atmosphere
Article
The Urban Heat Island Effect and the Role of
Vegetation to Address the Negative Impacts of Local
Climate Changes in a Small Brazilian City
Elis Dener Lima Alves 1, * and António Lopes 2
1
2
*
Instituto Federal de Educação, Ciência e Tecnologia, Ceres 76300-000, Brazil
Institute of Geography and Spatial Planning (IGOT-ULisboa), Center of Geographical Studies—Climate
Change and Environmental Systems Research Group, Universidade de Lisboa, 1649-003 Lisboa, Portugal;
[email protected]
Correspondence: [email protected]; Tel.: +55-62-3307-7100
Academic Editor: Robert W. Talbot
Received: 2 November 2016; Accepted: 13 January 2017; Published: 9 February 2017
Abstract: This study analyzes the influence of urban-geographical variables on determining heat
islands and proposes a model to estimate and spatialize the maximum intensity of urban heat islands
(UHI). Simulations of the UHI based on the increase of normalized difference vegetation index
(NDVI), using multiple linear regression, in Iporá (Brazil) are also presented. The results showed that
the UHI intensity of this small city tended to be lower than that of bigger cities. Urban geometry and
vegetation (UI and NDVI) were the variables that contributed the most to explain the variability of
the maximum UHI intensity. It was observed that areas located in valleys had lower thermal values,
suggesting a cool island effect. With the increase in NDVI in the central area of a maximum UHI,
there was a significant decrease in its intensity and size (a 45% area reduction). It is noteworthy that
it was possible to spatialize the UHI to the whole urban area by using multiple linear regression,
providing an analysis of the urban set from urban-geographical variables and thus performing
prognostic simulations that can be adapted to other small tropical cities.
Keywords: urban climate change; UHI; urban microclimates; mitigation
1. Introduction
The expansion of urban centers over the years has created a marked heating pattern in relation to
their rural environment [1–4], especially in densely built urban centers with little vegetation where
temperatures tend to be higher [4–9]. This pattern is known as urban heat islands (UHI) [4,10,11].
In 1833, Luke Howard hypothesized that the excess heat in cities during summer was due to a
greater absorption of solar radiation by the vertical surfaces of a city and the lack of available
humidity for evaporation [11]. Howard’s theories were surprisingly accurate. However, there are
several other reasons for the appearance of an UHI pattern: urban heat generated from heating,
air conditioning, transportation, and industrial processes [11], and a decrease in wind speed due to
a roughness length increase, which also slows down energy transfer from the surface to the urban
atmosphere [12,13]. Pollution also contributes to this scenario because air particles absorb and emit
heat to urban canyons [14,15].
Several other problems related to UHIs have been observed, such as increased urban pollution,
intense summer rainfalls, and high energy consumption. Also, there may be problems related to
thermal discomfort and heat stress impacting human health and causing a high death rate of already
physically vulnerable people.
Attenuating the effects of UHI in tropical climates is imperative and should be considered a key
element in urban design to address climate change in cities [14,16]. Despite its volume, studies of
Atmosphere 2017, 8, 18; doi:10.3390/atmos8020018
www.mdpi.com/journal/atmosphere
Atmosphere 2017, 8, 18
2 of 13
spatialization of the differences between intra-urban and rural temperatures can contribute to
mitigate the magnitude of UHIs [17,18] and improve quality of life.
Significant
Atmosphere
2017, 8, 18efforts have been made by the scientific community to assess the impact of UHIs
2 of on
14
the urban environment [4,19–21]: simulations of urban heat islands and multiple linear regression
have been used in numerous studies [22–29], obtaining good results. When combined with
urban heat islands have been an important tool to manage urban spaces. As a part of urban planning,
observations, they may provide prospects for the reorganization of territories. However, as
spatialization
of the differences
between
rural
can properly
contributeused
to mitigate
previously pointed
out by others,
veryintra-urban
often the and
results
oftemperatures
studies are not
by the
the
magnitude
of to
UHIs
[17,18]
and improve
quality
of life.
government
due
a lack
of detailed
and useful
information,
or simply due to the lack of interest by
Significant
have been
made by the
scientific
community
to assess
the impact
of changes
UHIs on[29].
the
urban
planners efforts
not sufficiently
preoccupied
with
the problems
related
with urban
climate
urbanThis
environment
[4,19–21]:
simulations
of
urban
heat
islands
and
multiple
linear
regression
have
research is a first UHI assessment in Iporá, and the main objectives are:
been used in numerous studies [22–29], obtaining good results. When combined with observations,
(i) may
to analyze
theprospects
influencefor
of the
urban-geographical
as However,
triggering as
factors
of the formation
of
they
provide
reorganization ofvariables
territories.
previously
pointed out
heat
islands;
by others, very often the results of studies are not properly used by the government due to a lack of
(ii) to propose
a model
to estimate
and spatialize
thelack
maximum
intensity
of UHIs
in the
city
core; and
detailed
and useful
information,
or simply
due to the
of interest
by urban
planners
not
sufficiently
(iii) to simulate
of urban
heaturban
islands
with an
increase
in vegetation index (NDVI) by
preoccupied
withthe
themitigation
problems related
with
climate
changes
[29].
using
multiple
linear
regression.
This research is a first UHI assessment in Iporá, and the main objectives are:
2. Materials
and
Methods
(i)
to analyze
the
influence of urban-geographical variables as triggering factors of the formation of
heat islands;
2.1. Data
Collection
(ii)
to propose
a model to estimate and spatialize the maximum intensity of UHIs in the city core; and
(iii) to
simulate
the mitigation
heat islands
with
increase
in vegetation
index
(NDVI)
by
The city of Iporá
is locatedofinurban
the western
part of
thean
state
of Goiás
(GO), Brazil
(Figure
1), and
2
using multiple
linear
regression. 1026.4 km [30]. The population of Iporá has not changed
its territorial
area is
approximately
substantially in recent decades (from 1980, there was an increase of approximately 4920 people), and
2. Materials and Methods
as of the writing of this paper the city currently counts 31,274 inhabitants [30]. From The economy is
based
onCollection
the third sector, followed by agriculture. The rainy season occurs from April to September
2.1.
Data
(with an average of approximately 180 mm), and from October to March (approximately 1400 mm).
city
of Iporá
is value
located
the western part
the state of Goiás (GO), Brazil (Figure 1), and its
The The
mean
total
annual
is in
approximately
1600ofmm.
