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Egodawatta,Prasanna(2018)Variability and uncertainty of particle
build-up on urban road surfaces.Science of the Total Environment,
640 - 641, pp. 1432-1437.
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1
Variability and uncertainty of particle build-up on urban
road
surfaces
Gustav Gbeddy, Ayomi Jayarathne, Ashantha Goonetilleke, Godwin
A. Ayoko, Prasanna
Egodawatta
Science and Engineering Faculty, Queensland University of
Technology (QUT), GPO Box
2434, Brisbane, 4001, Queensland, Australia
[email protected]; [email protected];
[email protected];
[email protected]; [email protected]
*Corresponding Author: Tel: 61 731384396; Fax: 61 7 31381170;
Email:
[email protected]
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2
Abstract
Particle build-up is a key stormwater pollutant process that is
typically replicated using a power
function with increasing antecedent dry days. Though the use of
power function is
recommended by a range of researchers, its applicability is
demonstrated primarily for
residential roads. Particle build-up process is also subjected
to significant variability due to
catchment heterogeneity and variability associated with source
characteristics such as traffic
volumes and land use. Variability in build-up process and use of
stereotype coefficients can
lead to significant model uncertainties. This study evaluates
particle build-up characteristics on
urban road surfaces using an extensive field investigation
program, giving specific priority to
industrial and commercial roads. Based on the outcomes, particle
build-up process
characteristics and respective uncertainties were evaluated and
compared for residential,
industrial and commercial road surfaces. The study primarily
found that both industrial and
commercial land-uses generally manifested greater particle
build-up loads compared with
residential land-uses. The study provides estimates for build-up
coefficients for a range of land-
uses, including industrial and commercial with their potential
uncertainties in build-up
predictions. This is a new addition to resources for stormwater
quality modelling. Aside from
land use, the proximity of sites to major road networks was
identified as a critical factor
influencing the variability and uncertainty of particle
build-up. Variability of the fraction of
particles
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3
1. Introduction
Stormwater pollution is of prime concern due to its significant
negative effects on the
ecosystem and human health. A comprehensive understanding of
stormwater pollutant
processes during the critical phase of pollutant build-up is
crucial for curbing stormwater
pollution. This is due to the fact that subsequent pollutant
wash-off is highly dependent on
preceding build-up process (Wijesiri et al., 2015). Ball et al.
(1998) noted that the estimation of
accumulated pollutants available for transport during a storm
event is a critical part of
stormwater quality modelling. Pollutant build-up refers to
pollutants accumulation on
impervious catchment surfaces during dry weather periods (Liu et
al., 2016). Significant spatial
variations have been observed in pollutant load and constituents
of built-up (Deletic and Orr,
2005; Wijesiri et al., 2016) due to differences in influential
factors such as land use and traffic
volumes (Helmreich et al., 2010). The build-up of particulate
pollutants is generally replicated
as a power function with an asymptotic pattern with increasing
antecedent dry days (Ball et al.,
1998; Goonetilleke et al., 2014). However, Wijesiri et al.
(2015) have shown that variability of
build-up processes for particles with varying characteristics
can affect the accuracy of power
function’s build-up prediction as well as the extent of
associated uncertainties. In this regard,
particles with varying characteristics such as size are expected
to exhibit different behaviour
and build-up patterns during an antecedent dry period.
Uncertainty is commonly used to describe the inaccuracies
associated with any predictive
model with respect to natural processes (Rabinovich, 2005).
Assessment of uncertainty in
stormwater quality modelling is an emerging field in urban
stormwater quality modelling. Thus
the influence of different land use and pollutant source
characteristics on the replicating
capacity of the power function has not been adequately
addressed. Considering the very
dynamic nature across different land uses (Wijesiri et al.,
2016) further knowledge is required
on the variability and uncertainty of pollutant build-up on
urban road surfaces as most of the
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4
existing studies are primarily focussed on residential land use.
