________________________________________

Environmental Costs of Commercial 
Motor Vehicle (CMV) Crashes
Phase II ? Part 2: Estimation Report

March 2007

Prepared by

Theresa Flieger, Adam Klauber, Jos Mantilla, and Paul Zebe
Environmental Engineering Division
Volpe National Transportation Systems Center
Research and Innovative Technology Administration (RITA)
U.S. Department of Transportation

Prepared for

					Michael Johnsen
Regulatory Evaluation Team
Federal Motor Carrier Safety Administration (FMCSA)
U.S. Department of Transportation

TABLE OF CONTENTS

Section											Page

	LIST OF TABLES		iii
	LIST OF FIGURES		iii

1.0	INTRODUCTION		1-1
1.1	Purpose		1-1
1.2	Focus			1-1
1.3	What Is a CMV Crash		1-2
1.4	Resource Categories		1-3
1.5	Limitations		1-3
1.5.1	Congestion		1-3
1.5.2	Air Quality and Greenhouse Gases		1-3
1.5.3	Hazardous Materials		1-4
1.5.4	Solid Waste		1-4
1.6	Overview of the Report		1-4

2.0	CONGESTION		2-1
2.1	Introduction		2-1
2.1.1	Congestion and Delay		2-1
2.1.2	Chapter Overview		2-2
2.2	Background		2-2
2.3	Congestion Delay Impact Methodology		2-3
2.4	Results			2-8
2.4.1	Overall CMV Crash Related Delay		2-8
2.4.2	Hazardous Materials (HM) CMV Crash Delay		2-11
2.4.3	Significant Regional Incidents		2-11
2.5	Congestion Delay Validation		2-12
2.6	Recommendations for Future Study	 2-14

3.0	AIR QUALITY AND GREENHOUSE GASES		3-1
3.1	Introduction		3-1
3.2	Background		3-3
3.3	Air Quality Emissions Methodology		3-6
3.4	Human Health Damages		3-18

4.0	HAZARDOUS MATERIALS		4-1
4.1	Introduction		4-1
4.2	Background		4-2
4.3	Methodologies		4-4
4.3.1	Data Sources		4-4
4.3.2	Data Query Descriptions		4-5
4.4	Estimates		4-10
4.4.1	Table 4-1 ? CMV Crashes and HM		4-10
4.4.2	Table 4-2 ? Releases of HM per Crash by CMV Category		4-13
4.4.3	Table 4-3 ? CMV HM Incidents and Releases of HM		4-18
4.5	Environmental Impact Summaries for Hazardous Materials		4-23
4.5.1	Hazardous Materials Overall		4-23
4.5.2	Hazardous Materials by Class		4-24
4.6	Conclusion		4-30

5.0	SOLID WASTES		5-1
5.1	Introduction		5-1
5.2	Background		5-1
5.3	Solid Waste Impact Methodology and Results		5-2
5.3.1	Automobiles		5-3
5.3.2	CMV Power Units		5-5
5.3.3	Buses		5-7
5.4	CMV Trailers		5-10
5.5	CMV Cargoes		5-10
5.6	Conclusions		5-11
5.7	Recommendations for Further Study		5-12

6.0	BIBLIOGRAPHY		6-1

LIST OF TABLES

Table											Page

2-1	Comparison of CMV Crash Road Delay by Severity Level		2-5
2-2	Total Number of Rural and Urban Crashes by Roadway Type		2-8
2-3	CMV Crash Congestion Delay and Costs by Crash Severity
And Roadway Type		2-9
2-4	HM and Non-HM Crash Vehicle Day Hours		2-11
3-1	Emission Rates (Grams/Mile) by Roadway Type		3-6
3-2	Total Tailpipe Emissions		3-12
3-3	Total Emissions		3-16
3-4	Total Per Crash Emissions		3-16
3-5	Carbon and Carbon Dioxide Emissions Per CMV Crash from 
	Wasted Fuel and Upstream Emissions from Fuel Refining and 
	Distribution by Congestion Scenario		3-18
3-6	Total Damage Costs and Damage Costs Per CMV Crash for 
	All Pollutants			3-20
4-1	CMV Crashes and HM		4-11
4-2	Releases of HM per Crash by CMV Category		4-14
4-3	CMV HM Incidents and Releases of HM		4-19
4-4	Summary:  Fuel Tank Spills Due to CMV Crashes		4-30
5-1	Quantity of ASR Sent to Landfill for Each Automobile Involved 
	in a CMV Crash		5-4
5-2	Percentage of Automobiles ?Totaled,? 2003-2005		5-4
5-3	List of Materials for a Hypothetical CMV Truck Tractor		5-6
5-4	Quantity of ASR Sent to Landfill for Each Truck Tractor Involved 
	in a CMV Crash		5-7
5-5	List of Materials for a Hypothetical Intercity Bus		5-8
5-6	Quantity of ASR Sent to Landfill for Each Intercity Bus Involved
in a CMV Crash		5-9

LIST OF FIGURES

Figure											Page

2-1	Time Flowchart of a CMV Crash		2-3
2-2	CMV Type by Roadway Facility (Rural and Urban)		2-7
2-3	Comparing Rural and Urban CMV Crashes and Delay		2-10
2-4	Volpe vs. Zaloshnja and Miller 2006 (Rural and Urban)		2-13
3-1	Carbon Monoxide Emissions vs. Velocity		3-10
3-2	VOC and NOX Emissions vs. Velocity		3-10
3-3	Carbon Dioxide Emissions vs. Velocity		3-11
3-4	Carbon Dioxide vs. Speed		3-11
3-5	Tailpipe Criteria Pollutant Emissions per Crash		3-12
3-6	Fuel Consumption per CMV Crash		3-15
3-7	CO2 Emissions per CMV Crash		3-15
3-8	Criteria Pollutant Emissions Per Crash		3-17
3-9	Total Criteria Pollutant and Carbon Damage Per CMV Crash
And Congestion Scenario		3-20

1.	 INTRODUCTION

The Federal Motor Carrier Safety Administration (FMCSA) is generally charged with 
preparing economic and environmental impact analyses to support proposed amendments 
and additions to the Federal Motor Carrier Safety Regulations (FMCSRs).  

Economic analyses are performed in support of amendments and additions to the FMCSRs 
as a consequence of Executive Order 12866 and Department of Transportation (DOT) 
Policies and Procedures.  For those economic analyses, FMCSA currently uses ?Revised 
Costs of Large Truck- and Bus-Involved Crashes? (2002) by Eduard Zaloshnja and Ted 
Miller of the Pacific Institute.   That report, commissioned by FMCSA, provides regulators 
and analysts at the agency (and elsewhere) with estimates of the economic costs associated 
with commercial motor vehicle (CMV) crashes.  

Environmental compliance analyses are performed in support of amendments and additions 
to the FMCSRs as a consequence of the National Environmental Policy Act (NEPA) and 
FMCSA?s Order 5610.1.  Up to now, FMCSA has not had a source comparable to Zaloshnja 
and Miller that could be used when evaluating the environmental impacts of proposed 
regulations.  As a consequence, generalizations and qualitative statements have been 
generally used when evaluating the potential environmental impacts of FMCSRs, their effect 
on changing the frequency and severity of CMV crashes and other safety related incidents, 
and their associated environmental impacts and costs.  

1.1	PURPOSE

The purpose of this report is to provide FMCSA with estimates for its environmental 
analyses comparable to those currently being used by the agency for its economic analyses.  
These environmental estimates, along with the included descriptions of the methodologies 
upon which they are based, will enable FMCSA to describe the inherent links between CMV 
safety and environmental protection.  The estimates presented in this report are meant to be 
used primarily in the preparation of Environmental Assessments (EAs) and related 
documents.  For Environmental Impact Statements (EISs), which look at potentially 
significant impacts on the environment, the estimates and methodologies presented in this 
report represent a starting point for analysts, who will probably want to develop their own 
more detailed estimates.  

1.2	FOCUS

Consistent with the primary safety focus of FMCSA, the focus of this report is on 
environmental estimates for commercial trucks with a gross weight rating of over 10,000 
pounds and buses.  In an effort to promote consistency, to the extent practicable, the 
methodologies and estimates presented in this report are for the same general vehicle 
categories used by Zaloshnja and Miller for economic analyses:  

*	Straight Trucks
*	Truck Tractors
*	Medium/Heavy Trucks
*	Buses 

1.3	WHAT IS A CMV CRASH?

For the purposes of this analysis, a CMV involved in a crash is defined as follows:  

*	Any truck having a gross vehicle weight rating (GVWR) of more than 10,000 
pounds or a gross combination weight rating (GCWR) over 10,000 pounds used on 
public highways,
   OR
*	Any motor vehicle designed to transport more than eight people, including the 
driver,
   OR
*	Any vehicle displaying a hazardous materials placard (regardless of weight)
[NOTE:  This criterion assumes that an officer at a crash site may not be familiar 
with the Federal Hazardous Materials Regulations (Specifically, 49 CFR Part 172).  
If an officer or associate is knowledgeable in those, any vehicle discovered to be 
transporting hazardous materials without a required placard should also be included.]
      AND
*	That vehicle is involved in a crash while operating on a roadway customarily open to 
the public, which results in:
o	A fatality:  any person(s) killed in or outside of any vehicle (truck, bus, car, 
etc.)  involved in the crash or who dies within 30 days of the crash as a 
result of an injury sustained in the crash, OR
o	An injury:  any person(s) injured as a result of the crash who immediately 
receives medical treatment away from the crash scene, OR
o	A tow-away:  any motor vehicle (truck, bus, car, etc.) disabled as a result of 
the crash and transported away from the scene by a tow truck or other 
vehicle.
   EXCEPT 
    	Crashes that involve:
*	A personally-owned truck or passenger vehicle meant for personal use only as the 
sole vehicle meeting the criteria above, OR
*	A driver with a disease condition (stroke, heart attack, diabetic coma or epileptic 
seizure) and no other injury or damage occurs,  OR
*	Deliberate intent (suicide, self-inflicted injury, homicide, etc.), with no 
unintentional injury or damage. 

The foregoing description matches the conditions for reporting crashes to FMCSA via 
Safetynet.  The description differs slightly from the definition of an ?accident? found at 49 
CFR 390.5, it should be noted.  

1.4	RESOURCE CATEGORIES

Consistent with the requirements of NEPA and FMCSA implementing regulations, estimates 
are presented in this report for the following four resource categories:

*	Congestion
*	Air Quality and Greenhouse Gases
*	Hazardous Materials 
*	Solid Waste

Other resource categories, such as noise, ecological resources, and water quality, which 
would appear to require significantly more elaborate and involved analyses, may be 
addressed in future updates of this report.

1.5	LIMITATIONS

To properly use the environmental estimates presented in this report, analysts and policy-
makers using this report must be aware of their limitations.  A summary of some of the more 
significant limitations by resource category is presented below.

1.5.1	Congestion 

*	Crashes affect traffic flow beyond the vehicles delayed at the crash site.  This report 
does not include CMV crash impacts attributable to re-scheduling, re-routing, 
reduced mobility, and reliability.  
*	No estimate of the delay associated with evacuations due to hazardous materials 
spills from CMVs is included in this report. 
*	The congestion impact of severe crashes (i.e., those involving the closure of major 
highway arteries for extended (crash related delay lasting more than 9 hours) 
periods) was not calculated.  In the case of extended road closures, traffic will be 
diverted to alternative routes and the associate delays will be dispersed over multiple 
roadways. 
*	Only limited real-world delay data could be obtained for the congestion analysis.
*	Breakdowns and other non-crash related delay is not considered in the report

1.5.2	Air Quality and Greenhouse Gases 

*	The air quality and greenhouse gases analysis does not consider idling time and 
instantaneous speed and acceleration throughout the duration of the congestion 
associated with the slow stop-and-go driving characteristics.  This is a significant 
limitation, since fuel consumption and emissions are highly dependent on 
acceleration and are significantly higher during idle and stop-and-go driving 
(especially at very slow speeds) than those resulting from unrestricted free-flow 
driving conditions.  Using average steady state speeds, as was done in this analysis, 
is expected to largely underestimate emissions and fuel consumption.
*	The air quality and greenhouse gases analysis uses time of delay rather than the total 
time in congestion.  Time of delay is a portion of the total time in congestion.  The 
accuracy of the estimates is limited by not estimating the difference betweeen the 
emissions and fuel consumption associated with the total time in congestion (at the 
congested driving conditions) and the emissions and fuel consumption associated 
with the time it would take to travel the distance affected by the CMV crash (at an 
average free-flow speed associated with the specific facility type affected in the 
incident).
*	The air quality analysis does not address impacts of air pollutants on visibility and 
the associated social costs.  Air pollutant impacts on health and greenhouse gases 
and impacts on climate change are addressed in this document. 

1.5.3	Hazardous Materials

*	FMCSA data inexplicably reports more crashes involving hazardous materials 
releases than PHMSA data.  
*	There was a possibility of double counting of certain non-crash incidents when 
quantities were categorized by hazardous materials class.  
*	No information was readily available on spills from bus fuel tanks.

1.5.4	Solid Waste  

*	Only rough approximations are readily available for the solid waste generated by 
automobiles, truck tractors, and buses involved in CMV crashes.  
*	No information is readily available for the solid waste generated by other types of 
CMVs involved in CMV crashes.
*	No information is readily available for the solid waste generated by truck trailers that 
are involved in CMV crashes.  
*	No information is readily available for the solid waste generated by cargoes being 
carried by CMVs involved in crashes (or for cargoes being carried by other 
conveyances that might be involved in CMV crashes).  This would appear to be 
counterintuitive, since the insurance industry is generally paying for cargoes lost due 
to CMV crashes.  The insurance industry should have the desired information.  The 
insurance industry, however, lumps cargoes for which they paid because of vehicular 
accidents with cargoes for which they paid because of cargo theft, which is a 
significantly bigger problem for insurers than cargo damage/loss due to vehicular 
accidents.

Because of the limitations to the environmental estimates presented in this report, in some 
cases, analysts developing EAs may need to develop their own estimates of the 
environmental impact of certain actions.  For instance, analysts working on a new safety rule 
for intermodal containers might need to develop ad hoc estimates of the solid waste typically 
generated in a CMV crash.  

1.6	OVERVIEW OF THE REPORT

The remainder of this report is organized as follows.  Chapter 2 looks at congestion.  
Congestion relief is one of the top U.S. DOT priorities.  Thus, having estimates of 
congestion impacts (traffic delay and associated economic and environmental costs) from 
changes in the frequency and severity of CMV crashes will allow FMCSA to support DOT 
in this strategic goal.  Additionally, congestion serves an important input for the 
environmental impact of CMV crashes on air quality and greenhouse gases.  

Chapter 3 discusses air quality and greenhouse gases.  With respect to air quality, Section 2a 
of the Clean Air Act charges the U.S. DOT with monitoring, evaluating, and controlling 
transportation activities (such as motor vehicle operation) in order to protect and enhance the 
quality of the environment.  Thus, estimates of the air quality impacts of CMV crashes will 
enable FMCSA to consider emissions impacts associated with evaluating safety regulations 
and programs by capturing their associated environmental and health costs.  Given the 
relevance and increased focus on the potential impacts of greenhouse gases (GHG) on global 
climate change, the air quality chapter also addresses GHG estimates.  

Chapter 4 addresses hazardous materials (HM) incidents.  FMCSA seeks accurate 
information on HM releases into the environment.  The hazardous materials chapter presents 
estimates of releases from CMV crashes on a per unit basis, and also estimates of releases 
from transportation incidents involving CMVs but not resulting from a CMV crash.  

Chapter 5 considers the solid waste generated by CMV crashes.  

Chapter 6 presents a bibliography containing all of references for the report.

2.  CONGESTION

2.1	INTRODUCTION

Congestion from CMV crashes reduces U.S. productive efficiency by delaying shipments 
of goods.  Extra time spent stopped or traveling at slower speeds could be spent in more 
economically productive and enjoyable activities.   Recurring congestion can eventually 
cause urban development to spread out as businesses attempt to decentralize jobs and 
housing to reduce travel time (Downs 2004).  The longer distances between workplaces 
and home in turn produce more demand for fossil fuels.  For CMVs, this means more 
deliveries of petroleum and more time on the road transporting household goods, which 
in turn results in higher consumption of fuel.

2.1.1	Congestion and Delay

Traffic congestion is a daily visible challenge to economic efficiency and quality of life.  
The National League of Cities (NLC) has found that city officials believe alleviating 
traffic conditions is their one of their most important goals (NLC 2006).  In addition, 
traffic delay is at the top of NLC?s list (34 percent of respondents) of conditions that have 
most deteriorated over the past five years.  The Texas Transportation Institute (TTI) 
estimates that the 85 largest metropolitan areas experienced 3.7 billion vehicle hours of 
delay (TTI 2004).  Traffic volumes are rising every year, so congestion is expected to 
increase.  More CMV?s will have to travel on roadways to move a volume of freight that 
is expected to nearly double by 2020 (FHWA 2006).  American businesses have realized 
huge productivity gains in part from reducing inventory requirements and use of ?just in 
time delivery.?  Growing congestion threatens to reverse some of these efficiencies and 
require businesses to carry larger inventories (US DOT 2006a).  In 2006, the U.S. DOT 
created an executive Congestion Working Group to prioritize solutions to our nation?s 
growing problems with traffic delay.  Collision related delays constitute a sizeable 
portion of total congestion in the U.S.  In 1999, as many as 1.7 billion vehicle hours were 
lost to delay due to collisions (Chin et al. 2004).  Vehicles involved in crashes often block 
traffic, need emergency response, and cause curious drivers to slow down to 
?rubberneck.?  Cambridge Systematics estimates that traffic incidents (including crashes) 
constitute approximately 25 percent of traffic congestion (Cambridge Systematics 2005).  
CMV crashes represent a sizable number of the overall number of crashes, and, due to the 
large size of trucks and buses, often result in longer delays than crashes where just cars 
are involved (Ullman and Ogden 1995, Glickman et al. 1991).  

CMV crash linked congestion also effects air quality and fuel use.  Increased idling, 
accelerating and decelerating, and longer engine run times all result in increased 
emissions above steady road speeds.  The congestion delay estimation will assist the 
evaluation of the air quality effects of CMV crashes, which is discussed in the next 
chapter of the report.  FMCSA regulations can have an indirect effect on congestion 
related to CMV collisions.  If regulations effectively reduce the number of large truck 
and bus crashes, associated delay will also decrease.  This chapter proposes a method to 
quantify the congestion impacts associated with CMV crashes.

For this report, economically productive and leisure activities are valued equally.  Both 
types of activities have an ?opportunity cost.? An individual makes a decision, based 
partly on economics, that they will forgo some action and its payoff in order to pursue a 
certain activity.  Congestion delay impedes both the selected action and the forgone 
alternative.

2.1.2	Chapter Overview

This chapter helps fulfill the U.S. DOT Congestion Working Group?s seven-point 
strategy for reducing congestion.  The chapter provides background information about 
congestion and how it occurs.  Following that background discussion,  two possible 
methodologies are outlined for calculating both congestion delay and its associated costs.  
The first method builds on Zaloshnja and Miller?s work using updated data.  The second 
method is an alternative slightly modifying a Battelle approach previously developed for 
FMCSA.  The preferred method is identified, including the reasons for the choice.  An 
approach to validate the results of the methodology is proposed.  The chapter then 
discusses accounting for significant regional incidents ? large crashes that can skew 
results due to the scale of their impact.  

2.2	BACKGROUND

CMV crashes cause traffic delay in a number of ways.  Crashes can cause damage to the 
driver?s own vehicle, other trucks, buses, or cars, requiring removal of some or all of the 
vehicles involved from the roadway.  The damaged vehicles may block roadway lanes, 
necessitating tow removal.  The tow vehicles themselves may impede traffic flow as 
other vehicles in the traffic bottleneck will stop and allow access.  Drivers and passengers 
may be injured or killed, requiring emergency medical vehicles to respond, rescue, and 
evacuate.  Cargo spills, especially hazardous materials, may require specialized clean up 
procedures.  Fires from vehicles or cargo may have to be extinguished.  In some cases, 
bridges and other road infrastructure could be damaged, requiring extended closure to 
assess and repair.  In all cases, curious drivers will likely slow down to view the incident 
causing even more congestion, which is referred to as rubbernecking or gaper?s block.

Any safety regulation that effectively reduces the number of CMV crashes has the 
potential to reduce congestion impacts.  Delay estimates can be derived through studying 
a number of metropolitan regions that represent large, medium, and small cities.  Police 
departments record crash delay times.  That information can be used to estimate the total 
number of hours of delay.  Economic costs can be calculated by multiplying the total 
delay by the value of time for all the individuals caught in the traffic (Miller 2006).  With 
this data, FMCSA can make a forecast of the monetary impact of the crash and combine 
it with the medical, air quality, and hazardous waste analyses to obtain a total social cost 
for each crash, and thus quantify the monetary benefit of regulations that reduce the 
number of incidents by a known or projected quantity.  

One well-regarded study of U.S. congestion from the Texas Transportation Institute 
estimates 3.7 billion hours of annual congestion delay valued at $63 billion in economic 
cost (Schrank and Lomax 2005).  The Texas Transportation Institute calculates 
congestion costs with productivity losses due to time spent in delay and extra fuel 
expended from longer travel times.  

Compared to other social costs (not covered by the price of vehicle operation and user 
fees), congestion may be the highest (Gomez-Ibanez 1997, K.T. Analytics 1997).  The 
Texas Transportation Institute found that incidents, including collisions, cause 52 percent 
of congestion in 85 urban areas it studied in 2003.  Studies have found that trucks are 
involved with 17 percent of crashes in the Washington D.C. capital beltway area 
(Glickman et al. 1991) and approximately 11 percent of incidents in the Houston area 
(Ullman and Ogden 1996).  The percentage of congestion caused by incidents varies 
greatly from region to region.  Ranges in one 1984 study found a low of 37 percent (San 
Diego) and a high of 100 percent (Indianapolis)  (Downs 2004).  This impact is greater 
when major incidents are analyzed.  Ullman and Ogden found trucks were involved in 90 
percent of major incidents (major was defined as having a median delay time of 2.5 
hours).  

FIGURE 2-1. TIME FLOWCHART OF A CMV CRASH

 
Note: Traffic diversions and repairs to roadway infrastructure are not represented in this 
flowchart.

Congestion delays also affect air quality.  Vehicles idling, decelerating and accelerating, 
and traveling over longer periods of time due to traffic slow downs will produce more 
emissions.  Congestion delay data can be used along with other information to determine 
the how much vehicle emissions increase when there is congestion, as compared to the 
emissions when there is an unimpeded traffic flow.

2.3	CONGESTION DELAY IMPACT METHODOLOGY

The highest accuracy could be obtained by studying crashes immediately after they occur 
and measuring the time traffic behavior and delay time at the scene.  Unfortunately, due 
to the stochastic nature of truck crashes and the difficulty of obtaining field data.  Crash 
delay models are linked either to capacity reductions (estimates in traffic flow on 
roadways with blocked lanes) or to broad estimates using traffic flow calculations based 
on speed estimates and the length of traffic queues.  Zaloshnja and Miller addressed this 
lack of data by collecting CMV crash delay duration information from local police 
departments (Zaloshnja and Miller 2000).  The same method was selected for use here.  

All 68 cities in the Texas Transportation Institute?s Urban Mobility Report (Schrank and 
Lomax 2005) were contacted for data relating to CMV crash delay.  Both state 
departments of transportation and police departments were contacted for each of the 
urban areas.  

Only three states: Pennsylvania, Kentucky, and Washington were able to provide relevant 
delay data to the Volpe research team.   Each of these states provided CMV crash vehicle 
delays for 2003-2005.  Kentucky provided the total duration of delay for each level of 
CMV crash severity.  The CMV delay data from Pennsylvania and Washington was 
provided in time ranges with crashes placed in the following time categories:

*	0-30 minutes
*	30-60 minutes
*	1-3 hours
*	3-6 hours
*	6-9 hours
*	9+ hours
*	Unknown duration

The ranges were converted into values for use in this study by assigning an average, 
maximum, and minimum time for each time period.  For example, 0-30 minutes was 
calculated as 15, 30, and 1 minute, respectively; 9+ hours was calculated as 9.5, 12, and 9 
hours, respectively.  Crash delay lasting more than 12 hours was not accounted for in the 
Pennsylvania and Washington data.  The unknown duration crashes were excluded from 
the estimate. 

