sensors

Article
Applying an Integrated System of Cloud Management and
Wireless Sensing Network to Green Smart Environments—Green
Energy Monitoring on Campus
Kuo-Hsiung Tseng 1 , Meng-Yun Chung 1 , Li-Hsien Chen 2, * and Ming-Yi Wei 1

1 Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
2 Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
* Correspondence: ntutlhchen@gmail.com

Abstract: With increasing urbanization, the application of Internet of things (IoT) technology to city
governance has become a trend in architecture, transportation, and healthcare management, making
IoT applicable in various domains. This study used IoT to inspect green construction and adopted
a front-end sensing system, middle-end wireless transmission, and a back-end multifunctional
system structure with cloud management. It integrated civil and electrical engineering to develop
environmental monitoring technology and proposed a management information system for the
implementation of green engineering. This study collected physical “measurements” of the greening
environment on a campus. Ambient temperature and humidity were analyzed to explore the
greening and energy-saving benefits of a green roof, a pervious road, and a photovoltaic roof. When
the ambient temperature was below 25 ◦ C, the solar panels had an insulation effect on the roof of
the building during both 4:00–5:00 and 12:00–13:00, with an optimal insulation effect of 2.45 ◦ C.
When the ambient temperature was above 25 ◦ C, the panels had a cooling effect on the roof of
Citation: Tseng, K.-H.; Chung, M.-Y.;
the building, whether during 4:00–5:00 or 12:00–13:00, with an optimal cooling effect of 5.77 ◦ C.
Chen, L.-H.; Wei, M.-Y. Applying an During the lower temperature period (4:00–5:00), the ecological terrace had an insulation effect
Integrated System of Cloud on the space beneath, with an effect of approximately 1–3 ◦ C and a mean insulation of 1.95 ◦ C.
Management and Wireless Sensing During the higher temperature period (12:00–13:00), it presented a cooling effect on the space beneath,
Network to Green Smart with an effect of approximately 0.5–9 ◦ C and a mean cooling temperature of 5.16 ◦ C. The cooling
Environments—Green Energy effect of the three greening areas on air and ground temperature decreased in the following order:
Monitoring on Campus. Sensors 2022, pervious road > photovoltaic roof > ecological terrace.
22, 6521. https://doi.org/10.3390/
s22176521
Keywords: cloud management; composite green roof; green information system; environmental
Academic Editor: Jikui Luo greening benefits

Received: 23 June 2022

Accepted: 28 August 2022
Published: 29 August 2022
1. Introduction
Publisher’s Note: MDPI stays neutral
The global environment is burdened by human development. With population growth,
with regard to jurisdictional claims in
environmental pollution, and climate change, many sectors have begun to improve their
published maps and institutional affil-
living environment to achieve the goal of sustainable development. Currently, environmen-
iations.
tal engineering mostly applies the long-term monitoring of environmental pollution and
employs greening techniques; however, it rarely includes the “benefits of greening engineer-
ing” among the items it inspects. In the early 19th century, the global population surged
Copyright: © 2022 by the authors.
rapidly, and the Industrial Revolution flourished when social and scientific conditions
Licensee MDPI, Basel, Switzerland. were met. Enormous changes in the environment, as well as the greenhouse effect, have
This article is an open access article occurred because of large-scale human activity and development and excessive greenhouse
distributed under the terms and gas emissions, which have led to global warming, climate change, and changes in other
conditions of the Creative Commons environmental factors [1,2]. Because of the major problem mentioned previously, a method
Attribution (CC BY) license (https:// to protect the environment was proposed recently. The Vertical Greening Modular System
creativecommons.org/licenses/by/ (VGMS) is an increasingly popular building envelope solution designed to enhance the
4.0/). aesthetic quality of new and existing façades while achieving energy-efficient performance.

