Descriptive Statistics 3S
The major cause of health discrepancies among people who live near major roads is traffic pollution. Traffic pollution is defined as the emission of aerosols from locomotives that in turn are inhaled by people living near major roadways (Ren et al, 2010). Research conducted focused on the difference in mortality rates of people diagnosed with air pollution cases and the prevalence of pollution related complications among people living close to heavy traffic areas. The study shows that traffic pollution is among the main health risks especially to people living near major road ways.
Bruggel, Durant and Rioux (2007) have used descriptive statistics very effectively in their article “Near-highway pollutants in motor vehicle exhausts”. It centers on pollutants produced by motor vehicle exhausts that in turn act as health risks to people especially those living near major roads. The article showed various distributions showing certain range values. For instance, the graph of normalized particle number concentration for different size ranges. It served the purpose of showing the various ranges at which particle concentration increased within a specific range of distance from the freeway. After measurements of mass concentration of 0.1–10 um particles and the size of distribution along major highways, the observation recorded was that there was a decrease in concentration that varied from 100 – 375 meters dependant on the wind direction and its speed. This helped show the particle number concentration with increase or decrease of distance from the freeway. With the help of these range values it was easier to reach the observation that it was between 15 – 200 meters from highways that the concentrations were highest presumably as a result of mixed highway pollutants and traffic emissions.
The arithmetic mean also served a major role in conducting and analyzing this specific study. The mean was used to determine the average risk ratio of mortality from cardiopulmonary diseases between the six locations that the particle concentration studies were being conducted. The separate locations gave different concentrations of fine particles which were 1.31, 1.31, 1.46, 1.37, 1.11 and 1.68 respectively. These concentrations were then added up and divided by the number of locations to give a mean of the range of concentrations obtained. The comparisons between the locations with the highest and the lowest fine particle concentration triggered the need for controlling the number of people living close to major high ways as well as aerosols present in motor vehicle exhaust fumes. Other than the highest and lowest fine particle concentrations, the median value was also of relevance in the analysis of the data collected as well as its interpretation. It served the purpose of showing a middle value in the entire range of distribution. It showed to which side the concentration, either lower or higher, was more than the other. If there was a higher accumulation of concentration value on the lower side of the median value, it translated to a lower overall concentration value. In the opposite case, if they were more on the higher side of the median value it meant that the overall concentration value would be high. The mode values showed that; of the six locations, two had similar concentration values. This translated to the locations being closer to each other or being subjected to the same environmental aspects for example, wind movement.
The extensive use of descriptive statistical methods in this article helped simplify the analysis of the data collected. Through them, results of traffic pollution are translated into statistical representations that are much easier to deduce information from.
Brugge1, D., Durant, J., & Rioux C. (2007). Near-highway pollutants in motor vehicle exhaust: A review of epidemiologic evidence of cardiac and pulmonary health risks. Retrieved: http://www.ehjournal.net/content/6/1/23
Ren. C., Park, SK., Vokonas, PS., Sparrow, D., Wilker, E., et al. (2010). Air pollution and homocysteine: more evidence that oxidative stress-related genes modify effects of particulate air pollution. Epidemiology 21: 198–206
Urdan, T. C. (2010). Statistics in Plain English. Routledge Academic.
Witte, R. S., & Witte, J. S. (2009). Statistics. Wiley.