Final Report
Project Title: Flows and Regional Risk Assessment of Transporting Hazardous
Waste in the U.S.-Mexico Border Region
SCERP Project Number: HW95-1
Principal Investigators:
Suleiman A. Ashur, Ph.D, P.E.
Hadi Baaj, Ph.D., P.E.
Heather O'connell
Ramon Carrasco
Department of Civil Engineering
University of Texas at El Paso
El Paso, Texas 79968-0516
and
K. David Pijawka, Ph.D.
Shuhbro Guhathakurta, Ph.D.
School of Planning and Landscape Architecture
Arizona State University
Tempe, Arizona 85287-2005
>Chapters 1-3<
Chapters 4 - 5
Chapters 6 - 7
Chapter 1 - Summary
Research Objectives
The objectives of this research project are:
1. To Create a database of the amount, type, and flows of hazardous waste from Mexico
to the US.
2. To Develop a risk assessment model applicable to the border region implemented on a
GIS platform and apply it to Sonora-Arizona case study.
3. To Analyze different management scenarios that will affect the transportation risks.
Research Methodology
To achieve the goals of this research project, the following tasks were undertaken:
Task 1: Literature review of all problem components.
Task 2: Data collection and analysis: Several data set are needed in this research:



Hazardous waste shipment data: these data include the quantities,
types, and flows of hazardous waste shipments from Maquiladoras
in the Mexican State of Sonora to the State of Arizona in the US.
Moreover, these data need to be confirmed through manifests
available from the USEPA.
Land use data: data on population densities in the US and Mexico
along shipment route.
Truck Accident Data: This is necessary to calculate the probability
of accidents of trucks carrying hazardous waste shipments.
Task 3: Development of solution framework: the proposed risk assessment and routing
models are discussed later in this chapter. This task will consider computer modeling by
establishing a GIS database of Arizona and Sonora highways with different attributes
(i.e., length of road segments, speed limits, type, population densities along the route),
and applying the risk assessment model to the GIS database.
Task 4: Development and analysis of different management scenarios: a major
contribution of this research is to identify the impacts of different risk management
scenarios. There are two sets of scenarios: the first set of scenarios focuses on changes in
the demand pattern (and amounts) such as (1) existing conditions, (2) increases in
hazardous flows due to strict enforcement of environmental laws in Mexico, and (3)
reduction hazardous flows due to construction of new recycling facilities in Sonora
and/or Arizona. The second set of scenarios focuses on the impacts of routing hazardous
waste under different preferences (such as minimum cost, minimum risk, risk equity,
and/or a combination of thereof).
Problems Encountered During the Research
The only problem encountered in this research was the difficulty in collecting data from
Mexico. We have contacted several institutions in Mexico and requested information on
the amount of hazardous wastes generated by the Maquiladora. However, the information
were not available to us because it was classified in Mexico or due the fact that it does
not exists. In addition, a major problem was finding an existing GIS database for the
highways in Mexico at the border region.
Research Findings and Conclusions
The anticipated growth of US industries in the northern part of Mexico render this
research urgent. As the number of US industries in Mexico continues to grow, an increase
in hazardous materials shipments will occur from the US to Mexico. This will create a
specific pattern of hazardous material shipments that will impose risks for the population
in the US, in general, and the population in the states of Arizona, California, New
Mexico, and Texas, in particular.
The US-Mexico border region is a growing source of hazardous waste. The research has
provided information showing a substantial increase in the number of shipments of
hazardous wastes over the last several years from Mexico to the US.
There is an urgent need to better manage the hazardous waste generated in Mexico by
either building new disposal facilities or recycling plants. This will enable the
development of policies dealing with the mitigation and reduction of risks of transporting
hazardous waste in the border region.
The actual quantities of hazardous waste generated by the maquiladora plants are still
unknown. The majority of the waste is not likely being shipped to the US or receiving
proper treatment, recycling, or disposal in Mexico. The problem will become more severe
as the maquila plants grow. Increasing hazardous waste shipments will dramatically
increase risk levels.
Following are some conclusions pertaining to the test case. These conclusions may or
may not be applicable to the whole US-Mexico border region. First, most of the
Maquiladoras plants that shipped hazardous waste from Sonora to Arizona were
concentrated in the city of Nogales (17 plants). Most of these Maquiladoras have
recorded one shipment, only six plants have recorded two or three shipments. This
turnout of shipments is very low. However, it is consistent with the turnout of shipments
of all Maquiladoras in general. This pattern reinforces the need for strict and better
management for the compliance of the Maquiladoras with hazardous waste shipment
regulations.
Studying several management scenarios was a successful tool for helping to develop new
policies in the border region. The United States has a plan to upgrade some ports of entry
in the border region to accommodate the anticipated influx of shipments under NAFTA.
This step may reduce the congestion at the ports of entry, but it will increase the risk
factors along the existing shipment routes.
Some management scenarios had little effect on reducing risk. However, this could be
limited to this test case only. A major reduction of risk occurred when building a new
treatment, storage, or disposal facility in the border region, specifically in Mexico. A
better scenario would be developing more than one TSD facility. A major risk and cost
increase were generated from Maquiladoras located away from the border cities.
The major concentration of maquiladora plants is at the border cities and border states.
The rest of plants are located in the middle and southern part of Mexico. The USEPA and
Arizona Department of Environmental Quality databases have recorded shipments only
from the border states. These unconsidered plants should be studied closely to find out
the way these plants manage their hazardous waste.
Recommendations for Further Research
The following areas could be considered for future research:
1. A study on the impact of building new ports of entry at the border region and the
degree to which such facilities would reduce risk level.
2. A study to estimate the quantities of hazardous waste generated in the future and
its adverse impact on human and environment
3. More research should be conducted on improving the environmental risk model.
4. An urgent need for building a GIS database for the entire border region.
5. More studies are needed to study the impacts of spills as a function of road type
and population density in both Mexico and the US.
Chapter 2 - Introduction
Problem Background
The maquiladora industry or Maquiladoras has grown substantially over the last decade.
Maquiladoras is the name of the US-owned assembly plants in Mexico. Most of these
industrial plants are located in the northern part of Mexico in an area known as the USMexico border region. The number of these industries grew from 585 in 1982 to 2,153
plants in 1993 (1). There is also growing evidence that these industrial plants produce
substantial amounts of hazardous waste as byproducts of their industrial processes. It is
estimated that the Baja California Maquiladora industry generates about 100,000 tons of
hazardous waste per year, the equivalent of 12% of California's 1989 estimate of in-state
hazardous waste generation (2,3). Under 1988 Mexican environmental law, hazardous
waste generated by US companies must be returned to the US for treatment and disposal
(1). However, only 10% of the companies in the Mexican states of Baja California and
Sonora have requested official shipments of hazardous waste to the US (3). The
remaining hazardous waste was either recycled, stored on site, or illegally dumped in the
border region.
The problem of transboundary movement of hazardous materials surfaced after the
Environmental Cooperation Agreement (La Paz Agreement) in 1983 between the US and
Mexico. This agreement established an area of 100 kilometers on both sides of the USMexico border within which parties agreed to cooperate "for protection, improvement
and conservation of the environment." Each government designated its national
coordinator to implement the agreement by its environmental authority: the United State
Environmental Protection Agency (USEPA) and the Secretariat of Ecology and Urban
Development (SEDUE). Since the La Paz 1983 agreement, there have been five annexes,
each dealing with a specific transborder problem. Most importantly, for the purpose of
this research, Annex III deals with the transborder shipment of hazardous waste and
hazardous substances between the two countries.
The problem addressed for Mexico in Annex III with respect to the transboundary
movement of hazardous waste is two-fold: (1) the need to prevent illegal and unregulated
dumping in Mexico from the US sources; and, (2) the lack of infrastructure (i.e. landfills,
recycling plants) in Mexico to treat and dispose of hazardous waste produced by the
maquiladora industry. The US agreed that hazardous waste generated by the
Maquiladoras would continue to be returned to the US, as required by Mexican law (4).
Environmental Transportation Issues in the Border Region
Generally speaking, most of the hazardous waste generated by the maquiladora industry
should be shipped back to the US, for treatment and storage, or adequate treatment
capacity should be developed in Mexico. These shipments contain constituents that are
hazardous to human health and to the environment. For example, 1,215 tons of ignitable
waste and 411 tons of corrosive waste were shipped from Mexico to the US in 1992.
These wastes have high risks in case of traffic accidents, especially if such events occur
in highly populated areas in Mexico or in the US (5).
The more worrisome part of this problem is the lack of knowledge of the pattern of
movement regarding the remaining hazardous waste. Authorities on both sides of the
border has been ignoring the problem for a long time as if it did not exist. Serious
enforcement activity of Mexico's environmental regulations pertaining to hazardous
waste is only now beginning to be recognized. With the amount of hazardous waste
shipments expected to increase dramatically in a short period of time, the transportation
risk problem will become difficult to manage. Currently, in the US, the disposal of
hazardous waste is a serious challenge because of escalating amounts and decreasing
capacity for treatment and disposal. The capacity of landfill disposal is reaching its
maximum because of increasing restrictions that have to be met with this option and the
closure of existing facilities. The potential for opening new disposal facilities is very
limited. With the implementation of the North American Free Trade Agreement and the
continuing growth of industries in the border region, the transportation of hazardous
waste has become a serious policy issue. It is now, more than ever, necessary to study the
problem and propose solutions under different management scenarios that may exist in
the near future.
Problem Description
Hazardous material is defined as any material that could present a danger during
shipment by ground, air, sea or any other mode of transportation such as pipe line. An
example would be the transporting of explosives from one place to another. Hazardous
waste, on the other hand, is defined by the Resource Conservation and Recovery Act
(RCRA) legislation (1976) as "a solid waste, or combination of solid wastes, which
because of its quantity, concentration, or physical, chemical, or infectious characteristics
may: (1) cause, or significantly contribute to, an increase in mortality or an increase in
serious irreversible or incapacitating reversible illness or (2) pose a substantial present or
potential hazard to human health or the environment when improperly treated, stored,
transported or disposed of or otherwise managed." While the definition refers to "solids,"
it has been interpreted to include semisolids and liquids. Hazardous waste account for
0.2% of all hazardous material traveled yearly in the US (6).
Currently, there is no complete database providing information on the pattern of
shipments of waste from the maquiladora industries as well as the shipment routes to
disposal facilities. Furthermore, it is widely expected that the amount of hazardous waste
will increase substantially as a result of the North American Free Trade Agreement
(NAFTA) and the accompanying anticipated relocation of US industries to Mexico. In
addition, binational environmental regulations and those in Mexico are beginning to be
seriously implemented. That will result in substantial flows in hazardous waste from
generator sources to the treatment and disposal facilities in the border region. The nature
of the risks in transporting hazardous materials and waste requires study to provide
management options to reduce risk and respond to transportation accidents involving
hazardous waste.
Moreover, environmental compliance is taking on new urgency in Mexico as a result of
inspections by the Secretariat of Social Development (SEDESOL), the federal successor
agency to SEDUE. For example, in the mid-July of 1992, an inspection took place at one
Mexicali environmental treatment firm, Mexaco, which resulted in a shutdown warrant of
the company and jailing of its general manager. The company had been storing drums
full of hazardous waste in its site from various maquiladora, and was unable to accurately
report on the contents of some drums (7).
In general, there are two types of shipments of hazardous substances across the USMexico border region: 1) shipments of hazardous materials (raw materials) from the US
to Mexico as part of the industrial process, which can be classified as many-origins-tomany-destinations shipments, as shown in Figure 2.1 (a) (8). This pattern is a complex
one because of the substantial amount and diversity of hazardous materials shipments
which render it extremely difficult to track; the lack of a regulatory tracking system adds
to the problem; 2) shipments of hazardous waste from Mexico to US as a by product of
US industries in Mexico. This pattern is less complex and is classified as many-originsto-few-destinations, as shown in Figure 2.1 (b) (8).
The goal of this USEPA-sponsored research is to develop an analytical framework for
environmental transportation issues by assessing the transportation risks of shipping
hazardous waste in the border region. The solution will be approached by utilizing the
Geographic Information System (GIS) technology. GIS is defined as a system for
collecting, storing, manipulating, and presenting geographic data. System refers to the
integration of computer hardware, software, and data.
In 1992, Perry et al. conducted a major and comprehensive research study on the
hazardous waste generated by the Maquiladora industry in the US-Mexico border region.
Based on data from the state of Baja California in Mexico, the quantities of hazardous
waste generated by Baja maquiladora were estimated based on the amounts of raw
material utilized by a sample of 34 assembly companies (1,2,3). Perry et al. assumed that
because most of these hazardous substances were used to facilitate assembly rather than
in manufacturing, the amount of hazardous waste generated would be close to the volume
or weight of the imported hazardous substances.
In total, there were 117 different hazardous materials imported annually. The study
reported that approximately 9,592 metric tons of solids and 2,063,606 liters of liquid
substances were imported to these sample plants. When extrapolated, the 318 Baja
maquiladora generated 100,000 tons of waste in 1988. On this basis, an individual plant
would generate 315 tons of hazardous waste per year. These materials can be classified
into three major categories: (1) a wide range of solvents such as acetone and methylene
chloride; (2) acids and alkaline substance such as sulfuric and hydrochloric acids; and (3)
heavy metals such as lead and nickel (3).
Figure 2.1 Patterns of hazardous waste shipments across the US-Mexico border
region.
2.4 PROBLEM DEFINITION
The study estimated the total quantity of hazardous waste generated, by assuming that
such quantities were equal to the amount of raw material imported by the sample plants.
Determining the quantities generated by the 34 sample plants and the total number of
plants, the study calculated the total quantities of hazardous waste generated by the
Maquiladora industry. Another major finding was the detection of traces of Volatile
Organic Chemicals (VOCs) used in printed circuit board assembly process in New River
at the US-Mexico border.
Although the above study was a major step in bringing the hazardous waste issue to
surface, it lacked precision by its emphasis on a small sample of industries, and did not
address the problem of transportation risk.
This research is an attempt to address two overriding issues regarding the transport of
hazardous waste in the US-Mexico border area. The first issue concerns the lack of a
current comprehensive database that tracks the amount and flows of hazardous waste
shipments, determines the risks to population and the environment based on actual
hazardous waste shipment data, and identifies of the patterns of shipments (their origins,
destinations, and transport routes). The second issue is the growing need for a risk
assessment transportation model that can readily be used for the border region and serve
as a valuable tool for formulating different management scenarios aimed at transportation
risk reduction and/or equity. The need for such tool is further emphasized by the
anticipated growth in hazardous waste shipments due to the NAFTA-related industrial
growth.
Chapter 3 - Methodology
Introduction
To achieve the goals of the research, the accomplishment of the following tasks are to
undertaken:
Task 1: Literature review of the different problem components.