2 [30]. The population of Iporá has not changed substantially
territorial
area
is
approximately
1026.4
km
Seven thermo-hygrometers were installed within the urban area of Iporá. The distribution of the
in
recent
decades (from
1980, to
there
was
an criteria:
increase(i)
of represent
approximately
4920 people),
and as of the
thermo-hygrometers
sought
meet
two
the different
urban-geographical
writing
of
this
paper
the
city
currently
counts
31,274
inhabitants
[30].
From
The
economy
is based on
characteristics of the city; and (ii) standardize the contact surface, following the recommendations
by
the
followed
by agriculture.
The rainy
season
occurs
fromonApril
to September
Okethird
[31].sector,
They were
installed
in white-painted
weather
shelters
fixed
wooden
poles at a (with
heightan
of
average
of
approximately
180
mm),
and
from
October
to
March
(approximately
1400
mm).
The
mean
1.80 m in roundabouts and road centers (Figure 1) with a SVF ≥ 0.8. With this procedure, it is assured
total
is approximately
1600ofmm.
that annual
there is value
no microclimatic
influence
buildings or nearby trees.
Figure 1. Location of the city of Iporá and the measurement sites.
Figure 1. Location of the city of Iporá and the measurement sites.
The OM-EL-USB-2-LCD thermo-hygrometers were manufactured by OMEGA. They have an
Seven
were Data
installed
within the
urban
area of Iporá.
distribution
of
accuracy ofthermo-hygrometers
0.5 °C for air temperature.
was recorded
from
20 October
2014 toThe
24 November
2014,
the
thermo-hygrometers
sought
to meet
two
criteria:
(i) represent the different urban-geographical
recording
data every 30 min,
totaling
1630
data
outputs.
characteristics of the city; and (ii) standardize the contact surface, following the recommendations by
Oke [31]. They were installed in white-painted weather shelters fixed on wooden poles at a height of
Atmosphere 2017, 8, 18
3 of 14
1.80 m in roundabouts and road centers (Figure 1) with a SVF ≥ 0.8. With this procedure, it is assured
that there is no microclimatic influence of buildings or nearby trees.
The OM-EL-USB-2-LCD thermo-hygrometers were manufactured by OMEGA. They have an
accuracy of 0.5 ◦ C for air temperature. Data was recorded from 20 October 2014 to 24 November 2014,
recording data every 30 min, totaling 1630 data outputs.
Atmosphere 2017, 8, 18
3 of 13
2.2. Methodology and Data Analysis
2.2. Methodology
Data Analysis
There is notand
an universal
criteria for calculating the intensity of urban heat islands [4,31–33].
In many
studies,
calculation
was performed
by subtracting
the temperature
recorded
the urban
There
is notsuch
an universal
criteria
for calculating
the intensity
of urban heat
islands in
[4,31–33].
In
area
from
the temperature
recorded
weather stations
or airports.
For example,
this technique
was
many
studies,
such calculation
was at
performed
by subtracting
the temperature
recorded
in the urban
applied
forthe
more
than 20 years
by Alcoforado
et stations
al. [4] fororthe
city of For
Lisbon,
Portugal.
et al.was
[1]
area from
temperature
recorded
at weather
airports.
example,
this Miao
technique
defined
as the
mean
air temperature
difference
between
inside Portugal.
(i.e., urban
areas)
and
applied UHI
for more
than
20 years
by Alcoforado
et al. [4]
for the stations
city of Lisbon,
Miao
et al.
[1]
outside
areas)
built
defined(i.e.,
UHIrural
as the
mean
airenvironments.
temperature difference between stations inside (i.e., urban areas) and
Based
onrural
Lopes
et al.built
[4], itenvironments.
was considered that an UHI occurred in Iporá when the temperature
outside
(i.e.,
areas)
of oneBased
of theon
collection
wasithigher
than the temperature
the otherincollection
sites.
intensity
Lopes etsites
al. [4],
was considered
that an UHIof
occurred
Iporá when
theThe
temperature
of
was
calculated
thehigher
difference,
at atemperature
given moment,
the sitesites.
withThe
the intensity
highest
ofan
oneUHI
of the
collection
sitesaswas
than the
of thebetween
other collection
temperature
(Thigher
) and the
the lowest
(Tlower
), according
to Equation
of an UHI was
calculated
assite
thewith
difference,
at atemperature
given moment,
between
the site
with the (1).
highest
temperature (Thigher) and the site with the lowest temperature (Tlower), according to Equation (1).
UHIt0 h→23 h = Thigher − Tlower
(1)
=
−
UHI →
(1)
The
variablesused
used
multiple
linear
regressions
altitude
(A),
The urban-geographical
urban-geographical variables
forfor
multiple
linear
regressions
were were
altitude
(A), terrain
terrain
aspect
(TA),
demographic
density
(DD),
NDVI,
terrain
slope
(TS),
and
urbanization
index
aspect (TA), demographic density (DD), NDVI, terrain slope (TS), and urbanization index (UI). Seven
(UI).
were(Figure
selected
2), corresponding
to each measurement
site. A with
square
areasSeven
wereareas
selected
2),(Figure
corresponding
to each measurement
site. A square
anwith
areaan
of
2 (200 m × 200 m) focusing on each measurement location was determined (Figure 2).
area
of
40,000
m
2
40,000 m (200 m × 200 m) focusing on each measurement location was determined (Figure 2). Mean
Mean
and mode
values
the urban-geographical
variables
for each
square
determined
(Table
and mode
values
of theofurban-geographical
variables
for each
square
werewere
determined
(Table
1). 1).
Figure 2. Measurement sites and method for obtaining urban-geographical variables.
Figure 2. Measurement sites and method for obtaining urban-geographical variables.
The normalized difference vegetation index ( NDVI ) and the urbanization index (UI) were
The normalized
difference
vegetation
and the urbanization index (UI) were
calculated
using Landsat
5 Thematic
Mapperindex
(TM) (NDVI)
satellite images.
calculated
using
Landsat
5
Thematic
Mapper
(TM)
satellite
images.
The NDVI is a simple indicator of vegetation biomass and strength, and can be used as cover
The NDVI
is a simplerelations
indicatorasofa vegetation
biomass
and strength,
and Difference
can be used
as cover
fraction
using empirical
possible basis
function.
Normalized
Vegetation
fraction
using
empirical
relations
as
a
possible
basis
function.
