Transport spatial model for the definition of green routes for city logistics centers

This paper presents a transport spatial decision support model (TSDSM) for carrying out the optimization of green routes for city logistics centers. The TSDSM model is based on the integration of the multi-criteria method of Weighted Linear Combination (WLC) and the modified Dijkstra algorithm within a geographic information system (GIS). The GIS is used for processing spatial data. The proposed model makes it possible to plan routes for green vehicles and maximize the positive effects on the environment, which can be seen in the reduction of harmful gas emissions and an increase in the air quality in highly populated areas. The scheduling of delivery vehicles is given as a problem of optimization in terms of the parameters of: the environment, health, use of space and logistics operating costs. Each of these input parameters was thoroughly examined and broken down in the GIS into criteria which further describe them. The model presented here takes into account the fact that logistics operators have a limited number of environmentally friendly (green) vehicles available. The TSDSM was tested on a network of roads with 127 links for the delivery of goods from the city logistics center to the user. The model supports any number of available environmentally friendly or environmentally unfriendly vehicles consistent with the size of the network and the transportation requirements.     

The effect of collaboration and IT competency on reverse logistics competency - Evidence from Brazilian supply chain executives

Business models have being designed, worldwide, to create sustainability competencies and in particular to incorporate reverse logistics, but Brazilian executives have not yet fully adhered to Law 12,305 on solid waste and reverse logistics. What would be the Brazilian executives' perception about reverse logistics competency and the support provided by a collaborative and IT competency? The objective of the paper is to investigate the effects of collaboration and IT competency in developing reverse logistics competency, as a strategy, and its consequences in economic and environmental performance. A survey was performed with 320 Brazilian supply chain executives and analyzed using Structural Equation Modeling. The models demonstrated that collaboration has a direct positive influence on the development of reverse logistics competency, by executives' point of view. The moderation effect between collaboration and IT competency for reverse logistics was not confirmed, since it was adopted a strategic view of reverse logistics. Therefore, despite there was not a moderation effect, IT presented a lower direct effect on reverse logistics competency. Results reinforce that organizations that develop reverse logistics competency tend to improve their economic and environmental performances.     

A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management Under Uncertainty

A superiority–inferiority-based inexact fuzzy stochastic programming (SI-IFSP) model was developed for planning municipal solid waste management systems under uncertainty. The SI-IFSP approach represents a new attempt to tackle multiple uncertainties in objective function coefficients which are beyond the capabilities of existing inexact programming methods. Through introducing the concept of fuzzy random boundary interval, SI-IFSP is capable of reflecting multiple uncertainties (i.e., interval values, fuzzy sets, probability distributions, and their combinations) in both the objective function and constraints, leading to enhanced system robustness. The developed SI-IFSP method was applied to a case study of long-term municipal solid waste management. Useful solutions were generated. A number of decision alternatives could be generated based on projected applicable conditions, reflecting the compromise between system optimality and reliability as well as the tradeoffs between economic and environmental objectives. Moreover, the consequences of system violations could be quantified through introducing a set of economic penalties, reflecting the relationships between system costs and constraint violation risks. The results suggest that the proposed SI-IFSP method can explicitly address complexities in municipal solid waste management systems and is applicable to practical waste management problems.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Immune response of earthworms (Lumbricus terrestris, Eisenia andrei and Aporrectodea tuberculata) following in situ soil exposure to atmospheric deposition from a cement factory

In order to reduce their energy costs, many cement plants use fuel product substitutes (old tyres and used oil). The combustion of these products generates a metal increase (e.g. Cu, Cd, Pb and Zn) in the atmospheric emissions. After their release, these elements are deposited into the environment and could eventually accumulate up to concentrations of concern. At the Saint-Laurent cement factory (Joliette, QC, Canada), maximum deposition of these elements occurs in the direction of prevailing winds (North-East). We evaluated the potential impact of these depositions upon the immune system of three earthworm species (Lumbricus terrestris, Eisenia andrei and Aporrectodea tuberculata) exposed in a natural environment. The exposure sites were 0.5, 1.0, and 2.0 km downwind from the cement factory, along with an upwind reference site. The immune parameters studied were the cell viability and phagocytic potential of the immune cells (coelomocytes). For both L. terrestris and E. andrei, after 7 d exposure, none of the measured parameters showed significant differences among the sites. On the other hand, for the indigenous worm A. tuberculata, in the most exposed zone (at 0.5 km), we observed an increase in cell viability and phagocytic potential. This increase could possibly be attributed to physicochemical effects such as the alkaline pH of the soil, or alternatively, it could result from beneficial effects induced by an increased calcium supply.     