Task 2: Data collection and analysis: Several data set are needed in this research:



Hazardous waste shipment data: these data include the quantities, types, and flows
of hazardous waste shipments from Maquiladoras in the Mexican State of Sonora
to the State of Arizona in the US. Moreover, these data need to be confirmed
through manifests available from the USEPA.
Land use data: data on population densities in the US and Mexico along shipment
route. These data were collected from different resources and are presented later
in this report.
Truck Accident Data: This is necessary to calculate the probability of accidents of
trucks carrying hazardous waste shipments. This subtask will be discussed later in
this chapter.
Task 3: Development of solution framework: the proposed risk assessment and routing
models are discussed later in this chapter. This task will consider computer modeling by
establishing a GIS database of Arizona and Sonora highways (implemented on
TransCAD platform) with different attributes (such as length of road segments, speed
limits, type, population densities along the route) and applying the risk assessment model
to the GIS database.
Task 4: Development and analysis of different management scenarios: a major
contribution of this research is to identify the impacts of different risk management
scenarios. There are two sets of scenarios: the first set of scenarios focuses on changes in
the demand pattern (and amounts) such as (1) existing conditions, (2) increases in
hazardous flows due to strict enforcement of environmental laws in Mexico, and (3)
reduction hazardous flows due to construction of new recycling facilities in Sonora
and/or Arizona. The second set of scenarios focuses on the impacts of routing hazardous
waste under different preferences (such as minimum cost, minimum risk, risk equity,
and/or a combination of thereof).
Risk Assessment Model
The solution methodology relies on the development of a suitable risk assessment model.
This is accomplished in three steps: (1) selecting a general category under which the risk
assessment model will be implemented; (2) review of different risk assessment models in
the selected area; and (3) selecting a risk assessment model appropriate for the border
region. To select a suitable model one may either choose an existing model (or a
combination of existing models) from the literature, or develop a new risk assessment
model.
Selecting Model's Category
After extensive review of all risk models and their advantages and disadvantages (Table
2.1), the category of probabilistic risk assessment models was selected as the appropriate
area of investigation. This selection was based largely on the fact that all data required for
the analysis was not complete or available.
In probabilistic risk assessment, risk is calculated as the product of the probability of
occurrence of a hazardous waste accident (or incident) and the consequences of that
accident. The outcome of the risk estimation process is the calculated risk associated with
each link in the transportation network model. If the objective is to minimize the risk in a
network, the calculated risk will be the input to the proposed routing model (9).
Alternatively, if the objective is to minimize cost, transportation cost will be the attribute
of each route segment. However, in this type of research, most of the time both cost and
risk need to optimized to construct a risk profile.
Review of Probabilistic Risk Assessment Models
A key component in probabilistic risk assessment models is the calculation of truck
accident probabilities and the probability of different outcomes of each accident. In the
following sections, the development of truck accident probabilities is presented.
Truck Accident Probabilities
In the current United States Department of Transportation (USDOT) guidelines, the
probability of a hazardous materials accident is computed as follows (10,11):
P(A)i = ARi x Li
(3.1)
where:
P(A)i = probability of a hazardous materials accident for route segment i .
ARi = accident rate per vehicle-mile for all vehicle types on Route Segment i .
Li = length (in miles) for route segment i.
In 1990, Harwood et al., proposed a modification to equation 3.1 (10). Their rationale
was that having a truck accident does not necessarily mean a release of hazardous
material. To remedy this, they proposed the following equation:
P(R)i = TARi x P(R/A)i x Li
(3.2)
where:
P(R)i = probability of an accident involving a hazardous materials release for
route segment i.
TARi = truck accident rate (accidents per vehicle-mile for route segment i).
P(R/A) = probability of hazardous materials release given an accident involving a
hazardous materials-carrying truck for route segment i.
There are two reasons that render equation (3.2) more appropriate than equation (3.1) for
hazardous materials routing analysis: (1) risk is based on truck accident rates rather than
just the probability of an accident, and (2) risk is based on truck accident rates rather than
all-vehicle types accident rates.
Based on equation (3.2), default truck accident rates and release probabilities for the use
in hazardous materials routing and analysis were calculated in Table 3.3. These default
values were calculated as shown in Tables 3.1 and 3.2 Table 3.3 (37) presents typical
values of truck accident rates and release probabilities that can be used as default values
in equation (3.2). A key aspect of Table 3.3 is that both truck accident rates and release
probabilities vary with area type (urban or rural) and roadway type. The data in Table 3.3
could be used as national default values, however, these data limit the analysis to the
level of release of hazardous materials given an accident. In reality, the outcome of the
accident could be: (1) spill, (2) fire, (3) explosion, and (4) threat.
Glickman in 1992, used the same procedure in a more detailed analysis for risk
assessment of transporting flammable liquids in bulk (13). He used the following
equation in his analysis:
P(X) = P(A) x P(R/A) x P(X/A,R)
(3.3)
where:
P(X) = probabilities of each of the possible outcomes X (i.e., spill, fire, and/or
explosion. No threat component were considered).
P(A) = probability of a truck accident.
P(R/A) = probability of a truck accident involving a release.
P(X/A,R) = probability of an outcome X given a truck accident and a release.
The probabilities P(A) that a truck will have an accident on a given route segment i is
estimated by multiplying the segment's length, in miles, by the estimated accident rate,
measured in accidents per truck-mile.
P(A)i = TARi x Li
(3.4)
TABLE 3.1 TRUCK ACCIDENT RATES BY STATE AN AREA AND ROADWAY
TYPES (9).
Highway Class
Area Type
Truck accident rate, TARi
(accidents per million veh-mi)
Roadway Type
California
Illinois
Michigan
Average *
Rural
Two-Lane
1.73
3.13
2.22
2.19
Rural
Multilane undivided
5.44
2.13
9.50
4.49
Rural
Multilane divided
1.23
4.80
5.66
2.15
Rural
Freeway
0.53
0.46
1.18
0.64
Urban
Two-Lane
4.23
11.10
10.93
8.66
Urban
Multilane undivided
13.02
17.05
10.37
13.92
Urban
Multilane divided
3.50
14.80
10.60
12.47
Urban
One-way street
6.60
26.36
8.08
9.70
Urban
Freeway
1.59
5.82
2.80
2.18
* weighted average by veh-mi of truck travel
TABLE 3.2 PROBABILITY OF HAZARDOUS MATERIALS RELEASE GIVEN
THAT
AN ACCIDENT HAS OCCURRED, AS A FUNCTION OF HIGHWAY CLASS (9).
Highway Class
Area
Type
Roadway Type
Probability of hazardous materials release given an accident,
P(R/A)i
California
Illinois
Michigan
Average *
Rural
Two-Lane
0.10
0.071
0.073
0.086
Rural
Multilane
undivided
0.10
0.071
0.064
0.081
Rural
Multilane
divided
0.09
0.064
0.062
0.082
Rural
Freeway
0.08
0.111
0.095
0.090
Urban
Two-Lane
0.08
0.059
0.069
0.069
Urban
Multilane
undivided
0.06
0.052
0.055
0.055
Urban
Multilane
divided
0.07
0.048
0.058
0.062
Urban
One-way street
0.07
0.050
0.056
0.056
Urban Freeway
0.06
0.055
0.067
0.062
TABLE 3.3 DEFAULT TRUCK ACCIDENT RATES AND RELEASE PROBABILITY
FOR
USE IN HAZARDOUS MATERIALS ROUTING AND ANALYSIS (9).
Highway Class
Area Type
Roadway Type
Truck accident
Probability of Releasing accident
rate (accidents per release given rate (releases per
million veh-mi)
an accident
million veh-mi)
TARi*
P(R/A)i**
Rural
Two-Lane
2.19
0.086
0.19
Rural
Multilane undivided
4.49
0.081
0.36
Rural
Multilane divided
2.15
0.082
0.18
Rural
Freeway
0.64
0.090
0.06
Urban
Two-Lane
8.66
0.069
0.60
Urban
Multilane undivided
13.02
0.055
0.77
Urban
Multilane divided
12.47
0.062
0.77
Urban
One-way street
9.70
0.056
0.54
Urban
Freeway
2.18
0.062
0.14
* : Calculated from Table 3.1
** : Calculated from Table 3.2
where:
P(A)i = probability of a truck accident for route segment i.
TARi = truck accident rate (accidents per million truck-miles for route segment i).
Li = length (in miles) of route segment i.
The conditional probability of a release given an accident occurrence P(R/A) can be
calculated from past historical data, if available, or can be expressed in terms of other
probabilities for which estimates can be derived from information that appears in the
literature:
P(R/A) = P(R, O/A) + P(R, N/A)
(3.5)
where:
O = the event that an overturn occurs during the accident.
N = the complement of the event O, a non-overturn.
This equation holds because the O and N events are mutually exclusive. Note that this
equation can be used in this specific case because the data for different element were
available or could be calculated. However, this may not hold in other cases.
Each term on the right hand side of equation (3.5) can be replaced with the product of
two probabilities, based on the operative definition of conditional probability:
P(R/A) = [P(O/A) x P(R/A,O)] + [P(N/A) x P(R/A,N)]
(3.6)
Substituting equations (3.4) and (3.6) in Equation (3.3):
P(X)i = {TARii x Li} x {[P(O/A) x P(R/A,O)] + [P(N/A) x P(R/A,N)]}i x P(X/A,R)i
(3.7)
By comparing this equation to equation 3.2, equation 3.7 can be presented as:
P(X)i = P(R)i x P(X/A,R)i
(3.8)
In summary, Harwood's modified equation (3.1) calculates the probability of an accident
by adding a component that considers the conditional probability of a release given an
accident while Glickman went further by considering outcomes of a release given the
occurrence of an accident.
Selecting the Probabilistic Risk Assessment Model
The model in equation (3.7) is considered an acceptable probabilistic risk model for the
assessment of hazardous materials shipments. This model can be used in calculating the
risks associated with the shipment of hazardous waste in the border region. However, it
requires an extensive data availability and analysis in order to evaluate the different
variables in the model. These specific data are very difficult to obtain. Moreover, one
main goal of this research is to study the transportation issues related to the hazardous
waste shipments based on considering both risk and cost rather than developing a risk
model only.
Developing a Risk Assessment Model for the Border Region
It is important to present a complete picture of different patterns of hazardous materials
and hazardous waste shipments generated by the Maquiladoras. To achieve this goal, it is
necessary to introduce transportation risk pathways generated by the shipments of
hazardous materials from/to different maquiladora plants in Mexico.
Transportation Risk Pathways
The analysis of the risks of transporting hazardous substances and waste in the USMexico border region is a complex task. Achieving this task requires the ability to
separate the various patterns of shipments into transportation pathways.
The first pattern of transportation pathways is characterized by shipments of hazardous
substances to individual maquiladora for processing and assembly purposes (Figure 3.1).
The majority of these substances are imported from the US industries as raw materials to
be used in treatment, degreasing, cleaning materials in assembly oriented industrial
processes. However, some of these transboundary shipments maybe regenerated by instate Mexican shipments of these substances. These shipments will create two groups of
pathways as shown in Figure 3.1: 1) pathways from the US to the maquiladora plants in
Mexico, and 2) pathways within Mexico (i.e., pathways of shipment
from Mexico industries to the Maquiladoras). Most importantly, there are transportation
risks associated with transboundary shipments of raw material to the maquiladora plants
both in the US and in Mexico.
The USEPA database shows that of the 2,153 Maquiladoras producing hazardous waste
in 1992, only 329 industries have records of shipments to the US. This means that only
15% of Maquiladoras have actually transported waste to the US.
As shown in Figure 3.2, two risk pathways are apparent from these shipments: waste may
go directly to a disposal facility in the US for incineration or landfilling, or may be reimported by a US recycling facility for treatment. In addition, two additional indirect risk
pathways are apparent from recycled hazardous waste: a) the transport of residual
hazardous waste from the recycled facility to a point of disposition, and b) shipments of
re-used and treated waste for industrial purposes.
At this point, Mexican environmental law provides a safe haven by allowing the storage
of hazardous waste at a maquiladora facility indefinitely, based on technical storage
standards that are being developed. Moreover, some of none-RCRA hazardous waste
generated at the plants can and are being shipped to recycling and reuse plants in Mexico.
In addition, some waste is also sent either to disposal facilities (i.e., landfills) or to the
off-site storage facilities in Mexico.
Transboundary shipments of hazardous waste from Mexico to the US have several
pathways. According to Mexican law, hazardous waste generated by the Maquiladoras
should be shipped back to the US for treatment, storage, or disposal (TSD).
The number of such shipments on a yearly basis is unknown but a handful of hazardous
waste recycling plants in northern Mexico are operating. The Titisa recycling facility in
Baja California has a capacity of recycling 15,000 tons of hazardous waste, representing
an estimated 15% of the waste produced by Baja maquiladoras (3).
Transportation risks are also incurred by shipments from these recycling facilities either
through the transport of usable byproducts from recycling or the transport of residual
hazardous waste requiring disposal. Information on shipment patterns of residual waste to
disposal facilities is not available at this time. Substantial growth is anticipated in
recycling facilities that utilize hazardous waste generated from the maquiladora industry.
Although a fairly robust database of generation-to-destination flows of hazardous waste
has been generated, the database is limited to reported waste streams from generators in
Mexico to first-line TSD facilities in the US. The pattern of shipments and the amount of
hazardous waste shipped from the Maquiladoras to different Mexican facilities remain
unclear. Moreover, the amounts of hazardous waste from Mexico that are not recorded in
RCRA manifests but nevertheless do enter the US are also not available. At this juncture
in the research, the available data will permit a reasonable picture of direct transboundary
shipments of hazardous waste to TSD facilities in the US.
Mathematical Development of the Risk Assessment Model
In general, any hazardous material shipment has the potential of generating risk at two
possible locations: at the facility during loading and unloading and at any point along the
shipment route. Transportation risk results from equipment failure during shipment or by
traffic accident. In this research it is assumed that the risk generated by loading and
unloading at different facilities and the possible risk generated by equipment failure is
minimal.
The total quantity of risk generated by hazardous material shipments is equal to the risk
of hazardous material shipped to the US industries in Mexico plus the risk of hazardous
wastes shipped out from these industries. This can be expressed by the following
equation:
Risk total = Risk(HM) + Risk(HW)
(3.9)
where:
Risk total = Total Risk generated by maquiladora plants.
Risk(HM) = Risk of shipments of hazardous material to the Maquiladoras.
