Comprehensive indicators of traffic-related premature mortality

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Comprehensive indicators of traffic-

related premature mortalityRaquel Beatriz Jimenez

a& Nicolas Caceres Bronfman

a

aEngineering Science Department, Universidad Andres Bello,

Santiago, ChileVersion of record first published: 25 Jul 2012.

To cite this article:Raquel Beatriz Jimenez & Nicolas Caceres Bronfman (2012): Comprehensive

indicators of traffic-related premature mortality, Journal of Risk Research, 15:9, 1117-1139

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Comprehensive indicators of trafc-related premature mortality

Raquel Beatriz Jimenez* and Nicolas Caceres Bronfman

Engineering Science Department, Universidad Andres Bello, Santiago, Chile

(Received 27 July 2011;nal version received 16 April 2012)

Motor vehicle circulation is associated with multiple social benets; nevertheless,it is widely acknowledged that it also produces a variety of adverse healtheffects, of which the most relevant are associated with mortality from roadaccidents and exposure to atmospheric pollution. Though in Chile these impacts

have been quantied and evaluated independently, no indicators have beendeveloped so far to account for this activitys global impact on public health, orto express the individual impact that can be attributed to each vehicle category.In order to ll this void, the present study aimed at designing and quantifyingindicators that account for the global impact on health that different motorvehicle categories impose on Chilean society. Health impact was quantied asthe number of expected premature deaths caused by road accidents and exposureto atmospheric pollutants. Total premature mortality was understood as the totalannual deaths that occurred as a consequence of road accidents and the exposureto O3 and PM2.5 derived from trafc-related emissions of its precursors. All esti-mations were made considering Chiles Metropolitan Region in 2005 as a basicscenario. Differentiated indicators were obtained for 15 vehicle categories as a

function of different parameters: traveled kilometer, vehicle, and vehicle lifetime.According to our results, when the health impact of trafc accidents andexposure to trafc-related air pollution are considered simultaneously, majordifferences were observed with the indicators traditionally used by regulatorsinvolved in the trafc-related decision-making process. The implications of ourresults on risk management strategies are discussed.

Keywords: trafc-related premature mortality; motorized vehicles; healthimpact; assessment; trafc accidents; exposure to trafc-related air pollution

1. Introduction

Chile has been experiencing accelerated economic growth during the past decades.

A great portion of its commercial activity takes place in the Metropolitan Region

(MR) where Santiago, the capital of Chile, is located. By the year 2005, the popula-

tion of the MR was 6,538,896, equivalent to 41% of the national population (Insti-

tuto Nacional de Estadsticas 2012).

Historically, this region has registered unusually high levels of atmospheric pol-

lution, and although they have decreased considerably in the last few years, there

are frequent incidents where levels of air pollution exceed the air quality standards

established by local authorities. Industrial activity and over one million vehicles cir-

culating in the region about 42% of the national total are the main contributors

*Corresponding author. Email: [email protected]

Journal of Risk Research

Vol. 15, No. 9, October 2012, 11171139

ISSN 1366-9877 print/ISSN 1466-4461 online

2012 Taylor & Francis

http://dx.doi.org/10.1080/13669877.2012.705314

http://www.tandfonline.com


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to this problem. While the increase in the proportion of private vehicles in relation

to the total MR vehicle eet reects a common tendency in developing countries,

poorly functioning public transportation has also been a determinant in the growing

number of private vehicles in recent years. All of the aforementioned factors con-tribute to the worsening of the already serious issues in this region, including trafc

congestion, air pollution, ambient noise levels, and road accidents, among others.

Nearly 40% of the countrys road accidents and nearly 25% of all resulting national

fatalities take place in the MR.1 Despite these gures, Chilean society does not per-

ceive vehicular risks as unacceptable (Bronfman and Cifuentes 2003).

1.1. Impacts on public health from motorized vehicle transit

Motor vehicle circulation causes external impacts that have negative effects on pub-

lic health, the most recognizable of which are road accidents and atmospheric pollu-

tion, followed by lesser-studied impacts such as those related to reduced physical

activity, high ambient noise levels, and the disposal of out-of-use vehicles, amongothers.

Negative health impacts from motorized trafc differ signicantly. For a clear

illustration of this difference, consider trafc accidents, which result in injuries of

varying severity, and exposure to air pollution, which results in cardiovascular and

respiratory diseases. Exposure to both trafc accidents and air pollution has been

associated with mortality, so mortality has been widely used to quantify these

effects. In fact, several studies measuring such impacts in terms of trafc-related

mortality have been published in recent years (Fisher et al. 2002; Knzli et al.

2000; World Heatlh Organization [WHO] 2009).

The development of impact indicators is fundamental for measuring and monitor-ing the effects of motorized transit on public health, especially in high-risk areas

(WHO 2000). For example, in the Netherlands, De Hartog et al. (2010) quantiedthe impact on all-cause mortality derived from the modal shift of 500,000 people

from car to bicycle, by calculating the number of life years gained and lost associ-

ated with increased physical activity, trafc accidents, and air pollution, respectively.

In Chile, the National Environmental Committee (CONAMA) has developed

health impact estimates of exposure to trafc-related air pollution in order to evalu-

ate how the implementation of new regulations benets public health. Similarly, the

National Commission of Trafc Safety (CONASET) annually develops road acci-

dent indicators based on historical records as a quantitative measure of the impact

of trafc accidents. Nevertheless, so far no indicators have been developed in Chile

to account for the global effect that vehicle circulation has on public health, particu-

larly in terms of premature mortality, whose major causes are exposure to air pollu-

tion and road accidents.

1.2. Traf c-related air pollution

As a result of different processes, motor vehicle circulation generates a number of

different pollutants resulting from varying processes, mainly through exhaust gases,

brake and tire wear, and Resuspended Dust (RSD). This results in a complex mix-

ture of pollution being emitted into the atmosphere, including particulate materialand gaseous pollutants such as NOx, SOx, VOCs, and greenhouse gases, among oth-

ers. These primary pollutants are precursors of the secondary formation of ne

1118 R.B. Jimenez and N.C. Bronfman


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particulate material (PM2.5) and ground level ozone (O3) in the atmosphere. There-

fore, the presence of these pollutants has also been associated with vehicle activity

(WHO 2000).

Road trafc contributes signicantly to the high levels of air pollution observedin urban areas around the world. In Chile, the latest emission inventory carried out

by CONAMA for the MR estimated that mobile sources contributed 37% of the

PM10, 35% of the PM2.5, and 73% of the NOx emitted into the atmosphere during

the year 2005 (Escobar 2007).

1.2.1. Emission estimates

Motor vehicle emissions are usually estimated as a function of the distance traveled

in a given time and area, and as a function of an emission factor (EF), as shown in

the following equation:

Ei NAjEFij 1

where Ei is the emitted amount of pollutant i in grams, NAj is the activity level or

the distance in kilometers traveled by a vehicle of the category j in the area and

period under observation, and EFij is the EF of pollutant i for this category, in

grams per kilometer. Both parameters in Equation (1) are inuenced by several fac-

tors. Caserini, Giugliano, and Pastorello (2008) estimated the relation of the NA of

passenger cars and their age according to the type of fuel, using information from

the circulating vehicle stock in Italy during 2004. These authors proposed the fol-

lowing function:

NAaf heba 2

where NA(a)f are the kilometers traveled by a vehicle of age a that uses type ffuel.