Normalized
Difference
Vegetation
Index
Index (NDVI) is determined by:
(NDVI) is determined by:
NIR − R
(2)
NDVI =
NIR + R
where NDVI is Normalized Difference Vegetation Index, NIR is Near InfraRed (band 4 in Landsat 5 TM),
and R (Red) is band 3 of the satellite. The Urbanization Index (UI) was determined using Equation
(3). The UI is generally used because of its easiness of mapping and characterizing built areas [34,35].
Atmosphere 2017, 8, 18
4 of 14
NDVI =
NIR − R
NIR + R
(2)
where NDVI is Normalized Difference Vegetation Index, NIR is Near InfraRed (band 4 in
Landsat 5 TM), and R (Red) is band 3 of the satellite. The Urbanization Index (UI) was determined
using Equation (3). The UI is generally used because of its easiness of mapping and characterizing
built areas [34,35].
UI =
SWIR − R
SWIR + R
(3)
where SWIR (Short Wave Infra-Red) is Landsat 5 TM band 7.
Table 1. Altitude (A), terrain slope (TS), demographic density (DD), NDVI, terrain aspect (TA),
and urbanization index (UI) at the measurement sites.
Sites
A1
TS 1
DD 2
NDVI 1
TA 3
UI 1
1
2
3
4
5
6
7
608.3
570.6
588.8
580.8
596.9
615.1
595.8
1
2.4
2.1
2.7
3.3
1.7
2.4
684.4
3560.3
2103
70.6
4002
2084.6
912.6
0.05
0.26
0.07
0.01
0.08
0.08
0.09
213
190
208
296
280
282
233
−0.09
−0.27
0
0.06
−0.02
0.01
−0.02
1
Mean value in a square with a 200 m side centered on the measurement site; 2 Population calculated by the
Brazilian Census of 2010; 3 Mode value in a square with a 200 m side centered on the measurement site.
Multiple linear regression is a multivariate technique whose main purpose is to obtain a
mathematical relation between one of the studied variables (dependent or response variable) and
the other variables that describe the system (independent or explanatory variables). It also seeks
to reduce the number of independent variables with a minimal loss of information. After the
mathematical relation is found, its main application is to produce values for the dependent variable
when independent variables are available [36].
In order to determine which independent variables had the greatest potential to contribute to
UHIs, a step-by-step multiple linear regression was used [37,38].
According to Montgomery, Peck, and Vining [39], the multiple linear regression model (MLRM)
with k control variables can be represented by Equation (4).
Y = β 0 + β 1 X1i + β 2 X2i + · · · + β k Xki + ε i
(4)
The regression coefficients β 0 , β 1 , ..., β k are described by Montgomery, Peck, and Vining [39] as
follows: β 0 is the intercept coefficient, which is the average of Yi when all control variables are equal to
zero; the coefficients β 1 , ..., β k are partial regression coefficients. The coefficient β k can be interpreted
as a partial derivative of Yi in relation to Xki , that is, the variation of Y caused by a unit change in Xk
since the other control variables are kept constant.
The criterion adopted for input and output variables in the model was their significance level
(p-value < 0.05). The best multiple linear regression model was used to estimate UHI values for the
entire urban area. The software used (Surfer 13) performed a semi-variogram analysis (presented in
Section 3), followed by the interpolation of UHI using the kriging technique.
Simulations of UHIs were made from the equations obtained by multiple linear regression with
several scenarios corresponding to the increase in vegetation cover.
3. Results and Discussion
As previously discussed, a total of 1630 data values (corresponding to the period from 20 October
2014 to 24 November 2016) were used to calculate the value of maximum heat islands.
Atmosphere 2017, 8, 18
5 of 14
3.1. A Case Study of a Hot and Dry Night (21 October 2014) in the Iporá Region
In tropical areas, very hot and dry nights may lead to extremely uncomfortable situations
and even to an excess of mortality, especially of more vulnerable people (young and old people).
This case study considers one of these situations, which was studied for the first time in the city of
Iporá. On 21 October 2014, the Intertropical Convergence Zone (ZCIT) oscillated from 08◦ N to 10◦ N in
the Pacific and from 07◦ N to 09◦ N in the Atlantic, and a hot and dry continental tropical air mass (mTc)
affected the study area. The Brazilian National Institute of Meteorology (INMET) station located on the
outskirts of the city (Figure 3) recorded an amplitude of 12.9 ◦ C in the air temperature: the minimum
(21.2 ◦ C) was recorded at 03:00 and the maximum at 16:00 (34.1 ◦ C). Relative humidity reached less
than
30% 2017,
during
the
Atmosphere
8, 18several hours, approximately from 14:00 to 17:00. There was little fluctuation in
5 of
13
atmospheric pressure (from 941 to 945 hPa) because a low system influenced the region. However,
due
to the
influence
of to
thethe
mTc
air mass,
was
solar and
radiation
was more
region.
However,
due
influence
ofthe
thesky
mTc
airclear
mass,and
thethe
skyglobal
was clear
the global
solar
−2 at noon.
than
3500 was
KJ·mmore
radiation
than 3500 KJ·m−2 at noon.
Figure 3.
3. Weather
Institute
of of
Meteorology
(INMET)
station
on
Figure
Weatherdata
datafrom
fromthe
theBrazilian
BrazilianNational
National
Institute
Meteorology
(INMET)
station
21
October
2014.
on 21 October 2014.
3.2. Multiple Linear Regression of Maximum Heat Islands
3.2. Multiple Linear Regression of Maximum Heat Islands
In order to observe the pattern of an UHI during its greatest intensity, multiple linear regressions
In order to observe the pattern of an UHI during its greatest intensity, multiple linear regressions
were calculated at 20:30, 21:00, and 21:30 on 21 October 2014. NDVI and UI variables were used in
were calculated at 20:30, 21:00, and 21:30 on 21 October 2014. NDVI and UI variables were used in three
three equations (Equations (5)–(7)) because these are the most important in the model and they
equations (Equations (5)–(7)) because these are the most important in the model and they represent an
represent an effective way to propose measures to address urban climate change. In Equation (5),
effective way to propose measures to address urban climate change. In Equation (5), NDVI explained
NDVI explained 92.3% of the spatial variability of the UHI at 20:30, the UI in Equation (6) explained
92.3% of the spatial variability of the UHI at 20:30, the UI in Equation (6) explained 86.5% at 21:00,
86.5% at 21:00, and Equation (7) explained 88.4% of the variability of the UHI at 21:30. All equations
and Equation (7) explained 88.4% of the variability of the UHI at 21:30. All equations (Table 2) had a
(Table 2) had a r2 > 0.9. Others urban-geographical areas were not included in the model because they
r2 > 0.9. Others urban-geographical areas were not included in the model because they presented a
presented a p-value < 0.05.
p-value < 0.05.