Risk(HW) = Risk of shipments of hazardous waste from the maquiladora plants to
TSD facilities in Mexico and in the US
Transportation Risk of Hazardous Material
The risk of hazardous materials shipped to the US industries in Mexico is equal to the
risk generated from the shipment of hazardous material from the US and Mexican
sources of raw materials to the Maquiladoras in Mexico. By ignoring the stop-over at
ports of entry, the transportation risk associated with these shipments can be presented in
the following equation:
M
Sl
TR(sim) +
{
Risk(HM) =
m=1
SJ
si=1
TR(sjm)}
sj=1
(3.10)
where:
i = an index, representing the country of Mexico.
j = an index, representing the country of US.
si = an industrial plant in the US that supplies raw material to the maquiladora.
sj = an industrial plant in Mexico that supplies raw material to the maquiladora.
SI = total number of the US suppliers of raw materials to the maquiladora.
SJ = total number of the Mexican suppliers of raw materials to the maquiladora.
m = maquiladora plant in Mexico.
M = total number of the maquiladora plants in Mexico.
TR = transportation risk associated with a hazardous materials shipment.
This problem is defined as the many-origin-to-many-destination problem (Figure 3.1).
This problem is also very complex and sometimes it is impossible to capture all
shipments of raw material due to the many reasons discussed previously in chapter 2.
3.3.2.2 Transportation Risk of Hazardous Waste
The risk of transporting hazardous wastes is defined as the many-origin-to-fewdestination problem as shown in Figure 3.2. This can be calculated using the following
equation:
M
Risk(HW) =
Risk (HW)m
m=1
(3.11)
whereby Risk(HW)m is defined as:
RJ
Risk(HW)m =
SI
TR(mrj) +
rj =1
RI
DJ
TR(msj) +
si=1
LI
+
TR(mdj)
+ TRj
+
dj=1
+ TRi
TR(mri)
TR(mli)
ri=1
li=1
(3.12)
where:
i = an index, representing Mexico.
j = an index, representing the US.
ri = recycling facility in the US.
li = disposal facility (landfill/incinerator) in the US.
rj = recycling facility in Mexico.
sj = storage facility in Mexico.
dj = disposal facility (landfill) in Mexico.
RI = total number of recycling facilities in the US.
LI = total number of disposal facilities in the US.
RJ = total number of recycling facilities in Mexico.
SJ = total number of disposal facilities in Mexico.
DJ = total number of disposal facilities (landfills) in Mexico.
m = maquiladora plant in Mexico.
M = total number of maquiladora plants in Mexico.
TR = transportation risk associated with a hazardous material shipment.
TRi and TRj are the transportation risk associated with the shipment of hazardous
substances from the recycling plants after their processing of hazardous waste. Such
shipments consist of two types: 1) shipments of recycled materials to reuse industries and
2) shipments of the remaining hazardous waste to disposal sites. In mathematical form,
TRi can be expressed as:
RI
FI
TR(rifi) +
{
TRi =
ri=1
LI
fi=1
TR(rili)}
li=1
(3.13)
where:
ri = recycling facility in the US.
li = disposal facility in the US.
fi = reuse material facility in the US.
RI = total number of recycling facilities in the US.
LI = total number of disposal facilities in the US.
TR = transportation risk associated with a hazardous materials shipments
FI = total number of the reuse material facilities in the US.
and TRj can be expressed as:
RJ
FJ
SJ
DJ
+
{
TRj =
rj=1
+
TR(rjfj)
TR(rjsj)
fj=1
sj=1
TR(rjdj)}
dj=1
(3.14)
where:
rj = recycling facility in Mexico.
sj = storage facility in Mexico.
dj = disposal facility (landfill) in Mexico.
RJ = total number of recycling facilities in Mexico.
SJ = total number of the storage facilities in Mexico.
DJ = total number of disposal facilities (landfills) in Mexico.
FJ = total number of the reuse material facilities in Mexico.
In this research, the risk addressed is only the component of the overall transportation
risk which covers the shipment of hazardous waste from the Maquiladoras in the
Mexican state of Sonora to treatment, storage, and disposal facilities in the US State of
Arizona. The mathematical component that addresses this problem is part of equation
(3.11):
M
Rl
TR(mri) +
{
Risk(HM) =
m=1
LI
ri=1
TR(mli)}
li=1
(3.15)
where:
i = an index, representing Mexican state of Sonora.
j = an index, representing the US state of Arizona.
ri = recycling facility in Arizona.
li = disposal facility in Arizona.
RI = total number of recycling facilities in Arizona.
LI = total number of disposal facilities in Arizona.
m = maquiladora plant in Sonora.
M = total number of maquiladora plants in Mexico.
TR = transportation risk associated with a hazardous materials shipment.
3.3.3 Transportation Risk Assessment Model
A major goal in developing this model is to capture the risk associated with the shipments
of hazardous waste from the maquiladora industries to the US, on humans and on the
environment.
The human factor is typically measured by population exposure to hazardous waste. The
population exposure can be measured by the number of people living within a one mile
along the transportation routes. With respect to measuring environmental impacts, one
approach is to obtain the costs of site clean-up of hazardous waste as a result of an
accident. A second approach that may be considered is the cost of traffic delay due to an
accident involving a hazardous waste shipments. In this case, the delay cost consist of
direct cost (fuel cost) and indirect cost (pollution to the environment) due to the
temporarily closure of a highway to clean up the accident site.
As stated previously, transportation risk is calculated as the product of the probability of
occurrence of a hazardous material accident and the consequences of that accident. i.e.,
TRk = P(R)k x V(R)k x A(R)k x C(P,E)k
(3.16)
where:
k = an index representing a route segment on a transportation network.
TREk = potential transportation risk associated with hazardous wastes shipments
for route segment k.
P(R)k = probability of an accident involving a hazardous waste release for route
segment k.
V(R)k = quantity of hazardous waste spilled by a release given an accident, in
gallons, for route segment k.
A(R)k = area impacted per gallon released, for route segment k.
C(P,E)k = consequences on population and the environment for route segment k.
Now, the probability of an accident is given in equation (3.2):
P(R)k = TARk x P(R/A)k x Lk
As stated before, the first two components are given in Table 3.2, while the third term,
Lk, is the length of the route segment.
Since hazardous waste consists of liquid and solids, the outcome of an accident could be
a spill or a threat only. However, some hazardous waste are ignitable and could catch fire,
but these outcomes are ignored in this research because of: 1) the hazardous waste is
usually shipped in drums which reduces the probability of an explosion, and 2) hazardous
waste in the drums consist mostly of a mixed waste. This will reduce the concentration of
the ignitable material and the probability of having an explosion.
The quantity of release given an accident is calculated as the average amount of
hazardous waste released in different hazardous waste accidents. A second approach to
estimate these values is by assuming that the quantity released in an accident is equal to
10% of the average load per shipment. Furthermore, the impacted area V(R) is assumed
to be 1 square foot per gallon of hazardous waste released.
Using these averages, equation (3.16) can be written as:
TRk = P(R)k x V(R)Average x A(R)Average x C(P,E)k
(3.17)
In this model, it is important to distinguish between risk to population and environmental
risk. Each component will be calculated separately (i.e., each route segment will have
two risk elements, the potential risk on humans (TRPk) and the potential risk on the
environment (TREk). Thus, equation (3.17) should be expressed as two equations:
TRPk = P(R)k x V(R)Average x A(R)Average x C(P)k
(3.18)
TREk = P(R)k x V(R)Average x A(R)Average x C(E)k
(3.19)
where:
C(P)k = total persons within the impact area.
C(E)k = clean-up cost per square footage.
However, the potential population risk in the impacted area is independent of the quantity
of hazardous waste released. This assumption is relevant because release quantities of
hazardous waste are relatively small and do not generally include hazardous gases.
Therefore, equation (3.18) is rewritten as:
TRPk = P(R)k x C(P)k
(3.20)
Risk Components
In the literature, there are three components of risk: population risk, environmental risk,
and property risk. The FHWA guildlines measured the first two components, but did not
specify when to consider both risks and how to combine or weight these risks when both
are considered (14). Most routing studies have avoided these issues by considering only
population risk. However, a 1987 Canadian screening method suggested some specific
weights for use in combining the three component of risks. The weight factors for
population, property and environmental risk were 60%, 10%, and 30% respectively. It is
unlikely that all users would agree on a single set of weight factors appropriate for all
circumstances.
The method used in this research largely follows the risk assessment method in the
FHWA routing guide. Environmental risk was calculated as the probability of an accident
times the consequences of the accident. In this research, consequences were calculated as
the clean-up cost per square footage as shown in equation (3.19). Although this method
follows the steps of the FHWA report, it provides an approxy procedure for measuring
the adverse effect of an accident of the environment. Another direct approach could be by
creating an index to measure the direct consequence of a release to the environment. This
index can be introduced as a function of the depth of the ground water table at the
location of potential accidents and be a function of the nature of the hazared. However,
this method was not considered in this research.
Accident Consequences on Population
In the earlier FHWA guidelines (15), population exposure for a given route segment was
determined as the total number of persons exposed in the impact area. The impact area
was defined as a band of equal width, usually 1/2 miles, on either side of the roadway
segment. That approach had drawbacks because it assumes that the entire population
along a route segment is exposed to the same amount of risk. Actually, only the
population within a specific distance, say 1/2 mile, from the release location should have
been considered. The first model has been modified and adjusted by dividing the
population within the impact area along the entire route segment by the length of the
route segment. This correction was suggested by Harwood and Russell and later
incorporated in the updated guidelines published by the Department of Transportation
Research and Special Programs Administration (RSPA) (11,16).
To understand the differences between the two approaches, assume a route segment (k) in
the transportation network has a population density of Pd in the impacted area Ia. As
shown in Figure 3.3(a), representative of the first method, the total number of persons
within the specified impact zone width are exposed to a hazardous waste release along
segment k [C(P)k]. This is calculated as follows:
C(P)k = Pd x Ia
(3.21)
where:
C(P)k = total persons within the impact area of route segment k.
Pd = population density given in persons per square miles.
Ia = impact area in square miles.
However, the impact area in this case is equal to the length of the segment (Lk)
multiplied by the band width (W). Substituting these variables in equation (3.21) will
yield:
C(P)k = Pd x Lk x W
(3.22)
In the corrected approach (shown in Figure 3.3 (b)), the impact area is W square miles,
therefore the population exposure is given by:
C(P)k = Pd x W
(3.23)
Although the objective of modifying equation (26) is to consider the population within
the same distance in all directions of a release, the equation (3.23) fails to achieve this
goal. Its linear population density is represented by a rectangle-shape area around the
point of release. This rectangular boundary does not provide an equal distance from the
release point.
Figure 3.3(c) represents the population exposure area that will be considered in this
research. The suggested model assumes that the personal injury consequences of a
hazardous waste release are proportional to the population potentially exposed to the
releases. Moreover, people in the exposed area are within equal distances from the
release point. It is important to note that in case of gaseous release (which is ignored in
the case of hazardous wastes, the wind conditions must be considered. Therefore, the
impact area is (0.25 x W2 x ) and the population exposure is given by:
C(P)k = Pd x (0.25 x W2 x )
(3.24)
Considering a 1 mile band width along the route,
C(P)k = 0.25 x x Pd
(3.25)
It is highly recommend that equation (3.25) should be used in calculating the population
exposure in the FHWA guidelines.
Transportation Routing Algorithm (TRA)
In routing, the total population risk and/or total environmental risk for each alternative
route is computed by summing all individual risk components along each route. No
attempt is made to combine or weight the population and environmental risks of a given
route, so these risks must be considered separately.
Figure 3.4 shows the flow chart of the Transportation Routing Algorithm (TRA)
implemented in this research. First, the quantities of hazardous waste (or demand) is
calculated for a selected scenario. Second, a flow matrix is developed which assigns the
demand of each Origin-Destination (O-D) pair in the transportation network. Third, TRA
computes the different link attributes for all links. These attributes are: 1) the population
risk, 2) the environmental risk, and 3) the transportation shipping cost. The first two link
attributes are computed using equations (3.20) and (3.19). The transport cost is calculated
assuming an overall average cost of $1 per mile of travel. This assumption, while robust,
is valid because the cost is approximately proportional to the travel distance. It is a
simplifying assumption given that the research focus is not on transport cost, but on the
risk side of the coin. The risk profile is a necessary tool which facilitates decision-making
regarding different management scenarios.
Fourth, utilizing a shortest path algorithm, different routes are generated for each O-D
pairs in the transportation networks (each having a specific set of values for the three
different route attributes). Based on the decision makers objective and the scenario
structure, if the objective of optimization is to minimize the population risk, transport
paths are identified which minimize the risk. For each route, the corresponding
environmental risk and the transportation cost will be calculated. The same procedure is
repeated for different objectives. It is important here to note the direct optimizing of all
the three attributes requires that the decision maker identify the trade-off between them.
Having identified the risk and cost values for each selected routes, a risk profile can be
developed which helps us in study this particular scenario. .
(a) Method used by earlier FHWA guideline (1980) (15)
(b) Method used by latter FHWA guideline (1989) (11,16)
(c) Method used in this research
Figure 3.3 Three possible impact area for population exposure
Figure 3.4 Transportation Routing Algorithm (TRA)
The goal of this problem is to optimize the following three objective functions:
where:
i = an index, representing state of Sonora in Mexico.
j = an index, representing state of Arizona the US.
k = an index, representing a route segment on a transportation network.
ri = recycling facility in Arizona.
li = disposal facility in Arizona.
RI = total number of recycling facilities in Arizona.
LI = total number of disposal facilities in Arizona.
m = maquiladora plant in Mexico.
M = total number of maquiladora plants in Mexico.
TRPk = potential risk on humans for route segment k.
TREk = potential risk on the environment for route segment k.
TCk = transportation cost associated with hazardous waste shipment on route
segment k.
Risk(HW)P = total population under potential risk associated with hazardous
waste shipments on a transport network.
Risk(HW)E = total environmental potential risk associated with hazardous waste
shipments on the same network.
Cost(HW) = total transportation cost associated with hazardous wastes shipments
on the same network.
Demonstration of Risk Assessment Procedure for a Hazardous Waste Routing Problem
To illustrate the risk assessment procedures for hazardous waste routing analyses, a
numerical example of the calculations will be provided. The example will address the
routing problem of a single origin-destination node pair (AE) shipment, on the highway
network shown in Figure 3.5. Calculations are summarized in Table 3.4.
In table 3.4, the values in columns (5) and (6) were from Table 3.3. Column (8) was
calculated as the product of (5), (6), and (7) using equation (3.2). Total persons exposed
(column 10) was calculated from equation (3.25). Population risk was calculated as in
equation (3.20). The environmental risk was calculated as in equation (3.19) assuming
that only 1% of the average number of shipments and that each shipment carried an
average of 10 tons. The average quantity released V(R)k is equal to:
V(R)ave = 0.01 x 10 tons/shipments x 2000 Ib/tons x 1/8.377 Ib/gallons= 239 gallons
released.