Sensors 2022, 22, 6521. https://doi.org/10.3390/s22176521 https://www.mdpi.com/journal/sensors

Sensors 2022, 22, 6521 2 of 15

The VGMS was designed and tested in a research project in Turin (northern Italy). The
VGMS achieves a 40% reduction in the equivalent thermal transmittance measurement,
significantly affecting the amount of energy passing through the exterior wall during warm
seasons. The outdoor surface temperature of the wall was reduced by 23 ◦ C in summer,
which had a positive impact on outdoor comfort and urban heat island mitigation [3].
Another research target was focused on pavement watering. A field study conducted at the
Louvre in Paris in the summer of 2013 showed that watering from the pavement lowered
the temperature in the afternoon by 5.9 ◦ C [4]. A study on a new PV panel thermal model
at the roof laboratory of the Czech University of Life Sciences analyzed the differences be-
tween freestanding and roof-integrated configurations in different locations (cold, medium,
warm, hot). The result showed that the panel can reduce annual power generation by
3–4% (cold, mild, warm) and by more than 5% in hot climates [5].
This study used relevant technology to assist with urban governance, green engineer-
ing techniques, and renewable energy to develop urban construction. Furthermore, Internet
of things (IoT) technology was incorporated to improve urban environments and energy-
saving problems for cities, where clean energy and environmentally friendly approaches
were utilized to facilitate clean urban environments and sustainable development. Based on
two elements, environmental engineering and architecture energy saving, an examination
of energy saving and the greening benefits of green construction in civil engineering was
implemented. The advantages of photovoltaic reusable energy in electrical engineering
for cooling and power-saving in buildings were also examined. The exploration of the
interaction between greening and energy saving and urban environments can provide
city governments with valid information, strengthen the development of urban green
environments, and enhance the growth and promotion of sustainable cities [6–8]. Based
on the concept of smart cities under the application of Internet of things technology, this
paper set up a wireless environmental monitoring and management system in the campus
environments of urbanized cities to monitor and quantify the benefits of greening projects
to the campus environment over a long period [9–11]. This research analyzed three different
cases. The subjects were a green roof, a pervious road, and a photovoltaic roof. The quanti-
tative results can be employed in estimating and calculating energy saving/consumption
or carbon emissions and serve as a reference for urban governance. The monitoring and
analysis of the environmental benefits of the actual greening project site in this study were
as follows:
1. Collected green roofs, permeable pavements, and solar photovoltaic system benefit
evaluations for the application representatives of the three indicators of “greening”, “base
water retention”, and “daily energy saving” in the green building evaluation system.
2. Integrated sensor technology and cloud monitoring to build a campus green
information system.
3. Analyzed the impact of environmental factors on the power generation efficiency of the
photovoltaic system, the effect of roof greening and photovoltaics on the cooling effect
of building shading, and the environmental greening benefits of permeable pavement.

2. Materials and Methods

2.1. System Architecture
Smart environments have measures for strengthened environmental monitoring. A
subdevelopment project for smart cities is gathering environmental information through
ICT, inspecting the environment using data analysis methods, and formulating environ-
mental management improvement plans to address environmental problems and enhance
urban residential environment safety and quality [12]. Rapid, massive industrialization
and urbanization have placed heavy burdens on the environment in Taiwan; therefore,
environmental management strategies should be comprehensive, with enhanced environ-
mental conservation concepts. The Industrial Economics & Knowledge Center (IEK) Net
of the Industrial Technology Research Institute (2016) specified three concerns regarding
domestic smart environment management:
 urban residential environment safety and quality [12]. Rapid, massive industrialization
and urbanization have placed heavy burdens on the environment in Taiwan; therefore,
environmental management strategies should be comprehensive, with enhanced environ-
mental conservation concepts. The Industrial Economics & Knowledge Center (IEK) Net
of the Industrial Technology Research Institute (2016) specified three concerns regarding
Sensors 2022, 22, 6521 3 of 15
domestic smart environment management:
1. Lack of environmental data due to markedly few environmental quality sensors;
2. Lack of measuring points due to the high cost of environmental quality sensors;
1. Lack of environmental data due to markedly few environmental quality sensors;
3. Lack of overall planning due to insufficient experience in solving environmental
2. Lack of measuring points due to the high cost of environmental quality sensors;
problems through big data analyses.
3. Lack of overall planning due to insufficient experience in solving environmental
In addition
problems to these
through bigconcerns, difficulties regarding environmental monitoring, in-
data analyses.
cluding the expiration of sensors, the harshness of the monitored environment, and prob-
In addition to these concerns, difficulties regarding environmental monitoring, includ-
lems of transmission and electricity because of remoteness occur. Thus, the establishment
ing the expiration of sensors, the harshness of the monitored environment, and problems
of environmental monitoring is extremely challenging. However, effective applications of
of transmission and electricity because of remoteness occur. Thus, the establishment of
environmental monitoring data are expected to reduce ecological and environmental
environmental monitoring is extremely challenging. However, effective applications of
damage, support the establishment of effective management strategies, and prevent arti-
environmental monitoring data are expected to reduce ecological and environmental dam-
ficialsupport
age, damagetheand natural disasters
establishment [13,14].management strategies, and prevent artificial
of effective
damage This study
and constructed
natural disastersa [13,14].
green information system using IoT technology, as depicted
in Figure 1. The monitoring data
This study constructed a green ofinformation
the perception layerusing
system wereIoT
relayed to dataasconvertors
technology, depicted
in Figure 1. The monitoring data of the perception layer were relayed to datainconvertors
through a wireless network in the network layer and forwarded to servers a physical
network.a A
through cloud management
wireless network in the system
networkwaslayer
thenand
constructed
forwarded to to
facilitate
servers user manage-
in a physical
ment.
network. A cloud management system was then constructed to facilitate user management.

