Comparison of pedestrians’ particulate matter inhalation
for different routes in urban centers
Inês Dias do Vale
Thesis to obtain the Master of Science Degree in
Environmental Engineering
Supervisors: Doutora Ana Leal de Serpa Vasconcelos
Doutor Gonçalo Nuno de Oliveira Duarte
Examination Comittee
Chairperson: Professor Doutor Ramiro Joaquim de Jesus Neves
Supervisor: Doutora Ana Leal Serpa de Vasconcelos
Member of the Comettee: Professor Doutor Filipe Manuel Mercier Vilaça e Moura
June 2014
Cover picture credits: © 2014 Rhythm Engineering
Acknowledgements
I would like to thank Doutora Ana Vasconcelos and Doutor Gonçalo Duarte for their
knowledge and expertise and for the constant and total support along the elaboration of this thesis.
I would like to thank also all the investigators from DTEA, especially: Eng. Marta Faria and
Doutor Gonçalo Gonçalves for sharing their knowledge and incentive. Also to Daniel Guimarães for
the generous company during the field work.
Furthermore, the researchers from the FMH, namely Prof. Dr. Fernando Pereira, Prof. Dr.
Helena Santa-Clara and Xavier Melo, for the possibility of utilizing their laboratory and apparatus and
also for sharing their know-how during the laboratory measurements.
Moreover, a special gratitude to all my friends and family who followed this process and were
by my side daily. To Julie Mundell, for her generosity and long distance support.
At last but not least my gratitude to Frederico who patiently supported me during all the
months of elaboration of the thesis, especially on the hardest days of work.
i
Resumo
O objectivo deste trabalho é desenvolver uma metodologia que permita comparar a inalação
de partículas (PM) por peões, para diferentes caminhos para o mesmo par origem-destino (OD), em
centros urbanos, recorrendo a métodos experimentais.
Três caminhos com diferentes características (distância, topografia e tipos de via) foram
comparados relativamente à inalação de PM, usando um laboratório portátil e metodologias
adequadas para quantificar a concentração de PM, taxa de ventilação pulmonar (VE) e tempo de
exposição. As concentrações de PM10, PM2.5 e PM1 e frequência cardíaca (HR) foram medidas ao
longo dos três caminhos recorrendo a um laboratório portátil, contudo os valores de VE foram obtidos
através da HR, cuja correlação provém de ensaios laboratoriais.
Os resultados indicam que o caminho mais curto (mas com mais volume de tráfego)
apresentou valores médios até 30% superiores de inalação de PM10 comparado com os outros
caminhos alternativos. Para PM2.5 e PM1, existe maior variação, sendo que os caminhos alternativos
apresentaram maiores valores de concentração média e de inalação, dependendo da medição. Dois
métodos de estimação de PM inaladas foram utilizados para comparação com os valores obtidos pelo
método experimental. Os valores estimados de PM inaladas foram até 85 % inferiores aos valores do
método experimental e nem sempre indicaram o caminho que proporciona o menor valor de inalação
de PM.
A metodologia desenvolvida provou ser uma boa ferramenta para apoiar o planeamento
urbano, ajudando a definir indicadores que caracterizam diferentes caminhos e alternativas,
nomeadamente medidas de redução de inalação de partículas em percursos urbanos.
Palavras-chave: inalação, peões, partículas, taxa de ventilação.
ii
Abstract
The aim of this thesis is to develop a method that allows a comparison of the total particulate
matter (PM) inhalation for pedestrians walking along different routes with the same origin-destination
pair (OD) in urban centers, based on experimental measurements.
Three routes with different characteristics (distance, topography and type of road) were
compared in terms of PM, using a portable laboratory and specific methodologies to quantify the
concentration of PM in the atmosphere, minute pulmonary ventilation rate (VE) and exposure duration.
The concentrations of PM10, PM2.5 and PM1 were measured along three routes using a portable
laboratory, although the VE values were obtained through the heart rate (HR), both correlated in
laboratory environment.
According to the results, the shortest route (which is also the one with higher traffic flow)
presented average values up to 30% higher PM10 inhalation compared to the alternative routes. For
PM2.5 and PM1 the alternative routes presented the highest average concentration and inhalation,
depending on the measurements. Two methods to estimate the PM inhalation were used for
comparison with the values obtained from the experimental method. The obtained values of estimated
PM inhalation were until 85% lower than the results of the experimental method and did not always
predicted the route that provides the lower PM inhalation value.
The methodology developed in this thesis is a good tool to support city planning through the
identification of indicators that characterize different routes and alternatives, namely policies to reduce
PM inhalation along urban corridors.
Keywords: inhalation, pedestrians, particulate matter, minute ventilation rate.
iii
Table of contents
Acknowledgements ............................................................................................................................. i
Resumo............................................................................................................................................... ii
Abstract ............................................................................................................................................. iii
Table of contents ............................................................................................................................... iv
List of figures ...................................................................................................................................... v
List of abbreviations ........................................................................................................................ viii
Nomenclature .................................................................................................................................. viii
1
Introduction .............................................................................................................................. 1
1.1
Motivation ................................................................................................................................... 1
1.2
Objectives .................................................................................................................................... 5
1.3
Structure of the thesis ................................................................................................................. 5
2
Particulate Matter - Background ............................................................................................... 6
2.1
Composition, formation and classification of particulate matter ............................................... 6
2.2
Legal framework and evolution of the PM10 and PM2.5 emissions’ level .................................. 11
3
State of the art ........................................................................................................................ 15
3.1
Factors that influence personal exposure concentrations in the urban transport
microenvironment ............................................................................................................................................ 15
3.2
Comparison of different routes according to particulate matter exposure .............................. 19
3.3
Methods used for measuring particulate matter concentration .............................................. 23
4
Methodology and Results ........................................................................................................ 25
4.1
Average speed for different slopes ........................................................................................... 26
4.2
Physiologic data (ergospirometry) ............................................................................................ 29
4.3
Fieldwork using a portable laboratory ...................................................................................... 34
4.3.1
Case study ........................................................................................................................... 37
4.3.2
Implementation of the field work ...................................................................................... 40
4.3.3
Particulate matter concentration ....................................................................................... 46
4.3.4
The influence of singularities on the particulate matter concentration ............................ 48
4.3.5
Particulate matter inhalation ............................................................................................. 50
4.4
Alternative numerical analysis .................................................................................................. 55
4.4.1
Constant minute ventilation rate ....................................................................................... 55
4.4.2
Estimated particulate matter data ..................................................................................... 59
4.5
5
Mitigation of pedestrians’ exposure ......................................................................................... 61
Conclusions and Discussion ..................................................................................................... 65
5.1
Future Research ........................................................................................................................ 67
References ........................................................................................................................................ 70
Annexes ............................................................................................................................................ 77
iv
List of Figures
Figure 1 Percentage of urban population exposed to harmful levels of air pollution in 2011, according
to EU limit/target values (left picture) and WHO guidelines (right picture) (EEA, 2013). ........................ 2
Figure 2 Sector contributions of emissions of primarily particulate matter in 2010 from anthropogenic
sources (for EEA 32-member countries) (EEA, 2012). ........................................................................... 2
Figure 3 Sources of particulate matter (Kelly and Fussell, 2012) ........................................................... 6
Figure 4 Sources and process of transportation of particles until impacting human health (EEA, 2013) 8
Figure 5 Formations steps of PM (Greenship, 2014). ............................................................................. 9
Figure 6 Schematic representation of diesel particulate matter (PM) formed during combustion of
atomized fuel droplets. The resulting carbon cores agglomerate and adsorb species from the gas
phase (Twigg and Phillips, 2009). ........................................................................................................... 9
Figure 7 Human respiratory system and particles dissemination according to the aerodynamic
diameter range while a person is exposed to a concentration of particles in the atmosphere. Source:
Ecoteck Pty Ltd. ..................................................................................................................................... 11
Figure 8 Emissions of primary PM2.5 and PM10 particulate matter from anthropogenic sources (EU 32).
(EEA, 2012). .......................................................................................................................................... 13
Figure 9 Percentage of urban population exposed to air pollution exceeding EU air quality standard. 14
Figure 10
Percentage of urban population exposed to air pollution exceeding WHO air quality
guidelines (EEA, 2013) .......................................................................................................................... 14
Figure 11 Air flow pattern in a canyon street. Source: A. Srivastava et al., 2011. National
Environmental Engineering Research Institute, India. .......................................................................... 19
Figure 12 Contribution of commute to total daily UFP number exposure. ............................................ 20
Figure 13 Schematic summary of the work steps followed (Correspondent section of the thesis in
brackets). ............................................................................................................................................... 26
Figure 14 Frequency of segments and the correspondent rounded slope. .......................................... 27
Figure 15 Distance of the segments and their slopes. .......................................................................... 28
Figure 16 Average speed and slope. .................................................................................................... 28
Figure 17 Average slopes for the volunteers and correspondent speed. ............................................. 29
Figure 18 Laboratory environment, during the measurements. ............................................................ 30
Figure 19 VE and HR laboratory data for the volunteer C. ................................................................... 32
Figure 20 VE vs. slope for three volunteers. ......................................................................................... 32
Figure 21 VE and HR for all the volunteers. .......................................................................................... 33
Figure 22 MoveLab. 1 – Particulate matter analyzer; 2 – Laptop. ........................................................ 34
Figure 23 Laptop (left) and numpad (right)............................................................................................ 34
Figure 24 Polar devices. Top left: GPS with arm band; Bottom left: HR measuring device with thorax
band. ...................................................................................................................................................... 35
Figure 25 Grimm PM measuring device and inlet tube. The tube is plugged to the device while the
device is in use. ..................................................................................................................................... 35
Figure 26 Optical measuring principles. Source: Grimm Portable Dust Monitor Series 1.100 manual. 36
Figure 27 LabView screenshot. ............................................................................................................. 37
v
Figure 28 Map with location of the three chosen routes. Source: Google Earth................................... 38
Figure 29 HR variation for route number 1 in downward direction and the influence of stops. ............ 43
Figure 30 HR for all measurements in route number 2 in downward direction and the influence of
stops. ..................................................................................................................................................... 44
Figure 31 HR for all measurements in route number 3 in downward direction. .................................... 44
Figure 32 Time-activity exposure profiles on walking (Kaur, 2006). ..................................................... 48
Figure 33 PM2.5 and PM10 concentration during one trip in route 2 on 13.03.2014. ............................. 49
Figure 34 Average mass of PM inhaled for each route, in % compared to route 2 in downward
direction. ................................................................................................................................................ 53
Figure 35 Average mass of PM inhaled for each route, in percentage compared to route 2 in upward
direction. ................................................................................................................................................ 53
Figure 36 Average PM inhalation for the 3 routes in downward direction. ............................................ 57
Figure 37 Average PM inhalation for the 3 routes in upward direction. ................................................ 58
Figure 38 Comparison of PM2.5 inhalation using numerical and measured data .................................. 61
Figure 39 Case study: sidewalk and boardwalk with a barrier in between (McNabola, 2008) .............. 62
Figure 40 Typical air flow patterns at the boardwalk (McNabola et al., 2008). ..................................... 63
Figure 41 Proposed paths for rehabilitation (CML, 2011) ..................................................................... 69
vi
List of Tables
Table 1 Classification of PM according to its size. ................................................................................ 10
Table 2 PM exposure concentration studies in urban environment for walking and cycling commutes 22
Table 3 Volunteers’ parameters. ........................................................................................................... 31
Table 4 Protocol used with slope and time at each step. ...................................................................... 31
Table 5 Characteristics of the three routes in downward direction. Source: Google Earth. ................. 39
Table 6 Trip time duration (minutes) for each measurement. ............................................................... 41
Table 7 Average HR in bpm for each measurement. ............................................................................ 42
Table 8 Average speed in km/h for each measurement. ...................................................................... 42
Table 9 Respiratory volume V in liters for each measurement. ............................................................ 45
Table 10 Average V and V per meter for each route and direction ....................................................... 45
Table 11 Meteorological conditions during the measurements. Source: IPMA (Instituto Português do
Mar e da Atmosfera) .............................................................................................................................. 46
Table 12 Average PM concentration in each measurement. ................................................................ 47
Table 13 Average PM concentration in route 2 on 13/03/2014 starting at 10:28:45 in downward
direction ................................................................................................................................................. 49
Table 14 PM inhalation values for each measurement, route and direction. ........................................ 50
Table 15 Summary of the values of time duration, Volume inhaled, mean VE, PM concentration and
PM inhalation on 11.03.2014 ................................................................................................................. 51
Table 16 Percentage of PM inhalation considering route 2 as reference (100% inhalation). ............... 52
Table 17 PM concentration for the three routes with upward and downward considered in the average
and deviation. ........................................................................................................................................ 54
Table 18 PM inhalation for numerical data and measured data............................................................ 56
Table 19 PM inhalation for numerical data and measured data............................................................ 57
Table 20 PM inhalation for the three routes using numerical data ........................................................ 60
Table 21 Summary of the results obtained............................................................................................ 65
vii
List of abbreviations
AEI – Average Exposure Indicator
APDP – Associação Protectora dos Diabéticos de Portugal
APA – Agência Portuguesa do Ambiente (Portuguese Environmental Agency)
BMI – Body Mass Index
bpm – Beats per minute
CCDR-LVT - Comissão de Coordenação e Desenvolvimento Regional de Lisboa e Vale do Tejo
(Commission of Coordination for the Region of Lisbon and Vale do Tejo)
CML – Câmara Municipal de Lisboa
DEP – Diesel Exhaust Particles
EU – European Union
EEA – European Environmental Agency
EF - Emission Factor
EMEP – European Monitoring and Evaluation Program
EPA – United States Environmental Protection Agency
FMH – Faculdade de Motricidade Humana
GPS – Global Positioning System
HR – Heart Rate
ICCT – International Council on Clean Transportation
IPMA – Instituto Português do Mar e da Atmosfera (Portuguese Institute of the Sea and Atmosphere)
LCD – Liquid Crystal Display
LEZ – Low Emissions Zone
OECD – Organization for Economic Co-operation and Development
OD – Origin-destination
PM – Particulate Matter
PTFE – Polytetrafluoroethylene
UFP – Ultra Fine Particles
WHO – World Health Organization
Nomenclature
PM10 – Particulate matter with an aerodynamic diameter under 10 µm
PM2.5 – Particulate matter with an aerodynamic diameter under 2.5 µm
PM1 – Particulate matter with an aerodynamic diameter under 1 µm
PMinh – Particulate matter inhalation
PMconc – Particulate matter concentration
V – Total Respiratory Volume (liters)
VE – Minute Pulmonary Ventilation Rate (liters/minute)
viii
1 Introduction
1.1 Motivation
In 2010 more than half of world’s population lived in urban areas. In Europe, 3 out of 4 people
live in cities (EEA, 2013), but studies indicate that this trend is increasing: 6 out of every 10 people by
2030 and 7 out of every 10 people by 2050 (WHO, 2014) will live in urban areas worldwide. In highly
populated cities, where pedestrians and motor vehicles coexist, mobility management is a big
challenge. Atmospheric pollution, noise and vibration from traffic and crowded sidewalks increase the
exposure to air pollutants in urban centers, imposing a considerable risk of development of several
diseases, such as asthma and lung cancer (Fruin, 1971).
It is estimated that PM2.5 (particulate matter with less than 2.5 µm of aerodynamic diameter)
ambient air pollution is responsible every year worldwide, for approximately 0.8 million premature
deaths and 6.4 million years of life lost (Cohen et al., 2005). The European Union (EU) estimates that
human exposure to fine particulate matter (PM2.5) is the cause of about 350000 premature deaths
each year, representing a reduction of almost a year in the average life expectancy (EEA, 2009).
Figure 1 presents the percentage of urban population exposed to harmful levels of air pollution
in 2011, according to EU limit/target values and WHO guidelines. EU limit for PM10 is 40 µg/m³ and
for PM2.5 is 25 µg/m³. WHO guidelines are stricter: 20 µg/m³ for PM10 and 10 µg/m³ for PM2.5. The
WHO (2008) explains this difference as follows:
'The 2005 AQG [Air Quality Guidelines] set for the first time a guideline value for PM.
The aim is to achieve the lowest concentrations possible. As no threshold for PM has been
identified below which no damage to health is observed, the recommended value should
represent an acceptable and achievable objective to minimize health effects in the context of
local constraints, capabilities and public health priorities.'
In 2011 in the EU, 31% of the urban population was exposed to PM2.5 concentrations above
the EU limit values and 33% was exposed to PM10 concentrations above the EU limit values, as
shown in Figure 1 (The legal limits will be further addressed in section 2.2).
1
Figure 1 Percentage of urban population exposed to harmful levels of air pollution in 2011, according to
EU limit/target values (left picture) and WHO guidelines (right picture) (EEA, 2013).
Figure 2 classifies, for PM10 and PM2.5, the concentration of particles emitted to the
atmosphere that are associated with each human activity in European Union (EU). Commercial,
Institutional and Households have the biggest share for both PM10 and PM2.5 (41.9% and 52.1%
respectively), followed by Road transport for PM2.5 (15.8%) and Industrial processes in the case of
PM10 (15.1%). In urban centers, especially in the case of the city of Lisbon, Road transport is the main
source of PM10 and PM2.5 particles and plays a major role when dealing with pedestrians’ exposure
(Farinha, 2009).
Figure 2 Sector contributions of emissions of primarily particulate matter in 2010 from anthropogenic
sources (for EEA 32-member countries) (EEA, 2012).