The A(R)k factor was assumed to equal 1 square foot per gallon of hazardous waste
released. The cost of cleaning was assumed to equal $10/square foot.
The results of three risk models (achieving the minimization of the three different
objectives: population risk, environmental risk, and transport cost) are listed in columns
(11-13). These results used to develop a risk profile as shown in Figure 3.6. This risk
profile will help in making different management decisions.
The population risk, environment risk, and transport cost computations are valid for one
node pair only (AE). To determine the yearly population risk, yearly environment risk,
and yearly transport cost for a given network consisting of many origin-destination node
pairs, each node pair's attributes (population risk, environmental risk, and transport cost)
has to be multiplied by the number of yearly shipments of that node pair.
Figure 3.5 Highway network demonstration of risk assessment model
(right click then select view image for larger image
)
Table 3.4 RISK ASSESSMENT FOR HAZARDOUS WASTE
ROUTING - HIGHWAY NETWORK DEMONSTRATION
Figure 3.6 Risk profile for the example problem
Chapter 4 - Data Collection and Analysis
Introduction
these in ways to facilitate the analyses. The collection of data constituted a major and
complex task. The difficulty in collecting data resulted from the sensitivity of the
hazardous waste issue, particularly during the period when NAFTA negotiations where
taking place. Moreover, the EPA's central file on hazardous waste shipments was
incomplete, and as of yet it appears that the 1995 shipment data is still being updated.
Consequently, some of the data had to be retrieved from individual states; much of these
data were not recorded into computer files.
In this chapter, four major elements are discussed: the development of Maquiladoras in
Mexico, the collection of data, data analysis by volume and pattern of shipments. An
approach developed to estimate actual quantities of hazardous waste generated by the
maquiladora industries is the fourth analytical element to be addressed in this chapter.
Development of The Twin Plant Industries (Maquiladoras) in Mexico
The Maquiladoras have become a vital source of income in Mexico. It is considered the
second largest source of foreign exchange, generating US $1.6 billion per year for
Mexico (1). It is considered as a valuable source for much-needed foreign currency,
employment, and regional development in Mexico. The growth of the industry has been
dramatic. The maquiladora industry began in 1965 with only 12 plants and approximately
3,000 workers and by September 1992, the number of plants reached 1,987 with 458,754
workers (1,5). The total number of maquiladora facilities which have been active in
shipping waste to the United States by the middle of 1995 is 2,053 with approximately
582,147 workers. The growth in Maquiladoras by year is shown in Figure 4.1.
Geographic Growth of the Maquiladora Industry
Tables 4.1 and 4.2 show the distribution of Maquiladoras by state and city and the
number of employees comparing the years 1992 to 1995. The numbers in the table were
supplied by the Secretariat of Commerce and Industrial Development (SECOFI) offices
and the economic development organization in Mexico. The raw figures were compared
with Solunet of El Paso, Texas, which publishes a complete directory of "twin plants" in
Mexico. The numbers are based on actual operating Maquiladoras. Not every city is
listed in Tables 4.1 and 4.2, but the total number of maquiladora and employment size for
all of Mexico is provided (6).
The country of Mexico has a surface are of 761,603 square miles. It consists of 23 states
with a total population of 81 million (17). Six Mexican states border the United States:
Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon, and Tamaulipas. The
number of maquiladora plants in each border states is shown in Figure 4.2. The total
number of plants in the border states is 1,773 plants which represents 89.2% of the total
Maquiladoras in Mexico in 1992; and more currently 1,755 plants in 1995. The percent of
employees working in the border region Maquiladoras in 1995 constitutes 94% of the
total maquiladora labor force in the country.
The number of cities that are at the border with the US or within a short distance from the
border is 15 cities. Tables 4.3 and 4.4 lists all six bordering states in 1992 and 1995, the
15 cities classified as border cities, and the number of Maquiladoras in each city. The
number of plants in these bordering cities is 1,524 Maquiladoras which is equivalent to
76.7% of the total number of plants and employ 82.2% of the total labor force. Thus,
focusing the study on the border cities as the source of hazardous waste shipments
provides a reasonable picture on the extent, size and inherent problems of hazardous
waste shipments.
Figure 4.1 Growth of the number of plants in the maquiladora
industry from 1968 to 1995, Mexico.
Figure 4.2 Number of Maquiladoras in each of the Mexican
states bordering the US in 1992.
Figure 4.3 Number of Maquiladoras in each of the Mexican
states bordering the US in 1995.
TABLE 4.1 DISTRIBUTION OF MAQUILADORAS BY
STATE AND CITY IN MEXICO, 1992.
CITY
NUMBER OF
MAQUILAS
NUMBER OF
EMPLOYEES
Tijuana
513
71,490
Mexicali
138
20,223
Tecate
82
5,024
Ensenada
39
2,730
BAJA CALIFORNIA
SUR
La Paz
16
1,101
SONORA
San Luis Rio
15
3,000
Nogales*
76
20,600
Naco
4
1,200
Agua Prieta
26
7,500
Hermosillo
15
5,000
Guaymas
2
900
Cd. Obregon
3
1,726
Other Cities
39
7,506
Palomas
5
137
Cd. Juarez
300
130,000
Cd.
Chihuahua
66
29,010
Ojinaga
1
300
Cd. Acuna
43
15,000
Piedras
Negras
40
7,436
Toreon
36
4,219
Saltillo
11
3,751
Moniclova
3
283
Other Cities
35
5,858
NUEVO LEON
Monterrey
86
17,130
TAMAULIPAS
Nuevo
Laredo
58
10,000
Reynosa
61
30,000
Matamoros
76
38,268
DURANGO
Gomez
Palacios
48
8,640
JALISCO
Guadalajara
41
6,722
BAJA CALIFORNIA
CHIHUAHUA
COAHUILA
Merida*
25
4,000
ZINALOA
n/a
5
n/a
ZACATECAS
n/a
4
n/a
SAN LUIS POTOSI
n/a
9
n/a
AGUASCALIENTES
n/a
10
n/a
GUANAJUATO
n/a
36
n/a
MICHOACAN
n/a
2
n/a
HIDALGO
n/a
13
n/a
VERACRUZ
n/a
2
n/a
CAMPECHE
n/a
3
n/a
1,987
458,754
YUCATAN
TOTAL
Table 4.2 DISTRIBUTION OF MAQUILADORAS BY
STATE AND CITY IN MEXICO, 1995.
CITY
NUMBER OF
MAQUILADORAS
NUMBER OF
EMPLOYEES
Tijuana
461
87,723
Mexicali
114
23,919
Tecate
79
8,320
Ensenada
49
4,919
BAJA CALIFORNIA
SUR
La Paz
6
995
SONORA
San Luis Rio
25
3,854
Nogales*
79
23,203
Naco
4
1,200
Agua Prieta
32
9,503
Hermosillo
15
5,000
Guaymas
2
900
Cd. Obregon
3
1,726
Other Cities
39
7,506
Palomas
5
137
Cd. Juarez
242
153,989
Cd.
Chihuahua
66
29,010
Ojinaga
1
300
Other Cities
17
7,357
Cd. Acuna
47
21,096
Piedras
Negras
42
9,573
Torreon
32
8,223
Saltillo
10
1,981
BAJA CALIFORNIA
CHIHUAHUA
COAHUILA
Monclova
2
1,968
Other Cities
47
10,705
NUEVO LEON
Monterrey*
95
18,821
TAMAULIPAS
Nuevo
Laredo
51
18,844
Reynosa
72
38,824
Matamoros
111
46,536
Valle
Hermoso
13
2,500
DURANGO
Gomez
Palacios
56
11,595
JALISCO
Guadalajara
50
10,186
YUCATAN
Merida*
31
5,654
MORELOS
n/a
15
1,189
MEXICO, D.F.
n/a
56
4,891
SINALOA
n/a
5
n/a
ZACATECAS
n/a
4
n/a
SAN LUIS POTOSI
n/a
9
n/a
AGUASCALIENTES
n/a
10
n/a
GUANAJUATO
n/a
36
n/a
MICHOACAN
n/a
2
n/a
HIDALGO
n/a
13
n/a
VERACRUZ
n/a
2
n/a
CAMPECHE
n/a
3
n/a
* Total includes surrounding areas
TABLE 4.3 MAQUILADORAS DISTRIBUTION BY BORDER STATE AND CITY, 1992.
n/a
TABLE 4.4 MAQUILADORAS DISTRIBUTION BY BORDER STATE AND CITY, 1995.
STATE IN
MEXICO
CITY
NUMBER OF
MAQUILADORAS
NUMBER
OF
EMPLOYEES
BAJA
CALIFORNIA
SONORA
Tijuana
461
87,723
Mexicali
114
23,919
Tecate
79
8,320
San Luis Rio
25
3,854
Nogales*
79
23,203
Naco
4
1,200
Agua Prieta
32
9,503
Palomas
5
137
242
153,989
Ojinaga
1
300
Cd. Acuna
47
21,096
Piedras
Negras
42
9,573
NUEVO LEON
Monterrey*
95
18,821
TAMAULIPAS
Nuevo
Laredo
51
18,844
Reynosa
72
38,824
Matamoros
111
46,536
1,460
465,842
CHIHUAHUA
Cd. Juarez
COAHUILA
TOTAL
* Total includes surrounding areas
Development of Maquiladora by Type of Industry
In the last ten years, the growth and diversification the Maquiladoras has resulted in a
substantial increase in the use of hazardous chemicals and, consequently, generation of
hazardous waste. In 1965, the majority of plants were textile assembly plants. By 1979,
the electronic and electrical equipment sectors represented 36% of the total number of
plants and 57% of the total maquiladora employment (1).
By 1990, the dominated industries were transportation equipment, furniture, and
electronic and electrical equipment as shown in Figure 4.4 . A major increase in
industries that are likely to use hazardous chemicals such as toys and sporting equipment
and mechanical equipment take place as shown in Figure 4.5 (2,3).
Figure 4.4 . Growth in number of plants for the major
maquiladora sectors from 1984 to 1990 (2,3).
Figure 4.5 . Growth in number of plants for some of the minor
maquiladora sectors from 1984 to 1990 (2,3)..
Data Collection
Information on hazardous waste shipments in the US-Mexico border region is available
from a manifest system which provides details about shipments of hazardous waste from
generators to treatment and disposal sites. Arizona environmental law requires all
transporters of hazardous waste to send a copy of the manifest of each shipment that
passes into or through Arizona. The manifests are then sent to the Hazardous Waste
Compliance Office at the Arizona Department of Environmental Quality (ADEQ). The
law also requires all TSD facilities operating in the State of Arizona to send a copy of the
manifest for each shipment that terminates at their facility to ADEQ.
Although the manifest tracking system is relatively new and has some shortcomings, it
can provide a basis for determining the amounts and shipment patterns of hazardous
waste transported by the maquiladora industry. There are three sources of manifest data:
(1) Regional or Federal Sources: The Federal regulations require transporters of
hazardous waste to submit, at the border crossing, a copy of the manifest of each
shipment entering the US. The manifests collected at border crossings are sent to the
USEPA Regional Offices in that area. In the southwest US, there are two regional offices:
the California regional office in San Francisco (representing region 9) and the Texas
office in Dallas (representing region 6). These regional manifests are then collected by
the Dallas EPA office and entered into a dBase IV program.
(2) State Sources: State regulations require all transportation firms, and TSD facilities to
submit a copy of each hazardous waste manifests passing through or terminating in that
state. Usually a copy is received by a state governmental agency that monitors
environmental compliance. The Arizona Department of Environmental Quality (ADEQ)
made copies available of all manifests of hazardous waste shipments that ended in
Arizona and that was generated by the maquiladora industry for the years 1991-1995.
(3) Mexican Sources: The Secretariat of Ecology and Urban Development (SEDUE)
requires transporters of hazardous waste and TSD facilities operating in Mexico to send a
copy of the Mexican manifest to their office. However, this manifest program in Mexico
is relatively recent and not available at this time. Due to the difficulty of obtaining these
manifests, this option was not pursued.
To validate the data and capture more information about shipments crossing the border,
interviews were conducted with the US Customs Service (USCS) officials at the Nogales
port of entry. The USCS office receives copies of manifests of each hazardous waste
shipment that crosses the border. All these manifests are then sent to the US Environment
Protection Agency regional office in San Francisco.
The USEPA office at Dallas provided three major databases: (1) a database of the
manifest system for the years 1987 to present, (2) a database of all US industries that
operate in Mexico, and currently registered with the USEPA, and (3) a database of all
TSD facilities in the US.
Data Analysis of The USEPA
The USEPA database contains all the information on RCRA reported shipments
originating in Mexico and ending in the US. In addition, the database also has
information about non-RCRA shipments from maquiladora plants to TSD plants in the
US. These data were collected from the border custom personnel and entered into a
database program using dBase IV software. The database structure captures all the data in
the manifest: name of the generator in Mexico and the parent firm in the US, name of the
transporter company, name of TSD facility and site address, US Department of
Transportation (USDOT) description of the material, quantities and type of waste
shipped, date of generation, date of transportation and the date of arrival at the TSD
facility in the US.
The database contains manifests of shipments from 1987 to 1995. However, there are
several shortcomings in this database: (1) the database is not complete, especially in the
earlier years. The reasons for this include the difficulties of collecting manifests either at
the border or from different collection offices, the serious lack of compliance by the
generators in Mexico and the transporters in completing and submitting these manifests;
(2) the quantity of waste accounted for by the manifests represents only a small
percentage of the total amount of hazardous waste generated by the maquiladoras.
Mexican environmental regulations permit all or part of the hazardous waste generated to
be stored at a storage facility or stored on-site. Storage may change with changes in
Mexican compliance efforts. The large extent of storage rather than treatment is one
possibility that can explain the low rate of hazardous waste shipped to the US. The
industries can keep hazardous waste in a facility for an unlimited time period, while US
law (under RCRA) limits such storage period to 90 days. A second possibility in
accounting for the low shipment rates to the US is the termination of hazardous waste
from Maquiladoras to recycling plants in Mexico (Mexican law prohibits the importation
of hazardous waste unless it can be recycled or reused). Hazardous waste recycling
facilities are being developed in Mexico, but are currently under-utilized. Finally, a third
possibility is that hazardous waste may also be transported by bypassing the RCRA
tracking system.
In the US, hazardous waste terminates in three possible locations: (1) in a disposal or
landfill facility; (2) in a treatment or recycling facility; and (3) in an incinerator facility
(Figure 3.2). The development of a database capturing regional movements of the
hazardous waste is a central step towards a comprehensive assessment of potential risk by
the Maquiladoras. Moreover, such a development would provide precise information
about the origin, destination, quantities and type of hazardous waste generated. At
present, it is important to develop an approach that estimates the total hazardous waste
generation by the maquiladora and associated shipment patterns.