Figure1.1.Structure
Figure Structureofofthe
thecampus
campus green
green information
information system
system (the (the
1 to 31 stands
to 3 stands for different
for different sensorsensor
nodes).
nodes).
2.2. Equipment
2.2.1. Sensors in the Perception Layer
2.2. Equipment
2.2.1.SHT-10
Sensorstemperature and humidity
in the Perception Layer sensors were established in the perception layer to
monitor the benefits generated by environmental
SHT-10 temperature and humidity sensorsgreening. The sensors
were established were
in the equipped with
perception layer
both sensor elements and signal processing functions, and every sensor
to monitor the benefits generated by environmental greening. The sensors were was individually
equipped
calibrated in a precise
with both sensor humidity
elements cabinet.
and signal They provided
processing functions,fully calibrated
and every digital
sensor output
was individ-
and
ually calibrated in a precise humidity cabinet. They provided fully calibrated digital and
measured relative humidity and temperature using capacitive sensor elements out-
bandgap sensors, respectively. CMOSens technology was adopted to ensure favorable
put and measured relative humidity and temperature using capacitive sensor elements
reliability and long-term stability.
and bandgap sensors, respectively. CMOSens technology was adopted to ensure favora-
ble reliability
2.2.2. and Wireless
Long-Range long-term stability.
Data Transmission Technology
The study primarily monitored the benefits of environmental greening, which is a
type of environmental monitoring in smart cities [15]. Considering the development and
challenges of smart cities and the problems they are likely to encounter during environ-
mental monitoring, a long-range (LoRa) low-power wireless data transmission technology
was adopted as the transmission interface for transmission and communication in the mon-
itoring system. The LoRa technology was presented by the Semtech Company in August
2013 and is categorized as a type of low-power, wide-area network (LPWAN) [16]. Wireless
Sensors 2022, 22, 6521 4 of 15

communication transmission was originally based on Wi-Fi, 3G/4G, and ZigBee; LPWAN
technology was designed to resolve the problem of transmitting few data over a long
distance. LoRa has a transmission distance range of 1–15 km and is relatively power-saving.
LoRa, with 200 pieces of transmission data per day, is a suitable communication transmis-
sion interface, particularly for industrial IoT, smart cities, and quality agriculture, though
it is unfeasible for transmitting large files, audio, or video. Communication techniques
have advantages and disadvantages. The AS32-TTL-100 LoRa transmission model was
selected for data transmission in the monitoring system because all sensing positions in
the monitored environments were established outdoors and the number of test points was
likely to be expanded.

2.2.3. Advantech WebAccess

The application layer was developed using Advantech WebAccess (hereafter referred
to as WebAccess) software. WebAccess is a human–machine interface, supervisory control,
and data acquisition system developed for IoT technology. It can be used for animated
graphic displays, real-time data, control, trends, reporting crimes, and keeping daily
journals, and these functions are available on standard web browsers. This study employed
a campus environment green information system and WebAccess graphic software for
data extraction, computing, and management. WebAccess professional edition was used
because it can manage 1500 monitoring points and up to 255 devices; in addition, the
professional edition does not limit the number of webpage users, which facilitates multi-
person connection, browsing, and management.

