Air Pollution 4 Essay, Research Paper

Damage Functions from Airborne Residuals

Air pollution is a growing concern that has gained an increasing level of attention by both scientists and legislators. This need to understand the degree of damages resulting from airborne residuals has sparked many projects to determine how these residuals are affecting us, and the cost that they bear to both our economy and our health. Once this information has been found, only then can we look at ways to estimate the benefit of pollution abatement.

Methods of determining mortality and morbidity related to airborne residuals are often unreliable and inadequate considering the amount of variables unknown. Epidemiological studies include data that are the best adapted to the estimation of air pollution effects. This data is gathered over long periods of time, however it is harder to pinpoint morbidity data because we don’t know all the things that made someone become ill. Another problem with gathering this is that a lot of morbidity simply isn’t reported. What is being reported is employment based, which includes things such as vacation and sick days. From this data we don’t have a way to relate sickness to that of pollution. To further complicate the problem, the use of death certificates aren’t relevant in the fact that they don’t tell what really killed the person, therefore making the information on them unreliable. Smoking habits, socioeconomic status, and many other immeasurable factors that can lead to death go unaccounted for.

A second method of investigating is by looking at episodic relationships. This deals mainly with mortality, and the information gathered reflects episodes recorded in discrete time blocks. It is gathered during a short term, for example, on a daily or weekly basis. The problem with this is that the measurements are crude and are based only on a single measurement for a large area. Once again, long-term exposure or habits can cause data to be unreliable, take for example someone smoking for 45 years who developed lung cancer. Lifetime exposure might bear little relation to currently measured levels of pollution. Conversions over time and space are sensitive to certain combinations of things in the atmosphere. It is evident that the ideology is undefined when talking about air pollution in the fact that no direct relationship exists.

Having utilized both of these methods in looking at the relationship between air pollution and human health are Lester B. Lave and Eugene P. Seskin. Their investigation has looked at the mortality experienced based on a certain smoke and deposit index between both males and females in different county boroughs in England. The results from these areas are in relation to such diseases as bronchitis, lung cancer, cardiovascular disease, and respiratory disease. Through a multiple regression analysis, they concluded that air pollution accounts for a doubling of the bronchitis mortality rate for urban, as compared to rural, areas. The variation of the mortality ranges from .3 to.8, which concludes that it is a significant explanatory variable in all cases. From this they proposed that if the air quality in all the boroughs were improved to the quality to that of the best air in all the samples, the average mortality rate would fall from 129 to 77. This represents a 40 percent drop in the death rate among males and a 70 percent drop among females. They also claim that bronchitis mortality can be reduced from 25 to 50 percent depending on the location and deposit index. This seems hard to imagine considering how and if the air could actually be made to the best air experienced.

The results comparing lung cancer and pollution are less conclusive but have been correlated with several indices of pollution and socioeconomic variables. For smokers, adjusted death rates ranged from 25 to 123 percent higher in urban areas than in 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 airborne residuals is in the agricultural sector. To appropriately deal with this issue requires looking at background information over time. This will allow a person to make their best guess for a model to illustrate what is taking place. The model requires air quality modeling which will help show what is happening to convert emissions into ambient concentrations. This is influenced by factors such as time, temperature, moisture, and wind direction. Emissions from both point and mobile sources should be viewed to relate ambient concentrations to agricultural losses, thereby formulating damage functions. However it is often difficult to accept them because the results aren’t certain. Many are uncomfortable in establishing a schedule dealing with crop loss to ambient concentration, however they can give their best guess.

A study dealing with agricultural losses from airborne residuals in the Ohio River Basin looked at producers of corn, soybeans, and wheat between 1976 and 2000 in regards to concentrations of SO2 and 03. The purpose of the study was to find estimates of damage functions and recognize monetary losses attributed to them. This required looking at utility related damages versus other sources, and to what extent they are related to nominal and peak load emissions. Three scenarios where devised to represent possible cases. The first scenario assumes that business is conducted as usual. This means that plants comply with performance standards, which requires replacing SIP units with new source performance ones. These new standards require a different physical capital stock to be applied to the plants. Compliance must be done in order to accurately judge air pollution and how effective pollution control is. This also includes looking at annual projected emissions and a schedule of bringing plants online. The second scenario is the same as the first except there is no compliance with the SIP’s. This is referred to as the dirty air scenario. The third scenario indicates a high growth in electric demand. Utility life of the SIP’s was upwards of 45 years. It is hard to determine which variable caused a change in the outcome.