Shipment Breakdown by Origin
Based on the USEPA data, the number of recorded shipments of hazardous waste from
Mexico to the US increased twenty-two fold, from 80 shipments in 1987 to more than
7,500 shipments in 1995 as shown in Figure 4.6. It is important to note here that all these
shipments originated from six bordering states (i.e., the total waste from Mexico is equal
to the total waste from the six bordering state). These states are: Baja California state
bordering California; Sonora state bordering Arizona; Chihuahua state bordering New
Mexico and Texas; and Coahuila, Nuevo Leon, and Tamaulipas states bordering Texas
(Figure 4.7). The quantity of hazardous waste shipped from Maquiladoras and recorded
by the USEPA database system is shown in Table 4.5. The majority of this waste stream
originated from the state of Tamaulipas and Baja California
The analysis focuses on data pertaining to the years 1992 and 1995 which have the most
updated and complete records of shipments. In 1992, hazardous waste was shipped to
eighteen states in the US from Mexico, and totaled 13,503 tons and 16,684 tons in 1995,
a 19% increase in just 3 years. The 1995 database is still continuously updated as more
reports are still being obtained, therefore this increase in waste is most likely greater than
the conservative 19%. Once again, it is also important to note that this was not the
amount of hazardous waste generated, but rather the actual amount recorded by the
USEPA based on manifest shipments.
Shipment Breakdown by Destination
The USEPA data also permitted the study of the distribution of shipments of hazardous
waste by the six bordering states in Mexico. The analysis assumed that all shipments
from Mexico to the US terminated at a TSD facility in a corresponding US-bordering
state. The analysis of the data proved that this was not the case. As shown in Table 4.4,
eighteen US states received waste from Maquiladoras in the border region. Some of the
receiving states were located in the southeastern part of the US such as Alabama or were
even located in the northeast part of the US such as New Jersey. This pattern indicates
that maquiladora-generated hazardous waste shipments do not impose risk only on the
population and the environment in the border region, but a much larger US region.
In regional transportation studies, the development of a flow matrix for OriginDestination (O-D) pairs in the transportation network is an important analytical first step.
In this research, the origin of hazardous waste has four spatial designations: state, city,
industrial park, and industrial plant, based on scale. Destinations, in turn can be
represented by a state, a city, or a TSD facility. Therefore, one can generate a state-state,
state-city, city-city, or any other combination in the flow matrix. In this section, the
spatial level of flow matrix between the Maquiladoras and TSD facilities in the US were
at the state to state level, as shown in Table 4.6.
The optimal basis for the flow matrix development should be the industrial park
(Mexico)-to-TSD (USA.). In this case, the total hazardous waste generated in each
industrial park can be considered as one (origin) node in the transportation network. It is
important, however, to take into consideration the size of each industrial park. In fact,
size criteria for each transportation node could be implemented (i.e., a node in the
transportation network should represent an industrial park of quarter of a mile).
Figure 4.6 Number of recorded shipments from the Maquiladoras by year.
TABLE 4.5: TOTAL QUANTITIES OF HAZARDOUS WASTE SHIPPED
BY THE MAQUILADORAS TO THE US, 1987-1995
Figure 4.7 Mexico's States and border ports of entry to the US.
Shipment Breakdown by Hazard Class (Type)
The USEPA has classified hazardous waste based on four characteristics that are
inherently hazardous in any substance (5,18). These four criteria are:
1. Ignitability: the characteristic indicative of the hazardous waste capability to cause a
fire during transport, storage, or disposal. Examples of ignitable waste include waste oils
and used solvents.
2. Corrosivity : a second characteristic of hazardousness, as indicated by pH, because
waste with high or low pH can react dangerously with other wastes or cause toxic
contaminates to migrate from particular wastes.
3. Reactivity: This characteristic was chosen because unstable waste can pose an
explosive problem at any stage of the waste management cycle. Examples of reactive
waste include water from TNT operations and used cyanide solvents.
4. Toxicity : The last fourth characteristic was chosen because a toxic waste on contact
with a living organism is capable of killing, injuring, or otherwise impairing that
organism. The inherent risk depends upon the level of exposure and the manner in which
such wastes are handled.
Based on the above four characteristics, EPA has classified hazardous waste into three
categories:
1. Nonspecific source wastes: These are generic wastes, commonly produced by
manufacturing and industrial processes. Examples from this list include spent
halogenated solvents and waste water treatment sludge from electroplating processes.
2. Specific source wastes: These are wastes generated from specifically identified
industries such as wood preserving, petroleum refining, and organic chemical
manufacturing.
3. Commercial chemical products: Such wastes include commercial chemical products or
manufacturing chemical intermediates. Examples include chemicals such as chloroform
and creosote, acids such as sulfuric acid and hydrochloric acid, and pesticides such as
DDT and Kepone.
The data show that most hazardous waste shipped to the US is transported in drums, with
each truckload carrying a number of drums containing hazardous waste. Hazardous waste
was classified into hazard classes as shown in Table 4.7 and as characterized by EPA.
Volume measurements in gallons were converted to tonnage using a waste unit
conversion factor of 8.377 Ib/gallon (19,20). As stated previously, the total number of
hazardous waste shipments amounted to 1,753 shipments in 1992 resulting in an
estimated 12,552 tons of hazardous waste. This averaged to 9.5 tons per shipment of
hazardous waste. Figure 4.8 shows the increase in the number of shipments and amount
of hazardous wastes shipped in tons by year based on manifest-reported waste shipments.
The amount of hazardous waste shipped increased from under a thousand tons in 1987 to
more than 28 thousands tons in 1995 for the six border states.
Figure 4.8 The number of shipments and amount of hazardous waste shipped by
year
TABLE 4.7 HAZARDOUS WASTE SHIPPED TO THE US BY
HAZARD FOR THE SIX MEXICAN BORDER STATES, 1992.
EPA Hazardous
Hazardous Waste Quantitie
TOTAL
Waste Number
GALLONS POUNDS KILOGRAMS TONS YARDS TONS
RCRA WASTE
D001
227156
241425
129834
0
0
1215.5
D002
77732
166438
2250
0
0
411.3
D003
270
0
0
0
0
1.1
D004
0
4169
0
0
0
2.1
D005
399
783
113
0
0
2.2
D006
2736
84033
18601
0
0
74.0
D007
5679
130482
150
0
11
98.1
D008
16702
799155
43717
0
30
542.7
D009
1889
0
0
0
0
7.9
D010
793
0
0
0
0
3.3
D011
6838
22450
0
0
0
39.9
D012
50
0
0
0
0
0.2
D013
55
0
0
0
0
0.2
D018
8177
20860
0
0
0
44.7
D019
0
1617
0
0
0
0.8
D021
275
8140
0
0
0
5.2
D022
0
0
675
0
0
0.7
D029
440
0
0
0
0
1.8
D030
1128
0
0
0
0
4.7
D035
9418
25831
38850
0
0
95.2
D039
5438
7792
0
0
0
26.7
D40
1464
0
300
0
0
6.5
D043
0
101
0
0
0
0.1
D CLASS TOTAL
366636
1513275
234489
0
40
2585.0
F001
175870
53119
49052
0
0
817.3
F002
134210
261132
4672
0
0
697.9
F003
174961
415058
57415
0
0
1003.7
F004
54
0
0
0
0
0.2
F005
124803
193902
45241
0
0
669.6
F006
33617
239669
0
0
0
260.6
F008
715
1866
0
0
0
3.9
F019
0
40199
0
0
0
20.1
F CLASS TOTAL
644230
1204944
156380
0
0
3473.4
U079
133
340
0
0
0
0.7
U220
83
0
0
0
0
0.3
U223
19
0
400
0
0
0.5
U226
0
83
0
0
0
0.0
U239
2338
3200
0
0
0
11.4
U CLASS TOTAL
2572
3623
400
0
0
13.0
NCHW**
6517
107975
1645
0
0
83.1
392,914
0
40
6,155
5,994,017
141,6733
12
2,530
6,368
1,273,497 8,823,833
543,587
12
2,570
12,522
TOTAL RCRA WASTE 1,019,955 2,829,816
NON-RCRA WASTE
TOTAL WASTE
253,542
Chapter 5 - the Gis Database and the Routing Model
Introduction
The objective of this chapter is to present the procedure required for preparing the GIS
database for the risk modeling. The various elements in the risk model are defined
explicated followed by applying the model to an example. The solution of the risk
objective is based on utilizing TransCAD routing procedure based on the shortest path
algorithm by Dijkstra. All steps pertaining to the development of the Arizona-Sonora GIS
database are also discussed in detail.
Arizona-Sonora GIS Database
The Arizona-Sonora GIS consists of three major layers. The first layer depicts the
transportation network which consists of links and nodes. A node is defined as a point of
intersection of two or more links. In some cases, a node could represent an end of a link.
The basic attribute of each node is its location which is determined by the longitude and
latitude coordinates. Links are defined as lines that represent highway segments. The two
basic attributes of each link are its identification number (ID) and length in miles.
The second layer is the census tract layer. This layer is necessary for calculating the
population corresponding to each transportation segment. The Third layer is a point layer
used to locate the maquiladora industries and the TSD facilities.
TIGER/Line™ Files
These files contain information that describe the points, lines, and areas on Census
Bureau Maps. These files are extracts of selected geographic and cartographic data from
the Census Bureau's Census TIGER™ (Topological Integrated Geographic Encoding and
Referencing) System, which is used to support mapping and other geographic activities
of census and sample survey programs (21).
TIGER/Line files are digital data files that contain street and water features, US Census
boundaries (including census blocks, block groups, and tracts), address ranges, ZIP
codes, and other features. While these files contain geographic data, they are not maps or
geographic information systems by themselves. Users must first import or reformat the
data into an applications system or software.
A major disadvantage of the TIGER files is its inaccuracy. The lengths of highway
segments may each have an error of ± 20 ft. However, such error is not a problem in this
research because it deals with a macroscopic (or regional routing) scale. On the other
hand, this error could have an effect on microscopic studies. The GIS database for the
State of Arizona has been built using TIGER files by using the TransCAD TCBuild
procedure.
The TCBuild Database Builder
The TCBuild Database Builder is a powerful tool for building, maintaining, and
exporting the information contained in TransCAD database. TCBuild has the capability
to modify databases and change their structures, optimize, copy, or rename databases,
build a TIGER/Line file into a GIS database, build a GIS database from ASCII data files,
append new data to an existing database, and export databases to ASCII data files.
TCBuild has translation functions that permits the user to automatically convert a
TIGER/Line file into a GIS database. The TIGER translator automatically extracts any
specified set of features or boundaries from a TIGER/Line file and creates a GIS
database. The translator can also extract the TIGER/Line data into ASCII files and create
a build file automatically. The user can modify the build file by adding or deleting data
fields and then reconstructing the database.
Using the TIGER/Line files and TCBuild utilities program, various data layers were
constructed for the Sate of Arizona and several other states. These GIS layers include: the
state boundary, county data, census tracts, transportation patterns, and others. Each layer
represents a different GIS database. TransCAD has the capability of containing up to
twenty databases in one application file. The application file is an ASCII file that has
blocks controlling the GIS database. The two blocks which are required in any
application file are the Boundary Box and Databases blocks. The objective of the
Boundary Box is to specify the range of coordinates that cover an application. The
Database block list the names of database in an application. One could add a Current Box
block to specify the range of coordinates displayed in the initial map for an application
and a Base Map block to specify the name of base map screen image file for the
application.
Using the TCBuild utility program, the transportation database was modified to include
more fields such as speed limits on different road segments, the types of the road
segments, the population within a one mile band width of each segment, the accident
probability field, and other field needed in the risk assessment calculations.
Population Density
The 1990 US census data can be loaded from a CD-ROM using utilities program. Data
were loaded from the CD-ROM onto a Lotus 1-2-3 file. The data contains the total
population of each census tract in Arizona. These data were imported into a TransCAD
GIS database, based on the Federal Information Processing Standard (FIPS) number that
exists in both files, the TIGER files, and the census data files.
Population within the one mile bandwidth of each segment was calculated using the
column aggregate command in TransCAD. This command performs spatial aggregation
of data from one layer (the source layer) to another (the current layer). In this case the
current layer is set to the transportation highway layer. Data is aggregated from the
census tract layer. The program proceeds by creating a buffer zone around each road
segment and calculates the total population in this buffer (Figure 5.1). If the buffer
intersects with more than one area with different population densities, it will add up the
attributes of these areas (i.e., their populations) that is totally or partially contained within
the zone. For areas that are completely contained within the buffer, the actual attribute
values are used. For areas that are partially contained within the buffer, a fraction of the
attribute value is calculated based on the proportion of the contained area within the zone,
to the total area.
Mathematically, the total population is equal to:
a1
TPk
=
a2
*
+
PD1
A1
a3
*
+
PD2
A2
*
PD3
A3
(5.1)
or to generalize:
(5.2)
where:
TPk = total population in the 1 mile buffer zone along a route segment k.
i = an index, representing a zone with a specific population density.
ai = portion of zone i located in the buffer area around the route segment k.
Ai = total area of zone i.
Z = total number of zones.
PDi = the population density in zone i.
As stated in equation (3.25) the total number of persons within the impact area, C(P)k, is
calculated as follows:
C(P)k = 0.25 x x Pd
(from equation 3.25)
where:
C(P)k = total number of persons within the impact area of route segment k.
Pd = population density given in persons per square miles.
This equation, however, considers a point population exposure regardless of the segment
length. For example, assume there are two routes (A and B) having the same length and
passing through the same area (i.e., population along both routes is the same). Route A
consists of one link and route B consists of four links. In reality, both routes have the
same population exposure. However, using equation (3.25) will result in route B having a
population exposure four times that of route A. Overcoming this error can be achieved by
considering the segment length in equation (3.25), therefore,
C(P)k = (0.25 x
x Pd) x Lk (5.3)
where:
Lk = length of route segment k.
Since the population density varies along the route as shown in Figure 5.1, the average
population density will be used. The average population density can be measured be
dividing the total population living in the impact area (TP) by the total area of the impact
zone, i.e.,
TP
Pd =
Areatotal
(5.4)
and,
Areatotal = (Lk x W) + (0.25 x
x W2)
(5.5)
where:
W = band width in miles
Lk = is the length of the route segment k.
As stated earlier in Chapter 2, bandwidth is assumed to equal 1 mile, therefore,
Areatotal = Lk + 0.25
(5.6)
a) A transportation segment passing through different population densities zones.
b) Calculation of population in the impact area.
Figure 5.1 Procedure for calculating the population of the impact area
Sonora GIS Database
The development of a GIS database for the State of Sonora proved to be a very difficult
task, because there isn't any complete GIS database for Mexico. However, there are
ongoing efforts in the US to create a GIS database for the US-Mexico border region.