2
According to the European Environmental Agency (EEA) report on air quality (EEA, 2013),
commuters in London and Budapest are most likely to travel more than an hour to work (23% and
32% more than the average European citizens, respectively). Moreover, assuming people work 222
days per year, OECD workers spend on average 8436 minutes or 140 hours and 36 minutes per year
commuting, but this figure is expected to grow by 30% for commuters in large metropolitan areas
(OECD, 2011). Since the concentrations of airborne pollutants near roads, especially the ones that are
constantly congested and during rush hour, is significantly higher than in other urban environments,
pedestrians were found to be more vulnerable to airborne pollutants, compared to other transportation
modes when following the same path. Under this scope, it is important that commuters are informed
about the ways to reduce personal exposure by using alternative routes. Such information is highly
useful for city planners and, still more research is needed. For this reason, permanent monitoring of air
quality near the area where pedestrians walk is extremely important in order to provide quality of life
for the population.
Several epidemiologic studies show that particles are the factors that play the major role for
most of the adverse effects of atmospheric pollution (Brook et al., 2003, Bhatnagar, 2006). The
consequences of the high concentrations of particles in the environment have been deeply studied
and the conclusions suggest that there are negative consequences for human health (Pope and
Dockery, 2006). The PM exposure can cause various cancers (Brunekreef and Holgate 2002), chronic
respiratory diseases, cardiovascular diseases (Miller et al 2007; Pandya et al 2002; Smith et al 2000;
Sorensen et al 2003), increased allergies and birth defects (Brunekreef and Holgate 2002).
Epidemiological and experimental studies also have identified PM in all its fractions as a new
and relevant risk factor for the development and increased severity of cardiovascular diseases,
independently of other commonly studied risk factors, such as smoking, diabetes, and hypertension.
Although many studies attribute greater toxicity to smaller size fractions, especially to fine particles,
evidence that these fractions make up part of the internal elements of coarse particles has to be taken
into consideration (Giuliano et al., 2009).
Some researchers have specifically linked proximity to traffic, with adverse health effects,
such as low birth weight and premature births among women living near roads with higher traffic levels
(Wilhelm and Ritz 2003), and increased allergies and respiratory illness among street vendors
(Kongtip et al 2006). The proximity to the source of pollution was also assessed in Boston's Roxbury
neighborhood (New York, USA) (Levy et al. (2001) where fine particulate matter was measured
(PM2.5) in a 1 mile radius around a large bus terminal and along bus routes. In this study the authors
found that PM2.5 concentration was significantly higher closer to the bus terminal, along bus routes,
and on roads reported to have heavy bus traffic.
Animal studies have suggested that fine particulate matter PM Brain inflammation is involved
in the pathogenesis of neurodegenerative diseases (Ulrich et al., 2009). The results of this study
indicate that chronic exposure to traffic-related PM may be involved in the pathogenesis of Alzheimer’s
disease.
3
Long-term research has also concluded that excessive exposure to air pollution (experienced
by those living in highly polluted cities) can cause neuroinflammation and an altered brain immune
response, which increases the likelihood of developing Alzheimer’s and Parkinson’s disease
(Calderon-Garciduenas et al., 2008). A large epidemiological study based on 23 European cities
estimated that 16926 premature deaths could be prevented annually if long-term exposure to PM2.5
levels were reduced to 15 μg/m³ in each city (Boldo et al., 2006).
Exposure to air toxins may be contributing to adverse health outcomes in urban
neighborhoods where polluted land uses are often adjacent to housing, schools, and highly
susceptible residents, such as children with asthma (Corburn, 2007). In Harlem, Kinney et al. (2002)
used personal exposure monitoring equipment to measure multiple air toxins and found that children
living closer to bus depots were exposed to higher pollutant levels than those living farther from the
source. Maantay (2001) showed that in New York City's South Bronx neighborhood, residents that live
within a quarter mile of the largest wastewater plant in the Northeast and the region's largest medical
waste incinerator were over five times more likely to have asthma than residents living farther away
from these noxious land uses.
Regarding the city of Lisbon, in recent years, issues concerning PM levels have been raised,
especially in the areas where traffic is more intense, posing a high level of risk not only for those who
live nearby but also for those who commute in the area. Avenida da Liberdade, located in downtown
Lisbon was considered the most polluted area in the city due to high traffic (Rodrigues, 2012)
according to the data obtained from the fixed air monitoring station located on this avenue from 2001
to 2010, as the values exceeded the daily and hourly legal limit in some consecutive days. Therefore,
a Low Emission Zone (LEZ) was implemented in Lisbon downtown area which brought some
modifications regarding restrictions to circulation of pre-Euro vehicles (vehicles previous to 1992), in
order to comply with the limit values established. A recent study (Rodrigues, 2012) stated that the
alteration of the circulation did not decrease significantly the traffic emissions considering the data
obtained from the implementation until the date of the study.
Consequently, considering the health impacts of high levels of PM exposure and in order to
ensure a sustainable quality of life for the population within a city, it is crucial to understand how to
effectively reduce the PM exposure, especially in areas where it is likely to be more threatening. The
present study intends to be a contribution in this area.
4
1.2 Objectives
Considering the framework presented, the aim of this study is to establish a method to
compare the pedestrian PM inhalation in different alternative routes for the same origin-destination
(OD), based on experimental measurements. In order to fulfill this major objective it is necessary to
perform several tasks, namely:
‒
Estimate Minute Pulmonary Ventilation Rate (VE) according to the value of Heart Rate
(HR). These variables are related to pollutants inhalation for pedestrians in urban
environment. A relationship between slope and speed for pedestrians in urban trips
was previously performed in order to better established the protocol needed to acquire
the correlation between VE and HR
‒
Measure PM concentration in situ for the three routes, using a portable laboratory.
This laboratory enabled the simultaneous real time acquisition of PM concentration,
HR and GPS (Global Position System) data.
‒
Estimate PM inhalation using different methodologies for comparison purposes: based
on the measurements previously collected and on alternative numerical approaches.
‒
Address some potential mitigation measures regarding pedestrians’ exposure to PM.
1.3 Structure of the thesis
The thesis is organized and divided into three main chapters:
Chapter 1 is the introductory chapter which addresses the relevance of the present thesis.
Chapter 2 elucidates upon the theoretical aspects of the topic of this thesis, namely the
characterization of particulate matter and its sources. The process of formation and the classifications
are also addressed. Regarding the legal framework, limits and targets are also explained.
Chapter 3 is dedicated to the state of the art on particulate matter (PM) concentration and
inhalation by urban population. The most relevant studies in the field are briefly explored as well as
relevant techniques for assessing PM concentration.
Chapter 4 comprises the methodology developed and the results that were obtained. This
chapter is subdivided according to the steps that were taken in order to obtain the values of PM
inhalation could be obtained. Alternative methodologies for PM inhalation calculation and possible
mitigation measures for the PM exposure of pedestrians are also presented.
Finally, in chapter 5, the conclusions and discussion of the main results are presented, as well
as suggestions for future research.
5
2 Particulate matter - Background
Particulate matter, also known as particles or atmospheric particulate matter (PM), is a
component of the atmosphere that may be in liquid or solid form. It is suspended in the atmosphere as
atmospheric aerosols (mixture of PM and air). A framework regarding PM classification, main sources,
production, exposure behavior and relevant legal framework will be addressed in this section.
2.1 Composition, formation and classification of particulate matter
Particulate Matter composition depends on the source and formation processes. The sources
of PM comprise both natural and man-made sources, as presented in Figure 3. Natural sources
include well known substances that are present almost everywhere and their concentration is difficult
to control in the atmosphere, like sea salt, pollens, volcanic ash and others. Man-made sources are
related to human activities and include fossil fuel combustion, industrial processes and cigarette
smoke, among others. These are the sources of PM that are possible to manage and control.
Figure 3 Sources of particulate matter (Kelly and Fussell, 2012)
A study made for the Lisbon northern region (Farinha et al., 2009) stated that in South
European regions, such as Portugal, the ambient aerosol has an important contribution from natural
dust due to local emissions from bare soil and an influence of episodic African dust transport
outbreaks. Detected concentrations of mineral elements that are present in the natural dust (in this
particular study) showed this phenomenon. Moreover, high concentrations of Na (sodium, sea salt
specie) were registered in Lisbon since the Portuguese coastal areas have an important input of
marine aerosol.
6
The contribution of sea salt to aerosol mass is highly dependent on distance from the sea, i.e.,
it varies from about 0.5% of aerosol mass at some inland sites to around 15% at sites closer to the
coast (EEA, 2013; Tørseth et al., 2012). Carbonaceous matter is also a significant component of the
atmospheric aerosol mass, accounting for between 10% and 40% of PM10 at the ‘European Monitoring
and Evaluation Programme’ (EMEP) sites (Yttri et al., 2007), and between 35% and 50% of the PM10
in southern sites of the Mediterranean (from wind-blow desert dust from Africa). Moreover, PM
chemical composition measurements in Europe change when moving from rural to traffic-based areas:
PM composition has more influence of agriculture related air pollutants in rural areas and more
contribution of carbon particles in traffic sites (Putaud et al., 2010; EEA, 2013)
Primary particles are released directly from their source. Consequently, particles from road
transport are included in this classification. The land and sea (natural sources) are also major sources
of primary particles, through soils carried by the wind and the generation of marine aerosol particles by
the bursting of air bubbles in breaking waves. Secondary particles, on the other hand, are produced in
the atmosphere through chemical reactions that have low volatility which easily condense into solid or
liquid phase, producing PM. Examples include sulfates and nitrates formed from oxidation of sulfur
dioxide (from power generation and industrial combustion processes) and nitrogen dioxide (from road
transport and power generation) in the atmosphere to acids, which are then neutralized by
atmospheric ammonia coming from agricultural sources (Kelly and Fussell, 2012). Secondary particles
take longer to produce and they are very persistent in the atmosphere.
The main source of PM in urban areas is road transport and burnt fossil fuels in power plants
and industries. The components of PM originated from traffic include engine emissions and wear,
brake and tires wear and dust from road surfaces (through resuspension). The largest source of
airborne PM from motor traffic is derived from diesel exhaust (much more than gasoline engines). It
was found that, in Lisbon, there are six aerosol sources: two related to soil, one related to the sea, an
anthropogenic source related to coal combustion, cement production, incineration, vehicles exhaust
and metallurgic industry (Farinha et al., 2009).
In industrialized countries, nowadays 55% of all new European cars have a Diesel engine
(ICCT, 2012). This increased demand results from the introduction of the powerful turbocharged highspeed diesel engine that provides excellent driving characteristics with high torque at low speed, and
very good fuel economy (Twigg and Phillips, 2009). Consequently Diesel exhaust particles (DEP’s)
account for most of the airborne particulate matter (up to 90%) in the largest cities in the world (Shah
et al., 2004; Riedl and Diaz- Sanchez, 2005). However in the USA, the majority of diesel engines are
trucks, buses and heavy equipment, not passenger cars. This is the reason why, in this country, truck
routes were determined to be a major source of diesel particulate air pollution (Corburn, 2007).
In summary, the concentration of PM in the atmosphere depends on dispersion
characteristics and on how the particles are introduced into the atmosphere, as can be seen in
Figure4.
7
Figure 4 Sources and process of transportation of particles until impacting human health (EEA, 2013)
Figure 5 demonstrates the process of formation of PM, for instance resulting from combustion
in Diesel car engines. In this specific case, there are several steps that lead to the formation of PM.
Initially, extremely small carbon PM (also called soot particles) are formed due to fuel rich zones in the
combustion chamber. The next step consists of collision of soot particles which causes an increase in
their size. Some of the resulting PM is still burned during the combustion process, still inside the
engine. In the exhaust system, where the exhaust gas is cooled, the less volatile substances
condensate. Sulfur oxides can react with water, condense and form sulfuric acid which adheres to the
particles as a liquid film. Nowadays, Diesel powered vehicles are equipped with diesel particulate
filters that trap the particles and burn when the temperature in the exhaust is high enough. Basically,
particles from Diesel powered vehicles are composed of a carbon core upon which high-molecular
weight organic chemical components (hydrocarbons) and heavy metals deposit, as shown in Figure 6.
8
Figure 5 Formations steps of PM (Greenship, 2014).
Figure 6 Schematic representation of diesel particulate matter (PM) formed during combustion of
atomized fuel droplets. The resulting carbon cores agglomerate and adsorb species from the gas phase
(Twigg and Phillips, 2009).
Besides the varying emission sources, PM also differs in size and composition. The size of
these particles is subdivided into several groups, depending on their aerodynamic diameter, as it can
be seen in Table 1. Furthermore:

Coarse particles are defined as PM with an aerodynamic diameter lower than 10 µm
of diameter (a human hair has an average diameter of 70 µm (EPA, 2013)) and higher
than 2.5 µm;

Fine particles are defined as PM with an aerodynamic diameter smaller than 2.5 µm;

Ultra-fine particles (UFP’s) are defined as PM with a diameter smaller than 0.1 µm.
(EPA)
9
Table 1 Classification of PM according to its size.
Particles classification
Size
PM10
PM2.5
PM1
PM0.1
< 10 µm
< 2.5 µm
< 1 µm
< 0.1 µm
Particles are classified in accordance with the aerodynamic diameter because the main
objective is assessing the effects on human health. The smaller the particle, the easier it is to travel
along sharp turns without colliding with the passage walls and consequently, smaller particles reach
deeper locations of the respiratory system, as seen in Figure 7. The deeper it settles, the more
threatening it is for human health (Oberdorster et al 2005).
Particles are also classified according to the norm ISO17708 and the Portuguese norm
1726:2007 as inhalable, thoracic and breathable:

Inhalable particles are substances that are potentially hazardous when deposited
anywhere in the region of the respiratory tract;

Thoracic particles are hazardous when deposited in the region of the lungs air ways
and in the region where gaseous exchanges take place (pulmonary and alveolar
region)

Respirable particles are substances that are potentially hazardous when deposited in
the region of gaseous exchanges.
Figure 7 summarizes the penetrations capacity of the particles according to their aerodynamic
diameter, in the respiratory system and the area that they affect.
10
Figure 7 Human respiratory system and particles dissemination according to the aerodynamic diameter
range while a person is exposed to a concentration of particles in the atmosphere. Source: Ecoteck Pty
Ltd.
2.2 Legal framework and evolution of the PM10 and PM2.5 emissions’
level
The thresholds for the concentration of PM that have an impact on human health were not
identified yet. Therefore, thresholds were established to help in quantifying air quality policies.
‒
World Health Organization (WHO) established thresholds in order to reach an optimum air
quality that protects the public health in different environments. These were based on
several studies (Pope et al., 2002; Dockery et al., 1993; Pope et al., 1995; HEI, 2000,
Jerrett, 2005):
o
PM2.5: 10 µg/m³ for the annual average and 25 µg/m³ for daily average (in force
from January 2005);
o
PM10: 20 µg/m³ for the annual average and 50 µg/m³ for the daily average.
11
‒
In the European Union, a directive of the European Parliament and Council (May 21st
2008, 2008/50/CE) established target values and limit values that the European Union
members shall reach. These are in the foundation of the member states’ policies on air
quality. The values were chosen according to the strategy for ambient air quality and a
cleaner air for Europe. Monitoring criteria, such as location, are also defined in the same
directive. The directive states that the concentration shall not exceed:
o
PM2.5: 25 µg/m³ for one calendar year. This value was the target value until
1.1.2010 and it will be the limit value from 1.1.2015. Target value for 2015 or
2020: 20 µg/m³;
o
PM10: 50 µg/m³ in 24 hours, not to be exceeded more than 35 times each
calendar year; 40 µg/m³ for the calendar year. In force since 1.1.2005.
‒
The Portuguese legislation on this topic (Decree-law no. 102/2010 from September 23rd)
based on the European directive, establishes limits for the ambient air quality together
with the WHO, in order to avoid or reduce the emissions of atmospheric pollutants. The
limits for particles are as follows:
o
PM2.5: 25 µg/m³ annual average (in force from January 2015);
o
PM10: 40 µg/m³ annual average and 50 µg/m³ daily average (that does not exceed
35 times each civil year).
The Portuguese legislation includes also the objective of reaching a continuous reduction of
population exposure to background urban PM2.5 concentration based on the calculations of an
Average Exposure Indicator (AEI). The AEI is the average annual concentration for three consecutive
years, determined for all background urban monitoring stations in a specific monitoring network.
Consequently, for January 2015, the PM2.5 annual average of the three last consecutive years (AEI of
2013, 2014, 2015) should not exceed 20 µg/m³ (APA, 2012). This supplementary limit was created
because it is not possible to define the value under which PM2.5 do not represent consequences for
human health.
Lisbon follows the Portuguese legislation which defines the responsibilities of the national
entities related to this subject as well as other guidelines for the monitoring process. Therefore there is
a network of air quality monitoring in Lisbon (Lisbon city has six stations) managed by the Portuguese
Environment Agency (Agência Portuguesa do Ambiente, APA) together with the Commission of
Coordination for the Region of Lisbon and Vale do Tejo (Comissão de Coordenação e
Desenvolvimento Regional de Lisboa e Vale do Tejo, CCDR-LVT). It provides information for
atmospheric pollutants (PM10 and PM2.5, for example) that is available for public consultation online
(www.qualar.org).
The established policies highly contributed to a modification of the air quality scenario in
Europe, since annual emissions of primary PM10 have reduced 26%, based on the most recent data
12
available (EEA, 2012), in the 32 EEA member states between 1990 and 2010 (Figure 8), with
significant reductions in anthropogenic sources that occurred within most countries driven by a 28%
reduction in emissions of PM2.5. Emissions of the fraction ‘PM10 minus emissions of PM2.5’ have
reduced 21% over the same period which is due to significantly increased emissions in PM 2.5 relatively
to PM10 from road transport and agriculture (50% and 15% more respectively) in the same period of
time. Of the reduction in PM10, 39% took place in the energy production and distribution sector due to
changes like switching from coal to natural gas in the electricity generation and general improvement
in the performance of the equipment that allows reducing the pollution at the source, at the industrial
facilities (further information on percentage of emissions of PM 10 and PM2.5 per source was seen in
Figure 2 for 2010 data).
