Spatial Variability of Particulate Matter (PM2.5) in the Ambient Air on ...
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Spatial Variability of Particulate Matter (PM 2.5 ) in the Ambient Air on the Campus of the University of Manchester A dissertation submitted to the University of Manchester for the degree of MSc Environmental Monitoring Modelling and Reconstruction in the Faculty of Humanities. 2014 Jorge B. Cevallos School of Environment, Education and Development
Transcript of Spatial Variability of Particulate Matter (PM2.5) in the Ambient Air on ...
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Spatial Variability of Particulate Matter (PM2.5)
in the Ambient Air on the Campus of the
University of Manchester
A dissertation submitted to the University of Manchester for the degree of MSc
Environmental Monitoring Modelling and Reconstruction in the Faculty of Humanities.
2014
Jorge B. Cevallos
School of Environment, Education and Development
2
Table of Contents
List of Illustrations .............................................................................................................................. 3
List of Tables ...................................................................................................................................... 3
Abstract ............................................................................................................................................... 4
Acknowledgements ............................................................................................................................. 5
Declaration .......................................................................................................................................... 6
Intellectual Property Statement ........................................................................................................... 6
Chapter 1: Introduction ..................................................................................................................... 7
1.1. Background .................................................................................................................................. 7
1.2. Scope of the Study ....................................................................................................................... 8
Chapter 2: Academic Context ......................................................................................................... 10
2.1. Characteristics of Particulate Matter .......................................................................................... 10
2.1.1. Characterization of Particulate Matter According to its Dimension ....................................... 10
2.2. Chemical Composition and Sources of Ambient Particulate Matter (PM2.5) ............................. 11
2.3. Factors Affecting PM2.5 Concentrations .................................................................................... 12
2.3.1. Traffic ..................................................................................................................................... 12
2.3.2.Meteorological Factors Affecting PM2.5 Concentrations ......................................................... 14
2.4. Methods to estimate PM2.5 ......................................................................................................... 16
2.4.1. Fixed Monitoring Stations ...................................................................................................... 17
2.4.2. Mobile Monitoring .................................................................................................................. 17
2.5. Human Exposure to Particulate Matter in Urban Environments................................................ 19
2.5.1. Human Exposure at Bus Stops and Street Intersections ......................................................... 20
2.6. Health Risks of Prolonged Exposure to PM2.5 ........................................................................... 21
2.7. Conclusion ................................................................................................................................. 21
Chapter 3: Research Questions ....................................................................................................... 22
Chapter 4: Methodology ................................................................................................................. 23
4.1. Monitoring Location .................................................................................................................. 23
4.2. Monitoring Equipment and Data Collection .............................................................................. 23
4.3. Data Analysis ............................................................................................................................. 24
Chapter 5: Results ........................................................................................................................... 26
5.1. Spatial Distribution of Ambient PM2.5 Concentrations at the University of Manchester .......... 26
5.1.1. PM2.5 concentrations on Oxford Road and Sackville Street .................................................... 27
5.1.2. Human Exposure to PM2.5 at Seven Bus Stops on Oxford Road ............................................ 28
5.1.3. Typical Pedestrian Exposure to PM2.5 along the Monitoring Route ....................................... 30
5.2. Temporal Variability of PM2.5 Concentrations .......................................................................... 31
5.2.1. Weekday-to-weekend PM2.5 Variability ................................................................................. 31
5.3. Factors Impacting PM2.5 Concentrations on the Campus of the University of Manchester ....... 34
5.3.1. Traffic ..................................................................................................................................... 34
5.3.2. Wind speed.............................................................................................................................. 35
5.3.3. Temperature ............................................................................................................................ 35
5.3.4. Relative Humidity ................................................................................................................... 36
5.3.5. Correlation between Incoming Buses and PM2.5 Concentrations at Bus Stops ....................... 37
Chapter 6: Discussion ..................................................................................................................... 38
6.1. Spatial distribution of ambient PM2.5 concentrations at the University of Manchester ............. 38
6.2. What is the temporal variability of PM2.5 concentrations at the University of Manchester? ..... 38
Chapter 7: Conclusions ................................................................................................................... 41
References ........................................................................................................................................ 42
Word Count
9,214
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List of Illustrations
Figure 1: Map displaying the area of the campus of the University of Manchester ..................... 9
Figure 2: Dimensions of PM10 and PM2.5 as compared to a human hair. .................................... 10
Figure 3: Mean chemical composition of atmospheric particles. ............................................... 11
Figure 4: Relationship between PM2.5 concentrations and traffic counts. ................................... 13
Figure 5: Vertical flow patterns in urban canyons. ..................................................................... 16
Figure 6: Location of the monitoring stations of the Automated Urban and Rural Network. .... 17
Figure 7: AEROFLEX bicycle (A) and van (B). ........................................................................ 18
Figure 8: Background PM2.5 concentrations in the study area. ................................................... 20
Figure 9: Typical bus stop design on Oxford Road, Manchester. ............................................... 21
Figure 10: Electron micrograph of a fine particle. ...................................................................... 21
Figure 11: Location of the monitoring sites. ............................................................................... 23
Figure 12: (A) Monitoring technique, (B) EPAM-5000 monitor and (C) anemometer. ............. 24
Figure 13: Distribution of PM2.5 levels on the campus of the University of Manchester. ........... 26
Figure 14: A) Site S.02 on Oxford Road. B) Site S.18 on Sackville Street.. .............................. 27
Figure 15: Mean PM2.5 concentrations for each monitoring site. ................................................ 28
Figure 16: Mean concentrations of PM2.5 at seven bus stops on Oxford Road. .......................... 29
Figure 17: Total number of exposed people to PM2.5 at seven bus stops. ................................... 29
Figure 18: Site S.04 -bus stop located in front of the City Labs Building. ................................. 30
Figure 19: Pedestrian exposure to PM2.5 along the monitoring route. ......................................... 30
Figure 20: Mean PM2.5 concentrations for all monitoring sites. .................................................. 31
Figure 21: Mean concentrations of PM2.5 for each day of the week. .......................................... 32
Figure 22: Mean PM2.5 concentrations for each monitoring site and day of the week ................ 33
Figure 23: Correlation between traffic counts and PM2.5 concentrations. ................................... 34
Figure 24: Correlation between wind speed and PM2.5 concentrations. ...................................... 35
Figure 25: Correlation between temperature and PM2.5 concentrations. ..................................... 36
Figure 26: Correlation between temperature and PM2.5 concentrations. ..................................... 36
Figure 27: Correlation between incoming buses and average PM2.5 concentrations. ................. 37
Figure 28: Average PM2.5 concentrations on Oxford Road and Sackville Street........................ 38
Figure 29: Correlation between variuos factors and PM2.5 concentrations. ................................ 39
List of Tables
Table 1: Natural and anthropogenic sources of ambient PM2.5 ................................................... 12
Table 2: Instrumentation for mobile monitoring of particulate matter. ...................................... 19
Table 3: Air Quality Index (AQI) for 24-hour fine particle concentrations. ............................... 21
Table 4: Summary of the obtained data ...................................................................................... 27
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Abstract
Ambient PM2.5 concentrations were monitored at 21 sites on the campus of the
University of Manchester during the month of June 2014. Concentrations were found to
vary spatially and temporarily. The evaluation of the spatial variability of PM2.5
concentrations showed average values ranging from 11.27 µg m-3
to 25.72 µg m
-3. The
temporal fluctuation of PM2.5 concentrations ranged from 4.54 µg m-3
to 34.71 µg m-3
.
Human exposure to PM2.5 at bus stops, street intersections and along Oxford Road and
Sackville Street was measured and found to occur within the limits recommended by
national and international air quality regulations. PM2.5 levels were more strongly
correlated with relative humidity and general traffic counts. This study is presented as a
preliminary evaluation of the spatial variability, temporal fluctuation and factors
governing the concentrations of ambient fine particulate matter on the campus of the
University of Manchester.
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Acknowledgements
I would like to first thank God for the great opportunity of studying at the University of
Manchester. I also wish to thank Sarah Lindley, the Supervisor of my dissertation, for
her support and patience in providing me with the necessary advice throughout the
preparation of this document. I would like to express my gratitude to the Government of
Ecuador for their financial support for the acquisition of the particulate matter
monitoring equipment (without their help, this study would not have been possible).