Examples of these efforts are the University of Utah's research group effort to develop a
comprehensive GIS database for the city of Nogales along both sides of the border (23).
The research group at the University of Texas at Arlington has also developed a GIS
database for the Arlington-Monterrey corridor area.
The state of Sonora's transportation network was developed by digitizing different
transportation maps using the AutoCAD software. These maps were then transferred into
TransCAD database by using the DXF file translator. The DXFXLATE utility program,
which is part of the TransCAD package, converts the geographic data stored in an
AutoCAD DXF (Drawing eXchange Format) file into a TransCAD database. DXF files
contain data that are normally prepared using a CAD system rather than a GIS. These
CAD drawings could have some problems which needed to be considered when
transferring these drawings into a GIS database. For example, CAD drawings may
contain some lines which cross, with no node indicating the intersection point. However,
the DXFXLATE program corrects these problems to some degree, producing point, line,
or area databases from the entries in the source DXF drawing file.
In addition to developing these data, three databases for the city of Nogales, Mexico,
were also developed. These databases included some of the maquiladora plants in
Nogales, the transportation network, and the population densities. However, these
databases were in ARC/INFO GIS database format. These files were then translated into
TransCAD using the ARCXLATE program which is ARC/INFO export file translator. It
has the capability of converting geographic data stored in an ARC/INFO export file into a
TransCAD database. Export files are text files containing data that are prepared using
ARC/INFO and exported using the EXPORT command. ARCXLATE constructs a
TransCAD database with all the attribute data contained in the Export file. The resulting
database has all geographic and attribute data from the original ARC/INFO coverage.
Both ARC/INFO and AutoCAD databases are then combined into one database that
represents a GIS database for the State of Sonora in Mexico. More data were added to the
database including the transportation network in Mexico. Information on the locations of
the Maquiladoras were obtained from maps of different industrial parks available in the
Solunet Guide directory. The Arizona and Sonora databases were merged together into
one GIS database consisting of three spatial layers: transportation, population density,
and Maquiladoras and the location of the TSD facilities.
Optimal Paths in Networks
To find the optimal paths implies finding the optimal path or the shortest "attribute"
routes. Attribute refers to a property that is associated with each link segment in the
transportation network. These attributes can be distance, time, risk, transportation cost,
etc. The general heading for this kind of problems is referred to as "shortest path
problems." There are several methods that deal with this problem, but the most widely
used, and one of the most efficient is the Dijkstra's method (24).
Dijkstra's Algorithm
In this method, the first step is to ensure that there is a distance value associated with
every pair of nodes in the transportation network. This distance will be the arc length or
any arc attribute (i.e., risk, cost) if there is an arc between the nodes (and the shortest arc
length if there are several arcs between a pair of nodes), with zero taken for the distance
from a node to itself, and infinity for the distance between any pair of nodes which are
not linked by an arc.
Arizona-Sonora Flow Matrix
As mentioned earlier in Chapter 4, this research focuses on the Arizona-Sonora area as a
preliminary test case for the entire border region as was discussed in Chapters 1-3, it is
important to develop a hazardous waste flow matrix of the hazardous waste shipments
from the maquiladora plants to the TSD plants in Arizona.
Using the 1992 ADEQ database and the dBase IV software, three flow matrices were
constructed. The first flow matrix, shown in Table 5.1, represents the shipments
originating by in Sonora maquiladora plants and terminating in one of the TSD facilities
in Arizona. It is worth mentioning that all shipments were terminated at the Romic
Chemical Southwest facility in Chandler, Arizona.
The total quantity of hazardous waste shipped from Sonora to Arizona in 1992 was 128
tons. This represents only 13% of the total hazardous waste shipped to Arizona and found
in the 1992 ADEQ database. The quantity of hazardous waste generated in Sonora and
terminating in Arizona is only 13% of the total waste that terminating or passing through
Arizona.
The quantity of hazardous waste that was generated in other Mexican states and
terminating in Arizona is shown in Table 5.2. The total quantity of hazardous waste was
612 tons, which represents 63% of total waste stream. This means the border states do not
necessarily take all the burden of the hazardous waste from the bordering Mexican states.
The waste that was generated form states other than Sonora and which terminated in
Arizona, were four times the quantity of hazardous waste which terminated from Sonora.
Table 5.3 shows the hazardous waste that was generated in Mexico and which passed
through Arizona. The total quantity of this waste was 247 tons which represents
approximately 25% of the total hazardous waste founded in the ADEQ database in 1992.
This portion of the waste streams is excluded from the analysis of the routing and
management scenarios because it is hard to know the entrance and exit points of each
shipment which passed through Arizona. The waste from other states that terminates in
Arizona will be used later to determine the potential risk generated to the population and
the environment in Mexico and in Arizona.
TABLE 5.1 FLOW MATRIX OF HAZARDOUS WASTE ORIGINATING IN
SONORA AND TERMINATING IN ARIZONA (TONS/YEAR)
From
To
TSD
FACILITIES
IN ARIZONA
CHANDLER
STATE
CITY
ORIGIN OF HAZARDOUS WASTE:
ROMIC
IN
MEXICO
MAQUILADORA PLANT IN
MEXICO
SONORA AGUA
PRIETA
ROGERS MAXICANA ROMEX
5.74
UNITRO DE MEXICO S.A. DE C.V.
2.3
CD. OBERGO AUTO CIRCUITOS DE OBREGON
3.92
HERMOSILLO AMP INC.
2.57
NOGALES
WHITNEY BLAKE
0.92
ADMAS RESSEL
1.61
BECTON DICKENSON
2.76
CIRCUITOS MEXICANOS DE
NOGALES
2.2
COMPONENS ASSEMBLY
3.92
GENERAL INSTRUMENT DE
MEXICO
9.81
GROUP CHAMBERLAIN
28.47
HASTA MEXICO
1.15
INDUSTRIALS DE CARSIAS
MECICANAS
3.35
ITT CANNON
2.08
ITT POWER SYSTEMS
7.99
MAGNETICS ELECTRONICAS
8.19
PRESTOLIEEWIRE
1.38
SAMSON SA. DE C.V.
3.23
TEMSA MEXICANA
17.97
THERMAX
5.89
WALBRO DE MEXICO
2.76
WEST CAP
6.68
Risk Analysis of Hazardous Waste Transportation: a Regional Test Case
In this section, a description of the attributes of the GIS database and the steps taken in
preparing the database to determine risk is presented. In addition, a detailed example for
calculating risk using the GIS TransCAD software is also presented.
TABLE 5.2 FLOW MATRIX OF NON-SONORA HAZARDOUS
WASTE THAT TERMINATED IN ARIZONA IN 1992 (TONS/YEAR)
From
STATE IN
MEXICO
TSD FACILITIES
IN ARIZONA
To
CITY
ORIGIN OF
HAZARDOUS
CHANDLER
PHOENIX
ROMIC
CHEMICAL RINCHEM
WASTE COMPANY
WASTE:
MAQUILADORA
PLANT IN MEXICO
BAJA
TIJUANA
CALIFORNIA
10.75
PACIFIC
MAGNETICS DE
MEXICO
16.13
MUEBLES PINOS
BUENOS
17.84
MEXICALI
CHROMIZING S.A.
94.38
TIJUANA
CWM DE MEXICO
SA DE CV
196
AIRESEARCH LOS
ANGELES FACILITY
0.5
CD. JUAREZ CONTROLES DE
PRESION
TORREON
3.92
ELCOM INC.
1.92
FAVESA-FORD-SAN
LORENZO
1.81
FAVESA-FORD S.A.
DE C.V. - OMEGA
2.24
FAVESA-FORD S.A.
DE C.V. - RIO BRAV
4.81
FAVESA-FORD SST
33.15
HONEYWELL
OPTOELECTRONICA
2.63
INDUSTRIAL HASE
S.A. DE C.V.
5.96
NUEVA HASE S.A.
DE C.V.
0.7
PRODUCTO
ELECTRICOS
INTERNACION
COAHHILA
4.38
MATSUSHITA
INDUSTRIAL DE
B.C.
TORRANCE
CHIHUAUA
SMK ELECTRONICS
S.A. DE C.V.
10.63
CONTROLES DE
TEMPERATURA
20.93
PRODUCTOS
MARINE DE
MEXICO
15.9
PRODUCTOS
ELECTRONICOS DE
LAGUNA
160.5
PRODUCTOS
11.75
ELECTRONICOS DE
LAGUNA
TABLE 5.3 FLOW MATRIX OF HAZARDOUS WASTE THAT
TERMINATED IN NON-ARIZONA TSD PLANTS IN 1992 (TONS/YEAR)
From
To
TSD FACILITIES IN OTHER STATES
CALIFORNIA
TEXAS
LOS
AZUZ DEER
ANGELES
PARK
SONOR NOGALES
AGUA
PRIET
FUGGITI
STUDIOS
CORPUS CLIVE SOUTHCHRISTI
SEND
0.5
PROGERS
MEXICANA S.A.
2.8
RIO
VIRSAN S.A. DE
COLORADO C.V.
16.11
VIRCO
MANUFACTURING.
CO.
56.08
VIRCO
MANUFACTURING.
CO.
120
VIRCO
MANUFACTURING.
CO.
B.C.
TIJUANA
UTAH INDIAN
GWM DE MEXICO SA
DE CV
38.53
12.1
The GIS Database: Attributes of the Transportation Network
The major transportation GIS network of the US highway system was available with the
TransCAD software package. The main attributes of the transportation network include:
length of each link, speed limits, median status (i.e., divided, undivided), urban flag (i.e.,
urban, suburban, rural), number of lanes, access (i.e., one way or two ways). To make the
network ready for the risk model, the following attributes we added to each link: the total
population living within a one mile buffer around the link, the average population density
for each link, the average number of people living within 1/2 miles of each link, the
probability of having an accident on each link, based on type of each link, the urban flag
and median status. The data were imported based on Table 3.3 and on the attributes of
each link. A sample of link attributes is presented in Table 5.4.
The population risk was calculated using equation (3.20),
TRPk = P(R)k x C(P)k
and the environmental risk is presented in equation (3.19)
TREk = P(R)k x V(R)Average x A(R)Average x C(E)k
It is to be noted that the three elements in the environmental risk equation V(R)Average,
A(R)Average, and C(E)k are assumed to be constant . Consequently, the multiplication of
these elements is constant (Ec). Thus, equation (3.19) can be presented as follows:
TREk = P(R)k x Ec
(5.7)
Therefore, optimizing the environmental risk will be a function of minimizing the
probability of transportation accident releases (P(R)k).
TABLE 5.4 SAMPLE OF THE GIS DATABASE: LINK
ATTRIBUTES OF THE TRANSPORTATION NETWORK.
(1) ID
(2)
(3) Route 1 (4)
Length
4001900
7.92 CXCOC
Truck
Route
(5) TOTAL
(6) POP. (7)
POPULATION
DENSITY
(Id) *
(8)
C(P)k* TARk x
P(R/A)k*
(9) P(r)k (10)
= (7) x (2) POPULATION
*
RISK *
-
51
6
5
0.36
2.852
51
4001920 8.081 I15
-
56
6
5
0.06
0.484
85
6000338
1.4 S111
-
261
120
94
0.36
0.503
78
6000340
10.9 S111
-
1404
120
94
0.18
1.966
185
6000350
1.25 S86
-
243
119
93
0.18
0.225
190
6000360
0.96 S86
-
208
119
93
0.18
0.172
50
6000370
8.3 S86
-
1090
120
94
0.36
2.987
68
6000380
6.84 S86
-
914
120
94
0.36
2.46
132
6000390
3.91 CX-RIV -
563
120
94
0.36
1.409
16
6000400
2.01 S195
-
335
120
94
0.36
0.725
231
6000410
10.3 S111
-
1327
120
94
0.36
3.695
281
6000440
7.43 I10
Truck
Route
986
120
94
0.06
0.445
621
6000450
8.8 I10
Truck
Route
1150
120
94
0.06
0.527
108
6000460
6.52 I10
Truck
Route
874
120
94
0.06
0.391
182
6000470
1.51 LDILLO -
274
120
94
0.36
0.542
16
6000480
22.1 I10
2743
120
94
0.06
1.323
14
6000490
18.3 LBOX- C
2282
120
94
0.36
6.57
347
6000510
4.64 S86
-
650
120
94
0.18
0.834
42
6000520
5.39 S195
-
740
120
94
0.36
1.94
124
6004752
4.6 I10
Truck
646
120
94
0.06
0.276
2
Truck
Route
Route
6011480
5.61 LHOPE -
766
120
94
0.36
2.018
21
6013600
0.49 LX-CHI -
151
119
93
0.36
0.175
37
32000080
30.8 U93
-
2244
71
56
0.36
11.09
618
32000140
25.3 I15
Truck
Route
1852
71
56
0.06
1.515
483
32000150
32.7 I15
Truck
Route
2334
70
55
0.06
1.961
47
32000160
23.9 S168
-
1757
71
56
0.36
8.616
26
* : (6)
(7)
From equation (5.2)
From equation (4.4)
(8)
From Table (3.3)
(9)
From equation (3.2)
(10)
From equation (3.20)
Sample Risks Calculations in the Border Region
To illustrate the methodology of calculating risks using the GIS database, sample
shipments were chosen from the city of Tijuana in Baja California destined to two
different cities in the US: Azusa in California and Salt Lake City in Utah.
The objective of the routing procedure is to find the minimum risk routes for the
shipments of hazardous waste from Tijuana to Azusa and Salt Lake City. Figure 5.2
shows the minimum population risk route from Tijuana in Mexico to Azusa in California
and data is shown in Table 5.5
As shown in Figures 5.2 and 5.3, the total population risk from Tijuana to Azusa is
5,771x10-6 based on one shipment. The total distance traveled was 133 miles. In the
second case (Tijuana Salt Lake City), the population risk was 4,181x10-6 and the distance
traveled was 746 miles. This case proves that population risk is independent of travel
distance. The distance traveled to Salt Lake City was about five times the distance
traveled to Azusa, but the population risk was less. The reason behind these differences is
the high population density in the vicinity of Azusa. The first 100 miles of travel were
common for both destinations. These 100 miles have a population risk of 2,252x10-6, the
final 33 miles has a population risk of 3,519x10-6 because of the high population density
around this final route. The environmental risk and the transportation cost were both less
in case of Azusa. The cost of hazardous waste shipment to Salt Lake City (SLC) was
about five times that of the cost of shipment to Azusa. In addition, the environmental risk
to SLC was four times the environmental risk to Azusa.
By analyzing the USEPA database, the quantity of hazardous waste shipped to Azusa in
California and SLC in Utah were 1,288 tons and 151 tons in 1992, respectively.