All the CMV crash delay times were summed and then divided by the number of crashes 
to yield the following ratios:

*	Low: 56 minutes- Property Damage Only (PDO): 64 minutes- Injury: 192 
minutes- Fatality
*	Mid:  72 minutes- PDO: 91 minutes- Injury: 261 minutes- Fatality
*	High: 89 minutes- PDO: 120 minutes- Injury: 336 minutes- Fatality

The mid-level calculation was selected, as the average duration is most likely the most 
accurate conservative approximation of the delay times.

 
TABLE 2-1.  COMPARISON OF CMV CRASH ROAD DELAY BY SEVERITY 
LEVEL
Type of Crash
Volpe 2007 (mid-level)
Zaloshnja and Miller 2006
Fatality
261 minutes
385 minutes
Injury
91 minutes
130 minutes
Property Damage Only
72 minutes
40 minutes

The mid-level figures developed for this study can be compared to the figures developed 
by Zaloshnja and Miller for comparable crashes:  40 minutes ? PDO; 130 minutes ? 
Injury; 385 minutes ? Fatality (Zaloshnja and Miller 2000).  Zaloshnja and Miller 
collected their police reported crash delay information from police departments in the 
early and mid 1990s.  

It is interesting to note that, based on the updated information, property damage crashes 
create more congestion, while both injury and fatal crashes create less delay than 
Zaloshnja and Miller found.  Property damage only crashes may create more congestion 
due to the increased volume of traffic on U.S. roadways.  More severe crashes may cause 
less traffic due to dramatic improvements in emergency response.  A recent National 
Cooperative Highway Research Program Report indicates that states are adopting a 
growing number of quick clearance procedures (TRB 2003).  Traffic fatality certification 
laws allow responders to remove the deceased from the roadway before medical 
examiners arrive to facilitate traffic flow.   Even though injuries and fatalities may create 
less delay following CMV crashes than they did in Zaloshnja and Miller?s original 
findings, overall delay is greater, since the vast majority of CMV crashes are limited to 
property damage only.

The National Highway Transportation Safety Administration (NHTSA) Fatal Analysis 
Reporting System (FARS) database was utilized for CMV crash information.  NHTSA?s 
General Estimates System (GES) was used as a supplement for injury and PDO crashes.

The steps below were followed to calculate the vehicle delay in hours discussed above:

*	Step 1: CMV crash delay: Determine crash delay times associated with each type 
of crash based on field-collected data.  Bus data was taken from MCMIS state 
reporting and normalized using 2004 truck ratios.  Crash delay time is in vehicle 
minutes.  Note actual hours for bus delay are likely greater as they are more likely 
to occur on urban roadways.  

*	Step 2: Highway Lane Miles: Use FHWA 2004 Highway Statistics to determine 
the Lanes per Facility Type.  Assumption 1 ? Delay only occurs to the traffic 
traveling in the same direction as the CMV crash.  

*	Step 3: Combine roadway lane miles with facility type traffic flow to determine 
traffic volume.  Lane volume was calculated based on traffic demand found in 
FHWA Highway Statistics.  Traffic flow, traveling in the opposite direction to the 
crash, was assumed to move at unrestricted speeds or at the highest Level of 
Service (LOS) ? LOS A.  See below for a more detailed explanation of LOS.  

*	Step 4: Weight time of day according to peak travel times.  Use FARS and GES 
data for crash times for CMVs.  Assumption 2 ? 50 percent of traffic flow occurs 
between 6-9am and 3-6pm (Schrank and Lomax 2005).  

*	Step 5: Use CMV crash data with facility type location information and assign the 
crash numbers to the traffic volume calculations from previous steps.  Assumption 
3 ? Unknown roadway types for CMV crashes have been assigned to roadway 
types in the same proportion as the known location data.

*	Step 6: Traffic Volume has been multiplied by the delay ratios from Step 1 and 
summed to obtain total delay results.  The minutes from the police department 
data were converted back into hours.

*	Step 7: Cost Calculation: Multiply total hours of delay by vehicle occupants using 
1.25 per vehicle.  The fleet mix (private vs. Commercial Motor Vehicle) is 
calculated according to the 2000 Highway Capacity Manual.  The hourly 
productivity delay cost was $13.45 (cars) and $71.05 (commercial vehicles) 
originally calculated in 1999 dollars (Zaloshnja and Miller 2002) multiplied by 
the 2005 US DOT guidance to obtain 2004 dollars (US DOT 2006b).

FIGURE 2-2. CMV TYPE BY ROADWAY FACILITY (RURAL AND URBAN)

 
 

CMV crash delay data is limited.  Ideally, actual field data could be collected at the exact 
time of a collision.  Each vehicle?s classification and additional time spent idling or at 
reduced speeds could be recorded.  Lack of predictability and technical recording, 
however, makes such field data only a theoretical possibility.  

Crash delay measurement is complex and the related challenges are listed in the 
methodology.  Ideally, complete information on the congestion delay from each large 
truck and bus crash could be assessed.   Point detection systems, with surveillance 
equipment spaced in short intervals, could record data on vehicle volume and lane 
occupancy following a crash (Cambridge Systematics, 2005).  As a proxy for recording 
every CMV crash event, at least one congestion delay event could be recorded for each 
level of crash severity on each roadway facility type.   Video recorders could record 
vehicle fleet composition and physical extent of the queue backup.  Ideally, a responding 
police officer could verify the accuracy of the electronically gathered information.    

2.4	RESULTS

2.4.1	Overall CMV Crash Related Delay

The report determined that there was an estimated 159,177,000 total vehicle hours of 
delay from CMV collisions in 2004.   The average vehicle delay associated with a CMV 
crash was 386 hours.  Table 2-1 provides the total rural and urban crashes for each 
roadway facility:

TABLE 2-2.	TOTAL NUMBER OF RURAL AND URBAN CRASHES BY 
ROADWAY TYPE

Severity Level
CMV crashes in 2004

Trucks
Buses

Rural
Urban
Rural
Urban
Fatality
2,929
1,511
184
95
Injury
54,750
28,250
3,427
1,769
Property 
Damage Only*
205,806
106,194
2,404
1,241
Sources: FAR, MCMIS, and GES, National Highway Traffic Safety Administration
Note: Adjusted for Unknown Crashes.  *MCMIS supplied information for bus crashes 
and non fatality/injury crashes are limited to ?Towaway? only (i.e. they are a smaller 
subset of all Property Damage Only crashes).

Associated costs for the delay are estimated to be approximately $3.7 billion (thousand 
million) in 2004 dollars.   The average vehicle delay for CMV crashes resulting in a 
fatality was 1,780 hours for urban roadways and 870 hours for rural roadways.  Crashes 
that caused injuries resulted in an average of 698 vehicles hours on urban roadways and 
341 on rural roadways.  Property damage only CMV crashes resulted in an average of 
539 vehicle hours of congestion delay on urban roads and 234 hours of delay on rural 
roads.  In the next table, this delay is broken down by crash severity (fatality, injury, or 
property damage only) and roadway type:

TABLE 2-3.	CMV CRASH CONGESTION DELAY AND COSTS BY CRASH 
SEVERITY AND ROADWAY TYPE

Congestion Delay from CMV crashes involving Fatalities in 2004(Vehicle Hours in 
bold, Cost in smaller font)

Roadway Type
(Urban Facility 
type  in Italics)
Rural
Urban

Facility Total
Per Crash
Facility Total
Per Crash
Interstate/ 
Interstate  
1,356,100
$38,045,721
1,914
$53,698
1,534,128
$37,289,770
3,107
$75,521
Other Principal/ 
Freeway 
Expressway
867,710
$21,584,080
878
$21,840
433,023
$9,959,766
2,434
$55,983
Minor Arterial/ 
Other Principal 
Arterial
372,759
$8,997,073
543
$13,106
613,592
$14,461,438
1,368
$32,242
Major Collector/ 
Minor Arterial
96,842
$2,337,421
203
$4,900
226,271
$5,217,208
898
$20,706
Minor Collector/ 
Collector
9,880
$238,468
102
$2,462
33,555
$775,595
480
$11,095
Local Road/ 
Local Road
4,556
$109,966
29
$700
18,197
$413,374
111
$2,522
Unknown* 
Numbers divided proportionally to known crash occurrence by facility type.
Total or Average
2,707,848
$71,313,000
870 average
$42,406
2,858,766
$68,117,000
1780 average
$16,632

Congestion Delay from CMV crashes involving Injuries in 2004(Vehicle Hours in 
bold, Cost in smaller font)

Facility Type
(Urban Facility 
type  in Italics)
Rural
Urban

Facility Total
Per Crash
Facility Total
Per Crash
Interstate/ 
Interstate  
9,939,145
$278,845,170
751
$21,069
11,244,111
$27,330,853
1,219
$29,630
Other Principal/ 
Freeway 
Expressway
6,359,691
$158,195,799
344
$8,557
3,173,632
$72,995,272
955
$21,966
Minor Arterial/ 
Other Principal 
Arterial
2,732,057
$65,942,116
213
$5,141
4,497,130
$105,990,568
536
$12,633
Major Collector/ 
Minor Arterial
709,777
$17,131,486
80
$1,931
1,658,354
$38,237,235
352
$8,116
Minor Collector/ 
Collector
72,409
$1,747,695

40
$965
245,925
$5,684,344
188
$4,345
Local Road/ 
Local Road
33,393
$805,988
11
$266
133,366
$3,029,618
43
$977
Unknown* 
Numbers divided proportionally to known crash occurrence by facility type.
Total or Average
19,846,472
$522,668,000
341 average
$8986
20,952,518
$253,268,000
698 average
$16,632

Congestion Delay from CMV crashes involving Property Damage Only in 
2004(Vehicle Hours in bold, Cost in smaller font)

Facility Type
(Urban Facility 
type  in Italics)
Rural
Urban

Facility Total
Per Crash
Facility Total
Per Crash
Interstate/ 
Interstate  
27,482,375
$771,024,825
580
$16,272
31,090,689
$755,715,717
942
$22,897
Other Principal/ 
Freeway 
Expressway
17,584,953
$435,421,517
266
$6,617
8,775,297
$201,836,632
738
$16,974
Minor Arterial/ 
Other Principal 
Arterial
7,554,313
$182,334,183
165
$3,983
12,434,852
$293,070,697
414
$9,757
Major Collector/ 
Minor Arterial
1,962,578
$47,490,318
61
$1,472
4,585,456
$105,728,425
272
$6,271
Minor Collector/ 
Collector
200,215
$4,832,476
31
$748
679,999
$15,717,589
145
$3,351
Local Road/ 
Local Road
92,334
$2,228,614
9
$217
368,764
$8,775,297
34
$772
Unknown* 
Numbers divided proportionally to known crash occurrence by facility type.
Total or Average
54,876,769
$1,445,332,000
264 average
$6,942
57,935,057
1,380,446,000
539 average
$12,850
* Adjusted for Unknown Crashes

Nearly twice as many CMV crashes occurred on rural roadways; however, the total delay 
for urban verses rural is roughly similar: 52.4 percent for urban vs. 48.6 percent for rural.   
Higher traffic density in urban areas causes greater delay per crash incident than for rural 
areas.  

FIGURE 2-3. COMPARING RURAL AND URBAN CMV CRASHES AND 
DELAY

 

2.4.2	Hazardous Materials (HM) CMV Crash Delay

CMV crashes that involve HM are assumed to cause greater delay than collisions without 
the presence of HM.  No reliable information is available detailing the recorded times for 
HM crashes vs. trucks carrying non-hazardous cargo.  For this report, the HM crash delay 
was calculated strictly on a proportional basis of HM vs. non-HM crashes.  The data was 
obtained from the MCMIS and HMIS databases which provide the number of crashes.  
Please note that the information likely underestimates the delay associated with 
Hazardous Material CMV crashes as the clean-up and added safety protocols most likely 
increases congestion delay.    

TABLE 2-4.  HM AND NON-HM CRASH VEHICLE DAY HOURS

Roadway Type
HM Adjusted*
Non-HM Adjusted*
 Interstate  
111,874,000
4,617,805,000
 freeway/expressway 
50,337,000
2,090,154,000
 Other Principal Arterial 
38,169,000
1,571,151,000
 Minor Arterial 
12,510,000
513,748,000
 Collector 
1,684,000
68,996,000
 Local Road 
1,675,000
69,939,000
 Unknown* 
N/A
N/A
 Grand Total Min. 
216,249,000
8,931,794,000
Grand Total Hours
3,604,000
148,863,000
*Unknown Crashes were assigned to both HM and non-HM according to the proportion 
of known crashes.  

The average duration for all crashes is less than the 2 hours for common HM incidents 
and less than the 12 hours for the 5 percent of incidents that could be classified as major 
(Battelle 2003).  This report estimates that 84 minutes is the average delay for all types of 
CMV crashes.  One study cites that evacuations were needed in 15 percent of hazardous 
waste incidents (TRB 1993).  No estimate of delay from HM evacuations is included , 
since there are no readily available models or tools to calculate the additional delays 
attributable to evacuations. 

2.4.3	Significant Regional Incidents

Severe crashes involving closure of major highway arteries can have a large effect on 
overall delay, even if they are few in number.  For example, on April 13, 2006, a truck 
crash in Washington DC caused traffic delays on Interstate 95 for over 5 hours and 10 
miles of backup during the Thursday evening commute home (Washington Post 2006).  
To keep consistency with the rest of the congestion methodology, CMV ?mega-crashes? 
were analyzed for 2004.  The HMIS database was searched but proved to be of limited 
value for calculating delay beyond providing the hours of road closure.  There is no 
accurate method to calculate delay associated with extended road closures.  Traffic will 
be diverted to alternative routes and the associate delays will be dispersed over multiple 
roadways. 

Although it is difficult to ascertain the frequency and total impact of significant regional 
incidents, one example is provided to highlight the possibilities.  On March 27, 2004, a 
truck carrying 12,000 gallons of heating oil skidded into an overpass on Interstate 95 near 
Bridgeport, Connecticut.  The truck and its cargo burst into flames and burned for over 2 
hours at 2000 degree Fahrenheit (NYT 2004). The fire caused extensive damage to a 
bridge-span and necessitated closing a 2-mile section of the interstate for over one week.  
An estimated 120,000 vehicles pass over the affected span on a daily basis.  Fortunately, 
no vehicle drivers or passengers were injured in the crash.  

Using this report?s methodology, a crash involving property damage, on an urban 
interstate, results in an average of 942 hours of delay.  The Bridgeport I-95 crash caused 
at least 138,000 hours delay based on conservative estimates from news reports.  

The foregoing Bridgeport I-95 estimate is based on the following assumptions:

*	Lane closures: 5 days for southbound, 7 days for northbound
*	Vehicle Fleet Composition: 95 percent passenger automobiles and 5 percent 
commercial trucks, which aligns with general congestion methodology.  
*	Lane closures: 5 days for southbound, 7 days for northbound
*	Diversion: 10 miles of extra mileage = 10 minutes delay for automobiles, and 40 
miles of extra mileage = 40 minutes delay for trucks
*	Actual Crash Event On Site Delay: Not included in the calculations due to lack of 
measurement of delay.

The delay from the Bridgeport I-95 CMV crash compares to an average of 942 hours 
predicted for an urban interstate PDO truck crash.  The I-95 crash is over two orders of 
magnitude greater in severity and cost at least $2,800,000 in lost productivity.  

Unfortunately, there is no standardized data collection for these huge crashes.  The delay 
resulting from the I-95 Bridgeport crash likely exceeded 138,000 hours estimate.   The 
delay from just this single mega-crash was equal to 0.4 percent of the total delay 
calculated for Urban Interstate Roadway.  In addition, road diversions likely caused 
additional delays due to extra travel time and traffic bottlenecks created on smaller 
alternative facilities during interstate repairs.  Significant regional incidents have the 
potential to cause delays exceeding the average CMV crashes and thus altering the 
overall estimates.  

2.5	CONGESTION DELAY VALIDATION

The total delay and associated productivity costs are a conservative estimate based on 
comparisons to other national estimates.  The Urban Mobility Report estimates that non-
recurring events, such as collisions, create half of all congestion related delays.  The 2005 
Urban Mobility Report estimates a total of 3.7 billion hours of vehicle delay in urban 
areas (Shrank and Lomack 2005).  Oak Ridge National Laboratories? (ORNL) 
comprehensive estimate of national vehicle delay estimated a total of 2.3 billion hours of 
vehicle delay on U.S. freeways and principal arterials for 1999 (Chin et al 2002).  ORNL 
also estimated 772.6 million hours of vehicle delay from all crashes.  The ORNL report 
includes all roadways and both rural and urban areas.  

This chapter?s delay estimate, approximately 159 million delay vehicle hours from CMV 
collisions, almost 4.3 percent of the Urban Mobility report total delay and about 20 
percent of ORNL?s 1999 total crash (automobile and CMV) delay estimate.

FIGURES 2-4. VOLPE VS. ZALOSHNJA AND MILLER 2006 (RURAL AND 
URBAN)

 

 

One potential factor causing the smaller Volpe estimates is that Zaloshnja and Miller 
used another report to scale up their urban delay numbers (Lan and Hu, 1999).  Lan and 
Hu found in Minneapolis, Minnesota, that crashes with heavy trucks involved caused an 
average of 5,057 hours of delay.  They also found that non-truck crashes caused 2,405 
hours of vehicle delay.  This non-truck estimate is approximately 5 times greater than 
ORNL found in their most recent report (ORNL 2004).  Based on the estimates of the 
Minneapolis study, Zaloshnja and Miller increased their delay numbers. ORNL estimates 
that an average crash (truck and non-truck) results in 506 hours of delay.  

Regional variations may account for a larger or smaller impact in specific locations.  
Actual CMV crashes in different cities will likely result in varying hours of vehicle delay. 
For example, the interstate roadways in the Los Angeles, California metropolitan region 
have many more lanes and traffic volume than those in Beaumont, Texas.  However, both 
locations are designated as urban areas.  

Crashes affect traffic flow beyond the vehicles delayed at the crash site.  This report does 
not include CMV crash impacts arising from re-scheduling, re-routing, reduced mobility, 
or reduced reliability.  No reliable method was found for estimating these associated 
delay impacts. 

2.6 	RECOMMENDATIONS FOR FUTURE STUDY 

The delay calculations provided in this report are conservative and are lower than noted 
by ORNL and Zaloshnja and Miller.  Estimates could be improved by accounting for 
traffic delay in the opposite direction of the crash (rubbernecking/gaper?s block).  In 
addition, using the location in the roadway for each of the crashes can yield more precise 
results.  With a specific crash location, highway capacity can be estimated using past 
research models.  For example, a crash that occurs on the left shoulder of the highway has 
an accepted value of 0.840 capacity for a three lane highway (Giuliano 1989).  That 
means that all three lanes would only be able to accommodate 84 percent of the 
maximum traffic volume while the crash is being cleared away.  The FARS and GES 
databases have recorded roadway locations for CMV crashes.  Also, using queuing theory 
from the TRB Highway Capacity Manual can provide a more precise estimate of delay, 
driver behavior, and associated speeds.  The speeds in turn can create more accurate 
estimates for associated emissions.  Lastly, a validation of the calculations can be made 
for a few specific cities using traffic volumes and crash data from that location.  The 
results can be compared with the TTI report.

In summary, the current recommendations with respect to congestion for future drafts of 
this report include:

*	Account for Rubbernecking in non-crash lanes.
*	Account for Capacity Reductions depending on location of crash
*	Account for LOS adjustments for traffic flow using ORNL?s approach
*	Make assumptions on length of the queue and traffic behavior to yield more 
precise delay results
*	Provide additional data on a truck type/configuration for fleet mix
*	Validation for specific cities

3.	AIR QUALITY AND GREENHOUSE GASES

3.1	INTRODUCTION

Commercial motor vehicle (CMV) crashes cause air quality impacts.  Congestion 
resulting from the crashes results in higher motor vehicle emissions than those associated 
with travel in free flowing conditions (FHWA 2002, Daniel and Bekka 2000).  In 
addition, motor vehicles delayed in traffic will consume more fuel due to longer engine 
run times and higher rates of fuel consumption in idling, stop-and-go traffic, and low 
speed conditions (Schrank and Lomax 2005).  This increase in vehicle emissions can 
contribute to local and regional air pollution problems.  The emissions can damage 
human and ecological health and can also harm agricultural crops and forests, damage 
buildings and cultural resources, and impair visibility.  In addition, some types of cargo 
released in crashes, such as volatile materials, can impact air quality.

This chapter describes the methodology and analysis used to quantify the criteria 
pollutant and greenhouse gas (GHG) emissions resulting from the congestion associated 
with CMV crashes.  This chapter also presents the main results of the analysis.  Air 
quality impacts carry a cost that is not contained within private transportation prices and 
thus are born by the general public, known as social costs.  This chapter also presents the 
methodology and estimation results for the social costs associated with air pollutant 
impacts on health and climate change (but not air pollutant impacts on visibility, it should 
be noted).  The methodology for estimating air quality impacts will provide estimates of 
the criteria pollutant and GHG emissions per CMV crash, and their associated damage 
costs.  

The methodology presented allows for the estimation of the fuel consumption, emissions, 
and damage costs associated with congestion resulting from CMV crashes.  The estimates 
can be calculated on an aggregate level for all CMV crashes and on a per crash basis.  
The estimates can also be calculated by facility type and accident severity, and the 
relevant combinations thereof.  This allows FMCSA to estimate the changes in emissions 
and damage costs resulting from regulations and rulemakings that affect CMV accident 
rates as a whole or a subset of accidents by severity or on particular facility types. 

The ideal approach would use idling time and instantaneous speed and acceleration 
throughout the duration of the congestion associated with a CMV crash to simulate the 
real world driving conditions associated with the slow stop-and-go driving 
characteristics.  Fuel consumption and emissions during idle and stop-and-go driving 
(especially at very slow speeds) are significantly higher than those resulting from 
unrestricted flow conditions.  In addition, fuel consumption and emissions are highly 
dependent on acceleration.  Therefore, using average steady-state speeds, as was done in 
this analysis, is expected to largely underestimate emissions and fuel consumption and 
provide only a general approximation to what actually would occur.  However, there is 
extremely limited information on the traffic conditions, idling time, travel speeds, and 
acceleration after an incident.  

In the absence of this data, work developed by Oak Ridge National Laboratory (ORNL) 
analyzing fuel economy and emissions as functions of speed and acceleration could be 
used.  ORNL developed a model to simulate emissions and fuel consumption for different 
driving schedules.  Driving schedules and drive cycles are synonymous terms for 
describing an identified range of vehicle velocities and their resulting tailpipe emissions.  
Thus, as an extension of the current analysis, a collection of driving schedules considered 
representative of driving conditions under congestion resulting from a CMV crash could 
be selected and used in the model to estimate emissions and fuel consumption and their 
associated damage costs.  Since the model data has acceleration and deceleration, once a 
cycle to be simulated is selected, the appropriate values from the model lookup tables 
could be used to estimate emissions and fuel consumption over the cycle.  An even better 
approximation of emissions and fuel consumption could be developed by selecting the 
section of the driving schedule that more closely resembles driving conditions under the 
congestion associated with a CMV crash.  A confidence interval based on the range of 
fuel consumption and emissions calculated using the different driving schedules could be 
developed to derive more accurate conclusions.  

The main shortcoming of this approach is that the data collected by ORNL is not a 
perfect representation of the vehicle fleet composition.  Additionally, there is no data that 
allows for accurately determining the driving conditions (and hence which schedule to 
select) after an incident.  Therefore, a decision regarding the conditions representative of 
driving after an incident needs to be made.  This last shortcoming also applies to the 
current analysis.  However, it can certainly provide a proper indication of changes among 
different congestion scenarios (defined by driving schedules).  Alternatively, assumptions 
regarding driving conditions after an incident could be developed and used in the ORNL 
model.

Another potential approach is to study the applicability of the EPA?s Motor Vehicle 
Emission Simulator (MOVES) model (US EPA 2007a).  This model estimates emissions 
for on-road and non-road sources, covers a broad range of pollutants, and can provide 
both fine-scale analysis and national inventory estimation.  The main advantage is that 
the model will estimate emissions for real world driving conditions.  In addition, when 
fully implemented, MOVES will serve as the replacement for MOBILE6.  Thus, studying 
how to use it for the current analysis would be helpful for future FMCSA environmental 
compliance activities.  The main shortcoming is that data is very limited (only a small set 
of vehicles have been tested for emissions and have not been selected to represent fleet 
composition).

