Abstract (300 words maximum)
This research study investigates the influence of traffic on Particulate Matter, specifically PM10 in and around school during the drop-off and pickup hours. The study will focus on in and around an Atlanta local elementary school. PM10 is a fine particulate matter produced from engine emissions that may pose significant health risks, particularly to children and individuals with pre-existing respiratory conditions such as asthma. To monitor the concentrations of PM10 emissions, we will employ Purple Air monitoring sensors. Data collection will include monitoring of PM10 levels during morning (drop off) and evening (pick up) peak hours, considering variations in weather and temperature. Additionally, we will collect PM10 from the surrounding areas of the school environment to understand aerial distance (approx. 1 mile, based on the settling velocity of PM10) as to how far the PM10 may come from and contribute to in and around schools. PM10 data will be compared with ambient air quality as well as with EPA MCL to see the level of elevation. Statistical analysis will be performed to see the significant difference of PM10 in and around schools with ambient air quality and EPA MCL. It is expected that PM10 in and around schools will be statistically higher than ambient air quality and EPA MCL and hence it will prove that high emissions of PM10 come from vehicles during drop off and pick up hours.
Academic department under which the project should be listed
SPCEET - Civil and Environmental Engineering
Primary Investigator (PI) Name
M.A. Karim
Impact of Traffic on Air Pollutant, PM10 in and around schools
This research study investigates the influence of traffic on Particulate Matter, specifically PM10 in and around school during the drop-off and pickup hours. The study will focus on in and around an Atlanta local elementary school. PM10 is a fine particulate matter produced from engine emissions that may pose significant health risks, particularly to children and individuals with pre-existing respiratory conditions such as asthma. To monitor the concentrations of PM10 emissions, we will employ Purple Air monitoring sensors. Data collection will include monitoring of PM10 levels during morning (drop off) and evening (pick up) peak hours, considering variations in weather and temperature. Additionally, we will collect PM10 from the surrounding areas of the school environment to understand aerial distance (approx. 1 mile, based on the settling velocity of PM10) as to how far the PM10 may come from and contribute to in and around schools. PM10 data will be compared with ambient air quality as well as with EPA MCL to see the level of elevation. Statistical analysis will be performed to see the significant difference of PM10 in and around schools with ambient air quality and EPA MCL. It is expected that PM10 in and around schools will be statistically higher than ambient air quality and EPA MCL and hence it will prove that high emissions of PM10 come from vehicles during drop off and pick up hours.