2.3. Greening of the Monitored Area and the Green Information System
This study adopted a campus reusable energy system for environmental monitoring
at three green engineering sites, namely, a photovoltaic roof, an ecological terrace, and a
pervious road. The monitored area and placement of sensors were discussed in [17–20]. To
understand the benefits of green engineering for the cooling of the environment, the three
greening positions were introduced and monitored for temperature and humidity both in
the air and on the ground.

2.3.1. Photovoltaic Roof

A photovoltaic system environment was constructed on the roof of a campus building.
Bother fixed- and single-axis photovoltaic systems were installed with a total capacity
of 70.38 kWp, and monitoring equipment was set up. To monitor the influence of solar
panels on the cooling effect on the top floor and that of ambient temperatures on the
power generation of the solar modules, this study examined the sensing approaches of the
solar panels with regard to ambient temperatures and the shading effect [21,22]. The fixed
photovoltaic system and areas of the roof were selected as sensing areas, and temperature
and humidity sensors were placed in four locations: in the air under the solar panels, on the
ground in a shaded area, in the air without shade, and on the roof without shade [23–25].
The sensing positions are depicted in Table 1 and Figure 2.

Table 1. Sensing positions on the photovoltaic roof.

Site Photovoltaic Roof

Sensing Area Shaded Area Unshaded Area
Test point positions In the air On the roof In the air On the roof
 Table
Table1.1.Sensing
Sensingpositions
positionson
onthe
thephotovoltaic
photovoltaicroof.
roof.
Site
Site Photovoltaic
PhotovoltaicRoof
Roof
Sensing
SensingArea
Area Shaded Area
Shaded Area Unshaded
UnshadedArea
Area
Sensors 2022, 22, 6521 Test point positions
Test point positions In the air
In the air On the roof
On the roof In the air
In the air On
Onthe
theroof
5 of 15
roof

(a)
(a) (b)
(b)
Figure
Figure 2.2.2.
Figure (a)
(a) Photograph
(a) Photograph
Photograph and
and
and (b)
(b)
(b) schematic
schematic
schematic ofof
of the
the
the sensing
sensing
sensing positions
positions
positions on
ononthe
the
the photovoltaic
photovoltaic
photovoltaic roof.
roof.
roof.

2.3.2.
2.3.2.Ecological
2.3.2. EcologicalTerrace
Ecological Terrace
Terrace
An
AnAn ecologicalterrace,
ecological
ecological terrace,aaatype
terrace, typeof
type ofofgreen
greenroof,
green roof,on
roof, onaaacampus
on campusbuilding
campus buildingwas
building wasselected
was selectedfor
selected forfor
examination
examination [26,27].
[26,27]. The
The building
building was
was built
built according
according toto
thethe campus
campus
examination [26,27]. The building was built according to the campus eco-environment de- eco-environment
eco-environment de-
design
sign
signplanplan
plan tototo provide
provide
provide biological
biological
biological habitats
habitats
habitats andand
and restore
restore
restore urbanurban
urban green
green green
spaces.
spaces. spaces.
The The plants
Theplants
plants ononthe
the
on the
terrace terrace absorb CO and have a shading effect on the building; the floor area is
terraceabsorb
absorbCO CO2 22and
andhave
2
have aashading
shadingeffecteffecton
onthethebuilding;
building;the thefloor
floorarea
areaisisapproxi-
approxi-
approximately
mately 2 80 mstudy . This study selected the greening area and an unshaded area on the
mately80 80mm.2.This
This studyselected
selectedthe thegreening
greeningarea areaand
andan anunshaded
unshadedarea areaononthetheroof
roofas as
roof as
sensing sensing areas and placed temperature and humidity sensors in four locations: in the
sensingareasareasandandplaced
placedtemperature
temperatureand andhumidity
humiditysensors
sensorsininfour fourlocations:
locations:ininthetheairair
air within the
within greening area,ononthe the roofininthethe shadeof of the greening
greening area, in the air without
withinthe thegreening
greeningarea, area, on theroofroof in theshadeshade ofthe the greeningarea, area,ininthetheair
airwithout
without
shade,
shade, and on the roof without shade. A sensor was also placed in aaclassroom beneath
shade, and on the roof without shade. A sensor was also placed in a classroombeneath
and on the roof without shade. A sensor was also placed in classroom beneath
the
theecological terrace. The sensing positions at the ecological terrace were as described in
theecological
ecologicalterrace.
terrace.The Thesensing
sensingpositions
positionsatatthe theecological
ecologicalterrace
terracewere wereas asdescribed
describedinin
Table
Table 2 and Figure 3.
Table22and andFigure
Figure3.3.
Table
Table2. Sensing positions on the ecological terrace.
Table2.2.Sensing
Sensingpositions
positionson
onthe
theecological
ecologicalterrace.
terrace.
SiteSite Ecological
Ecological Terrace
Terrace
Site Ecological Terrace
Sensing
Sensing AreaArea
Sensing Area Greening
Greening Area Area
Greening Area Unshaded
Unshaded Area
Area
Unshaded Area
Test
Test point
point positions
positions
Test point positions In In
the the
air air
In the air On On
the the
roofroof
On the roof In In
thethe
air air
In the air On
On
On the
the roof
roof
the roof