Figure 8 Emissions of primary PM2.5 and PM10 particulate matter from anthropogenic sources (EU 32).
(EEA, 2012).
The same trend of reduction was verified for the percentage of urban population exposed to
PM10 concentration values that are higher than EU air quality standard, according to the EEA report on
air quality (EEA, 2013). Available data (Figure 9 and Figure 10) indicates that in 2011 about 30% of
the European urban population was exposed to values of PM10 exceeding EU air quality standard,
while this value rises up to 85 % of the population if the WHO air quality guidelines are considered.
The highest percentage of population in these conditions was registered in 2003 using both entities
thresholds, corresponding to 45% for EU air quality standard and 95% according to WHO air quality
guidelines.
13
Figure 9 Percentage of urban population exposed to air pollution exceeding EU air quality standard
Figure 10 Percentage of urban population exposed to air pollution exceeding WHO air quality guidelines
(EEA, 2013)
Although the emissions have reduced progressively, the high levels verified still represent a
decreased quality of life in urban environments. In fact, one author demonstrated an increase in life
expectancy as a result of reductions in PM2.5 air pollution that occurred in the US during the 1980’s
and 1990’s, suggesting at least partial reversibility of the mortality effect (Pope et al., 2009), based on
the respiratory and cardiovascular mortality associated with acute and chronic exposures to PM.
14
3 State of the art
Considering impacts of PM in urban areas, several authors studied the exposure or inhalation
of PM either by comparing different modes of transportation, different routes or using different
measuring devices and methodologies. The different approaches take into consideration the different
factors which influence PM exposure and inhalation for pedestrians. In this section, these factors are
individually assessed and analyzed according to the studies of other authors.
3.1 Factors that influence personal exposure concentrations in the
urban transport microenvironment
There are numerous variables that can highly influence personal exposure in the transport
microenvironments that can be classified into four categories (Kaur, 2007):
‒
Personal/individual factors
‒
Mode of transport factors
‒
Traffic factors
‒
Meteorological factors
Each category has an underlying spatial and/or temporal dependency to a varying extent
which consequently influences the pollutant exposures experienced by a pedestrian. In the context of
the urban transport microenvironment, the spatial scale goes from meters to kilometers and the
temporal scale goes from minutes to several hours, which might be influenced by local and
neighboring pollutant sources, varying pollutant levels in one day (depending on location and human
activities taking place) and different exposures at different periods of time.
Personal factors
The variation of exposure levels are dependent on factors dictated by each individual. First of
all, PM exposure is considered via inhalation at a certain rate Minute Pulmonary Ventilation Rate (VE)
that depends on individual characteristics and according to Int Panis et al. (2010) VE is 1.6 times
higher for men when compared to women. The positioning of the individual breathing zone can
influence the exposure, since the air pollution changes from place to place. Considering that there is a
vertical gradient of air pollutants, children are more exposed than adults (Rudolf,1994); Limasset et
al.,1993; Ishaque and Noland, 2008) as well as those in wheel chairs (Ishaque and Noland, 2008).
Furthermore, seating during travels on public transportation or choosing the ground floret or on an
upper story (usually happens in the train), for example, can influence the individual exposure level.
For pedestrians, some people tend to walk close to the building side of the pavement, others
walk very close to the road and others do not walk straight along the sidewalk. The pollutants
concentration decrease with increasing distance from the road (Weijers et al., 2004; Buonanno et
15
al.,2010; Kingham et al., 2011) and the side of the road also has an impact on the concentration of
pollutants (Kaur et al., 2005 b).
Kingham et al. (2011) compared different travel modes and concluded that on-road cyclists
are exposed to higher levels of PM1 (25%) and ultra-fine particles (UFP) (over 100%) than off-road
cyclist. Consequently, locating cycle lanes just a short distance from roads can reduce pollution
exposure significantly; for example, locating a cyclist 5-7 m away can reduce exposure by 20 to 40%.
Whitlow et al. (2011) monitored curbside (sidewalk edge that is the closest to the road, usually
separated from the sidewalk by vegetation and typically present in the American street design, for
example) airborne particulate matter (PM) concentrations using fixed curbside monitors (2011). It was
observed that UFP concentrations decayed exponentially with distance from the street, reaching
background levels within 100 m of the source.
Int Panis et al. (2010) compared the PM10 and PM2.5 concentration and inhalation for cyclists
and car drivers and found that minute ventilation while cycling was on average 4.3 times higher
compared to driving a car. Inhaled PM2.5 µg/km and PM10 µg/km was found to be significantly higher
while cycling compared to driving in a car and the PM2.5 and PM10 concentration was also significantly
higher for the bicycle compared to the car. The trip time difference between the car trip and the bike
trip was kept to a minimum (between 3-8 min) in order to minimize the effect of the timing on the
analysis because of intraday variation in concentrations due to changes in traffic, wind speed and
direction.
Zuurbier (2009) also verified that minute ventilation levels of cyclists are on average two times
higher than those of bus and car passengers. This study illustrated the importance of the inclusion of
minute ventilation data in comparing air pollution inhaled doses between different modes of transport.
Pedestrians are more vulnerable to environmental pollution exposure and have physical
efforts that require higher air inhalation than other transportation modes (Adams, 1993). Despite
walking being a universal and common form of transportation globally, few studies have been
conducted specifically for pedestrians (Kaur, 2007).
Modes of transportation
Several authors have studied the PM concentrations in urban centers and some studies
compare transportation modes according to PM exposure. For example, Jiao and Frey (2013)
compared the PM exposure for each mode (pedestrians, cars and buses) that were conducted within
one and half hour time period on pre-selected round trip routes in Raleigh, North Carolina. They
concluded that, in general, pedestrian and bus modes had higher PM2.5 concentrations among the
measured transportation modes (42% more for pedestrians compared to car mode and 37% more for
bus mode compared to car trips).
Kingham et al. (2011) made a study in New Zealand that included cyclists, car drivers,
pedestrians and bus passengers and found that car drivers and bus passengers are exposed to higher
16
average levels of UFP than cyclists. However, very short acute exposures (a few seconds), on-road
cyclists can be exposed to higher peaks.
Although there seemed to be differences in the studies depending upon the mode of transport,
there are specific features and characteristics of each transportation mode that can lead to the
exposure concentrations experienced. Kaur (2007) deeply analyses the potential factors influencing
exposure within different modes of transportation according to several other authors’ studies. For
example, exposure while using car as the transportation mode depends on: position of the passenger
in the car (front, back), ventilation and air intake point, vehicle passengers (e.g. smokers), vehicle fuel
type, vehicle speed, previous vehicle use, height of the vehicle from the ground, position of the vehicle
in traffic, acceleration, number of stops and vehicle model (year and design).
Additionally, the exposure while walking is influenced by the position of the pedestrian in the
street canyon, position across the pavement, direction of travel, passing smokers, passing bins with
cigarette butts and other rubbish, traffic light stops (duration of wait and traffic proximity) and indoor
sources (Dennekamp, 2002; Kaur, 2007).
Traffic
Some studies refer the correlation between traffic count and PM exposure levels of road
transport users. Experiments made along a busy roadway in Sydney, Australia, included PM2.5
measurements made at second-by-second intervals using a portable aerosol monitor, while
simultaneously recording location with a personal GPS device (Greaves, Issarayangyun et al. 2008),
allowed concluding that PM exposure is affected by wind speed, traffic volumes and clearway
operations (independent of traffic volumes) and were found to be significant predictors of PM 2.5
concentrations. Doubling traffic volumes increased PM 2.5 concentrations by 26%, while each 5 km/h
increase in wind speed decreased PM2.5 concentrations by 10%. Several PM2.5 hotspots were
identified where concentrations exceeded 100 µg/m³. These were attributed to specific traffic
(intersections, trucks, buses) and non-traffic sources (pedestrians smoking), that typically only lasted a
few seconds.
Whitlow et al. (2011) monitored curbside airborne PM concentrations using fixed curbside
monitors and its inflammatory capacity during three weekends when vehicle traffic was excluded from
Park. Avenue, New York City. Both fine and UFP spiked in response to traffic conditions but while
UFP is directly responsive to traffic flow, PM2.5 is not, according to the author. PM2.5 appears to
respond to traffic light timing but this plays a subsidiary role to local dispersion conditions. It is
important to note that even without morning traffic contributing to the pollution load, PM concentrations
were typically higher during the morning hours. Cooler morning air is inherently more stable and with
less convective mixing, vertical mixing and ventilation, near ground which makes PM concentrations
typically higher. As a result, the traffic exclusion did not overcome the diurnal effects of atmospheric
processes.
17
Ishaque
and
Noland
(2008)
used
a
traffic
modeling
tool
(VISSIM)
and
considered Marylebone Road in London to simulate pedestrians’ exposure to vehicle emissions. For
all pedestrians on sidewalks and crossings, those with lowest walking speed are the most exposed,
namely, the elderly, ill and disabled. Similarly those who comply more with the traffic signals at
signalized crossings are more exposed to vehicle emissions in comparison to those who comply less.
Kingham (2011) compared several modes of transportation and concluded that, at some parts
of their journeys, travelers are exposed to very high levels of pollution, often for short periods of time
which have potential health implications. For example for cyclist in which the majority of these events
occurred at intersections or when cycling (or waiting behind a diesel vehicle), while general congestion
accounted for a much smaller percentage of UFP. Jiao and Frey (2013) in their study concluded that
passing trucks were associated with many peaks in PM2.5 exposure concentration. Furthermore, one
hour of commuting (4% of the day) could contribute up to 20% of the total daily dose of UFP.
Meteorological factors
Concerning all the meteorological variables (wind speed and direction, temperature and
relative humidity), wind speed has been the most closely scrutinized with regard to exposure knowing
it disperses and dilutes pollutants. Generally, both exposure (Krausse and Mardaljevic, 2005) and
ambient (Boarnet et al., 2011) studies have identified that an increase in wind speed results in a
decrease in exposure concentrations to PM2.5.
Some studies have found wind direction to have a significant impact on PM2.5 concentration
levels. For instance, Lung et al. (2005) measured UFP count pedestrian exposures levels during
northerly prevailing winds to be higher on the south side compared to the north side of an intersection.
This is most likely due to the wind direction and road and building configuration causing recirculation
of the wind in the street causing pollutant concentrations to be higher on one side of the street in
comparison to the other side.
Kaur et al. (2005b) recorded different levels of count of UFP exposures of pedestrians on the
opposite sides of the road which might be due to recirculation in the street canyon generated by the
prevailing wind direction during measurements. In another study, higher concentrations were
associated with street canyons with buildings of over five stories (Boarnet et al., 2011) (Figure 11).
18
Figure 11 Air flow pattern in a canyon street. Source: A. Srivastava et al., 2011. National Environmental
Engineering Research Institute, India.
3.2 Comparison of different routes according to particulate matter
exposure
Similarly to objectives of this work, some authors compared different routes in accordance
with PM exposure. In one hand, Adams et al. (2001) studied the PM exposure for cyclists in a central
route and secondary side streets and found that the central route exposure levels were higher than the
other routes, and there was a significant difference between the main route exposures and the side
street route exposures. On the other hand, Kaur (2005) investigated the pedestrian exposure to PM2.5
along a major road in Central London, UK. During a four-week field campaign, groups of four
volunteers collected samples at three timings (morning, lunch and afternoon), along two different
routes (a heavily trafficked route and a backstreet route) using five transport modes (walking, cycling,
bus, car and taxi). The authors concluded that there was no evidence to suggest a statistically
significant difference between the two routes analyzed in terms of PM exposure, independently of the
mode of transportation.
Int Panis et al. (2011) measured PM10 and PM2.5 concentrations and ventilator parameters
(minute ventilation, breathing frequency and tidal volume) in three Belgium locations (Brussels,
Louvain-la-Neuve and Mol). Subjects were first driven by car and then cycled along identical routes in
a pairwise design. Concentrations and lung deposition PM mass were compared between biking trips
and car trips, indicating that the size and magnitude of the differences in concentrations were proved
to depend on the location.
Zuurbier et al. (2010) compared a high traffic route and a low traffic route for several
transportation modes (diesel buses, electric buses, bicycles and personal cars), according to the
exposure and inhalation of PM10 and PM2.5. For cyclists, the PM exposure was higher along bicycle
routes near high-traffic roads compared to bicycle routes near low-traffic roads. Because of their
19
increased minute ventilation, the inhaled doses of all studied air pollutants were highest for cyclists.
Exposure to PM10 and PM2.5 did not differ significantly in the high traffic route and in the low traffic
route. This happened probably because the routes have a traffic level that does not present a high
difference. Similarly, Ragettli (2013) compared UFP contribution to total daily exposure in two routes,
one had high traffic and the other had low traffic (Figure 12) in Basel for cyclists. Highest values of
contribution to the total cumulative exposure were due to the high exposure route.
Figure 12 Contribution of commute to total daily UFP number exposure.
Regarding PM measurements, it was observed that some authors use static monitors while
others use dynamic monitors. Beckx (2009) did a deep analysis of these two types of approaches that
are presented in previous studies. He concluded that significant differences in pollutant concentrations
can occur over one day and between different locations. His results for the exposure to PM10 and
PM2.5, reveal big differences between the static and the dynamic approach with the values given by
the static approach being significantly lower than the values that resulted from the dynamic approach,
which is more accurate considering personal exposure. Gulliever (2004) also found a weak correlation
between static and dynamic measurements knowing the fixed monitor presented a poor
representation of PM10 concentrations recorded during walking on a route over 1 km away.
Several studies reached opposite conclusions, namely when comparing different transport
mode. It might be important for future studies to establish common parameters such as vehicle
characteristics used in the analysis, identical equipment for measuring PM concentration or physical
characteristics of the volunteers.
20
After analyzing the studies that have been done so far, it is clear the existence of a gap in this
field. In the first place, the actual mass of PM that exists in the atmosphere that affects human quality
of life is only possible to analyze if PM inhalation values are calculated. It necessarily changes
according to the individual characteristics of the pedestrian. Moreover, there is a lack of investigation
regarding the comparison of actual alternative routes for the same origin-destination (OD) and the
impact of choosing an alternative route to decrease PM inhalation. This thesis will try to fill this gap
and contribute to future studies that might deeper explore this problematic.
Table 2 summarizes the studies where pedestrians’ exposure to PM was assessed,
considering the methods used as well the major results.
21
Table 2 PM exposure concentration studies in urban environment for walking and cycling commutes.
Author
Location
Equipment
(sampling duration)
General remarks on the characteristics of the study (sample
size)
Dennekamp
et al. (2002)
Aberdeen,
UK
DustTrak TSI Model
8520
Gulliver and
Briggs
(2004)
Northampton,
UK
OSIRIS (15 to 20 min)
Kaur et al.
(2005b)
London, UK
Casella vortex
ultraflow at 16 L/min
(~20 min)
London, UK
Casella vortex
ultraflow at 16 L/min
(~20 min)
McCreanor
et al. (2005)
Hyde Park;
Oxford St.
London, UK
Casella vortex
ultraflow at 16 L/min
(~20 min)
Lung et al.
(2005)
Taiwan
DustTrak aersol
monitor (15 min)
McNabola
et al. (2008)
Dublin,
Ireland
Walking in curbside of a heavy traffic road and it was observed the
median exposure concentration (10)
Two routes were used for personal monitoring, in order to provide
contrasting traffic conditions and thus to give a wide range of
exposure levels. The results for the several routes were not analyzed
separately. (74)
PM2.5 exposure assessment specifically designed to examine
pedestrian exposure concentrations with a higher sample number,
compared to Kaur et al., 2005a. (155)
PM 2.5 pedestrian exposure concentrations as part of a multi-mode
exposure assessment. No significant differences in PM 2.5 pedestrian
exposure concentration depending upon the position on the
pavement or the direction of travel (56)
Personal pedestrians’ exposure was assessed while walking.
Hyde Park: location with low traffic intensity; Oxford St: location
where only pedestrians, diesel powered buses and taxi cabs are
permitted.
Stationary individuals made measurements around their breathing
zone while waiting at traffic lights at two different intersections—one
intersection with buildings roadside and the other with buildings
located approximately 20 m from the roadside. (16)
PM2.5 along a footpath and the adjoining boardwalk in Dublin. Two
pedestrians walked side by side, one in each path with personal
samplers carried by the pedestrian commuters. (10)
Int Panis et
al. (2010)
Three
regions in
Belgium
Zuurbier et
al. (2010)
Arnhem,
Germany
Kaur et al.
(2005a)
1
Casella vortex
ultraflow at 1 m3/h
(~20 min)
Ptrack and Dusttrack
TSI Model 8525
Chosen individuals rode a bicycle measuring PM concentration and
VE. Values of inhalation for Brussels city are also shown.
*1
Two routes with different traffic level were rode by cyclists.
(Mean exposure ± SD), µg/m3
Range, µg/m3
PM2.5: Median~22
-
PM10: 38.18±25.17
PM2.5: 15.06±16.15
PM1: 7.14±9.62
-
PM2.5: 37.7±16.4
6.4–88.7
PM2.5: 27.5
5.3–64.4
Hyde Park: PM2.5: 22.5
Oxford St.: PM2.5: 47.9
5–107
10–150
PM2.5: Surrounded by buildings:
268±144; surrounded by open
space: 145 ±84
-
PM2.5: 2.83 times more
concentration in sidewalk
compared to boardwalk.