EFs for exhaust emission depend directly on circulation speed and fuel

consumption. These factors are generally differentiated according to the emission

abatement technology, quality standards performance, and fuel type. Ntziachristos

and Samaras (2000) developed EFs for different sizes of particulate matter and a

number of gas pollutants, in the frame of the COPERT III2 project, a European

model for vehicular emission estimates.

Exhaust EFs are also affected by usage, age, and poor vehicle maintenance.

The effect of these factors on the emission quantity and its chemical compositioncan be integrated by correcting the EF with damage factors (DF). Ntziachristos and

Samaras (2000) developed damage curves for passenger cars according to

kilometers traveled and circulation speed. In Chile, CONAMA has estimated local

DF based on measurements made in technical revision plants (TRP).

The EFs for RSD traditionally used in literature are found in the US EPAs AP-

42,3 a compilation of EFs for different activities. According to this publication, the

RSD-related EF for evaluations that consider prolonged periods of time is dened

as follows:

FELP k sL2

0;65 W

3

1;5C

" # 1 P

4N

3

Journal of Risk Research 1119


5/24

where, FELP= paved road RSD-related EF, g/vehicle-km; k= particle size-related

constant, g/vehicle-km; sL = silt load, g/m2; W= average weight of categories under

consideration, ton; C= brake and tire wear correction factor, g/vehicle-km; P= days

with rainfall over 0.254 mm during the evaluation period; N= number of days in theevaluation period.

1.2.2. Environmental pollutant concentration

Environmental pollution levels are determined by complex transportation and trans-

formation processes that pollutants go through once they are emitted into the atmo-

sphere. The relationship between the atmospheric concentration of a secondary

pollutant and the emission of its precursors can be estimated using a simple speciat-

ed rollback model (National Research Council 1993). This model assumes that the

concentration of a secondary pollutant x (Cx) exceeding the background concentra-

tion (Cbx) is directly proportionate to the total emission ofy (Ey), an x precursor, in

the area and period of study, shown in the following equation:

CxCbxkxy Ey 4

The proportionality constant kxy can be used as an emission concentration factor

(ECF). Its estimation is based on historical records of atmospheric pollutant mea-

surements, emission inventories, and the chemical constitution of atmospheric pollu-

tion in the area under study. This apparently simple approximation has been applied

in cost-benet assessments for implementation of environmental standards for air

pollution showing good results (CONAMA 2009).

1.3. Health impacts from exposure to traf c-related air pollution

Adverse health effects from exposure to atmospheric pollutants have been inten-

sively studied and analyzed in recent years, establishing that short- and long-term

exposure to atmospheric pollutants constitute a signicant health risk factor (see

studies by Jerrett et al. 2009; Laden et al. 2006; Pope III et al. 2004). The effects

from such exposure vary in severity: from loss of work days, increases in medical

care needs and diseases, to premature death; the most frequently studied effect.

The association between premature mortality and exposure to PM2.5 has been

widely analyzed in epidemiologic literature throughout the last decades. Pope III

et al. (2002) evaluated the relationship between mortality and long-term exposure to

PM2.5, identifying a 4% increase in risk of premature death due to all causes facing

a 10 g/m3 increase in the environmental PM2.5 concentration. No signicant associ-

ation was found between mortality and the coarse fraction of PM10 (PM2.510). In

another study, Cifuentes et al. (2000) estimated the effects of PM2.5 vs. PM2.510and other pollutants have on daily mortality in Santiago, Chile, and found consis-

tent relationships between PM2.5 and the gaseous pollutants CO, NOx y O3. More

specically, it has been determined that combustion particles from mobile sources

in the ne fraction are strongly associated with increased mortality (Cifuentes et al.

2000; Finkelstein, Jerrett, and Sears 2004; Laden et al. 2000).

Numerous studies have also shown negative effects on human health from expo-sure to ground-level ozone. However, only in the last decade could a signicant

relationship be established between mortality mainly because of respiratory



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diseases and chronic and acute exposure to this pollutant (Bell et al. 2004). For

instance, Jerrett et al. (2009) studied the relationship between death by various

causes and chronic exposure to O3. The authors found an increase of 1.029 (95%

CI; 1.0101.048) in the relative risk of death due to respiratory diseases, associatedwith an increase of 10 ppb in environmental O3 concentration.

The increase in the risk of premature death due to exposure to trafc-related air

pollution has been estimated as a function of various parameters. Hoek et al. (2002)

developed risk indicators in accordance with the residential proximity to highly

transited roads, as did Finkelstein, Jerrett, and Sears (2004), Kim et al. (2004) and

Brauer et al. (2003), among others. Biwer and Butler (1999) developed death risk

indicators per traveled kilometer, associated with the emissions of heavy duty

vehicles during transportation of hazardous substances. In London, Kaur and

Nieuwenhuijsen (2009) estimated death risk indicators from personal exposure to

PM2.5 and carbon monoxide in a transport microenvironment for different modes of

transport.

1.4. Estimates of health impacts from exposure to air pollution

In recent years, several studies have been published that measure the impacts of air

pollution from motor vehicles on health, in terms of trafc-related mortality. Knzli

et al. (2000) estimated the impact of exposure to trafc-related air pollution on pub-

lic health in Austria, France, and Switzerland, by calculating the number of deaths

attributable to exposure to trafc-related air pollution in these countries. Their nd-

ings indicate mortality rates of 370 annual premature deaths per million habitants

for Austria, and of 340 for France and Switzerland. Similarly, the impact of traf

c-related air pollution on public health has been estimated for New Zealand by Fisher

et al. (2002), and for the city of Hai Pong in Vietnam by Dhondt et al. (2010).

Generally, estimates of public health impacts of air pollution are derived from

concentration-response (C-R) functions that describe the relative increase in an

observed effect in response to changes in exposure levels for a given population.

C-R functions are obtained from epidemiologic studies that use multivariate

models (Poisson, Cox proportional hazards survival, among others) in order to asso-

ciate environmental pollution levels with mortality records in the population under

investigation, while controlling the inuence of confounding factors, such as humid-

ity or temperature, and additional risk factors, such as smoking and others.

In these models, the expected number of health effects j as a consequence ofexposure to a pollutant i (Ei

j) is estimated as a function of coefcient C-R (ij),

the base rate of incidence of effect j in the affected population (TB0j), and the

affected population (Pobj) when atmospheric concentration of pollutant i changes

by Ci. The functional form of the model most frequently used in the literature is

the log-linear form (US EPA 1999), represented in Equation (5):

E ji e

bj

iCi 1 Pob j TB jo 5

The selection of parameters to be used in the model must be made with special pru-

dence and care. If possible, locally obtained C-R coefcients should be used, giventhat they account for the unique characteristics of both local population and local

pollutant mixture. The use of coefcients obtained from studies developed in other



7/24

countries is considered reasonable when there is no local information available.