The analysis could also be extended by calculating the total time in congestion instead of 
only using the time of delay, which is the unit of delay used in this analysis.  The time of 
delay is a portion of the total time in congestion.  The analysis would be more accurate by 
estimating the difference between the emissions and fuel consumption associated with the 
total time in congestion (at the congested driving conditions) and the emissions and fuel 
consumption associated with the time it would take to travel the distance affected by the 
CMV crash (at an average free flow speed associated with the specific facility type 
affected by the incident).  This would provide an estimate of the delay emissions and fuel 
consumption, which given the non-linearity of emissions and fuel consumption, is not the 
same as calculating the emissions and fuel consumption for the delay time, which is what 
was done in this analysis.  Free-flow speeds by facility type are available from the 
FHWA Highway Performance Monitoring System.  However, in order to accurately 
perform the analysis in this proposed way, data on the queue length associated with a 
CMV accident would be needed; this data is not available.  If free-flow emissions and 
fuel consumption are subtracted from the delay emissions and fuel consumption, we 
would be systematically underestimating things since we would be subtracting from the 
delay portion and not the total congestion emissions and fuel consumption.  In the 
absence of data on queue length associated with a CMV accident, the current analysis 
based on delay provides the most accurate representation of congestion emissions and 
fuel consumption.

However, estimates of the distance traveled under congested conditions following an 
incident could be developed.  Queuing theory modeling could be used to develop 
confidence intervals of the distance traveled under congested conditions to determine the 
?real additional? emissions and fuel consumption associated with a CMV crash.  Data on 
arrival rates by facility type (number of vehicles per unit of time) would be needed to be 
collected.  The service rate (smaller after an incident) by facility type for different 
scenarios would then be estimated for different scenarios: (1) no accident: service rate = 
capacity; (2) one lane closure; (3) two lane closure; etc.  Time of day flow and resulting 
congestion delay could be included in the analysis.  The main objective of this approach 
would be to develop a confidence interval by facility type of the queue length (number of 
vehicles), to then translate queue length information into confidence intervals for distance 
by considering fleet composition (car lengths) by facility type and car-following 
assumptions (based on speed).  

As explained, using steady-state speed does not provide very accurate estimates of 
emissions and fuel consumption associated with congested conditions.  However, given 
the lack of data and in the absence of a much more complex and time intensive approach, 
the three scenarios of travel characterized by different average (steady-state) speeds 
selected for the analysis provide useful information to quantify the emissions and costs 
associated with congestion resulting from CMV crashes.  Further refinements to the 
analysis and the accuracy of the estimates could be obtained with the use of the above-
mentioned recommendations. 

3.2	BACKGROUND

The United States Environmental Protection Agency (EPA) sets standards for the 
maximum concentration of known harmful air pollutants defined as ?criteria pollutants? 
(US EPA 1998).  The 1990 Clean Air Act Amendment provides authority for the EPA to 
set limits on these pollutants to protect the public and the environment.  The EPA 
monitors emission that impact air quality and helps to track emissions trends for each 
pollutant.  The agency also sets limits on quantity of allowable pollution.  
Air quality is measured by determining the concentration of air pollutants present within 
the air mass of a region, in parts per million (ppm) or micrograms per cubic meter 
(pg/m3).  Air pollutants are a significant cause of concern for both public health and 
welfare.  In response to both of these concerns, Federal regulations have been developed 
for six criteria pollutants, under the National Ambient Air Quality Standards (NAAQS), 
that are considered harmful to public health and the environment.  The six criteria 
pollutants are carbon monoxide (CO), lead (Pb), nitrogen dioxide (NO2), ozone (O3), 
sulfur dioxide (SO2), and particulate matter (PM).  Nitrogen dioxide reacts in the 
atmosphere over the course of several hours and is often referred to simply as nitrogen 
oxides (NOX).  Similarly, sulfur dioxide is often referred to simply as sulfur oxides 
(SOX).

The ambient concentration of pollutants is compared with the EPA?s NAAQS in order to 
measure air quality.  There are two types of standards ? primary and secondary.  Primary 
standards protect against adverse health effects; secondary standards protect against 
adverse welfare effects, such as damage to farm crops and vegetation and damage to 
buildings.  Because different pollutants have different effects, the NAAQS for each 
pollutant is different.  Some pollutants have standards for both long-term and short-term 
averaging times.  The short-term standards were designed to protect against acute, or 
short-term, health effects, while the long-term standards were established to protect 
against chronic health effects.

When a geographic area falls within the NAAQS established by the Clean Air Act, it is 
called an attainment area; when concentrations of criteria pollutants in the region exceed 
the standards, it is called a non-attainment area.  The EPA continuously monitors ambient 
air quality within counties and air basins in the U.S.  A description of the relevant criteria 
pollutants is presented below (US EPA 2006a):

*	Carbon Monoxide (CO): colorless, odorless, and poisonous gas produced by the 
incomplete combustion of carbon in fuels.  CO reduces the delivery of oxygen to 
the body?s organs and tissues when it enters the bloodstream and is a serious 
health risk for people with cardiovascular disease.
*	Nitrogen Dioxide (NO2) and NOX: brownish highly reactive gases that can irritate 
lungs, cause bronchitis and pneumonia, as well as lower resistance to respiratory 
infections.  Nitrogen oxides are precursors to ozone (O3) and acid rain, which 
negatively impact aquatic and terrestrial ecosystems.
*	Sulfur Dioxide (SO2) and SOX:  high concentrations of these gases can affect 
breathing and aggravate existing respiratory and cardiac disease.  People with 
asthma, bronchitis or emphysema, or children and elderly are sensitive to elevated 
SO2 levels.  SO2 is a primary component of acid rain, and SOX compounds reduce 
visibility.
*	Particulate Matter (PM): particulate matter pollutants include dust, dirt, soot, 
smoke, and liquid droplets emitted by sources such as automobiles.  PM is a 
major concern for human health and possible effects are breathing and respiratory 
symptoms, aggravation of existing respiratory and cardiovascular disease, 
alterations to immunological response, lung tissue damage, carcinogenesis, and 
premature death.  In addition, PM damages soils and crops, and impairs visibility.
*	Hydrocarbon (HC): Hydrocarbons are a precursor to ground-level ozone - a key 
component of smog.  Ground-level ozone causes health problems such as 
difficulty breathing, lung damage, and reduced cardiovascular functioning.  A 
number of hydrocarbons can cause cancer or other health issues.
*	Volatile Organic Compounds (VOC): A notable subset of hydrocarbons, volatile 
organic compounds includes benzene, toluene, ethylbenzene, m-/p-/o-xylene, and 
formaldehyde (Chan et al. 1991).  VOCs can cause eye, nose, and throat irritation; 
headaches, loss of coordination, nausea; damage to liver, kidney, and central 
nervous system.  Some VOCs can cause cancer in animals; some are suspected or 
known to cause cancer in humans.  Note: benzene may be independently 
analyzed, as the EPA is strengthening its hazardous air emissions control largely 
due to increased concern over the effects of this pollutant (EPA 2006b).
*	Greenhouse gases (GHG): Recent government and scientific studies strongly 
suggest that anthropogenic GHG emissions are contributing to a rise in surface 
and lower atmosphere temperatures beyond the naturally occurring greenhouse 
?effect? (CCSP 2006).  Carbon dioxide (CO2) is the most prevalent GHG 
emission from vehicles.  Methane (CH4) and nitrous oxide (N2O) are also formed 
from vehicle emissions and cause GHG impacts.
Transportation sources in the U.S. account for the highest or second highest levels of 
emissions for several pollutants of concern for environmental and public health reasons.  
The transportation sector continues to be a substantial source of air pollutants at the 
national level, and is responsible for most of the total carbon monoxide (CO) and 
nitrogen oxide (NOX) emissions, close to half of the total volatile organic compounds 
(VOC), and a quarter of total particulate matter (PM) emissions.  The contributions to 
lead (Pb) and sulfur oxide (SOX) emissions from vehicles are relatively less, partly due to 
their reduced presence in transportation fuels (lead has essentially been eliminated from 
gasoline).  However, SOX is formed when fuel that contains sulfur, such as coal and oil, 
is burned, and when gasoline is extracted from oil in petroleum refineries.  Thus, the 
analysis of criteria pollutant emissions from CMV crashes will focus on the effects of on 
CO, NOX, VOC, PM, and SOX emissions.

The transportation sector ? specifically, motor-vehicle operation ? is also a substantial 
contributor to GHG emissions, accounting for approximately one third of all GHG 
emissions in the U.S.  The operation of motor vehicles accounts for the majority of these 
emissions.  Thus, this chapter examines the effects of CMV collision related congestion 
on GHG emissions.  GHG occur naturally, but also result from human activities, such as 
fossil fuel combustion, industrial processes, agricultural activities, deforestation, and 
waste treatment activities.  CO2 is one of the main products of motor vehicle exhaust and, 
although it does not directly impair human health and is not regulated, in recent years it 
has started to be viewed as an issue of concern for its global climate change potential. 
Vehicles stopped or running at slow speeds, as those associated with congestion, can 
create more ambient air emissions than drivers traveling without delays at higher speeds 
(Daniel and Bekka 2000).  CMV crashes and the related congestion delay induced 
emissions contribute more greenhouse gases than free flowing traffic.  Emissions carry a 
number of social costs that are not directly paid for in either vehicle operation or related 
transportation taxation and tolls.  Our methodology includes analysis of carbon 
monoxide, nitrogen dioxide, sulfur dioxide, particulate matter, hydrocarbons, and 
greenhouse emission from current CMV crashes.

Emissions from motor vehicles occur from either fuel that is either not fully combusted, 
or formed by chemical processes during combustion, and others occur through chemical 
reactions in the atmosphere.  The processes are complex and are influenced by fuel and 
engine type, ambient temperature, and natural background concentrations of the chemical 
compounds (Harrington and McConnell 2003).  Emissions profiles of gasoline and diesel 
engines are different.  Gasoline engines produce high levels of carbon monoxide, volatile 
organic compounds, and N2O.  Diesel engines produce high levels of NOX and particulate 
matter in comparison to gasoline engines (Harrington and McConnell 2003).  Both 
engine types produce CO2 in fairly direct proportion to fuel consumption.

Diesel exhaust is a complex mixture containing hundreds of organic and inorganic 
materials, in gaseous and particulate forms, from diesel engine combustion processes.  
Diesel engines include light- and heavy-duty engines in trucks, buses, and some 
automobiles.  Diesel emissions include 40 hazardous air pollutants listed under the Clean 
Air Act, 15 of which are known or probable carcinogens (U.S. EPA 2007).  Diesel 
exhaust includes both gases and particles (PM).  The gaseous fraction contains nitrogen, 
oxygen, carbon dioxide, water vapor, and many toxic substances including aldehydes.  
The particles consist of an elemental carbon core with volatile organic compounds, 
sulfates, nitrogen oxides, heavy metals, trace elements, and irritants (such as acrolein, 
ammonia and acids) adsorbed to the surface.  Specific toxic chemicals of concern include 
polycyclic aromatic hydrocarbons (PAHs) and nitroarenes which are concentrated in the 
particle phase.  The particle size distribution and chemical composition of diesel exhaust 
emissions can vary greatly depending on the engine type (light vs. heavy duty), the speed 
and load at which it is run, the fuel composition, the lubricating oil, and the emission 
control technology.  The mass, composition, and particle size distribution of diesel 
exhaust have also changed over time as fuel and engine technology have improved (U.S. 
EPA 2007).  

3.3	AIR QUALITY EMISSIONS METHODOLOGY

Ideally, any analysis of total pollutant emissions would be based on accurate knowledge 
of factors that determine emissions while vehicles are slowed after a large truck and bus 
crash.  For the purposes of this paper, we propose analyzing CMV crash related health 
impacts in total and on a per unit basis so that FMCSA can have a general assessment of 
how regulatory changes may benefit air quality and health.  The per unit analysis will use 
the emissions estimates for and the damage cost figures to calculate total air quality 
damages for an average large truck and bus crash.    

Based on a review of scientific literature, vehicle speeds and vehicle fleet composition 
information are critical variables to estimate emissions.  One study found that congestion 
can increase fuel consumption by 40 percent and emissions by 800 percent (De Vlieger et 
al. 2000).  The three groups of factors that influence air quality emissions are

*	Vehicle Speeds: acceleration, idling, and total times for each vehicle caught in the 
crash congestion influence emissions (Kulkarni et al. 1996).

*	Vehicle Fleet Composition: precise numbers of the vehicles involved in every 
traffic delay created from large truck and bus crashes along with each type of 
engine, fuel, and emissions profile (Romilly 1999).  

*	Vehicle Age and Maintenance:  the age and maintenance history of vehicles are 
also factors that influence emissions (Romilly 1999).  

Comprehensive and precise data sources for congestion conditions in general, and 
specifically for those associated with CMV crashes, are extremely limited.  Therefore, an 
analytical methodology that allows for the estimation of air quality impacts resulting 
from the delay associated with CMV crashes was developed.  The first step in the process 
was the calculation of the total vehicle miles traveled (VMT) under the congested 
conditions generated by CMV crashes in the United States (see Chapter 2, which 
addresses congestion, for an explanation of the delay estimation process).  VMT under 
congested conditions is calculated by multiplying the total hours of delay for each facility 
type and accident severity type by the average speed during the congestion resulting from 
CMV crashes.  

Data on speed and acceleration under congested conditions is very scarce.  The Texas 
Transportation Institute (TTI) estimated average speeds of approximately 36 mph for 
congested conditions (Schrank and Lomax 2005).  DeLucci (1998) estimated average 
speeds under congested conditions at between 27.5 and 37 mph.  Both estimates were 
developed for congested conditions at full capacity for a variety of facility types.  
Therefore, these estimates are high for the kind of driving conditions that can be expected 
following a CMV crash.  Even though drive cycles used for estimating fuel economy and 
emissions are not designed to represent driving conditions following a CMV crash, they 
are representative of real world conditions associated with different driving situations.  
Therefore, drive cycles were studied to determine which ones could be more 
representative of conditions following a CMV crash.  Examining the drive schedules used 
by EPA and state agencies, the EPA urban drive cycle and New York City driving 
schedule were selected as the two that most closely represent conditions similar to the 
ones experienced following a CMV crash.  The EPA urban drive cycle is designed to 
represent the slow speed and stop-and-go traffic conditions associated with rush hour 
driving in urban areas.  The average speed for the urban drive cycle is 19.59 mph.  The 
New York City driving schedule is designed to represent very low speed stop-and-go 
traffic conditions.  The average speed for this schedule is 7.1 mph.  Based on this 
information, three scenarios of congestion represented by different average speeds were 
selected: high-level congestion (5 mph), mid-level congestion (10 mph), and low-level 
congestion (20 mph).  These three values were used to estimate the corresponding total 
VMT under congested conditions for each facility, accident severity, and congestion 
scenario.

The second step was the estimation of emission rates for criteria pollutants.  Emissions of 
the speed sensitive criteria pollutants (carbon monoxide, volatile organic compounds, and 
nitrogen oxides) were estimated by using the EPA Mobile 6.2 model (MOBILE6) to 
calculate emission rates by speed and facility type.  The Highway Vehicle Particulate 
Emission Modeling Software - PART5 was used to estimate the emission rates of PM2.5, 
PM10 and SOx.  Calculations were made for 2008, since the Low Sulfur Fuel Rule is 
being phased-in over 2006-07 and affects some emission rates significantly, and 
combining the 2006 values for the speed sensitive criteria pollutants with the 2008 values 
for particulate emissions was considered inappropriate.  It is important to note that 
emission rates of the speed sensitive criteria pollutants decline significantly between 
2006 and 2008.  Thus, using 2008 values will understate today's (i.e., 2006-07) 
emissions.  However, since the analysis presented in this document is intended for future 
use, the use of 2008 values was considered more appropriate and useful for FMCSA.  In 
summary, MOBILE6 and the PART5 model, along with a typical vehicle fleet 
composition for each facility type, were used to estimate composite emission rates for 
criteria pollutants.  The criteria pollutant emission rates used (for the roadway types and 
speeds, in miles per hour ? mph ?, of interest) in the analysis are presented in Table 3-1.  

The vehicle fleet composition for each facility type is needed to estimate the emission 
rates for criteria pollutants.  The vehicle fleet composition was derived using the Federal 
Highway Administration Highway Economic Requirements System (HERS) computer 
model (FHWA 2002).  The HERS-ST model is a highway investment/performance model 
that considers engineering and economic concepts and principles in reviewing the impact 
of alternative highway investment levels and program structures on highway condition, 
performance, and user impacts.  Specifically, the HERS-ST model simulates highway 
condition and performance levels and identifies deficiencies through the use of 
engineering principles.  As stated above, it is important to consider the changes in CMV 
emission rates associated with EPA diesel engine regulations being phased in over the 
following years.

Volpe estimated the mix of MOBILE6 vehicle classes (MOBILE6 uses 28 of them) 
typically operating on each type of roadway, using extremely detailed data on vehicle 
classes and roadway types obtained from a special 1999 FHWA study, which were 
supplied by the FHWA Office of Policy.  The same data was used to estimate the mix of 
PART5 vehicle classes operating on each type of roadway.  Then, MOBILE6 and PART5 
were used to estimate the emission rates for the characteristic mix of vehicles operating 
on each roadway type defined by HERS at different speeds.  These results are stored in 
the HERS model, and HERS uses the rate appropriate to the roadway type and estimated 
speed before and after improvements are made to the roadway.

TABLE 3-1.  EMISSION RATES (GRAMS/MILE) BY ROADWAY TYPE

 
CO
VOC
NOX
PM10
PM2.5
SO2
ROADWAY TYPE
Speed (mph)
Speed (mph)
Speed (mph)
 
 
 

5
10
20
5
10
20
5
10
20
 
 
 
 Rural 
 

 

 

 
 
 
 Interstate  
28.030
19.292
15.517
2.685
1.518
1.008
4.057
3.347
2.734
0.160
0.123
0.162
 Other Principal 
30.931
21.733
17.097
2.999
1.665
1.074
2.569
2.127
1.677
0.098
0.080
0.126
 Minor Arterial 
31.493
22.139
17.467
3.047
1.681
1.084
2.214
1.829
1.445
0.075
0.067
0.115
 Major Collector 
31.942
22.445
17.734
3.079
1.694
1.093
1.988
1.640
1.300
0.078
0.062
0.110
 Minor Collector 
31.942
22.445
17.734
3.079
1.694
1.093
1.988
1.640
1.300
0.078
0.062
0.110
 Local Road 
31.942
22.445
17.734
3.079
1.694
1.093
1.988
1.640
1.300
0.078
0.062
0.110
 Urban 
 

 

 

 
 
 
 Interstate  
25.061
17.609
14.707
1.861
1.060
0.744
2.327
1.847
1.510
0.077
0.060
0.068
 
freeway/expressway 
25.410
17.868
15.061
1.866
1.049
0.739
1.806
1.392
1.137
0.062
0.045
0.054
 Other Principal 
Arterial 
26.300
18.793
15.178
1.992
1.133
0.744
1.791
1.471
1.158
0.059
0.043
0.053
 Minor Arterial 
27.282
19.450
15.489
2.080
1.184
0.801
1.785
1.471
1.168
0.053
0.041
0.048
 Collector 
26.470
18.932
15.365
1.995
1.128
0.771
1.530
1.249
0.983
0.051
0.035
0.045
 Local Road 
26.470
18.932
15.365
1.995
1.128
0.771
1.530
1.249
0.983
0.051
0.035
0.045
Source: Calculated using MOBILE6 and PART5 vehicle emission factor models.

Figures 3-1 and 3-2 present the composite emission rates (for CO, VOC and NOX) by 
speed for an urban interstate roadway as an example of the highly speed sensitive (and 
distinct) nature of CO, VOC and NOX emissions.  For scaling purposes, CO data is 
presented in a separate figure, while VOC and NOX are presented together.  Figures 3-3 
and 3-4 present the relationship between speed and carbon dioxide emissions for two 
roadway types.

Criteria pollutant tailpipe emissions associated with the congestion conditions for each 
roadway type, accident severity, and congestion scenario were calculated as the product 
of the total incident-delayed VMT and the emission rates.  Since emissions are estimated 
for each accident severity and roadway type, depending on the projected results of the 
rulemaking or regulatory action, the emissions associated with the respective delay can 
easily be determined.  The total tailpipe emissions for each congestion scenario were 
calculated as the sum of the tailpipe emissions for each facility and accident severity 
type.  Table 3-2 presents the total tailpipe emissions for each criteria pollutant and 
congestion scenario.  Figure 3-5 presents the tailpipe emissions per crash for each criteria 
pollutant by accident severity and congestion scenario.

FIGURE 3-1.  CARBON MONOXIDE EMISSIONS VS. VELOCITY

 

FIGURE 3-2. VOC AND NOx EMISSIONS VS. VELOCITY

 

FIGURE 3-3.  CARBON DIOXIDE EMISSIONS VS. VELOCITY

 

FIGURE 3-4. CARBON DIOXIDE VS. SPEED

 
Source: Dr. Lawrence Frank, Center for Clean Air Policy, January 2004 TRB 
Presentation

TABLE 3-2.  TOTAL TAILPIPE EMISSIONS
 
Emissions (tons)
 Pollutant 
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
 CO 
23399
32801
53162
 VOC 
2012
2266
3031
 NOX 
2246
3659
5911
 PM10 
82
163
326
 PM2.5 
64
127
255
 SO2 
85
170
340

FIGURE 3-5.  TAILPIPE CRITERIA POLLUTANT EMISSIONS PER CRASH
 
 
 
 
 

In addition to the criteria pollutant emissions associated with the fuel wasted in incident-
related delays caused by CMV crashes, there are emissions associated with the refining 
and distribution of that wasted fuel.  To calculate those emissions, it is necessary to 
determine the fuel consumption for the incident-delayed VMT.  The fuel consumption is 
calculated simply by dividing the incident-delayed VMT for each accident severity, 
roadway type, and congestion scenario by the fuel economy associated with the average 
speed for each congestion scenario.  However, there is very little information available 
with respect to the relationship between speed and fuel economy.  

For the low-level congestion scenario (representative of the urban drive cycle), the 
estimated fuel economy determined by EPA for 1975-2006 vehicles for the adjusted city 
driving schedule can be used (EPA 2006).  The report has values for the combined 
average fuel economy of new cars and light trucks on the urban test cycle that range 
between approximately 18-19 miles per gallon (mpg) for approximately the last 20 model 
years.  It can be assumed that the fleet average in urban driving is within this range, since 
the fleet is composed primarily of vehicles from these model years.  The value for the 
2006 vehicle models (18.6 mpg), roughly the midpoint of this range, was selected for this 
analysis.  The test value has already been adjusted downward (by 10 percent) to reflect 
differences between laboratory test conditions and real-world driving conditions.  

For the mid-level and high-level congestion scenarios, the fuel economy associated with 
the respective average speeds was estimated using the results of a methodology 
developed by ORNL for FHWA to determine modal vehicle emissions and fuel 
consumption models (SAE 1997 and FHWA 1999).  The models, in the form of look-up 
tables of fuel economy and emission rates as a function of speed and acceleration, are 
designed to simulate fuel consumption and emissions for different driving schedules.  
Differences in emission rates as a result of more stringent EPA regulations would have to 
be considered.  The authors derived their results from tests with a limited number of 
vehicles from different model years, selected to be representative of the overall vehicle 
fleet.  Even though the number of vehicles tested and used in the model is small, the 
weighted averages (based on their selected vehicle fleet composition as represented by 
the tested vehicles) for curb weight, engine displacement, and number of cylinders 
closely resembles the real world vehicle fleet composition.  In addition, the authors 
compared their results to those of a test in Detroit with 30 vehicles, and obtained very 
similar results.  

The average speed for the mid-level and high-level congestion scenario (10 and 5 miles 
per hour ? mph ?, respectively) was matched with the corresponding fuel economy 
figures from the ORNL study.  The resulting values are 15 and 9.5 mpg, respectively.  
These numbers seem somewhat high for the driving conditions expected after a CMV 
crash.  Overestimation of fuel economy is expected since the fuel economy values are for 
the respective average speeds and do not consider acceleration and stop-and-go 
conditions.  However, the values are reasonable in the context of the fuel economy figure 
for the urban drive cycle.  When comparing the fuel economy values from the ORNL 
study (22.6 mpg) to the urban drive cycle figure (18.6 mpg), it is seen that using the 
ORNL steady-state speed of approximately overestimates fuel economy by 20 percent.  
However, in the absence of more detailed information, the ORNL fuel economy figures 
(for the vehicle fleet) for steady-state 10 and 5 mph are used in the current analysis.  