PM10: Median~62 µg/m3;
11.5±4.5 µg PM10inh/km;
3.4±1.3 µg PM2.5inh/km
PM10: 72.3±67 for high traffic
and 71.7±65.5 for low traffic.
PM2.5: 38.8±14.1 for high traffic
37.2±11.6 for low traffic.
Sidewalk:~7-158
Boardwalk:~5-47
40-92
-
DataRAMs MIE Inc. Model 1200 with PM2.5 cyclones KTL Model GK 2.05 and pumps BGI Inc. Model AFC400s. PM10 on 37-mm 2-µm pore-size Teflon filters, using Harvard Impactors
(Air Diagnostics and Engineering Inc.) and pumps Air Diagnostics and Engineering Inc Model SP-280E.
22
3.3 Methods used for measuring particulate matter concentration
The design of previously implemented in several studies that were made in the area to
measure PM concentration varied widely. They may be personal, for dynamic measurements, or fixed,
for static measurements. The protocol created for the studies followed also varied considerably. The
following section focus specifically on the description of some of those processes.
McNabola et al. (2008) measured PM2.5 pedestrians’ exposure along a footpath and the
adjoining boardwalk in Dublin (see section 4.5). Two pedestrians walked side by side, one in each
path with personal samplers carried by the pedestrian commuters. Samples typically lasted 20
minutes and the personal sampling equipment was carried in a satchel by the pedestrian. The
sampling inlet was placed in the breathing zone of the pedestrian to avoid abnormal disturbances and
to be representative of the air the pedestrian is breathing.
Personal measurements of exposure to particulate air pollution (PM 10, PM2.5, and PM1) were
simultaneously made during walking and in-car journeys on two suburban routes in Northampton, UK
(Gulliver and Briggs, 2004). For walking journeys, the portable particle analyzer was carried on the
chest in a custom-made pouch, and powered by a battery held in a separate pouch. For in-car
journeys, the analyzer was placed on the front passenger seat. Two routes were used for individual
monitoring, in order to assess different traffic conditions. The measurements were made in several
days, in each day starting always at 8 am and 3 pm, both of which are characterized by increased
traffic associated with the ‘school-run’. Both modes started at the same time and made 2 trips in each
direction per measurement. Route 2 was not performed in the same days as route 1. In total, 74
samples for car and 74 for walking commuters were gathered. Footpaths were never more than a few
meters from the road and the only difference between the modes was the type of microenvironment.
Data was also collected from fixed monitoring devices within the study area, which was located about
10 m from roadside, close to a busy road junction. The performance of the portable monitor was
initially validated by co-locating samplers alongside the fixed device. Results showed a strong
agreement between the two measuring devices.
In another study (Int Panis et al., 2010), individuals rode a bicycle measuring PM
concentration and VE and values of inhalation for Brussels city were obtained. The bicycle was
equipped with different instruments: TSI P-TRAK, TSI DustTrak, and a commercial GPS was used in
this study. A GRIMM 1.108 spectrometer (Grimm Technologies Inc, USA) was added for calibration
purposes, but only the results of the calibrated DustTrak were used because second-by-second data
is necessary for the synchronization with the other instruments. Calibrations were performed in dry
conditions for 5 days in an urban background site versus a Partisol Plus model 2025 filter sampler
which is an equivalent sampler for PM10 dust. Simultaneous respiratory measurements were made
during each trip (one while driving and another while cycling) and synchronized with the P-Trak,
DustTrak and GPS datasets. During the field tests, breathing frequency, tidal volume and oxygen
uptake were measured using a portable cardiopulmonary with an indirect breath-by-breath calorimetry
23
system (MetaMax 3B, Cortex Biophysik, Germany) fixed into a chest attachment. A flexible facemask
covered the mouth and nose. Before each test, gas and volume calibration took place and ambient air
was measured before each test according to the manufacturer’s guidelines. Heart rate was recorded
via a Polar X-Trainer Plus system (Polar Electro OY, Finland).
Taking the several studies into consideration and considering the comparison of personal PM
exposure or inhalation in different routes for pedestrians, it is possible to conclude that the study
design must:
‒
Contain a dynamic portable monitor of PM exposure and the inlet of the device must
be placed in the breathing zone of the pedestrian;
‒
Be done in a short time period in all the routes to ensure similar conditions in all
routes;
‒
Include a period of rest between trips to allow the pedestrians to start each trip without
fatigue (about 100 bpm) so the previous trip does not influence the following one. To
avoid differences in trip effort due to slope, for very steep routes the upward and
downward direction must be distinguished;
‒
Distinguish between peak and off-peak hours so as to avoid different traffic flows;
It is also relevant to mention that PM exposure (or PM concentration) and total PM exposure
(or total PM concentration) are different concepts. The difference is irrelevant in instances such as
daily exposures because the time is explicitly present (24 hours). However, in the case of exposure
when commuting the situation is not the same because besides PM concentration, the time exposed is
variable.
24
4 Methodology and results
The comparison of PM inhalation for pedestrians in three routes with the same OD pair was
assessed within this work. In order to associate a PM inhalation value to each route, a methodology
was created using laboratory and field measurements. The PM inhalation depends on the trip time,
human minute ventilation rate (that depends on physical activity due to speed and road
characteristics) and PM concentration. It can be generically defined by equation 1:
(1)
Where:
 PMinhalation is the mass of PM (µg) that enters the pedestrian respiratory system;
 PMair-concentration is the PM concentration (µg/m³) presented in the atmosphere;
 VE is the minute pulmonary ventilation rate (m³/min).
 t is the trip time, in minutes
To fulfill this objective, different approaches could be considered, for instance using average
values of PM concentration or Ventilation rate; however, this work intends to integrate micro-scale
data collected under field measurements - in a second-by-second basis - to present a better
assessment of total PM inhaled in each route.
Figure 13 schematically describes the variables and relations required to characterize urban
trip characteristics and consequent human impacts, to assess the PM inhaled. To each block, a
section number is linked (in brackets), where a description of the method used will be presented.
25
MoveLab
(4.3)
Speed vs.
slope curve
(4.1)
HR (4.3.2)
HR vs. VE
curve (4.2)
Time (4.3.2)
VE (4.3.2)
PMconc
(4.3.3)
PMinh
(4.2.5)
Figure 13 Schematic summary of the work steps followed (Correspondent section of the thesis in
brackets).
The MoveLab (section 4.3) is a portable laboratory for personal use that provides the time
spent in each trip (analyzed in section 4.3.2), PM concentration (in the section 4.3.3), position, speed
and Heart Rate (HR) (in the section 4.3.2) at a micro-level scale. However, in order to obtain
information about VE at the same time-scale, which is necessary to estimate PM inhalation, it would
be necessary to use a spirometer, which is not practical under field measurements. Consequently,
before starting field measurements with MoveLab, an initial step was necessary to find a relationship
between pedestrians’ walking speeds in urban environments according to the slope of the paths. This
procedure was used to calibrate a relation between heart rate (bpm – beats per minute) and VE
(l/min), under laboratorial environment, considering the values of speed and slope obtained from
several field tests specifically made for the purpose.
This way it was possible to estimate VE from the values of HR measured with the portable
laboratory (MoveLab), while walking in urban environments. The final results for PM inhalation are
found in the section 4.3.5. The following sections (indicated in Figure 13) present a detailed
description and analysis of the results obtained in each step.
4.1 Average speed for different slopes
In this section, the main objective is to obtain a correlation between slope and walking speed
in urban environments. This step is important because it allows the determination of an average
comfort speed for individuals within the same age and fitness characteristics for the typical range of
slopes found in the city of Lisbon. This relation between pedestrian comfort speed and slope is
relevant in order to allow proceeding to the following steps of the methodology.
Four volunteers walked several routes located in downtown Lisbon, along the sidewalk, in a
total of 116 segments. The volunteers were in the same age range and fitness condition (Table 3).
26
The measurements took place between November 2013 and January 2014. The meteorological
conditions were approximately similar for all essays with atmospheric air temperature between 8 and
14 degrees Celsius, relative humidity between 60% and 90% and 0 ml of precipitation. In order to
simulate common commuting events in the city, volunteers did not carry weights higher than 4 kg and
wore comfortable casual clothes.
The time spent walking each path was registered, either using a simple chronometer or based
on the laptop computer used in the Movelab portable laboratory (the description of the laboratory is
further discussed in section 4.3). The volunteers walked in free flow conditions on selected routes that
did not include discontinuities such as obstacles or on routes that did not involve crossing the road.
The distribution of road slope of the segments (calculated using the values of altitude)
according to their frequency is presented in Figure 14, indicating that 19 segments had a slope of 0
degrees. There is also observed symmetry, as most of the segments were walked back and forth , so
the same segments was walked more than once.. This is observed for segments with a value of slope
of -5 and 5 degrees, for instance. About 68% of the segments had soft slopes, between -4 and 4
degrees.
Figure 14 Frequency of segments and the correspondent rounded slope.
The paths had different slopes, as previously analyzed, but also different lengths. The
distribution of the slope of the paths according to its distance is presented in Figure 15. Each column
comprises the sum of the distances of the paths with the same slope. The values for distances were
obtained using Google Maps. The biggest values of distance were found for slopes between -2 and 2
degrees although the number of trips with this slope was relatively low (12 trips for both -2 and 2
degrees while there were 19 trips for trips with slope of 0 degrees). It is also shown that most of the
trips (76%) have a short distance, below 200 m long. More details regarding the length of each trip is
presented in Annex A.
27
Figure 15 Distance of the segments and their slopes.
For each slope, the respective speed was assigned for each volunteer, as shown in Figure 16.
The average speed was calculated dividing the total distance by the time spent to walk the route. A
wide dispersion for slopes from 4 to 8 degrees between volunteers is observed, but the general trend
is an increasing speed for more negative values, which means the more negative the slope, the higher
the speed. At 0 degrees the average speed is 5.5 km/h. Literature value for young adults indicate
average speeds between 4.7 and 5.4 km/h (Himann, Cunningham et al. 1988), which is coherent with
the values that were measured.
Figure 16 Average speed and slope.
Since all volunteers are in the same age category and in similar fitness condition, the values
obtained for walking speed according to the road slope led to similar values, resulting in a general
trend for the conditions mentioned. A correlation between slope and speed for hiking (Tobbler, 1950)
28
presented results that are similar to the data obtained in this work, except in the highest slopes. For
slopes higher than -6 degrees and under 5 degrees (Figure 17), the volunteers’ average speed for
several slope levels is similar to the results obtained by Tobbler.
Figure 17 Average slopes for the volunteers and correspondent speed.
4.2 Physiologic data (ergospirometry)
The relationship between VE and HR was estimated based on laboratory measurements
performed at Faculdade de Motricidade Humana (FMH) for the same four volunteers. The relationship
between VE and slope was also obtained in order to test different methodologies, which will be
presented in section 4.4.2. This step of the methodology is extremely relevant since in this way it is
possible to calibrate the individual characteristics of the volunteers in order to calculate PM inhalation
more accurately. Other previous studies use a constant VE reference value from the literature but this
might not be representative of the actual physical effort, particularly in the city of Lisbon that has a hilly
topography, which requires more physical effort to walk, compared to other European capitals.
The measurements were performed in order to be representative of daily commuting,
therefore, the laboratory ambient conditions were kept similar as the outside, with a temperature range
(18-20 degrees Celsius) kept constant, just as the relative humidity of 50% and pressure of 761
mmHg. Also, the volunteers wore casual daily clothes and shoes in order to simulate common
commuting events in urban environment.
Measurements of VE, HR and other parameters (not used in the present study) were obtained
for each volunteer at different slopes while walking on a treadmill. The volunteers’ comfort speed was
a result of the measurements obtained in the previous section. Correlations between minute
ventilation and slope or heart rate were obtained using an ergometer (treadmill) and a spirometer
under laboratory environment, as can be seen in Figure 18.
29
Figure 18 Laboratory environment, during the measurements.
The experimental protocol was based on the results obtained by the urban measurements
(speed vs. slope, Figure 16) and was established in accordance with the recommendations of the
FMH experts. Previous to the measurements, each volunteer registered the number of heart beats per
minute (bpm) in resting conditions, right after waking up, for three consecutive days and, in the
laboratory, weight and height were measured and registered. In resting conditions the VE (l/min) was
measured with the participants in seated position. Inspired and expired gases as well as ventilation
rate were measured continuously, breath by breath, through a portable spirometer (K4b2, Cosmed,
Rome, Italy), which had been previously validated by McLaughlin (Melo et al., 2011). The
K4b2 weighs 475g and is not expected to significantly affect the energy demands of the
subjects (Melo et al., 2011); none of the volunteers of this study had any negative remarks on the
system’s weight or on their mobility and vision during the test.
In the same conditions, HR was also measured for 3 minutes using a portable heart rate
measuring device (Polar T31 coddedTM transmitter), similar to the model used by Int Panis et al.
(2010). This compact device was easy to attach without restricting the volunteers’ movements. Table 3
presents a description of the volunteers’ parameters.
30
Table 3 Volunteers’ parameters.
Name,
(gender)
Age
(yrs)
Height
(cm)
Weight
(kg)
BMI
(kg/m2)
Speed
(km/h)
A (M)
B (F)
C (F)
D (F)
29
34
24
28
171.2
171.9
165.0
164.6
65.9
64.8
50.0
49.9
22.5
21.9
18.4
18.4
5.80
5.22
5.47
5.65
HR
resting
(bpm)
64
74
61
73
HR
seating
(bpm)
72
95
82
86
VE
seating
(l/min)
8.69
8.36
9.89
12.42
BMI stands for Body Mass Index (kg/m 2), which indicates the nutrition status of an individual. It
was calculated dividing the weight by the squared of the height. According to WHO for adultus over 20
years old, volunteers A and B have normal weight and volunteers C and D are underweight.
The protocol used consisted of an eight-step process in which the volunteers walked always
with the same speed based on the personal average speed at 0 % (Table 3) slope obtained during the
fieldwork described above. The speed was kept constant since it did not vary considerably according
to the FMH experts, as shown in Figure 16. Each step lasted two or three minutes, in order to obtain
steady-state physiological conditions, and the slope was increased in steps of a 1 to 2 %, according to
Table 4. The objective of the protocol chosen was to cover the slopes found under field
measurements, achieve steady-state physiological conditions and avoid fatigue near the end of the
laboratorial proceeding.
Table 4 Protocol used with slope and time at each step.
Slope, %
Time, min
0
2
2
2
4
2
5
2
6
3
8
3
10
3
Only positive values of slope were allowed in the treadmill available in the laboratory. The
volunteer D was excluded in the data analysis process since the results were significantly different
from the other volunteers due to difficulties in adapting to the laboratory apparatus.
An example of the HR and VE data collected during the laboratorial procedure is presented in
Figure 19 for volunteer C. It can be observed that the variation of HR and VE has a similar trend: both
increased slowly with time, while slope was gradually increased. The separations between the seven
31
steps were also plotted in vertical lines. The last vertical line marks the beginning of the resting period
in seated position, which lasted 7 min and led to an abrupt reduction in HR and VE.
Figure 19 VE and HR laboratory data for the volunteer C.
The values of VE measured during the laboratory procedure for each step may be associated
to the correspondent slope of the laboratory procedure to evaluate the relationship between these two
variables. Finding a relation between these variables allows estimating VE values based only on road
slope, which can be easily achieved using numerical tools, such as Google Earth. This result is
presented in Figure 20 for each volunteer, showing that the VE increased with slope linearly for the
three volunteers.
Figure 20 VE vs. slope for three volunteers.
Equation 2 presents an estimate of the value of VE (l/min) given slope S (%), with a high
determination coefficient R2 of 0.95.
32
(2)
The same approach was also used to correlate two other variables: HR and VE. The
laboratory data for VE and HR for all the volunteers at each step of the experimental protocol is shown
in Figure 21. Using the data from Figure 21, it was possible to find an estimate of VE (l/min) according
to the HR (bpm), as shown in equation 3, with a determination coefficient of 0.83, for the three
volunteers. Other studies also found a good correlation between VE and HR (average R2=0.90, for a
study measuring VE and HR while cycling in a ergometer at increasing cycling intensity, Zuurbier et
al., 2009).
Figure 21 VE and HR for all the volunteers.
(3)
Similar analyses were performed by Wilhelm and Roth (1998) and Adams (2004) however,
1
Wilhelm and Roth estimated VE from HR based on a graded exercise including standing, walking, etc,
although the relation found is similar to equation 3. Adams (1993) used conditions that were similar to
the laboratory protocol followed in the present work, however the slope was kept constant.
Consequently, the slope of the equation was smaller than the one from equation 3.
With the approach chosen it was possible to cover a wide range of physical efforts, as well as
a broad range of HR and VE, which allows assessing the VE at a small-scale level while performing
on-road tests, by measuring second-by-second HR with the MoveLab portable laboratory. The use of
direct measurements in the field would not be practical and could influence the inhalation of pollutants
(Zuurbier et al., 2009). The minimum value of 120 bpm presented in the Figure 21 was the minimum
value allowed by the protocol established and it corresponds to the value of HR obtained for the
33
volunteers when they started walking. Despite that, the curve will be used to estimate lower values of
HR considering the relation is linear (just as Adams’ curve, 2004) for values of VE lower than 25 l/min.
4.3 Fieldwork using a portable laboratory
In order to acquire PM concentration and HR data, a portable laboratory, MoveLab (Figure
22), was used. It consists of a 40-liter backpack, weighting 7.5 kg, with a built-in structure designed to
place all the components: a PM analyzer, a laptop (Figure 23) and connector cables.