Moreover, the number of shipments were 173 to Azusa and 16 to SLC. Therefore, the
total population risk, total environmental risk, and transportation cost should be
calculated by multiplying each component by the total number of shipments. The total
components of risks and cost will increase substantially.
At this point, several questions surface. For example, What is the total population
exposed to these shipments in the US and in Mexico?, What is the change in the risk and
cost under an anticipated increase of wastes under NAFTA?. Could some management
measures solve or reduce the risk?. In the following Chapter 6, different management
scenarios will be formulated and analyzed so as to answer these questions and some
others.
TABLE 5.5 RISKS AND COST FOR THE EXAMPLE PROBLEM
FROM
TO
POPULATION
ENVIRL. DISTANCE
RISK
RISK * Ec
Persons/Million $/million veh
veh
Miles
Tijuana,
Mexico
Azusa, CA
5,771
12.33
133
Tijuana,
Mexico
Salt Lake
City, UT
4,181
50.11
746
Figure 5.2 Minimum population risk route from Tijuana to Azusa.
Figure 5.3 Minimum risk and transport cost routes from Tijuana to Azusa and Salt
Lake City.
Chapter 6 - Management Scenarios
Introduction
This chapter develops the risk analysis for transporting hazardous waste under current
conditions. This will be followed by assessing the risks under various management
scenarios. The first set of scenarios focuses on changes in the demand pattern and in
amounts of hazardous waste transported. The second set of scenarios addresses the
impacts of routing of hazardous waste under different options that include minimum
transportation cost, minimum risk, risk equity considerations, and/or a combination
thereof.
The objective of formulating and analyzing such scenarios is to help in facilitating and
developing transportation policy objectives for hazardous waste in the US-Mexico border
region. In this chapter, the Sonora-Arizona region (Figure 6.1) serves as a test case for the
above analyses.
Arizona-Sonora Ports of Entry
To establish the routing pattern of cross-country shipments of hazardous waste, the ports
of entry were first identified. There are five ports of entry in the Arizona-Sonora border
region: San Luis Rio Colorado, Luckville, Nogales, Naco, and Douglas. However, trucks
cross-passing is permitted at only three ports: San Luis, Nogales, and Douglas. The San
Luis Port is located on SR 95 just north of the Arizona-Mexico border. In 1987 the
average daily traffic was 109 trucks and the total annual traffic was 33,856 (25). The
Nogales port of entry (Figure 6.2) is located on the southwest side of the town on SR 189.
This port of entry is the major one for carriers. In 1987 the average daily traffic was 321
trucks and the total annual traffic was 100,268 (25). The Douglas port of entry is located
on the south side of the town on SR 666. In 1987 the average daily traffic was 17 trucks
and the total annual traffic was 4,363 (25).
Risk Assessment under Present Conditions
Present conditions refer to the pattern of hazardous waste shipments, the amounts, and
the three ports of entry for the baseline year 1992. The quantity of hazardous waste and
the number of shipments by each maquiladora plant are shown in Table 6.1. The ArizonaSonora transportation network and ports of entry were shown in Figure 6.1.
By using the Dijkstra shortest path algorithm, four objectives were optimized for the
shipment routing of all 22 Maquiladoras that shipped hazardous waste from Sonora to
Arizona in 1992. The four objectives were: 1) minimizing the population risk; 2)
minimizing the release risks; 3) minimizing the population exposure; and 4) minimizing
the transport cost. As mentioned earlier, population risk is calculated as the product of the
probability of release by the potential level of population exposure. As stated previously,
the transport cost is assumed to be $1/mile and the environmental risk is directly
proportional to the release probability.
Figure 6.1 Test Case area: Arizona-Sonora transportation network and ports of
entry.
Figure 6.2 Nogales port of entry
The reason for optimizing the release probability and population exposure independently
is to study several management options to reduce risk. For example, assume that a
network consists of two links A and B. These two links begin and end at the same nodes.
Link A has a population exposure of 1000 and a 0.01 probability of a release. Link B has
a population exposure of 100 and a 0.70 probability of a release. The risks for link A and
link B are 10 and 70, respectively. If the routing decision is based only on risk, route A
will be chosen. However, route A has a large population exposure, which means a high
number of people can be exposed to a release of hazardous material if an accident were to
occur. While route B incurs a high risk, it has a low level of population exposure, i.e., the
occurrence of a release in this case may be higher, but the population exposed to the risk
is relatively small. In the first case (route A), one management option is to prevent
shipments from using this route or reducing the number of shipments. In the case of route
B, the option may be to improve the conditions of the route in order to minimize the
release probability on that route. Selecting a route to reduce risk based on one index alone
may be problematic; it is necessary to look closely at the possible routes when making a
final decision.
TABLE 6.1 PATTERNS OF HAZARDOUS WASTE SHIPMENTS ORIGINATING
IN SONORA AND TERMINATING IN ARIZONA (TONS/YEAR), 1992.
CITY
ID ORIGIN OF HAZARDOUS
NO. WASTE:
MAQUILADORA PLANT IN
MEXICO
NOGALES
HAZARDOUS NUMBER
OF
WASTE(TONS)
SHIPMENTS
1
ADMAS RESSELL
1.61
1
2
BECTON DICKENSON
2.76
1
3
CIRCUITOS MEXICANOS DE
NOGALES
2.2
1
4
COMPONENS ASSEMBLY
3.92
1
5
GENERAL INSTRUMENT DE
MEXICO
9.81
2
6
GROUP CHAMBERLAIN
28.47
3
7
HASTA MEXICO
1.15
1
8
INDUSTRIALS DE CARSIAS
MECICANAS
3.35
1
9
ITT CANNON
2.08
1
10
ITT POWER SYSTEMS
7.99
3
11
MAGNETICS
ELECTRONICAS
8.19
2
12
PRESTOLIEEWIRE
1.38
1
13
SAMSON SA. DE C.V.
3.23
1
14
TEMSA MEXICANA
17.97
2
15
THERMAX
5.89
1
16
WALBRO DE MEXICO
2.76
1
17
WEST CAP
6.68
1
18
ROGERS MAXICANA
ROMEX
5.74
2
19
UNITRO DE MEXICO S.A.
DE C.V.
2.3
1
CD.
OBERGON
20
AUTO CIRCUITOS DE
OBREGON
3.92
1
HERMOSILLO
21
AMP INC.
2.57
1
AGUA
PRIETA
22
WHITNEY BLAKE
0.92
1
The minimum risk routes for the test case are shown in Figure 6.3. These are based on the
locations of the Maquiladoras, the destination locations, population along the routes and
accident-release probabilities. The risk factors are summarized in Table 6.2 based on
minimum population risk routes. Of all the possible routes, the one selected reflects the
least total population risk. The probability of release and the size of the population within
one mile band width of the route were calculated for each possible route by adding the
risk and population attributes of individual links in the transportation system. Notice that
the risk factors are calculated assuming one shipment for each O-D pair. Then, the total
risk factors for each pair is calculated by multiplying the risk factors by the number of
shipments between each pair.
To evaluate different management scenarios and the four optimizing objectives, the risk
factors where added for all Maquiladoras. Thus, these factors represents the total risk for
all 22 Maquiladoras for the year 1992. Under current shipment conditions, the system
total population risk is 1,259,398 x 10-6. This risk represents the minimum total
population risk for all Maquiladoras in Sonora in 1992. For the same set of routes, the
system release probability, the total population, and the annual transport cost are (893 x
10-6), (949,880) persons, and ($12,719), respectively. When minimizing the release
probability alone or population exposure, a different set of routes was generated as shown
in Figure 6.4. The risk data of these two options are presented in Tables 6.3 and 6.4.
Optimizing the release only reduced the total probability to 732 x 10-6 (18% reduction).
However, it increased the population risk to 1,489,870 x 10-6 (17% increase) and the total
population exposed has increased to 1,099,565 persons (16% increase). Optimizing only
the population exposure increased the population risk to 2,456,163 x 10-6 (95% increase)
and increased the release probability to 4,162 (950%).
Optimizing the transport cost (Table 6.5) reduced the cost to $11,946 (6% reduction) and
the release probability to 733 (18% reduction). However, the total risk increased to
1,498,229 x 10-6 (17% increase) and the total population exposed has increased to
1,099,910 persons (16% increase).
The risk is directly proportional to the number of shipments by each Maquiladoras as
shown in Figure 6.5. The number of shipments is multiplied by several factors shown in
Figure 6.5. The total population risk was calculated for each case. This figure proves that
any change in the policy at the border region, will increase the risk substantially.
6.3 Minimum population risk routes.
Figure 6.4 Minimum release routes.
TABLE 6.2 MINIMUM RISKS OF ALL SHIPMENTS ORIGINATING IN
SONORA AND TERMINATING IN ARIZONA IN 1992 UNDER CURRENT
CONDITIONS.
TABLE 6.3 MINIMUM RELEASE PROBABILITY OF ALL SHIPMENTS
ORIGINATING
IN SONORA AND ENDING IN ARIZONA IN 1992 UNDER CURRENT
CONDITIONS.
TABLE 6.4 MINIMUM POPULATION EXPOSURE OF ALL SHIPMENTS
ORIGINATING
IN SONORA AND TERMINATING IN ARIZONA IN 1992 UNDER CURRENT
CONDITIONS.
Figure 6.5 Population risk versus increase in shipments.
TABLE 6.5 MINIMUM TRANSPORT COST OF ALL SHIPMENTS
ORIGINATING IN SONORA AND TERMINATING IN ARIZONA IN 1992
UNDER CURRENT CONDITIONS.
Risk Assessment under Improved Conditions
In the previous section, different risk factors were calculated regardless of the geographic
location of the route. In order to study equity problems, it is important to calculate the
risk factors separately in Sonora and Arizona.
Each route consists of a chain of transport links. For example, the minimum risk route
from maquila 1 (Admas Ressell) in Sonora to Romic in Arizona is listed in Table 6.6.
Although the minimum risk route length from the plant in Nogales to the port of entry at
the border region was 7.5 miles, the population risk was 7,634 x 10-6. The route length in
Arizona was 162 miles and the population risk is 9,637 x 10-6. A 21 fold increase in route
length in Arizona had little effect on the population risk. These data suggest an imbalance
in risk in both countries (i.e., equity problem). To study this problem closely, the data of
each route was then listed in Table 6.7. The system risk factors were calculated for the 22
Maquiladoras in Sonora.
By studying the data closely, the main reason for imbalances in risk between the two
countries is the high population density in Mexico. In general, the Maquiladoras are
located at several industrial parks around different border cities. In the case of Nogales,
the majority of the Maquiladoras are located in the southern part of the city. This means
each hazardous waste shipment drives north across the city toward the border's port-ofentry. Given the high population density in Nogales, this will expose a large number of
people to transport risk.
To search for risk reduction solutions, several management scenarios have been studied.
The scenarios include free flow through the existing 5 ports of entry, restricting flow by
closing ports of entry at Nogales and by establishing recycling plants in Sonora and in
Arizona.
The first scenario was to open all six ports of entry to traffic. All four risk factors were
calculated as in the first case and the summary of the results is listed in Table 6.8. In
general, this scenario minimizes the total risk slightly relative to the current pattern. The
reason for this is the distribution of the set of Maquiladoras in this test case: seventeen
plants are located in Nogales, two in Agua Prieta, two in Hermosillo, and one in Cuidad
Obergon. The 19 plants gravitate toward two specific ports of entries because of distance.
The other three ports are farther from the border. In this case the number of ports used
were four: two in Nogales, one in Agua Prieta, and one in Naco.
TABLE 6.6 SAMPLE CALCULATIONS FOR MINIMUM RISK ROUTE FOR
SHIPMENT
ORIGINATING AT MAQUILA PLANT 1 AND TERMINATING AT ROMIC.
TABLE 6.7 SAMPLE CALCULATIONS FOR MINIMUM RISK ROUTE FOR
SHIPMENT ORIGINATED IN SONORA AND TERMINATED AT
ROMIC.
This scenario, however, is not practical because it is hard to utilize every port of entry for
the shipment of hazardous waste. The ports of entry should be customized and equipped
to handle such shipments. The second scenario was to close the ports of entry at Nogales
and open a port of entry at Naco. The reason for selecting this scenario was to eliminate
shipments through a populated city of Nogales. Only the population risk factor was
calculated in this case. The results are summarized in Table 6.8. In this case the total risk
and the subtotal risks in Sonora and Arizona have increased. This increase in total risk
occurred because of the increase in route length.
The last two scenarios included building a TSD facility in Arizona and in Sonora close to
the border region. Building a TSD facility in Sonora would reduce the risk in Arizona to
zero but would increase risks in Sonora. Building a TSD facility in Arizona increases the
risk in Sonora and reduces the risk in Arizona. In these scenarios the location of the TSD
facilities was arbitrary.
Since the first set of TSD-related scenarios did not reduce the risk, another option was to
find the location for a TSD facility that minimized risk. Notice that the configuration of
this test case substantially affected the results.
Locating a TSD Facility to Minimize Risk
The next step was to study the best location for locating a TSD facility. This was
approached by using a TransCad procedure LOCATE01. This procedure finds the
optimal location for a stationary facility on a network. This procedure solves the one-
median location problem by identifying a location for a facility such that the weighted
average generalized cost (of travel or service) from demand points to the facility is
minimized. The procedure evaluates the weighted average cost from candidate locations
to all demand points using the demand values as the weight and finds the locations with
the minimum weighted average cost.
FIGURE 6.8 SYSTEM ATTRIBUTES FOR DIFFERENT MANAGEMENT SCENARIOS
MANAGEMENT
SCENARIO
I)
II)
OBJECTIVE
RISK
AZ
SO
P(R)k
Total
AZ
C(P)k
SO Total
AZ
SO
Total
AZ
TRANSP.
COST
SO Total
1. MIN. RISK
246,479 888,228 1,134,707 379
390
769 145,185 584,631 729,816 3,867 1,102 4,969
CURRENT
2. MIN.
RELEASE
226,263 1,138,517 1,364,780 292
317
610 141,140 739,011 880,151 3,683 908 4,591
CONDITIONS
3. MIN.
POPULATION
668,340 1,358,420 2,026,760 2,128 1,026 3,154 107,710 459,230 566,940 6,812 2,830 9,641
4. MIN. TRANS..
COST
226,271 1,147,028 1,373,299 293
318
611 141,062 730,837 871,899 3,678 908 4,585
1. MIN. RISK
240,678 874,896 1,115,574 367
378
744 142,841 544,597 687,438 3,806 1,069 4,875
IMPROVED
2. MIN.
RELEASE
226,269 1,138,836 1,365,105 293
316
609 141,182 711,974 853,156 3,885 903 4,788
CONDITIONS
3. MIN.
POPULATION
495,494 1,379,104 1,874,598 1,876 1,152 3,028 99,338 232,950 332,288 6,279 3,200 9,479
[FREE
FLOW]
4. MIN. TRANS..
COST
226,475 1,138,836 1,365,311 299
1. MIN. RISK
313,604 1,412,144 1,725,748 749 1,233 1,982 161,548 275,772 437,320 4,610 3,425 8,035
III) IMPROVED
615 141,154 712,564 853,718 3,680 903 4,583
2. MIN.
RELEASE
CONDITIONS
3. MIN. POPULATION
[RESTR.