More intimately, I wish to thank my wife Luisita Vélez for always encouraging me to
do my best throughout the course of my education abroad. Finally, my gratitude also
goes to my mother Silvia Bravo, sister María Zambrano, and all my siblings who in
many ways supported me while I was away from home.
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Declaration
I declare that no portion of the work referred to in this dissertation has been submitted
in support of an application for another degree or qualification of this or any other
university or other institute of learning.
Intellectual Property Statement
I. The author of this dissertation (including any appendices and/or schedules to this
dissertation) owns certain copyright or related rights in it (the “Copyright”) and he has
given The University of Manchester certain rights to use such Copyright, including for
administrative purposes.
II. Copies of this dissertation, either in full or in extracts and whether in hard or electronic
copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988
(as amended) and regulations issued under it or, where appropriate, in accordance with
licensing agreements which the University has entered into. This page must form part of
any such copies made.
III. The ownership of certain Copyright, patents, designs, trademarks and other intellectual
property (the “Intellectual Property”) and any reproductions of Copyright works in the
dissertation, for example graphs and tables (“Reproductions”), which may be described in
this dissertation, may not be owned by the author and may be owned by third parties. Such
Intellectual Property and Reproductions cannot and must not be made available for use
without the prior written permission of the owner(s) of the relevant Intellectual Property
and/or Reproductions.
IV. Further information on the conditions under which disclosure, publication and
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Reproductions described in it may take place is available in the University IP Policy (see
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restriction declarations deposited in the University Library, The University Library’s
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University’s Guidance for the Presentation of Dissertations.
7
Spatial Variability of Particulate Matter (PM2.5) in the Ambient Air on the Campus of the University of Manchester.
Chapter 1: Introduction
1.1. Background
Airborne particulate matter (PM) is constituted by a colloid of solid or liquid particles
that can be classified according to their physical form and chemical composition. PM2.5
is the term used to describe fine particles whose dimensions are ≤2.5 microns (µm) in
diameter (Durant et al., 2014). PM2.5 can be comprised of organic or inorganic particles
depending on the source of emission (e.g. pollen, sawmilling processes, motor vehicles
and forest fires) (Ediagbonya et al., 2013; Kurth et al., 2014 and Shen et al., 2014).
Links between human exposure to PM2.5 and insidious health effects have been
consistently demonstrated by epidemiological studies (Colburn and Johnson, 2003; Nel,
2005 and Dominici et al., 2006). Prolonged human exposure to high PM2.5
concentrations can cause deleterious consequences to the respiratory and cardiovascular
systems (Pope et al., 2002). The ease with which these fine particles can reach the lungs
and heart may result in health disorders such as emphysema, bronchitis, asthma, lung
cancer and coronary artery disease (Ghio, 2014 and Rohr et al., 2014).
In the European Union, Directive (2008/50/EC) establishes standards for Member States
in relation to PM2.5 and other air pollutants. The Automated Urban and Rural Network
(AURN) is the major stationary monitoring network in the UK used for verifying
compliance of this Directive and other ambient air quality regulations (Chen et al.,
2008). This fixed monitoring network has the advantage of providing a good indication
of the temporal variability of ambient PM2.5 levels at the monitoring site. However, as
indicated by Van Poppel et al. (2013), stationary monitoring networks are incapable of
capturing the complete evolution of airborne particles, especially in polymorphic urban
environments with insufficient density of monitoring stations. Thus, large uncertainties
are expected in the estimation of PM2.5 concentrations for areas in-between monitoring
stations (Reyes and Serre, 2014). In contrast, mobile monitoring campaigns can be more
suitable to understand the spatial distribution patterns of PM2.5 concentrations and to
produce more accurate exposure estimates in cities with variegated structural and
topographical characteristics (Peters et al., 2013).
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The majority of PM2.5 studies have generally used data originated from stationary
monitoring networks (e.g. Blanchard et al., 2014; Wu et al., 2014 and Zhao et al.,
2014). However, with the increasing availability of relatively inexpensive portable
monitoring equipment, mobile monitoring is now becoming more abundant in the
literature. Despite this, most of the existing mobile monitoring studies are focused on
the understanding of the spatial distribution of PM2.5 levels without placing an
appropriate emphasis on specific human exposure locations (Weijers et al., 2004;
Wallace et al., 2009 and Tunno et al., 2012). Moreover, as indicated by Reyes and Serre
(2014), stationary and mobile monitoring can be unified to create a more robust
methodology, which considers human exposure estimates in urban environments
through the evaluation of both the spatial and temporal variability of ambient PM2.5
concentrations.
Therefore, in view of the scarcity of mobile monitoring studies providing an indication
of ambient PM2.5 concentrations at street locations with a large number of people, the
principal objective of this research is to evaluate the spatial and temporal variability of
PM2.5 concentrations using bus stops and street intersections as monitoring sites along
two major roads on the campus of the University of Manchester. In addition to PM2.5
monitoring, the obtained particle concentration values were correlated with traffic
counts and meteorological variables collected in situ and from a weather station.
Finally, monitoring is here presented with a different approach by integrating the
advantages of the stationary and mobile monitoring methodologies in order to produce a
more precise estimate of PM2.5 concentrations in an urban environment.
1.2. Scope of the Study
Due to time constraints and lack of personnel, this research work was restricted to
Oxford Road and Sackville Street —two important streets on the campus of the
University of Manchester (Figure 1). This area was selected due to the prominent
outdoor human exposure to PM2.5 occurring along the two streets. The physical
incapability of one person to perform multiple monitoring activities simultaneously
restrained the collection of in-situ meteorological variables to temperature and wind
speed. However, other data from the Whitworth Meteorological Observatory were also
used in the analyses. PM2.5 data were collected in the form of particle concentrations,
with no inclusion of chemically speciated data or details of the physical characteristics
9
of the measured particles. Data collection took place in the month of June 2014 during
the summer break of the University of Manchester.
Figure 1: Map displaying the area of the campus of the University of Manchester (red contour). Oxford Road and Sackville Street are highlighted in darker blue lines. Source: Open Street Map, 2014.
Manchester
100 m
Oxford Road
Sackville Street
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Chapter 2: Academic Context
2.1. Characteristics of Particulate Matter
Airborne particles are chemically an extremely diversified class of substances in the
atmosphere (Lee, 1972; Kelly, 2012 and Kurth et al., 2014). Despite this, they do have a
number of physical properties in common (e.g. dimension, surface area, and mass), and
for this reason are normally categorized with a distinct nomenclature, generally referred
to as particulate matter (PM) (AQEG, 2012). The concept of particulate matter may be
improperly allocated only to the solid phase. However, this distinction is difficult to
make in practice. Thus, it is more suitable to define PM as any dispersed matter, solid or
liquid, in which the individual particles are larger than single molecules (about 0.002
µm in diameter), but smaller than approximately 100 µm (Finlayson-Pitts and Pitts,
2000).
2.1.1. Characterization of Particulate Matter According to its Dimension
Initial attempts to quantify atmospheric particles in terms of their size were based on
gravimetric fractionation. For instance, in a study conducted by Lee (1972) using the
NASN cascade impactor, it was found that PM measured in urban air in the United
States and Great Britain was considerably uniform and composed of particles with
diameters less than 1 µm in size. In contrast, modern light scattering instruments (for
example, see APS 3321 Particle Sizer® and the Electrostatic Classifier Model 3080, TSI
Incorporated, 2014) are used today to classify PM according to its size. See Figure 2 for
the illustration of two common PM size categories.
Figure 2: Dimensions of PM10 and PM2.5 as compared to a human hair. Source: Adapted from U.S. EPA (2014).
11
The most common dimension categories that particulate matter has been
correspondingly classified into are PM10 (≤10 µm in diameter); Fine Particles/PM2.5
(≤2.5 µm in diameter); PM1 (≤1.0 µm in diameter) and Ultrafine Particles/UFPs (≤0.1
µm in diameter) (Kurth et al., 2014). Larger particles, also known as coarse particles
(≥2.5 ≤100 µm), have a little contribution to particle number estimation (actual amount
of particles in the air), but they substantially contribute to the main proportion of total
particle mass (mass of the particles expressed in µg or mg). The opposite occurs with
fine particles (≤2.5 µm).