Table 2. Equations obtained for maximum urban heat islands (UHI) intensity.
Table 2. Equations obtained for maximum urban heat islands (UHI) intensity.
Estimated UHI
Estimated
UHI : = 29.84 + (0.0000753
× DD)UHI
− (13.510 × NDVI) + (0.744 × UI)
UHI20:30
=
29.84
+
0.0000753
×
DD
−
13.510
× NDVI
× UI×) UI)
(
)
(
) + (+0.744
(5.933
(0.00309
UHI
=
29.741
−
×
NDVI)
−
× TA)
(6.478
:
(7.302
UHI
=
28.927
−
×
NDVI)
+
(5.508
×
UI)
UHI21:00
=
29.741
−
5.933
×
NDVI
−
0.00309
×
TA
+
6.478
×
UI
(
)
(
)
(
)
:
UHI21:30 = 28.927 − (7.302 × NDVI) + (5.508 × UI)
Number of Equations
Number
of Equations
Equation
(5)
Equation
(5)(6)
Equation
Equation
Equation
(6)(7)
Equation (7)
The semi-variogram used for kriging UHIs at 20:30 (Figure 4) was an exponential model, with a
r2 = 0.69, a nugget effect = 0.06 ( ), and a silo ( + ) = 1.23. From this point, it is considered that
there is no spatial dependence between samples. The spatial dependence on UHI intensity, obtained
by the range ( ) was 188 m. For the kriging of UHI at 21:00, the Gaussian model was used, with r2 = 0.78,
nugget = 0.48, silo = 1.13, and spatial dependence = 295 m (the maximum distance with the influence
of space on each point). At 21:30, the best semi-variogram was obtained with r2 = 0.94,
= 0.82, and
+ = 2.69. The maximum range of spatial dependence was greater than the others (758 m).
The semi-variograms (Figure 4) were used as an input model for the spatialization (with kriging)
Atmosphere 2017, 8, 18
6 of 14
The semi-variogram used for kriging UHIs at 20:30 (Figure 4) was an exponential model,
with a r2 = 0.69, a nugget effect = 0.06 (C0 ), and a silo (C0 + C) = 1.23. From this point, it is considered
that there is no spatial dependence between samples. The spatial dependence on UHI intensity,
obtained by the range (A0 ) was 188 m. For the kriging of UHI at 21:00, the Gaussian model was used,
with r2 = 0.78, nugget = 0.48, silo = 1.13, and spatial dependence = 295 m (the maximum distance
with the influence of space on each point). At 21:30, the best semi-variogram was obtained with
r2 = 0.94, C0 = 0.82, and C0 + C = 2.69. The maximum range of spatial dependence was greater than
the others (758 m).
Atmosphere 2017, 8, 18
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Atmosphere 2017, 8, 18
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Figure 4. Semi-variograms used for the kriging of the maximum UHI intensities at 20:30 (A);
Figure 4. Semi-variograms used for the kriging of the maximum UHI intensities at 20:30 (A); 21:00 (B);
21:00
(B);4.and
21:30 (C).
Figure
Semi-variograms
used for the kriging of the maximum UHI intensities at 20:30 (A);
and 21:30 (C).
21:00 (B); and 21:30 (C).
Figure 5. Thermal patterns estimated on the 21 October 2014, at 20:30 (A); 21:00 (B); and 21:30 (C).
Figure 5. Thermal patterns estimated on the 21 October 2014, at 20:30 (A); 21:00 (B); and 21:30 (C).
Figure
5. Thermal
patterns
estimated
onthe
thehighest
21 October
2014,
at 20:30
21:00 for
(B);the
andUHI
21:30
(C).
According
to the
boxplots
(Figure 6),
median
value
was (A);
observed
at 21:30
(2.7 °C),
whereastothe
value was
at median
21:00 (4.5
°C). was
The observed
highest values
corresponding
According
themaximum
boxplots (Figure
6), observed
the highest
value
for the
UHI at 21:30
to
50%
ofwhereas
the datathe
were
observed
21:30
(2.2–3as°C),
20:30
21:00
thevalues
interval
was
lower:
The
semi-variograms
(Figure
4)atwere
anatwhile
input
model
for
spatialization
(with
kriging)
(2.7
°C),
maximum
value
wasused
observed
21:00 at
(4.5
°C).and
Thethe
highest
corresponding
1.9–2.8
°C
anddata
1.8–2.8
°C,observed
respectively.
Therefore,
the
data patterns
set
of boxplots
shows
that
therewas
was
little
to 50%
of resulting
the
were
at 21:30
°C),
at 20:30
and
21:00
the
interval
lower:
of UHIs.
The
maps
are shown
in (2.2–3
Figure
5. while
The
of UHIs
were
similar
for
all three
in the
intensity
ofatthe
UHI.
1.9–2.8
°C
and
1.8–2.8
°C,
respectively.
Therefore,
data
set of an
boxplots
shows
therecorresponding
was little
times,change
and the
area
located
the
southwest
of the the
maps
shows
intense
heatthat
island
Urban
heat
islandsof are
often regarded as the most important factor influencing the urban
change
in the
intensity
the UHI.
environment
and islands
its surroundings
However,
the phenomenon
knowninfluencing
as urban cool
Urban heat
are often[2,4,40].
regarded
as the most
important factor
theislands
urban
may
also occur
with
significant effects.
This
pattern was
first detectedknown
by Hu,asSu,
and cool
Zhang
[41]
environment
and
its surroundings
[2,4,40].
However,
the phenomenon
urban
islands
based
on an
analysis
microclimate
characteristics
ofwas
a reservoir
in the Hexi
Region,
China.
may also
occur
withof
significant
effects.
This pattern
first detected
by Hu,
Su, and
Zhang [41]
based on an analysis of microclimate characteristics of a reservoir in the Hexi Region, China.
Atmosphere 2017, 8, 18
7 of 14
to the center of Iporá. Its peak was at 21:30. In addition, the spatial patterns of UHI < 0, also called
urban cool islands (UCI), were maintained.
