Graphics summarizing and comparing the case study results are presented below in Table 5.1, Figure 5.1 for PM2.5, and Figure 5.2 for NOx.
Emissions from commercial vehicles are significantly larger than those from the privately owned vehicles even though there are substantially more privately owned vehicles crossing the border at Ysleta-Zaragoza. Commercial vehicles make up only about 15 percent of the volume in this case study, but have on the order of 11 times the PM2.5 emissions and six times the NOx emissions than privately owned vehicles.
Delay and queuing account for approximately half of the emissions associated with traffic crossing the border at port of entry. While this is an approximate estimate, it has important implications for the maximum level of emission reductions that can be achieved by strategies focused on the port of entry itself.
|Commercial Vehicle PM2.5||Privately Owned Vehicle PM2.5||Commercial Vehicle NOx||Privately Owned Vehicle NOx|
|No Delay (Hypothetical)||6.29||0.70||85.46||14.70|
|Commercial Vehicle Strategy||12.48||1.16||168.08||29.60|
These results are estimated based on daily average conditions for northbound traffic in 2010. One finding is that southbound travel information, in terms of typical delay in queue length, is extremely limited. There also is little agreement among stakeholders over the quality of the southbound data that are available. The same issues crop up with traffic forecasts because there are often multiple jurisdictions preparing estimates of future conditions for a variety of purposes. Because these are case studies designed to illustrate the application of the analysis template, this work focused on 2010 northbound travel, rather than attempting to resolve some of those data concerns.
The template also is designed to be relatively straightforward to apply as improved travel data becomes available. Therefore we encourage results to be updated as appropriate.
Source: Cambridge Systematics, Inc.
Source: Cambridge Systematics, Inc.
All recommendations for best management practices focus on minimizing queue delay and congestion at the border. Another important consideration is that commercial traffic contributes proportionately much more pollution than passenger vehicle traffic.
Examples of strategies that would be compatible with these best management practices would be: consolidating toll and inspection booths; appointment systems; and preclearance of more vehicles and vehicle occupants through programs such as SENTRI, FAST, and the use of Ready Lanes.
Candidate performance measures include:
All of the above metrics should be minimized.
This section supports decision-making about the transferability of estimation protocols for emissions and delay. More detailed information can be found in the Task 3 technical reports included as an attachment to this white paper and analysis template. Parameter categories examined generally include:
In reviewing the data collected, the El Paso POE Group has been identified as having the following characteristics: truck volumes exceeding 700,000 in 2010, and comprising 7 percent of total vehicle counts; and passenger vehicle counts nearing 10 million and bus counts exceeding 22,000 in 2010. The El Paso POE Group carries high volumes of truck traffic coupled with high passenger and bus traffic. Approximately half of El Paso NB truck crossings are laden, and truck counts are relatively constant by season (as is the percent of trucks that are laden). Examining proxies for relative levels of congestion and delay, the El Paso POE Group carries 55,000 trucks per commercial lane annually, and 4,600 trucks per lane per daily hour of operation – and El Paso crossings are open 12 or 16 hours per day for commercial traffic.
Noting these characteristics, the most similar POE Groups to El Paso appear to include: Laredo, Texas; Hidalgo, Texas; Calexico East, California; Nogales Arizona; Brownsville, Texas; and Otay Mesa, California. For all of these crossings, relatively high truck and passenger vehicle volumes are present. At Brownsville and Calexico East, the percentage of NB laden trucks is consistent with El Paso, though Laredo, Otay Mesa, Hidalgo and Nogales do have higher proportions of laden trucks (it is important to note that trucks maybe laden when traveling NB, but unladen upon return, so these proportions may balance out when both NB and SB traffic are considered). In terms of lane configurations, these crossings all have several commercial truck lanes, and the presence of a FAST lane. In terms of relative volume and congestion, Brownsville, Hidalgo, Laredo, Nogales, Otay Mesa, and Calexico East all have at least 50,000 trucks per commercial lane, or over 3,500 trucks per lane-hour. Hours of operation at each of these POEs are typically between 12 and 16 hours, though Hidalgo operates for fewer hours. Additionally, grade is relatively consistent among these crossings (0-1.99 percent), although grade at Otay Mesa is higher (it is important to note that actual grade at crossing may differ from topography, due to presence of bridges or other infrastructure with differing grade). Temperature and humidity profiles do differ at Brownsville and Hidalgo, though Laredo, Nogales, and Calexico East profiles are similar to El Paso.
For these conditions, the dominant ports that are unlike El Paso are Lukeville, Sasabe, Naco, Roma, Columbus, and Presidio. For all of these POE Groups, truck volumes are relatively low (less than 10,000 per year) and there are only 0-2 dedicated commercial truck lanes present – and no FAST lanes. In terms of relative volume and congestion, these POEs have less than 10,000 trucks per lane per year, and have roughly 1,000 or less trucks per lane per hour of operation. Lukeville, Sasabe, Columbus, and Presidio are identified as being in regions with high grade compared to the major crossings. However, all of these crossings do have similar temperature and humidity profiles when compared to El Paso (with the exception of Roma).
The above analysis is a first-order consideration of POE location variables and examine their applicability as inputs for the MOVEs model along the entire U.S.-Mexico border as a whole. In addition to the size, use, and influence of each specific POE, other site-specific variables can be analyzed in more detail for a better understanding of emissions related to each POE.