Depending on the time frame in which the evaluated effects manifest themselves,

coefcients from cohort studies should be used to estimate chronic effects, and from

time series studies should be used to estimate acute effects.Generally, air pollutants are highly correlated in time and space, as they share

common sources. Therefore, including various pollutants in the analysis of a spe-

cic effect may result in an overestimation of the total effects if the C-R functions

have been determined independently and their effects added later (Knzli et al.

2000). On the other hand, an analysis that considers only one pollutant would

underestimate the total effects, given the already established fact that the total risk

associated with exposure to air pollution increases when more than one pollutant is

considered in the estimation (Cifuentes et al. 2000).

Bearing this in mind, certain pollutants are used as representative indicators of

the mixture of chemical species that constitute air pollution. Given that their effects

contribute consistently and independently to increasing morbidity and mortality

levels observed in the population (Jerrett et al. 2009), this study will consider twopollutants as indicators of trafc-related air pollution: secondary PM2.5 and ground-

level O3.

1.5. Road traf c accidents

Road accidents are a well-known public health problem all over the world. Over

1.2 million people die each year on roads around the world, and between 20 and

50 million suffer nonfatal injuries (WHO 2009). In Chile, road accidents constitute

the main external cause of death, and the rst cause of death among youth (Medina

and Kaempffer 2007). During 2005, a total of 2301 road accident fatalities wereregistered nationwide, of which 30% occurred in the MR.4 The number of road

accident fatalities is expected to continue growing worldwide in the coming years(Jacobs et al. 2000).

Differences in physical attributes of vehicles, such as weight, size, materials,

and height of the center of gravity, to name just a few, make certain vehicle catego-

ries incompatible with others in terms of trafc safety. The presence of one class of

vehicle may become detrimental for another in terms of risk of accidents with seri-

ous consequences (Attewell, McFadden, and Seyer 1999). For example, the heavier

the vehicle involved in a car crash, the lower its passengers risk of death, and the

higher the risk for the passengers of the other vehicle involved (Evans 2004).Generally speaking, these attributes are highly correlated, which makes it difcult

to establish each partys relative responsibility regarding the consequences of the

accident (Evans and Frick 1992).

1.6. Overview

Motorized transportation plays a fundamental role in the economic development

process and in satisfying personal transportation needs. However, it also generates

negative externalities that signicantly impact the populations quality of life,

mainly through atmospheric pollution and road accidents. The current organizationof modern societies and their unsustainable mobility patterns have made these

impacts more and more evident.



8/24

A number of studies have quantied direct and indirect impacts of motor trans-

portation on public health. On one hand, epidemiologic studies have established

indicators that relate the exposure to atmospheric pollutants to an increase in the

incidence of adverse health effects, including premature death. On the other hand,annual indexes of road accident mortality show that this is a manifested public

health problem and one of the major reasons for death due to external causes

worldwide. Although the elaboration of health impact indicators is fundamental in

order to identify, evaluate, and monitor impacts that can be attributed to vehicle cir-

culation, no global indicators have been developed so far to reect the accumulation

of impacts that this activity causes on public health.

Furthermore, no existing indicators express the individual impact of each class

of vehicle on public health and safety simultaneously. For instance, factors such as

type of fuel, age of vehicle, and motor technology, among others, are responsible

for differences in emission quantity and composition. At the same time, differences

in vehicle attributes such as weight, height, and circulation speed inuence accident

risk and the magnitude of its consequences.The main goal of this study was to design and quantify indicators of premature

mortality caused by exposure to atmospheric pollutants and road accidents that

different motor vehicle classes inict on society.

Given that motor vehicle circulation generates a number of pollutants, each of adifferent nature, whose effects are highly correlated, we considered the pollutants

PM2.5 and O3 as indicators of trafc-related air pollution. This is supported by the

fact that the effects of these pollutants independently contribute to increase the

observed levels of morbidity and mortality in the population (Jerrett et al. 2009).

2. Methodology

In this study, we designed indicators to account for the total premature mortality

associated with the circulation of different types of vehicles. Total premature

mortality is understood as the total number of premature deaths that occurred as a

consequence of road accidents and exposure to O3 and PM2.5 derived from the

pollutant emissions from the MRs vehicle eet, in a given year.

Indicators for different vehicle categories will be obtained as a function of

different parameters: per traveled kilometer, vehicle, lifetime of the vehicle, and

category. All estimations will be made with the MR in 2005 as a basic scenario, as

this is the most recent year for which the information required for this study isavailable.

2.1. Vehicle stock characteristics

The vehicles in circulation were described according to year of manufacture,

weight, technology, fuel type, activity level, and number of vehicles per category,

among others. The disaggregation level for each category was determined by the

information requirements needed to assign EF to the different vehicle categories in

the emission estimation process.

The technical characteristics of the vehicle eet were obtained from informationprovided by the MR TRP. The total number of vehicles in circulation during 2005,

by vehicle category, was provided by the National Bureau of Statistics (INE).



9/24

2.2. Premature mortality from exposure to traf c-related air pollution

Health impact from exposure to trafc-related air pollution was estimated as the

number of premature deaths attributable to acute and chronic exposure to O3 and

PM2.5, respectively. For this purpose, Equation (1) will be used, following the

guidelines established in previous studies (Cifuentes et al. 2000; Knzli et al. 2000;Ostro 1996; US EPA 1999).

The PJ coefcients used to estimate the effects of acute and chronic exposure

were obtained from studies performed by Cifuentes et al. (2000) in Chile and by

Pope III et al. (2002) in the US.

The base rate of effects BR0J was obtained from public records, namely by pro-

cessing death certicates from the year 2005 in the MR. Deaths due to all causes

were considered, excluding those in which traumatisms were a secondary cause

(Letters AU of the ICD-10). The affected population is the total MR inhabitants in

2005, obtained from census information.

In order to achieve impact estimates differentiated by category, the selected val-

ues forCi from Equation (5) correspond to the change in ambient concentrations

of pollutants O3 and PM2.5 attributed to each type of vehicle per traveled kilometer.

Each type of vehicles contribution to the atmospheric levels of O3 and PM2.5was estimated based on their precursors emission levels and relationship to the

environmental concentration of the secondary pollutants O3 and PM2.5 observed

during the study period. This was done using the ECF corresponding to the given

area and period of study.


Emissions of O3 and PM2.5 precursors from motor vehicle circulation were esti-mated as a function of their activity level and the EF, using Equation (1).

The distance traveled annually by private passenger cars was estimated using

Equation (2), and the adjustment to this model made by Lepeley and Cifuentes

(1999) in order to achieve a proper representation of the local scenario. For the

remaining vehicle categories, activity levels were obtained from the available litera-

ture, particularly from the contribution of Samaras and Ntziachristos (2008).

In exhaust emission estimations, EF from Ntziachristos and Samaras (2000) was

used, evaluated at 41 and 23 km/h for private passenger cars and public transportation,

respectively.5 For motorcycles, trucks, and rural and interurban buses, the following

circulation speeds were considered: 45, 50, 40, and 55 km/h, respectively. Fuel con-sumption was estimated based on the previously determined vehicular activity level

and the fuel consumption factors provided by Ntziachristos and Samaras (2000). The

sulfur content of the fuel was obtained from information provided by ENAP (Empresa

Nacional del Petrleo). The NOx and VOC EFs for light vehicles were corrected

according to vehicle age, using local DF provided by CONAMA (2007).