According to the boxplots (Figure 6), the highest median value was observed for the UHI at 21:30
(2.7 ◦ C), whereas the maximum value was observed at 21:00 (4.5 ◦ C). The highest values corresponding
to 50% of the data were observed at 21:30 (2.2–3 ◦ C), while at 20:30 and 21:00 the interval was lower:
1.9–2.8 ◦ C and 1.8–2.8 ◦ C, respectively. Therefore, the data set of boxplots shows that there was little
change
in the2017,
intensity
of the UHI.
Atmosphere
8, 18
7 of 13
Atmosphere 2017, 8, 18
7 of 13
Figure 6. Boxplots of UHIs at 20:30, 21:00, and 21:30.
Figure 6. Boxplots of UHIs at 20:30, 21:00, and 21:30.
Even at a maximum UHI, cool islands may occur. This is shown in Figure 7, which was designed
Urban
heat islands
are over
oftentheregarded
as be
theseen
most
factorin influencing
the
urban
by superimposing
the UHI
relief. It can
thatimportant
the areas located
valley bottoms
had
the lowestand
values,
suggesting a cold
air drainage.
Thisthe
is inphenomenon
accordance with
the study
by Lopes
[42],
environment
its surroundings
[2,4,40].
However,
known
as urban
cool
islands
which
reported
cold-aireffects.
lakes inThis
Oeiras,
Portugal,
formed
in topographically
depressed
may also
occur
with that
significant
pattern
was first
detected
by Hu, Su, and
Zhang areas,
[41] based
were
double
fed
by
the
cool
air
formed
in
situ
by
irradiation
and
also
by
the
cold
air
drained by
on an analysis of microclimate
characteristics
a reservoir
in the
Figure
6. Boxplots ofof
UHIs
at 20:30, 21:00,
andHexi
21:30.Region, China.
gravity that formed in the upper sections of the valleys.
Even
at a maximum UHI, cool islands may occur. This is shown in Figure 7, which was designed
Even at a maximum
UHI, the
coolrelief.
islandsItmay
This
is shown
in Figure
7, which
was designed
by superimposing
the UHI over
canoccur.
be seen
that
the areas
located
in valley
bottoms had
by superimposing the UHI over the relief. It can be seen that the areas located in valley bottoms had
the lowest values, suggesting a cold air drainage. This is in accordance with the study by Lopes [42],
the lowest values, suggesting a cold air drainage. This is in accordance with the study by Lopes [42],
which reported that cold-air lakes in Oeiras, Portugal, formed in topographically depressed areas,
which reported that cold-air lakes in Oeiras, Portugal, formed in topographically depressed areas,
were were
double
fed by the cool air formed in situ by irradiation and also by the cold air drained by gravity
double fed by the cool air formed in situ by irradiation and also by the cold air drained by
that formed
in
upper
the valleys.
gravity thatthe
formed
in sections
the upperofsections
of the valleys.
Figure 7. Maximum UHI intensities.
Figure 8 shows the quantification of the area of each urban heat island class. The UHI classes > 1 °C
had an area of 14.91 km2, which corresponds to 90% of the urban area. The UHI classes < 1 °C had an
area of 1.67 km2, which corresponds to 10% of the total area. Therefore, there was a predominance of
heat islands in high intensity classes. The cool islands (classes with UHI < 0 °C), were concentrated
in the valley bottoms, which present
vegetated areas and water bodies (Figure 8).
Figure 7. Maximum UHI intensities.
Figure 7. Maximum UHI intensities.
Figure 8 shows the quantification of the area of each urban heat island class. The UHI classes > 1 °C
had an area of 14.91 km2, which corresponds to 90% of the urban area. The UHI classes < 1 °C had an
area of 1.67 km2, which corresponds to 10% of the total area. Therefore, there was a predominance of
heat islands in high intensity classes. The cool islands (classes with UHI < 0 °C), were concentrated
in the valley bottoms, which present vegetated areas and water bodies (Figure 8).
Atmosphere 2017, 8, 18
8 of 14
Figure 8 shows the quantification of the area of each urban heat island class. The UHI
classes > 1 ◦ C had an area of 14.91 km2 , which corresponds to 90% of the urban area. The UHI
classes < 1 ◦ C had an area of 1.67 km2 , which corresponds to 10% of the total area. Therefore, there was
a predominance of heat islands in high intensity classes. The cool islands (classes with UHI < 0 ◦ C),
were concentrated
in the valley bottoms, which present vegetated areas and water bodies8 of
(Figure
8).
Atmosphere 2017, 8, 18
13
Figure 8. Areas of UHI and cool island classes.
Figure 8. Areas of UHI and cool island classes.
3.3. The Influence of Vegetation on the Mitigation of Urban Heat Island Patterns
3.3. The Influence
of Vegetation on the Mitigation of Urban Heat Island Patterns
In this section, several simulations were performed to understand the potential role of the
in theseveral
mitigation
of UHI intensity
in Iporá.
Changesto
in vegetation
(which
be monitored
In vegetation
this section,
simulations
were
performed
understand
thecan
potential
role of the
by the NDVI) may be a good alternative to minimize the UHI intensity and excessive temperatures
vegetation in the mitigation of UHI intensity in Iporá. Changes in vegetation (which can be monitored
in the tropics, mainly in the Brazilian Midwest region, which presents high temperatures during
by the NDVI)
be a year.
goodTrancoso
alternative
minimize
UHIbetween
intensity
and and
excessive
in
almost may
the entire
et al.to[43]
found a the
relation
NDVI
biomasstemperatures
for that
the tropics,
mainly
in this
the index
Brazilian
region,
which
presents
temperatures
during
region.
Because
is an Midwest
abstract figure
to urban
planners,
this high
relation
can be very useful
in almost
determining
how much
is needed
in urban
areas.NDVI and biomass for that region. Because
the entire
year. Trancoso
et al.vegetation
[43] found
a relation
between
(8)–(12)
(Table
3) wereplanners,
prepared based
on Equation
multiple
linear
regressions
this index isEquations
an abstract
figure
to urban
this relation
can(7)
bebyvery
useful
in determining
how
regarding the maximum heat island on 21 October 2014, at 21:30. With the equation, the most
much vegetation is needed in urban areas.
influential variables were UI, which explained 88.4% of the spatial variability of heat islands, and
Equations (8)–(12) (Table 3) were prepared
based on Equation (7) by multiple linear regressions
NDVI, which explained 4.1%, with a r2 = 0.92. The UI indicates the degree of urbanization of an area.
regarding
maximum
island
21 Octoberthe2014,
at 21:30.
