Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

基于MODIS地面温度数据的成都市热岛时空变化

研究利用2000、2005和2010年逐月的MODIS/Terra和MODIS/Aqua卫星的地表温度数据产品,通过等间距法进行分析,得出成都市建成区2000~2010年间昼夜热岛的时空变化以及相互间差异。研究结果表明:成都市昼夜热岛存在显著差异,日间热岛主要呈散点分布,夜间热岛集中在城市中心区域。日夜间热岛变化趋势有显著不同,日间热岛继续分散而夜间热岛范围则有所扩大。日间热岛分布与植被覆盖存在一定的负相关性,但道路也在一定程度上促使了地面温度的增加,夜间城市热岛范围及变化则与其下垫面组成存在关系。     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Methodology of industry scale analysis under the constraint of regional resource capacity using HSY algorithm

This paper develops a methodology of proper scale analysis for regional industry development, which can be used in industry planning with the consideration of regional resource capacities. In the face of different data sources and even data scarcity, alternative methods based on linear programming and quadratic programming algorithms for calculating the resource intensity factors are designed. Based on the empirical study of industrialization, initial scenarios of industry development were set. Using HSY algorithm, sensitive industries that may cause exceeding regional resource capacity can be identified, and the risk of exceeding can be predicted and expressed in probability. Furthermore, a proper scale range can be designed for these sensitive industries according to resource capacity. Taking the case of Dalian city in China, this paper estimated the regional urban development plan, various resources capacities were studied, and land resources were estimated to be the most critical resource for the city. The land resource depletion intensities of different industries are calculated by quadratic programming algorithm. Under the constraint of 427.56 km² available industrial land resources, the electronic and power industries have the most significant impact on total land use, if the scale of power industry exceeds 1.27 billion USD, the probability of land resource capacity breakthrough will be 50%.     

Arsenic concentration in porewater of an alkaline coal ash disposal site: Roles of siderite precipitation/dissolution and soil cover

The geochemical behavior of As in porewaters of an alkaline coal ash disposal site was investigated using multilevel samplers. The disposal site was in operation from 1983 until 1994 and was covered with 0.3–0.5 m thick soils in 2001 when this study was initiated. Sequential extraction analyses and batch leaching experiments were also performed using the coal ash samples collected from the disposal site. The results suggest the important roles of siderite (FeCO₃) precipitation/dissolution and soil cover, which have been ignored previously. Arsenic levels in the porewater were very low (average of 10 μg L⁻¹) when the site was covered with soil due to coprecipitation with siderite. The soil cover enabled the creation of anoxic conditions, which raised the Fe concentration by the reductive dissolution of Fe-(hydr)oxides. Because of the high alkalinity generated from the alkaline coal ash, even a small increase in the Fe concentration (0.66 mg L⁻¹ on average) could cause siderite precipitation. When the soil cover was removed, however, an oxidizing condition was created and triggered the precipitation of dissolved Fe as (hydr)oxides. As a result, the dissolution of previously precipitated As-rich siderite caused higher As concentration in the porewater (average of 345 μg L⁻¹).     

A decision framework for possible remediation of contaminated sediments in the River Kymijoki, Finland