(a)
(a) (b)
(b)
Figure
Figure3.3.(a)
(a)Photograph
Photographand
and(b)
(b)schematic
schematicofofthe
thesensing
sensingpositions
positionson
onthe
theecological
ecologicalterrace.
terrace.
Figure 3. (a) Photograph and (b) schematic of the sensing positions on the ecological terrace.
2.3.3. Pervious
2.3.3.Pervious
2.3.3. Road
PerviousRoad
Road
Three
Three
Three types
types
types ofofofwater-pervious
water-pervious
water-pervious roads
roads
roads were
werewere discoveredat at
discovered
discovered atthe
the thecampus,
campus,
campus, namely,
namely,
namely, pervi-
pervi-
pervious
ous
ous concrete,
concrete, interlocking
interlocking block,
block,and
and grass
grassblock
block paver
paverroads,
roads, representing
representing
concrete, interlocking block, and grass block paver roads, representing the three major the
thethree
threemajor
major
approaches to pervious
approachestotopervious
approaches roads
perviousroads currently
roadscurrently used
currentlyused (i.e.,
used(i.e., material,
(i.e.,material, splicing,
material,splicing, and
splicing,and structural
andstructural brick).
structuralbrick).
brick).
This study selected the grass paver road (Figure 4). The total area of the pavement was ap-
proximately 800 m2 . According to monitoring approaches for pervious roads, sensing areas
were set above and under the road; temperature and humidity sensors were established
50 cm above the ground (in the air) and 5–10 cm in the soil (underground) to serve as test
points. The sensing positions were as described in Table 3 and Figure 4.
 areas were set above and under the road; temperature and humidity sensors were estab-
lished 50 cm above the ground (in the air) and 5–10 cm in the soil (underground) to serve
as test points. The sensing positions were as described in Table 3 and Figure 4.

Table 3. Sensing positions on the pervious road.

Sensors 2022, 22, 6521 Site Pervious Road (Grass Pavers) 6 of 15

Sensing Area Pervious Road
Test point positions Above ground (in the air) In the soil (underground)

(a) (b)
Figure 4. The sensing positions on the pervious road: (a) photograph and (b) schematic.
Figure 4. The sensing positions on the pervious road: (a) photograph and (b) schematic.
2.3.4. Cloud Green Data System
Table 3. Sensing positions
This study on the
established pervious
a cloud road. system for the campus green information
monitoring
system. The system structure was as depicted in Figure 5. WebAccess graphic software
was used to Site
develop the cloud monitoring system, Pervious Road (Grass
which processed, Pavers)and cal-
converted,
culated the data
Sensing Area gathered from sensors for instant display, graphical
Pervious Roaddemonstration, and
historical data search. The system was convenient and quick to learn for managers and
Test point positions Above ground (in the air) In the soil (underground)
could be operated through a browser. Instant data from the regional environment of each
monitored area were gathered. Data were accompanied by photographs of the overall
2.3.4.greening
Cloudarea to inform
Green users of the status of the environment, sensing positions, and ad-
Data System
ditional environmental greening benefits. The sensing results for each area were revealed
inThis study established
the information on campus a green
cloudengineering
monitoring system
benefits andfor the campus
quantified green
using the information
campus
system. The system
geographic map afterstructure was compared.
the data were as depicted Thein Figure
system was5.also
WebAccess
designed withgraphic
a his- software
wastorical
used data
to develop the which
search page, cloudprovided
monitoring system,
managers which
the ability processed,
to retrieve converted,
sensing data and
calculated the data
from previous timegathered from
periods; the sensors
historical fordata
sensing instant
of updisplay, graphical
to 12 sensing demonstration,
points could be
and retrieved simultaneously
historical data search.(Figure 6). Sensorwas
The system locations (i.e., the photovoltaic
convenient and quick roof, ecological
to learn for managers
and terrace,
could and pervious road)
be operated were encoded
through a browser.(TableInstant
4). data from the regional environment
of each
Tablemonitored areacodes.
4. Sensor location were gathered. Data were accompanied by photographs of the
overall greening area to inform users of the status of the environment, sensing positions,
Items
and additional environmental greening benefits. The sensing resultsCodes
for each area were
Air A
revealed in the information on campus green engineering benefits and quantified using the
Ground G
campus geographic map after the data were compared. The system was also
Sensors 2022, 22, x FOR PEER REVIEW designed with
7 of 15
Underground UG
a historical data search page, which provided managers the ability to retrieve sensing data
Temperature T
from previous time periods; the historical sensing data of up to 12 sensing
Humidity H
points could be
retrieved simultaneously (Figure Roof Roof
6). Sensor locations (i.e., the photovoltaic
Difference D roof, ecological
Room encoded (Table 4).
terrace, and pervious road)Photovoltaic
were Room
roof PV
Ecological terrace (green roof) Green
Pervious road PR