With
theUIequation,
the most
Duethe
to the
difficulty ofheat
changing
builton
environments,
simulations
did not
change
values. Only
influential
variables
were because
UI, which
explained
88.4%
spatial
variability
ofsimply
heat islands,
and NDVI,
NDVI
was changed
it may
be increased
by of
thethe
creation
of green
areas or
by planting
2 = 0.92.
trees on sidewalks
road
TheThe
simulations
were performed
by increasing
NDVI by 20%
which explained
4.1%, and
with
a rmedians.
UI indicates
the degree
of urbanization
of an area.
(Equation
(8)),
40%
(Equation
(9)),
60%
(Equation
(10)),
80%
(Equation
(11)),
and
100%
(Equation
(12)),
Due to the difficulty of changing built environments, the simulations did not change UI values.
which corresponds to a proportional biomass increase in the streets. Trancoso et al. [43] observed that
Only NDVI was changed because it may be increased by the creation of green areas or simply
a lower biomass correlates with a higher relative variation of NDVI between dry and wet seasons.
by planting trees on sidewalks and road medians. The simulations were performed by increasing
NDVI by 20% (Equation
(8)),
40% (Equation
(9)),the60%
(Equation
(10)),
80%in(Equation
(11)), and 100%
Table 3.
Equations
used to simulate
maximum
UHI with
increases
NDVI.
(Equation (12)),
which
corresponds
to
a
proportional
biomass
increase
in
the
streets.
Trancoso
Simulated UHI
Equations
Number of Equations et al. [43]
observed that
a lower
with×aNDVI)
higher
relative
NDVI between
dry and
UHI correlates
= 28.927 − (7.302
+ (5.508
× UI) variation of Equation
Maximum
UHI biomass
(7)
UHI = 28.927 − (7.302 × 1.2 × NDVI) + (5.508 × UI)
Simulated UHI 1
Equation (8)
wet seasons.
UHI = 28.927 − (7.302 × 1.4 × NDVI) + (5.508 × UI)
Simulated UHI 2
Equation (9)
The simulations
of Figure
9 show how the intensity of UHIs can be minimized
by increasing the
UHI = 28.927 − (7.302 × 1.6 × NDVI) + (5.508 × UI)
Simulated UHI 3
Equation (10)
NDVI. LiuSimulated
et al. [44]
and
et al.−[45]
observed
a similar
With
the increase
in NDVI,
(7.302
UHI = 28.927
× 1.8 × NDVI)
+ (5.508 relation.
× UI)
UHI
4 Mackey
Equation
(11)
the UHI gradually
decreased:
areas
close×to
Tamanduá
stream significantly
UHI the
= 28.927
− (7.302
2 ×the
NDVI)
+ (5.508 × UI)
Simulated UHI
5
Equation (12) decreased the
values of UHI, or even presented a negative UHI (−4 ◦ C, also known as cool island). In simulations
simulations of Figure 9 show how the intensity of UHIs can be minimized by increasing the
with NDVI,The
they
increased by 80% and 100%. In some areas (mainly in the city center) with less
NDVI. Liu et al. [44] and Mackey et al. [45] observed a similar relation. With the increase in NDVI,
vegetation, the model shows no significant decreases in UHI intensity.
the UHI gradually decreased: the areas close to the Tamanduá stream significantly decreased the
Atmosphere 2017, 8, 18
9 of 14
Table 3. Equations used to simulate the maximum UHI with increases in NDVI.
Simulated UHI
Equations
Maximum
UHI2017, 8, 18
Atmosphere
UHI = 28.927 − (7.302 × NDVI) + (5.508 × UI)
Number of Equations
Equation (7)
9 of 13
UHI = 28.927 − (7.302 × 1.2 × NDVI) + (5.508 × UI)
Equation (8)
values of UHI, or even presented a negative UHI (−4 °C, also known as cool island). In simulations
UHI = 28.927 − (7.302 × 1.4 × NDVI) + (5.508 × UI)
Equation (9)
with NDVI, they increased by 80% and 100%. In some areas (mainly in the city center) with less
Simulated
UHI 3 the model
UHI
= 28.927
− (7.302 ×decreases
1.6 × NDVI
+ (5.508
× UI)
Equation (10)
vegetation,
shows
no significant
in)UHI
intensity.
UHI
a 100%
increase
NDVI) +
had
the ×
highest
the highest
Simulated The
UHI simulated
4
UHI with
= 28.927
− (7.302
× 1.8 in
× NDVI
UI) data variation,
Equation
(11)
(5.508
minimum,
mean −
and
median
thereby
theEquation
ability (12)
of the
Simulated
UHI 5 and the lowest
UHI = 28.927
× 2 × (Table
NDVI) 4),
+ (5.508
× UIdemonstrating
(7.302
)
vegetation to minimize UHI intensity.
Simulated UHI 1
Simulated UHI 2
Figure 9. Simulations of spatial patterns of heat islands with increases in NDVI.
Figure 9. Simulations of spatial patterns of heat islands with increases in NDVI.
Table 4. Simulated UHI intensity statistics with NDVI increases.
The simulated UHI with a 100% increase
had theUHI_3
highest data
variation,
the highest
UHI_1 in NDVI
UHI_2
UHI_4
UHI_5
UHI
Statistics
(Simulated)
minimum, and
the lowest (Observed)
mean and median (Table 4), thereby
demonstrating the ability of the
vegetation to minimize UHI intensity. (1.2 × NDVI) (1.4 × NDVI) (1.6 × NDVI) (1.8 × NDVI) (2 × NDVI)
Minimum
−1.79
−2.48
−2.75
−3.08
−3.71
Maximum
4.40
4.50
4.35
4.27
4.34
Table 4. Simulated
UHI2.86
intensity statistics
with NDVI
Mean
2.99
2.74
2.62 increases.
