Air Pollution 4 Essay Research Paper Damage — страница 2

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rural areas. For non-smokers, all differences were well over 120 percent. They argued that the differences in the quality of diagnosis could not account for the observed differences for urban and rural areas. Mortality from heart disease concluded that a substantial abatement of air pollution would lead to a 10 to 15 percent reduction in death rates. What is still questionable is how much is a ” substantial ” abatement. This value is very vague and therefore should not be accepted without a more definitive number. It is easy to recognize many factors that were not included in these experiments. Such factors such as general habits, inherited characteristics, and lifetime exercise patterns were not taken into account. Also, there was no attempt made to control for income or

social status. Failure to use a control such as this can often lead to bias results. One other factor deals with the population the sample was taken from. A sampling error can occur which could have a tremendous impact on results. All of these factors combined must be taken into account before accepting the results. To determine the economic cost of diseases, we must ask ourselves how much we are willing to pay as a society to improve our health. That is, how much is it worth to society to relieve painful symptoms and to extend our lives? The appropriate measure is what people would be willing to pay to reduce mortality and morbidity. One suggested way is to pay a certain amount of dollars for every level of pollution that is decreased. The underlying cost here is the cost of

lowering the level of pollution. Waving a wand or snapping a finger won’t lower pollution. A decrease by one level could cost more than the benefits experienced after doing so. These decisions are difficult to make and require careful thought before being implemented. Walter P. Page and William Fellner conducted a similar study. The purpose of the study was to compare results obtained using multivariate statistical techniques for exploring the dose-response relationships among human health and air pollution. Earlier studies have used techniques such as multiple regressions to analyze the relationship between human health and air pollution. However, the accuracy of the results is questionable. Part of the problem deals with the multifactorial nature of the relationship between

air pollution and human health. For example, variables such as income distribution, ethnic composition, life styles, and migration have all been shown to have an influence on mortality and morbidity. Another problem is when generating regressions; anything and everything may be included. This is known as forming “garbage” regressions, which create inconclusive results. Two techniques used for analysis in hopes of finding more accurate results were factor analysis on pollution and disease experience across SMAS followed by appropriate correlation analysis; and canonical correlations between pollution and disease experience across SMASs. The pollutants used in the analysis included NO2, SO2, and SO4. The determination of disease categories was based on technical medical advice,

which should indicate that they are not fact, rather only a best guess. Looking at the factor analysis results, the adjusted mortality rates indicate five dimensions being dominated by particular diseases. This is different from the unadjusted mortality rates having only three dimensions. The importance of this is the change in dimensions. It appears that the larger percentage of variance by dimension 1 requires more than one disease category. What this suggests is that larger numbers of diseases might be factor analyzed to acquire dimensions for use with correlation. The correlations between diseases and human health showed highly significant results relating SO2 and SO4 to gastrointestinal cancer and arteriosclerotic heart disease. Also, breast cancer was found highly

correlated and very significant in relation to NO2. Asthma and emphysema show a negative sign meaning that they are inversely related, and therefore significant. Looking now at the canonical correlation it is suggested that this technique produced stronger results in terms of dose-response relationships. The same diseases were showing up in both the adjusted and unadjusted data. The significant correlation among gastrointestinal malignancy, arteriosclerotic heart disease, respiratory system cancer, and hypertensive heart disease all were found to be consistent with known dose-response relationships. Although all pollutants are significant when looking at relationships, NO2 is less significant when talking about damage functions. Another area in which it is relevant to assess