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.     

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).     

宜兴市环保产业供给侧改革的启示与建议

当前我国环保产业普遍存在着企业规模较小、缺乏核心技术、中低端供给过剩、优质供给不足等现象,环保产业的供给侧水平明显无法满足日益提高的环境治理需求。以全国最大的环保产业基地宜兴市为例,分析上述问题特质及产生原因,总结政府及企业改革创新中的经验和不足,提出环保产业供给侧改革的参考对策与建议。     

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.     

Characterization of products formed in the reaction of ozone with alpha-pinene: case for organic peroxides

The generation of reactive oxygen species (ROS) and their subsequent induced pulmonary and systemic oxidative stress has been implicated as an important molecular mechanism of PM-mediated toxicity. However, recent work has shown that there is significant ROS associated with ambient PM. In order to understand the formation mechanisms as well as understand the potential health effects of particle-bound oxidative species, the alpha-pinene-O(3) oxidation chemical system was studied to elucidate the structures of reaction products using liquid chromatography-multiple stage mass spectrometry (LC-MS(n)). The classes of compounds identified based on their multiple stage-MS fragmentation patterns, mechanistic considerations of alpha-pinene-O(3) oxidation, and general fragmentation rules, of the products from this reaction system were highly oxygenated species, predominantly containing hydroperoxide and peroxide functional groups. The oxidant species observed were clearly stable for the 1-3 h that elapsed during aerosol collection and analysis, and probably for much longer, thus rendering it possible for these species to bind onto particles forming fine particulate organic peroxides that concentrate on the particles and could deliver concentrated doses of ROS in vivo to tissue.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

A fuzzy multi-stakeholder multi-criteria methodology for water allocation and reuse in metropolitan areas

In this paper, a fuzzy decision making methodology is proposed to find a socially optimal scenario for allocating effluent of wastewater treatment plants and urban and suburban runoffs to agricultural regions and recharging aquifers. The presented methodology named modified fuzzy social choice (MFSC) considers multi-stakeholder multi-criteria problems under uncertainties inherent in a decision making process utilizing a fuzzy ranking method and the fuzzy social choice (FSC) theory. A set of water and wastewater allocation scenarios are proposed for water quantity and quality management of the study area, while six main stakeholders with conflicting utilities and different negotiation powers are involved. The proposed methodology is applied to Tehran metropolitan area, the capital city of Iran with the population of about 8 million people, to examine its applicability and effectiveness. The results shows that using fuzzy multi-stakeholder multi-criteria decision making method considering equal and different negotiation powers can lead to different outcomes. Based on the results, the MFSC method, which considers a number of decision makers having different negotiation powers, degrees of importance of decision making criteria, and some important uncertainties, performs more promising in real water resources management problems.     

Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China

In order to enrich and improve the groundwater quality assessment system, a new coupled assessment model based on rough set attribute reduction and the technique for order preference by similarity to ideal solution (TOPSIS) was proposed. The proposed model was applied in the groundwater quality assessment of a semi-arid area, northwest China. The results show that most chemical indices except NH (4) (+) , F(-), and Mn meet the Standards for Drinking Water of China and the groundwater quality overall is good. All assessed water samples are found to be fit for human consumption according to the comprehensive assessment results. Rough set attribute reduction for groundwater quality assessment is practical. The assessment results after attribute reduction show a good consistency with those before attribute reduction. Rough set attribute reduction and TOPSIS evaluation coupled model is clear in ideas and simple in calculation, and evaluation results are reasonable as well. The coupled model can be applied to solve many multiple criteria decision making problems such as groundwater quality assessment.     

Modeling the plant–soil interaction in presence of heavy metal pollution and acidity variations

On a mathematical interaction model, developed to model metal uptake by plants and the effects on their growth, we introduce a modification which considers also effects on variations of acidity in soil. The model relates the dynamics of the uptake of metals from soil to plants and also variations of uptake according to the acidity level. Two types of relationships are considered: total and available metal content. We suppose simple mathematical assumptions in order to get as simple as possible expressions with the aim of being easily tested in experimental problems. This work introduces modifications to two versions of the model: on the one hand, the expression of the relationship between the metal in soil and the concentration of the metal in plants and, on the other hand, the relationship between the metal in the soil and total amount of the metal in plants. The fine difference of both versions is fundamental at the moment to consider the tolerance and capacity of accumulation of pollutants in the biomass from the soil.     