This will facilitate the holistic
appreciation of these concepts in stormwater quality modelling
thereby aiding the formulation
of up-to-date stormwater remediation strategies. The development
of an effective stormwater
management strategy is highly dependent on the availability of
comprehensive knowledge
(Hvitved-Jacobsen et al., 2010) and thus the need for further
investigations on pollutant build-
up variability and uncertainty.
The research under discussion seeks to examine the influence of
varied land use types and
source characteristics on particle build-up variability and
uncertainty using the most applicable
build-up replication equation for pollutants on urban road
surfaces. Secondly, the role of
particle size on build-up pollutant variability across a varied
set of land uses will be assessed.
This is highly crucial since the sorption of toxic pollutants in
the stormwater environment is
highly influenced by particle size distribution. Finally,
recommendations will be proposed
based on the research findings in order to enhance sustainable
management of stormwater
quality during the critical phase of pollutant build-up.
2. Materials and methods
2.1 Study area and site description
Gold Coast region of Queensland, Australia was selected as the
study area. Gold Coast is the
sixth largest and one of the rapidly growing cities in
Australia. Due to the diversity of land uses
it offers, Gold Coast can be regarded as a truly representative
urban setting to carry out an in-
depth study on particulate pollutant build-up on urban road
surfaces. In this regard, five road
sites were selected for particle build-up investigations in the
field. These sites entail two
industrial roads located within Nerang suburb, and two
residential roads and one commercial
road located within Benowa suburb. Each site has varying traffic
characteristics such as daily
traffic volumes as shown in Table 1 and proximity to major road
network other than primary
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5
variability in land-use. The locations of the study sites are
represented in Fig.S1 in the
Supplementary Information.
2.2 Sampling and Laboratory testing
Build-up samples were collected from half-width of each road
site for 1, 4, 7 and 11 antecedent
dry days (ADDs). Half-width was considered due to non-uniform
distribution of particulate
build-up across road surfaces (Sartor et al., 1974). A portable
dry and wet vacuuming system
(Delonghi Aqualand Model) was used for sample collection. The
sampling efficiency of the
vacuum system was assessed prior to field sampling. During the
assessment, a sandy loam
sample with particle sizes ranging from 0.45 to 3000 µm was
collected with 92% efficiency. In
this study, particles less than 0.45µm are considered as soluble
fractions. A total of twenty (20)
build-up samples were collected from all the study sites at the
end of the sampling period.
Further details on similar build-up sampling can be found in
Gunawardana et al. (2012) and
Jayarathne et al. (2017).
The build-up samples were tested for total suspended solids
using similar gravimetric methods
as reported by Wijesiri et al. (2015). The particle size
distribution (PSD) was determined using
Malvern Mastersizer 3000 instrument, which is capable of
measuring particle sizes ranging
from 10nm to 3.5mm based on a laser diffraction technique
(Malvern Instruments Ltd, 2015).
-
6
3. Results and discussion
3.1 Mathematical replication of pollutant build-up
The mathematical replication of the observed particle build-up
data conforms very well to the
power function as shown in Fig. 1. The power function, M (t) =
atb adopted in this study is
indicated in the Supplementary Information as Equation (S1). In
order to characterize particle
build-up, the build-up coefficients, ‘a’ and ‘b’ in the power
function were estimated such that
the root mean standard error (RMSE) associated with the observed
and predicted build-up is
minimal (Billo, 2007; Egodawatta and Goonetilleke, 2006). The
build-up coefficients and their
uncertainties in predicting pollutant build-up are presented in
Table 1. The uncertainties
accompanying the build-up coefficients were assessed in the form
of prediction and confidence
intervals as illustrated in Equations (S2) and (S3) respectively
in the Supplementary
Information. The prediction interval (PI) indicates that there
is a 95 percent probability of
predicted pollutant loads falling within this interval whilst
the confidence interval (CI) shows
that there is a 95 percent probability that the true best-fit
line for the measurement will occur
within the confidence limits (Verschuuren, 2013). The resultant
PI and CI plots associated with
industrial and commercial, and residential land uses are shown
in Fig.1 and Table 1.