2.49
Median
3.21
3.11
3.01
2.90
2.80
UHI_22.46
UHI_3
UHI_4
First quartile
2.75 UHI_1 2.61
2.31
2.15
Third quartile UHI
3.49
3.42
3.33
3.25
3.18
(Simulated)
Statistics
(Observed)
Variance
0.63
0.87
(1.2 × NDVI)0.79 (1.4 × NDVI)
(1.6 × 1.00
NDVI)
(1.81.19
× NDVI)
Standard deviation
0.79
0.89
0.93
1.00
1.09
Minimum
−1.79
−2.48
−2.75
−3.08
−3.71
Coefficient of variation
0.27
0.31
0.38
0.44
Maximum
4.40
4.50
4.35 0.34
4.27
4.34
−4.27
4.35
2.37
2.69
2.01 UHI_5
3.11
1.38
(2 × NDVI)
1.17
−4.27
0.50 4.35
Mean
2.99
2.86
2.74
2.62
2.49
2.37
3.21
3.11
3.01
2.90
2.80
2.69
Median
In general, the2.75
increase in NDVI
a decrease in the
2.61 resulted in2.46
2.31 intensity of the
2.15 UHI, and the
2.01
First quartile
3.49 classes increased
3.42
3.25
3.11
Third
quartile in cool island
frequency
with the 3.33
increase in NDVI.
Classes from −53.18
°C to −4 °C were
Variance
0.63
0.79
0.87
1.00
1.19
1.38
only
verified in the0.79
simulation with
cool islands, the
0.89a 100% increase
0.93 in NDVI (Figure
1.00 10). Among1.09
1.17
Standard
deviation
0.31
0.38 10).
0.44
0.50
Coefficient
of variation
class
−1 to 0 °C had0.27
the greatest increase
in relative0.34
frequency (Figure
In the heat island classes 0–1 °C, 1–2 °C, and 2–3 °C, an effective increase in frequency was
observed (Figure 10), which is in line with a decrease in the frequency of UHIs of a greater magnitude
In general, the increase in NDVI resulted in a decrease in the intensity of the UHI, and the
(3–4 °C and 4–5 °C). This occurred because, with the increase in NDVI, UHIs decreased, therefore
frequency
in cool island
classes increased with the increase in NDVI. Classes from −5 ◦ C to −4 ◦ C
representing
lower classes.
were only verified in the simulation with a 100% increase in NDVI (Figure 10). Among cool islands,
the class −1 to 0 ◦ C had the greatest increase in relative frequency (Figure 10).
Atmosphere 2017, 8, 18
10 of 14
Atmosphere 2017, 8, 18
10 of 13
Atmosphere 2017, 8, 18
10 of 13
Figure 10. Relative frequency of simulated UHI classes.
Figure 10. Relative frequency of simulated UHI classes.
3.4. Simulation of the Maximum Urban Heat Island in a Highly Built Density Area of Iporá
◦ C, 1–2 ◦ C, and 2–3 ◦ C, an effective increase in frequency was
In the The
heatmaximum
island classes
0–1
intensity
of the
UHI was found in the commercial center of Iporá (Figure 9).
observed
(Figure
10), which
is in line
with
a decrease
the frequency
UHIsin
ofNDVI
a greater
magnitude
Figure
11 shows
the maximum
heat
island
(observed)insimulated
with an of
increase
by 100%,
◦ C). This
respective
NDVIs
(normalbecause,
NDVI andwith
simulated
NDVI). It in
is noteworthy
in Figure
11 that the
(3–4 ◦ Cand
andthe
4–5
occurred
the increase
NDVI, UHIs
decreased,
therefore
maximum
intensity
heat island and its size decreased with the increase in NDVI in its core area. It is
representing
lower
classes.
also observed that, in some points, the NDVI hardly changed, which did not cause a significant
decreaseof
inthe
the Maximum
intensity of
the UHI
because
of the
biomass
vegetation
the area.
Figure
10. Relative
frequency
simulated
UHIDensity
classes.
3.4. Simulation
Urban
Heat
Island
in insignificant
a ofHighly
Built
Area ofinIporá
3.4.maximum
Simulation ofintensity
the Maximum
Urban
Heatwas
Islandfound
in a Highly
Builtcommercial
Density Area ofcenter
Iporá of Iporá (Figure 9).
The
of the
UHI
in the
Figure 11 shows
the
maximum
heat
island
(observed)
simulated
with
an
increase
NDVI9).
by 100%,
The maximum intensity of the UHI was found in the commercial center of Iporáin(Figure
11 shows
the maximum
islandand
(observed)
simulated
withItanisincrease
in NDVI
100%, 11 that
and theFigure
respective
NDVIs
(normalheat
NDVI
simulated
NDVI).
noteworthy
inby
Figure
and the respective
(normaland
NDVI
NDVI).
It is
noteworthy
that
the maximum
intensityNDVIs
heat island
itsand
sizesimulated
decreased
with
the
increase in
inFigure
NDVI11in
itsthe
core area.
maximum intensity heat island and its size decreased with the increase in NDVI in its core area. It is
It is also observed that, in some points, the NDVI hardly changed, which did not cause a significant
also observed that, in some points, the NDVI hardly changed, which did not cause a significant
decreasedecrease
in the in
intensity
of the UHI because of the insignificant biomass vegetation in the area.
the intensity of the UHI because of the insignificant biomass vegetation in the area.
Figure 11. (A)—Effective UHI; (B)—Simulated UHI; (C)—Observed NDVI; (D)—Simulated NDVI.
In the core area of the maximum heat island (Figure 11), the intensity ranged from 3.2 to 4.3 °C.
In the simulated UHI, however, the maximum and minimum values were lower (2.7–4.2 °C), with an
average gain of approximately 0.1 to 0.5 °C. It was noted that the curve of the simulated UHI was
Figure 11. (A)—Effective UHI; (B)—Simulated UHI; (C)—Observed NDVI; (D)—Simulated NDVI.
Figure 11. (A)—Effective UHI; (B)—Simulated UHI; (C)—Observed NDVI; (D)—Simulated NDVI.
In the core area of the maximum heat island (Figure 11), the intensity ranged from 3.2 to 4.3 °C.
In the simulated UHI, however, the maximum and minimum values were lower (2.7–4.2 °C), with an
average gain of approximately 0.1 to 0.5 °C. It was noted that the curve of the simulated UHI was
Atmosphere 2017, 8, 18
11 of 14
UHI (°C)
In the core area of the maximum heat island (Figure 11), the intensity ranged from 3.2 to 4.3 ◦ C.
