总量控制与线性规划

探索了运用运筹学的线性规划解决环境保护中出现的多约束条件、多变量问题的方法,同时论证了总量控制理论的实际 意义。     

Simulation and Evaluation of Control Strategies for Ozone Reduction in a Complex Terrain in Southwestern Spain

During the central months of the year, southwestern Spain is under strong insolation and weak synoptic forcing, promoting the development of sea breezes and mountain-induced winds and creating recirculations of pollutants. The complex topography of the Southwestern Iberia Peninsula induces the formation of vertical layers, into which the pollutants are injected and subjected to long-distance transport and compensatory subsidence. The characteristics of these highly complex flows have important effects on the pollutant dispersion. Air pollution studies in very complex terrains require high-resolution modelling for resolving the flow dynamics. This paper shows the results obtained from using the MM5-CAMx multiscale-nested air quality model to relate the sensitivity regimes for ozone, nitrogen oxides and volatile organic compounds in an area of high geographical complexity. The article assesses the impact on the hourly and eight-hourly maximum daily ozone concentrations of four reduction strategies during two ozone pollution episodes. This analysis of the ozone response has led to a preliminary evaluation of the effectiveness of the most common control strategies: traffic, industry, mixed traffic and industry, and closure of some of the largest industries (oil and petrochemical refineries). Photochemical indicators show that ozone chemistry in southwestern Spain is strongly sensitive to NO _x . However, volatile organic compound-sensitive points are found in areas with anthropogenic influence (highways, cities and industrial parks). Our results indicate that reductions in road traffic lead to ozone reductions over large areas, whereas reductions in industrial emissions, despite sometimes showing greater decreases in the maximum hourly and eight-hourly ground-level ozone levels, lead to ozone reductions in a local area only. In the control study case, with the oil refinery and the petrochemical plants closed, decreases in ozone hourly concentrations are up to 40% higher than in the other emission control scenarios studied. This analysis provides an assessment of the effectiveness of different policies for controlling precursor emissions by comparing the modelled results for different scenarios.     

Blockwise AICc for Model Selection in Generalized Linear Models

The corrected Akaike information criterion (AICc) is a widely used tool in analyzing environmental and ecological data, and it outperforms the Akaike information criterion (AIC), especially in small-size samples. To take advantage of this property, we propose a modified version of the AICc in a generalized linear model framework, referred to as the blockwise AICc (bAICc). Compared with some other information criteria, extensive simulation results show that the bAICc performs well. We also analyzed two environmental datasets, one for snail survival and the other for fish infection, to illustrate the usefulness of this new model selection criterion.     

Some Dependence Models for the Time Between Ozone Exceedances in Mexico City

In this paper, we consider several modelling approaches for the mean time between exceedances of a given environmental threshold. The interest here resides in the time between ozone exceedances (also called ozone inter-exceedances times). The proposed models assume two basic density functions for the time between surpassings: the Weibull and the generalised exponential functions. Considering those distributions, a random effect with autoregressive structure is taken into account to determine unexpected changes in the mean of the inter-exceedances density functions. Those unexpected changes could be captured either by their scale parameter or by both their scale and shape parameters. The models are applied to ozone data from the monitoring network of Mexico City. Selection of the model that best explains the data is performed using the deviance information criterion and also the sum of the absolute values of the differences between the estimated and observed means of the inter-exceedances times. An analysis to detect the possible presence of change-points is also presented.     

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semi-infinite programming and parametric programming, the IFSSIP method is more reasonable than others.     

An Linear Programming Model for Batch Optimization in the Ecological Zinc Production

This paper presents a mathematical model for solving the blending composition problem used in the zinc smelting process. The model proposes an objective function maximizing profit in terms of the total income from the sale of zinc on the market and the cost of producing zinc. In addition to the constraints contained in the basic mathematical model, the constraints, which are in accordance with the specific production process of zinc and environmental requirements, are developed. This paper proposes a model that can align three contradictory demands (technological, economic, and environmental) present in zinc production. The results, obtained by numerical experiments, confirm the assumption that it is possible to assemble such an optimal batch that will “reconcile” all contradictory demands. This model can be applied to other similar processes of production with minor changes.     