2.2. Chemical Composition and Sources of Ambient Particulate Matter (PM2.5)
The chemical constituents forming ambient PM2.5 in urban environments is largely
dependent on its origin (localized or regional), meteorological conditions, long-range
transport effects and chemical reactions in the atmosphere. For instance, black carbon is
associated with localized sources such as road traffic, industrial emissions and domestic
(solid fuel and oil) combustion (Saarikoski et al., 2008). Sillanpa¨a et al. (2005) show
that the major components in PM2.5, at six urban locations in Europe, were
carbonaceous compounds (elemental carbon + organic matter), sea salt and secondary
inorganic ions. Carbonaceous compounds are among the most abundant components
found in atmospheric PM2.5 in urban environments. Ammonium sulphate, sodium
chloride, ammonium nitrate, sodium nitrate, potassium, calcium and iron also make a
significant contribution to PM2.5 in urban zones (Yin and Harrison, 2008) (Figure 3).
Figure 3: Mean chemical composition of atmospheric particles (PM10, PM2.5, PM1 and PM2.5-10) from three sites in the United Kingdom: Bristol Road site (BROS), Birmingham City Centre site (BCCS) and Churchill Pumping Station site (CPSS). Bars representing PM2.5 have been highlighted in bold. Source: Reproduced from Yin and Harrison (2008).
PM10 PM2.5 PM1 PM2.5-10 PM10 PM2.5-10 PM2.5 PM1 PM10 PM2.5 PM1 PM2.5-10
BROS BCCS CPSS
Iron-rich Dust
NaCl
Organics
NH4NO3/NaNO3
Calcium Salts
EC
(NH4)2SO4
Other
30.0
Ma
ss C
on
ce
ntr
atio
n (
µg
m-3
)
25.0
20.0
15.0
10.0
5.0
0.0
12
In addition to localized emissions, regional sources can contribute to a substantial
fragment of the total mass of urban particulate matter. Examples of some natural
regional sources are volcanic dust, desert dust and vegetation pollen (Table 2.1). In a
study by Abdeen et al. (2014) conducted at three populated locations in Palestine,
Jordan and Israel, it was found that total PM2.5 mass was highly impacted by
contributions of sulphate and crustal components from dust storms originated in
regional desert areas. Thus, atmospheric PM2.5 in urban environments is a combination
of particles coming from localized and regional emission sources.
Table 1: Natural and anthropogenic sources of ambient PM2.5. Source: Extracted from European Environmental Agency, 2012.
Natural Regional Sources Anthropogenic Localized Sources
Soil erosion Motor vehicle exhaust
Sea salt particles Domestic biomass combustion (especially in winter)
Desert dust Industrial incinerators/boilers
Volcanic dust Restaurant cooking
Wild-land fires Power generation
Pollen Mining processes
Spores Cement plants
Livestock Sawmilling processes
2.3. Factors Affecting PM2.5 Concentrations
The most direct indicator of high concentrations of PM2.5 in urban locations is the
omnipresence of pollutant sources. It can be argued that a street filled with a great
number of fossil-fuel-powered vehicles is more likely to have higher PM2.5
concentrations than a rural site with few or no emission sources (Merbitz et al., 2012).
For instance, in the UK, rural annual average concentrations of PM2.5 range from 3.5 µg
m-3
to 10 µg m-3
(SNIFFER, 2011), and increase in cities by 100 % at the kerbside (1
meter from the kerb) of roads with a high density of traffic (Laxen et al., 2001). Apart
from traffic, there are other factors that can vary spatially and temporarily, and as a
consequence, can influence the levels of particulate matter in the atmosphere. The
following is a selection of the most influential factors affecting ambient PM2.5
concentrations in urban environments:
2.3.1. Traffic
Traffic is one of the major emission sources of PM2.5 in urban environments (Lee et al.,
2011). In the UK, road traffic accounts for a significant contribution to the existing
background PM2.5 concentrations (approximately 30-50% of total mass) (AQEG, 2012).
13
Traffic-related emissions of PM2.5 can occur in two main ways. The first type of
emission is constituted by particles derived from the internal fossil fuel combustion (Ye
et al., 2003). The second type of emission is composed of non-exhaust particles that can
be originated from various physical processes such as tyre abrasion of the road surface,
tyre and break wear, and blowing of dust particles with the wind turbulence caused by
the vehicle’s motion (Wahlin et al., 2006; Apeagyei et al., 2011 and Lawrence et al.,
2013). Non-exhaust emissions are more difficult to be traced, and as a consequence
have been, to some extent, overlooked in the majority of air quality studies and
regulations (Pant and Harrison, 2013). In most air pollution studies, traffic is often
qualitatively and quantitatively categorized. For instance, Freiman et al. (2006) classify
traffic into five different categories: Private, Diesel, Truck, Bus and Motorcycle. Of this
group, private vehicles have been the major contributor to total traffic counts in most of
these studies, and for this reason, are also the greatest source of PM2.5 emissions.
The effect of traffic contributions to PM2.5 concentrations can be determined through
various methods. For example, Reponen et al. (2003) evaluate the variability in PM2.5
concentrations on roads as a function of the distance from traffic. In this way, it can be
possible to produce a lateral exposure gradient along a road. Other studies (e.g. Zhang
and Batterman, 2010 and Tunno et al., 2012) have used traffic counts to examine the
correlation between particulate matter levels and traffic density for a particular street or
highway (Figure 4).
Figure 4: Example of the relationship between PM2.5
concentrations and general traffic counts at a major highway in Detroit, Michigan during the summer season. Source: Redrawn from Zhang and Batterman (2010).
4
2
0
-2
-4 0 2 4 6 8
Traffic counts (1000 vph)
Cen
tre
d P
M2
.5 µ
g m
-3
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2.3.2. Meteorological Factors Affecting PM2.5 Concentrations
Variability in mass concentration of atmospheric particles can be dominated by a
number of meteorological factors such as temperature, wind speed and direction,
amount of precipitation, and the height of the atmospheric boundary (Pohjola et al.,
2000 and Pu et al., 2011). Studies have showed that in addition to local transport
emission characteristics, local meteorological conditions are also a driving factor
affecting PM2.5 levels in urban environments (Tai et al., 2010).
2.3.2.1. Wind Speed
The literature suggests that stagnant days (days with stable meteorological conditions)
with low wind speed (<8 m s-1
) are accounted for higher PM2.5 concentrations. For
example, Tai et al. (2010) found that, on average, concentrations became
2.6 μg m−3
higher on days with minor wind speed in the United States. Pohjola et al.
(2004) also indicate that low-wind speed conditions are associated with higher
concentrations of particulate matter. Contrastingly, Vallius (2005) argues that high
pollutant levels may result from intense wind flows blowing natural dust provoking
concentrations to rise, sometimes at sites located hundreds or even thousands of
kilometres from the emission sources. Therefore, the correlation between wind speed
and PM2.5 concentrations is not always positive or negative, but depends on other factors
such as wind direction and proximity to emission sources.
2.3.2.2. Wind Direction
Wind direction, in combination with wind speed, can affect the spread and transmission
of pollutants in urban environments. As remarked by Hitchins et al. (2000), wind
direction is an important factor impacting pollutant concentrations in urban settings.
Jones et al. (2010) indicate that wind flow patterns can significantly affect the spatial
distribution of airborne particles. However, in considering wind direction as a variable
affecting air pollution, the emission sources in relation to such direction should also be
taken into account.
2.3.2.3. Temperature
Concentrations of organic and semi-volatile nitrate particles can vary with the amount
of oxidants present, which is dependent on photolysis rates, and consequently, cloud
cover (Dawson et al., 2007). A positive correlation was reported between average
values of PM2.5 concentrations and atmospheric temperatures in a study by Tai et al.,
15
(2012) using data from about 1000 fixed monitoring stations in the United States.
However, when the temperature drops as a consequence of an atmospheric inversion,
higher particulate matter concentrations have been reported (Chen et al., 2012).
2.3.2.4. Absolute and Relative Humidity
Dawson et al. (2007) concluded that absolute humidity had the largest impact on
ammonium nitrate aerosol concentrations of all the other meteorological variables
considered in a modelling case study for Eastern United States. The effects of increased
absolute humidity were an increase in aerosol concentrations. Similarly, Galindo et al.
(2011) state that a rise in relative humidity facilitates the fractionation of nitrate to the
aerosol phase enlarging average PM2.5 concentrations during the summer and fall
seasons. In their study, mean PM2.5 concentrations were 13.82% higher during the
summer with a mean relative humidity value of 64% than during the winter with 59%.
