Date of Award

January 2013

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Brian P. Leaderer

Second Advisor

Theodore Holford

Abstract

Black carbon is a component of fine particle pollution that exerts adverse effects on both global climate change and human health. It is produced through incomplete combustion of fossil fuels and biofuels. Therefore, some of its major emission sources are diesel engines, residential heating and industry. Due to its strong ability to absorb solar energy, black carbon is one of the major contributors to global warming. In addition, black carbon and other fine particle pollutants can be inhaled into the lower respiratory track, causing and exacerbating health conditions such as asthma, chronic obstructive pulmonary disease, low birth weight, cardiovascular diseases and lung cancer. Current black carbon monitoring sites are located in close proximity to roads with heavy traffic volume. The dispersion of ambient black carbon is subjected to many influencing factors. Therefore, the concentration of ambient black carbon in residential areas is much lower than that in heavy traffic areas. Environmental health studies using these data are likely to overestimate the concentration of ambient black carbon. As a result, the true threshold for adverse health effects may be lower than what have been reported in these studies. This paper aims to use existing data on ambient black carbon, traffic volume, daily temperature, daily wind speed and daily precipitation level in Connecticut to construct a linear regression model which can be used to reliably predict the level of ambient black carbon in places where black carbon monitors are not available. Data from five black carbon monitor stations, geographically matched traffic count stations and meteorology stations were collected and analyzed. Results showed that black carbon was significantly influenced by seasonal cycle, days of the week, daily temperature and daily average wind speed. In contrast, traffic volume did not have statistically significant influence on ambient black carbon concentration. This study suggests that ambient black carbon is strongly affected by seasonal cycle, daily temperature and inversely affected by daily wind speed, but not by traffic volume, especially as the point of interest moves further away from traffic source. However, given that this study did not account for other sources of emission, such as residential heating and industry emission, the relationship between ambient black carbon and various emission sources could be further explored.

Comments

This is an Open Access Thesis.

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