. School noise exposure was measured on three different school days in front of children's schools. Socioeconomic status (mother's highest level of education and family income) was used as a statistical control. There were no significant main effects of ambient noise levels on EF, however, a significant interaction indicated adverse noise impacts on boy's EF. We discuss possible reasons for male EF vulnerability to noise.     

Prediction of global solar radiation using support vector machines

This article utilizes Support Vector Machines (SVM) for predicting global solar radiation (GSR) for Sharurha, a city in the southwest of Saudi Arabia. The SVM model was trained using measured air temperature and relative humidity. Measured data of 1812 values for the period from 1998–2002 were obtained. The measurement data of 1600 were used for training the SVM, and the remaining 212 were used for comparison between the measured and predicted values of GSR. The GSR values were predicted using the following four combinations of data sets: (i) Daily mean air temperature and day of the year as inputs, and global solar radiation as output; (ii) daily maximum air temperature and day of the year as inputs, and GSR as output; (iii) daily mean air temperature and relative humidity and day of the year as inputs, and GSR as output; and (iv) daily mean air temperature, day of the year, relative humidity, and previous day’s GSR as inputs, and GSR as output. The mean square error was found to be 0.0027, 0.0023, 0.0021, and 7.65 × 10^?4 for case (i), (ii,), (iii), and (iv) respectively, while the corresponding absolute mean percentage errors were 5.64, 5.08, 4.48, and 2.8%. Obtained results show that the SVM method is capable of predicting GSR from measured values of temperature and relative humidity.     

关于水中总α总β放射性测量监测方法改进的探讨

目前国内关于水中总α、总β放射性测量相关标准还不够细化,操作规程在一些细节方面还存在部分问题,为减小实验误差,提高实验准确度,对监测方法进行了改进.对前期水中总α、总β放射性监测的实验过程进行经验总结,对实验条件进行优化改进,通过回收率和准确度对实验结果进行验证.对水中总α、总β放射性监测实验过程中的水样放置时间、蒸发...     

APPLICATION OF PARAMETER OPTIMIZATION METHOD FOR CALIBRATING TANK MODEL1

ABSTRACT: Many automatic calibration processes have been proposed to efficiently calibrate the 16 parameters involved in the four‐layered tank model. The Multistart Powell and Stuffed Complex Evolution (SCE) methods are considered the best two procedures. Two rainfall events were designed to compare the performance and efficiency of these two methods. The first rainfall event is short term and the second designed for long term rainfall data collection. Both rainfall events include a lengthy no‐rainfall period. Two sets of upper and lower values for the search range were selected for the numerical tests. The results show that the Multistart Powell and SCE methods are able to obtain the true values for the 16 parameters with a sufficiently long no‐rainfall period after a rainfall event. In addition, by using two selected objective functions, one based on root mean square error and one based on root mean square relative error criteria, it is found that the no‐rainfall period lengths necessary to obtain the converged true values for the 16 parameters are roughly the same. The SCE method provides a more efficient search based on an appropriate preliminary search range. The Multistart Powell method, on the other hand, leads to more accurate search results when there is no suitable search range selected based on the parameter calibration experience.     

Economic and social effects analysis of mineral development in China and policy implications

Mineral development has contributed greatly to China's economic and social development. Many challenges remain, however, including environmental pollution and resource waste in practice, as well as a dearth of systematic theoretical research. The goal of this study is to analyze the economic and social effects of various mineral developments in China from diversified perspectives, so as to provide the basis for the formulation of China's mineral development policy. The input–output effects, industrial linkage effects and income distribution effects of different mining industries are quantitatively analyzed by adopting basic hypotheses of input–output economics, industrial linkage model and income distribution antitheses based on the latest available official data from China Statistical Yearbook from 2004 to 2010 and the 2007 Input–Output Table of China. The empirical results obtained in this study indicate that all mineral development industries, especially coal mining and washing, and petroleum and natural gas extraction industries, have given a strong impetus to the increase of China's fixed asset investment and GDP. Moreover, they have provided a large number of jobs, thereby alleviating ongoing employment pressure, and they have also played a positive role in promoting China's technology investment. The analysis of industrial linkages demonstrates that mining industries are basic to the national economy and produce a significant impetus to its downstream industries, but create weak pull effects in terms of national economic development. From the perspective of income distribution, mining industries play an important role in increasing China's fiscal revenue and per capital income. Hence, China's mineral development policy should (1) encourage additional investment in technology for exploration and development to insure sufficient supply and expand the input effects; (2) attract additional talent to work in remote regions; (3) optimize the industrial structure and promote the industrial transformation in resource regions; (4) adjust the interest distribution between the central and local governments to enable the local regions to become more self-sufficient; and (5) enhance the legal environment so that companies can more readily undertake their social responsibilities voluntarily.