Since the high-level congestion (5 mph) scenario is representative of conditions with 
significant idling, considering idling fuel consumption will result in more realistic 
estimates of congestion related fuel consumption.  Information on idling fuel 
consumption is very limited.  However, a recent study motivated by the concern over 
idling emissions from CMVs was prepared by ORNL (SAE 2003).  The results can be 
used to account for CMV idling emissions during congestion.  Even though there is no 
information on the idling time during congestion associated with crashes, it was 
considered useful to include them in the analysis.  A TTI study estimates the fleet 
composition at 95 percent vehicles and light trucks and 5 percent CMV.  However, as this 
estimate seems very low, data from the 2000 Transportation Research Board Highway 
Capacity Manual was selected as a better representation of the relative fleet composition 
by facility type (TRB 2000).  The actual values for the percentage of CMVs by facility 
type are presented in the Congestion section.  Thus, for this scenario, it was assumed that 
the CMVs (represented by the corresponding fleet contribution by facility type) were 
idling during the congestion incident and the rest of the vehicles were traveling at a 
steady-state speed of 5 mph.  Using CMV idling emissions somewhat offsets the 
underestimation of fuel consumption that results from using steady-state conditions 
instead of acceleration and stop-and-go.  The results show that the high-level congestion 
scenario results in higher fuel consumption and CO2 emissions than the mid-level 
congestion scenario.

The estimates of fuel consumption for each scenario were then used to calculate the 
upstream emissions associated with the refining and distribution of this wasted fuel.  The 
energy content of gasoline and emission rates for criteria pollutants associated with 
refining and distribution were calculated from the GREET model (Argonne National 
Laboratory 2006).  The upstream emissions are calculated as the product of the fuel 
consumption (gallons of wasted fuel) and the emission rates (grams per gallon).  These 
estimates are then added to the total tailpipe emissions for the respective pollutants, to 
calculate the total criteria pollutant emissions associated with congestion generated by 
CMV crashes.  The total emissions per crash are estimated by dividing the total emissions 
by the number of CMV crashes.  Figure 3-6 presents the carbon (carbon dioxide) 
emissions per crash and by accident severity and congestion scenario.  Table 3-3 presents 
the total criteria pollutant emissions (tailpipe and upstream) for each congestion scenario.  
Table 3-4 and Figure 3-7 present the total criteria pollutant emissions per crash for each 
congestion scenario.

FIGURE 3-6.  FUEL CONSUMPTION PER CMV CRASH

FIGURE 3-7.  CO2 EMISSIONS PER CMV CRASH

TABLE 3-3.  TOTAL EMISSIONS

 
Emissions (tons)
 Pollutant 
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
 CO 
23767
33155
53732
 VOC 
2267
2510
3426
 NOX 
2800
4192
6770
 PM10 
161
239
449
 PM2.5 
120
181
341
 SO2 
418
490
856

TABLE 3-4.  TOTAL PER CRASH EMISSIONS

 
Emissions (kilogram/crash)
 Pollutant 
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
 CO 
54.06
75.41
122.22
 VOC 
5.16
5.71
7.79
 NOX 
6.37
9.53
15.40
 PM10 
0.37
0.54
1.02
 PM2.5 
0.27
0.41
0.78
 SO2 
0.95
1.11
1.95

FIGURE 3-8. CRITERIA POLUTANT EMISSIONS PER CRASH

The estimation of GHG focused on carbon dioxide (CO2), as it represents 97-98 percent 
of total GHG from producing, distributing, and consuming motor fuel.  Carbon (C) 
emissions associated with the wasted fuel resulting from CMV crash-induced congestion 
are estimated as the product of the total fuel consumption, the mass density of fuel 
(averages 2,364 grams per gallon for the current U.S. gasoline mix), and the percentage 
of fuel mass represented by carbon (averages 84.7 percent for current U.S. gasoline mix).  
CO2 emissions are estimated as the product of the carbon emissions and the ratio of 
molecular weight of CO2 to that of its carbon component.  Since the atomic mass of 
carbon is 12 g/mol and the atomic mass of oxygen is 16 g/mol, the ratio is 44:12.  

In addition, as in the case of criteria pollutants, there are upstream emissions associated 
with the refining and distribution of the wasted fuel.  From GREET, upstream emissions 
per gallon of fuel refined and distributed average (for the current U.S. gasoline mix) 557 
grams per gallon for carbon, and 2,043 grams per gallon for CO2.  The upstream 
emissions are estimated as the product of fuel consumption and the average upstream 
emissions per gallon of fuel refined and distributed.  GREET can also be used to 
determine the non-carbon dioxide GHG emissions (CH4 and N2O) from refining and 
burning fuel, but they are much smaller in magnitude and thus not considered here.  
Table 3-5 presents carbon and CO2 emissions per CMV crash from wasted fuel and 
upstream emissions from its refining and distribution.  

TABLE 3-5.  CARBON AND CARBON DIOXIDE EMISSIONS PER CMV 
CRASH FROM WASTED FUEL AND UPSTREAM EMISSIONS FROM FUEL 
REFINING AND DISTRIBUTION BY CONGESTION SCENARIO

   
Emissions (kilogram/crash)
 Pollutant 
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
Carbon
686.92
659.61
1063.88
Carbon dioxide
2518.70
2418.56
3900.90

3.4	HUMAN HEALTH DAMAGES

Human exposure to the vehicle emissions from CMV crashes has health impacts.  Direct 
emissions from ozone and other emissions result in a number of pathologies including 
impaired breathing, cardiac conditions, and can be fatal (US EPA 2006d).  If FMCSA 
regulations reduce emissions from CMV crashes, they will also be reducing overall 
concentrations of harmful air pollution.

The formation of ambient pollution from emissions is a non-linear process and to some 
extent the effect of ambient pollution on people?s health is not linear (McCubbin and 
Delucchi 1996).  A marginal and discreet reduction of air emissions will not necessarily 
reduce air pollution or health impacts on a corresponding per unit basis.  Despite these 
complexities, the Office of Management and Budget has accepted damage cost values for 
each of the criteria pollutants (OMB draft 2006) and based their numbers on two 
Regulatory Impact Analysis Reports from the EPA (US EPA 2005a, US EPA 2005b).  
The OMB?s estimates have been thoroughly reviewed by analysts, and the executive 
branch recommends policy-makers use them in benefit-cost analyses (OMB draft 2006, 
OMB 1992).

OMB?s estimates are derived from the EPA?s 2005 Regulatory Impact Analysis for Final 
Clean Air Act Interstate Rule.  The report analyzes the economic impact of Clean Air Act 
regulations including only estimates for values that can be monetized (US EPA 2005a).  
The EPA developed the estimates based on detailed analysis of the health costs 
associated with air pollution and includes both on-road and off-road emissions.
Damage cost values were obtained from the OMB Draft 2006 Report to Congress on the 
Costs and Benefits of Federal Regulations (OMB 2006).  OMB does not provide a value 
for carbon monoxide since it estimates that damage costs associated with this pollutant 
are very low.  Damage cost values are:

*	Hydrocarbon:			$600 to $2,700 per ton
*	Nitrogen Oxide (mobile): 	$1,100 to $11,600 per ton
*	Particulate Matter: 		$10,000 to $100,000 per ton
*	Sulfur Dioxide: 		$1,700 to $18,000 per ton

A value for carbon monoxide was taken from the research literature.  In addition, a value 
for carbon (representative of carbon dioxide emissions) was estimated by Volpe in a 
previous study and is used in this analysis.  Adjusting all values to 2008 dollars, the 
damage costs are

*	Carbon Monoxide		A value of $20 is used (McCubbin and DeLucci)
*	Hydrocarbon:			$691 to $3,110 per ton
*	Nitrogen Oxide (mobile): 	$1,267 to $13,359 per ton
*	Particulate Matter: 		$11,517 to $115,167 per ton
*	Sulfur Dioxide: 		$1,958 to $20,730 per ton
*	Carbon (carbon dioxide):	$20 (Volpe 2006)

Note that OMB damage estimates for particulate matter don?t necessarily break out PM10 
and PM2.5, although MOBILE6 does have the capability to measure each pollutant 
independently.  There is near consensus among researchers that PM2.5 is more dangerous 
than coarser particles, since it can penetrate deeper in the lung (Dockery et al 1995, 
Marrack 1995, Pope et al.1995).  The OMB damage cost for particulate matter does not 
currently differentiate between PM10 and PM2.5 for total emissions.  There may be 
future OMB guidance on the different impact costs of each of these two particle sizes.  
For the purposes of this analysis, the estimation of damage costs is done accounting only 
for the PM2.5 emissions.  For the other criteria pollutants, the cost figure used is the 
midpoint of the range provided.  

The damage costs are estimated as the product of the total emissions and the damage cost 
values.  This estimation includes air quality impacts, which are compared with human 
exposure, physical damage, and assesses the cost associated with the health damage.  
OMB does not consider the economic impact costs to ecological and agricultural 
resources, since consensus has not been reached on methods to estimate those (US EPA 
2005).  

Table 3-6 presents the total damage costs and damage costs per CMV crash for criteria 
pollutants and carbon (carbon dioxide).  Figure 3-8 presents the damage costs per crash 
for criteria pollutants and carbon (carbon dioxide) for each congestion scenario.  

TABLE 3-6.  TOTAL DAMAGE COSTS AND DAMAGE COSTS PER CMV 
CRASH FOR ALL POLLUTANTS

 
Total Damage Costs (2008 $)
Per Crash Damage Costs (2008 $)
Pollutant
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
High-level 
congestion
Mid-level 
congestion
Low-level 
congestion
CO
 $475,336 
 $663,096 
 $1,074,633 
 $1.2 
 $1.7 
 $2.7 
VOC
 $4,307,816 
 $4,770,002 
 $6,509,438 
 $10.8 
 $12.0 
 $16.3 
NOX
 $20,477,503 
 $30,653,222 
 $49,508,481 
 $51.3 
 $76.9 
 $124.1 
PM
 $7,578,042 
 $11,470,325 
 $21,623,018 
 $19.0 
 $28.8 
 $54.2 
SO2
 $4,740,264
 $5,555,693 
 $9,708,288 
 $11.9 
 $13.9 
 $24.3 
Carbon (CO2)
 $6,039,883
 $5,799,740 
 $9,354,420 
 $15.1 
 $14.5 
 $23.5 
Total Damage 
Costs
 $ 43,618,845 
 $ 58,912,078 
 $ 97,778,279 
 $109 
 $148 
 $245 

FIGURE 3-9.  TOTAL CRITERIA POLLUTANT AND CARBON DAMAGE PER 
CMV CRASH AND CONGESTION SCENARIO
 
Contrary to what would be expected, emissions, fuel consumption, and costs for the low-
level congestion scenario are the highest and those for the high-level congestion scenario 
are the lowest.  Given that a high-level congestion scenario is intended to represent a 
situation with very slow speeds and stop-and-go driving, the opposite results might be 
expected.  However, the results can be easily explained by the fact that the ratio of 
average speeds for each congestion scenario (and thus the estimated VMT under 
congested conditions) is much higher than the corresponding ratios of emissions and fuel 
economy.  For example, the same vehicle delayed for one hour in congestion will create 
more emissions traveling at 10 mph than it would at 5 mph because it travels twice the 
distance.  Longer distances result in more fuel consumption along with all the related 
emissions.  This result is driven by the fact that the analysis uses average speeds verses 
actual time in congestion and does not consider idling and stop-and-go acceleration.  

To correct this, one alternative would be to consider a fourth scenario (or change the 
high-level congestion scenario to a lower average speed).  MOBILE6 uses 2.5 mph as the 
speed representative of idling conditions.  In reality, what this means, is that 2.5 mph is 
the slowest speed at which the model can reliably estimate emission rates (in grams/mile, 
which is the unit used by the model).  Idling emissions should be estimated in emissions 
per unit of time.  However, there is very little data on idling emissions.  Since the 
emission rates (for the speed sensitive pollutants) at 2.5 mph are significantly higher than 
those at 5 mph, this alternative scenario might provide a more realistic representation of 
emissions.  As an extension of the current analysis, a scenario considering emissions and 
fuel consumption at 2.5 mph could be developed to provide a ?conservative? scenario.

4.	HAZARDOUS MATERIALS

4.1	INTRODUCTION

CMV crashes can result in the release of hazardous materials (HM) into the environment.  
Crashes occur in varying locations and with varying amounts and types of HM being 
released from the CMV cargo.  Environmental impacts are dependent on these variables. 
In addition, HM from the CMV cargo is also released into the environment from non-
crash related incidents that occur during in-transit movements, loading, unloading and in-
transit temporary storage.  Similar to CMV crashes, the environmental impacts are 
dependent on the location, quantity and type of HM being released.

In general, HM are substances that may pose a threat to public safety or the environment 
during transportation, because of their physical, chemical, or radioactive properties.  The 
potential for environmental damage or contamination exists when packages of HM are 
involved in crashes or en route incidents resulting from cargo shifts, valve failures, 
package failures, or loading, unloading, or handling problems.  Accidental releases of 
HM can result in explosions or fires. Radioactive, toxic, infectious, or corrosive HM can 
have short- or long-term exposure effects on humans or the environment.  Diesel fuel 
released during a CMV crash from a fuel tank rupture, although not classified as a HM 
under federal HM transportation law (see Section 4.2) can also adversely impact the 
environment.  

This chapter contains 1) a brief overview of HM transportation law, 2) a description of 
the different classes of HM, 3) quantification tables for use by the FMCSA responsible 
federal official to estimate the releases of HM per commercial CMV crash (or non-crash 
related incident), 4) general narrative summaries of environmental impacts by HM class, 
5) specific narrative summaries of HM releases into the environment based on database 
research conducted for 2003, 2004 and 2005, and 5) quantification and environmental 
impact summaries of diesel fuel releases that occur during a CMV crash from a fuel tank 
rupture.

This chapter can be used as an informational tool for FMCSA?s responsible federal 
official when preparing a NEPA document and/or it can be inserted in whole or in part as 
an appendix to a NEPA document when a FMCSA action may affect the release of HM 
into the environment.  For example, the narrative portion of the report (see Section 4.5) 
can be inserted as an appendix into certain NEPA documents to provide context and 
background information on HM in transportation; however, not all quantification tables 
may be applicable.  Quantification tables can be used to estimate the reduction in the 
amount of HM released into the environment if the number of avoided CMV crashes or 
non-crash related incidents is known, but not necessarily included in the appendix of a 
NEPA document.

Note that this chapter focuses on the HM transportation law. There are numerous other 
federal and state laws and regulations that apply to HM and can also be referenced for use 
in FMCSA?s HM analyses.

4.2	BACKGROUND

The federal hazardous materials transportation law, 49 U.S.C. ? 5101 et seq., (formally 
the Hazardous Materials Transportation Act, 49 App. U.S.C. ? 1801 et seq.) is the basic 
statute regulating hazardous materials transportation in the U.S.  The purpose of the law 
is to provide adequate protection against the risks to life and property inherent in 
transporting HM in commerce by improving the regulatory and enforcement authority of 
the Secretary of Transportation (Secretary). 

The Secretary has the authority to designate a material or a group or class of materials as 
hazardous when the Secretary decides that transporting the material in commerce in a 
particular amount and form may pose an unreasonable risk to health and safety or 
property.  The HM regulations (HMR) can be found at Title 49 CFR Parts 171-180.  The 
HMR cover five areas as follows:

*	Hazardous materials definition/classification
	(Part 172, Subparts A-B, and Part 173);
*	Hazard communication (Part 172, Subparts C-G);
*	Packaging requirements (Parts 173, 178, 179 and 180);
*	Operational rules (Parts 171, 173, 174, 175, 176 and 177); and
*	Training (Part 172, Subpart H).

The U.S. DOT, Pipeline and Hazardous Materials Safety Administration (PHMSA), has 
public responsibilities for the safe and secure movement of HM to industry and 
consumers by all transportation modes, including the nation's pipelines.  PHMSA?s 
Office of Hazardous Materials Safety (OHMS) is the federal safety authority for the 
transportation of HM by air, rail, highway and water.  

The HMR (49 CFR Section 171.16) require that certain type of HM incidents be reported 
to PHMSA on US DOT Form F 5800.1, Hazardous Material Incident Report (HMIR).  
The information in the report is fundamental to HM transportation risk analysis and risk 
management by government and industry.  Additional information on transportation and 
HM can be found on the PHMSA website at (www.phmsa.dot.gov).
Classes of Federal Hazardous Materials
The DOT divides regulated HM into nine classes, some of which are further divided into 
divisions (see also HMR at 49 CFR Part 173). HM in transportation must be placarded. 
Some materials must always be placarded; others may only require placarding in certain 
circumstances. A listing of all HM classes and divisions is given below.  Some examples 
of the various classes and divisions are also given.  Section 4.5 of this chapter includes a 
general description of the potential environmental impacts that can occur by HM class.  
In addition, specific quantities of actual releases from 2003, 2004 and 2005 are given.
Class 1: Explosives ? Six subclasses.
1.1 ? Explosives with a mass explosion hazard. (nitroglycerin/dynamite) 
1.2 ? Explosives with a blast/projection hazard. 
1.3 ? Explosives with a minor blast hazard. (rocket propellant, display 
fireworks) 
1.4 ? Explosives with a major fire hazard. (consumer fireworks, ammunition) 
1.5 ? Blasting agents. 
1.6 ? Extremely insensitive explosives. 
Class 2: Compressed Gases ? Three subclasses.
2.1 ? Flammable gases. (propane, hydrogen) 
2.2 ? Non-flammable gases. (helium, nitrogen) 
2.3 ? Poison gases. (chlorine, phosgene) 

Class 3: Flammable Liquids ? Liquids with a flash point at or below 140 F (gasoline, 
some alcoholic beverages)
Combustible Liquids ? Liquids with a flash point between 140 and 200 F 
Class 4: Flammables ? Three subclasses.
4.1 ? Flammable solids. (magnesium powder, red phosphorus, etc.) 
4.2 ? Spontaneously combustible materials. (white phosphorus) 
4.3 ? Water reactive materials. (sodium, potassium) 

Class 5: Oxidizing Materials ? Two subclasses.
5.1 ? Oxidizers. (ammonium nitrate, hydrogen peroxide) 
5.2 ? Organic peroxides. (benzoyl peroxide) 

Class 6: Toxic Materials ? Three subclasses.
6.1 ? Poisonous liquids or solids. (potassium cyanide, mercuric chloride) 
6.2 ? Infectious/biohazardous substances. (anthrax, HIV) 
6.3 ? Liquids and solids with a lower toxicity than those in group 6.1. 

Class 7: Radioactive Materials ? Three subclasses.
7.1-7.3 ? Radioactive I, II, III. (uranium, plutonium, radioactive waste) 

Class 8: Corrosive Materials ? acids and bases (sodium hydroxide, sulfuric acid)
Class 9: Miscellaneous Dangerous Goods ? materials that are hazardous during 
transportation but do not meet the definition of any of the other hazard classes, for 
example dry ice in an airplane or hot asphalt).
4.3	METHODOLOGIES
4.3.1	Data Sources 
Three databases were used to estimate the release of HM from cargo and diesel fuel from 
a CMV fuel tank:  1) the Hazardous Material Information System (HMIS), 2) the Motor 
Carrier Management Information System (MCMIS), and 3) The Spill Center, Acton, MA, 
Spill Database.  Details about these sources are presented below.

Hazardous Material Information System (HMIS) 

The federal hazardous materials transportation law (49 U.S.C. Sec. 5101 et seq.) and 
implementing regulations (HMR 49 CFR Parts 171-180) apply to persons who transport 
hazardous materials in commerce, which includes intrastate motor carriers.  

Carriers from any mode of transportation are required to complete a HMIR when there is 
an unintentional release of a HM during transportation (including loading, unloading, and 
temporary storage related to transportation).  The most current form used is Form DOT F 
5800.1 (01-2004).  The form is submitted to the US DOT, PHMSA, OHMS for database 
entry.

The form contains key entries for use in the HM analysis, including but not limited to 1) 
mode of transportation (e.g. highway, rail, etc.), 2) transportation phase (in-transit, 
loading, unloading, etc.), 3) hazardous class/division and quantity of the HM released, 
and 4) if the HM vehicle was involved in a crash.  The HM form does not contain a field 
for fuel spills from fuel tanks, only a field for cargo spills.

Motor Carrier Management Information System (MCMIS) 

MCMIS is a system managed by FMCSA.  MCMIS contains CMV crash data submitted 
by the states.  The states collect state crash data from police accident reports (PAR) for 
large trucks and buses (including HM placarded vehicles) using the Safetynet system.  
Safetynet information is subsequently forwarded to FMCSA?s MCMIS.  Each state has 
adopted the National Governors? Association?s (NGA) uniform accident data elements 
for their PAR.  

Reportable crashes include those that involve 1) any truck weighing more than 10,000 
pounds, 2) any motor vehicle designed to transport more than eight people (including 
driver), or 3) any vehicle displaying a hazardous material placard (regardless of weight).  
In addition, the vehicle involved in the crash must be operating on a public road.  Finally, 
to be reportable, the crash must result in 1) a person being killed or receiving bodily 
injury requiring immediate medical treatment away from the scene of the accident, or 2) 
the vehicle being disabled as a result of the crash and transported from the scene by a tow 
truck or other vehicle.  (Note: in theory, this would capture less info for HM spills than 
the HMIS; however, it is assumed that most spills - including those from fuel tanks - 
would result in a tow-away.)  Reports are not required for crashes caused by a driver with 
a disease condition.  Additionally, they are not required for crashes caused as part of a 
suicide attempt.   

It should be understood that MCMIS is not a complete collection of crashes, and the 
quality of the reporting varies by state.  The FMCSA?s Analysis and Information (A&I) 
Online (http://ai.fmcsa.dot.gov ) presents State Safety Data Quality (SSDQ) reports that 
rate each state on a quarterly basis on the completeness, timeliness and accuracy of state 
reported crash data in MCMIS.  

The Spill Center, Acton, MA, Spill Database

Neither the HMIS nor MCMIS include information specific to CMV crashes and fuel 
spills from the CMV fuel tanks of trucks or buses.  The Acton Spill Center (ASC) tracks 
HM spill data at the national level.  They are able to query the data for CMV gas tank 
spill releases separate from HM cargo releases.  

Their database contains information on approximately 900 fleets nationally and goes 
back 16 years.  The Acton Spill Center Database has information allowing the 
identification of spills from large trucks (> 10,000 pounds) and small trucks ( 10,000 
pounds).  The Acton Spill Center was not able to identify any bus data for this analysis.  
4.3.2	Data Query Descriptions
The following queries were run to estimate the releases of HM and diesel fuel spills from 
the CMV fuel tank:

For CMV Crashes

HMIS Query 1 Crashes - This query was run for years 2003, 2004 and 2005.  Its purpose 
was to identify all CMV crashes that resulted in a release of HM.  Applicable query fields 
included the city, state and date where the HM release from the CMV crash occurred, as 
well as the land use type where the HM was released.   

HMIS Query 1 was used to obtain data that could be used to estimate the following:  

*	The total number of unique CMV crashes with a HM release per year. 
*	The percentages of HM releases in certain land use areas such as agricultural, 
commercial, industrial, residential and undeveloped.

The following were the primary HMIS Query 1 limitations and assumptions: 

*	Since PHMSA does not have a minimum reportable HM spill quantity for 
crashes, all HM spills from CMV crashes (in theory) should be reported and 
captured in the HMIS database.  MCMIS also contains a field for crashes that 
result in a release of HM. There are some discrepancies between the HMIS and 
MCMIS databases however.  MCMIS includes a slightly higher number of CMV 
crashes with an HM release than HMIS as follows:   1) 2003 ? 263 for HMIS vs. 
282 for MCMIS, 2) 2004 ? 205 for HMIS vs. 325 for MCMIS, and 3) 2005 - 233 
for HMIS vs. 300 for MCMIS.  Although MCMIS includes information indicating 
that a HM spill occurred, the HM Class is sometimes missing (between 22 and 27 
percent of the time) and there is no information on the spill quantity.  The 
accuracy of this data with respect to HM releases from CMV crashes is 
questionable, consequently.  

*	Approximately 2 percent of the reported incidents did not include information on 
land use types.  In addition, the land use fields were only available for the years 
2003 and 2004, due to the changes in the HMIR form that occurred in January 
2005.  The land use percentages calculated from the HMIS were based on those 
reported.  It is assumed that those reported are representative of the entire 
population.  

HMIS Query 2 Crashes - This query was run for years 2003, 2004 and 2005.  It lists the 
CMV crashes by city, state and date and also lists the HM Class, quantity and units of the 
HM released into the environment.  Land use type and CMV type (truck type or other 
vehicle type) is also listed for 2003 and 2004.  