RSD EFs for the different vehicle categories were calculated according to Equa-tion (3), using parameters proposed by the US EPA in the AP-42, and meteorologi-

cal information pertinent to the study area.

2.2.2. Pollutant atmospheric concentration

The PM2.5 and O3 atmospheric concentration increment associated with its precur-

sors emission was estimated for each vehicle type using a simple rollback model,



10/24

as described in Equation (4). The ECF used in this study was obtained from Rizzi

(2008) and CONAMA (2009), as detailed in the Tables 1 and 2.

Their authors estimated these ECF specically for the MR, based on information

pertinent to the year 2005.

2.3. Mortality resulting from traf c accidents

The health impact associated with road accidents was estimated as the number of

deaths that occurred in the MR during 2005 as a consequence of this type of acci-

dent, attributable to different vehicle categories.

The total number of deaths from road accidents in the MR during 2005 was

obtained from information from death certicates. Only fatalities with secondary

diagnoses classied under the letter V of the ICD-10 were considered. However,

death certicates do not contain details about characteristics of the accident, nor

about the vehicles implicated, information that is essential to determine the respon-sibility of the fatalities resulting from trafc accidents between the participating

vehicles. In order to explore the contribution of each type of vehicle on total mor-

tality from trafc accidents, data from historical records of road accidents from

CONASET were used. Both the level of involvement in accidents with fatal results

and the magnitude of the impact were estimated.

The characteristics of all accidents that occurred in the area and period of study,

and that resulted in at least one fatality, were analyzed, classifying the accidents

according to the number of vehicle categories involved. In cases where two or more

categories were involved, it was assumed that each participating vehicle carries the

responsibility for all the resulting deaths. In order to attribute the responsibility of

the resulting fatalities to the different vehicle categories, in cases where two or more

categories were involved, it was assumed that each participating vehicle was

responsible for all the resulting deaths. The implications of this assumption, among

Table 1. Emission concentration factor (ECF) for PM2.5.

Primary pollutantsECF

[g/m3] PM2.5/ton

NOx 1.01E04

SOx 2.01E04NH3 1.59E04PM2.5 3.04E03RSD 4.42E05

Source: CONAMA (2009).

Table 2. Emission concentration factor (ECF) for O3.

Primary pollutants

ECF

ppbO3/tonNOx 5.80E04VOC 2.64E04

Source: Rizzi (2008).



11/24

others, are analyzed further on. The percentage of the total number of deaths from

historical records for trafc accidents associated with each category was used to

determine each categorys responsibility in the real mortality levels from road acci-

dents, according to the registry of death certicates.Finally, the total number of deaths per category was distributed across the num-

ber of vehicles in circulation belonging to each category and their annual activity

level, in order to obtain differentiated indicators of mortality from road accidents

per driven km for each category.

3. Results

3.1. Vehicle stock characteristics

Signicant variations were found among data from different sources, especially with

regard to the total number of vehicles. This may be due to the rule that exempts

new vehicles from the otherwise mandatory vehicle inspections and maintenanceduring the rst years of use.6 Furthermore, some drivers have their required vehicle

inspections done outside the region, while others simply evade the process.

The circulating vehicle eet was classied into 15 categories. In order to obtain

vehicle subcategories compatible with the EF used in the emission estimation pro-cess, all categories were disaggregated according to fuel type, emission abatement

technology, and compliance with emission standards. The technological composition

of each category was obtained from TRP-derived information and then projected

over the total number of vehicles per category reported by INE. The results of these

analyses are shown in the rst four columns of Table 3. The total sum of vehicles

in circulation in the area and period of study was 1,068,294, of which 63% were

private passenger cars.

3.2. Mortality from exposure to traf c-related air pollution


Table 3 shows the results of the emission estimation process for O3 and secondary

PM2.5 precursors (primary PM2.5, NOx, SOx, NH3, VOC, and RSD), for each sub-

category. Our results indicate that heavier vehicle categories (buses and trucks) gen-

erate the highest amount of all primary pollutants per traveled kilometer, with two

exceptions: VOC and NH3. The highest VOC contribution per traveled kilometer is

made by motorcycles, followed by gasoline vehicles without a catalytic converter.

The highest contribution of NH3 is made by gasoline vehicles with a catalytic con-

verter. Bus categories generate the highest quantities of SOx.As expected, given their vehicular attributes, heavy trucks emit the highest RSD

quantity per traveled kilometer: almost twice as much as the next category (Urban

Public Transportation Buses, four times higher than the RSD emission attributed to

other trucks and buses, and nine times higher than private passenger cars).

Natural gas vehicles emit neither PM2.5, NH3 nor SOx exhaust, which are

directly related to the use of diesel and gasoline. However, their VOC and NOxemission levels are comparable to those of heavier vehicles, such as Van Type I,

Type 2 Heavy Trucks, or even higher.The results in Table 3 indicate that almost all PM2.5 exhaust emissions can be

attributed to circulation of diesel vehicles. The conventional bus and truck



12/24

Table3.

VehiclestockMR2005disagg

regatedbycategory,emissionabatementtechnologyandfuelty

pe,andpollutantemissioning/km.

Category

Numberof

vehicles

Subca

tegory

Composition

(%)