Figure 5.5.Structure
Figure Structureof the cloud
of the management
cloud monitoring
management system. system.
monitoring
Sensors 2022, 22, 6521 7 of 15

Figure 5. Structure of the cloud management monitoring system.

Figure 6.
Figure 6. Historical
Historical data
data search
search in
in the
the campus
campusgreen
greendata
datamonitoring
monitoringsystem.
system.

3. Environmental
Table Data
4. Sensor location Analyses of the Green Information System
codes.
3.1. Benefit AnalysesItems
of the Photovoltaic Roof Codes
This study monitored
Air
ambient temperature and humidity atAthe fixed photovoltaic
system. Monitoring data were collected every 5 or 15 min over 5 consecutive days in
Ground G
March, April, and May to inspect the hourly mean temperature and humidity in higher
(12:00–13:00) and Underground
lower (04:00–05:00) temperature periods. UG
A photovoltaic roof has both a power generation function and
Temperature T a shading effect on a
building. The measurement
Humidity results for the influence of solar panels
H on the cooling of the
temperature in the building indicate that the solar panels reduced building temperature
Difference D
and were beneficial regarding energy saving and human comfort. The shading effect of
the solar panelsPhotovoltaic roof in high (12:00–13:00) and low (04:00–05:00)
during spring PV temperature
periods were analyzed
Ecological terraceseparately.
(green roof) Green
As Table 5Pervious
suggests, in low-temperature periods during 17–21
road PR March, the ambient
temperature on the roof under the panels was higher than that of the unshaded roof, and
Roof Roof
the air temperature without shade was higher than the air temperature under the panels.
Room Room

3. Environmental Data Analyses of the Green Information System

3.1. Benefit Analyses of the Photovoltaic Roof
This study monitored ambient temperature and humidity at the fixed photovoltaic
system. Monitoring data were collected every 5 or 15 min over 5 consecutive days in
March, April, and May to inspect the hourly mean temperature and humidity in higher
(12:00–13:00) and lower (04:00–05:00) temperature periods.
A photovoltaic roof has both a power generation function and a shading effect on a
building. The measurement results for the influence of solar panels on the cooling of the
temperature in the building indicate that the solar panels reduced building temperature
and were beneficial regarding energy saving and human comfort. The shading effect of the
solar panels during spring in high (12:00–13:00) and low (04:00–05:00) temperature periods
were analyzed separately.
As Table 5 suggests, in low-temperature periods during 17–21 March, the ambient
temperature on the roof under the panels was higher than that of the unshaded roof,
and the air temperature without shade was higher than the air temperature under the
panels. During the high-temperature periods, the roof and air temperatures under the
solar panels were generally lower than those in unshaded areas. During 4–8 April, in the
low-temperature period, the ground and air temperatures under the solar panels were
generally lower than those in the unshaded area; during the high-temperature period, the
ground and air temperatures under the panels were both lower than those in the unshaded
areas. During 25–29 May, in the low-temperature period, the ground and air temperatures
under the panels were lower than those in the unshaded areas. The highest temperature
in May occurred at noon on 27 May and was the highest May temperature recorded since
the establishment of the Taipei Weather Station. As displayed in Table 5, a comparison
Sensors 2022, 22, 6521 8 of 15

of the ambient temperatures on the roof and under the panels suggests that, in the high-
temperature period (12:00–13:00), ground and air temperatures under the solar panels were
lower than those in the unshaded areas.