1
2
Figure 22 MoveLab. 1 – Particulate matter analyzer; 2 – Laptop.
A numpad was carried by hand by the user, which is connected via Bluetooth to the laptop, in
order to register crosswalks, starting and ending points of the trip and specific occurrences such as
trucks and buses, each associated to a number (Figure 23).
Figure 23 Laptop (left) and numpad (right).
34
The user also carried a Polar model RS 800 with an armband GPS and a HR measuring
device in the thorax area synchronized with a watch. This system acquires at 1 Hz the following
signals: altitude, speed, position and HR (Figure 24). A similar system was used by Int Panis et al.
(2010).
Figure 24 Polar devices. Top left: GPS with arm band; Bottom left: HR measuring device with thorax band.
The particulate matter analyzer used is a portable continuous measuring device of PM in the
atmosphere, Grimm 1.101 (Figure 25), a similar model was used by (Int Panis et al., 2010). It allows
measuring the concentration of particles (number of particles per liter) or mass concentration of
particles (micrograms per cubic meter), which is the most appropriate unit for this work. The inlet of air
is extended using a tube when the PM analyzer is installed in the backpack, in order to collect
aerosols within the individual breathing zone (about 30 cm from the inlet to the front of the head of the
individual using the MoveLab).
Figure 25 Grimm PM measuring device and inlet tube. The tube is plugged to the device while the device
is in use.
The PM analyzer uses light-scattering technology for single-particle counts, where a
semiconductor-laser serves as the light-source. The scattered signal from the PM passes through the
laser beam and is collected at approximately 90° by a mirror and transferred to a recipient-diode. The
35
signal of the diode passes, after a corresponding reinforcement, a multi-channel size classifier. A
pulse height analyzer then classifies the signal transmitted in each channel. These counts are
displayed and transferred to the laptop via the RS-232 serial port, in which a dedicated data
acquisition software developed in LabView (Figure 27) was used to register the data.
Figure 26 Optical measuring principles. Source: Grimm Portable Dust Monitor Series 1.100 manual.
Ambient air samples are drawn into the PM analyzer via an internal volume-controlled pump at
a rate of 1.2 l/min. The sample passes through the sample cell, past the laser diode detector and is
collected onto a 47 mm polytetrafluoroethylene (PTFE) filter. The entire sample is collected on the
PTFE filter, which can then be analyzed gravimetrically for verification of the reported aerosol's mass.
Additionally, further chemical analysis can be performed on the deposited residue. The pump also
generates the necessary clean sheath air, which is filtered and passes through the sheath air regulator
back in to optical chamber. This process ensures that no dust contamination comes into contact with
the laser-optic assembly. This particle free airflow is also used for the reference zero test during the
auto-calibration performed at the beginning of each measurement, when the instrument starts a selftest, which last approximately 30 seconds. The actual dust-measurement begins when the Liquid
Crystal Display (LCD) displays the first result. Subsequent results will be displayed every 6 seconds
for PM1, PM2.5 and PM10. The values obtained have 5% standard deviation. This device was calibrated
according to manufacturer’s standards prior to the measurements.
The following figure (Figure 27) is a screenshot of the dedicated data acquisition software
developed in Labview. The acquired data is displayed continuously during the measurements and it
includes (from left to right): the data from the GPS such as latitude, longitude, altitude, speed over
ground; time elapsed, which is useful to synchronize the Polar devices in post possessing; and finally,
the PM concentration divided into the three classes. The data is recorded and saved in a Microsoft
Excel sheet for easier analysis and processing.
36
Figure 27 LabView screenshot.
The MoveLab was used previously in research works with valid results (Baptista, 2013;
Mendes et al., 2014).
4.3.1 Case study
Three routes were chosen to evaluate PM concentration and potential inhaled dose. They
have the same origin and destination location and are intended to verify whether different
characteristics lead to different PM inhalation for pedestrians. For convenience, the three routes are in
close proximity to IST, Lisbon. They have the same origin in Av. Duque de Ávila/Av. Defensores
Chaves (81 m of altitude) and destination in Av. Fontes Pereira de Melo/Rua Martens Ferrão (67 m of
altitude), as shown in Figure 28.
37
N
209 m
Figure 28 Map with location of the three chosen routes. Source: Google Earth.
These routes were chosen in such a way that it was possible to walk the three routes back
and forth in less than two hours, to ensure the meteorological and traffic conditions do not change
significantly (Jiao and Frey, 2013).
These routes have different characteristics such as: length, traffic volume, topography and
road width. The route number 2 is the central route that was chosen to represent the near-capacity
route, where traffic is very influenced by the signalization cycles and with little vegetation. Routes
number 1 and 3 are secondary routes. Route number 3 was chosen to represent the longest route but
with lower traffic flow (only one lane - one way - in a significant part of the length of this route) and
narrow streets with high density vegetation (from building to building across the road). This
characteristic and the high walkable and wide sidewalks promote a pleasant route to walk in but
creates a canyon effect (see Figure 11), which restricts the dispersion of PM. Route 1 does not have
as much vegetation and has more traffic flow, though it is shorter than route 3.
This selection of routes will allow comparing PM inhalation according to the characteristics of
the routes. The specifications of each route are detailed in Table 5 where, for each route, the length,
maximum and minimum slope, mean slope, and the number of crosswalks are presented. Each route
was divided into segments, which have an approximately constant slope.
38
Table 5 Characteristics of the three routes in downward direction. Source: Google Earth.
Characteristic
Route 1
Route 2
Route 3
Length
1114 m
904 m
1289 m
Maximum slope
3%
0%
2%
Minimum slope
-6 %
-2.5 %
-6 %
Weighted arithmetic mean slope
-1.95 %
-1.73 %
-1.57 %
With traffic lights
10
8
4
No traffic lights
1
0
6
Number of crosswalks
The weighted arithmetic mean ( x w [%]) of the slope was calculated according to the following
equation 4:

(4)
Where x [m] is a section of the route with approximately constant slope, w is the slope [%] of
the section and i = 1,2,…,n is the number of sections. The slopes were obtained from Google Earth.


Route 3 is the longest and route 2 is the shortest one and they have different properties in
terms of topography. Considering downward direction, route 2 has a constant negative slope, while
the other routes have both positive and negative slopes. According to the value of weighted mean
slope it is possible to conclude that although route 1 and route 3 have the lowest values of slope, route
3 has steep slope segments (just like route 1) but in shorter distances. This may be indicative of a
lower pedestrian effort, since the value of respiratory volume (V, in litters) per meter (
Table 10, shown later) is the lowest in route 3 compared to route 1 in downward direction (2%
less) and upward direction (7.8% less). Route 2 provides the lowest effort for the pedestrian in
downward direction and is the second for upward direction, where route 3 is the route that has a lower
average V value.
Furthermore, these routes were characterized according to traffic volume and number of lanes
(the number of lanes comprises both directions of the road in which the traffic flows). Traffic volume
stands for the volunteer’s perception while walking during the fieldwork (the field work will be deeply
explained in the following chapter). Route 1 has 1 or 2 lanes with light traffic level in 86% of the length,
while 14 % of its length has 3 or 4 lanes with medium traffic level. Route number 2 has the biggest
length with 6 lanes (78% of total length), 8 lanes (10% of total length) with high traffic volume, while
12% of the length has medium traffic level and only one lane. 53 % of the length of route 3 has light
traffic level and 2 lanes, 41% with 1 or 2 lanes and medium traffic level and only 6% of high traffic level
and 6 or 8 lanes. Route 1 also has the highest number of crosswalks with traffic light and route 3 has
the lowest number of crosswalks with traffic lights.
39
4.3.2 Implementation of the field work
Between February 25th and March 10th field measurements were performed walking in routes
chosen (Figure 28), using the Polar device which includes a GPS, HR measuring device and watch.
The same process was done later between March 11th and March 13th with the addition of the
MoveLab to perform PM concentration measurements. For this second approach, in order to ensure
constant traffic level and meteorological conditions, the 3 routes were walked upwards and
downwards in less than 2 hours (off peak periods), which is an important assumption when PM
concentration is measured. The volunteer C was the only individual that performed the measurements,
which means the results obtained might be similar for individuals with the same physical
characteristics. The volunteer took a short break between trips to avoid fatigue in the trips caused by
the previous one. Due to the fact that the influence of the weight of the backpack did not influence
average HR (Annex B), which means that, among the average HR in each trip, the value of average
HR while carrying the backpack was not higher than in the trips in which the volunteer did not carry the
backpack. The analysis will consider all measurements together (with and without backpack) when
analyzing this variable.
The physiologic behavior of the subject during the measurements will be presented, including
the trip time, HR and V values, for the measurements performed in the 3 routes. The number of
samples guarantees at least a 10% error for 80% confidence interval, considering a normal
distribution, for the volunteers of this work with their intrinsic characteristics.
Table 6 presents the trip time duration of the measurements done for each route and direction.
The average values did not show a significant variation for each route. There is a small difference in
time duration according to the direction: it takes longer to walk upwards (1 % more for route 1 and 2 %
more for both route 2 and 3). Route 2 is the quickest path with an average value of 11.1 min, while
route 3 is the longest with an average value of 15.3 min.
Although the variability is not significant, some values (highlighted in bold) seem to have some
contrast compared to the other measurements. This factor directly influences the total V, as shown in
Table 10. Time spent walking the routes showed a regular range of values along the measurements.
The non present values in some measurements were due to the period of adaptation to the MoveLab
and routes when the volunteers walked only one or the other route.
40
Table 6 Trip time duration (minutes) for each measurement.
Direction
26.02.2014
26.02.2014
28.02.2014
03.03.2014
06.03.2014
10.03.2014
11.03.2014
12.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Route 1
Route 2
Route 3
Route 1
Route 2
Route 3
Down
15.1
14.0
14.6
13.6
12.8
14.3
14.1
0.8
Down
12.0
12.4
11.4
11.1
9.7
10.0
10.6
10.6
11.3
11.0
0.9
Down
15.8
15.2
15.2
14.1
14.8
15.0
15.4
15.1
0.5
Up
15.2
15.3
13.1
13.6
14.6
14.1
14.2
14.3
0.8
Up
11.7
12.3
12.0
10.2
10.0
10.4
10.9
12.5
11.2
1.0
Up
17.3
13.3
16.1
14.4
15.5
15.3
15.6
15.4
1.3
The values of measured HR are presented in Table 7. It is noticeable a higher mean value of
HR for the upward direction for all measurements, which is explained by the extra effort the volunteer
makes in that direction. In downward direction, route 3 presents the higher mean value of HR, while
route 2 has the lowest. In upward direction, route 2 presents the highest mean value of average HR
while route 3 has the lowest. The differences found in route 3 compared to the downward direction is
mainly due to the average value of HR on 03.03.2014 that presents higher time duration (see Table 6)
which leads to a lower velocity and consequently, lower HR values along the route. On the opposite,
the average HR for route 2 was the highest considering upward direction because there was one
measurement on 11.03.2014 which presents an average HR 12% higher than the average.
Summarizing, for most of the measurements, route 2 presents the lowest HR average values and
route 3 presents the highest HR average values.
41
Table 7 Average HR in bpm for each measurement.
Direction
26.02.2014
26.02.2014
28.02.2014
03.03.2014
06.03.2014
10.03.2014
11.03.2014
12.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Route 1
Route 2
Route 3
Route 1
Route 2
Route 3
Down
118.9
129.5
115.1
112.9
117.6
121.0
119.2
5.8
Down
115.2
119.2
119.1
112.9
131.9
115.7
117.0
117.3
117.0
118.4
5.4
Down
122.7
114.8
131.8
122.0
118.5
120.1
124.8
122.7
6.7
Up
112.6
126.7
126.0
129.8
119.0
123.3
128.4
123.7
6.1
Up
106.7
124.3
115.3
139.5
120.7
127.4
123.4
124.1
125.0
9.5
Up
101.8
124.3
136.1
124.4
124.0
125.9
130.6
123.3
10.0
The speed (Table 8) was calculated considering the time spent in each trip and its length.
Route 1 had an average speed of 4.7 km/h, route 2 had a value of 4.9 km/h and route 3 registered the
lowest value, 4.6 km/h in upward direction. This difference between routes is highly related to the
number of crosswalks (Table 5) that is the lowest in route 2. Although route 1 has more crosswalks
than route 3, the crosswalks in route 1 did not require stopping as much as route 3.
Table 8 Average speed in km/h for each measurement.
Direction
26.02.2014
26.02.2014
28.02.2014
03.03.2014
06.03.2014
10.03.2014
11.03.2014
12.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Route 1
Down
4.4
4.8
4.6
4.9
5.2
4.7
4.8
0.3
Route 2
Down
4.5
4.4
4.8
4.9
5.6
5.4
5.1
5.1
4.8
5.0
0.4
Route 3
Down
5.0
5.2
5.2
5.6
5.4
5.3
5.1
5.3
0.5
Route 1
Up
4.4
4.4
5.1
4.9
4.6
4.8
4.7
4.7
0.3
Route 2
Up
4.6
4.4
4.5
5.3
5.4
5.2
5.0
4.3
4.9
0.4
Route 3
Up
4.6
6.4
4.9
5.9
5.1
5.2
5.1
5.2
0.6
Figure 29 HR variation for route number 1 in downward direction and the influence of stops.
presents the variation of HR for all measurements for route number 1 in downward direction where it
shows the influence of stops in HR. Figure 30 presents the same information for route 2 and Figure 31
for route 3. Altitude, speed and crosswalks are also presented and were plotted for the measurements
42
made on 11.03.2014. These are exclusively for downwards trips (upwards in Annex C). In each
figure, for most of the trips, the volunteer had a similar physiologic response, as observed, except the
measurements made in the three routes on 11.03.2014 that may be due to high fatigue level of the
volunteer. It is also visible the influence of crosswalks (vertical green lines) by a decrease in HR that
is observed for most of the trips.
It is relevant to mention that not all the crosswalks marked in the graphs imply a decrease of
the volunteer’s speed and HR. In fact, some crosswalks do not show any influence in the speed and
HR of the volunteer, for example, at 180 m in route 1 and at 270 m in route 2, where the pedestrian
did not stopped at the crosswalk.
Altitude has also a big impact on the HR variation, since a higher effort is asked to pedestrians
in positive slopes compared to negative slopes. For example, considering downward direction, at 1000
m in route 1 (Figure 29), HR increases from 100 bpm up to 137 bpm due to the positive slope at the
end of the route1; for route 3 (Figure 31), in a section of the route from 100 to 700 m, the slope is
positive and led to a very gradual increase in the HR for all measurements, traduced by an average of
15 bpm increase.
Figure 29 HR variation for route number 1 in downward direction and the influence of stops.
43
Figure 30 HR for all measurements in route number 2 in downward direction and the influence of stops.
Figure 31 HR for all measurements in route number 3 in downward direction.
Table 9 shows the total respiratory volume V (liters) during each trip for each route and each
direction and Table 10 shows the average V value for each route and V value per meter. It was
calculated by summing the multiplication of time by VE in each second. The VE was calculated using
the equation 3, which relates HR and VE. It was previously seen than HR had a similar variation along
the trip in all the trips (Figure 29, Figure 30 and Figure 31). Comparing the three routes, the average
value of V was the highest in route 3 and the lowest in route 2. Since V consists of the total volume
44
inhaled during the trips and being route 3 the one that takes longer to walk, the average values for V
were found to be higher in this route. The same explanation justifies the low values for route 2, which
means that the lower time duration leads to a lower average V, consequently.
In some trips V was significantly higher than average, for example, in the measurements
performed on 11.03.2014, for route 1 and 3 in downwards direction. The value of time duration was
not higher than on the other days, which indicates that these higher values of volume are explained by
higher average HR values (shown in Figure 29 for route 1 and in Figure 31 for route 3).
For route 3, V was higher in both directions just as HR, which may be related to the fact that
this trip was the last one from the total of six trips in all days of measurement and the volunteer may
have been experiencing more fatigue to walk the route; also, route 3 has steeper slopes compared to
the other routes.
Table 9 Respiratory volume V in liters for each measurement.
Direction
26.03.2014
26.03.2014
28.03.2014
03.03.2014
06.03.2014
10.03.2014
11.03.2014
12.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Route 1
Down
366
406
329
294
303
361
343
46
Route 2
Down
240
293
220
279
240
291
229
249
246
265
257
24
Route 3
Down
409
341
465
366
357
373
416
389
45
Route 1
Up
325
425
359
398
355
369
406
385
23
Route 2
Up
326
271
338
251
293
286
333
300
36
Route 3
Up
282
329
487
359
413
419
460
411
49
Table 10 Average V and V per meter for each route and direction
Route
Average V, l
Average V, l/m
Downwards
1
2
3
343
257
389
0.308 0.285 0.302
45
1
385
0.346
Upwards
2
3
300
411
0.332 0.319
4.3.3 Particulate matter concentration
The objective of this section is to compare and relate PM10, PM2.5 and PM1 concentration in
the three routes during the measurements. The data was collected during the fieldwork, as described
in the section 4.3.2.
Meteorological conditions for the days and day time when the PM concentration was
evaluated and is presented in Table 11, as well as the starting and finishing time of each
measurement. The days when the fieldwork was performed where chosen to ensure a low relative
humidity, according with the operational limits of the PM analyzer.