Background, aim, and scope  The paper describes the spatial contamination of the River Kymijoki, South-Eastern Finland, and the coastal region of the Gulf of Finland with PCDD/Fs and mercury. The findings of ecotoxicologial and human health studies are also reported, including environmental and human risk assessments. Sediments from the River Kymijoki, draining into the Gulf of Finland, have been heavily polluted by the pulp and paper industry and by chemical industries. A wood preservative, known as Ky-5, was manufactured in the upper reaches of the river between 1940 and 1984 causing severe pollution of river sediments with polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDF). Moreover, the sediments have been polluted with mercury (Hg) from chlor-alkali production and the use of Hg as a slimicide in pulp and paper manufacturing. Materials and methods  An extensive sediment survey was conducted as well as sediment transport modeling, toxicity screening of sediment invertebrates, and a survey of contaminant bioaccumulation in invertebrates and fish. Studies on human exposure to PCDD/Fs and the possible effects on hypermineralization of teeth as well as an epidemiological study to reveal increased cancer risk were also conducted. An assessment of the ecological and human health risks with a null hypothesis (no remediation) was undertaken. Results  The sediment survey revealed severe contamination of river and coastal sediments with PCDD/Fs and Hg. The total volume of contaminated sediments was estimated to reach 5 × 10⁶ m³ and hot spots with extremely high concentrations (max 292,000 ng g⁻¹ or 1,060 ng I-TEQ g⁻¹ d.w.) were located immediately downstream from the pollution source (approximately 90,000 m³). Sediment contamination was accompanied by changes in benthic assemblages, but direct effects were masked by many factors. The fish showed only slightly elevated PCDD/F levels in muscle, but orders of magnitude higher in the liver compared with reference freshwater sites and the Baltic Sea. The concentrations in human fat did not reveal high human exposure in the Kymijoki area in general and was lower than in sea fishermen. The relative risk for total cancer among farmers was marginally higher (RR = 1.13) among those living close to the river, compared with farmers living further away, and the possibility of increased cancer risk cannot be ruled out. A conservative risk assessment revealed that the present probability of exceeding the WHO upper exposure limit of 4 pg WHO-TEQ kg⁻¹ d⁻¹ for PCDD/Fs and DL-PCBs was 6%. The probability of exceeding the WHO limit value of 0.23 μg kg⁻¹ d⁻¹ for methyl mercury was estimated to be notably higher at 62%. Based on these studies and the estimated risks connected with different remediation techniques a general remediation plan with cost benefit analysis was generated for several sub-regions in the river. Dredging, on-site treatment, and a close disposal of the most contaminated sediments (90,000 m³) was suggested as the first phase of the remediation. The decision regarding the start of remediation will be made during autumn 2008. Conclusions  The sediments in the River Kymijoki are heavily polluted with PCDD/Fs and mercury from earlier chlorophenol, chlor-alkali, and pulp and paper manufacturing. A continuous transport of contaminants is taking place to the Gulf of Finland in the Baltic Sea. The highly increased PCDD/F and Hg levels in river sediments pose an ecotoxicological risk to benthic fauna, to fish-eating predators and probably to human health. The risks posed by mercury exceed those from PCDD/Fs and need to be evaluated for (former) chlor-alkali sites and other mercury releasing industries as one basis for remediation decision making. Recommendations and perspectives  The studies form the basis of a risk management strategy and a plan for possible remediation of contaminated sediments currently under consideration in the Southeast Finland Regional Environment Centre. It is recommended that a detailed restoration plan for the most seriously contaminated areas should be undertaken. Based on current knowledge, the restoration of the whole river is not feasible, considering the current risk caused by the contaminated sediment in the river and the costs of an extensive restoration project. The experiences gained in the present case should be utilized in the evaluation of PCDD/F- and mercury-contaminated sites in other countries. The case demonstrates that the historic reservoirs are of contemporary relevance and should be addressed, e.g., in the national implementation plans of the Stockholm Convention. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

砒霜生产化工厂危险废物处置工程设计

通过对砒霜生产化工厂原场址废渣及污染场地调查的基础上,进行了处置方案比选及技术经济比较,确定了就地安全填埋的处置方案。根据区域性危险废物集中安全填埋场的设计经验,本工程采用先进的HDPE双层防渗的安全填埋方式,并设置了雨水分流系统、渗滤液收集系统、封场处理和填埋气体导排系统,整个场区布置合理,对周围环境的影响小,投资少,取得了良好的社会效益和环境效益,并对危险废物处理处置工程设计中应注意的问题提出了建议。     

垃圾填埋场微生物气溶胶粒径分布研究

为了了解垃圾填埋场微生物气溶胶粒径分布规律,在北京市某垃圾卫生填埋场填埋区、渗滤液处理区、生活区分别选定监测点,利用安德森六级微生物采样器,对填埋场空气微生物进行了系统的定点取样、测定和分析。研究结果表明,空气细菌粒径分布均为第Ⅰ级(>8.2 μm)最高,填埋区空气细菌粒径呈偏态分布,渗滤液处理区、生活区分别在第Ⅳ级和第Ⅲ级出现第2个峰值。携带细菌的可吸入微粒在渗滤液处理区比例最大。空气真菌与放线菌均在第Ⅳ级分布最高,携带真菌和放线菌的可吸入粒子的比例显著大于细菌(P<0.05)。填埋区不同作业时段空气微生物粒径在各级分布比例基本一致。填埋区细菌气溶胶中值直径为5.7 μm,渗滤液处理区为3.7 μm,生活区为5.3 μm,显著大于真菌气溶胶和放线菌气溶胶的中值直径(P<0.05)。     

River channel network design for drought and flood control: A case study of Xiaoqinghe River basin, Jinan City, China

Vulnerability of river channels to urbanization has been lessened by the extensive construction of artificial water control improvements. The challenge, however, is that traditional engineering practices on isolated parts of a river may disturb the hydrologic continuity and interrupt the natural state of ecosystems. Taking the Xiaoqinghe River basin as a whole, we developed a river channel network design to mitigate river risks while sustaining the river in a state as natural as possible. The river channel risk from drought during low-flow periods and flood during high-flow periods as well as the potential for water diversion were articulated in detail. On the basis of the above investigation, a network with “nodes” and “edges” could be designed to relieve drought hazard and flood risk respectively. Subsequently, the shortest path algorithm in the graph theory was applied to optimize the low-flow network by searching for the shortest path. The effectiveness assessment was then performed for the low-flow and high-flow networks, respectively. For the former, the network connectedness was evaluated by calculating the “gamma index of connectivity” and “alpha index of circuitry”; for the latter, the ratio of flood-control capacity to projected flood level was devised and calculated. Results show that the design boosted network connectivity and circuitry during the low-flow periods, indicating a more fluent flow pathway, and reduced the flood risk during the high-flow periods.