Assessment of Air Quality Trends Near Roadways

Executive Summary: In this project our group set out to determine what effect, if any, an air monitor's distance from the roadway had on the measured air pollution levels of carbon monoxide, nitrogen dioxide, and fine particulate matter (PM2.5). The group was also searching to see if we could identify pollution emitters using wind data. "Fine particles," such as those found in smoke and haze, are 2.5 micrometers in diameter and smaller. We obtained yearly, hourly, and daily data for each pollutant from 1999-2010 from the U. S. Environmental Protection Agency (USEPA). For both carbon monoxide and nitrogen dioxide the air monitors that are less than 30 meters from the road had higher concentrations than the other monitors located further from the road. For fine particulate matter, the monitors located 30 to 100 meters from the road were significantly different from monitors located closer to the road (less than 30 meters) or monitors located further than 100 meters from the road. Regression models were developed which examined the air pollution concentration as a function of elevation, the annual average daily traffic count, the distance the monitor is located from the roadway, the interaction of traffic count and distance from the roadway and the effect of year, which reflects air pollution controls implemented over time. The explanatory variables: elevation, the interaction of traffic count and distance from the roadway and the effect of year are significant for all three pollutants (p<.001). The annual average daily traffic count and distance from the roadway were also highly significant for NO2 (p<0.0001). Finally, the distance from the roadway was also highly significant for PM fine (p<0.0001). Carbon monoxide and nitrogen dioxide have higher levels in the winter months. A majority of CO monitors reported their smallest concentration at 0.3 parts per million. Only NO2 was found to have a day of the week effect with weekend days having lower concentrations than weekdays at all three set back distances from the roadway. When examining the wind data our group was able to use hourly wind speed multiplied by the hourly concentration of each sites individual pollutants to create a wind graph showing pollutant readings at degree intervals of 10. Using Google Images of the individual sites our team was able to see that our wind graphs correctly identified primary and secondary pollutant sources for all 8 case study sites. Each graph showed that the main …