Table 11 Meteorological conditions during the measurements. Source: IPMA (Instituto Português do Mar
e da Atmosfera)
Date and time
(yyyy-mm-dd
hh)
2014-03-11 15h
2014-03-11 16h
2014-03-11 17h
2014-03-12 10h
2014-03-12 11h
2014-03-12 12h
2014-03-12 13h
2014-03-13 10h
2014-03-13 11h
2014-03-13 12h
2014-03-13 15h
2014-03-13 16h
2014-03-13 17h
T
(ºC)
21.2
21.9
21.9
12.2
13.6
16.3
17.8
12.7
14.5
15.9
19.4
19.7
19.4
Relative
Humidity
(%)
NA
35
33
67
59
48
40
76
66
57
48
51
49
Pressure
(mbar)
Wind speed
(km/h)
1016.2
1015.9
1015.9
1022.6
1022.9
1022.5
1021.8
1025.0
1025.0
1024.6
1022.9
1022.5
1022.5
16.6
15.8
14.4
16.9
11.9
10.1
12.2
12.2
9.4
6.8
9.7
13
14
Start
(mm:ss)
Stop
(mm:ss)
15:03
17:03
10:39
12:37
10:28
11:52
15:01
17:02
The average PM concentration is shown in Table 12. The average concentration was divided
into PM10, PM2.5 and PM1 for each measurement. There is a visible difference between routes and
even between days that can be explained by the singularities present such as cigarette smoke, buses
and trucks at the time the measurements were done and in the weather conditions during the
measurements. The values vary especially from day to day.
46
Table 12 Average PM concentration in each measurement.
PM10, µg/m³
PM2.5. µg/m³
PM1, µg/m³
11.03.2014
12.03.2014
13.03.2014
13.03.2014
11.03.2014
12.03.2014
13.03.2014
13.03.2014
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Route 1
Down Up
10.5 20.9
24.5 18.7
26.6 27.9
26.2 25.0
4.1
5.1
8.7
7.3
13.3 12.1
11.8
9.0
3.0
3.4
5.5
5.0
9.8
8.8
8.4
6.2
Route 2
Down Up
21.2 18.7
39.3 23.3
49.5 43.5
34.9 26.3
7.0
6.0
8.5
13.1
16.7 15.6
15.7 12.3
5.1
4.1
5.4
8.1
11.0 10.1
11.1
9.1
Route 3
Down
Up
13.9
15.7
20.6
14.7
26.8
27.0
20.5
25.3
4.1
4.9
7.1
5.9
11.4
12.0
10.6
10.2
2.7
3.2
3.5
3.8
8.3
8.6
7.3
7.0
The average concentration of PM10 was the highest in route 2 (2 times higher than route 3 in
some measurements and the difference is slightly smaller for comparing to route 1, in downward
direction). In upward direction routes 1 and 3 have nearly the same average PM10 concentration.
Route 2 did not have the highest value in upward direction only on 11.03.2014 where route 1 had 2.2
µg/m³ more and route 3 though route 3 had still the lowest value (15.7 µg/m³).
The average PM2.5 concentration was the highest in route 2 (about 30% more than route 1 and
route 3) except on 12.03.2014 in downward direction when route 1 had 0.2 µg/m³ higher average
concentration.
For average PM1 concentration, just as PM2.5, concentrations were always higher in route 2
compared to route 1 and 3 (about 60% more) except on 12.03.2013 in downward direction when route
1 had 0.1 µg/m³ more compared to route 1.
In summary, route 2 had the highest average PM concentrations compared to the other routes
and route 3 had the lowest average values (here average indicates the average of the values
measured in one trip along each route). Since there was a big variability between measurements, the
average PM concentration (considering average in all the trips of one route and one specific direction)
is not displayed due to this variability. The consequences of the differences in PM concentration will
be further analyzed in a following section.
47
4.3.4 The influence of singularities on the particulate matter concentration
Although the average value of PM concentration gives a good idea of the PM concentration for
comparison between routes, it does not always provide an understanding of the causes of peak
exposure and the extent to which exposure fluctuates, according to Kaur et al. (2006).
Kaur et al. (2006) created an exposure visualization system that allows the presentation of
mobile video synchronized with measured UFP count exposure of an individual. This way, it is
possible to identify the influence of the singularities in the measured UFP count, as shown in Figure
32. It is also possible to identify the duration and the source of the higher UFP count in a route located
in central London, namely smokers and crossing the road.
Figure 32 Time-activity exposure profiles on walking (Kaur, 2006).
Regarding this work, it was not possible to implement such a detailed analysis that account for
image recording or more routes and measurements due to time constraints and also because it was
not the main objective. Nevertheless it is of interest to know the singularities that might influence the
PM concentration values applied to the presented case study.
In order to further understand the importance of studying the peak exposure for pedestrians, a
detailed analysis of one of the three routes is presented, as an example of the influence of
singularities such as crosswalks, buses passing or cigarette smoke on PM exposure. The trip along
route 2 performed on 13/03/2014 starting at 10:28:45 in downward direction is the example studied.
Table 13 presents the average PM10, PM2.5 and PM1 concentrations for that measurement.
48
Table 13 Average PM concentration in route 2 on 13/03/2014 starting at 10:28:45 in downward direction
PM10
PM2.5
PM1
Average PM
concentration,
µg/m³
49.5
16.7
11.0
Figure 33 presents the PM concentration for each second of the trip and the singularities,
which were registered using the numpad. It was marked the initial moment of actual stops in
crosswalks (while waiting) and the initial moment when a truck, bus or smoke from cigarette was
observed. While stops in crosswalks do not seem to have a significant impact on PM concentration,
probably due to the fact that the time spent waiting to cross was very short (for example, at 10:36:15,
when the marking was done the PM concentration did not increase considerably), other singularities
detected had a significant impact on the concentration of PM, for example, at 10:32:00, when a bus or
truck was signalized.
As observed, the PM10 average concentration was 49.5 µg/m³ however, there were peaks
around 180 µg/m³ and one with 404.44 µg/m³ which are not represented through the analysis of
average values. Both peaks happened because of a bus or truck passing by. The same situation
caused peaks of PM2.5 and PM1. A slight delay between the detection of the singularities and the
increase in PM concentration is due to the normal delay of the PM analyzer.
PM10 = 401.44 µg/m3 caused by a singularity
Figure 33 PM2.5 and PM10 concentration during one trip in route 2 on 13.03.2014.
49
This analysis illustrates important features of urban air pollution, especially peak exposures
that highly contribute to the pedestrians’ PM inhalation. This analysis is useful to identify practices to
minimize risk of exposure and contribute either through urban planning or through education of urban
citizens in order to avoid depletion of their health. Further analysis would be very useful as a future
work having in mind the continuation of this work for further route characterization.
4.3.5 Particulate matter inhalation
For the three routes, PM inhalation was calculated, using equation 5 for each second. It allows
calculating the PM inhalation in one trip. The direction was distinguished because there is a significant
difference on PM inhalation related to the direction, due to the higher value of VE and t in upward trips.
The value of t was higher because the volunteer speed was lower due to the positive slopes. Table 14
presents the values obtained.
(5)
Where:
 PMinh. is the total mass of PM (µg) that enters the pedestrian respiratory system in the
whole trip;
 PMconc. is the PM concentration (µg/m³) presented in the atmosphere;
 VE is the minute pulmonary ventilation rate (m³/min) acquired in each second
(estimated based on HR)
 t is the trip time, in seconds
 i is each second of the trip.
Table 14 PM inhalation values for each measurement, route and direction.
PM10, µg
PM2.5, µg
PM1, µg
11.03.2014
12.03.2014
13.03.2014
13.03.2014
11.03.2014
12.03.2014
13.03.2014
13.03.2014
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Route 1
Down Up
4.3
7.2
8.8
4.7
8.1
10.2
9.4
9.6
1.6
1.8
3.4
1.7
4.0
4.5
4.3
4.8
1.2
1.2
2.2
1.1
3.0
3.2
3.1
3.4
50
Route 2
Down Up
6.5
6.5
10.4 10.0
12.3 13.0
9.1
10.4
2.0
2.1
2.0
3.7
4.2
4.5
4.1
4.3
1.5
1.4
1.2
2.4
2.7
2.9
3.0
3.1
Route 3
Down Up
3.6
5.7
6.2
4.3
10.0 11.4
8.4
11.9
1.1
1.7
2.1
1.7
4.3
5.2
4.4
4.7
0.7
1.2
1.1
1.1
3.1
3.8
3.0
3.2
In order to perform a deeper analysis of the differences from day to day found on the values of
PM inhalation, each day is observed separately. Table 15 summarizes the parameters of the equation
5 which leads to PM inhalation values: total volume inhaled (V), time and PM concentration. The
measurements for one day are presented, since the traffic and meteorological conditions were
considered similar within the 2 hours required to perform the measurement and consequently it was
assumed as reasonable to compare the PM inhalation values between routes. The measurements for
the other days can be found in annexes F, G and H.
Table 15 Summary of the values of time duration, Volume inhaled, mean VE, PM concentration and PM
inhalation on 11.03.2014
PM 10
PM2.5
PM1
Direction
t, min
V, l
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
Route 1
Down
14.0
406.5
10.5
4.3
4.1
1.6
3.0
1.2
Route 2
Down
9.7
291.5
21.2
6.5
7.0
2.0
5.1
1.5
Route 3
Down
15.2
464.8
13.9
3.6
4.1
1.1
2.7
0.7
Route 1
Up
13.1
358.8
20.9
7.2
5.1
1.8
3.4
1.2
Route 2
Up
10.2
338.5
18.7
6.5
6.0
2.1
4.1
1.4
Route 3
Up
16.1
487.0
15.7
5.7
4.9
1.7
3.2
1.2
Table 15 shows the measurements made on 11.03.2014, in which the value of V was the
smallest in route 2 for both directions: in downward direction, 63% and 72% the value of V in route 1
and 3 respectively; in upward direction it was 6% and 70 % the value of route 1 and 3, respectively.
The smaller V for route 2 is due to the shorter time it took to walk the route, as seen in Table 6.
Considering downward direction, although the values of average PM10, PM2.5 and PM1
concentrations are the highest in route 2 (between 68% and 101% more compared to route 1; the
values of PM10, PM2.5 and PM1 inhalation do not have as higher differences to the other routes (only
19 to 50% more PM inhalation compared to route 1). A similar situation is observed for upward
direction, although only for PM10, the inhalation was higher in route 1 (11% more than route 2). The
PM inhalation and concentration are the highest in route 2 considering upward direction although V is
the lowest compared to the other routes, which demonstrates that, for this specific case, PM
concentration is the parameter that influenced the total PM inhalation the most.
For upward direction, route 3 has an average PM1 concentration of 3.2 µg/m³ and route 1 of
3.4 µg/m³, PM1 inhalation has the same value for both routes. An explanation is the difference of V
between the two routes (36% higher in route 3), which may have played the main role in the PM1
inhalation. The different number and magnitude of the peaks of PM1 concentration was not significant.
In order to minimize daily variation of PM concentration due to the effect of external factors
(climate conditions, traffic, etc.), the PM inhalation values between routes are compared in percentage
51
instead of using absolute values, considering route 2 (the fastest, main route) as the reference value
(100%), as shown in Table 16. It is shown that route 2 is, on average, the highest in PM10 inhalation
(100%) compared to the other routes in both directions. In upward direction PM 2.5 and PM1 are, on
average, the highest in route 2 (27% and 22% more than route 1; 23% and 10% more than route 3, for
PM2.5 and PM1, respectively). In downwards direction, route 1 is, on average, 12% and 18% higher in
PM2.5 and PM1 respectively, compared to route 2. Route 3 is the lowest in downward direction with 9%
and 12% less than route 2 for PM2.5 and PM1 respectively.
Table 16 Percentage of PM inhalation considering route 2 as reference (100% inhalation).
%
PM10 inh, %
PM2.5 inh, %
PM1 inh, %
Direction
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Route 1
Down
66
85
66
103
80
18
81
168
97
103
112
38
84
177
108
102
118
41
Route 2
Down
100
100
100
100
100
0
100
100
100
100
100
0
100
100
100
100
100
0
Route 3
Down
56
60
81
92
72
17
53
104
103
106
91
26
48
88
113
102
88
28
Route 1
Up
72
48
79
92
73
18
49
74
99
110
83
27
51
77
112
111
88
30
Route 2
Up
100
100
100
100
100
0
100
100
100
100
100
0
100
100
100
100
100
0
Route 3
Up
57
44
88
114
76
32
47
76
116
110
87
32
48
75
132
106
90
37
Figure 34 and Figure 35 present the comparison of the three routes regarding PM10, PM2.5 and
PM1 inhalation using the same data from Table 16. Figure 34 shows the values for downward direction
and Figure 35 shows the values for upward direction.
In the Figure 34 it is presented the average PM inhalation for each route considering all the
measurements. It can be observed that route 2 is the worst option regarding PM10 inhalation,
presenting, on average, 20% higher values than route 1 and 28% higher values than route 3.
Regarding PM2.5 and PM1, route 1 is the worst option. For PM2.5 there was found on average 12%
higher PM inhalation than route 2, while route 3 presents 9% less PM inhalation than route 2. For PM1,
route 1 had on average 18% higher PM inhalation than route 2 while route 3 had 12% lower PM
inhalation than route 2. Route 3 provides the best option for all PM classes with, on average, between
28% and 9% less PM concentration than route 2.
52
Figure 34 Average mass of PM inhaled for each route, in % compared to route 2 in downward direction.
For the upward direction (Figure 35), it is also presented the average PM inhalation for each
route considering all the measurements. Route 2 was the worst option, followed by route 3 for all PM
classes. The highest difference was found for PM10 inhalation: route 2 presented 27% higher PM
inhalation compared to route 1 and 24% higher PM inhalation than route 3. The difference between
the routes for PM2.5 and PM1 was lower, from 10 to 17% less in routes 1 and 3 compared to route 2.
For PM2.5 and PM1, route 1 indicated 17% and 12% lower PM inhalation than route 1, respectively and
route 3 showed 13% and 10% lower PM inhalation than route 2.
Figure 35 Average mass of PM inhaled for each route, in percentage compared to route 2 in upward
direction.
After this analysis, it is important to mention the fact that PM concentration plays a major role
in the values of PM inhalation in most of the measurements done. The PM concentration might not
have had the higher values in the routes with more traffic level due to the fact that the wind speed had
a bigger influence in this route, where the dispersion of particles is higher, compared to the secondary
routes.
53
The values of PM concentration and inhalation are now compared with the values obtained in
similar studies. The values of average PM concentration measured within this work are presented in
Table 12. However, to easily compare with other studies where direction was not differentiated the
average values of PM concentration presented in Table 17 include both upward and downward
directions without differentiation.
Table 17 PM concentration for the three routes with upward and downward considered in the average and
deviation.
PM10
PM2.5
PM1
Average, ug/m3
SD, ug/m3
Average, ug/m3
SD, ug/m3
Average, ug/m3
SD, ug/m3
Route 1
22.5
5.7
8.9
3.3
6.3
2.5
Route 2
32.1
11.4
11.9
4.2
8.0
2.8
Route 3
20.6
5.4
8.3
3.1
5.6
2.5
Only one study was found that compared PM concentration between two different routes with
different levels of traffic (Zuurbier et al., 2010), similarly as it was done in this work. The study was
made for cyclists, not pedestrians, though it has similar exposure characteristics as pedestrians
except for the value of VE, which does not interfere with the values of PM concentration. The PM2.5
concentration for the high traffic route was 38.8±14.1 µg/m3 and, in the present work, the value for
PM2.5 concentration in route 3 was 11.9±4.2 µg/m3, which was more than 3 times lower though very
close in variability. Moreover, PM10 in that study was 71.7±65.5 µg/m3 while it was 32.1±11.4 µg/m3 in
the present thesis in route 2, where PM10 concentration is more than 2 times lower, and the variability
was smaller than Zuurbier’s study. This difference found in the absolute values of PM concentration
might be related with the fact that the values obtained in this thesis were under off-peak conditions
and in an area with different traffic flow. For the low traffic road, Zuurbier et al. obtained, for PM2.5,
about 37.2±11.6 µg/m3 and in this thesis the value obtained was 8.3±3.1 µg/m3, again about 10 times
lower in absolute value, although Zuurbier referred that the difference in traffic level in the routes
chosen was little. In the contrary, for this thesis a significant difference was observer between route 2
and route 3.
Kaur et al. (2005a; 2005b) obtained for PM2.5: 37.7±16.4 µg/m3 and 27.5 µg/m3, respectively
for each study. The values are significantly higher than route 2, the worst case in PM concentration in
this thesis. Again, deviation was about half the average for Kaur’s study (2005a) higher than in this
thesis.
Dennekamp et al. (2002) obtained a median PM2.5 of 22 µg/m3 for pedestrians walking along
a route near the curbside, about twice the average value obtained in the route 2, the more polluted
one. Gulliver and Briggs (2004) obtained for PM10, PM2.5 and PM1 respectively: 38.18±25.17 µg/m3,
15.06±16.15 µg/m3 and 7.14±9.62 µg/m3. The average values obtained in this thesis are lower
54
although very similar compared to route 2. It is noticeable a high standard deviation from these
average values, reaching more than 100% the average for PM2.5 and PM1, even with a big sample
size. In the present thesis, 8 measurements per route were made (due to time constraints) and lower
variability (less than one third of percentual standard deviation). This indicates that performing more
measurements do not ensure a higher representativeness of the results.
4.4 Alternative numerical analysis
In the following, two alternative numerical analysis are used to calculate PM inhalation values.
This step was performed in order to understand the importance of measuring VE and PM
concentration along the routes
‒
The first alternative numerical analysis (section 4.4.1) considers the measured values of PM10,
PM2.5 and PM1 concentrations and time duration of the trips (presented in section 4.3.3 and
4.3.2, respectively). As stated before, average VE values are commonly used to calculate PM
inhalation and it is a useful approximation when it is not possible to measure HR or VE in situ,
although that approximation does not accurately describes the real PM inhalation. This is due
to the fact that VE varies greatly during a walk (example of variation of VE along one route is
shown in the Annex D). A comparison of PM10, PM2.5 and PM1 inhalation values was made
between this alternative analysis and the experimental analysis presented in this thesis.