The way to begin this study was to look at what output was versus what output could have been. Declining productivity, for example, can be viewed in bushels per acre. This will give the physical damages, which then can be converted into monetary losses. Considering the size of the region being studied, the best that could be done was to be given a damage coefficient, which represented a number indicating the percent reduction in activity. The damage coefficient is probable, not a precise value. The observed production in any year is a point estimate of dirty air production. From this a weighted average price was calculated. This in turn will help determine the change in producer surplus. This change is distinguished by the shift in the supply curves, which reveals a number considered to be a legitimate number of the loss experienced. To arrive at this number first requires determining dirty air and clean air output. Dirty air is found when the damage coefficient is subtracted from clean air output, then multiplied by clean air output. Clean air output is found by dividing dirty air output by one minus the damage coefficient. These values now show the location and magnitude of the supply curves.

The next problem in deriving this calculation deals with the time value of money. Dollar values change over the years and in order to use an appropriate value in the calculation, future losses must be discounted to a present value in time. The discount rate picked for this must be in the area of what a relatively informed person can earn over the time horizon. Choosing a low discount rate will have losses maximized in the future while a high discount rate will have losses minimized in the future. In this study, a discount rate of 10 percent was chosen.

The results of the study indicate that monetary losses were 12 percent of the present discounted value of clean air production. Looking at scenario one, the total region losses constituted roughly 10.3 percent of the discounted value of pollution free output. These losses were during peak load emissions and half the losses, 4.3 percent, are utility related from coal-fired activity. The rest of the losses are from point and mobile sources. Looking at the second scenario the results are almost identical showing a total regional loss of 10.4 percent and utility related losses at 4.3 percent. This indicates that compliance or noncompliance with the SIP’s did not significantly influence the losses experienced in the region. The third scenario showed similar results except that losses from utilities increased from 4.3 to 5.5 percent. The loss distribution among this scenario was comparable to scenarios one and two, however this was the only one where agricultural losses increased over the time period. This can be largely due to the higher growth rate in electric demand.

Another study dealing with damages in the agricultural sector dealt with damages to corn and hay in the northeast from acidic deposition and Nox. The study looked at the region consisting of five states including Maine, Massachusetts, New Hampshire, New York, and Vermont. The focus of this study was to calculate monetary losses for the year 1979. In this study was a sensitivity analysis where both physical and economic parameters were varied in order to assess the sensitivity of final results to physical economic estimates. Due to the lack of information regarding the movement of airborne residuals, the study is based on total measurements of rainfall acidity and concentrations of Nox.

Looking at the results from this study, it is clear that potential losses to producers do exist. The results indicate a potential loss of about $65 million in 1979. These losses are at a point in time in comparison with the ORBES study covering the period from 1976 to 2000. Of the five states, New York suffered the highest losses, which attributed to 78.1% of the total losses. The losses associated with corn and hay were roughly 8.2% of producer surplus related to a clean air situation. However, there is no split between utility and background sources. The study uses ambient regions to get ambient concentrations and acidity of rainfall. In the presence of dirty air, 6% was the losses attributed to corn production, while 10% was attributed to hay. It should be noted that no research of their own was used in forming the damage coefficient for hay. They used literature to estimate for the hay.

Regional losses were found to be more sensitive to variations in prices of crops, rather than that of supply elasticity’s for the crops. However, damage coefficients are difficult to come up with when using this analysis. The damage that is occurring is not related to point sources in the area. The particulates are being blown into the area from prevailing winds. In the end it is clear that damage is occurring to these crops, but the true amount of damage is difficult and maybe even impossible to calculate.

In conclusion, it is becoming necessary to recognize the degree of damage that airborne residuals have on both our health and sectors of our economy. Studies like the ones mentioned above are only a few of the many which attempt to put a dollar value on the losses experienced in order to help us decide how relevant pollution abatement is. Realizing that every study is not accurate and is only a best guess should suggest that more research be done before spending money on corrective actions.