HMIS Query 2 was used to obtain data that could be used to estimate the following:  

*	HM quantities overall and by HM Class per CMV type per year 
*	Percentages of HM spills by HM Class in certain land use areas such as: 
agricultural, commercial, industrial, residential and undeveloped

The following were the primary HMIS Query 2 limitations and assumptions: 

*	Since the release of more than one HM Class may result from a CMV crash, 
Query 2 may list some crashes more than once.  As a result, the number of crashes 
identified in Query 2 is greater than Query 1 as follows: 1) 2003 ? 263 for Query 
1 vs.294 for Query 2, 2) 2004 - 205 for Query 1 vs. 230 for Query 2, and 3) 2005 
? 233 for Query 1 vs. 270 for Query 2.  In order to keep the HM Class estimates 
consistent with each unique crash, a manual search was performed of the Query 2 
data to identify the crashes listed more than once as a consequence of there being 
more than one HM Class released.  The crash incident was then assigned to the 
HM Class that resulted in the largest release.  Although not as accurate as the 
quantities per crash that can be calculated using the data from Query 1, this 
constitutes a reasonable approximation of the amount of HM released by Class 
based on the best available information.  

*	Approximately 2 percent of the reported incidents did not include information on 
land use types.  In addition, the land use fields were only available for the years 
2003 and 2004, due to the changes in the HMIR form that occurred in January 
2005.  The land use percentages calculated from the HMIS were based on those 
reported.  It is assumed that those reported are representative of the entire 
population.  

*	The ?other vehicle? category was relied on to determine the HM Class released 
per vehicle type.  This field was only available for 2003, and 2004 due to changes 
in the HMIR form that occurred in January 2005.  If the vehicle type did not 
match one of the CMV descriptions listed on the form, those submitting reports 
were required to list identify the ?other vehicle type?.  There were only a few 
?other vehicle? types listed, including an auto, pickup truck and passenger 
vehicle.  Assuming those submitting reports were accurate in identifying their 
vehicle type, it was concluded that the vast majority of HM releases are from 
large trucks (> 10,000 pounds), not buses or other CMVs.

MCMIS Query Crashes - This query was run for years 2003, 2004 and 2005.  It identified 
crashes by the following CMV types:  1) trucks less than or equal to 10,000 pounds, 2) 
trucks between 10,001-26,000 pounds, 3) trucks more than 26,000 pounds, 4) buses with 
seats for 9-15 people, and 5) buses with seats for > 15 people.  

The MCMIS Query was used to obtain data that could be used to estimate the following:  

*	The number of crashes by CMV type including; 1) trucks > 10,000 pounds, 2) 
trucks < 10,000 pounds and 3) buses (seats for greater than 15 people including 
the driver). 
*	The number of crashes with HM placards by CMV type.

The following were the primary MCMIS Query limitations and assumptions: 

*	In order to obtain the desired estimates, certain MCMIS database fields needed to 
be combined.  This was a basic addition exercise.  Also, some of the MCMIS 
crash reports did not have information on the type of CMV.  For this analysis, the 
unidentified CMVs were proportionately assigned to a CMV type based on the 
percentages of the known vehicle types.  This was done for each year as follows 
1) 2003 - 32,553 of 127,725 (CMV type not reported for 25 percent of crashes); 
2) 2004 - 39,486 of 84,593 (CMV type not reported for 25 percent of crashes), 
and 3) 2005 - 38,019 of 145,259 (CMV type not reported for 26 percent of 
crashes).  This exercise was also performed for the CMVs with HM placards for 
which the CMV type was not reported at the following percentages:  2003 - 17 
percent, 2004 ? 20 percent, 2005 - 21 percent.

Acton Spill Center Query Crashes - This query was run for years 2002 through 2006.  
The Acton Spill Center provided the estimates for the percentage of CMV crashes based 
on a single 5,000 tractor fleet group.  

The Acton Spill Center Query was used to obtain data that could be used to estimate the 
following:  

*	The percentage of truck crashes that result in release of diesel fuel from the fuel 
tank and the average amount of fuel per CMV truck type (e.g. trucks > 10,000 
pounds and trucks < 10,000 pounds). 

The following were the primary Acton Spill Center Query limitations and assumptions: 

*	The Acton Spill Center database included spills from one CMV truck fleet group, 
not the entire Nation?s CMV truck fleet.  It should be noted that this particular 
fleet group reports all fuel spills, no matter the quantity.  In addition, the group 
should be highly representative of the U.S. trucking industry, because the Spill 
Center subscribers include all segments of the trucking industry such as LTL (less 
than truck load, TL (truck load), private fleets, tank and straight trucks.  Based on 
five years of data from this group, the Acton Spill Center estimated that 12 
percent of all CMV trucks experienced a crash that resulted in a release of diesel 
fuel from the vehicle?s fuel tank.  Quantities could also be estimated for trucks > 
10,000 pounds - 72 gallons per crash - and trucks < 10,000 pounds - 40 gallons 
per crash.  The percentage and quantity per crash figures developed using the 
Acton Spill Center data were applied to the total number of truck crashes 
estimated using the MCMIS database.  It was not possible to estimate diesel fuel 
releases for buses, it must be noted. 

For CMV Incidents

HMIS Query 1 Non-crash Incidents - This query was run for years 2003, 2004 and 2005.  
It identifies the CMV non-crash incidents by transportation phase (e.g. In-Transit, 
Loading, Unloading and In-Transit Temporary Storage) during which the HM release 
occurred.  Applicable query fields used included the city, state and date for the HM 
release, as well as the land use type where the HM was released.  

HMIS Query 1 was used to obtain data that could be used to estimate the following:  

*	Total number of unique CMV non-crash incidents with a HM release per year 
*	Total number of unique CMV non-crash incidents per year by transportation 
phase with a HM release 
*	Percentages of HM releases in certain land use areas such as: agricultural, 
commercial, industrial, residential and undeveloped

The following were the primary HMIS Query 1 limitations and assumptions: 

*	PHMSA does not require reporting for minimal amounts of HM released during 
loading or unloading incidents, so long as no property damage is caused.  In 
addition, since MCMIS does not capture HM non-crash related incidents, it 
cannot be used to cross check the HMIS incident data. 

*	Approximately 2 percent of the reported non-crash incidents did not include 
information on land use types.  In addition, the land use fields were only available 
for the years 2003 and 2004, due to the changes in the HMIR form that occurred 
in January 2005.  The land use percentages calculated from the HMIS were based 
on those reported.  It is assumed that those reported are representative of the 
entire population.  

HMIS Query 2 Non-Crash Incidents - This query was run for years 2003, 2004 and 2005.  
It identifies the CMV non-crash incidents by city, state and date and also identifies the 
HM Class, quantity and units of the HM released into the environment.  

HMIS Query 2 was used to obtain data that could be used to estimate the following:  

*	HM quantities overall by transportation phase and by HM Class per CMV type 
per year 

The following were the primary HMIS Query 2 limitations and assumptions: 

*	Since the release of more than one HM Class may occur from each unique CMV 
non-crash incident, Query 2 may identify the same incident more than once.  As a 
result, the number of non-crash incidents in Query 2 is greater than 
(approximately 1-2%) the unique non-crash incidents listed in Query 1, due to 
multiple incident counts when more than one type of HM Class is released. 
Unlike Query 2 for crashes, the number of non-crash incidents was too large to 
manually identify those incidents listed multiple times.  As a result, the number of 
non-crash incidents assigned to each HM Class within each transportation phase 
was based solely on the Query 2 results.  Although the HM Class estimates are 
not as accurate as the per incident quantity by transportation phase (e.g., total HM 
quantities/Query 1 unique incidents), it is believed that the HM quantities by HM 
Class in Query 2 is a reasonable representation for general NEPA analyses 
purposes as it represents the best available information at this time.

*	Since there were a statistically insignificant number of ?other vehicle types? in the 
HMIS incident data, it was assumed that all releases occurred from large trucks.  

4.4	RESULTS

The following tables summarize the information related to HM releases for CMV crashes 
and non-crash incidents for all HM classes (1-9) and fuel spills to be used by FMCSA in 
NEPA documents.  Note that in the following tables, the units of measurement are 
defined as follows: ?gal? = gallons, ?lbs? = pounds, ?cf? = cubic feet, and ?ci? = curies.

4.4.1	Table 4-1 ? CMV Crashes and HM

Table 4-1 includes the following information:

*	The total number of CMV crashes per year (based on a 3-year average using 
2003, 2004 and 2005 data) categorized by large trucks buses and other CMVs.  
Source: MCMIS data.
      Purpose: Background information and for use in other tables

*	The total number crashes per year (based on a 3-year average using 2003, 2004 
and 2005 data) involving either an HM vehicle (includes HM crashes with no 
release of material per FMCSA Order 5610.1 or any CMV crash with a release of 
HM cargo categorized by large trucks and buses). 
      Source: Primarily MCMIS cross-checked using HMIS data
Purpose: Background information and for use in other tables

*	Total number of CMV crashes per year (based on a 3-year average) that resulted 
in a release of hazardous materials as cargo with a subset of HM Class type (1-9) 
with corresponding quantities broken out by trucks, with references to buses and 
other CMVs. 
      Source: Primarily MCMIS cross-checked using HMIS data
Purpose: Background information and for use in other tables

*	Number of CMV truck crashes (based on that resulted in a fuel tank spill and the 
average number of gallons released per year (based on a 5-year average).  
      Source: Acton Spill Center database
      Purpose: Background information and for use in other tables

TABLE 4-1.  CMV CRASHES AND HM

Categories of CMV Crashes
Annual Averages

By CMV Type 

Total Number
     Large Trucks  (> 10,000 pounds)
136,395
     Buses  (seats for > 15 people including 
driver)
9,332
     Other CMVs (Trucks < 10,000 pounds & 
Other vehicles  w/HM Placards Required)
2,117
All CMVs Combined
147,844
Involving a Hazardous Materials Vehicle 
(with and without a Hazardous Material cargo 
release)

Total Number
     Large Trucks  (> 10,000 pounds)
1,998
     Buses  (includes > 15 passengers)
2.0
     Other CMV?s (Trucks < 10,000 pounds & 
Other vehicles w/HM Placards Required)
37
All HM Vehicle Crashes Combined
2,037
Involving a Cargo Release by CMV Type 
and Hazardous Materials Class 
   Total Number 
Total Quantity 
Released
     Large Trucks (> 10,000 pounds)
(# HMIS database w/ 
MCMIS cross check)
(HMIS database) 
Hazard Class 1
Explosives
3
437.50 gal
27,017 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
30
57,852.30 gal
58.71 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
136
391,117.69 gal
Hazard Class 4 
Flammable solids
3
1,829.21 gal
8,971.75 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
10
4,860.27 gal
47,270.63 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
4
1,443.88 gal
15,351.67 lbs
Hazard Class 7
Radioactive Materials
2
18.88 gal
200.09 ci
Hazard Class 8  
Corrosive Substances
33
20,794.37 gal
2,791.33 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
12
14,291.07 gal
57,628.73 lbs

TABLE 4-1.  CMV CRASHES AND HM

Categories of CMV Crashes
Annual Averages
Involving a Cargo Release by CMV Type 
and Hazardous Materials Class 
   Total Number (#)
(# HMIS database w/ 
MCMIS cross check)
Total Quantity 
Released
(HMIS database)
Buses (seats for > 15 people including driver) 

Note, based on 2003 and 2004 HMIS data and using the ?other vehicle? category, there were no 
HM releases from buses.  Data was not available for 2005, as the new HMIS database does not 
include categories for vehicle type.

   Total Number (#)
Total Quantity 
Released
Other CMVs (Trucks < 10,000 pounds and 
other vehicles w/HM Placards)
(# HMIS database w/ 
MCMIS cross check)
(HMIS database)
Note, based on 2003 and 2004 HMIS data and using the ?other vehicle? category the following  
releases occurred:
Pickup Truck - 113.63 cf release of Class 2 HM (based on one event only)
Passenger Vehicles ? 4,020 gal release of Class 3 HM (based on two events only)

HM data from 2005 was not available by vehicle type.  In summary, HM releases by ?Other 
CMVs? is statistically insignificant (with only three individual events) and a separate table is not 
warranted.  
CMVs (Trucks) Involving Fuel Tank 
Spills from Fuel Tanks
(Information from the Acton Spill Center was 
extrapolated and applied to MCMIS data for all 
truck crashes)
# of Crashes Per 
Year with Fuel 
Tank Spills 
(12% of Truck 
Crashes?)
Avg. 
Release 
per Crash
Est. Total amount of 
fuel released annually 
Large Trucks (> 10,000 pounds)
16,367
72 gal
1,178,424 gal
Non-Large Trucks (< 10,000 pounds)
254
40 gal
10,160 gal
Total Large and Non-Large Trucks
16,621

1,188,584 gal

4.4.2	Table 4-2 ? Releases of HM per Crash by CMV Category

Table 4-2 presents information for four FMCSA regulatory options and includes the 
following information:

*	?Option I? - The amount overall of HM released and the average release of HM 
per CMV crash (including HM classes 1-9).  All quantities of HM releases were 
added by class and divided by the total number of CMV crashes. 
      Source:  HMIS and MCMIS database
      Purpose:  For use when FMCSA regulations apply to all CMVs

*	?Option II? ? The amount overall of HM released and the average release of HM 
per large truck crash (including HM classes 1-9).  All quantities of large truck 
releases were added by class and divided by the total number of large truck 
crashes.
      Source:  HMIS and MCMIS database.
      Purpose:  For use when FMCSA regulations apply to all large trucks.

*	?Option III? - The amount overall of HM released and the average release of HM 
per HM carrier type.  All quantities of HM carrier releases were added by class 
and divided by the total number of HM carrier crashes. 
      Source:  MCMIS and HMIS database. 
      Purpose: For use when FMCSA regulations apply to all HM carriers only.

*	?Option IV? - The average release per HM carrier by HM carrier class.  
Quantities of each HM class are divided by the total number HM carriers for that 
particular class
      Source: HMIS only database.  
      Purpose: For use when FMCSA regulations apply to a particular HM class only.

TABLE 4-2.  RELEASES OF HM PER CRASH BY CMV CATEGORY

Note:  Table 4-2 contains four NEPA analysis options
Option I. - All CMVs

Use the below information when FMCSA proposes new or modified regulations that apply 
to all CMVs

Instructions for NEPA Analysis:  Multiply the number of avoided CMV crashes by the 
average release per crash overall or by each HM class.  For example, if FMCSA regulations 
would result in 100 fewer CMV crashes per year, the reduction in the overall HM quantities 
released would be (100 x 3.3322 gallons, 1.0757 pounds, 0.0004 cubic feet and 0.0014 curries) 
= 333.22 gallons, 107.57 pounds, 0.04 cubic feet and 0.14 curries respectively, and the 
reduction for HM Class 2 would be (100 x 0.3913 gallons and .0004 cubic feet) = 39.13 
gallons and 0.04 cubic feet, respectively. 
Total HM 
Quantities
Average # 
CMV 
Crashes
HM Quantity per 
CMV Crash
492,645.17 gal
147,844
3.3322 gal
159,031 lbs
147,844
1.0757 lbs
58.71 cf
147,844
0.0004 cf
200.09 ci
147,844
0.0014 ci

Hazardous Material Class

Quantities of Average Annual HM Releases
(average annual release HM class/average 
annual # CMV crashes)
Hazard Class 1
Explosives 
437.50 gal/147,844
27,017 lbs/147,844
0.0030 gal
0.1827 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
57,852.30 gal/147,844
58.71 cf/147,844
0.3913 gal
0.0004 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
391,117.69 gal/147,844

2.6455 gal
Hazard Class 4 
Flammable solids
1,829.21 gal/147,844
8,971.75 lbs/147,844
0.0124 gal
0.0607 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
4,860.27 gal/147,844
47,270.63 lbs/147,844
0.0329 gal
0.3197 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
1,443.88 gal/147,844
15,351.67 lbs/147,844
0.0098 gal
0.1038 lbs
Hazard Class 7
Radioactive Materials
18.88 gal/147,844
200.09 ci/147,844
0.0001 gal
0.0014 ci
Hazard Class 8  
Corrosive Substances
20,794.37 gal/147,844
2791.33 lbs/147,844
0.1407 gal
0.0189 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
14,291.07 gal/147,844
57,628.73 lbs/147,844
0.0967 gal
0.3898 lbs

TABLE 4-2.  RELEASES OF HM PER CRASH BY CMV CATEGORY

Note:  Table 4-2 contains four NEPA analysis options
Option  II.- All Large Trucks

Use the below information when FMCSA proposes new or modified regulations that apply 
to Large Trucks

Instructions for NEPA Analysis:  Multiply the number of avoided large truck crashes by 
the average release per crash overall or by each HM class.  For example, if FMCSA 
regulations result in 50 fewer large truck crashes per year, the reduction in the overall HM 
quantities released would be (50 x 3.6120 gallons, 1.1660 pounds, 0.0004 cubic feet and 
0.0015 curries) = 180.6 gallons, 58.3 pounds, 0.02 cubic feet and 0.75 curries respectively, and 
the reduction for HM Class 3 would be (50 x 2.865 gallons) = 143.25 gallons.
Total HM 
Quantities
Average # 
Lg. Truck 
Crashes
HM Quantity per 
Large Truck Crash
492,645.17 gal
136,395
3.6120 gal
159,031 lbs
136,395
1.1660 lbs
58.71 cf
136,395
0.0004 cf
200.09 ci
136,395
0.0015 ci

Hazardous Material Class

Quantities of Average Annual HM Releases
(average annual release HM class/average 
annual # large truck crashes)

Hazard Class 1
Explosives 
437.50 gal/136,395
27017 lbs/136,395
0.0032 gal
0.1981 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
57,852.30 gal/136,395
58.71 cf/136,395

0.4242 gal
0.0004 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
391,117.69 gal/136,395

2.865 gal
Hazard Class 4 
Flammable solids
1,829.21 gal/136,395
8,971.75 lbs/136,395
0.0134 gal
0.0658 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
4,860.27 gal/136,395
47,270.63 lbs/136,395
0.0356 gal
0.3446 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
1,443.88 gal/136,395
15,351.67 lbs/136,395
0.0106 gal
0.1126 lbs
Hazard Class 7
Radioactive Materials
18.88 gal/136,395
200.09 ci/136,395
0.0001 gal
0.0015 ci
Hazard Class 8  
Corrosive Substances
20,794.37 gal/136,395
2,791.33 lbs/136,395
0.1525 gal
0.0205 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
14,291.07 gal/136,395
57,628.73 lbs/136,395
0.1048 gal
0.4225 lbs

TABLE 4-2.  RELEASES OF HM PER CRASH BY CMV CATEGORY

Note:  Table 4-2 contains four NEPA analysis options
Option  III.- All HM Carriers

Use the below information when FMCSA proposes new or modified regulations that apply 
to all HM carriers.

Instructions for NEPA Analysis:  Multiply the number of avoided HM carrier crashes by 
the average release per crash overall or by each HM class.  For example, if FMCSA 
regulations would result in 10 fewer HM carrier crashes per year, the reduction in the overall 
HM quantities released would be (10 x 241.8484 gallons, 78.0712 pounds, 0.0288 cubic feet 
and 0.0982 curries) = 2418 .48 gallons, 7807.12 pounds, 2.88 cubic feet and 9.82 curries 
respectively, and the reduction for HM Class 4 would be (10  x 0.8980 gallons and 4.4043 
pounds) = 8.980 gallons and 44.043 pounds, respectively.
Total HM 
Quantities
Average # 
HM Carrier
Crashes
HM Quantity per HM 
Carrier Crash 
492,645.17 gal
2,037
241.8484 gal
159,031 lbs
2,037
78.0712 lbs
58.71 cf
2,037
0.0288 cf
200.09 ci
2,037
0.0982 ci

Hazardous Material Class

Quantities of Average Annual HM Releases
(average annual release HM class/average 
annual # HM placarded crashes)
Hazard Class 1
Explosives 
437.50 gal/2,037
27,017 lbs/2,037
0.2147 gal
13.2631 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
57,852.30 gal/2,037
58.71 cf/2,037

28.4007 gal
0.0288 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
391,117.69 gal/2,037

192.0067 gal
Hazard Class 4 
Flammable solids
1,829.21 gal/2,037
8,971.75 lbs/2,037
0.8980 gal
4.4043 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
4,860.27 gal/2,037
47,270.63 lbs/2,037
2.3860 gal
23.2060 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
1,443.88 gal/2,037
15,351.67 lbs/2,037
0.7088 gal
7.5336 lbs
Hazard Class 7
Radioactive Materials
18.88 gal/2,037
200.09 ci/2,037
0.0093 gal
0.0982 ci
Hazard Class 8  
Corrosive Substances
20,794.37 gal/2,037
2,791.33 lbs/2,037
10.2083 gal
1.3703 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
14,291.07 gal/2,037
57,628.73 lbs/2,037
7.0157 gal
28.2910 lbs

TABLE 4-2.  RELEASES OF HM PER CRASH BY CMV CATEGORY

Note:  Table 4-2 contains four NEPA analysis options.

Option  IV. ? HM Carriers by HM Class

Use the information below when FMCSA proposes new or modified regulations that apply 
to HM carriers of a specific HM Class.

Instructions for NEPA Analysis:  Multiply the number of avoided crashes by the average 
per crash release of the applicable HM Class.  For example, if FMCSA regulations would 
result in 5 fewer Class 7 crashes per year, the reduction in the quantity of Class 7 released 
into the environment each year would be as follows:  (5 x 9.44 gallons and 100.5 curies) or 
47.2 gallons and 502.5 curies, respectively.

Hazardous Material Class

Quantities of Average Annual HM Releases
(average annual release HM class/average 
annual # HM Class crashes)
Hazard Class 1
Explosives 
437.50 gal/3
27,017 lbs/3
145.83 gal
9005.67 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
57,852.30 gal/30
58.71 cf/30

1928.41 gal
1.96 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
391,117.69 gal/136

2875.87 gal
Hazard Class 4 
Flammable solids
1,829.21 gal/3
8,971.75 lbs/3
609.74 gal
2,990.58 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
4,860.27 gal/10
47,270.63 lbs/10
486.83 gal
4,727.06 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
1,443.88 gal/4
15,351.67 lbs/4
360.97 gal
3,837.92 lbs
Hazard Class 7
Radioactive Materials
18.88 gal/2
200.09 ci/2
9.44 gal
100.05 ci
Hazard Class 8  
Corrosive Substances
20,794.37 gal/33
2,791.33 lbs/33
630.13 gal
84.59 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
14,291.07 gal/12
57,628.73 lbs/12
1,190.92 gal
4,802.39 lbs

4.4.3	Table 4-3 ? CMV HM Incidents and Releases of HM

Table 4-3 presents information for four regulatory options and includes the following 
information:

*	?Option I? - The amount overall of HM released per in-transit incident and also 
by each HM class.  Quantities of each HM class were divided by the total number 
of in transit incidents for that HM Class. 
      Source: HMIS
      Purpose: For use when FMCSA regulations apply to all CMVs In-transit.

*	?Option II? - The amount overall of HM released per loading incident and also by 
each HM class.  Quantities of each HM Class were divided by the total number of 
loading incidents for that HM Class. 
      Source: HMIS
      Purpose: For use when FMCSA regulations apply to all CMV loading incidents

*	?Option III? - The amount overall of HM released per unloading incident and also 
by each HM class.  Quantities of each HM Class were divided by the total number 
of unloading incidents for that HM Class. 
      Source: HMIS
      Purpose: For use when FMCSA regulations apply to all CMV unloading 
      incidents.  

*	?Option IV? - The amount overall of HM released per in transit storage incident 
and also by each HM Class.  Quantities of each HM Class were divided by the 
total number of in transit storage incidents for that HM Class. 
      Source: HMIS
      Purpose: For use when FMCSA regulations apply to all CMV in transit storage 
      incidents.  

This table is for use as an informational resource for NEPA documents if FMCSA 
regulations are modified and that modification is expected to result in a decrease of HM 
releases due to both CMV crashes and incidents.