Presenting a conceptual pattern of HSE performance of oil trucks

Accidents are among the main problems in the oil product supply chain. The most important effective factors in these events are the kind of trucks used and their health, safety, and environment (HSE) condition. The aim of this study was to present a conceptual pattern of the HSE performance of oil trucks in oil industries. In this study, 20 truck models (with fixed tanks), in use over different periods of time, were investigated. In this regard, the criteria and sub-criteria were first determined in two parts—carrier and tank—and weighted by fuzzy analytical hierarchy process (FAHP). The results showed that the most important sub-criteria regarding the HSE factors of the carrier were resistance and strength of the front and rear shields, the brake system, and the ventilation system. The most important sub-criteria regarding the HSE factors of the tank were tank shell thickness and a good tank design shape with respect to portable material. It should be noted that the weight of the criteria with each other and sub-criteria with each other are not equal. This issue is important for decision-making. The main reason for the use of trucks with the lowest score in developing countries is the lack of attention by managers to safety issues and international standards and agreements such as the ADR.     

Fuzzy clustering analysis in environmental impact assessment — A complement tool to environmental quality index

In spite of rapid progress achieved in the methodological research underlying environmental impact assessment (EIA), the problem of weighting various parameters has not yet been solved. This paper presents a new approach, fuzzy clustering analysis, which is illustrated with an EIA case study on Baoshan-Wusong District in Shanghai, China. Fuzzy clustering analysis may be used whenever a composite classification of environmental quality/impact incorporates multiple parameters. In such cases the technique may be used as a complement or an alternative to comprehensive assessment. In fuzzy clustering analysis, the classification is determined by a fuzzy relation. After a fuzzy similarity matrix has been established and the fuzzy relation stabilized, a dynamic clustering chart can be developed. Given a suitable threshold, the appropriate classification can be accomplished. The methodology is relatively simple and the results can be interpreted to provide valuable information to support decision making and improve management of the environment.     

Holistic cognitive conflict chain management framework in supply chain management

Supply chain management is an integral part of most businesses and crucial to company performance and customer satisfaction. The demanding characteristics in supply chain management include lack of customer service quality, increased cost, risk management, and lack of inefficiency, which are considered indispensable factors in this research. This paper proposes a holistic cognitive conflict chain management framework (HCCCMF) to enhance quality customer service and supply chain management efficiency. This proposed HCCCMF method reduces interruption during the process and the cost of raw materials, labour, and energy in supply chain management. Behavioural monitoring analysis is carried out to improve quality service to consumers, creating an excellent customer-supplier relationship to function effectively in supply chain management. Policy matrix analysis is introduced to overcome disruptions during operation, which effectively manages affordable rates. The experimental results show that the proposed HCCCMF method enhances the productivity ratio of 94.3%, performance ratio of 98.4%, efficiency ratio of 96.5%, reliability ratio of 95.5%, accuracy ratio of 97.8%, and low trade cost ratio of 15.3% when compared to other existing methods.     

智能算法及其在环境预警中的应用

智能算法具有学习非线性问题的能力,可有效优化环境模型结构与参数,是环境预警的重要工具。重点分析了遗传算法和人工神经网络的相关特征,并以太湖蓝藻水华预报预警为例,介绍其在提高环境模型精度中的应用。     

Upstream flood pattern recognition based on downstream events

Reverse stream flood routing determines the upstream hydrograph in a stream reach given the downstream hydrograph. The Muskingum model of flood routing involves parameters that govern the routed hydrograph. These parameters are herein estimated using simulation methods coupled with optimization tools to achieve optimized parameters. Different simulation methods are shown to perform unequally in the estimation of nonlinear Muskingum parameters. This paper presents two simulation methods for nonlinear Muskingum reverse flood routing: (1) Euler equations and (2) Runge-Kutta 4th order equations. Moreover, the generalized reduced gradient (GRG) is used as the optimization tool that minimized the sum of the squared deviations (SSQ) between observed and routed inflows in a benchmark flood routing problem. Results show the Runge-Kutta 4th order equations yield better routed hydrographs with smaller SSQ than obtained in previous research and with the first simulation method (Euler equations).