2.3.3. Urban Morphology
Air circulation at street level is dissimilar from that which occurs at higher altitudes
where physical barriers are essentially non-existent. Previous studies suggest that the
physical configuration within the urban canopy layer substantially affects the physics of
urban microenvironments (Oke, 1988). There are a variety of aspects in urbanism that
can affect the distribution of particulate matter (De Nicola et al., 2013). For instance,
man-made structures, vegetation, physical geometry, anthropogenic activity and
industrial operations are all factors involved in the formation and spatial dispersion of
contaminants at street level (Edussuriya et al., 2011).
2.3.3.1. Street Canyons
Street canyons are places where the street is lined by buildings on both sides creating a
microenvironment resembling a natural canyon (Oke, 1988) (Figure 5). The dispersion
of pollutants in a street canyon is generally determined by the rate at which the air is
perpendicularly exchanged with the above roof-level atmosphere and laterally with the
adjoining streets (Vardoulakis et al., 2003).
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Figure 5: Vertical flow patterns in urban canyons for various aspect ratios. Source: Redrawn from Oke (1988).
There are three major dispersion conditions that can be identified in street canyons: a)
low wind conditions, b) vertical or near-vertical flow and c) parallel or near-parallel
wind flow. Figure 5 illustrates some vertical flow patterns in urban canyons for various
aspect ratios. For long canyons without contiguous streets, studies (see Vardoulakis et
al., 2003) have reported that an increase in pollutant concentrations is more likely to
occur when wind flows are parallel to the street axis. Research suggests that even when
the traffic density decreases at street canyons, the levels of particulate matter can remain
larger than the urban background (Dos Santos-Juusela et al., 2013). Thus, the wind
circulation patterns in street canyons may lead to higher concentrations of pollutants
and increased risk of human exposure with serious health consequences (Michaels and
Kleinman, 2000).
2.4. Methods to estimate PM2.5
PM2.5 can be estimated through in-situ monitoring techniques or by means of
computational methods. Monitoring can be either fixed (Blanchard et al., 2014) or
mobile (Weijers et al., 2004). Some examples of computational methods are land use
regression, chemical transport models, satellite data and a variety of geospatial
approaches (e.g. kriging interpolation) (Reyes, 2014).
Isolated roughness flow
Wake interface flow Skimming flow
17
2.4.1. Fixed Monitoring Stations
Fixed monitoring stations are the most common method to estimate PM2.5
concentrations in countries with air pollution control programs (Figure 6). They have
the advantage of capturing the temporal variability of PM2.5 and other air pollutants, and
can be used in rural or urban locations (AQEG, 2012). Data from fixed monitoring
stations can be geospatially analysed to provide estimates of concentrations in between
stations (Reyes, 2014). The accuracy of such estimates is largely determined by the
density of the monitoring network, which must be capable of capturing the geospatial
changes in the variables influencing PM2.5 levels (Van Poppel et al., 2013).
2.4.2. Mobile Monitoring
Mobile monitoring can provide a solution to evaluate the spatial variability of
particulate matter in a wide range of sizes, with a reduced number of instruments and in
a restrained timeframe (Van Poppel et al., 2013). It is especially useful for air quality
mapping in places with no existing fixed monitoring stations, or for hot spot
identification and assessment of human exposure locations as a complement to
stationary networks. Mobile monitoring can use a variety of techniques. For instance,
traditional mobile studies have been conducted in vans or other type of motor vehicles
Figure 6: Location of the monitoring stations of the Automated Urban and Rural Network in 2010. The colours (blue, green and red) differentiate the type of PM2.5 monitor used at each station. The pictures of the instruments only symbolize the brand, not the specific model. Source: Redrawn and adapted from AQEG (2012).
Partisol FDMS BAM
100 Km
18
equipped with modern air quality monitors and GPS logging system (Figure 7). All
instruments are generally synchronized to simultaneously record geographical
coordinates and particle concentration measurements (Wallace et al., 2009 and Tunno et
al., 2012).
Other mobile studies have used bicycles especially assembled for air pollution
monitoring. For example, Elen et al. (2012) carried out a mobile monitoring study in
Belgium using the AEROFLEX bicycle with which it was possible to assess people's
real life exposure to particulate matter and other air pollutants by identifying hot spots
within the city of Antwerp, Belgium (Figure 2.7).
Figure 7: AEROFLEX bicycle (A) and van (B) used for mobile PM2.5 monitoring studies. Source: Elen et al. (2012) and Wallace et al. (2009) respectively.
Mobile monitoring of PM2.5 can also be performed on foot (Gulliver and Briggs, 2004
and Dionisio et al., 2010). The principal advantages of monitoring on foot are that
measurements can be taken at pedestrian speed and at the normal height of an exposed
individual. However, the research scope (area to be monitored) and the dimensions and
weight of the monitoring instruments can be a constraint for on-foot monitoring. Table
2.2 shows some of the most widely used particulate matter monitors used in mobile
monitoring studies.
A
B
19
Table 2: Instrumentation for mobile monitoring of particulate matter. Source: Extracted and adapted from Elen et al. (2012).
Study Instrumentation Sampling
Rate (s)
Westerdahl et al.
(2005, 2007) EcoChem PAH analyser, model PAS 2000 2
Kaur et al. (2007) High-Flow Personal Sampler (HFPS) variable
Greaves et al. (2008) SIDEPAK™ AM510 Personal Monitor variable
Wallace et al. (2009)
and Adams et al.
(2012)
DustTrak model 8520 monitors (TSI Inc.,
Shoreview, MN, USA). GRIMM model 1.107 1
Dionisio et al. (2010) DustTrak model 8520 monitors (TSI Inc.,
Shoreview, MN, USA) 60
Vogel et al. (2011) GRIMM OPC, GRIMM Nano-Check 6
Tunno et al. (2012)
Hazdust monitor (Model EPAM-5000,
Environmental
Devices Corporation (EDC) 1
2.5. Human Exposure to Particulate Matter in Urban Environments
Pedestrians are an important group being exposed to particulate matter in cities
(Gulliver and Briggs, 2004). Their exposure can be governed by different factors such
as walking speed, direction and side preference (adjacent to or distant from traffic
emissions) (Greaves et al., 2008). Other groups affected by air pollution are bus
commuters and cyclists. For example, in Manchester, England, research by Gee and
Raper (1999) showed that a commuter on bus route 85 was exposed to mean PM2.5
concentrations of 338 µg m-3
, and cyclists to concentrations of approximately 54 µg m-3
.
However, with the application of national emission controls to meet air quality
standards, those values have decreased over the last decade.
Expected annual background PM2.5 concentration data for the UK are available from the
Department for Environment, Food and Rural Affairs (Defra) from 2011 up to 2030. In
Figure 8, it can be observed that on the campus of the University of Manchester, the
average background PM2.5 levels expected for 2014 range from 8 µg m-3
to 10 µg m-3
.
Additionally, a report by the Manchester City Council (2011) shows that by 2009 and
2010, the average concentrations of PM2.5 at Piccadilly Station were 12 µg m-3
and 18
µg m-3
respectively. These updated values are considerably below the National Air
Quality Strategy objective of 25 µg m-3
(Defra, 2007). However, since particulate matter
concentrations are not generally homogeneous even within a same urban area, different
(higher or lower) values may be obtained from monitoring at different locations.
20
2.5.1. Human Exposure at Bus Stops and Street Intersections
Human exposure to fine particulate matter can also occur while waiting at bus stops
(Dales et al., 2007) and street intersections (traffic light change) (Slavin, 2013). In a
study by Hess et al. (2010) carried out in Buffalo, New York, it was found that
concentrations of particulate matter inside bus stops were 18% greater than those
outside, mainly due to the contribution of a number of factors including cigarette smoke
and incoming bus emissions. The bus stop design (open or enclosed) and the type of
buses (electric, hybrid, fossil-fuel-powered, etc.) can reduce or exacerbate the
concentrations of pollutants at these microenvironments. Likewise, street intersections
can be characterized by high concentrations of particulate matter owing to the emissions
originated from idling vehicles at both intersecting streets (He et al., 2009). Yet, studies
concerning pollutant exposure at bus stops and street intersections remain severely
limited (Dales et al., 2007 and Kaur et al., 2005). Figure 9 shows the type of design that
is characteristic of most bus stops on Oxford Road, Manchester.
100 m
Figure 8: Background PM2.5 concentrations expected for 2014 in the study area. Source: Defra, 2014.