TABLE 4-3.  CMV HM INCIDENTS AND RELEASES OF HM

Note: Table 4-3 contains four NEPA analysis options.
Option 1 ? In-Transit HM Incidents

Use the below information when FMCSA proposes new or modified regulations that apply 
only to CMVs In-Transit 

Instructions for NEPA Analysis:  Multiply the number of avoided CMV in-transit 
incidents by the average release per incident overall or by each HM class.  For example, if 
FMCSA regulations would result in 50 fewer in-transit incidents, the reduction in overall HM 
quantities would be (50 x 36.39 gallons, 11.76 pounds and 39.75 cubic feet) = 1,819.5 gallons, 
588 pounds and 1,987.5 cubic feet, respectively, and the reduction for HM Class 3 would be 
(50 x 50.93 gallons) = 2,546.5 gallons.
Total In-Transit 
HM Quantities
In-Transit 
Average # 
Incidents
Per Incident Quantity
48,832.31 gal
1,342
36.39 gal
15,778.82 lbs
1,342
11.76 lbs
53,348.86 cf
1,342
39.75 cf

Incident Type 
Annual Averages

In Transit (non crash related)
# of In-Transit 
Incidents
Total Units 
Released
Release HM 
Class per In 
Transit 
Incident
Hazard Class 1 
Explosives
3
8.05 gal
400.06 lbs
2.68 gal
133.35 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
53
4,309.87 gal
59.5 lbs 
53,348.86 cf
81.32 gal
1.12 lbs
1,006.58 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
625
31,828.35 gal
50.93 gal
Hazard Class 4 
Flammable solids
6
33.68 gal
678.48 lbs
5.61 gal
113.08 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
42
424.47 gal
1,051.73 lbs
10.11 gal
25.04 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
210
379.95 gal
75.33 lbs
1.81.gal
0.36 lbs
Hazard Class 7
Radioactive Materials
1
0 ci
0
Hazard Class 8  
Corrosive Substances
470
10,533.77 gal
1,706.84 lbs
22.41 gal
3.63 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
48
1,314.17 gal
11,806.88 lbs
27.38 gal
245.98 lbs

TABLE 4-3.  CMV HM INCIDENTS AND RELEASES OF HM

Note: Table 4-3 contains four NEPA analysis options.
Option II ? Loading HM Incidents

Use the below information when FMCSA proposes new or modified regulations that apply 
only to CMV Loading HM incidents

Instructions for NEPA Analysis:  Multiply the number of avoided CMV loading incidents 
by the average release per incident overall or by each HM class.  For example, if FMCSA 
regulations would result in 200 fewer loading incidents the reduction in the overall HM 
quantity released would be (200 x 13.30 gallons, 8.97 pounds and 0.08 cubic feet) = 2,660 
gallons, 1,794 pounds and 16 cubic feet, and the reduction for HM Class 4 would be (200 x 
2.67 gallons and 22.16 cubic feet) = 534 gallons and 4,432 pounds.

Total Loading 
HM Quantities
Loading
Average # 
Incidents
Per Incident 
Quantity
21,328.03 gal
1,604
13.30 gal
14,384.28 lbs
1,604
8.97 lbs
128.44 cf
1,604
0.08 cf

Incident Type 
Annual Averages

Loading
# of Loading 
Incidents
Total Units 
Released
Release per 
Loading 
Incident
Hazard Class 1
Explosives
1
0.02 gal
2.20 lbs
0.02 gal
2.20 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
68
3,771.08 gal
180.47 lbs
128.44 cf
55.46 gal
2.65 lbs
1.89 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
748
10,782.21 gal
14.41 gal
Hazard Class 4 
Flammable solids
10
26.7 gal
221.60 lbs
2.67 gal
22.16 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
50
418.44 gal
107.49 lbs
8.37 gal
2.15 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
66
334.38 gal
115.14 lbs
5.06.gal
1.74 lbs
Hazard Class 7
Radioactive Materials
0
0
0
Hazard Class 8  
Corrosive Substances
614
4,298.79 gal
145.77 lbs
7.00 gal
0.24 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
44
1,696.41 gal
13,611.61 lbs
38.55 gal
309.35 lbs

TABLE 4-3.  CMV HM INCIDENTS AND RELEASES OF HM

Note: Table 4-3 contains four NEPA analysis options.
Option III ? Unloading HM Incidents

Use the below information when FMCSA proposes new or modified regulations that apply 
only to CMV Unloading HM incidents

Instructions for NEPA Analysis:  Multiply the number of avoided CMV unloading 
incidents by the average release per incident overall or by each HM class.  For example, if 
FMCSA regulations would result in 50 fewer unloading incidents, the overall reduction of the 
HM quantity released would be (50 x 12.82 gallons, 1.315 pounds and 0.18 cubic feet) = 641 
gallons, 65.75 pounds and 9 cubic feet and the reduction for HM Class 5 would be  (50 x 3.41 
gallons and 9.38 pounds) = 170.5 gallons and 469 pounds.

Total Unloading 
HM Quantities
Unloading
Total #
Per Incident 
Quantity
117,062.81 gal
9,129
12.82 gal
12,012.14 lbs
9,129
1.315 lbs
1,633.56 cf
9,129
0.18 cf

Incident Type 
Annual Averages

Unloading
#  Unloading 
Incidents
Total Unit 
Released
Release per 
Unloading 
Incident
Hazard Class 1
Explosives
1
0.01 gal
0.79 lbs
0.01 gal
0.79 lbs
Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
387
6,645.70 gal
1,109.26 lbs
1,633.56 cf
17.17 gal
2.87 lbs
4.22 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
4433
50,755.92 gal
0.17 lbs
11.45 gal

Hazard Class 4 
Flammable solids
64
9.76 gal
726.49 lbs
0.15 gal
11.35 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
396
1,351.52 gal
3,715.98 lbs
3.41 gal
9.38 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
438
2,568.51 gal
1,478.56 lbs
5.86 gal
3.38 lbs
Hazard Class 7
Radioactive Materials
0
0
0
Hazard Class 8  
Corrosive Substances
3218
40,614.68 gal
3,441.50 lbs
12.62 gal
1.07 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
230
6,050.47 gal
1,539.39 lbs
26.31 gal
6.70 lbs
	

TABLE 4-3.  CMV HM INCIDENTS AND RELEASES OF HM

Note: Table 4-3 contains four NEPA analysis options.
Option IV ? In-Transit Storage 

Use the below information when FMCSA proposes new or modified regulations that apply 
only to CMV In-Transit Storage incidents

Instructions for NEPA Analysis:  Multiply the number of avoided CMV in-transit storage 
incidents by the average release per incident overall or by each HM class.  For example, if 
FMCSA regulations would result in 20 fewer in-transit storage incidents, the reduction in the 
overall HM quantity release would be (20 x 31.14 gallons, 16.58 pounds and 0.39 cubic feet) = 
622.8 gallons, 331.6 pounds and 7.8 cubic feet, and the reduction for HM Class 8 would be  
(20 x 51.30 gallons and 1.72 pounds) =  1,026 gallons and 34.4 pounds.
Total In-Transit 
Storage HM 
Quantities
Unloading
Total #
Per Incident 
Quantity
18,183.78 gal
584
31.14 gal
9,680.04 lbs
584
16.58 lbs
225.25 cf
584
0.39 cf

Incident Type 
Annual Averages

In Transit Storage
# In-Transit 
Storage 
Incidents
Total Unit 
Released

Average 
Release Per 
Incident 
(Quantity/#HM 
Incidents)
Hazard Class 1
Explosives
0

0.13 gal
0 gal

Hazard Class 2 
Gases (flammable, nonflammable, toxic/non-
toxic)
30

2,306.38 gal
13.01 lbs
225.25 cf
76.88 gal
0.43 lbs
7.51 cf
Hazard Class 3 
Flammable and Combustible Liquids Hazard 
233
5,264.69 gal
22.60 gal

Hazard Class 4 
Flammable solids
3
0.01 gal
5.54 lbs
0.003 gal
1.85 lbs
Hazard Class 5 
Oxidizing Substances and Organic Peroxides
20
78.92 gal
9,062.66 lbs
3.95 gal
453.13 lbs
Hazard Class 6 
Toxic Substances and Infectious Substances
59
103.87 gal
143.74 lbs
1.76 gal
2.44 lbs
Hazard Class 7
Radioactive Materials
0
0 
0
Hazard Class 8  
Corrosive Substances
202
10,363.50 gal
347.0 lbs
51.30 gal
1.72 lbs
Hazard Class 9
Miscellaneous Hazardous Materials
34
66.29 gal
251.83 lbs
1.95 gal
7.41 lbs
6.70 lbs

4.5	ENVIRONMENTAL IMPACT SUMMARIES FOR HAZARDOUS 
MATERIALS 

The adverse environmental impacts associated with releases of most HM in highway 
transportation are usually short-term impacts that can be greatly reduced or eliminated 
through prompt clean up of the accident scene.  Most HM transported by CMVs is not in 
quantities sufficient to cause significant, large scale, long-term environmental damage if 
they are released; however, environmental damage can occur depending on the amount 
and location of the spill/release.  

The natural resources that could be adversely affected by a HM release include air, 
surface and ground water, soil, wetlands, and ecological resources (for example, wildlife 
habitats or endangered species).  Historic properties protected under Section 106 of the 
National Historic Preservation Act and parklands or wildlife refuges protected under 
Section 4(f) of the DOT Act can also be adversely impacted.  

Impacts vary depending on the class and amount of the HM and the location where it is 
released. Below is a general description of the environmental impacts that can be 
expected from a release as well as a more specific description of the quantities, types and 
locations where the releases for each HM class have occurred.  Specific information is 
based on a three year analysis (2003, 2004 and 2005) of PHMSA?s HMIS database, 
which relies on the submission the HMIRs.  

In addition, information on fuel released from fuel tanks was obtained from the Acton 
Spill Center.  

4.5.1	Hazardous Materials Overall

There are 688,986 gallons, 211,030 pounds, 53, 925 cubic feet and 201 curies released 
into the environment on an average annual basis from both CMV crashes and CMV non-
crash related incidents combined.

CMV Crashes - On average, there are 234 CMV crashes a year that result in the release of 
a hazardous material from the cargo.  For all hazardous material classes combined, there 
is an average of 492,645 gallons, 159,031 pounds, 59 cubic feet and 201 curies released 
into the environment annually.  Most hazardous material releases from CMV crashes 
occur in undeveloped (38 percent) and commercial (32 percent) areas, followed by 
agricultural (12 percent), residential (10 percent) and industrial (8 percent) areas.

CMV Non-Crash Incidents - On average, there are 12,659 CMV non-crash related 
incidents a year that result in the release of a hazardous material.  Most of these incidents 
occur during unloading (9,129) followed by loading (1,604), in-transit (1,342) and in-
transit temporary storage (584).  For all HM combined, there is an average of 196,341 
gallons, 51,999 pounds, and 53, 866 cubic feet released into the environment annually.  
Most HM releases from CMV non-crash related incidents occur in commercial (53 
percent) and industrial (44 percent) areas, followed by residential (2 percent) and 
undeveloped (1 percent) areas.

4.5.2	Hazardous Materials by Class

Class 1 - Explosives

General - The potential environmental impacts from a release of a Class 1 material 
depend on the nature of the explosive.  Spills or fires related to explosive materials can 
cause destruction of the immediate area and may require local evacuations up to a mile 
away.  Explosives have the potential to throw fragments over a mile.  Besides their 
immediate threat to public health (human injuries and fatalities), fires related to an 
incident may produce irritating, corrosive, or toxic gases and harm plants and wildlife as 
well.  Additionally, fire control methods may produce runoff pollutants into wetlands, 
waterways and contaminate ground water. 

In total, there are 445.71 gallons and 27,420.05 pounds of Class 1 HM released into the 
environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately three (3) CMV crashes a year that 
result in the average release of 437.50 gallons and 27,017 pounds of Class 1 HM into the 
environment.  Most Class 1 releases occurred in undeveloped (57 percent) and 
commercial (29 percent) areas.  

CMV Non-crash Incidents ? On average, there is approximately one (1) non-crash related 
incident a year that results in the release of 8.21 gallons and 403.05 pounds of Class 1 
HM into the environment.  The release usually occur in-transit. 

Class 2 - Compressed Gases

General ? Releases of Class 2 materials can lead to injuries and fatalities, as well as 
carrier damage and incident delay costs.  Depending on the actual material, there could be 
catastrophic impacts and extensive cleanup costs.  Flammable gases are extremely 
flammable and can easily ignite by heat, sparks, or flames.  They can also form explosive 
mixtures with air and have environmental impacts similar to Class 1 explosives.  
Containers may explode when heated and ruptured cylinders may rocket.  Vapors may 
travel to a source of ignition and flash back; some vapors also are heavier than air and 
may spread along the ground.  Some corrosive gases may react violently with water.  
Oxidizing gasses do not burn, but still support combustion, and may react explosively 
with fuels.  In incidents with compressed gases, vapors may also travel to a source of 
ignition and flash back.  Runoff from fire control could subsequently cause pollution. 

Potential health effects of compressed gases include asphyxiation, dizziness, and 
irritation when inhaled.  Contact with liquefied gas may cause burns.  Some flammable 
gases may also be fatal if inhaled or also through absorption of the skin.  Potential spills 
or leaks would need to be isolated.  Large spills require evacuations downwind up to a  
mile, while fires may require evacuations up to a mile radius from the incident.  

In total, there are 74,885.33 gallons, 1,362.24 pounds and 53,924.82 cubic feet of Class 2 
HM released into the environment on an average annual basis from both CMV crashes 
and CMV incidents combined.

CMV Crashes ? On average, there are approximately 30 CMV crashes a year that result 
in the release of 57,852.30 gallons and 58.71 cubic feet of Class 2 HM into the 
environment.  Most Class 2 releases occurred in commercial (42  percent) and 
undeveloped (27 percent) areas. 

CMV Non-crash Incidents ? On average, there are approximately 135 non-crash related 
incidents a year that result in the release of 17,033.03 gallons 1,362.24 pounds and 
53,866.11 cubic feet of Class 2 HM into the environment.  Releases are most likely to 
occur during unloading. 

Class 3 - Flammable Liquids

General - Flammable liquids are extremely flammable and can easily ignite by heat, 
sparks, or flames.  Their vapors can also form explosive mixtures with air.  Containers 
containing flammable liquids can explode when heated and ruptured cylinders can rocket.  
Vapors may travel to a source of ignition and flash back; some vapors also are heavier 
than air and may spread along the ground.  Many liquids in this category are lighter than 
water.  Vapors may also travel to a source of ignition and flash back.  Runoff from fire 
control could subsequently cause pollution.      

Public health and safety concerns include irritation or burns of the skin and eyes via 
inhalation or contact with the material.  Dizziness and suffocation from vapors are more 
serious effects.  Spills should be isolated and larger spills may necessitate downwind 
evacuations up to 1000 feet.  Fires may produce irritating, corrosive, or toxic gases, and 
runoff from fire control also may cause pollution.  Fires also necessitate isolation and 
evacuation measures up to a  mile radius from the incident.  

In total, there are 489,748.86 gallons and 0.17 pounds of Class 3 HM released into the 
environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately three 137 CMV crashes a year that 
result in the average release of 391,117.69 gallons of Class 3 HM into the environment.  
Most class 3 releases occurred in undeveloped (42 percent) and commercial (30 percent) 
areas.  

CMV Non-crash Incidents ? On average, there is approximately 1,510 of non-crash 
related incidents a year that result in the release of 98,631.17 gallons and 0.17 pounds of 
Class 3 HM into the environment.  The release is most likely to occur during unloading. 

Class 4 - Flammables

General - Flammables are considered hazardous because they are easily ignited and many 
burn rapidly if ignited.  Flammables may be ignited by friction, heat, sparks, or flames.  
Flammable powders, dusts, shavings, borings, turnings, or cuttings can explode or burn 
with explosive violence.  Vapors from toxic or corrosive substances may form explosive 
mixtures with air when heated.  Toxic and corrosive flammable solids may also produce 
flammable hydrogen gas when in contact with metals.  Some spontaneously combustibles 
react explosively on contact with water.  Containers carrying these substances can 
explode if heated.  Additionally, these substances have the capability to re-ignite after a 
fire is extinguished.  In the case of spontaneously combustible materials, runoff may also 
create a fire or explosion hazard.    

Impacts to human health can occur if fires produce irritating or toxic gases.  Additionally, 
contact with these materials or associated molten substances may cause burns to the skin 
and eyes.  Ingestion or inhalation of a spontaneously combustible substance or its 
decomposition products is toxic and can cause severe injuries and death.  Fires can 
produce toxic gases, and water-reactive substances may generate heat reacting with 
water, which can also increase the concentration of fumes in the air.  

Spills should be isolated immediately at a radius of at least 75 feet.  Large spills may also 
required downwind evacuations up to 330 feet.  Fires associated with these materials may 
necessitate isolation and evacuation up to a  mile in all directions.  Fire control may 
also subsequently cause runoff pollution.

In total, there are 1,899.36 gallons and 10,603.86 pounds of Class 4 hazardous materials 
released into the environment on an average annual basis from both CMV crashes and 
CMV non-crash related incidents combined.

CMV Crashes ? On average, there are approximately three (3) CMV crashes a year that 
result in the average release of 1,829.21 gallons and 8,971.75 pounds of Class 4 HM into 
the environment.  Most class 4 releases occurred in commercial (71 percent) and 
undeveloped (29 percent) areas.  

CMV Non-crash Incidents ? On average, there are approximately 21 non-crash related 
incidents a year that result in the release of 70.15 gallons and 1,632.11 pounds of Class 4 
HM into the environment.  The release is most likely to occur during unloading. 

Class 5 - Oxidizing Materials

General - Oxidizing materials accelerate burning if they are involved in a fire.  They can 
explode from friction, heat, or contamination.  Some oxidizers can react explosively with 
fuels, water, or ignite other combustibles.  Some water-reactive oxidizers can produce 
flammable hydrogen gas upon contact with metals.  Containers holding organic peroxides 
can explode if heated.  Some organic peroxides can be particularly sensitive to 
temperature rises, and can decompose violently and catch fire if a give control 
temperature for a substance is exceeded.  Runoff from incidents with oxidizing materials 
can also pose a fire or explosion hazard.  

Oxidizing materials are toxic if ingested or if associated dusts are inhaled.  Contact with 
oxidizing substances can cause severe burns to the skin or eyes.  Toxic/flammable fumes 
may accumulate in confined areas.  Fires involving these materials can produce irritating, 
corrosive, and toxic gases.  Ingestion or inhalation of oxidizing materials or their 
decomposition products is toxic and can cause severe injuries and death.  

Spills of these materials may require isolation and evacuations from 75 to 800 feet.  Fires 
involving these materials should be isolated up to a half mile radius surrounding the 
incident.  Fire control may also subsequently cause runoff pollution.

In total, there are 7,133.62 gallons and 61,208.49 pounds of Class 5 HM released into the 
environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately 10 CMV crashes a year that result 
in the average release of 4,860.27 gallons and 47,270.63 pounds of Class 51 HM into the 
environment.  Most Class 5 releases occurred in undeveloped (58 percent) and 
commercial (17 percent) areas.  

CMV Non-crash Incidents ? On average, there are approximately 127 non-crash related 
incidents a year that result in the release of 2,273.35 gallons and 13,937.86 pounds of 
Class 5 HM into the environment.  The release is most likely to occur during unloading. 
.
Class 6 - Toxic Materials

General - Toxic substances may or may not be combustible.  In the case of non-
combustible toxic substances, the substance itself does not burn but may decompose upon 
heating to produce corrosive and toxic fumes.  Combustible toxic materials may burn but 
do not ignite readily.  Containers carrying toxic materials can explode if heated.  
Infectious materials do not ignite readily but are sometimes transported in flammable 
liquids.  Runoff from an incident could also pollute waterways.  

Toxic materials are by nature highly toxic, and can be fatal if inhaled, swallowed, or 
absorbed through the skin.  Contact with molten substances may cause severe burns.  
Health effects from these substances may be delayed.  Contact with or inhalation of 
infectious substances may cause infection, disease, or death. Fires involving these 
substances may also produce corrosive and toxic gases.  Runoff from fire control 
methods could also cause pollution.  Spills should be isolated up to 150 feet.  Fires should 
be isolated up to a  mile in all directions.  

In total, there are 4,830.59 gallons and 17,164.44 pounds of Class 6 HM released into the 
environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately four CMV crashes a year that result 
in the average release of 1,443.88 gallons and 15,351.67 pounds of Class 6 HM into the 
environment.  Most Class 6 releases occurred in undeveloped (36 percent) and 
commercial (36 percent) areas.  

CMV Non-crash  Incidents ? On average, there is approximately 193 non-crash related 
incidents a year that result in the release of 3,386.71 gallons and 1,812.77 pounds of 
Class 6 HM into the environment.  The release is most likely to during unloading. 

Class 7 - Radioactive Materials

General - Radioactive materials do not typically ignite readily but may burn.  
Additionally, radioactivity does not change the flammability or other properties of 
materials.  

Radioactive material presents minimal risk to transport workers, emergency response 
personnel, and the public during transportation accidents.  Packaging durability increases 
as the potential hazard of radioactive content increases.  Radioactive materials that are 
packaged as Type A contain non-life endangering amounts.  Type B and C packages 
contain the most hazardous amounts of radioactive material.  Life threatening conditions 
may exist only if the contents are released or the package shielding fails.  Because of the 
design, evaluation, and testing of the packages, these conditions would only be expected 
for accidents of utmost severity.  For spills and fires involving these materials, isolation 
and evacuations may be necessary from 330-1000 feet.  

In total, there are 18.88 gallons and 200.09 curies of Class 7 HM released into the 
environment on an average annual basis.   

CMV Crashes ? On average, there are approximately two CMV crashes a year that result 
in the average release of 18.88 gallons and 200.09 curies of Class 7 HM into the 
environment.  Most class 1 releases occurred in undeveloped (50 percent) and 
commercial (20 percent) areas.  

CMV Non-crash Incidents ? On average, no Class 7 releases occurred from CMV non-
crash related incidents. 

Class 8 - Corrosive Materials

General - Corrosive materials may or may not be combustible; some do not ignite readily 
while others are highly flammable.  When heated, corrosive materials can release vapors 
with the potential to explode upon contact with air.  Corrosive materials may produce 
flammable hydrogen gas upon contact with metals.  Some substances react violently with 
water, releasing flammable, toxic, or corrosive gases and runoff.  Runoff from incidents 
with corrosive materials may pollute waterways.    

Corrosive materials are highly toxic.  Inhalation, ingestion, or skin contact with corrosive 
materials may cause severe burns, injuries, or death.  Effects of these materials may also 
be delayed.  Reaction with water or moist air can release toxic, corrosive, or flammable 
gases, and can generate heat, which will increase the concentration of fumes in the air.  
Most vapors are heavier than air and will collect in low or confined areas.  Fires 
involving these substances may also produce corrosive and toxic gases.  Runoff from fire 
control methods could also cause pollution.  Spills should be isolated up to 75-150 feet.  
Fires should be isolated up to a  mile in all directions.  

In total, there are 86,605.11 gallons and 8,432.44 pounds of Class 8 HM released into the 
environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately 33 CMV crashes a year that result 
in the average release of 20,794.37 gallons and 2,791.33 pounds of Class 8 HM into the 
environment.  Most Class 8 releases occurred in undeveloped (43 percent) and 
commercial (41 percent) areas.  

CMV Non-crash Incidents ? On average, there are approximately 1,126 non-crash related 
incidents a year that result in the release of 65,810.74 gallons and 5,641.11 pounds of 
Class 8 HM into the environment.  The release is most likely to occur during unloading. 

Class 9 - Miscellaneous Dangerous Goods

General - Mixed goods could contain any or many types of HM.  As such, the potential 
hazards and health impacts could be similar to any of the categories listed above.  Mixed 
goods could explode from heat, sparks, shock, friction, or contamination.  Mixed goods 
could react explosively with air, water, or foam.  Vapors may travel to the source of 
ignition and flash back.  Containers may explode if heated and ruptured cylinders may 
rocket.  

Health risks include severe injury, infection, disease, or death from inhalation, ingestion, 
or contact with substances.  Contact with mixed materials may cause burns to the skin 
and eyes.  High concentrations of gas may cause asphyxiation without warning.  Fire or 
contact with water may produce irritation, toxic, and corrosive gases.  Fire control 
methods may subsequently produce polluted runoff.   

In total, there are 23,418.41 gallons and 84,838.44 pounds of Class 9 HM released into 
the environment on an average annual basis from both CMV crashes and CMV non-crash 
related incidents combined.

CMV Crashes ? On average, there are approximately 12 CMV crashes a year that result 
in the average release of 14,291.07 gallons and 57,628.73 pounds of Class 9 HM into the 
environment.  Most Class 9 releases occurred in commercial (41 percent) and 
undeveloped (35 percent) areas.  

CMV Non-crash  Incidents ? On average, there are approximately 89 non-crash related 
incidents a year that result in the release of 9,127.34 gallons and 27,209.71 pounds of 
Class 9 HM into the environment.  The release is most likely to occur during unloading. 

Potential Environmental Impacts of Fuel Tank Spills 

General ? Petroleum is a mix of many chemicals, many of which (especially in gasoline) 
evaporate quickly, which is why it is flammable and why it evaporates quickly from 
warm pavement.  If a petroleum spill is not recovered, it can pose health threats in 
drinking water wells if it percolates into the ground water.  Petroleum can kill aquatic life 
and wildlife if it reaches surface water directly or through a storm sewer.