Industrial and commercial land uses have similar observed
particle build-up patterns and were
therefore combined during the uncertainty assessment. All the
observed pollutant loads fall
within the prediction interval bands for all land uses under
study; an indication of excellent
fitting of the prediction line where the majority of the
observed loads also lie within the 95%
confidence interval as indicated by Fig. 1a and 1b. The
anticipated particle build-up load across
various land uses are shown in Fig. 1c including the outcomes of
a research conducted by
Egodawatta & Goonetilleke, 2006.
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7
Estimated build-up coefficients ‘a’ and ‘b’ indicate that the
variability of particle build-up
across various land uses is highly influenced by the magnitude
of these parameters (see Table
1). The ‘a’ mostly determines the rate and magnitude of the
particle load whilst ‘b’, the
exponent parameter normally dictates the asymptotic net particle
built-up pattern as indicated
by Fig. S2. In this regard, industrial and commercial land uses
in this study have higher particle
accumulation rate (1-8 g/m2/day) than residential areas (1-4
g/m
2/day). This is further evident
in the large uncertainty band of the ‘a’ estimated for
industrial and commercial areas as shown
in Table 1 and Fig. 1a. Particle build-up load, therefore,
correlates positively with ‘a’ and
further indicates the potential particle generation capacity of
a particular land use. It was also
determined from the data simulation (see Fig. S2) that ‘b’
values greater than 0.20 will result in
particle build-up pattern reaching an asymptotic value at longer
antecedent dry days (ADD). On
the other hand, those land-uses with b
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8
(a)
(b) (c)
Fig. 1: Pollutant build-up and associated uncertainties for
different land-uses: (a) industrial and
commercial; (b) residential; and (c) projected build-up patterns
(Residential areas (ii)
and (iii) were studied by Egodawatta and Goonetilleke
(2006)).
0
2
4
6
8
10
12
14
16
18
20
0 5 10 15
Par
ticl
e b
uild
-up
load
(g/
m2 )
Antecedent dry days
Observed loadPrediction linePrediction interval bandEgodawatta
& GoonetillekeEgodawatta & GoonetillekeConfidence interval
band
0
2
4
6
8
10
12
14
16
18
20
0 10 20
Par
ticl
e b
uild
-up
load
(g/
m2 )
Antecedent dry days
Industrial&Commercial
Residential(i)
Residential(ii)
Residential(iii)
0
2
4
6
8
10
12
14
16
18
20
0 5 10 15
Par
ticl
e b
uild
-up
load
(g/
m2)
Antecedent dry days
Observed load
Prediction line
Prediction interval band
Egodawatta & Goonetilleke
Egodawatta & Goonetilleke
Confidence interval band
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9
Table 1: Study site characteristics with estimated build-up
coefficients and uncertainties
Land and road use characteristics Estimated daily traffic
volume*
Power function build-up
coefficient
a (95% PI) b (95% PI)
Urban industrial (NSS &NHC) &
commercial (BST), roads with through
traffic and close proximity to major road
500, 3500, 3000 for NSS,
NHC & BST respectively
6.74
(2.46 ̶ 11.03)
0.26
(0.18 ̶ 0.53)
Urban residential (BVH & BMT ) with
high population density (townhouse and
duplex housing), roads located in close
proximity to major road
1537, 750 for BVH & BMT
respectively
3.62
(1.03 ̶ 6.37)
0.43
(0.28 ̶ 0.86)
Urban residential (G ) with high
population density (townhouse and
duplex housing), roads located far away
from a major road
2.90
0.16
Urban residential (L & P ) with moderate
population density (single detached
housing), with no through traffic located
far from a major road
1.65 0.16
Notes: PI is predictive interval; Build-up coefficients for G, L
& P were adapted from
Egodawatta and Goonetilleke (2006); and * data for daily traffic
volume was acquired from
Mummullage et al. (2016).