In the simulated UHI, however, the maximum and minimum values were lower (2.7–4.2 ◦ C), with
an
Atmosphere 2017, 8, 18
11 of 13
◦
average gain of approximately 0.1 to 0.5 C. It was noted that the curve of the simulated UHI was
always below
below that
that of
of the
the observed
observed UHI,
UHI, with
with minor
minor differences
differences occurring
occurring at
at higher
higher intensities
intensities due
due to
to
always
low
NDVI
values
in
points
with
a
greater
intensity
(Figure
12).
low NDVI values in points with a greater intensity (Figure 12).
Figure
Figure 12.
12. Maximum
Maximum UHI
UHI and
and maximum
maximum simulated
simulated UHI
UHI with
with NDVI
NDVI increased
increased by
by 100%.
100%.
After a simulation of UHI with the NDVI increased by twice the simulated UHI, the area
After a simulation of UHI with
the NDVI increased by twice the simulated UHI, the area decreased
decreased by 45% (from
94,296 m2 to 51,931 m2).
by 45% (from 94,296 m2 to 51,931 m2 ).
4.
4. Conclusions
Conclusions
The
The UHI
UHI in
in Iporá
Iporá presents
presents different
different spatial
spatial and
and time
time configurations
configurations in
in relation
relation to
to big
big cities,
cities, with
with
intensities
tending
towards
lower
values.
intensities tending towards lower values.
It
It is
is also
also noteworthy
noteworthy that:
that:
11
The
Theurban-geographical
urban-geographical variables
variables NDVI
NDVI and
and UI
UI were
were those
those that
that contributed
contributed the
the most
most to
to explain
explain
the
thevariability
variabilityin
inthe
themaximum
maximum UHI.
UHI.
2
It was observed that the areas located in the valleys had lower thermal values, suggesting a cold
2
It was observed that the areas located in the valleys had lower thermal values, suggesting a cold
air drainage as defined by Lopes [42].
air drainage as defined by Lopes [42].
3
During the occurrence of a maximum UHI, UHI > 1 °C
classes had an area of approximately
3
During the occurrence of a maximum UHI, UHI > 1 ◦ C classes had an area of approximately
15,000 km22, which corresponded to 90% of the urban area.
15,000 km , which corresponded to 90% of the urban area.
4
With the increase in NDVI, the UHI gradually decreased: the areas close to the Tamanduá stream
4
With the increase in NDVI, the UHI gradually decreased: the areas close to the Tamanduá stream
significantly decreased the values of UHI, or even presented a negative UHI (−4 °C,
also known
significantly decreased the values of UHI, or even presented a negative UHI (−4 ◦ C, also known
as cool islands), in simulations with NDVI increased by 80% and 100%.
as cool islands), in simulations with NDVI increased by 80% and 100%.
5
In some areas of the city, there was little decrease in the intensity of UHIs due to little or no
5
In some areas
of theeven
city,with
therea was
decrease
in theit intensity
due insufficient
to little or no
greening.
Therefore,
100%little
increase
in NDVI,
would beof
tooUHIs
low and
to
greening.
Therefore,
even
with
a
100%
increase
in
NDVI,
it
would
be
too
low
and
insufficient
to
significantly minimize UHIs.
significantly
UHIs.classes increased with the increase in NDVI. The cool island class
6
The
frequencyminimize
of cool island
6
The
frequency
of
cool
island
classes
with the
increase
NDVI. The
cool island
−5 °C to −4 °C was only observed
in increased
the simulation
with
a 100%inincrease
in NDVI.
There class
was
◦
◦
−
5
C
to
−
4
C
was
only
observed
in
the
simulation
with
a
100%
increase
in
NDVI.
There
was
also a decrease in UHI classes 3–4 °C and 4–5 °C and an increase in low intensity classes due
to
◦ C and 4–5 ◦ C and an increase in low intensity classes due to
also
a
decrease
in
UHI
classes
3–4
the migration of more intense UHI to lower intensity classes.
the migration
of more
intense
UHI
to lower
classes.UHI (dense urban area), there was
7
With
the increase
in NDVI
in the
central
areaintensity
of a maximum
7
the increase
in NDVI indecrease
the central
of a maximum
(dense
area), there was a
aWith
perceptible
and significant
in area
its intensity
and sizeUHI
(a 45%
areaurban
decrease).
perceptible and significant decrease in its intensity and size (a 45% area decrease).
As final conclusions, it is noteworthy that, by using multiple linear regression, it was possible to
As final
it is noteworthy
by using
multiple
linear regression,
it was
possible
spatialize
the conclusions,
UHI to the whole
urban area,that,
thereby
providing
an analysis
of the urban
setting
from
to spatialize the UHIvariables
to the whole
area, therebyprognostic
providing simulations.
an analysis ofUnger
the urban
urban-geographical
and urban
thus performing
et al.setting
[25],
László and Szegedi [26], Bottyán and Unger [27], and Hart and Sailor [28] also concluded that a statistical
type of modelling of the spatial distribution of maximum urban heat islands based on urban surface
factors is an appropriate process because of the important roles it plays in increasing urban temperature.
The relation between NDVI and biomass is a potentially useful way to determine how much
vegetation planners need to plant to decrease 1 °C in city centers in order to properly address urban
Atmosphere 2017, 8, 18
12 of 14
from urban-geographical variables and thus performing prognostic simulations. Unger et al. [25],
László and Szegedi [26], Bottyán and Unger [27], and Hart and Sailor [28] also concluded that a
statistical type of modelling of the spatial distribution of maximum urban heat islands based on
urban surface factors is an appropriate process because of the important roles it plays in increasing
urban temperature.
The relation between NDVI and biomass is a potentially useful way to determine how much
vegetation planners need to plant to decrease 1 ◦ C in city centers in order to properly address
urban climate changes. In the city center of Iporá, local planners may obtain a maximum gain
of approximately 0.5 ◦ C by doubling the vegetation on the streets. Future research must address the
problem related to choosing the best plant species, taking into account that ventilation on streets should
not be reduced by dense canopies in order to prevent higher concentrations of air pollution [29].
Acknowledgments: The authors would like to thank the São Paulo Research Foundation (FAPESP) for granting
the scholarship (12/10450-0). They would also like to thank the anonymous reviewers and the editor for their
helpful and constructive comments that greatly contributed towards improving the final version of this paper.
Author Contributions: The manuscript was prepared by Elis Dener Lima Alves and revised by Antonio Lopes.
All the authors reviewed the manuscript and contributed their part to improve the manuscript. All authors have
read and approved the final manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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