CMV Crashes ? On average, each year approximately 1,188,584 gallons of diesel fuel 
(petroleum) are released into the environment as a result of CMV crashes.  The majority 
of the fuel is released from large trucks (> 10,000 pounds) (see Table 4-4).  

TABLE 4-4.  SUMMARY:  FUEL TANK SPILLS DUE TO CMV CRASHES

CMVs (Trucks) Involving Fuel Tank 
Spills from Fuel Tanks
(Information from the Acton Spill Center was 
extrapolated and applied to MCMIS data for all 
truck crashes)
# of Crashes Per 
Year with Fuel 
Tank Spills 
(12% of Truck 
Crashes?)
Avg. 
Release 
per Crash
Est. Total 
amount of 
fuel released 
annually 
Large Trucks (> 10,000 pounds)
16,367
72 gal
1,178,424 gal
Non-Large Trucks (< 10,000 pounds)
254
40 gal
10,160 gal
Total Large and Non-Large Trucks
16,621

1,188,584 gal

4.6	CONCLUSION

The estimates outlined in Tables 4-1 to 4-3 can be used to quantify the environmental 
benefits from FMCSA regulatory changes reducing crashes and/or non-crash related 
incidents.  Estimates for crashes can be used in concert with Zaloshnja?s and Miller?s 
report for FMCSA on the Revised Cost of Large Truck ? and Bus Involved Crashes.  
Non-crash incidents will be used independently, since Zaloshnja?s and Miller?s report 
only considers CMV crashes.  In addition, FMCSA Order 5610.1, Appendix 18, 3. 
Hazardous Materials, requires FMCSA to document how a proposed action will impact 
the use, transportation, and storage of HM or the number or severity of HM crashes or 
non-crash related incidents.  The above methods and estimates address all of these 
regulatory requirements.

Order 5610.1 further states that the NEPA analysis must identify any moderate to 
significant adverse impacts to public safety and health, transportation, property damage, 
water resources, and biological resources from releases of hazardous materials associated 
with safety-related crashes and incidents.  The above methods and estimates can be used 
to assess the potential adverse or beneficial impacts.  Most of the anticipated impacts will 
be beneficial. In cases where there are adverse impacts, Tables 4-1 to 4-3 can be used to 
calculate the increases in HM releases.  Quantification of the HM releases avoided can 
also be tracked and used by FMCSA as an optional ?mitigation banking? measure. 

The HM environmental impact summary narrative contained in Section 4.5 describes (in 
general) the types of impacts that can occur to various natural resources and public health 
as required in FMCSA Order 5610.1.  Section 4.5 also summarizes the quantities of HM 
released into the environment by HM Class and in aggregate.  The data is comprehensive 
for the years 2003, 2004 and 2005.  This narrative can be used in NEPA analyses to 
supplement the HM quantities derived from Tables 4-1 to 4-3. 

5.  SOLID WASTES

5.1	INTRODUCTION

CMV crashes can generate solid wastes.  The Resource Conservation and Recovery Act 
(RCRA) and related regulations establish the waste management requirements that apply 
to CMV crash generated waste.  The chassis and engines, as well as associated fluids and 
components of trucks, buses, and automobiles and the contents of the vehicle can all be 
deemed waste.  The waste can also include damage to the roadway infrastructure 
including road surface, barriers, bridges, and signage.   The purpose of this report section 
is to quantify solid waste generated from CMV crashes.

Waste material has one of three fates.  First, the material can be returned to the originally 
intended use: vehicles can be repaired, cargo can be still be utilized.  Second, damaged 
vehicles parts or cargo may be recycled, depending on market demand and availability of 
recycling technology and facilities.  Third, material that cannot be repaired or recycled 
must be abandoned.  The term abandoned refers to material that is either disposed in a 
landfill or burned in an incinerator (US EPA).

According to RCRA regulations the definition of a solid waste encompasses the 
following materials: (1) materials that are abandoned; (2) materials that are recycled, (3) 
materials that are inherently waste-like, and (4) waste military munitions (40 CFR 261.2).  
Recycled materials still fall under regulatory classification of solid waste depending on 
the type of material and recycling method.  For example, for CMV crashes, scrap metal 
from automobiles is regulated as a solid waste (40 CFR 261.1(c)(6)).  All materials that 
are permanently disposed of due to CMV crashes are technically solid waste (including 
liquids).  These materials can include components of vehicles that are discarded during 
repair.  

FMCSA?s actions and proposed regulations to reduce the number and severity of CMV 
crashes may impact the quantity and number of occurrences of collisions producing solid 
waste generated in the U.S.  Less solid waste translates into cost savings from reductions 
in the following areas: (1) transport of waste material, (2) energy required for recycling 
efforts, and (3) landfill or incinerator fees. 

The goal of this report is to provide data on both solid waste mass and volume generated 
from CMV collisions.   Unfortunately, accurate data on waste from CMV crashes is 
extremely limited.  Due to limitations of available information, the report is only able to 
make estimations of vehicle based solid waste with a known percentage of the 
car/truck/bus impacted.  No aggregated national level or average CMV crash solid waste 
mass or volume is provided. 

5.2	BACKGROUND

CMV crashes involve at least one CMV and may also involve 

*	Other CMVs, 
*	Non-CMV vehicles (e.g., one or more automobiles)
*	Roadway infrastructure (e.g., bridge abutments)
*	Roadside objects and structures (e.g., trees, boulders, telephone poles, or 
buildings),  
*	Animals (wildlife, livestock, or pets), and 
*	People.  

A crash can also involve only a single CMV without damage to any other vehicle, 
structure, or individual (e.g., if an axle breaks).  Any CMV involved in a crash may be 
carrying cargo or traveling empty.  Some non-CMV vehicles involved in CMV crashes 
may be carrying cargo, as well.

CMV crashes can generate solid waste in several ways.  Debris dislodged from vehicles 
as the result of crashes is solid waste.  Likewise, any cargo that is not removed from the 
crash site can be solid waste.  Even when removed from the crash site, some portion of a 
damaged CMV and/or cargo may end up as solid waste.  Damaged roadway 
infrastructure and roadside objects and structures, as well as other vehicles damaged in a 
CMV crash, are also solid waste.  There are items carried by individuals struck by CMVs, 
such as groceries, strollers, bicycles, walkers which may end up as solid wastes.  Pets, 
livestock, and wildlife killed in crashes are also considered solid waste.

At the ends of their useful life, most CMVs and many of the products that move by truck 
are ?junked,? meaning that they are removed from operation.  Some portion of CMVs 
and their cargoes eventually enter the solid waste stream (or end up as roadside and 
similar trash).  As a consequence, FMCSA?s regulations impacting CMV crashes affect 
the timing of when solid waste is generated and not the ultimate generation of solid 
waste.  FMCSA?s rules do not prevent trucks and cargoes from becoming solid waste.  
The rules affect the circumstances and the rate at which trucks and cargoes (as well as 
any other materials damaged in a CMV crash) may become solid waste.  The functional 
use and lifespan of CMVs and their constituent materials is extended by a reduction in 
CMV collisions.

5.3	SOLID WASTE IMPACT METHODOLOGY AND RESULTS

Currently in the U.S., between 10 and 11 million vehicles of all types reach the end of 
their useful lives each year.  An estimated 94 percent of those vehicles are reprocessed by 
the Nation?s vehicle recycling industry (Environmental Defense).  Most of the remaining 
6% is either stored or abandoned (Staudinger and Keoleian; Gesing).  

The Nation?s vehicle recycling industry consists of the following:  

*	Vehicle dismantlers (also known as salvage yards or salvagers), where useful 
parts are stripped from the vehicles, 
*	Vehicle shredders, where vehicles obtained from dismantlers are shredded into 
fist-sized pieces and receive their initial sorting with steel being the primary 
material recovered at this stage of the process, 
*	Sink-float plants, where more comprehensive metal separation is performed, and 
finally 
*	Metal sorters, where final metal sorting can be performed (Staudinger and 
Keoleian; Gesing; ?Auto Recycling Demonstration Project?).  

In total in North America, there are less than 6 thousand salvage yards, approximately 
200 vehicle shredders, approximately 10 sink-float plants, and 1 metal sorter (Gesing).  

Generally, the useful materials from vehicle recycling are usable parts and bulk metals.  
Vehicle tires and batteries have their own recycling networks and are generally removed 
from scrapped vehicles.  All other materials accumulated during the vehicle recycling 
process are considered ?auto shredder residue? (ASR or ?fluff?), which is separated from 
the useful materials and sent to landfills for disposal.  

The focus of this chapter is on the CMV crash generated solid wastes that are abandoned 
otherwise referred to as the ASR.  The generation of ASR diminishes available landfill 
capacity.  Furthermore, unlike recycling, the disposal of ASR in landfills results in 
potentially useful materials being thrown away.  The ASR generated by CMV crashes is 
derived primarily from the automobiles, CMV power units, CMV trailers, and CMV 
cargoes involved in those crashes.  In the remainder of this chapter, the ASR generated 
by CMV crashes from automobiles, CMV power units, CMV trailers, and CMV cargoes 
are considered.  

5.3.1	Automobiles

Automobiles are frequently involved in CMV crashes.  Damage to automobiles in CMV 
crashes can run the gambit from minor dents to complete destruction.  It is not possible, a 
priori, to predict the amount of damage that each automobile involved in a CMV crash 
will receive.  Assuming that (1) a typical automobile contains 1,050 kg of metals and 350 
kg of ASR (Environmental Defense--Auto Shredder Residue), (2) any metal scrap 
resulting from a CMV crash will be recycled and any other scrap will be ASR that is sent 
to a landfill, and (3) as mentioned above, 94% of all automobiles at the end of their lives 
are recycled, it is possible to develop estimates for the amount of ASR going to landfills 
as a result of an automobile being involved in a CMV crash.  These estimates are 
presented in Table 5-1.  

TABLE 5-1.  QUANTITY OF ASR SENT TO LANDFILL FOR EACH 
AUTOMOBILE INVOLVED IN A CMV CRASH

Percent of 
vehicle needing 
to be replaced
Kilograms of 
ASR sent to 
landfill
10
33
20
66
30
99
40
132
50
165
60
197
70
230
80
263
90
296
100
329

To simplify the estimation, the calculations performed for Table 5-1 assume that metal 
and other material are uniformly distributed throughout a vehicle.  Furthermore, the 
estimates in Table 5-1 assume that only metal parts are salvaged from automobiles prior 
to shredding.  A weighted average for damaged parts could potentially be determined 
from insurance damage payments, provided that insurance companies were willing to 
supply the data.

Table 5-2 presents estimates of the U.S. percentage of automobiles that were ?totaled? for 
all crashes (CMV and non-CMV) during the years 2003 through 2005.  Definitive data is 
available quantifying just CMV totaled vehicles.  ?Totaled,? means that the cost of 
repairing a vehicle is greater than the market value of that vehicle.  It roughly 
corresponds to the 100 percent row in Table 5-1 (?totaled? vehicles may still be drivable, 
and some may continue to be used, with or without repairs).  

TABLE 5-2.  PERCENTAGE OF ALL U.S. AUTOMOBILES ?TOTALED,? 2003-
2005

Year
Percentage of Automobiles 
?Totaled?
2003
12.1
2004
13.4
2005
13.0
Note:  The estimates in this table include only appraisals and do not include 
??vehicles that were obvious totals where no appraisal was generated?.?
Source:  Susanna Gotsch, ?Crash Course,? CCCUpFront, Vol. III, Issue 2, 
http://www.cccis.com/static/upfront/issue08/feature_01.htm.  

There are no public accident databases that include damage severity.  An analyst might 
use the information in Table 5-2 to develop a rough estimate for the percentage of 
automobiles needing to be 100 percent replaced; however, information that could be used 
for other levels of replacement is not readily available.  The well-known Principle (or 
Criterion) of Insufficient Reason could be used by analysts to develop a full set of 
estimates.  The Principle of Insufficient Reason states that, if there is no reason to believe 
that one alternative is more likely than any other, then all can be assumed to have the 
same probability.  For this to be true, the set of alternatives under consideration must be 
mutually exclusive and exhaustive.  According to the Principle of Insufficient Reason, the 
ten categories presented in Table 5-1 could each be assumed to have a likelihood of 10%.  
Using this estimate, a weighted average for the percentage of automobiles needing to be 
replaced could be developed.  

5.3.2	CMV Power Units

The CMV power units that might be involved in a CMV crash include truck tractors, 
straight trucks, medium/heavy trucks, and buses.  Damage to any of power units in a 
CMV crash is likely to result in the creation of solid wastes.  Conversely, if such damage 
can be prevented through regulations or other actions, material will be delay from 
entering the Nation?s solid waste stream.  

Truck Tractors

Truck tractors are frequently involved in CMV crashes.  Damage impact to truck tractors 
in those crashes can range from minor dents to complete destruction.  It is not possible to 
predict the amount of damage that each truck tractor involved in a CMV crash will 
receive.  

The average or typical amount of scrap metal and ASR that could be obtained from a 
typical truck tractor as a result of a CMV crash is unknown.  Some crashes may destroy 
potentially reusable or recyclable components due to fire or compression.  As a 
consequence, a hypothetical truck tractor is used for this analysis.  This hypothetical 
truck tractor is based on information available from Volvo on certain of its FM12 and 
FH12 truck tractor models.  Table 5-3 contains information on the materials in the 
hypothetical truck tractor.  

TABLE 5-3.  LIST OF MATERIALS FOR A HYPOTHETICAL CMV TRUCK 
TRACTOR

Material
Weight (Kg)
Potential End-of-
Life Destination
Iron

-Wrought, tempered
1,196
Recycled
-Cast
1,478
Recycled
Steel

-Rod
198
Recycled
-Hot-rolled
1,645
Recycled
-Cold-rolled
925
Recycled
Other metals

-Aluminum
201
Recycled
-Lead (battery)
95
Recycled
-Copper
14
Recycled
-Brass, bronze
9
Recycled
-Stainless steel
15
Recycled
Plastics

-Thermoplastics
339
Solid waste stream
-Reinforced thermoplastics
74
Solid waste stream
-Thermosetting plastics
6
Solid waste stream
Other materials

Rubber
459
Recycled
Glass
60
Solid waste stream
Textile, other fibers
57
Solid waste stream
Paint
13
Solid waste stream
Brake pads
22
Solid waste stream
Oil, grease
62
Solid waste stream
Electronics
56
Solid waste stream
Sulfuric acid (battery)
36
Recycled
Bitumen
6
Solid waste stream
Wood
11
Solid waste stream
Cooling agent (R134a)
1
Solid waste stream
Glycol
17
Solid waste stream
Ethanol
4
Solid waste stream
TOTAL
7,000

    Source for columns 1 and 2:  Volvo, ?Environmental Product Declaration, Volvo FH12 and Volvo 
    FM12, Euro 3,? p.5; source for column 3:  various.  

In total, the hypothetical truck tractor weighs 15,432.2 pounds (7,000 kilograms).  Of 
this, metals, tires, and the battery accounts for 13,827.3 pounds (6,272 kilograms).  If the 
truck tractor were to be totaled in a CMV crash, this material would likely be recycled.  
The remaining 1604.9 pounds (728 kilograms) would be ASR.  

Assuming that (1) a truck tractor contains 13,827.3 pounds (6,272 kilograms) of 
recyclable material and 1604.9 pounds (728 kilograms) of non-recyclable material, (2) 
any metal scrap resulting from a CMV crash will be recycled and any other scrap will be 
ASR that is sent to a landfill, and (3) 94 percent of all vehicles at the end of their lives are 
recycled, it is possible to develop estimates for the amount of ASR going to landfills as a 
result of a truck tractor being involved in a CMV crash.  These estimates are presented in 
Table 5-4.  

TABLE 5-4.  QUANTITY OF ASR SENT TO LANDFILL FOR EACH TRUCK 
TRACTOR INVOLVED IN A CMV CRASH

Percent of 
vehicle needing 
to be replaced
Kilograms of 
ASR sent to 
landfill
10
68
20
137
30
205
40
274
50
342
60
411
70
479
80
547
90
616
100
684

To simplify the estimation, the calculations performed for Table 5-4 assume that metal 
and other material are uniformly distributed throughout a vehicle.  The estimates in Table 
5-4 assume that only metal parts are salvaged from truck tractors prior to shredding.  

Straight Trucks

Information on straight trucks that could be used to estimate the amount ASR that would 
be sent to a landfill as a result of a CMV crash is not readily available.  No estimate of 
the ASR can be made at this time.  

Medium/Heavy Trucks

Information on medium/heavy trucks that could be used to estimate the amount ASR that 
would be sent to a landfill as a result of a CMV crash is not readily available.  No 
estimate of the ASR can be made at this time.  

5.3.3	Buses

There are a variety of CMV buses that might be involved in a CMV crash, including 
intercity buses, suburban commuter buses, transit buses, school buses, tour buses, and 
entertainer buses.  

Intercity Buses

Intercity buses, which tend to be 40-45 feet in length, carry 46-55 passengers, and have 
restrooms and underfloor baggage compartments, are sometimes involved in CMV 
crashes.  The damage to these buses resulting from those crashes can run the gambit from 
minor dents to complete destruction.  It is not possible to predict the amount of damage 
that each bus involved in a crash will receive.  

The amount of scrap metal and ASR that could be generated by a typical or average 
intercity bus involved in a CMV crash is unknown.  As a consequence, a hypothetical 
intercity bus is used for this analysis.  This hypothetical bus is based on information 
available from Volvo on one of its bus models.  Table 5-5 contains information the 
materials in the hypothetical bus.  

TABLE 5-5.  LIST OF MATERIALS FOR A HYPOTHETICAL INTERCITY BUS

Material
Weight (Kg)
Potential End-of-
Life Destination
Iron

-Wrought
502
Recycled
-Cast
1,029
Recycled
Steel

-Rod
2,408
Recycled
-Hot-rolled
1,590
Recycled
-Cold-rolled
568
Recycled
Other metals

-Aluminum
1,666
Recycled
-Lead (battery)
90
Recycled
-Copper
109
Recycled
-Brass, bronze
3
Recycled
-Stainless steel
690
Recycled
Plastics

-Thermoplastics
295
Solid waste stream
-Reinforced thermoplastics
132
Solid waste stream
-Thermosetting plastics
126
Solid waste stream
Other materials

Rubber
405
Recycled
Glass
490
Solid waste stream
Textile, other fibers
23
Solid waste stream
Paint
30
Solid waste stream
Oil, grease
78
Solid waste stream
Electronics
56
Solid waste stream
Sulfuric acid (battery)
34
Recycled
Bitumen
54
Solid waste stream
Wood
396
Solid waste stream
Glycol
26
Solid waste stream
Other
182
Solid waste stream
TOTAL
10,900

    Source for columns 1 and 2:  Volvo, ?Environmental Product Declaration, Volvo 8500 Low Entry,? 
    p.5; source for column 3:  various.  

In total, the hypothetical bus weighs 24,030.1 pounds (10,900 kilograms).  The bus 
identified for the calculations can also be referred to as a ?10+ metric ton bus.?  Of the 
total mass, metals, tires, and the battery account for 20,048.6 pounds (9,094 kilograms).  
If the bus were to be totaled in a CMV crash, this material would likely be recycled.  The 
remaining 4,157.9 pounds (1,886 kilograms) would be ASR.  

The report makes the following assumptions: (1) a CMV bus contains kilograms of 
recyclable material and kilograms of non-recyclable material, (2) any metal scrap 
resulting from a CMV crash will be recycled and any other scrap will be ASR that is sent 
to a landfill, and (3) 94 percent of all vehicles at the end of their lives are recycled. The 
amount of ASR going to landfills as a result of a CMV bus involved in a crash can be 
calculated using these assumptions.  These estimates are presented in Table 5-6.  

TABLE 5-6.  QUANTITY OF ASR SENT TO LANDFILL FOR EACH 
INTERCITY BUS INVOLVED IN A CMV CRASH

Percent of 
vehicle needing 
to be replaced
Kilograms of 
ASR sent to 
landfill
10
177
20
355
30
532
40
709
50
886
60
1,064
70
1,241
80
1,418
90
1,596
100
1,773

To simplify the estimation, the calculations performed for Table 5-6 assume that metal 
and other material are uniformly distributed throughout a vehicle.  The estimates in Table 
5-6 assume that only metal parts are salvaged from intercity buses prior to shredding.  

It should be noted that suburban commuter, long-distance airport, and top-end tour and 
entertainer buses are all generally similar to intercity buses.  

Other Buses

Solid waster information on other types of bus including transit and school buses, as well 
as on mid-size buses (20-35 feet in length, 16-35 passengers, some with restrooms) is not 
readily available.  The ASR estimate is limited to the intercity 10+ metric ton bus.

5.4	CMV TRAILERS

When a CMV crash occurs, sometimes at least one of the CMVs involved is a tractor-
trailer in combination.  If the trailer is damaged, some part of it may end up in a landfill.  
It should be noted that a CMV crash will not necessarily involve a CMV trailer.  CMV 
crashes may involve bobtails, tractors without trailers, or buses.  

The trailers variations include 

*	Box semi-trailers, 
*	Liquid tank trailers, 
*	Gas tank trailers, 
*	Dry bulk trailers, 
*	Flatbed trailers, 
*	Automobile trailers, 
*	Special trailers, and
*	Intermodal chassis.

Information on trailer material composition is not readily available.  Consequently, 
estimates of the amount of material from a trailer that might be sent to a landfill after a 
CMV crash cannot be estimated.  Analysts interesting in pursuing this further might want 
to start their effort by contacting the Truck Trailer Manufacturers? Association (TTMA). 

5.5	CMV CARGOES 

Cargo is another key source of potential solid waste from CMV crashes.  Cargo damaged 
in a CMV incident may be sent to a landfill or be burned in an incinerator.  It should be 
noted that a CMV crash will not necessarily involve a CMV carrying a cargo.  The CMV 
crash may involve a tractor without a trailer, a bus, or a CMV making an empty backhaul.  

Thousands of different types of cargoes are carried by CMVs.  Those cargoes are so 
varied and diverse that it is not possible to empirically determine the fate of the goods 
after a crash impact.  Consider the following possible CMV crash examples: a CMV 
crash involving a full garbage truck, a truck hauling a full load of watermelons, and a 
truck hauling a full load of anvils.  The cargo of the garbage truck, already destined for a 
landfill, will not be impacted by the crash.  Some or all of the watermelons will likely be 
too damaged to consume and will be diverted from their original destination to a landfill.  
The anvils, on the other hand, are likely to survive the crash unscathed.  If some do not, 
the metal that they contain can be recycled.  For another example, consider a tanker load 
of gasoline involved in a CMV crash with an automobile.  If no gasoline leaks from the 
tanker, the gasoline can be transferred to another vehicle and sent on to a gasoline station.  
Additionally, a portion of any gasoline that does leak from the tanker may be retrieved 
during site cleanup, reprocessed, and sold.  

5.6	CONCLUSIONS

The usefulness of the information on the solid wastes resulting from CMV crashes is 
severely limited.  Estimates can be derived for additions to the solid waste stream due to 
automobiles involved in CMV crashes.  Rough estimates can be derived for solid waste 
stream additions due to truck tractors and intercity buses involved in CMV crashes.  
Estimates are not readily available for any other additions to the solid waste stream.

At least in theory, the manufacturers of straight trucks, medium/heavy trucks, transit 
buses, and various types of truck trailers should be able to provide information on the 
composition of their conveyances.  This information could be used to develop estimates 
of the additions to the solid waste stream that would result when the conveyances are 
involved in a CMV crash.  Rough estimates could be developed using cargo composition 
straight averages conveyance composition if it was available.   More truly representative 
estimates could be developed with information on the nature and composition of the 
national fleet.  Better estimates of the additions to the solid waste stream could be 
developed if additional truck and bus manufacturers provided vehicle composition 
information.  

It is likely that cargoes damaged in CMV crashes contribute more to the solid waste 
stream than anything else.  Unfortunately, information on the damages sustained by 
cargoes in crashes is not readily available, and it may not exist.  The insurance industry is 
generally paying for cargoes lost due to CMV crashes and should have information on 
those losses.  Unfortunately, the information must be derived from insurers? general 
category of losses, which includes theft.  Cargo theft is reportedly a significantly bigger 
problem for insurers than cargo damage/loss due to vehicular accidents.  Potentially, data 
on cargo losses due to accidents could be purchased from the insurance industry.  
Alternatively, the insurance industry might be able to provide a percentage breakdown 
between theft and accident losses.  With that information and the total cost of both theft 
and accidents, a rough estimate of the dollar value of losses due to accidents could be 
developed.  This introduces another problem: there is not necessarily a predictable 
correlation between the value of a cargo and its pollution potential.  Furthermore, there is 
not necessarily a one-to-one correspondence between the value of a cargo and its volume.  
Small cargoes may have a large cash value and other larger cargoes may have nominal 
market value.  Conversely, both loads would contribute differing amounts of solid waste.  
Consequently, the insured value of cargoes would not necessarily be representative of 
their impact on the solid waste stream or on the Nation?s landfills.