The proximity of study sites to major road networks has been
identified as a critical factor in
particle build-up load on roads. This emphasizes the important
role of surrounding pollutant
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10
emitting sources as noted by Goonetilleke et al. (2014). In this
context, urban residential areas
with single detached housing and low population density located
far away from major road
networks attain faster asymptotic particle build-up. This is
evident in the build-up patterns
reported by Egodawatta and Goonetilleke (2006) compared to those
in the current study.
Furthermore, residential areas in this study (Residential (i))
are most likely to exhibit delayed
asymptotic net particle load compared to industrial and
commercial land uses as evident in the
build-up patterns of Fig. 1c. This observation further
collaborates with the higher uncertainty
margins in ‘b’ estimate for residential land-use. A previous
study conducted by Ball et al.
(1998) in a suburban residential area with similar
characteristics as Residential area (i) in this
research obtained comparable empirical ‘a’ and ‘b’ values of
3.77 and 0.57 respectively. This,
therefore, underscores the spatial variation of particle
build-up coefficients with respect to
changes in residential land use characteristics. The differences
in particle build-up coefficients
for different sites may be an indication of the disparities in
site location, pollutant generation
capacity, pollutant composition and the influence of natural and
artificial redistribution factors
such as wind and vehicular movement across various land-uses.
The influence of these factors
on ‘a’ and ‘b’ is however, interlinked thereby presenting a
unique challenge during the generic
modelling of particle build-up (Goonetilleke et al., 2014). The
need to adopt the most
appropriate stormwater management system for various land-uses
must, therefore, be based on
an in-depth investigation.
The values determined for ‘a’ and ‘b’ as shown in Table 1 will
enable stormwater quality
modelling possible for catchments with varied land uses. This
will facilitate the accurate
prediction of particle build-up behaviour with reference to a
particular land-use type. It will
further assist in estimating the most probable ADD to carry out
cost and time effective street
sweeping practices with respect to different land-uses. In the
context of this study, the
maximum ADD of 11 days employed is relatively inadequate to
clearly observe and also
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11
determine the most applicable asymptotic values for each
land-use. However, with the help of a
detailed examination of the particle size distribution pattern
in the observed particle build-up
load, a viable ADD can be projected for an effective protection
of stormwater quality.
3.2 Role of particle size distribution in particle build-up
variability
The assessment of particle size distribution (PSD) of particle
built-up load is highly essential.
This is due to the significant influence of particle size on
particle mobility and their associated
high pollutant concentrations (Deletic and Orr, 2005). It also
influences the health risk posed by
particles to humans and other animals as finer particles are
highly capable of reaching the
alveoli of the respiratory systems. Moreover, Wijesiri et al.
(2015) noted that the behavioural
variability of size-fractionated particulate build-up influences
pollutant build-up process
variability. As a result, the assessment of build-up variability
across particle size ranges for
different land-uses is absolutely vital to develop management
strategies for stormwater
pollution.
The built-up particles generally ranged from 0.46 ̶ 3080 µm. In
order to fully comprehend
particles behaviour during build-up on road surfaces across
varied land-uses, particle size
distribution data for built-up particles were fractionated into
three size ranges of 0.45-75µm,
75-150µm and 150-3500µm as specified in Table S1. Fig. 2
illustrates the variations of build-
up particles in the form of percentage by volume for different
particle size ranges against the
ADDs for each study site. The highly dynamic nature of particle
build-up due to the influence
of re-distribution forces is clearly evident in Fig.2. First of
all, particles >150 µm generally
show an increasing relationship with ADD across all land-uses.
This observation agrees with an
earlier study conducted by Wijesiri et al. (2015). As they
noted, re-distribution forces such as
natural wind and vehicular movement (Egodawatta and
Goonetilleke, 2006; Namdeo et al.,
1999) results in re-suspension and relocation of finer particles
thereby enabling coarser
particles to accumulate with increasing ADD.