Application of the UAM-V and Use of Indicator Species to Assess Control Strategies for Ozone Reduction in the Lower Fraser Valley of British Columbia

Modelled and measured indicator species ratios of O₃/NOz, O₃/NOy, H₂O₂/HNO₃, HCNO/NOy for the Lower Fraser Valley were compared with VOC-NO_x-O₃ sensitivity threshold values reported in previous studies. Modelled region - specific indicator ratio thresholds derived from 50% NOx and 50% VOC reduction scenarios are provided. They show strongest agreement with the H₂O₂/HNO₃ ratio values found elsewhere. A clear transition region for the LFV from VOC to NOx sensitivity could not be identified, but there is evidence that O₃ concentrations in the western valley, dominated by metropolitan Vancouver, are VOC sensitive, and the more rural eastern valley O₃ concentrations exhibit greater NO_x sensitivity. The UAM-V Process Analysis utility was used to identify physical and chemical mechanisms which contributed to O₃ formation and destruction and indicate the key importance of entrainment from elevated layers generated by the highly complex meteorological conditions in determining near surface O₃ concentrations.     

Minimax Statistical Models for Air Pollution Time Series. Application to Ozone Time Series Data Measured in Bordeaux

This paper deals with the application of l(infinity) (or minimax) optimization techniques to statistical modelling of high frequency air pollution data. The method was applied to ground-level ozone time-series data measured in Bordeaux over 4 years from 1998 to 2001. The aim of model building was to develop predictive models in order to provide forecasts of the maximal daily ground-level ozone concentration. Experimental results from this case study indicate that such techniques could be more appropriate than the commonly used l2 setting if only good estimation of high levels is of interest. When the free parameters are fitted by means of l(infinity) optimization techniques, the forecasting errors are more evenly distributed amongst the data points, resulting in a better estimation of high values. The paper compares the quality of forecasts produced by both a linear and a nonlinear model, using l2 and l(infinity) parameter optimization.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

DMSP-IEES: A Stochastic Programming Model Based on Dual-Interval and Multi-Stage Scenarios Modeling Approaches for Energy Systems Management and GHG Emissions Control

Energy-related activities contribute a major portion of anthropogenic greenhouse gas (GHG) emissions into the atmosphere. In this study, a dual-interval multi-stage stochastic programming model for the planning of integrated energy-environment systems (DMSP-IEES) model is developed for integrated energy-environment systems management, in which issues of GHG-emission mitigation can be reflected throughout the process of energy systems planning. By integrating methodologies of interval linear programming (when numbers are described as interval values without distribution information), dual-interval programming (when lower and upper bounds of interval values are not available as deterministic values but as discrete intervals), and multi-stage stochastic programming, the DMSP-IEES model is capable of dealing with uncertainties expressed as discrete intervals, dual intervals, and probability distributions within a multi-stage context. Decision alternatives can also be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that the developed model can tackle the dual uncertainties and the dynamic complexities in the energy-environment management systems through a multi-layer scenario tree. In addition, it can reflect the interactions among multiple system components and the associated trade-offs.     

Using Geographically Isolated Loading Scenarios to Analyze Nitrogen and Phosphorus Exchanges and Explore Tailored Nutrient Control Strategies for Efficient Management

A set of geographically isolated differential nitrogen (N) and phosphorus (P) load model scenarios from major Chesapeake basins provides information on the relative impact of nutrient loads on primary production and dissolved oxygen in the Chesapeake Bay. Model results show the relationships of deep water dissolved oxygen with nutrient limitation-related algal blooms, organic carbon loads from the watershed, estuarine circulation, nutrient cycling, and nutrient diagenesis. The combined effect of changes in load from multiple basins is additive for changes in both chlorophyll-a and deep water dissolved oxygen concentrations. Management of both N and P are required in the Chesapeake watershed and tidal waters to achieve water quality standards, but overall efficiencies could be gained with strategies that place greater emphasis on P control in the upper Bay and greater emphasis on N control in the lower Bay. The areas of the Bay with the highest degree of dissolved oxygen degradation that generally drive management decisions are mostly P-limited and are significantly influenced by the load from the upper Bay’s basins. Reducing P from the upper Bay’s basins will intensify P limitation and would allow an increase in N of about six times the weight of P reduction. Combining the relative nutrient reduction effectiveness with the relative control cost information could improve management efficiency and provide benefits at a lower cost. This article describes initial steps that can be taken to examine the benefits from N-P exchanges.     

Selecting Variables for Habitat Suitability of <Emphasis Type="Italic">Asellus</Emphasis> (Crustacea,Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.