21
2.6. Health Risks of Prolonged Exposure to PM2.5
There is a broad consensus that particulate matter can provoke deleterious consequences
to human health (Kaiser, 2000; Colburn and Johnson, 2003 and Nel, 2005). According
to the United Nations, 500,000 people are estimated to be killed by PM each year (UN,
1994). Epidemiological studies confirm a constant increase in respiratory and
cardiovascular morbidity and mortality from exposure to fine particulate matter (Nel,
2005). Fine and ultrafine particles (see Figure 10) have been considered as the most
toxic category of particulate matter largely due to their size, which facilitates their
intrusion into the lungs and heart. The most common diseases caused by prolonged
exposure to PM2.5 are emphysema, bronchitis, asthma, lung cancer and coronary artery
disease (Ghio, 2014 and Rohr et al., 2014). Directive (2008/50/EC) provides the
legislative framework for regulating atmospheric PM2.5 concentrations in the UK and
other Member States of the European Union. The recommended annual PM2.5
concentration values that a person can be exposed to are 25 µg m-3
for an averaging
period of 1 year. This standard is consistent with the American Environmental
Protection (EPA) limits and thresholds presented in Table 2.3.
Table 3: Air Quality Index (AQI) for 24-hour fine particle concentration (PM2.5). Source: EPA, 2012.
AQI Category Index
Values
Revised Breakpoints
(µg m-3
, 24-hour average)
Good 0-50 0.0-12.0
Moderate 51-100 12.1-35.4
Unhealthy for
Sensitive Groups 101-150 35.5-55.4
Unhealthy 151-200 55.5-150.4
Very Unhealthy 201-300 150.5-250.4
Hazardous 301-400 250.5-350.4
401-500 350.5-500
Figure 9: Typical bus stop design on Oxford Road, Manchester. Source: Author’s own image.
Figure 10: Electron micrograph of a fine particle probably composed of polar organic compounds and inorganic salts. Source: Nel (2005).
22
Chapter 3: Research Questions
1. What is the spatial distribution of ambient PM2.5 concentrations on the campus
of the University of Manchester?
From the analysis to the projected average background PM2.5 concentrations for 2014
(obtained from Defra) for the study area, it is hypothesized that atmospheric PM2.5
levels will be higher on the north of the campus of the University of Manchester (refer
to Figure 8: on page 20). However, monthly PM2.5 concentrations may be different from
annual average values as a consequence of the meteorological conditions of the
prevailing season. For this reason, evidence of the spatial variability of ambient PM2.5
concentrations during the month of June 2014 (summer) will be presented in the form of
average values for each monitoring site covering the entire period of the study.
2. What is the temporal variability of PM2.5 concentrations on the campus of the
University of Manchester?
In addition to varying spatially, PM2.5 concentrations can also change temporally. These
variations can be evaluated on an hourly, daily, weekly, monthly and annual basis. For
the purpose of this study, the variability in PM2.5 concentrations on the campus of the
University of Manchester will be evaluated from one monitoring day to another for all
days of the month of June 2014.
3. What are the most influential factors impacting PM2.5 concentrations during
the summer season on the campus of the University of Manchester?
Meteorological factors, for example temperature, wind speed and humidity can exert an
influence upon PM2.5 concentrations in urban environments (Pohjola et al., 2000).
Similarly, traffic conditions, which can vary in time (weekdays vs. weekends) can
influence the spatial ambient PM2.5 concentration patterns (Lee et al., 2011). Evidence
of the influence of the above-mentioned factors on ambient PM2.5 levels on the campus
of the University of Manchester will be presented in the form of statistical correlation
analysis.
23
Chapter 4: Methodology
4.1. Monitoring Location
Ambient PM2.5 concentrations were monitored on the campus of the University of
Manchester along Oxford Road and Sackville Street (Figure 11). An additional site at
Whitworth Park was selected with the purpose of evaluating the effect of trees on
atmospheric PM2.5 levels. A total of 21 monitoring sites were considered, of which 14
sites were located at the major street intersections on Oxford Road and Sackville Street,
and 7 sites corresponded to the existing bus stops on the right-hand side of Oxford Road
following the monitoring trajectory in Figure 11.
4.2. Monitoring Equipment and Data Collection
Following the methodology of Tunno et al. (2012), a light scattering nephelometer, the
Hazdust monitor (Model EPAM-5000, Environmental Devices Corporation (EDC),
Plaistow, NH 03865) was used for daily measurements of PM2.5 concentrations at 21
sites on the campus of the University of Manchester (Figure 12). Temperature and wind
speed were measured with the handheld LAC-EA3000 anemometer. Humidity data
S.01 S.03 S.02
S.04 S.05
S.06
S.07 S.08
S.09 S.10
S.12
S.11
S.13
S.15
S.14
S.16
S.17
S.18
S.19 S.20
S.21
100 m
Oxford Road
Sackville Street
Whitworth Park
Figure 11: Map showing the location of the monitoring sites on the campus of the University of Manchester. The arrows on the map show the monitoring trajectory employed in this study. Source: Open Street Map, 2014.
24
were obtained from the Whitworth Meteorological Observatory. Additionally, a
qualitative description (sunny, rainy, cloudy, etc.) of the weather conditions for each
monitoring day was recorded for facilitating the interpretation of the obtained
meteorological data. Incoming bus counts were taken at each bus stop during the
complete monitoring period. However, general traffic was counted only during the final
monitoring week. Walking was the only means of transport while monitoring PM2.5
concentrations (Dionisio et al., 2010) and meteorological variables. All variables were
simultaneously measured during each monitoring run.
The Hazdust monitor has a sampling rate of 1 second; however, PM2.5 measurements
were taken for two minutes to make the data recording process more manageable.
Monitoring was done from 4.00 to 6.00 pm (afternoon traffic peak hours) and each run
lasted for approximately 90 minutes, including the return time. The monitoring
campaign was performed during all the days of June 2014. For safety concerns,
monitoring was performed facing incoming traffic, and the monitoring trajectory was
from South to North. In order to avoid inconveniences (being interrogated or causing
discomfort), the Hazdust monitor was placed inside a backpack. Thus, monitoring was
carried out in a discrete manner (Figure 12).
4.3. Data Analysis
For the spatial and temporal PM2.5 concentration analysis, raw values were pre-
processed by averaging the concentrations obtained during the two-minute period at
each monitoring site. The resulting values were then incorporated onto an Excel-2010
spreadsheet for further analysis. Average PM2.5 concentration values were calculated
taking into consideration the location (spatial analysis) and the monitoring campaign
duration (temporal analysis) (Elen et al., 2012). Boxplots were used for illustrating the
PM2.5 concentration data through their quartiles. Maps showing the study location and
Figure 12: (A) Monitoring technique, (B) HAZ-DUST EPAM-5000 monitor and (C) LAC-EA3000 anemometer. Source: Author’s own images.
C
B
A
25
PM2.5 concentrations were downloaded from the internet and processed on Adobe
Illustrator CS6. For the evaluation of the factors affecting PM2.5 concentrations,
incoming buses and general traffic were considered as a spatial rather than a temporal
variable. In contrast, all meteorological variables were evaluated as factors changing
from one monitoring day to another. Simple linear regression analysis was performed
on Excel 2010 for evaluating the relationship of each considered factor and PM2.5
concentrations.
26
Chapter 5: Results
5.1. Spatial Distribution of Ambient PM2.5 Concentrations on the Campus of the
University of Manchester
The campus of the University of Manchester lies on two areas; one centred on Oxford
Road (south) and the other on Sackville Street (north). Ambient PM2.5 concentrations
were varied along these two areas (Table 5.1, Figure 13 and 14). The highest mean
PM2.5 concentration (25.72 µg m-3
) was obtained at site S.02 (bus stop near Hathersage
Road) on Oxford Road. The lowest mean PM2.5 level (11.26 µg m-3
) corresponded to
site S.18 (intersection, Service Road) on Sackville Street. Results show that the average
PM2.5 concentrations are higher on the south than on the north of the campus of the
University of Manchester (Figure 13).
Figure 13: Map showing the distribution of PM2.5 concentrations on the campus of the University of Manchester. Each coloured circle represents the mean PM2.5 values obtained from June 1
st to June 30
th, 2014. Source: Bing Maps,
2014.