Annual

km

a

PM

2.5

b

NO

x

SO

x

NH3

VOC

RSD

Privatep

assenger

car

677.773

Privatepassengercarcatalytictype1

c

80

19,052

N

Ad

0.56

0.003

0.07

0.14

0.28

Privatepassengercarnoncatalytic

20

9859

N

A

3.66

0.004

0.002

1.91

0.54

Taxi

26.539

Catalytictaxitype1

96

60,000

N

A

0.48

0.01

0.07

0.17

0.31

Noncatalytictaxi

2

60,000

N

A

2.22

0.01

0.002

2.01

0.31

Natur

algastaxi

3

60,000

N

A

0.40

NA

NA

0.41

0.31

Collectiv

etaxi

14.264

Collectivetaxicatalytictype1

87

60,000

N

A

0.48

0.01

0.07

0.17

0.31

Collectivetaxinoncatalytic

1

60,000

N

A

2.22

0.01

0.002

2.01

0.31

Collectivetaxinaturalgas

12

60,000

N

A

0.40

NA

NA

0.41

0.31

Pickup

150.470

CatalyticpickupType1

70

30,000

N

A

0.39

0.00

0.07

0.21

1.02

Picku

pnoncatalytic

17

30,000

N

A

2.50

0.005

0.002

1.81

1.02

Picku

pdiesel

13

30,000

0.23

1.05

0.01

0.001

0.08

1.02

Jeep

30.775

JeepcatalyticType1

83

30,000

N

A

0.39

0.005

0.07

0.21

1.02

Jeepnoncatalytic

11

30,000

N

A

2.50

0.005

0.002

1.81

1.02

Jeepdiesel

7

30,000

0.23

1.05

0.01

0.001

0.08

1.02

Minibus,

van

65.632

Minib

us,vancatalyticType1

39

30,000

N

A

0.39

0.005

0.07

0.21

1.55

Minib

us,vannoncatalytic

18

30,000

N

A

2.50

0.005

0.002

1.81

1.55

Minib

us,vandiesel

41

30,000

0.23

1.05

0.01

0.001

0.08

1.55

Minib

us,vannaturalgas

2

30,000

N

A

0.40

NA

NA

0.41

1.55

Schoolbus

7.338

Schoo

lbuscatalyticType1

12

30,000

N

A

0.39

0.005

0.07

0.21

1.55

Schoo

lbusnoncatalytic

4

30,000

N

A

2.50

0.005

0.002

1.81

1.55

Diese

lschoolbus

84

30,000

0.23

1.05

0.01

0.001

0.08

1.55

Lighttruck

36.244

Conventionallightdieseltruck

23

55,000

0.25

2.47

0.01

0.003

1.30

5.44

Light

dieseltruckType1

37

55,000

0.16

1.73

0.01

0.003

0.97

5.44

Light

dieseltruckType2

40

55,000

0.10

1.23

0.01

0.003

0.64

5.44

Medium-

sized

truck

16.453

Conventionalmediumdieseltruck

37

46,000

0.53

5.13

0.02

0.003

1.30

13.69

MediumdieseltrucklType1

33

46,000

0.35

3.59

0.02

0.003

0.97

13.69

Mediumdieseltrucktype2

30

46,000

0.21

2.57

0.02

0.003

0.91

13.69

Heavytruck

8.184

Conventionalheavydieseltruck

45

86,000

0.66

11.74

0.03

0.003

1.30

59.03

HeavydieseltruckType1

32

86,000

0.43

6.46

0.03

0.003

0.65

59.03

(Continued)



13/24

Table3.

(Continued).

Category

Numberof

vehicles

Subca

tegory

Composition

(%)

Annual

km

a

PM

2.5

b

NO

x

SO

x

NH3

VOC

RSD

HeavydieseltruckType2

23

86,000

0.16

4.70

0.03

0.003

0.58

59.03

Motorcycle

15.728

Conventionaltwo-strokemotorcycle

35

4500

N

A

0.03

0.002

0.002

8.78

0.01

Conventionalfour-strokemotorcycle

65

4500

N

A

0.15

0.002

0.002

1.17

0.01

Urbanpu

blic

transpo

rtation

bus

7.936

Diese

lurbanbusType3

44

80,000

0.21

6.14

0.04

0.003

0.85

33.76

Diese

lurbanbusType1

25

80,000

0.48

12.28

0.03

0.003

1.30

33.76

Diese

lurbanbusType2

31

80,000

0.30

8.77

0.04

0.003

1.21

33.76

Interurbanbus

4.303

Conventionaldieselinterurbanbus

32

40,000

0.61

8.49

0.02

0.003

1.26

29.41

Diese

linterurbanbusType1

15

40,000

0.25

4.67

0.02

0.003

0.63

29.41

Diese

linterurbanbusType2

51

40,000

0.16

3.40

0.02

0.003

0.57

29.41

Diese

linterurbanbusType3

2

40,000

0.11

2.38

0.02

0.003

0.40

29.41

Ruralpublic

transpo

rtation

bus

4.394

Conventionaldieselruralbus

25

35,000

0.77

11.19

0.02

0.003

1.68

21.43

Diese

lruralbusType1

17

35,000

0.32

6.15

0.02

0.003

0.84

21.43

Diese

lruralbusType2

27

35,000

0.20

4.47

0.02

0.003

0.75

21.43

Diese

lruralbusType3

31

35,000

0.14

3.13

0.02

0.003

0.53

21.43

Privateb

us

2.261

Privatebus

100

25,000

0.32

6.15

0.02

0.003

0.84

21.88

Source:ElaborationbasedonTRP,INE,Lepe

leyandCifuentes(1999),Caserinietal.(2008).

aValuesobtainedfromCaserinietal.(2008),LepeleyandCifuentes(1999),andothers.

bPM2.

5

fro

mexhaustemissions.

cType1:E

UROI;Type2:EUROII;Type3:

EUROIII,orhigher.

dNotapplicable.



14/24

subcategories are the greatest contributors to the emissions of this pollutant, fol-

lowed by the remaining buses and trucks. Estimations obtained for diesel pickups,

jeeps, and vans are comparable with medium-sized trucks and buses EURO I or

higher. Additionally, these categories emission levels of NH3 and VOC are compa-rable to those of natural gas vehicles.

In general, motorcycles show the lowest emission rates for all the pollutants per

kilometer traveled, except for VOC, as mentioned earlier.

3.2.2. Changes in PM2.5 and O3 environmental concentration

Table 4 shows the marginal increase in the environmental concentration of second-

ary pollutants PM2.5 and O3, estimated per vehicle, lifetime, category, and kilometer

traveled for each vehicle category.

Motorized vehicles circulating in the MR contributed a total of 25.5 ppb of O3and 13.8 g/m3 of secondary PM2.5 to the ambient levels of these two pollutants in

2005. The heavy truck category is the greatest contributor of these two pollutants tothe environmental concentration per traveled kilometer, followed by Urban Public

Transportation Buses, and by buses and trucks in general. Inversely, the motorcycle

category presents the lowest PM2.5 rates per traveled kilometer, while the O3 rates

per kilometer traveled obtained for motorcycles are comparable with the light truckcategory rates. The greatest contribution per category is made by private passenger

cars and heavy trucks.

3.2.3. Mortality attributable to exposure to PM2.5 and O3

Indicators of mortality attributable to exposure to O3 and PM2.5 were obtained foreach category as a function of different parameters: traveled kilometers, vehicle,

Table 4. Contributions to O3 and PM2.5 ambient concentration, in ppb and g/m3,

respectively, per traveled kilometer, annual contribution per vehicle and per category.

Categoryappb

O3/kmppb

O3/vehppb

O3

g/m3

PM2.5/kmg/m3

PM2.5/vehg/m3

PM2.5

Heavy truck 5.10E09 4.40E04 3.62 4.90E09 2.30E04 3.45Urban public

transportation bus5.20E09 4.20E04 3.3 3.30E09 2.60E04 2.09

Rural publictransportation bus

3.20E09 1.30E04 0.57 2.80E09 1.10E04 0.49

Interurban bus 3.70E09 1.30E04 0.56 2.60E09 9.10E05 0.39Private bus 3.80E09 9.50E05 0.21 2.60E09 6.40E05 0.15Medium-sized truck 2.50E09 1.20E04 1.9 2.10E09 1.20E04 1.62Light trucks 1.20E09 6.80E05 2.45 8.90E10 2.70E05 1.78School bus 6.40E10 1.90E05 0.14 7.60E10 2.30E05 0.17Minibus, van 7.30E10 2.20E05 1.43 4.60E10 2.10E06 0.91Pickup 6.10E10 1.80E05 2.77 2.30E10 6.90E06 1.03Jeep 4.80E10 1.40E05 0.45 1.70E10 5.00E06 0.16Private passenger car 8.10E10 1.10E05 7.26 1.40E10 2.50E06 1.37Taxi 3.50E10 2.10E05 0.55 7.70E11 4.60E06 0.12

Collective taxi 3.40E10 2.00E05 0.29 7.40E11 4.40E06 0.06Motorcycle 1.10E09 4.90E06 0.02 1.20E11 1.00E06 0.0002

aCategories sorted in descending order of emissions ofg/m3 PM2.5/km.