FLOW]
4. MIN. TRANS.. COST
1. MIN. RISK
(IV) IMPROVED
316
0
2. MIN.
RELEASE
CONDITIONS
3. MIN. POPULATION
[TSD IN
SONNORA]
4. MIN. TRANS.. COST
1,304,183 1,304,183
0
972
972
0
263,231 263,231
0
2,699 2,699
(V)
1. MIN. RISK
53,088 1,412,144 1,465,232 285 1,233 1,982 12,058 275,772 437,320 793 3,425 8,035
IMPROVED
2. MIN.
RELEASE
CONDITIONS
3. MIN. POPULATION
[TSD IN
ARIZONA]
4. MIN. TRANS.. COST
The first set of locations were in Maricopa County (Figure 6.6). The location with the
minimum population risk was the closest location to the border region (i.e., node 1000 in
Figure 6.6). The second set of locations were 15 sites in Arizona close to the Mexican
border. The best location that minimized the population risk was node 1200 (Figure 6.7).
To find the optimal location on the transportation network, the number of potential sites
increased to include most of the border region nodes and all Maquiladoras sites. The site
that minimized population and release risks was actually one of the Maquiladoras plant
site (Figure 6.8). A sample of the location procedure output to minimize risk in the last
case is shown in Table 6.9. These results means that the best location for a TSD plant
would be a portion close to the industrial plants.
Figur 6.6 Set of TSD locations in Maricopa County
RISK EQUITY PROBLEM IN THE BORDER REGION
This test case suggests that the way to achieve equity is by building new ports of entry
away from the highly populated areas and force all shipments to go through these ports.
However, this scenario could produce an equal amount of risk in both countries but will
not minimize risk. The way to reduce risk is by building new TSD facilities in Sonora
that must be close to waste generation. It is important to mention that recycling plants in
Mexico will produce secondary benefits to both countries, in addition to reducing risk.
For example, the risk was reduced from 1,134,707 x 10-6 to 881,381 x 10-6 (22%
reduction) when using a TSD facility in Nogales.
Figure 6.7 Set of TSD locations at border region
Figure 6.8 The best TSD locations at the border region
TABLE 6.9 SAMPLE REPORT FOR MINIMUM RISK SITE LOCATION.
LOCATE01 Report
==============
Network: C:\TRANSCAD\FINAL1.NET, 453 nodes, 1120 links
Table:C:\TRANSCAD\MAQUILADORAS.TAB, 22 rows
37 locations were previously selected from database.
22 demand points were chosen from table.
Node 4900365 is an optimal 1-median with minimum cost 881381.84.
Location
4900365
4900353
4900369
4900367
4900368
ID Cost
881381.84
881397.29
881417.54
881636.75
881925.67
4900370
882218.28
4900371
882307.98
4900355
882310.34
4900372
882480.74
4900363
883640.83
4900361
883706.40
4900364
883873.81
4900351
887125.06
4900349
887242.25
4900347
887566.48
4900357
919746.27
4900346
925885.19
400088
979960.30
400074
1016239.30
400090
1023932.46
400062
1032858.81
400058
1040911.53
4900380 1085018.85
4900383 1107172.58
4900165 1124539.21
400050
1142988.25
4900299 1186942.17
4900169 1268644.56
4900168 1608161.89
400148
1885711.79
4900170 2157263.34
4900173 3293495.53
4900175 3467166.28
4900377 4225865.82
4900374 4226248.32
4900378 8169897.14
4900399 8333849.78
CHAPTER 7 - CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
The focus of this research has been on hazardous waste shipment activities in the US.Mexico border region. The anticipated growth of US industries in the northern part of
Mexico render this research urgent. As the number of US industries in Mexico continues
to grow, an increase in hazardous materials shipments will occur from the US to Mexico.
This will create a specific pattern of hazardous material shipments that will impose risks
for the population in the US, in general, and the population in the states of Arizona,
California, New Mexico, and Texas, in particular. Growth in the number of hazardous
waste shipments to the US from Mexico is also anticipated.
The US-Mexico border region is a growing source of hazardous waste. The research has
provided information showing a substantial increase in the number of shipments of
hazardous waste over the last three years from Mexico to the US in general, and from
Sonora to Arizona, in particular. Most of these shipments originated from border cities.
The assumption is that these quantities represent only 10-15% of the industries in
Mexico; extrapolating these values will increase the number of shipments of hazardous
waste dramatically.
There is an urgent need to better manage the hazardous waste generated in Mexico by
either building new disposal facilities or recycling plants. The pattern of shipments has
been changing over the last few years with more industries shipping their waste to remote
US areas due to disposal availability. Under NAFTA, Mexican trucks will be able to
travel directly in the US. It is important that truck standards in Mexico be compatible
with these in the US. With the development of a transportation GIS for the US-Mexico
border and the resulting appropriate risk assessment modeling, there will be opportunities
to use these tools in formulating and determining the impacts of different risk
management scenarios on the border region. Although GIS technology has some
disadvantages, it proved to be a powerful tool in transportation routing and risk modeling.
By improving the technology, most of the disadvantages will be eliminated. This will
enable the development of policies dealing with the mitigation and reduction of risks of
transporting hazardous waste in the border region. Building new ports of entry and strict
enforcement of shipments would reduce risk. Improving the existing ports of entry will
probably help in speeding the paper work at the ports of entry, however, it will increase
risk.
Risk equity is a major problem in the border region. However, the people in the US are
incurring some risk during the shipment of hazardous materials to the Maquiladoras.
When addressing the equity problem, all risk pathways should be considered. In addition,
reaching equity will increase risks on both sides. The best solution is to reduce risk in
Sonora by implementing the management options addressed in this research.
The quantities of hazardous waste generated by the maquiladora plants are unknown. The
majority of the waste is not likely being shipped to the US or receiving proper treatment,
recycling, or disposal in Mexico. The problem will become more severe as the
Maquiladora plants grow. Increasing hazardous waste shipments will dramatically
increase risk levels.
Following are some conclusions pertaining to the test case. These conclusions may or
may not be applicable to the whole US-Mexico border region. First, most of the
Maquiladora plants that shipped hazardous waste from Sonora to Arizona were
concentrated in the city of Nogales (17 plants). Most of these Maquiladoras have
recorded one shipment, only six plants have recorded two or three shipments. This
turnout of shipments is very low. However, it is consistent with the turnout of shipments
of all Maquiladoras in general. This pattern reinforces the need for strict and better
management for the compliance of the Maquiladoras with hazardous waste shipment
regulations.
Studying several management scenarios was a successful tool for helping to develop new
policies in the border region. The United States has a plan to upgrade some ports of entry
in the border region to accommodate the anticipated influx of shipments under NAFTA.
This step may reduce the congestion at the ports of entry, but it will increase the risk
factors along the existing shipment routes.
Some management scenarios had little effect on reducing risk. However, this could be
limited to this test case area only. A major reduction of risk occurred when building a
new treatment, storage, or disposal facility in the border region, specifically in Mexico. A
better scenario would be developing more than one TSD facility. A major risk and cost
increase comes from the Maquiladoras located away from the border cities. For example,
in this test case two Maquiladoras were located in Hermosillo and one Cuidad Obergon.
Both cities are located at least 200 miles from the closest port of entry. Most of the
Maquiladoras were located with 10 to 15 miles from the ports of entries. The three
Maquiladora had contributed about the same transportation risk cost as all other
Maquiladoras in the test case (19 plants).
The major concentration of maquiladora plants is at the border cities and border states.
The rest of plants are located in the middle and southern part of Mexico. The USEPA and
ADEQ databases have recorded shipments only from the border states. These
unconsidered plants should be studied closely to find out the way they manage their
hazardous waste.
RECOMMENDATIONS
The northern part of Mexico is home to a large number of US industries. Under NAFTA,
the number of industries will increase dramatically due to economic and legislative
factors. The USEPA should communicate with the Mexican Environmental Agency to
control and regulate the environmental impact of these industries. Some mitigation
options would be to create a recycling plant or landfill in Mexico so as to prevent or
reduce the problem of illegal dumping and cross-boundary shipments. It is important to
develop these plants in the border region as well as in other parts of Mexico where the
Maquiladora plants are concentrated. Although non-border Maquiladoras are less that
20% of the total Maquiladoras, however, they may contribute to the transportation risk
and cost as much as all plants located in the border cities because of the long travel
distances from these plants to the ports of entry at the border region.
Some policy options should be developed by both countries to manage the problem.
Some options include a mandatory educational program to be established to inform the
Maquiladora industries about the benefits of recycling in-plant and reducing waste on
site. In addition, greater control of shipments through the manifest system should be
considered in both countries. The US EPA database lacks a substantial number of
manifests. The main USEPA database contains data from the six bordering states only.
However, 11% of the Maquiladoras are located in other states. This leads to a conclusion
that there are uncertainties in how these Maquiladoras manage and transport their waste.
In addition, only about 15% of the Maquiladoras in the border region have reported
hazardous waste shipments over the last six years. The maximum reporting year was
1992, with 230 Maquiladoras reporting. A cooperation between the USEPA and SEDUE
should be established to control the shipment of hazardous waste in both countries.
ADEQ should put more emphases on the hazardous waste received by Arizona TSD
facilities from foreign countries. Only 144 tons of hazardous waste have been received by
Arizona from all foreign countries as reported by FAR. However, analyzing the ADEQ
data showed that Mexico contributes 740 tons to TSD facilities in Arizona.
The GIS database is a new technology that should be utilized in future risk development.
It should considered as one of tools in helping solve waste management problems.
It is highly recommended that a model for accident rates of hazardous waste
transportation in the border region be developed. In is important to further develop a GIS
database and/or expert system for the transportation routes, TSD facilities, and
Maquiladora industries. The extension of this study to the entire border region is
necessary. An important effort would also be to improve the risk model to include the
development of environmental risk factors.
The following areas could be considered for new research:
1. A study of building new ports of entry at the border region and the degree to which
such facilities would reduce risk level.
2. A study to project the quantities of hazardous waste generated in the future and its
impact on human and environmental risk.
3. More studies are needed to study the impacts of spills as a function of road type and
population density in both Mexico and the US.
REFERENCES
1. D. M. Perry, R. C. Segal, and P. Cicero . The Maquiladora Industry:
Generation, Transportation and Disposal of Hazardous Waste at the CaliforniaBaja California, US-Mexico Border: First Maquiladora Report. Report submitted
to California Department of Health Services, October, 1990.
2. D. M. Perry and D. J. Kloosler. The Maquiladora Industry: Generation,
Transportation and Disposal of Hazardous Waste at the California-Baja
California, US-Mexico Border: Second Maquiladora Report. Report submitted to
Department of Toxic Substances Control, California Environmental Protection
Agency, June, 1992.
3. D. M. Perry, R. Sanchez, W. Glaze, and M. Mazari. Regional Management of
Hazardous Waste: The Maquiladora Industry at the US-Mexico Border.
Environmental Management, Vol. 14, No. 4, pp. 441-450, 1990.
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6. C. A. Wentz. Hazardous Waste Management, McGraw-Hill, Inc., 1989.
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8. S. Ashur, K. D. Pijawka, and M. H. Baaj. Risk Assessments of Transporting
Hazardous Wastes in the USA.-Mexico Border Region. Center for Environmental
Studies, Arizona State University, 1994.
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Hazardous Waste Transportation and Disposal, In Transportation Research
Record 1245, TRB, National Research Council, D.C., 1989, pp. 52-59.
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10. Research and Special Programs Administration, Guidelines for Applying
Criteria to Designate Routes for Transporting Hazardous Materials, Report No.
DOT/RSOA/OHMT-89-02, 1989.
11. Research and Special Programs Administration, Guidelines for Applying
Criteria to Designate Routes for Transporting Hazardous Materials, Report No.
DOT/RSOA/OHMT-89-02, 1989.
12. M. A. Chaparrofarina. Arizona Statewide Network Optimal Network for the
Collection of Waste Tires Using GIS. thesis presented to the Arizona State
University, Tempe, in partial fulfillment of the requirements for the degree of
Master of Science, 1994.
13. W. D. Rowe. NCHRP Report 103: Risk Assessment Processes for Hazardous
Materials Transportation, TRB, National Research Council, Washington D.C.,
1983.
14. E. J. Barber and L. K. Hildebrand. Guidelines for Applying Criteria to
Designate Routes for Transporting Hazardous Materials, Report No. FHWA-IP80-15, Federal Highway Administration, 1980.
15. K. G. Zografos and C. F. Davis. A Multiobjective Programming Approach for
Routing Hazardous Materials, Journal Of Transportation Engineering, Vol. 115,
No. 6, 1989, pp. 661-673.
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Transport. State and Local Issues in Transportation of Hazardous Wastes
Materials: Towards a National Strategy. Proceedings of the National Conference
on Hazardous Materials Transportation, 1990.
17. American Automobile Association, Mexico Travel Book, American
Automobile Association, 1994.
18. United States Environmental Protection Agency. 1993 Hazardous Waste
Report: Instructions and Forms, GPA Form 8700-13 A/B (5-80), 1993.
19. K. D. Pijawka and A. E. Radwan. The Transportation of Hazardous Materials:
Risk Assessment and Hazard Management. Dangerous Properties of Industrial
Materials, Vol. 5, No. 5., 1985, pp. 2-11.
20. A. J. Sosilio. A transportation Geography of Hazardous Materials: Risk
Assessment and Hazard Management in Arizona. Dissertation presented to the
Arizona State University, Tempe, in partial fulfillment of the requirements for the
degree of Doctor of Philosophy, December 1986.
21. G. F. Hepner et al. Geographic Information Systems Development. Technical
Conference, Proceedings of the Southwest Center for Environmental Research
and Policy, 1993.
22. D. K. Smith. Network Optimization Practice: A Computational Guide, Ellis
Horwood Limited, 1982.
23. Bureau of the Census, 1992 TIGER/Line(tm) User Guide, 1992.
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TRB, National Research Council, Washington D.C., 1985, pp. 6-11.
25. J. C. Gentner and G. N. Bays. Arizona Ports of Entry: Master Plan, Motor
Vehicle Division, Arizona Department of Transportation, 1989.
Please contact the principal investigators about this project.
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