11.1-12.0 12.1-13.0 13.1-14.0 14.1-15.0 15.1-16.0 16.1-17.0 17.1-18.0 18.1-19.0 19.1-20.0 20.1-21.0 21.1-22.0 22.1-23.0 23.1-24.0 24.1-25.0 25.1-26.0
PM2.5 (µg m-3
)
100 m
Lowest value
Highest value
27
Figure 14: A) Site S.02 (Bus stop near Hathersage Road) on Oxford Road. B) Site S.18 (Intersection, Service Road) on Sackville Street. Source: Google Earth, 2014.
5.1.1. PM2.5 Concentrations on Oxford Road and Sackville Street
On Oxford Road, with the exception of site S.01 located in the middle of Whitworth
Park, the first six monitoring locations were characterized by relatively high PM2.5
concentrations ranging from 21.45 µg m-3
(site S.03) to 25.72 µg m
-3 (site S.02) (Figure
15). It can be hypothesized that PM2.5 concentrations at site S.01 have been diminished
by the removal of the ambient particulate matter by the existing trees, which form a
natural barrier that improves the air quality at Whitworth Park. This effect has been
widely documented in the literature (Beckett et al., 1998 and Nowak et al., 2013). PM2.5
levels at sites beginning from S.07 to S.17 ranged from 13.29 µg m-3
(site S.11) to 17.18
µg m-3
(site S.13). In the case of Sackville Street, PM2.5 concentrations ranged from
Table 4: Summary of the obtained data. Values are expressed in averages.
Site
Name References
Location PM2.5
µg m-3
Wind
Speed
m/s T
°C RH
(%) Traffic
counts/min N W
S.01 Whitworth Park 53.458828 -2.22957 17.19 1.58 23.66 60.93 0
S.02 Bus stop (near Hathersage Road) 53.458696 -2.22726 25.72 2.46 23.30 60.99 24
S.03 Intersection (Hathersage Road) 53.459163 -2.22741 21.45 5.06 23.29 60.84 24
S.04 Bus stop (City Labs) 53.461905 -2.22892 22.65 2.46 22.56 61.24 15
S.05 Intersection (Nelson Street) 53.462295 -2.22931 22.01 4.35 22.43 61.47 16
S.06 Intersection (Grafton Street) 53.462931 -2.22991 22.14 3.37 22.29 61.48 15
S.07 Bus stop (across From RBS) 53.463952 -2.23111 16.28 3.08 21.45 61.67 12
S.08 Intersection (Ackers Street) 53.464107 -2.23131 14.22 5.05 21.81 61.67 12
S.09 Bus stop (across From Student's Union) 53.464522 -2.23178 15.29 2.36 22.12 61.67 13
S.10 Intersection (Dover Street) 53.464893 -2.23222 15.65 2.66 22.18 61.47 12
S.11 Intersection (Brunswick Street) 53.465571 -2.23292 13.29 3.94 22.23 61.44 12
S.12 Bus stop (across From Waterloo Place) 53.467324 -2.23444 16.29 1.75 21.81 61.87 11
S.13 Bus stop (Saint Peter's Chaplaincy) 53.467604 -2.23476 17.18 1.55 21.95 62.41 12
S.14 Intersection (Booth Street) 53.468328 -2.23526 14.74 3.70 22.18 62.74 17
S.15 Bus stop (Manchester Aquatics Centre) 53.469684 -2.23645 13.66 1.03 21.99 62.92 13
S.16 Intersection (Grosvenor Street) 53.470104 -2.23688 15.09 2.74 22.29 62.78 15
S.17 Intersection (Sidney Street) 53.47109 -2.23776 16.43 2.30 22.31 62.54 10
S.18 Intersection (Service Road) 53.473644 -2.23401 11.27 5.88 21.57 62.32 3
S.19 Intersection (Altrincham Street) 53.474868 -2.23484 12.61 2.06 21.54 61.89 3
S.20 Intersection (Granby Row) 53.47536 -2.23513 11.96 2.38 21.51 61.79 4
S.21 Intersection (Whitworth Street) 53.476197 -2.23562 13.21 3.57 21.37 61.86 14
Site S.02
Site S.18
A B
28
11.27 µg m-3
(site S.18) to 13.21 µg m-3
(site S.21) (Figure 15). PM2.5 concentration
variability is also evident within the same monitoring site. For example, some sites were
characterized by maximum values above 50 µg m-3
(S.02, S.04, S.05, S.06 and S.07),
and in all sites, there were minimum concentration values below 5 µg m-3
(Figure 15).
In general, there is a declining trendline defining average PM2.5 concentrations from site
S.02 to S.21.
Figure 15: Mean PM2.5 concentrations for each monitoring site on the campus of the University of Manchester from June 1
st to June 30
th, 2014.
5.1.2. Human Exposure to PM2.5 at Seven Bus Stops on Oxford Road
Of the seven monitored bus stops, the one located near the intersection of Oxford and
Hathersage Roads obtained the highest PM2.5 concentrations with an average value of
25.72 µg m-3
(Figure 16). The lowest concentrations were obtained at the bus stop in
front of the Manchester Aquatics Centre with a mean PM2.5 concentration value of 13.66
µg m-3
. Despite having similarities in their basic design, there were differences in the
dimensions of some of the bus stops considered in this study. For example, bus stop
S.04 is the largest in comparison to all the other monitored bus stops (Figure 16). Bus
stop dimension and design are variables that in contribution with localized emissions
and meteorological factors can play a substantial role in the levels of particulate matter
inside these microenvironments (Hess et al., 2010). However, details concerning bus
stop design are outside the scope of this study. PM2.5 concentrations inside bus stops
follow the same general pattern of relatively high concentrations on the south and low
concentrations on the north of the campus of the University of Manchester.
0
5
10
15
20
25
30
35
40
45
50
55
60
S.01 S.02 S.03 S.04 S.05 S.06 S.07 S.08 S.09 S.10 S.11 S.12 S.13 S.14 S.15 S.16 S.17 S.18 S.19 S.20 S.21
PM
2.5
(µg
m-3
)
Monitoring sites South North
29
Figure 16: Boxplot showing the mean concentrations of PM2.5 at seven bus stops on Oxford Road from June 1st
to June 30
th, 2014.
5.1.2.1. Number of Exposed People
The bus stop with the greatest number of exposed individuals is the one located in front
of the City Labs Building (S.04) (Figure 17 and 18). In total, 213 people were counted
in the two-minute monitoring period during the thirty days of the month of June 2014.
In contrast, the lowest number of exposed people was recorded at bus stop S.09 located
across from the University of Manchester Student’s Union building. The number of
exposed individuals at each bus stop is largely dependent on the annual schedule of the
academic activities at the University of Manchester and other educational institutions
adjacent to the study area. In particular, bus stop S.09 was severely affected by the
significant absence of students due to the start of the summer break at the University of
Manchester. In fact, it can be hypothesized that if this research had been performed in a
different month with regular academic activities, S.09 might have been the bus stop
with the largest number of exposed individuals of all the seven bus stops considered in
this study.
Figure 17: Total number of exposed people to PM2.5 at seven bus stops on Oxford Road from June 1st
to June 30th
, 2014.
0
10
20
30
40
50
60
S.02 S.04 S.07 S.09 S.12 S.13 S.15
PM
2.5
(µg
m-3
)
Bus stops South North
151
213
93 90 95
137 142
0
50
100
150
200
250
S.02 S.04 S.07 S.09 S.12 S.13 S.15
Num
be
r o
f e
xp
ose
d p
eo
ple
Bus stops
30
Figure 18: Site S.04 corresponding to the bus stop located in front of the City Labs Building. Source: Google Earth, 2014.
5.1.3. Typical Pedestrian Exposure to PM2.5 along the Monitoring Route
With the aim of evaluating the typical exposure of a pedestrian using the walking route
employed in this monitoring study, data are presented in Figure 19 illustrating the range
of PM2.5 concentrations that an individual can be exposed to at afternoon rush hours
(4.00 to 6.00 pm). The data corresponds to a regular sunny day. A total of six relatively
high PM2.5 concentrations ranging from 30 µg m-3
to 40 µg m-3
were recorded near sites
S.02, S.04, S.11, S.13, S.14 and S.17 (Figure 19). These higher PM2.5 concentrations
can be attributed to traffic contribution due to the fact that no other immediate sources
were observed. In general, most of the data reflect low PM2.5 concentrations fluctuating
from 5 µg m-3
to 20 µg m-3
.
Figure 19: Pedestrian exposure to PM2.5 along the monitoring route. These values correspond to Thursday June 5th
. 2014. The arrows in the graph show the approximate location of each monitoring site (S.01 -first left arrow to S.21-last right arrow).