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12
(a) (b)
(c) (d)
(e)
Fig. 2: Behaviour pattern of particle size ranges for different
land-uses: (a) & (b) residential
BVH & BMT; (c) commercial BST; and (d) & (e) industrial
NHC & NSS respectively
(Legend in (a) is valid for (b) - (e)).
0
20
40
60
80
100
0 5 10
% b
y vo
lum
e P
SD
Antecedent dry days
0.45 - 75 75 - 150150-3500
0
20
40
60
80
100
0 5 10
% b
y vo
lum
e P
SD
Antecedent dry days
0
20
40
60
80
100
0 5 10
% b
y vo
lum
e P
SD
Antecedent dry days
0
20
40
60
80
100
0 5 10
% b
y vo
lum
e P
SD
Antecedent dry days
0
20
40
60
80
100
0 5 10
% b
y vo
lum
e P
SD
Antecedent dry days
-
13
The behaviour pattern of particles within 75-150 µm size range
shows partial variations for
different land-uses. Some of the sites such as a and e in Fig. 2
exhibit gentle decline of particles
in this size range with ADD whilst sites b, c and d in Fig. 2
show gradual increase of particles
with ADD. Finally, from Fig. 2 (a) and (c), particles within
0.45-75 µm range exhibit distinct
characteristics where the % by volume decreases significantly
with ADD. However, in terms of
Fig. 2 (b) and (d), this sharp decrease reaches a minimum point
around the 7th
ADD before
ascending with higher ADDs. This particle behaviour can be
attributed to the attainment of
build-up equilibrium where the rate of 0.45-75 µm particles
generation and loss becomes equal
around the 7th
ADD at industrial site of Hilldon Court and residential site of
Mediterranean
Drive. However, after the 7th
ADD more particles are produced by the sources than lost due
to
redistribution factors at these sites.
This study partly affirms the conclusion made by Wijesiri et al.
(2015) that particle
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14
(Kayhanian et al., 2012). In this regard, fine particles could
pose an enormous challenge to the
effective performance of commonly deployed stormwater particle
removal processes such as
street sweeping, filtration and sedimentation. In addition, this
presents a unique health
implication to humans, other animals and plants since
particles
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15
4. Conclusions
This research shows the existence of significant variation and
uncertainty in particle build-up
across residential, commercial and industrial land-uses.
Industrial and commercial land-uses
generally manifested greater build-up of particles compared with
residential land-uses. The
study provides estimates of coefficients in particle build-up
replicating equation for a range of
land-uses, including industrial and commercial with their
potential uncertainties in particle
build-up predictions. This is a new addition to resources for
stormwater quality modelling and
will greatly help in enhancing the accuracy and reliability of
modelling outcomes.
Estimated built-up coefficients for the power function indicated
that industrial and commercial
land-uses accumulate more particles (1-8 g/m2/day) compared to
residential areas (1-4
g/m2/day) which are evident in the large difference of the
estimated coefficients compared to
residential areas. Commercial and industrial land-uses must,
therefore, receive greater attention
during the deployment of stormwater mitigation measures coupled
with the fact that higher
loads of deleterious stormwater pollutants such as PAHs and
heavy metals are normally
associated with these areas. The behaviour and variability of
particles between 0.45-3500 µm
are influenced by either particles in size ranges of >150µm
and 75µm and
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16
Acknowledgement
The authors will like to acknowledge the Queensland University
of Technology (QUT) for
extending the postgraduate research award to Gustav Gbeddy to
undertake this study. Further
appreciation goes to the Central Analytical Research Facility
(CARF) under the Institute of
Future Environments, QUT where the data employed in this paper
were acquired. Access to
CARF was facilitated by generous funding from the Science and
Engineering Faculty, QUT.
Finally, the significant role of the Ghana Atomic Energy
Commission (GAEC) is highly
recognized for granting study leave to Gustav Gbeddy in order to
embark upon this study.
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