‒
The second alternative numerical analysis (section 4.4.2) considers measured values of HR
along the trips and its time duration (presented in section 4.3.3 and 4.3.2, respectively). Using
the measured values of HR, VE was estimated. In this alternative, instead of using measure
values for PM, it was estimated the value of PM2.5 concentration based on literature data. Only
PM2.5 was assessed since it is the only class that the author of the estimates considered. A
comparison of PM inhalation values was made between this alternative analysis and the
experimental analysis presented in this thesis.
4.4.1 Constant minute ventilation rate
In order to evaluate de influence of VE in the total PM inhalation, its value was calculated
using a VE reference value. PM inhalation was calculated from equation 5 using a constant value of
16.5 l/min, according to the weight and age of the volunteer (volunteer B) and considering that walking
is a moderate exercise (EPA, 2009). The PM concentration and time duration were the same obtained
in the measurements (Table 12 and Table 6, respectively) and the analysis is done for each direction
separately.
Table 18 presents the PM inhalation for constant VE and PM inhalation for measured VE
(estimated from measured HR) in downward direction. It is clear that the estimated values of PM
inhalation using this alternative method results in a much lower value. The values of average PM
inhalation for a constant VE are 67% the average PM inhalation for measured VE values.
55
Nevertheless, although the method provides a rough approximation to PM inhalation from measured
VE, the trend for the three routes and PM classes is still very similar.
Table 18 PM inhalation for numerical data and measured data.
PM10 inh, µg
PM2.5 inh, µg
PM1 inh, µg
Route
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Estimated data
1
2
3
2.5 3.5 3.3
6.1 7.2 4.6
5.6 8.3 6.6
6.2 6.5 5.2
5.1 6.4 5.0
1.8 2.0 1.4
0.9 1.1 1.0
2.4 1.5 1.6
2.8 2.9 2.8
2.8 2.9 2.7
2.2 2.1 2.0
0.9 0.9 0.9
0.7 0.8 0.6
1.5 0.9 0.8
2.1 1.9 2.1
2.0 2.1 1.9
1.6 1.4 1.3
0.6 0.7 0.7
Measured data
1
2
3
4.3 6.5 3.6
8.8 10.4 6.2
8.1 12.3 10
9.4 9.1 8.4
7.6 9.6
7
2.3 2.4 2.8
1.6
2
1.1
3.4
2
2.1
4
4.2 4.3
4.3 4.1 4.4
3.3 3.1
3
1.2 1.2 1.6
1.2 1.5 0.7
2.2 1.2 1.1
3
2.7 3.1
3.1
3
3
2.4 2.1
2
0.9 0.9 1.3
Figure 36 presents the average PM inhalation for a constant value of VE and for the measured
VE values from HR, in comparison with route 2 in downward direction. For both methodologies, the
average PM inhalation for all measurements has the same trend: the lowest average value for one
route in one method corresponds also to the lowest average for the other method. Route 2 is the worst
option (23% on average) compared to route 3 and route 1 for PM10 in both methods. For PM 2.5 and
PM1, route 1 is the worst option (between 10 and 18 % more than route 2) followed by route 2 and
route 3, which registered 3 to 12% less PM inhalation than route 2.
56
Figure 36 Average PM inhalation for the 3 routes in downward direction.
Table 19 presents the same analysis in upward direction. As observed for downward direction,
there is a clear difference between the average PM inhalation values given by each methodology. The
values of average PM inhalation for a constant VE are 66% the average PM inhalation for measured
VE values.
Table 19 PM inhalation for numerical data and measured data.
PM10 inh, µg
PM2.5 inh, µg
PM1 inh, µg
Route
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
11.03.2014
12.03.2014
13.03.2014
13.03.2014
Average
SD
Estimated data
1
2
3
4.5 3.1 4.3
4.3 10.0 3.3
6.5 7.9 6.8
5.5 6.4 6.5
5.2 6.9 5.2
1.0 2.9 1.7
1.1 1.0 1.3
1.5 3.7 1.3
2.8 2.8 3.0
2.7 2.7 2.6
2.1 2.6 2.1
0.9 1.1 0.9
0.7 0.7 0.9
1.0 2.4 0.9
2.0 1.8 2.2
2.0 1.9 1.8
1.4 1.7 1.4
1.7 0.9 0.7
Measured data
1
2
3
7.2
6.5
5.7
4.7 10.0 4.3
10.2 13.0 11.4
9.6 10.4 11.9
7.9 10.0 8.3
2.5
2.7
3.9
1.8
2.1
1.7
1.7
3.7
1.7
4.5
4.5
5.2
4.8
4.3
4.7
3.2
3.6
3.4
1.7
1.1
1.9
1.2
1.4
1.2
1.1
2.4
1.1
3.2
2.9
3.8
3.4
3.1
3.2
2.2
2.4
2.3
1.2
0.7
1.4
Figure 37 shows the average PM inhalation comparison between the two methodologies using
route 2 as reference and considering the average for all measurements. Route 2 is the route that
presents the highest inhalation for PM10, PM2.5 and PM1, with about 20% more than the other routes.
57
Regarding the method with constant VE, route 1 and route 3 provide approximately the same value of
PM10, PM2.5 and PM1 inhalation, which indicates that route 1 the best option since the pedestrian walks
from the origin to destination location in the shortest distance and for the shortest time. On the other
hand, the method in which VE is estimated from the measured HR values along the routes, route 3
corresponds to a higher PM10, PM2.5 and PM1 inhalation compared to route 1: 3% more for PM10, 4%
more for PM2.5 and 2% more for PM1.
Figure 37 Average PM inhalation for the 3 routes in upward direction.
In conclusion, using a constant value of VE did provide lower absolute values of PM10, PM2.5
and PM1 inhalation compared to using estimated VE values from measured HR values for both
upward and downward direction. However, this methodology provides a good estimate for comparison
of the routes in downward direction, when the walk does not require a high effort for the pedestrian.
For higher effort walks (going upward), the PM inhalation given by this alternative methodology
indicates that the worst route was not the same as the one indicated by the measured data. When
walking upwards, VE was, on average, higher
Using a constant value of VE did provide lower absolute values of PM10, PM2.5 and PM1
inhalation compared to using estimated VE values from measured HR values. However, this
methodology provides a good estimate for comparison of the routes in downward direction, when the
walk does not require a high effort for the pedestrian. For higher effort walks (going upwards), the PM
inhalation given by this alternative methodology indicates that the worst route was not the same as the
one indicated by the measured data. When walking upwards, VE was, on average, higher
58
4.4.2 Estimated particulate matter data
The methodology to estimate the value of PM2.5 inhalation presented in section 4.4.1 took into
consideration measured values of PM2.5 concentration and time duration while VE was considered
constant along the routes using a reference value. In this method, PM2.5 inhalation is calculated taking
into consideration VE estimated from HR values and time duration measured along the routes but
PM2.5 concentration was estimated using reference values (Liu and Frey, 2011). PM10 and PM1 were
not considered since the literature review focus only on this range of PM to be estimated.
In order to define the PM2.5 concentration values for each route, the following process was
undertaken:
‒
Route 1 was divided into 7 segments, route 2 into 3 and route 3 into 7 segments. The
division of the routes was made in sections that have approximately constant slope.
‒
Average time duration and average speed considered are the ones mentioned in
Table 6 and Table 8, correspondingly. Assuming constant average speed along each
route, for each segment and knowing the length of each segment, it was associated a
time duration value.
‒
Using the correlation shown in equation 2, each route segment was assigned a
correspondent VE, based on its slope. Since equation 2 only considers positive slope
values, in this section it is considered only the upward direction. For negative slope
values along the routes (it occurs in route 1 and 3), it was assumed the same value of
VE for 0 degrees of slope;
‒
PM2.5 concentrations were estimated using data from Liu and Frey (2011) where PM2.5
concentration factors are presented for different road types, road level of service and
climate conditions (annex J and annex K). Off-peak period was assumed,
representing free flow traffic conditions, wind speed representative of Lisbon values
was considered to be equal to 3.3 m/s and a D stability class (average value between
classes A and G) was assumed, following the guidelines proposed by the
Environmental Protection Department of Hong Kong (EPD, 2005). The PM2.5
concentrations were obtained using this method that refers only to the increment of
traffic and excludes the other sources of PM2.5 such as natural sources. More details
on thevalues chosen according to this requirements are presented in Annex L.
Table 20 presents the results obtained after completing the previous steps, as well as the
measured values presented in section 4.3.3. Route 2 presents the highest values of PM concentration,
as expected considering that this is the main road, with values of route 3 presenting about 57% of the
average PM concentration of route 2. Considering that route 2 is the less physiologically demanding
(because of the lower average VE value compared to the other routes) and that it is the shortest route
(Table 5), when analyzing PM inhalation, this variation is less significant, considering route 2 as a
reference (100%). A pedestrian that chooses route 3 inhales about 81% of those who choose route 2
and 75.2% if choosing route 1.
59
The absolute values of PM inhalation estimated using this method are about 7 times lower
than the values obtained when PM concentration was measured. An explanation for this result is the
fact that Liu and Frey (2011) estimated PM concentration for the American fleet which is composed of
a lower percentage of diesel vehicles and also the fact that the estimation refers only to the increment
of traffic, excluding the other sources like resuspension and natural sources, for example. This
approximation was made because doing the same analysis Liu and Frey did would require modeling
the dispersion of pollutants and more meteorological measurements in situ, which was beyond scope
of this work.
Table 20 PM inhalation for the three routes using numerical data
Route
Time (min)
Average Speed (m/s)
Average VE (l/min)
Average PM concentration (µg/m 3)
Total PM inhalation (µg)
Total PM inhalation (% of route 2)
Numerical data
1
2
3
14.2 11.1 16.8
4.7
4.9
4.6
27.9 26.2 28.4
1.0
1.8
1.2
0.4
0.6
0.5
75
100
81
Measured data
1
2
3
14.3 11.2 15.4
4.7
4.9
4.6
26,4 27,0 26,2
8.4 11.8 8.3
3.2
3.6
3.4
83
100
87
Figure 38 shows the Total PM2.5 inhalation (percentage from route 2) from estimated values of
PM2.5 concentration and from measured values of PM2.5 concentration. It is possible to observe that
both methodologies present similar results regarding PM inhalation in a comparative approach.
Route 2 presents the highest values followed by route 3, with 81% the value of route 2 for
numerical data and 87% the PM2.5 inhalation of route 2 for measured data. Regarding route 1 for
numerical data, it corresponded to 75% of the PM2.5 inhalation of route 2 and, for the measured data,
83% of the value of route 2. The two approaches present the highest differences in route 1 due to a
higher value of the ratio (PM2.5inh. in route 2 / PM2.5inh. in route 1) which is 1.8 for numerical data and
1.4 for measured data, as can be seen in Table 20. This way, it is observed a higher difference
between routes for the numerical data when compared to the difference in measured data.
60
Figure 38 Comparison of PM2.5 inhalation using numerical and measured data
The estimates of PM inhalation obtained using this method were substantially lower (about
15% of the measured data) than the ones measured in situ. However it was proved that this
methodology could be a good alternative to compare routes but should not be considered for the use
of absolute values.
The two numerical analyses do not allow obtaining the same results not even as accurate and
personalizes as the ones obtained in the method presented in this thesis. For this reason, the
approach proposed in this thesis should be taken into consideration in future works.
4.5 Mitigation of pedestrians’ exposure
This section addresses some possible options that might reduce the pedestrians’ PM
exposure creating a pedestrian-friendly environment, especially in route 2 which generally showed the
highest average PM concentration values. This might be applied to all routes with the objective of
reducing pedestrians’ PM exposure or it might be applied in such a way to ensure route 2 does not
provide a value of PM inhalation higher than the other alternatives, since it is more convenient due to
being faster to walk from origin to destination.
These options might include physical barriers made of vegetation or synthetic materials,
changing the sidewalk width or using different approaches in traffic management such as reduction of
traffic level, limitation for the most pollutant vehicles, decrease of traffic lights or bus stops, etc.
Increasing distance from the source
Kingham et al. (2011) stated that locating a cycle lane 5 to 7 m away from the road can reduce
exposure by 20 to 40%, which indicates significant potential health benefits. The same trend is
61
probably applicable for pedestrians, according to the author. This option can be applied to the case
study discussed in this thesis by eliminating one lane in one or both directions in route 2. This option
must be evaluated through a traffic modeling analysis in order to optimize pollutant exposure because
decreasing the number of lanes might create conditions for traffic congestion, which increases the
emission factor (EF) of the vehicles (Vasconcelos et al., 2005). This situation would pose a higher PM
exposure of pedestrians. In order to avoid this situation, one measure could be to undertake a traffic
study with the intention of decreasing the traffic flow in this zone.
Physical barriers
McNabola et al. (2008) studied the PM concentration and noise modeling of air pollution in a
boardwalk near a river and the adjacent sidewalk (Figure 39). The results demonstrate that increasing
the height of the boundary wall (existent in between the boardwalk and sidewalk) by 1 m leads to an
average reduction of 51.8% in exposure to pollutants. The average reduction was about 96% lower for
a 2 m boundary wall (McNabola et al. 2008).
Figure 39 Case study: sidewalk and boardwalk with a barrier in between (McNabola, 2008)
On the other hand, the exposure is higher (2.83 times higher) in the sidewalk with the
presence of the barrier, as observed in the modeling of the dispersion of air pollutants in Figure 40.
A barrier can be a solution for route 2 if the segregation of the pedestrians is completely done
by positioning the barrier right next to the car lanes avoiding having pedestrians walking in the highest
concentration zone. Depending on the costs, vegetation would be a better option for aesthetical
reasons and as a way to increase the comfort of the pedestrians.
62
Figure 40 Typical air flow patterns at the boardwalk (McNabola et al., 2008).
Traffic management
There are several ways the traffic management might influence the pedestrians’ exposure to
PM. Ishaque and Noland (2008) observed that short traffic light cycles increases vehicles’ emissions
because of the higher number of time they have to accelerate after stopping due to the traffic lights.
Shorter traffic cycles might reduce the overall emissions from motor vehicles which is beneficial to
pedestrians walking on sidewalks of the main road but not for pedestrians that cross the main road
(which have higher PM concentrations that further from crossings), which was also supported by
Buonanno et. al. (2010). In this case, shorter cycle lengths are beneficial in terms of exposure of PM.
This would be something to have in mind in the case of route 2 since it was verified previously (section
4.3.4) that buses (with frequent stops along route 2) are responsible for some peaks of PM exposure.
Consequently, fewer bus stops might bring lower PM exposure for pedestrians walking along route 2.
The global exposure depends on the pedestrians’ decision on choosing one route or the other
and on the amount of time walking along a main congested road against waiting to cross it. The
consequences of these results are that the objective of traffic management policies should be to
optimize pollutant exposure and not just either pollutant concentration or travel time. A short signal
cycle might be beneficial in reducing the global exposure for intersections with large volumes of
crossing pedestrians. Fruin (1971) had already emphasized the importance of taking into
consideration the pedestrians’ flow when dealing with street design in order to avoid congestion,
especially in crossings.
Another possibility for route 2 is restricting the access of the most pollutant vehicle class in the
case study area to verify if it can significantly reduce the concentration of PM in this route. A Low
Emissions Zone (LEZ) was already established in downtown Lisbon and other areas in the city which
63
brought some modifications regarding restrictions to circulation of pre-Euro vehicles in order to comply
with the limit values established by the EU.
Restricting the access to Diesel powered pre-Euro vehicles in route 2 might also contribute to
a lower PM exposure for pedestrians walking in this route. To test this possibility, some calculations
were made. The objective is to obtain the emissions for only pre-Euro diesel vehicles:
‒
10.8 % of the Portuguese fleet corresponds to pre-Euro vehicles and, of these, 43.4% are
diesel vehicles (Vasconcelos et al., 2012). Consequently, the percentage of pre-Euro
diesel vehicles is 4.7 %.
‒
The EF of diesel cars is 0.28 g/km (Ntziachristos and Samaras, 2000) considering that the
average speed in route 2 is 15 km/h.
‒
With this data it is possible to estimate that the average EF of PM for the pre-Euro diesel
vehicles is 0.02 g/km.
‒
Considering that average emission of PM for the representative vehicle of the Portuguese
fleet is 0.03 g/Km (Vasconcelos et al., 2012) which includes the diesel cars and the
others, that means that restricting the access of this vehicles in route 2 results in a
reduction of PM concentration (in the source) of 59 %.
The value obtained must be interpreted as a potential reduction in PM exposure for
pedestrians because it refers only to the reduction in the source and the fleet that is found in the area
might not be representative of the Portuguese fleet. Nevertheless, if one considers that the PM
concentration in the source is proportional to the PM exposure and that the zone is representative of
the Portuguese fleet, applying this restriction to the route 2 might mean up to 59% decreasing of PM
exposure, which is a significant amount.
All the other alternatives mentioned might also be implemented together to more effectively
reduce pedestrians’ exposure, for whatever the routes chosen.