0
5
10
15
20
25
30
35
40
45
16:19:12 16:33:36 16:48:00 17:02:24 17:16:48 17:31:12 17:45:36 18:00:00
PM
2.5
(µg
m-3
)
Time
Site S.04
31
5.2. Temporal Variability of PM2.5 Concentrations
PM2.5 concentrations varied temporarily from mean values of 4.54 µg m-3
(June 12th
,
2014) to 34.71 µg m-3
(June 2nd
, 2014) (Figure 20). Measurements taken on June 2nd
were affected by light precipitation that lasted for the complete monitoring period. Thus,
relative humidity was also high during the same time interval (78% to 100%). Previous
research suggests that high relative humidity has a positive impact on atmospheric
PM2.5 by increasing its concentration as a result of the chemical reactions occurring in
the atmosphere during high humidity events (Galindo et al., 2011). In contrast, the
lowest average PM2.5 concentrations were recorded during a sunny day with
temperatures of approximately 27 °C. June 9th
was the day with the greatest degree
of variability in the PM2.5 concentrations recorded at the 21 sites with minimum and
maximum values fluctuating from 3.83 µg m-3
to 45.75 µg m-3
respectively.
Figure 20: Mean PM2.5 concentrations for all monitoring sites on the campus of the University of Manchester from June 1
st to June 30
th, 2014.
5.2.1. Weekday-to-weekend PM2.5 Variability
On average, the highest PM2.5 concentrations were recorded on Mondays, whereas the
lowest levels correspond to Tuesdays (Figure 21). Most of the variability in mean PM2.5
concentrations occurred from Monday to Thursday with mean values ranging from
14.98 µg m-3
to 20.76 µg m-3
. From Friday to Sunday, the variability was minor and
only fluctuated from 15.06 µg m-3
to 15.83 µg m-3
. These results indicate that PM2.5
levels on the campus of the University of Manchester remain relatively stable during the
weekends.
0
10
20
30
40
50
60
Jun
. 1
Jun
. 2
Jun
. 3
Jun
. 4
Jun
. 5
Jun
. 6
Jun
. 7
Jun
. 8
Jun
. 9
Jun
. 10
Jun
. 11
Jun
. 12
Jun
. 13
Jun
. 14
Jun
. 15
Jun
. 16
Jun
. 17
Jun
. 18
Jun
. 19
Jun
. 20
Jun
. 21
Jun
. 22
Jun
. 23
Jun
. 24
Jun
. 25
Jun
. 26
Jun
. 27
Jun
. 28
Jun
. 29
Jun
. 30
PM
2.5
(µg
m-3
)
Monitoring days
32
Figure 21: Boxplot showing the mean concentrations of PM2.5 for each day of the week from June 1st
to June 30th
, 2014.
By examining the temporal variability of PM2.5 at each monitoring site, it can be
observed that most variation occurs at the first seven sites (S.01-S.07) (Figure 22 on the
next page). For example, Mondays are the days with the largest PM2.5 levels, whereas
the rest of the days appear to be substantially consistent. From sites S.08 to S.21, the
temporal variability of PM2.5 levels is more equally distributed among the other days of
the week. In general, PM2.5 concentrations in the ambient air of the campus of the
University of Manchester are higher on Mondays, decrease during the rest of the week
and finally stabilize on the weekends. For the first seven sites, the PM2.5 variability
responds positively to traffic conditions as it has been found in previous studies (Zhang
and Batterman, 2010).
0
5
10
15
20
25
30
35
40
Sun. Mon. Tue. Wed. Thu. Fri. Sat.
PM
2.5
(µg
m-3
)
Monitoring days
33
Figure 22: Mean PM2.5 concentrations for each monitoring site and day of the week from June 1st
to June 30th
, 2014.
0 5 10 15 20 25 30 35 40
S.01
S.02
S.03
S.04
S.05
S.06
S.07
S.08
S.09
S.10
S.11
S.12
S.13
S.14
S.15
S.16
S.17
S.18
S.19
S.20
S.21
PM2.5 (µg m-3)
Mo
nito
rin
g s
ite
s
Saturday Friday Thursday Wednesday
Tuesday Monday Sunday
34
5.3. Factors Impacting PM2.5 Concentrations on the Campus of the University of
Manchester
As it has been observed, PM2.5 concentrations on the campus of the University of
Manchester have varied both spatially and temporarily. However, despite the fact that
all the examined factors influencing ambient PM2.5 levels can also vary in terms of
space and time, traffic was here considered as a spatial rather than a temporal factor.
This is because traffic counts were taken during the final monitoring week and not for
the entire duration of the study. In contrast, all other factors (wind speed, temperature
and relative humidity) were considered as temporal variables. Due to this consideration,
the maximum and minimum average PM2.5 concentrations presented in the following
evaluation of the factors affecting PM2.5 concentrations are not the same as those
presented in the PM2.5 spatial distribution analysis on page 27.
5.3.1. Traffic
The correlation analysis between traffic and PM2.5 concentrations on the campus of the
University of Manchester shows a positive trend in the relationship between both
variables (Figure 23). Traffic counts at sites beginning from S.02 to S.06 are higher than
at the majority of the other sites, with the exception of sites S.14 and S.16. This
characteristic is also evident in the PM2.5 concentrations at the first above-mentioned
locations. However, the low R2 value of 0.26 indicates that the relationship between
traffic and PM2.5 concentrations is not satisfactorily robust. Traffic counts proved to be a
difficult factor to measure due to the lack of a research assistant and the relatively short
period of time (2 minutes) allocated for the counts at each site. It can be hypothesized
that with more precise traffic counts carried out for a longer period of time, this
correlation could become stronger.
y = 0.3306x + 13.273 R² = 0.2636
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30
PM
2.5
(µg
m-3
)
Traffc counts (vpm)
Line 1:1
Figure 23: Correlation between traffic counts and PM2.5 concentrations in the ambient air on the campus of the University of Manchester.
35
5.3.2. Wind speed
PM2.5 concentrations and wind speed have a negative correlation suggesting that as
wind speed increases, PM2.5 concentrations decrease and vice versa (Figure 24). Wind
speed measurements were taken at each site and no wind data from a meteorological
station were used. Repeating wind speed and direction patterns were observed at a few
sites as a consequence of the existing physical geometry surrounding the monitoring
location. For example, at site 18 (highest average wind speed, 5.88 m/s), wind speed
often appeared to be particularly higher when compared to the other sites, and at this
location, wind always flowed from the Northeast until it reached Faraday building. In
contrast, wind speed inside Whitworth Park (lowest average wind speed, 1.58 m/s) was
often low, most probably due to the surrounding trees in all directions of the monitoring
location. The days with the lowest and highest average wind speed values were June
30th
(0.73m/s) and June 5th
(5.28 m/s) respectively. Finally, in order to attenuate the
effects of wind direction on PM2.5 concentrations, the particulate matter monitor was
always pointed at a different direction from the wind flow.
5.3.3. Temperature
From the correlation analysis, it can be observed that PM2.5 concentrations respond
negatively to temperature values (Figure 25). Most of the PM2.5 concentrations
including the lowest (4.54 µg m-3
) occur within the 20 °C to 27 °C. However, the largest
PM2.5 concentration (34.71 µg m-3
) took place during a day with an average temperature
of 16 °C. This correlation analysis suggests that on sunny and bright days, PM2.5
concentrations on the campus of the University of Manchester were lower than they
were on cloudy days with lower temperatures. These results contribute to the existing
y = -2.1149x + 22.965 R² = 0.1341
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6
PM
2.5
(µg
m-3
)
Wind speed (m/s)
Figure 24: Correlation between wind speed and PM2.5 concentrations in the ambient air on the campus of the University of Manchester.
Line 1:1
36
literature (e.g. Tai et al., 2010 and Tran and Mölders, 2011) suggesting a negative
correlation between temperature and atmospheric PM2.5 concentrations.