15/24

and lifetime. The highest magnitudes of indicators of premature mortality from

exposure to both pollutants were from the buses and trucks categories. Results for

all categories are shown in Table 5, in descending order of the number of premature

deaths per traveled kilometer.Our results indicate that motor vehicle circulation was responsible for at least

2590 premature deaths that occurred in the MR during 2005, as a consequence of

Table 5. Indicators of premature mortality from exposure to O3 (acute) and PM2.5 (chronic)per traveled kilometer, annual deaths per vehicle, and per lifetime.

CategoryDeaths/

kmAnnual deaths/

vehicleExpected deathsduring lifetimea

Heavy truck 8.92E07 4.23E02 7.35E01Urban public transportation bus 6.09E07 4.87E02 1.41E+00Interurban bus 5.11E07 2.05E02 5.91E01Rural public transportation bus 4.81E07 1.68E02 4.86E01Private bus 4.74E07 1.19E02 3.43E01Medium-sized truck 3.90E07 2.13E02 3.71E01Light truck 1.64E07 5.12E03 7.84E02School bus 1.37E07 4.10E03 6.28E02Minibus, van 8.59E08 5.01E04 7.68E03Pickup 4.40E08 1.32E03 2.02E02Jeep 3.24E08 9.73E04 1.49E02Private passenger car 3.02E08 5.07E04 9.35E03Taxi 1.56E08 9.36E04 1.72E02Collective taxi 1.51E08 9.04E04 1.67E02Motorcycle 8.78E09 2.13E04 5.23E03

aLifetime values for car (18.4 years), motorcycle (24.5 years), bus (28.9 years), commercial vehicle(15.3 years), and heavy truck (17.4 years) were taken from Samaras and Ntziachristos (2008).

Table 6. Number of cases attributable to exposure to O3 (Acute) and PM2.5 (Chronic) percategory.

Categorya Acute O3 Chronic PM2.5 Total

Heavy truck 22 612 634Urban public transportation bus 20 369 389

Light truck 15 314 329Medium-sized truck 12 285 297Private passenger car 45 241 286Pickup 17 182 199Minibus, van 9 161 170Interurban bus 4 86 90Rural public transportation bus 3 69 72Jeep 3 27 30School bus 1 29 30Private bus 1 26 27Taxi 3 21 24Collective taxi 2 11 13

Motorcycle 0.09 0.03 0.12Total 157 2433 2590

aCategories sorted in descending order of total attributable cases.



16/24

exposure to trafc-related O3 and secondary PM2.5. This gure is equivalent to

8.1% of the total deaths registered in this period, representing a rate of 405 prema-

ture deaths per million habitants. The number of premature deaths per category for

the year 2005 is shown in Table 6. Most of the attributable cases (94%) werecaused by chronic exposure to secondary PM2.5, of which 50% is attributable to

trucks. Similarly, 31 and 28% of premature mortality caused by exposure to O3 can

be attributed to trucks and private passenger cars, respectively. As observed, 13, 11,

and 24% of the total mortality from exposure to trafc-related pollution can be

attributed to light, medium, and heavy trucks, respectively. Motorcycles do not sig-

nicantly contribute to annual mortality caused by exposure to these pollutants.

Table 7. Annual deaths caused by different types of road accidents.

Category

Annual deaths

Simpleaccidentsa

Doubleaccidentsb

Multipleaccidentsc

Totalcategory

(CONASET)dInvolvement

(%)

TotalCategory(Ministry

ofHealth)e

Privatepassenger car

102 48 12 162 29.9 218

Taxi 4 3 0 7 1.2 9Collective taxi 3 1 0 4 0.7 5Pickup 49 39 6 94 17.3 126Jeep 5 5 0 10 1.9 14Minibus, van 8 0 3 11 2.1 15

School bus 2 13 0 15 2.7 20Light truck 24 35 8 67 12.3 90Medium-sized

truck2 5 3 10 1.9 14

Heavy truck 1 0 1 2 0.3 2Motorcycle 10 14 1 25 4.7 34Urban public

transportationbus

66 43 3 112 20.6 150

Rural publictransportation

bus

2 0 0 2 0.4 3

Interurban bus 9 4 2 15 2.7 20Private bus 1 1 5 7 1.2 9Total 288 211 44 543f 100 729

aSimple accidents deaths include run overs, overturns, and crashes that do not involve other categories.bDouble accident deaths include crashes between two categories or between one category and a bicy-cle.cMultiple accident deaths include crashes of more than two categories, considering bicycles.dTotal deaths registered by CONASET. This database contains disaggregated information about eachvehicle categorys involvement in fatal accidents, but does not consider deaths caused by the accidentthat occurred posterior to it.eTotal deaths according to Ministry of Health. This database contains information of the total numberof deaths caused by road accidents, but does not contain information regarding vehicles involvementin accidents causing those deaths.f

This number does not match the total number of annual deaths registered by CONASET (465 deaths).The reason for the difference is that the responsibility for deaths in double accidents (126 deaths) andmultiple accidents (23 deaths) was attributed to each participating vehicle category, resulting in a dou-ble count of the number of fatalities.



17/24

3.3. Mortality from road accidents

Road accidents that occurred in the MR during 2005 were classied according to

the number of vehicle categories involved: simple (one category), double (two cate-

gories), and multiple (three or more categories) accidents. Table 7 shows the distri-

bution of fatalities resulting from traf

c accidents according to the number ofvehicle categories involved, the percentage of each categorys involvement in the

total deaths, and the total number of deaths attributable to each vehicle category.

Out of all the accidents registered by CONASET, 432 resulted in one or more fatal-

ities: 68% occurred as a consequence of a simple accident, 27% from double acci-

dents, and 5% from multiple accidents (see columns 2, 3 and 4 of Table 7).

The fth column of Table 7 shows that private passenger cars generate the

greatest number of deaths followed by the urban public transportation buses, and

pickups and medium-sized truck categories. On the other hand, the heavy truck cat-

egory shows the lowest number of road accident fatalities, followed by the rural

public transportation buses and collective taxi categories.

Analyses of death certicate-based information indicate that 729 deaths had a

road accident-related secondary cause of death in the MR during 2005. This is

equivalent to 2.3% of all deaths and a rate of 114 premature deaths per million

habitants, considering the total population of the MR in 2005.

Table 8 shows indicators of mortality per kilometer traveled, vehicle, and life-

time of each category, in descending order of magnitude of deaths/km. Motorcycles

show the highest rate of deaths per kilometer traveled, followed by urban public

transportation buses, private buses, interurban buses, and school buses. Mortality

indicators per traveled kilometer for school buses were 5 times, 15 times, and 24

times higher than those for private cars, taxis, and heavy trucks, respectively.

3.4. Premature deaths associated with motor vehicle circulation

Our results indicate that at least 3319 premature deaths occurred in the MR during

2005 as a consequence of motor vehicles external impacts on health, of which

Table 8. Indicators of mortality caused by road accidents, per traveled kilometer, annualdeaths per vehicle, and per lifetime.