64
5 Conclusions and discussion
The aim of this study was establishing a methodology that allows comparing the total PM
inhalation for different alternative routes for the same OD (origin-destination) pair. In order to obtain
more accurate values for total PM inhalation that take into consideration the physiologic response of
pedestrians, it was important to define a way to account for the VE along a trip. Since it is difficult to
measure VE in situ, since it requires a spirometer, it was necessary to use indirect methods to
estimate VE. Therefore, it was obtained a relationship between slope and speed for pedestrians in
urban trips. Taking into consideration the average values of speed for each value of slope, VE and HR
were obtained for each slope which allowed estimating VE from the measured values of HR (Heart
Rate). This was used to obtain VE values from the HR (easily measured in situ). PM concentration
was measured in situ for three routes with the same OD pair and PM inhalation was obtained based
on PM concentration, VE and time. The comparison of the three routes was made assuming one of
the routes as a reference (representing 100%) in order to avoid the variability of PM concentration due
to traffic and climatic conditions. The results are summarized in Table 21.
Table 21 Summary of the results obtained.
Direction
Route
Average time (min)
Average HR (bpm)
Average speed (km/h)
Average VE, (l/min)
Average V (l/min)
Average PM10 concentration (µg/m3)
Average PM2.5 concentration (µg/m3)
Average PM1 concentration (µg/m3)
Average PM10 inhalation (% of route 2)
Average PM2.5 inhalation (% of route 2)
Average PM1 inhalation (% of route 2)
Furthermore, an estimate
Route 1
14.1
119.2
4.8
24.3
343.0
22.0
9.5
6.7
80
112
118
of
Downwards
Route 2 Route 3
11.0
15.1
118.4
122.7
5.0
5.3
23.5
25.7
257.0
389.0
23.1
36.2
8.4
12.0
5.9
8.2
100
72
100
91
100
88
particles
inhalation was
Route 1
14.3
123.7
4.7
26.4
385.0
28.0
11.8
7.9
73
83
88
performed
Upwards
Route 2
11.2
125.0
4.9
27.0
300.0
20.5
8.3
5.5
100
100
100
using
Route 3
15.4
123.3
5.2
26.5
411.0
20.7
8.3
5.7
76
87
90
alternative
methodologies for comparison purposes.
The conclusion and discussion of the methodology must be done in five parts:
1.
In order to obtain a relation between speed and slope, to use as basis for the
development of an indirect method to estimate VE, four volunteers walked in several
urban paths in the Lisbon area. The paths had an approximate constant slope which was
verified through GoogleEarth prior to the measurements. There was a big variability of
speed between volunteers and within each volunteer for the same slope values. This
65
might be explained by specific individual variables like fatigue, pleasantness of the path,
pavement conditions or distractions like storefronts. This variability of speed did not
compromised the following steps since the speed did no vary significantly for the slopes
that were established in the next step, according to the FMH laboratory experts, where
the measurements took place.
2.
Furthermore, it was obtained a strong correlation between VE and slope for pedestrians
in laboratory environment which indicated a linear increase of VE by incrementing the
slope. Likewise for VE and HR relationship, a strong correlation for the volunteers was
found. These results show that the selected volunteers had similar physical responses to
the same level of effort.
3.
HR was measured in situ in order to obtain VE values, using the VE vs. HR correlation,
along the three chosen routes (case study). The HR values were very consistent along
each route with values for upward direction trips highest than downwards. Between the
three routes, it was observed that the main route presented the lowest average HR since
it has a regular slope along the route, contrary to the other routes which have irregular
slope and some segments that alternate between negative and positive slopes,
considering only one direction.
Likewise, PM10, PM2.5 and PM1 concentrations were
measured in situ. It was observed a very wide variability of the average values between
trips and between routes due to specific events at the time and place the measurements
were made. In general, route 2 (the main route) presented the highest average PM
concentrations.
4.
After calculating PM10, PM2.5 and PM1 inhalation, it was observed that route 2 (the main
route) presented the highest average PM10 inhalation up to 30% more than the
alternatives. For PM2.5 and PM1, route 1 or route 2 presented the highest concentration
and inhalation values, depending on the measurements. This result proved the fact that
average PM concentration along one route does not directly indicate more PM inhalation
for pedestrians: average PM concentration was generally higher in route 2, but this was
not always the route that presented higher PM inhalation. This also proves the
importance of taking into account the physical effort of pedestrians (assessed by VE) in
the calculation of PM inhalation and the importance of assessing PM inhalation instead of
only PM concentration, which most of the previous studies did, when studying the effect
of PM in human health. This is especially relevant for pedestrians and cyclists since other
transportation modes do not require as much physical effort.
5.
Two methods of estimation of PM10, PM2.5 and PM1 inhalation were performed for
comparison with the method presented in this thesis.
a. The values obtained for PM inhalation for a constant VE did provide about 67% of the
absolute values of PM inhalation using measured data. However, this methodology
66
provides a good estimate for comparison of the routes in downward direction, when
the walk does not require a high effort for the pedestrian. For higher effort walks, the
PM inhalation given by this alternative methodology indicates that the worst route was
not the same as the one indicated by the measured data. When walking upwards, VE
was, on average, higher. It was proved that using a constant VE for estimation of PM
inhalation did not predict the route that is actually the worst option for pedestrians;
b. The estimates of PM inhalation obtained estimated PM data this method were only
about 15% of the ones measures in situ, however it was proved that this methodology
could be a good alternative to compare routes but should not be considered for the
use of absolute values.
Comparing the results found within this work and the literature review, in general, the PM
concentration obtained was lower in the case study chosen in the city of Lisbon. The main difference
might be the fact that off-peak period was chosen and also Lisbon probably has lower traffic levels
compared to the cities studied by the other authors. It is relevant to mention that the deviation
obtained in this thesis is significantly better compared to previous studies.
The methodology created is appropriate for comparison of the 3 routes chosen for this study
regarding PM inhalation values and might be a good method to be included in future tools for
comparison of routes with the same OD regarding PM inhalation. In fact, it proved to be a better
approach when compared with the other alternative analysis since it is adjusted to the topography and
personal characteristics of the pedestrian. The results from this study should be incorporated in city
planning improvement, defining policies to reduce PM concentration on major pedestrian corridors or
deploying infrastructures that contribute to reduce the physical effort in areas where PM concentration
is higher.
5.1 Future research
The time frame of this these proved not to be enough to perform further experimental analysis.
Thus, some suggestions for future work that would complete the present study are addressed:
‒
More measurements need to be done in order to decrease the variability of the values of PM
inhalation obtained, especially in routes 1 and 3 in order to understand if there is one route that,
on average, presents higher values of PM2.5 inhalation and PM1 inhalation. This would be useful
to compare the three routes but, as seen for previous studies, a higher sample size might not
lead to a lower deviation within one route. Other step could have been added in order to obtain
more data for PM10, PM2.5 and PM1 inhalation for the pedestrian walking the three routes in
downward direction (even without doing more measurements): inverted values of PM
concentration in upward direction trips could be inverted and assuming the same physiologic
data for downward direction; similarly, inverting PM concentration for trips in downward direction
67
and using physiologic data in upward trips in order to obtain more PM 10, PM2.5 and PM1
inhalation data in upward direction. It is reasonable to proceed in such a way because the PM
concentration is independent of the pedestrian physiologic response to the effort of walking the
route. Moreover, the study would be further improved if other age and characteristics range of
the volunteers such as: children, seniors or diabetics were considered.
‒
Another aspect that was not possible to include in the present study due to time constraints is
related with the use of a portable spirometer (K4b2, Cosmed, Rome, Italy) used in the laboratory
measurements, which is suitable to take into the field and could be added to the MoveLab, used
in this study. This way, it would be possible to validate the VE values that were estimated using
equation 3.
‒
Moreover, as mentioned in section 4.3.4, a deeper analysis of peak exposure would be an
important addition to the present thesis. In order to do it, the numpad must allow distinguishing
the more common singularities present in the case study, which are the causes of exposure
peaks, namely: buses, trucks, pre-Euro personal diesel vehicles, cigarette smoke and while
waiting in crossings and crosswalks. In order to do this more effectively, an image method could
be used, just as proposed by Kaur et al. (2006).
‒
McNabola et al. (2008) proposed adding the analysis of noise (being an important urban
pollutant) in the studies that assess PM exposure. The reason is the fact that the high frequency
of noise contained in local traffic related events would be informative, especially for quantifying
exposure risk to pedestrians. To exemplify this situation: a fast acceleration of a motor vehicle is
a source of high emissions of PM and is related to a characteristic noise. Thus, including noise
measurements to the present study would be useful, especially to help identifying the events
that lead to the peaks of PM related to traffic.
‒
The municipality of Lisbon developed a plan (“Pedestrians Accessibility Plan of Lisbon”, CML,
2013). The objective is to define the best strategy for promoting accessibility in Lisbon until the
end of 2014. The plan takes into consideration several problems that pedestrians find in public
places and intends to promote walking as a preferable mode of transportation. Walking brings
better health for the pedestrians because it is a way of locomotion and a form of transportation
at the same time. It is also beneficial for the environment due to the very low emissions
compared to motor vehicles. The plan proposes increasing the walkability of the existing
sidewalks and other urban structures for pedestrians in order to fulfill the objectives mentioned.
If more citizens prefer walking instead of driving a private car, naturally the air pollution will
decrease in the long run but that scenario is a situation that will not be observed in a short time
period which might take decades. Unfortunately the plan does not take into consideration the
exposure of pedestrians to the air pollution and measures to mitigate the effect of pollutants in
human health. The methodology developed in this thesis might be very useful if included in the
Municipality’s Plan leading to the improvement of the conditions of paths that promote lower
inhalation of pollutants.
68
‒
The Association for the Protection of Portuguese Diabetics (APDP) and CML proposed the
creation of “healthier paths for pedestrians”. These paths are regular urban paths, integrated in
the sidewalks network that are adapted in order to promote walking as a way of doing exercise,
which is especially important for diabetics. The proposal includes four paths (Figure 41) and
several measures to make them easier and more pleasant to walk in.
Figure 41 Proposed paths for rehabilitation (CML, 2011)
One of the paths lead to the headquarters of the Association and the four paths start or
finish (depending on the direction), in Largo do Rato. This busy crossroad has been one of
the main arterial road of traffic circulation in Lisbon and has several bus stops and one
subway station. These factors make this area not really recommended for pedestrians
considering the high level of air pollution, especially during peak hours. It would be
beneficial for the pedestrians’ health to include the air pollution factor into consideration in
this plan. Once again, the methodology proposed in this thesis might be a good
contribution for the goal.
69
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Annexes
Annex A - Slope and length of each trip (each column represents one segment).
77
Annex B – HR along all trips on route 1, route 2 and route 3 (top to bottom figure order)
78
Annex C – Average HR for each measurement in route 1, route2 and route 3 respectively from top to
bottom figure.
Striped columns correspond to the average HR values without carrying the MoveLab and the
darker columns correspond to the average HR values carrying the MoveLab. Both downward and
upward direction is represented next to the labels with the letter “d” and “u” according to respective
direction.
79
Annex D - PM10 concentration, PM10 inhalation and VE for one trip.
The graph presents the PM10 concentration and PM10 inhalation for route 1 and VE on
11.03.2014. As expected, PM inhalation presents a high variation with VE.
There is a bigger
difference between PM10 concentration and PM10 inhalation when the value of VE is higher (33 l/min),
for example, at 16:05:48 and when the VE is lower (13 l/min), the difference between PM10
concentration and PM10 inhalation is lower, for example at 15:59:00. This graph shows the importance
of using measured data of VE instead of average values from the literature.
PM10 concentration
peak =57.77 ug/m3
80
Annex E - Average time spent per day performing activities within specified intensity categories, and
average ventilation rates associated with these activity categories, for females according to age category
(EPA, 2009).
81
Annex F - Summary of the values of time duration, Volume inhaled, mean VE, PM concentration and PM
inhaled on 12.03.2014.
The following analysis is based on the same kind of analysis that was made for the
measurements on 11.03.2014 (section 4.3.5) but here for the rest of the measurements.
On 12.03.2014 the average PM concentration is, in general, higher than the previous day (as
high as 12%, daily average for both directions and the three routes) regarding PM10, PM2.5 and PM1.
PM10 inhalation was the highest in route 2: with a higher average PM concentration the
consequence is a higher PM inhalation for this particular day (between 107 and 129% more compared
to route 1). Route 3 had the lowest values of PM10 concentration and PM10 inhalation: total PM10
inhalation was only 6.2 and 4.3 in downward and upward directions, respectively, while the PM10
inhalation for route 1 was 8.8 and 4.7, respectively.
PM2.5 and PM1 concentrations were the highest in route 1 in downward direction and this was
the route that registered a higher PM2.5 and PM1 inhalations, 68% more compared to route 2. In
upward direction, route 2 had the highest values of average PM2.5 and PM1 concentrations and the
higher PM2.5 and PM1 inhalation (50% more than the other routes). Route 3 presented the same PM2.5
and PM1 inhalation compared to route 1 in upward direction (1.7 and 1.1 respectively). In downward
direction, PM2.5 was 0.1 higher in route 3 than in route 2 and PM1 was 0.1 lower in route 3 than in
route 2.
PM10, PM2.5 and PM1 inhalation directly changed with the average value of PM10, PM2.5 and
PM1 concentration: the higher the average concentration, the higher the inhalation.
Route 2 did not register a higher PM inhalation only for PM1compared to the other route The
higher value of mean PM concentration in the routes where the traffic level is considerably lower. One
possible explanation for this fact that the value for wind speed (Table 11) on this day was higher than
11.03.2014. Due to the fact that route 2 is a lot wider than routes 1 and 3, the tunnel effect may be the
cause of the higher concentration in these routes. PM10 concentration had the highest value in route 2
and the lowest value in route 3, for both directions. Consequently, the PM10 inhalation followed the
same trend. The higher concentration in route 2 is expected since PM10 concentration increases
through resuspension. So, the higher the traffic level, the more the PM settled in the ground
resuspends, so the higher the PM concentration.
82
PM 10
PM2.5
PM1
Direction
t, min
V, l
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
Route 1
Down
14.6
328.9
24.5
8.8
8.7
3.4
5.5
2.2
Route 2
Down
10.0
229.0
39.3
10.4
8.5
2.0
5.4
1.2
Route 3
Down
14.1
365.7
20.6
6.2
7.1
2.1
3.5
1.1
Route 1
Up
13.6
398.0
18.7
4.7
7.3
1.7
5.0
1.1
Route 2
Up
10.0
251.0
23.3
9.7
13.1
3.7
8.1
2.4
Route 3
Up
13.4
358.8
14.7
4.3
5.9
1.7
3.8
1.1
Annex G - Summary of the values of time duration, Volume inhaled, mean VE, PM concentration and PM
inhaled of the first measurements on 13.03.2014
PM 10
PM2.5
PM1
Direction
t, min
V, l
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
Route 1
Down
12.8
302.9
26.6
8.1
13.3
4.0
9.8
3.0
Route 2
Down
10.6
246.0
49.5
12.3
16.7
4.2
11.0
2.7
Route 3
Down
15.0
372.6
26.9
10.0
11.4
4.3
8.3
3.1
Route 1
Up
14.1
369.4
27.9
10.2
12.1
4.5
8.8
3.2
Route 2
Up
10.9
286.2
43.5
13.0
15.6
4.5
10.1
2.9
Route 3
Up
15.3
419.3
27.0
11.4
12.0
5.2
8.6
3.8
Annex H - Summary of the values of time duration, Volume inhaled, mean VE, PM concentration and PM
inhaled of the second measurements on 13.03.2014
PM 10
PM2.5
PM1
Direction
t, min
V, l
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
PM, µg/m³
PM inh, µg
Route 1
Down
14.3
360.9
26.2
9.4
11.8
4.3
8.4
3.1
Route 2
Down
11.3
264.9
34.9
9.1
15.7
4.1
11.1
3.0
Route 3
Down
15.4
415.6
20.5
8.4
10.6
4.4
7.3
3.0
83
Route 1
Up
14.2
405.7
25.0
9.6
10.7
4.8
7.2
3.4
Route 2
Up
12.5
333.0
26.3
10.4
12.3
4.3
9.1
3.1
Route 3
Up
15.6
460.4
25.3
11.9
10.2
4.7
7.0
3.2
Annex I - Comparison between route alternatives according to PM inhalation. Cumulative PM 2.5 inhalation
for the three routes along the distance measured on 11.03.2014.
84
Annex J - Characteristics of road types and traffic conditions (Liu and Frey, 2010).
Annex K - Near road PM2.5 increment (µg/m3) for selected traffic flow and road type, wind speed and
stability class scenarios (Liu and Frey, 2010).
85
Annex L – Detailed data used for calculating estimated PM2.5 inhalation.
Length
(m)
Road
type
Distace from
origin (m)
114
Local I
280
182
69
89
293
87
280
Local I
Local I
Local I
Local III
Local I
Local I
367
660
749
818
1000
1114.0
701
91
112
Local III
Local III
Local III
112
203
904
19
296
189
388
218
67
112
Local III
Local I
Local I
Local I
Local I
Local III
Local I
112
179
397
785
974
1270
1289
PM2.5
concentration
(µg/m³)
Route 1
0.90
0.90
0.90
0.90
1.75
0.90
0.90
Route 2
1.75
1.75
1.75
Route 3
1.80
0.90
0.90
0.90
0.90
1.75
0.90
86
Slope
(%)
VE
(l/min)
Time (s)
PM2.5
inhalation
(µg)
-3.7
23.7
87
0.03
6.0
1.0
2.0
-3.0
3.0
6.0
38.3
26.2
28.6
23.7
31.0
23.7
139
53
68
224
67
214
0.08
0.02
0.03
0.16
0.03
0.08
2.5
0.0
0.5
23.7
23.7
23.7
515
67
82
0.46
0.05
0.06
-7.0
5.3
4.2
-0.8
-4.8
-0.5
4.1
23.7
36.5
34.0
23.7
23.7
23.7
33.6
15
232
148
304
171
52
88
0.01
0.13
0.08
0.11
0.06
0.04
0.04