5.3.4. Relative Humidity
From all the evaluated factors, relative humidity had the strongest correlation with
PM2.5 concentrations with an R2
value of 0.32 (Figure 26). In fact, the highest seven
mean PM2.5 concentration values were obtained during either cloudy or rainy days with
relative humidity values above 50 %. Other studies (see DeGaetano and Doherty 2004
and Galindo et al., 2011) have also found that during high relative humidity conditions,
ambient PM2.5 concentrations increase as a result of the formation of wet droplets when
dry inorganic particles absorb water from the atmosphere.
y = -1.0177x + 39.161 R² = 0.2355
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30
PM
2.5
(µg
m-3
)
Temperature °C
Line 1:1
Figure 25: Correlation between temperature and PM2.5 concentrations in the ambient air on the campus of the University of Manchester.
y = 0.2404x + 1.9937 R² = 0.3289
0
5
10
15
20
25
30
35
40
0 10 20 30 40 50 60 70 80 90 100
PM
2.5
(µg
m-3
)
Relative humidity (%)
Line 1:1
Figure 26: Correlation between temperature and PM2.5 concentrations in the ambient air on the campus of the University of Manchester. The dotted-line square encloses the days with the highest PM2.5 concentrations (2
nd, 7
th, 15
th, 19
th,
24th
, 25th
and 26th
of June 2014).
37
Though the results indicate a relatively high correlation (in comparison with the other
factors) between PM2.5 levels and relative humidity, this relationship lacks the sufficient
strength so that PM2.5 levels can be entirely attributed to relative humidity conditions.
However, in a modest way, it can be argued that during the summer season, relative
humidity is the most influential meteorological factor affecting ambient PM2.5
concentrations on the campus of the University of Manchester.
5.3.5. Correlation between Incoming Buses and PM2.5 Concentrations at Bus Stops
Incoming buses and PM2.5 concentrations are remarkably correlated (R2: 0.52) at the
seven monitored bus stops on Oxford Road (Figure 27). The results show that bus stops
with a greater number of incoming buses have larger PM2.5 concentrations than those
with fewer buses stopping during the monitoring activity. Bus stop S.02 had the largest
number (60 in total) of incoming buses during the monitoring period, and as it has been
previously mentioned, also the highest PM2.5 concentrations. The lowest number of
incoming buses (21 in total) was obtained at bus stop S.12 (across From Waterloo
Place). Stagecoach was the most predominant bus company operating on the monitoring
route followed by Megabus, and with fewer occurrences, Quids in and Oxford Road
Link. Previous studies have shown that exhaust emissions from incoming buses can
increase the PM2.5 concentrations in these microenvironments (Hess et al., 2010).
y = 0.2506x + 9.0254 R² = 0.5286
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70
PM
2.5
(µg
m-3
)
Total number of incoming buses
Line 1:1
Figure 27: Scatter plot showing the correlation between total number of incoming buses and average PM2.5 concentrations at seven bus stops on Oxford Road from June 1
st to June 30
th, 2014.
38
Chapter 6: Discussion
6.1. Spatial Distribution of Ambient PM2.5 Concentrations on the Campus of the
University of Manchester
Average PM2.5 concentrations on the campus of the University of Manchester varied
spatially among the 21 selected monitoring sites during the month of June 2014. The
range of variation is 11.27 µg m-3
to 25.72 µg m
-3. During the monitoring period, the
annual 25 µg m-3
PM2.5 standard established by
Directive (2008/50/EC) was only
exceeded by the highest mean concentration obtained at site S.02. In general, by
comparing the average concentrations obtained at all sites, it can be observed that none
of them can be considered as hazardous to human health, but fall into the category of
good as recommended by Directive (2008/50/EC) and the American EPA standards
(Figure 28). However, it is important to bear in mind that national and international
PM2.5 recommended values are based on annual averages obtained from hourly mean
concentrations, whereas the results presented through this research are based on two-
minute average values during one month of the year.
This research confirms that PM2.5 concentrations on Oxford Road have decreased over
the last decade in comparison to the results obtained by Gee and Raper (1999) of PM2.5
concentrations ranging from 54 µg m-3
(cyclist’s exposure) to 338 µg m-3
(bus
commuter’s exposure) in the same area. The results also show that in terms of ambient
PM2.5 levels, during June of 2014, the university campus was safer on Sackville Street
than on Oxford Road. This is contradictory to the initial hypothesis, which stated that
02468
1012141618202224262830
Oxford Road Sackville Street
PM
2.5
(µg
m-3
)
Location
Directive (2008/50/EC) recommendation
Figure 28: Average PM2.5 concentrations on Oxford Road and Sackville Street from June 1
st to June 30
th, 2014.
39
PM2.5 concentrations would be higher on Sackville Street than on Oxford Road.
Nonetheless, it is important to consider that this is only a one-month research work and
that space-related variables (e.g. traffic) on Sackville Street may change significantly
and be more influential during the rest of the year leading to more ambient particulate
matter concentrations.
6.2. Temporal Variability of PM2.5 Concentrations on the Campus of the University
of Manchester
Ambient PM2.5 concentrations on the campus of the University of Manchester varied
temporarily during the monitoring period. In fact, the range of variation (4.54 µg m-3
to
34.71 µg m-3
) is larger temporally than spatially. This finding confirms that PM2.5 on the
campus of the University of Manchester is more sensitive to time-related factors than to
spatial variables. The highest and lowest average concentrations corresponded to
Monday and Tuesday respectively, and not to the weekend, as it could have been
expected. The subsequent analysis of the time-related factors considered in this study
provided an illustration of the possible causes of the temporal variation in PM2.5
concentrations. Finally, the assessment of the temporal variability of PM2.5
concentrations was, to some extent, limited by the scope of this study, which had a
stronger emphasis on the spatial distribution of the PM2.5 concentrations on the campus
of the University of Manchester.
6.3. Factors Impacting PM2.5 Concentrations during the Summer Season on the
Campus of the University of Manchester
The statistical analysis of the correlation between the variables considered in this study
and PM2.5 concentrations showed that relative humidity is the factor exerting the
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70
PM
2.5
(µg
m-3
)
Total number of incoming buses
Line 1:1
Figure 29: Correlation between A) General traffic: R2: 0.26, B)
wind speed: R2: 0.13, C) Temperature: R
2: 0.23, D) Relative
humidity: R2: 0.32, E) Incoming buses: R
2: 0.52, and average
PM2.5 concentrations on the campus of the University of Manchester.
A
B C
D
E
40
strongest influence (R2: 0.32) on PM2.5 levels on the campus of the University of
Manchester (Figure 29). Traffic was also found to be an important factor (R2: 0.26) in
determining the levels of PM2.5 on the university campus. However, general traffic
counts were restrained by the relative short time allocated for each monitoring site and
the physical limitation of the person involved in this study to perform simultaneous
monitoring activities. In contrast, incoming bus counts, which were done more
exhaustively, proved to be a determinant factor (R2: 0.52) governing PM2.5
concentrations at the seven evaluated bus stops.
41
Chapter 7: Conclusions
The integration of mobile and fixed monitoring techniques provided a preliminary
illustration of the spatial and temporal variability of ambient PM2.5 concentrations on
the campus of the University of Manchester. The results showed that in June of 2014,
PM2.5 concentrations occurred within the limits established by national air quality
legislation. However, the average PM2.5 concentrations obtained in this study are based
on a two-minute monitoring period, whereas the limit values recommended by Directive
(2008/50/EC) are expressed as annual average values obtained from hourly
measurements. For this reason, our results are not intended to be entirely comparable to
the values recommended by Directive (2008/50/EC) and international standards such as
the EPA Air Quality Index (AQI). Much research is still needed for a better
understanding of the spatial and temporal variability of PM2.5 concentrations on the
campus of the University of Manchester.
The initial hypothesis stating that PM2.5 concentrations would be higher on the north of
the campus of the University of Manchester was refuted by the obtained results
evidencing that the north area of the campus had indeed the lowest levels of fine
particulate matter according to the results. The collection of data was limited to the
month of June of 2014, and for this reason, the results presented in this study are only
representative for the summer season. This study does not provide any suggestion or
argument that PM2.5 levels will remain constant throughout the other seasons of the
year.
Ambient PM2.5 concentrations were found to be more dependent on relative humidity
than on the other evaluated factors. However, inside bus stops, incoming traffic was the
dominant factor influencing the PM2.5 concentrations. No monitoring sites were located
inside an urban canyon, thus this was not a factor to be considered in this study. It is
recommended that more studies be done in relation to the spatial variability of PM2.5
concentrations at the remaining streets that were not considered in this research work.
The temporal variation of PM2.5 concentrations must also be more thoroughly evaluated
in order to understand the evolution of ambient PM2.5 levels during the different seasons
of the year. This will also provide a more comprehensive identification of the main
factors affecting PM2.5 concentrations on the campus of the University of Manchester.
42
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