Authors: Yogita Karale*, University of Texas At Dallas, May Yuan, University of Texas at Dallas
Topics: Geographic Information Science and Systems, Spatial Analysis & Modeling, Geography and Urban Health
Keywords: geographic context, PM2.5, urban, health, spatial, variability
Session Type: Paper
Start / End Time: 8:40 AM / 9:55 AM
Room: Governors Square 16, Sheraton, Concourse Level
Presentation File: No File Uploaded
Fine particulate matter, abbreviated as PM2.5 is one of the major pollutants that cause many cardiovascular and respiratory health problems. PM2.5 monitoring stations are located in an open area to avoid a local effect of buildings and trees. Because of their fixed locations, PM2.5 observations from these stations provide limited opportunity to study spatial variability in PM2.5 where most people live and the health effects are most impactful. Spatial obstructions like buildings and trees affect the dispersion and therefore concentration of PM2.5. Additionally geographic contextual factors like proximity to emission source, amount of area available for dispersion and wind direction with respect to emission source play an important role in determining the concentration and variability of PM2.5 at a location. This study investigates the effect of geographic context on PM2.5. The study chooses a University campus as a representative site for an urban area and collects PM2.5 observations at varied geographical settings using a mobile monitoring platform. Geographical settings are measured by distance and orientation of a location corresponding to emission source, spatial openness and wind direction relative to the emission source. This study develops a framework to relate geographical settings to PM2.5 using machine learning and presents findings of this research.