5.7	RECOMMENDATIONS FOR FURTHER STUDY

There are many missing data elements that would be needed in order to reliably estimate 
solid waste generated from CMV crashes.  The missing data includes the following: 

*	Vehicles Involved - Number and type of vehicle involved in each type of CMV 
collision.  
*	Totaled Vehicles ? Number of vehicles that have to be completed scrapped or 
significantly repaired to resume function
*	Type of Truck ? The type of truck influences the potential solid waste generated. 
For example concrete mixer will have different 
*	Cargo loss ? Mass and volume of cargo loss.  Mass and Volume could potentially 
be determined by studying average freight cargos and making assumptions that 
crashes involve average freight.

Additional research could potentially establish values for many of these missing items.  
The insurance industry might prove to be an excellent source of information to address 
these gaps.   The insurance industry, however, is likely to require payment for 
developing, estimating, or locating any of the missing data relating to solid wastes.

 6.	 REPORT BIBLIOGRAPHY

Abkowitz, M.D., Delorenzo, J.P., Duych, R., Greenberg, A., and McSweeney, T. (2001).  
Assessing the Economic Effect of Incidents Involving Truck Transport of 
Hazardous Materials.  Transportation Research Record 1763, Paper No. 01-0156, 
125-129

Acton Spill Center Spill Database (2007). The Spill Center, Acton, Massachusetts, 
Database Query

Agent, K., et al. (1995).  Evaluation of Advanced Surveying Technology for Accident 
Investigation, Transportation Research Board, No. 1485.

American Trucking Association (ATA) Foundation (1997).  Incident Management: 
Challenges, Strategies, and Solutions for Advancing Safety and Roadway 
Efficiency.  Final Report for Federal Highway Administration, ATA, Alexandria, 
VA, February 1997.

Argonne National Laboratories, Transportation Technology R & D Center (2006).  
GREET Model description: http://www.transportation.anl.gov/software/GREET/.  
Last updated: 2006.

Battelle (2001).  Comparative Risks of Hazardous Materials and Non-Hazardous 
Materials Truck Shipment Accidents/Incidents.  Final Report for Federal Motor 
Carrier Safety Administration, Battelle, Columbus, OH, March 2001.

Brookshire, D.S., Crocker, T.D., and D?Arge, R.C. (1979).  Methods Development for 
Assessing Air Pollution Control Benefits.  EPA 600/5-79-001.  Washington, DC: 
US Environmental Protection Agency.

Brookshire, D.S., Thayer, M.A., and Schulze, W.D., and D?Arge (1982).  Valuing Public 
Goods: A Comparison of Survey and Hedonic Approaches.  The American 
Economic Review, 72, 165-177.

Bureau of Census (2002).  Statistical Abstract of the United States.  Washington DC: US 
Department of Commerce.

CCSP (2006).  Temperature Trends in the Lower Atmosphere: Steps for Understanding 
and Reconciling Differences.  Karl, T. R., Hassol, S.J., Miller, C.D., Murray, 
W.L., editors Climate Change Science Program and Subcommittee on Global 
Change Research, Washington DC, Synthesis and Assessment Product 1.1 ? Final 
Report

Chan, C.C., Spengler, J.D., Ozkaynak, H., and Lefopoulou, M. (1991).  Commuter 
Exposures to VOCs in Boston, Massachusetts.  Journal of Air Waste 
Management, Number 41, Volume 12, 1594-1600.

Cambridge Systematics (2005). Traffic Congestion and Reliability: Trends and Advanced 
Strategies for Congestion Mitigation.  Final Report for Federal Highway 
Administration, Cambridge Systematics, Cambridge, MA, September 2005.

Chestnut, L.G., and Dennis, R.L. (1997).  Economic Benefits of Improvements in 
Visibility: Acid Rain Provisions of the 1990 Clean Air Act Amendments.  Journal 
of the Air and Waste Management Association 47, 395-402.

Chestnut, L.G. and Rowe, R.D. (1990a).  Economic Valuation of Changes in Visibility: a 
State of the Science Assessment for NAPAP.  In Acid Deposition: State of 
Science and Technology, Report 27, Methods for Valuing Acidic Deposition and 
Pollution Effects.  Pp. 27-153 to 127-175.  Washington, DC: National Acid 
Precipitation Assessment Program.

Chestnut, L.G. and Rowe, R.D. (1990b).  Preservation Values for Visibility Protection at 
the National Parks.  Research Triangle Park, NC: US Environmental Protection 
Agency, Office of Air Quality Planning and Standards, Economic Analysis 
Branch.

Chin, S.M., Franzese, O., Greene, D.L., Hwang, H.L., and Gibson, R.C. (2002).  
Temporary Losses of Highway Capacity and Impacts on Performance.  Oak Ridge 
National Laboratories for U.S. Department of Energy.  Under Contract DE-AC05-
00OR22725

Chin, S.M., Franzese, O., Greene, D.L., and Hwang, H.L. (2004). Temporary Losses of 
Highway Capacity and Impacts on Performance: Phase II. Oak Ridge National 
Laboratories for the U.S. Department of Energy.  Under DE-AC05-00OR22725

Daniel, J. I, and Bekka, K. (2000).  The Environmental Impact of Highway Congestion 
Pricing.  Journal of Urban Economics 47, 180-215.

Delucchi, M.A., Murphy, J., McCubbin D.R., and Kim, J. (1996a). The Cost of Reduced 
Visibility due to Particulate Air Pollution from Motor Vehicles.  UC Davis, 
California, Institute for Transportation Studies, Report 13 in series: The 
Annualized Social Cost of Motor-Vehicle Use in the United States, based on 
1990-1991 data.

Delucchi, M.A. (1997).  Emissions of Non-CO2 Greenhouse Gases from the Production 
and Use of Transportation Fuels and Electricity.  UC Davis, California, Institute 
for Transportation Studies.

Delucchi, M.A. (2000).  Environmental externalities of motor vehicle use in the US. 
Journal of Transport Economics and Policy 34, 135-168.

Delucchi, M.A., Murphy, J.J., and McCubbin, D.R. (2002).  The health and visibility cost 
of air pollution: a comparison of estimation methods.  Journal of Environmental 
Management 64, 139-152.

Delucchi, M.A., (2004) Personal Non-Monetary Cost of Motor-Vehicle Use: Report #4 in 
the series The Annualized Social Cost of Motor Vehicle Use in the United States.  
Institute of Transportation Studies, University of California, Davis, Revised 2004.

De Vlieger, I., De Keukeleere, D., Kretzschmar, J.G. (2000).  Environmental Effects of 
Driving Behavior and Congestion Related to Passenger Cars. Atmospheric 
Environment, 34, 4649-4655.

Dockerty, D.W., Schwartz, J., and Pope, C.A. (1995).  Comments for Original 
Investigators. Particulate Air Pollution and Daily Mortality, Replication and 
Validation of Selected Studies.  Health Effects Institute, Cambridge, MA, pp. 115-
122.

Downs, A. (2004).  Still Stuck in Traffic: Coping with Peak-Hour Traffic Congestion, 
Brookings Institute Press, Washington DC 2004.

Environmental Defense (ED), (2006a). Green Cars: End-of-Life Vehicle Management, 
http://www.environmentaldefense.org/documents/891_GC%5Feol%2Ehtm. 

ED (2006b). Green Cars, End-of-Life Vehicle Management:  Auto Shredder Residue, 
Volume-Percent Estimation, 
http://www.environmentaldefense.org/documents/893_GC%5Fresidue%2Ehtm.  
Federal Highway Administration (FHWA) 1999.  Development and Validation of Light-
Duty Vehicle Modal Emissions and Fuel Consumption Values for Traffic Models.  
Publication No. FHWA-RD-99-068.  Federal Highway Administration.  March 
1999.
FHWA (2000).  2000 National Statistics.  Federal Highway Administration, U.S. 
Department of Transportation, Washington, DC

FHWA (2002).  HERS-ST 2.0: Highway Economic Requirements System-State Version.  
Federal Highway Administration.

FHWA (2004).  2004 Highway Statistics.  Report No. FHWA-PL-05-013, Office of 
Highway Policy Information (HPPI), FHWA US Department of Transportation, 
Washington, DC.

Federal Motor Carrier Safety Administration (FMCSA). (2001).  Accident and Incident 
online crash profiles (CPOL) database. 

FMCSA (2004).  Order 5610.1, NEPA Implementing Procedures and Policy for 
Considering Environmental Impacts. 2004. (Appendix 18 ? Special Areas of 
Consideration When Implementing NEPA?#3)

Gesing, A. (2004). Assuring the Continued Recycling of Light Metals in End-of-Life 
Vehicles:  A Global Perspective, August 2004, JOM:  A Hypertext-Enhanced 
Article, http://www.tms.org/pubs/journals/JOM/0408/Gesing-0408.html. 

Giuliano, G. (1989).  Incident Characteristics, Frequency, and Duration on a High 
Volume Urban Freeway.  Transportation Research Part A: General 23(5), 
Transportation Research Board, National Research Council, Washington DC, 27-
37.

Glickman, T.S., Macauley, M.K., and Portney, P.R. (1991).  Highway Robbery: Social 
Costs of Hazardous Materials Incidents on the Capital Beltway.  Transportation 
Research Record 1313, 27-32.

Gomez-Ibanez, J.A. (1997).  Estimating Whether Transport Users Pay Their Way: The 
State of the Art.  In David L. Greene, Donald W. Jones, and Mark A. Delucchi, 
Eds.  The Full Costs and Benefits of Transportation, Chapter 3.  Heidelberg, 
Germany: Springer-Verlag Berlin.

Gotsch, S. (2006). Crash Course, CCCUpFront, Vol. III, Issue 2, 
http://www.cccis.com/static/upfront/issue08/feature_01.htm   

GREET (2002).  The Greenhouse Gas and Regulated Emissions for Transportation 
(GREET) Model, Version 1.6, Argonne National Laboratories.

Grenzeback, L.R. et al. (1990).  Urban Freeway Gridlock Study: Decreasing the Effects 
of Large Trucks on Peak-Period Urban Freeway Congestion,? Transportation 
Research Record, No. 1256

Harrington, W. and McConnell, V. (2003). Motor Vehicles and the Environment: RFF 
Report, Resources for the Future, Washington, DC April 2003.

Great Lake Institute for Recycling Materials (1998). Auto Recycling Demonstration 
Project Final Report, Great Lakes Institute for Recycling Materials, January 23, 
1998, http://www.deq.state.mi.us/documents/deq-ead-recycle-autofinal.pdf.

International Panel on Climate Change (IPCC) (1996).  Climate Change 1995: The 
Science of Climate Change. Intergovernmental Panel on Climate Change; J.T. 
Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. 
Maskell, eds.; Cambridge University Press. Cambridge, U.K.

IPCC (2001).  Climate Change 2001: The Science of Climate Change. Intergovernmental 
Panel on Climate Change; J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. 
van der Linden, X. Dai, K. Maskell, and C.A. Johnson, eds.; Cambridge 
University Press. Cambridge, U.K.

K.T. Analytics, Inc. and Victoria Transport Policy Institute.  (1997). Review of Cost of 
Driving Studies, 1997.  Paper prepared for the Metropolitan Washington Council 
of Governments.

Kulkarni, R.G., Stough R.R., and Haynes, K.E. (1996).  Spin glass and interactions of 
congestion and emissions: an exploratory step.  Transportation Research Part C: 
Emerging Technologies, Volume 4, Issue 6.

Lan, C., and Hu, P. (1999).  Delays and Congestion Costs Caused by Truck Incidents.  
Oak Ridge National Laboratories.

Leach, M.A. Bauen, and N. Lucas, (1997). A systems approach to materials flow in 
sustainable cities: A case study of paper. J. Environmental Planning and 
Management, 40 (6): 705-723, which adapted data from a literature review by 
Hormandinger, G (1995): Fuel cells in transportation, unpublished MSc thesis 
(University of London, Imperial College of Science, Technology and Medicine, 
Centre for Environmental Technology).

Malm, W.C. (1999).  Introduction to Visibility.  Cooperative Institute for Research in the 
Atmosphere (CIRA), National Park Service (NPS), NPS Visibility Program 
CA2350-97-001, Fort Collins, CO 

Marrack, D. (1995).  All PM-10 are Not Biologically Equal. Presentation at International 
Conference on Particulate Matter, Health, and Regulatory Issues.  Pittsburgh, 
PA, April 4-6 1995.

McClelland, G. et al. (1991) Valuing Eastern Visibility: A Field Test of Contingent 
Valuation Method.  USEPA Cooperative Agreement #CR-815183-01-2.  
Washington, DC: US Environmental Protection Agency, Office of Policy, 
Planning, and Evaluation.

McCubbin, D.R. and Delucchi, M.A. (1996). The Social Cost of the Health Effects of 
Motor-Vehicle Air Pollution Motor.  UC Davis, California, Institute for 
Transportation Studies, Report 11 in series: The Annualized Social Cost of Motor-
Vehicle Use in the United States, based on 1990-1991 data.

McCubbin, D.R. and Delucchi, M.A. (1999). The health costs of motor vehicle related air 
pollution. Journal of Transport Economics and Policy 33, 253-286.

Miller, Ted (2006).  Phone conversations regarding methodology used in Revised Costs 
of Large Truck- and Bus-Involved Crashes

Mineta, M.Y. (2006).  National Strategy to Reduce Congestion on America?s 
Transportation Network.  Speech delivered to the National Retail Federation, May 
16, 2006 Washington, DC

Meyer, M. (1994).  Alternative Methods for Measuring Congestion Levels, in Curbing 
Gridlock: Peak Period Fees to Relieve Traffic Congestion, Vol. 2, National 
Academy Press, Washington DC, pp 32-61

National League of Cities (2005).  The State of America?s Cities 2005: The Annual 
Opinion Survey of Municipal Elected Officials.  National League of Cities, 
Washington, DC 2005.

National League of Cities (2006).  The State of America?s Cities 2005: The Annual 
Opinion Survey of Municipal Elected Officials.  National League of Cities, 
Washington, DC 2005.

New York Times (2004). Bumper to Bumper, Travelers Ride Out An I-95 Nightmare. 
McFadden, R.D. and Cowan, A.L., March 26 Edition.

Office of Management and Budget (OMB), (1992).  Memorandum for Heads of 
Executive Departments and Establishments: Guidelines and Discount Rates for 
Benefit-Cost Analysis of Federal Programs.  Circular No. A-94.

OMB (2006).  Draft 2006 Report to Congress on the Cost and Benefits of Federal 
Regulations; Notice.  Congressional Federal Register.  Washington, DC.  White 
House, Office of Management and Budget.

 PHMSA (2004).  Guide for Preparing Hazardous Materials Incident Reports.  U.S. 
Department of Transportation, Office of Pipeline and Hazardous Materials Safety 
Administration (PHMSA) Washington, DC.  January 2004 Revision. 

PHMSA (2006). Hazardous Materials Information System (HMIS). 

Pope, C. Arden III et al.  (1995).  Particulate Air Pollution as a Predictor of Mortality in a 
Prospective Study of U.S. Adults.  American Journal of Respiratory and Critical 
Care Medicine 151, 669-674.

Romily, P. (1999).  Substitution of bus for car travel in urban Britain: an economic 
evaluation of bus and car exhaust emission and other costs.  Transportation 
Research Part D 4. 109-125.

SAE (1997).  Development of Data-Based Light-Duty Modal Emissions and Fuel 
Consumption Models.  Society of Automotive Engineers.  972910.

SAE (2003).  Particulate Matter and Aldehyde Emissions from Idling Heavy-Duty Diesel 
Trucks.  Society of Automotive Engineers.  2003-01-0289.

Schrank, D. and Lomax, T. (2005).  2005 Urban Mobility Report.  Texas Transportation 
Institute, The Texas A & M University System, May 2005.

Staudinger, J., and Keoleian, G.A. (2001). Management of End-of Life Vehicles (ELVs) in 
the US, Center for Sustainable Systems, University of Michigan, Report No. 
CSS01-01, March 2001, http://css.snre.umich.edu/css_doc/CSS01-01.pdf.  

Texas Transportation Institute (2006).  Mobility Monitoring Program for FHWA.  
Collaborative effort of Texas Transportation Institute and Cambridge Systematics.  
http://mobility.tamu.edu/mmp/. 

Transportation Research Board (1993).  Hazardous Materials Shipment Information for 
Emergency Response,? TRB Special Report 239, published in book format 
National Academy Press, Washington, DC.

Transportation Research Board (2003).  Safe and Quick Clearance of Traffic Incidents: A 
Synthesis of Highway Practice, National Cooperative Highway Research Program 
Report 318.  National Academy Press, Washington, DC.

Turner, S.M., Lomax, T.J., and Levinson, H.S. (1996).  Measuring and Estimating 
Congestion Using Travel-Time Based Procedures, TRB Paper No. 961238, 
presented at the 75th Annual Meeting of the Transportation Research Board, 
Washington, DC.

Ullman, G.L., and Ogden, M.A. (1996).  Analysis of Major Freeway Incidents in 
Houston, Texas.  Transportation Research Record 1554, 221-227.

USC Title 42 (2005). United States Code pertaining to non-attainment areas 
http://www.law.cornell.edu/uscode/html/uscode42/usc_sec_42_00007509----000-
.html 

US Department of Energy (2007).  Fuel Economy Guide.  U.S. Department of Energy.  

US Environmental Protection Agency (EPA), (1998). National Air Quality and Emissions 
Trends Report 1996.  EPA-454/R-97-013. Research Triangle Park, NC: US 
Environmental Protection Agency, Office of Air Quality Planning and Standards.

US EPA (2000).  2018 Milestone Benefits Assessment of BART reductions in Nine 
Western States (Draft Report).  US Environmental Protection Agency, August 
2000.

US EPA (2001).  RCRA, Superfund, & EPCRA Call Center Training Module: Definition 
of Solid Waste and Hazardous Waste Recycling.  

US EPA (2002). MOBILE Motor Vehicle Emission Factor Model, version 6.1/6.2, US 
Environmental Protection Agency, June 2002.

US EPA (2003).  User?s Guide to Mobile 6.1 and Mobile 6.2: Mobile Source Emission 
Factor Model, Ann Arbor, MI: US Environmental Protection Agency, Office of 
Assessment and Standards Division, Office of Transportation and Air Quality.

US EPA (2005a).  Regulatory Impact Analysis for Final Clean Air Act Interstate Rule. 
US Environmental Protection Agency, Office of Air and Radiation, Air Quality 
Strategies and Standards Division, Emission, Monitoring, and Analysis Division, 
and Clean Air Markets Division, Washington DC, March 2005.

US EPA (2005b).  Regulatory Impact Analysis for the Final Clean Air Visibility Rule or 
Guidelines for Best Available Retrofit Technology (BART) Determinations Under 
the Regional Haze Regulations. US Environmental Protection Agency, Office of 
Air and Radiation, Air Quality Strategies and Standards Division, Emission, 
Monitoring, and Analysis Division, and Clean Air Markets Division, Washington 
DC, June 2005.

US EPA (2006a).  EPA Green Book, Criteria Pollutants Section. US Environmental 
Protection Agency, Office of Air and Radiation.  
http://www.epa.gov/air/oaqps/greenbk/o3co.html#Ozone 

US EPA (2006b).  Draft Regulatory Impact Analysis: Control of Hazardous Air 
Pollutants from Mobile Sources. U.S. Environmental Protection Agency, Office 
of Transportation and Air Quality, Assessment and Standards Division, 
Washington DC, February 2006.

US EPA (2006c). 2006 Inventory of US Greenhouse Gas Emissions and Sinks. 
Washington DC, US Environmental Protection Agency.

US EPA (2006d).  US Environmental Protection Agency.  Toxic Air Pollutants: 
http://www.epa.gov/oar/toxicair/newtoxics.html 

US EPA (2006e).  Light-Duty Automotive Technology and Fuel Economy Trends: 1975 
through 2006.  Office of Transportation and Air Quality.  EPA420-R-06-011.  
U.S. Environmental Protection Agency.

US EPA (2007).  US Environmental Protection Agency.  Mobil Source Air Toxics.  
http://www.epa.gov/otaq/toxics.htm#what 

US Department of Transportation (DOT), (2001).  Senate Confirmation Committee 
Hearing for then Secretary-Designate Norma Y. Mineta, January 24, 2001.

US DOT (2002). Status of the Nation's Highways, Bridges, and Transit: 
2002 Conditions and Performance Report.  U.S. Department of Transportation, 
Federal Highway Administration (FHWA).

US DOT (2002a). Code of Federal Regulations, Title 49, Volume 2, Part 173, October 
2002

US DOT (2004). Emergency Response Guidebook, A Guidebook for First Responders 
During the Initial Phase of a Dangerous Goods/Hazardous Materials Incident, 
developed jointly by the US DOT, the Secretariat of Transport and 
Communications of Mexico (SCT) and with the collaboration of CIQUIME 
(Centro de Informacion Quimica para Emergencias) of Argentina.

US DOT (2005). Hazardous Materials Incident Reporting System, HMIRS Data 
Definitions and Codes, prepared by: Catapult Technology Ltd., April 2005.

US DOT (2006a).  Congestion Working Group Action Plan: A Seven Point Strategy for 
Reducing Congestion on the U.S. Transportation Network.  Presentation, May 
2006.

US DOT (2006b).  Memorandum ? Rulemaking Cost-Benefit Charts.  Washington, DC: 
US Department of Transportation, Office of the Secretary

US DOT (2006c). Memorandum ? Rulemaking Cost-Benefit Charts.  Washington, DC: 
US Department of Transportation, Office of the Secretary

Volvo (2001a). Environmental Product Declaration, Volvo 8500 Low Entry, 
http://www.volvo.com/NR/rdonlyres/F3E032FE-CBB1-4ED3-9AEE-
8E1D5D33F519/0/epd_volvo_8500_low_entry.pdf 

Volvo (2001b) Environmental Product Declaration, Volvo FH12 and Volvo FM12, Euro 
3  http://www.volvo.com/NR/rdonlyres/E8FD3F6B-B06B-4EBE-BA7D-
A529AFE0BFD0/0/euro3_03.pdf 

Wang, M. (2002).  Assessment of Well-to-Wheels Energy Use and Greenhouse Gas 
Emissions of Fishcer-Tropsch Diesel.  Report for US Department of Energy, 
Office of Energy Efficiency and Renewable Energy. Produced by the Center for 
Transportation Research, Argonne National Laboratories.  

Wang, Q.W. and D.J. Santini, (1995). Monetary Values of Air Pollutant Emissions in 
Various US Regions. Transportation Research Record 1475. Washington, DC: 
National Academy Press.

Washington Post (2006).  Fatal I-95 Wreck Felt Across Region: 10-Mile Traffic Backup 
Spills Onto Surrounding Arteries.  Reporters: Dan Morse and Clarence Williams.  
Washington Post Newspaper, April 14, 2006; Page B01.

Watkins, P., Arnoff, D., Downing, T., Hepburn, C., Hope, C., Hunt, A., and Tol, R. 
(2005). The Social Cost of Carbon: Methodological Approaches for Using SCC 
Estimates in Policy Assessment.  Oxford, United Kingdom.

Watson, J.G. and Chow, J.C. (1994).  Clear sky as a challenge for society. Annual Review 
of Energy and Environment 19, 241-266.

Zaloshnja, E., and Miller T. (2002).  Revised Costs of Large Truck- and Bus-Involved 
Crashes.  Calverton, Maryland.  For the US Department of Transportation, 
Federal Motor Carrier Safety Administration (FMCSA).

  This report was updated in 2006, and it is expected that this update will replace the 2002 version in FMCSA 
economic analyses in the very near future.
  Defined in the American National Standard Institute ? ?Manual on Classification of Motor Vehicle Traffic 
Accidents? (ANSI D1.6, Section 2.4.1).
  Defined in the American National Standard Institute ? ?Manual on Classification of Motor Vehicle Traffic 
Accidents? (ANSI D16.1, Section 2.4.2).
  It should be recognized that there is considerable uncertainty in the data used to generate this estimate.  
  The website for TTMA can be found at http://www.ttmanet.org/.  
 
 
 
 

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