Traffic-Related Air Pollution (TRAP) Exposures from Border ... Objectives Traffic-Related Air...
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Traffic-Related Air Pollution (TRAP) Exposures from Border Crossings:
Assessing Affected Populations in El Paso, Texas Rohit Jaikumar1, Tara Ramani1, Amber Trueblood1, Inyang Uwak1,2, Suriya Vallamsundar1, Natalie Johnson2, Josias Zietsman1
1Center for Advancing Research in Transportation Emissions, Energy and Health, Texas A&M Transportation Institute, 2Department of Environmental & Occupational Health, Texas A&M University School of Public Health
Traffic Density Values by Census Block Group
• The city of El Paso shares a border with Mexico and is
home to some of the busiest border crossings in the U.S.
• In 2015, El Paso was found to be in non-attainment of the
National Ambient Air Quality Standards for 𝑃𝑀10.
• Traffic-related air pollution (TRAP) from the border
crossings may play a significant role.
• Populations living in proximity to the border crossings may
be disproportionately exposed to TRAP.
"b% EPBorderCrossing ADT_CUR
25022 - 58237
58238 - 101809
101810 - 177953
Hot Spot Analysis showing affected neighborhoods
Results • Significant findings revealed that the higher the level of
education (OR=0.6, p-value= 0.003) and median income
(OR=0.9, p-value= 0.001), the lower the odds of being in a
Conclusions • A systematic GIS-based approach revealed worse air
quality affected El Paso populations with lower
• Findings can inform efforts to address TRAP exposure,
health impacts, and environmental justice issues in border
Future Work • These preliminary findings will further inform our site
selection for the second phase of the study, which will
involve conducting personal air monitoring to identify and
characterize TRAP exposures within the hotspots.
High Volume Traffic Roadways
Population risk (Odds Ratio)
Hot spot areas
Traffic Density Block Groups Buffers VMT
High Volume Roadways/Truck Routes
El Paso Border Crossing Traffic
• To characterize exposures of people affected by TRAP in
the El Paso border crossing region in two phases.
• Phase 1 – Examine affected environment and populations
• Phase 2 – Monitor personal exposures to traffic emissions
• Identified high traffic volume roadways and truck
• Used GIS to overlay demographic data with traffic
density data to identify “hotspot” areas most affected
by border crossing emissions.
• Employed logistic regression to identify the probability
of living in a “hotspot” area and the demographics of