El Paso Socio-Economic-Health Data Assessment
Communities are vulnerable. While transportation can benefit local communities—facilitating evacuations during disasters, for example—it can also present hazards. Knowing more about a local population can help engineers, planners, and policymakers minimize potential threats and better serve a community’s needs. The goals of this project included identifying where data about social, economic, and health vulnerability can be obtained, how agencies in El Paso are currently using these data, and making recommendations for improving both processes. CIITR researchers queried local/state agencies and other organizations in El Paso regarding their use of social, economic, and health data. The team then cataloged federal, local/university, and private data resources and identified limitations with currently available data sources. Suggestions for stakeholders to effectively use population data in their planning processes include:
- Train personnel on data sources and availability and ensure resources are available to integrate data into community planning.
- Improve communication with national/state agencies that might already have existing data resources for a given topic.
- Leverage existing expertise by using a third party to access the data needed.
- Establish relationships with Census Data Research Centers to access micro-level data not otherwise easily accessible.
- Identify specific information particularly relevant to stakeholders and use appropriate statistical techniques to achieve more thorough analyses across variables.
- Use social, economic, and health variables to enhance assessment and analysis aspects of planning studies for transportation, health, and emergency
For more information contact David Bierling at (979) 317-2563 or email@example.com.
Estimating Traffic-Related Air Pollution in El Paso
According to the Texas Commission on Environmental Quality (TCEQ), El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to the problem, along with other sources including industrial, residential, and cross-border. CIITR researchers used multivariate receptor modeling—specifically positive matrix factorization (PMF)—to separate unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. They collected and analyzed two sets of multivariate air pollution data: 1) speciated PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 microns) mass concentrations (measured every 3 days from 2006 to 2008) and 2) hydrocarbon hourly concentrations (measured in 2008) at El Paso’s Chamizal monitoring station. The team also used wind-direction analysis to estimate the contribution of sources from Mexico. Regression models were applied to relate traffic levels to vehicle emissions (taking the other sources into account). The findings of this project may interest local stakeholders—such as the El Paso Metropolitan Planning Organization, the Texas Department of Transportation, the City of El Paso, and TCEQ—in understanding source apportionment of pollutants measured in El Paso. It can potentially inform transportation planning strategies aimed at reducing emissions across the region or the development of more efficient traffic-management strategies. Other broader applications of this approach include supporting health impact analyses and risk analyses for border communities.
For more information contact Eun Sug Park at (979) 317-2466 or firstname.lastname@example.org.