Categorya Deaths/km Annual deaths/vehicle Deaths/lifetime

Motorcycle 4.80E07 2.16E03 5.31E02Urban public transportation bus 2.36E07 1.89E02 5.46E01Private bus 1.59E07 3.98E03 1.15E01Interurban bus 1.33E07 4.55E03 1.32E01School bus 9.09E08 2.73E03 4.18E02Light truck 4.51E08 2.48E03 3.81E02Pickup 2.79E08 8.37E04 1.28E02Private passenger car 1.87E08 3.22E04 5.93E03Medium-sized truck 1.85E08 8.51E04 1.30E02Rural public transportation bus 1.71E08 6.97E04 2.01E02Jeep 1.52E08 4.55E04 6.97E03Minibus, van 7.62E09 2.29E04 3.50E03Collective taxi 5.84E09 3.51E04 6.46E03

Taxi 5.65E09 3.39E04 6.25E03Heavy truck 2.84E09 2.44E04 4.25E03

aCategories sorted in descending order of deaths/km indicators.



18/24

22% is due to road accidents and 78% to the exposure to pollutants generated by

motor vehicle circulation. This corresponds to 10.4% of all mortality registered in

the MR in this period, and a rate of 520 premature deaths per million habitants.

Table 9 shows global indicators of premature mortality and the total fatalities

estimated for all categories. The heavy truck, urban public transportation bus, and

private passenger car categories are responsible for 19, 16, and 15% of the esti-mated total mortality that can be attributed to motor vehicle trafc, respectively.

Heavy trucks and urban public transportation buses present the highest indica-

tors per kilometer traveled. The impact magnitudes of these two categories are con-

gured quite differently: urban transportation buses indicator of total mortality is

formed almost evenly between road accidents and pollutant exposure, while mortal-

ity attributed to heavy trucks is dominated by pollutant exposure. The opposite

occurs with motorcycles: 98% of the global indicator of premature deaths per kilo-

meter traveled is determined by road accidents, and only 2% to pollutant exposure.

The motorcycle category presents high indicators of premature deaths per kilo-

meter traveled in comparison to other categories, even compared to indicatorsobtained for rural public transportation buses and medium-sized trucks. For this cat-

egory, the estimated premature deaths indicator per traveled kilometer is 7, 10, and

23 times higher than the estimation for pickups, private passenger cars and taxis,

respectively.

The results obtained for taxi and collective taxi categories were the lowest. For

these categories, the indicators per traveled kilometer are 11, 10, and 4 times lower

than the estimated indicators for school buses, pickups and private passenger cars,

respectively.

4. Discussion

This study presents two main results. First, a set of comprehensive indicators of

trafc-related impacts on public health was developed, in order to yield complete

Table 9. Indicators of premature mortality for MR 2005 associated to motor vehiclecirculation, per traveled kilometer, annual deaths per vehicle, per lifetime, and per category.

CategoryaPrematuredeaths/km

Annual prematuredeaths/vehicle

Deaths/lifetime

Annual prematuredeaths

Heavy trucks 8.94E07 4.25E02 0.74 636Urban public transportation bus 8.45E07 6.76E02 1.95 539Private bus 6.34E07 1.58E02 0.46 36Interurban bus 6.14E07 2.14E02 0.62 92Rural public transportation bus 5.28E07 2.12E02 0.61 93Motorcycle 4.89E07 2.37E03 0.06 34Medium-sized truck 4.09E07 2.22E02 0.39 311School bus 2.27E07 6.82E03 0.10 50Light truck 2.09E07 7.60E03 0.12 419Minibus, van 9.36E08 7.29E04 0.01 185Pickup 7.19E08 2.16E03 0.03 325Private passenger car 4.89E08 8.29E04 0.02 503

Jeep 4.76E08 1.43E03 0.02 44Taxi 2.12E08 1.27E03 0.02 34Collective taxi 2.09E08 1.25E03 0.02 18

aCategories sorted in descending order of premature deaths/km indicators.



19/24

information about the total impact on premature mortality resulting from motorized

trafc. Second, estimates of total impact on premature mortality were obtained for

each vehicle category, as a function of different parameters (premature deaths per

driven kilometer, year, and lifetime).Traditionally, trafc-related mortality is associated only to direct mortality, i.e.

death as a consequence of trafc accidents, where the relationship between cause

and effect is clearly evident and straightforward. However, the extent to which

motor vehicles inuence premature mortality is much greater than their involve-

ment in trafc accidents, and when additional impacts from this activity are

considered, the real impact of road trafc on mortality is signicantly larger.

One of the major implications of this common misinterpretation is that both

community and regulators base their decisions on incomplete information, and

therefore, it would be expected that results from such decision-making processes

be biased.

For example, in Chiles MR, trafc accidents were responsible for a mortality

rate of 114 annual deaths per million inhabitants during 2005, equivalent to 2.3%of all-cause mortality registered in this period. According to our results, when the

two main factors that inuence trafc-related mortality are considered simulta-

neously, the rate of annual deaths per million inhabitants increases to 520, and the

equivalent percentage of the total deaths registered in the same area and time periodincreases to 10.4%.

The results of this study indicate that the public transportation buses generate

the greatest health impact on MRs population in terms of annual premature deaths

per vehicle and premature deaths during a vehicles lifetime. However, the number

of transported passengers was not considered within this study. If this variable were

integrated in the analysis, the numbers would show that public transportation busesare signicantly safer and less pollutant, which makes them the most efcient

means for transporting passengers. While it is essential that authorities with compe-

tence in the transport sector strengthen the participation of public transport in the

MRs transportation matrix and encourage the public to use this means of transpor-

tation, equally important is the need to adopt measures to reduce this activitys neg-

ative impacts on society.

The MR health and environment authorities have assessed the implementation

of different measures in the transport sector, with the aim of decreasing its contri-

bution to the alarming pollution levels observed in the region and reducing their

impact on public health. Also, various governmental organisms make constantefforts to reinforce road safety plans and control vehicles in circulation, in order

to reduce the risks of accidents. However, the adoption of these measures alone

and separately is not enough to deal with the expected impacts of the regions

accelerated growth of vehicles in circulation: coordinated and integrated multisec-

tor action is essential, including public participation throughout the decision-

making process.

The comprehensive indicators developed in this study are a useful tool which

can support the complex process of deciding between transportation modes by

delivering complete disaggregated information of premature mortality attributable to

different types of vehicle.

Multiple impacts and benets of each means of transportation must be consid-ered when making such decisions, because of the multiple trade-offs existing

between road safety, a clean environment, comfort, and the satisfaction of personal



20/24

needs. For example, the change from gas vehicles into hybrid electric vehicles is a

cost-effective measure for the society, in terms of emission reduction. On the other

hand, these vehicles are generally smaller and lighter, and therefore in disadvantage

when facing bigger and heavier vehicles in an accident, given that death and seriousinjury risks are higher for passengers in the lighter vehicle involved in the accident.

Another example is the case of motorcycles: using this type of vehicle has several

benets for the user, mainly linked to low operational cost and avoidance of trafc

congestion. Yet, motorcycles present the highest death risk due to accidents. Our

indicators allow the inclusion of public health requirements when such decisions

are being made.

