Air Quality Modeling and Metamodeling Approach

This chapter deals with modeling and metamodeling of air quality in the Pardubice region of the Czech Republic. From a regional point of view, the Pardubice district is the most problematic area in regards to air pollution. Concentrations of traffic, industry and power stations (Opatovice and Chvaletice) activities are the cause of this situation, although emissions of all pollutants have markedly decreased within the last ten years. A decrease in air pollution was achieved particularly by restriction and restructuring of industrial production, use of emission standards, changes in legislation in the area of air protection, etc. The mentioned air quality modeling belongs to classification tasks. It means the authors deal with the classification problem, with the creation of classification models (classifiers) and they focus on metamodeling (combining classifiers). Through the application of modeling and metamodeling the authors use selected algorithms of decision trees (C5.0, chi-squared automatic interaction detection and classification and regression trees) that belong to useful explanatory techniques.

Modeling the Ecological Footprint of Nations via Evolutionary Computation and Machine Learning Models

The per capita Ecological Footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth’s biological capacity required to support human activity. This study uses gene expression programming and Self-organizing Maps (SOM) to predict, classify and cluster the EF of 140 nations. A Bayesian approach was used to formally test the research hypotheses. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific simulation method, i.e., Markov Chain Monte Carlo (MCMC), is utilized to estimate the model parameters. Bayesian MCMC methods allow a richer and more complete representation of complex EF data. It also provides a computationally attractive and straightforward method to develop a full and complete description of the inherent uncertainty in parameters, quantiles and performance metrics.

Air Quality Modeling by Fuzzy Sets and IF-Sets

The chapter presents a design of parameters for air quality classification of districts into classes according to their pollution. Therefore, the chapter presents basic notions of fuzzy sets introduced by L. A. Zadeh for design hierarchical fuzzy inference systems Mamdani type and IF-sets introduced by K. T. Atanassov for design of hierarchical IF-inference systems Mamdani type. In the next part of the chapter the authors describe air quality modeling by hierarchical fuzzy inference systems, hierarchical IF-inference systems and we analyze the results. Moreover, the chapter describes air quality modeling, the design of membership functions and non-membership functions, if-then rules of individual subsystems and inference mechanism. Further, the authors present basic notions of IF-relations and their determination by Kohonen’s Self-organizing Feature Maps and K-means algorithms and process air quality classification.

Air Quality Assessment by Neural Networks

The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen’s self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality.

Modeling and Understanding the Interrelationship between Natural Resource Extraction, Poverty and Inequality

Many rural households in developing countries derived their income partly from forest extraction yet, very little is known about the distributional implications of this income source on poverty and household welfare in general. Applying Gini and poverty decomposable techniques to community level datasets in rural Nigeria for analyzing the distributional implications of forest income on household welfare, the study finds that forest income reduces both income inequality and poverty. 2-step Ordinary Least Square (OLS) analysis of the determinants of forest income indicates that the decision to participate in forest extraction increases with more access to natural forest areas, larger and poorer households; and decreases with membership in forestry management and higher educational attainment. Furthermore, poverty simulations revealed that poverty can be reduced in the short run, through programs that raise the price that households receive for minor forest products. However, in order to synergize forest-led poverty reduction and forest conservation, the study recommends the planting and use of minor forest products outside of the natural forest areas. Other policy options and implications of the study are fully discussed.

Economic Performance and Environmental Quality at the Regional Level An Approach to Modeling Depletion Adjusted Macro Aggregates for the Czech Coal Mining Regions

The chapter describes an approach for the calculation of coal depletion adjusted regional macroeconomic aggregates for the coal mining regions of the Czech Republic. In the first part of the chapter, the concept of depletion adjusted macroeconomic aggregates is discussed. The next two parts provide a description of the coal mining regions as well as the position of the coal mining industry in the Czech economic structure. The final part of the chapter describes the methodological approach to resource rent and depletion modeling. After that, the coal depletion adjusted macroeconomic aggregates are presented, followed by our conclusions.

A Spatial Multicriteria Decision Analysis to Manage Sewage Sludge Application on Agricultural Soils

In this chapter, the spatial problem of disposing sewage sludge on agricultural soils is addressed. Sewage sludge application on agricultural soils is recommended by governments in order to recycle nutrients and organic matter. Moreover, a new utility is given to a by-product of wastewater treatment. However, this managing practice may lead to environmental and human health risks. Soil amendment has also several related economic costs. In order to solve this decision problem, a spatial multicriteria decision analysis is presented. This method allows solving the decision problem taking into account the geographical peculiarities of each agricultural site. The purpose of this chapter is to present a methodology to solve the decision problem of managing sewage sludge on agricultural soils. For that, the most used multicriteria decision analysis procedures reported in the literature are reviewed and other novel methods are suggested. By the end of the chapter, a brief example of the method application is presented.

Estimation of Irrigation Water Demand on a Regional Scale Combining Positive Mathematical Programming and Cluster Analysis in Model Calibration

Mathematical programming tools are widely used to simulate agriculture water use thanks to their ability to provide a detailed technical and economic representation of farm choices. However, they also require a significant amount of basic information and appropriate methods for the organization of such information. The objective of the paper is to test a methodology for the estimation of irrigation water demand using a combination of Positive Mathematical Programming (PMP) at farm level, and a cluster analysis. The methodology is applied in an area of Northern Italy. The main outcome of our empirical application is the variety and complexity of reactions of different farms. The scenarios considered highlight the potential importance of the effects of price and cost variables, while the changes in the (area-based) tariff system appear less significant. The change in water cost/pricing appears somehow relevant, but does not motivate major changes in present water management policy, at least in the range of scenarios considered.

Large-Scale Computational Modeling for Environmental Impact Assessment

Input-output table plays a central role in the Economic Input-Output Life Cycle Assessment (EIO-LCA) method. This chapter presents an integrated and distributed computational modeling system capable of estimating and updating large-size input-output tables. The complexity of national economy leads to extremely large-size models to represent every detail of an economy. In order to construct the table reflecting the underlying industry structure faithfully, multiple sources of data are integrated and analyzed together. The major bottleneck of matrix estimation is the lack of memory allocation. In order to include more memory, this unique distributed matrix estimation system runs over a parallel supercomputer to enable it to estimate a matrix with the size of more than 1,000-by-1,000 with relatively high accuracy. This system is the first distributed matrix estimation package for such a large-size economic matrix. This chapter presents a comprehensive example of facilitating this estimation process by integrating a series of components with the purposes of data retrieval, data integration, distributed machine learning, and quality checking.

Remote Sensing of Bush Encroachment on Commercial Cattle Farms in Semi-Arid Rangelands in Namibia

Bush encroachment is one of the most extensive changes in land cover in semi-arid rangelands and an urgent problem for cattle farming, rapidly reducing the productivity of the rangeland. Despite the severity of these consequences, a complete and accurate assessment of bush encroached areas is still missing at large. This study aims at assessing bush encroachment on commercial cattle farms in central Namibia by employing remote sensing methods to distinguish between areas covered by bush and open rangeland. The authors use different classification techniques and vegetation indices to characterize the nature of vegetation cover. Their analysis shows that results are sensitive to specific classifications of indices. As an accuracy assessment could not be run on these results the authors could not analyze which classification approximates real bush encroachment best. Hence, this study highlights the need for further analysis. Ground truth data, in the form of field mappings, high resolution aerial photographs or local expert knowledge are needed to gain further insights and produce reliable results.