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An Aggregate Index for Environmental Quality
Introduction
The concept of environmental quality can be interpreted either objectively or more subjectively in socio psychologically terms (Rapoport, 1983). The first and he simpler meaning relies on objective standards and scientific criteria for the measurement of quality, while the second, the more complexinterpretation is related to the more variable qualities which gives satisfaction to the people (Odermerho Francio O, 1991). Objective measures of the physical world, over ehich decision makers have absolute control fail to define those appropriate environmental quality profiles that are congruent with and supportive of the life style and activities which a group finds desirable (Odermerho, 1991). The objective and subjective measures both are likely to lead to a balanced judgement in the evaluation of quality. It is the aim of the study to develop an aggregate index of environmental quality that combines professional and lay view points in a way that would be beneficial to planning in the third world.
Physical Parameters
Environment pollution may be defined as the presence of substances in the environment in such a concentration that may tend to be injurious to any of human, animal, plant/crops, buildings/human welfare and environment itself. Such a substance is known as pollution. These substances may result from man-made activities or from natural processes, causing adverse effects to man and the environment. Pollution is of many types like water pollution, solid waste pollution, noise pollution, air pollution etc. Vehicle emission is main source of polluted air in cities. Transport activities have a wide variety of effects on the environment such as air pollution, noise pollution from road traffic in mega cities. Due to data and time constraints, only air and noise pollution is considered in this study. The physical environment refers to surroundings and conditions, in which a person or a community lives. In the context of this report, the well-being and poverty related components of the physical environment and how these relate to the physical and natural environments, are the points of focus. It is important to note that elements of the physical environment overlap and interact with the natural environment. Poverty tends to increase people’s reliance on the natural environment and may heighten the vulnerability to environmental degradation. Certain physical environmental factors like topography, population, urban greenery, Land use etc have deep impact on the UEQ. Urban greenery plays a vital role in controlling pollution. It gets effected with the land use changes.
- Components of Environmental quality assessment
Environmental Pollution and physical environment are the two main components used in the study
- Criteria for Environmental quality assessment
Criterion is used to evaluate what class the environmental quality in some spatial evaluation unit belongs. Here the criteria are air, noise, land use, population, urban greenery and topography.
- Indicators for Environmental quality assessment
Environmental indicators are designed to quantify the criteria of environmental quality. Indicators must be able to describe and access urban environment concisely and explicitly. Carbon monoxide, sulphur dioxide, nitrogen dioxide, suspended particulate matters for air, slope, aspect for topography are taken in the study. Land use as per the availability of remote sensing satellite image, has been categorised into commercial, residential, transportation, institutional, open space, institutional and water bodies.
Analytical Hierarchy process for multi criteria decision making
Analytic Hierarchy Process (AHP) is one of the most commonly used utility-based methods for environmental decision-making (Sadiq, 2007). Uncertainty is an unavoidable and inevitable component of any environmental decision-making process. Sadiq (2007) have broadly categorized uncertainty into vagueness and ambiguity. The AHP inherently involves both vagueness and ambiguity in assigning pair wise comparisons and evaluating alternatives. Vagueness (imprecision) refers to lack of definite or sharp distinction, whereas ambiguity is due to unclear distinction of various alternatives, which is further divided into discord (conflict) and non-specificity.
Analytical Hierarchy Process method is based on three principles: decomposition, comparative judgment and synthesis of priorities. AHP is a mathematical method used to determine the priorities of different decision alternatives via pair wise comparison of decision elements with respect to a common criterion. The pair wise comparison approach coupled with a ratio scaling method is used to uncover the relative importance among all decision criteria in multiple attribute decision-making environments.
This study primarily focuses on air and noise quality evaluation of the urban environment for Bhopal city. The choice of experts is based on the parameters taken for the evaluation. Further all the experts should have well enough idea about the chosen parameters. According to the above scenario, it is advisable to choose the experts from different fields like environmental planning, town planning, geography, urban planning etc. The selection of final pairwise comparison matrix depends on the research under consideration. Final decision is completely based on discretion and experience of the decision maker who has chosen the experts. The pair wise comparison ratio, for eg. SO2/NO2 is chosen on the basis of weight given by the decision maker to a particular expert and consistency of that ratio across various experts. For example an expert in environmental field is definitely given more weightage to SO2/NO2 than a geographer. Similarly for aspect and slope affecting the air and noise pollution, the decision of geographer for overall environmental degradement is more valuable. Therefore the choice of the final pair wise comparison matrix has no preset mechanism. Yet, one can evaluate consistency measure of pair wise comparison matrices given by each expert and in those cases where CR is less than 0.1 has been considered more effective. In the similar note, the convergence of pair wise comparison can be tested on the basis of mean and standard deviation of a particular ratio like SO2/NO2.
Geographic Information System with remote sensing and AHP
High resolution satellite data CARTOSAT-1 PAN has been used to map land use map. IRS P6 LISS IV MX data has been used for extracting urban greenery by extracting NDVI. Population ward wise has been calculated and has been considered in the form of polygon map. Topography has been developed from DEM. It has been made where by contour maps of the study area. The data varies in dimension and it is difficult to aggregate them all in one unit. Taking into account, the overlaying functions of GIS, the raster data structure is more suitable. Continuous surface of air and noise data has been generated by IDW interpolation (geostatistical analyst tools). The cell size is maintained to be of 2.5m which is that of satellite image which is considered to be the spatial unit for the analysis.. Much environmental information has the obvious spatial character that can be addressed by GIS. Factors involved in environmental quality assessment changes in the different spatial unit. The multiple layers of information of evaluation criteria can be integrated in different combinations in GIS.
Use of AHP and Fuzzy Scores to have membership value
Uncertainty is an unavoidable and inevitable component of any environmental decision-making process. Uncertainty is categorized into vagueness and ambiguity. Many attempt has been made to analyze these uncertainties into AHP using fuzzy score between 0 to 1 (Sadiq, 2007). Many attempts have been taken earlier to analyze different kind of uncertainty with AHP. Sadiq has combined AHP with Intuitionistic fuzzy sets. Yager and Kelman (1999) have used AHP with ordered weighted averaging operators. Here, uncertainty is incorporated by combining AHP scores with fuzzy scores using fuzzy algebraic sum. Later by the help of fuzzy scores, all the indicators maps have values between zero to one that is in one unit (Martin, 2001). Defuzzification is done where by applying some operators and by designing fuzzy inference network.
Conclusion
It is essential to develop an aggregate index of environmental quality that combines professional viewpoint in a way that would be beneficial to planning in India. More specifically it is required to develop a method of measuring environmental quality taking into account the drawbacks of existing methods. The combination of remote sensing, GIS, AHP has given a platform to bring environmental quality evaluation unit. The aim of making cities healthier is possible through proper plans which incorporate the environmental consideration concepts into urban planning.