4.1. Implications for risk communication

The question of how best to compare and communicate information regarding

health impacts from motor trafc, once quantied, is a complicated issue inherentto health impact assessments. Moreover, peoples perceptions, worries, and interests

regarding transportation risk management and the need for regulation must be taken

into account if successful communication strategies are to be implemented.

The objective of risks communication is to provide relevant information with

which the public can make responsible decisions concerning the risks that they face

as individuals, and as society as a whole. Adequate communication of the magni-

tude of the impact that vehicle circulation imposes on certain dimensions of public

health is essential. In this sense, the indicators designed and quantied in this study

represent an important contribution to the mixture of stakeholders participating in

the transport sector, by providing adequate understanding of the global socialimpact that the circulation of different vehicle categories have on public health.

Moreover, indicators based on local information provide more useful results, by

reecting the current situation and tendencies of vehicle trafc in Chiles MR, and

by reducing the bias from transferring results obtained elsewhere.

At an individual level, this information is especially useful for heightening

awareness of the importance of individual actions contributing to these impacts,

encouraging informed, efcient, and effective civic participation in the process of

public policy development and responsible decision-making regarding changes in

lifestyle or motor vehicle purchase options.

From the regulators perspective, the results of this study are a useful tool with

which to more efciently confront the complex transportation-related decision-making process. This will allow for the optimization of resources invested in

implementing public policies, minimization of the potential conicts caused by dif-

fering interests, and recognition of the multiple trade-offs between economic

development and health.

Our main indicators were structured in such a way to quantify the total impact

of trafc on mortality as annual deaths per million habitants. By expressing the

impact of transportation using this common measure of risk, comparisons are estab-

lished between risks associated with different technologies and industrial sectors,

such as forestry, manufacturing, chemical, agriculture, mining, and energy genera-

tion, among others. This will facilitate the publics understanding of the impact oftheir personal preferences and actions.



21/24

4.2. Further considerations

As discussed previously, negative health impacts from exposure to air pollution and

trafc accidents differ signicantly. Nevertheless, exposure to both environmental

risk factors has been consistently associated with mortality, so we quantied the

potential impact on health from motorized vehicles as a whole, in terms of total pre-mature mortality by using this specic outcome as a common metric. This logic

assumption relies on the fact that a death is a death regardless of its cause, and

therefore can be considered equal in terms of its health and social consequences.

From a different perspective, the methodology used to quantify the number of

deaths caused by road accidents is very different from that used for estimating the

number of premature deaths caused by exposure to air pollution. Road accident-

related deaths are caused directly by the accidents impact on the person and are

considered a specic event (count data), while pollutant exposure acts indirectly

by increasing the risk of illness or pathological conditions that may lead to prema-

ture death, i.e. death is the delimiter of survival time or life expectancy (Knzli

2002).

Age structure of victims affected by these two causes is an important issue in

the comparability of risk from exposure to air pollution and trafc accidents. In

general, associations between exposure to air pollution and premature mortality are

more consistent for children (Kim et al. 2004; Loomis et al. 1999) and the elderly

(Pope III, Ezzati, and Dockery 2009), while road accidents are a major global cause

of death for young people between 15 and 44 years old (WHO 2009). The implica-

tions of this are especially signicant when calculating the economic valuation of

these deaths based on per capita income, according to life expectancy reduction and

its subsequent loss in national productivity. This is explained by the fact that the

attribution of similar economic benets to society as a whole derived from theavoidance of air pollution deaths, with a life expectancy reduction in the order of

months, as opposed to a death by trafc accident, where the life expectancy reduc-

tion is in the order of decades, is not reasonable.

Individuals willingness to pay for reductions in a given risk or to prevent a

fatality caused by it is a different approach widely used to estimate the economic

value of a fatality, which is also affected by differences in the at-risk context of

deaths from exposure to air pollution and trafc accidents. This method relies on

the fact that members of the public not only stand to benet from, say, improved

public health, but also ultimately pay for it either direct or indirectly, and therefore

social decisions are to reect the rate at which members of the society are willingto trade health benets for other desirable benets (Sommer et al. 1999). Public per-

ception of risks from air pollution and trafc accidents have a strong inuence on

the monetary value they are willing to pay in order to prevent undesirable effects

from these causes. More specically, WTP to reduce air pollution risk is inuenced

by low degrees of dread, severity, controllability, and personal exposure, while

WTP to reduce trafc accident risk is inuenced by perceived immediacy of its

occurrence (Vassanadumrongdee and Matsuoka 2005).

Beyond the real impact derived from air pollution and trafc accidents, peoples

perceptions of the risks they face from these hazards are determined by attributes

such as the degree of voluntariness of exposure, controllability, familiarity, dreadful-

ness, and immediacy of the consequences, among other attributes of risks. In

contrast to environmental hazards (i.e. air pollution), hazards directly related to



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transportation (i.e. trafc accidents) are perceived by the Chilean population as rela-

tively not dreadful, known, and with a small personal effect. Motor vehicles and

public transportation are signicantly associated with high personal and social bene-

ts, according to Bronfman and Cifuentes (2003).Another important difference lies in the levels of uncertainty inherent to the

assessment process of deaths by these two causes. An important part of the uncer-

tainty concerning the assessment of air pollution-related deaths is determined by the

uncertainty in the estimation of C-R functions, the differences in exposure at indi-

vidual levels, variability in the population, and the various assumptions used in the

calculations, among others (Knzli 2002). On the contrary, the number of accident

related-deaths is obtained based on historical records and death certicates, so

uncertainty associated to this process comes from databases, errors, and biased

information.

4.3. Limitations of the study

During the development of this study, serious problems were detected concerning

the details and quality of the information on vehicles in circulation and road acci-

dent characteristics. A signicant difculty emerged from the lack of a methodology

to attribute the responsibility of the accident victims to each of the participating

vehicles that would integrate both the effects of the vehicles attributes and the dri-

vers attitude on accident risk and death risk. The use of ECF when estimating the

increase in environmental concentration (especially that of O3) instead of emission

and dispersion models, is also a limitation that detracts from the precision of the

obtained results.

In order to tackle these difculties, various assumptions were adopted, whichalong with the uncertainty inherent to the different models used in this study, add

uncertainty to the assessments made in this work. However, the objective in per-

forming this kind of modeling is to develop tools to aid the decision-making pro-

cess, and therefore aims at identifying relations and tendencies or an order of

magnitude of the problem, rather than exactgures.

Acknowledgments

The authors would like to acknowledge the nancial support of Chiles CONICYT through

the National Fund for Scienti

c and Technological Research (FONDECYT) for havingpartially funded this work through Regular Fund N 1090577.

Notes

1. The information source is the National Committee of Transit Security (CONASET) ofthe Transport and Telecommunications Ministry.

2. Computer program to calculate emissions from road transport, 3rd version.3. Available at http://www.epa.gov/ttn/chief/ap42/ch13/nal/c13s0201.pdf.4. Information from the Ministry of Health.5. Average speeds are obtained from the starting pointdestination survey of traveling in

Great Santiago 2006, performed by the Ministry of Transportation and Telecommunica-

tion (2008).6. This is due to the assumption that emission abatement systems would maintain their ef-ciency during the rst years of use.

http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdfhttp://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdfhttp://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdfhttp://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf


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Comprehensive indicators of traffic-related premature mortality

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