Table 5. Shading effect of the photovoltaic panels.

Date Period PV AT (◦ C) PV GT (◦ C) Roof AT (◦ C) Roof GT (◦ C) D AT (◦ C) D GT (◦ C)

Low 19.22 19.84 19.96 19.56 0.74 −0.28
3/17
High 22.14 22.38 23.4 22.89 1.26 0.5
Low 20.27 20.89 21.04 20.63 0.77 −0.26
3/18
High 32.23 28.95 32.2 32.34 −0.03 3.39
Low 21.97 22.71 22.6 22.31 0.64 −0.4
3/19
High 33.11 30.95 32.85 34.87 −0.26 3.92
Low 23.04 23.94 23.76 23.81 0.72 −0.13
3/20
High 20.41 21.31 21.1 18.91 0.69 −2.4
Low 16.49 17.42 16.88 15.28 0.39 −2.14
3/21
High 17.04 17.77 17.85 17.7 0.8 −0.08
Low 22.44 32.59 23.1 35.86 0.66 3.26
4/4
High 35.08 32.59 37.08 35.86 1.99 3.26
Low 23.91 31.06 24.49 34.76 0.58 3.7
4/5
High 31.22 31.06 32.1 34.76 0.88 3.7
Low 24.22 22.34 24.95 21.5 0.72 −0.85
4/6
High 20.27 22.34 21.08 21.5 0.81 −0.85
Low 15.92 18.73 16.56 19.21 0.65 0.47
4/7
High 19.21 18.73 20.92 19.21 1.7 0.47
Low 15.7 25.86 16.44 28.16 0.74 2.3
4/8
High 27.93 25.86 28.7 28.16 0.77 2.3
Low 26.52 27.77 27.34 28.06 0.82 0.29
5/25
High 33.81 33.88 35.02 37.89 1.21 4.01
Low 27.9 29.21 28.74 29.61 0.84 0.4
5/26
High 37.51 35.96 38.72 39.45 1.21 3.49
Low 27.57 29.09 28.43 29.81 0.86 0.73
5/27
High 39.16 37.66 39.04 43.43 −0.12 5.77
Low 29.14 30.37 30.04 31.06 0.9 0.69
5/28
High 34.84 34.41 35.86 38.73 1.02 4.31
5/29 Low 28.76 29.91 29.64 30.36 0.88 0.44

3.2. Benefit Analyses of the Ecological Terrace

3.2.1. Environmental Greening Benefits of the Ecological Terrace (Green Roof) for the
Top Floor
This study monitored the ambient temperature and humidity of the ecological terrace
(green roof) and a top-floor classroom beneath the terrace. The environment was monitored
from the middle of May (spring) to June (summer), and temperature and humidity data
were gathered every 5 min for 8 consecutive days. The mean hourly temperature and
humidity in the highest (12:00–13:00) and lowest (04:00–05:00) temperature periods were
examined. The highest and lowest daily temperature periods were determined based on
the mean hourly temperature in March.
 monitored from the middle of May (spring) to June (summer), and temperature and hu-
midity data were gathered every 5 min for 8 consecutive days. The mean hourly temper-
Sensors 2022, 22, 6521 ature and humidity in the highest (12:00–13:00) and lowest (04:00–05:00) temperature 9 ofpe-
15
riods were examined. The highest and lowest daily temperature periods were determined
based on the mean hourly temperature in March.
Thetemperature
The temperaturemeasured
measuredat atnoon
noonon on2727May
Maywas was38.2
38.2◦°C, thehighest
C, the highesttemperature
temperature
ever recorded in Taipei in May. The temperature was similar to
ever recorded in Taipei in May. The temperature was similar to summer temperatures summer temperatures in
Taiwan; therefore, such data could be adopted as a reference for the
in Taiwan; therefore, such data could be adopted as a reference for the benefits of the benefits of the envi-
ronmental cooling
environmental effect
cooling of green
effect roofsroofs
of green in summer.
in summer.ThisThis
study collected
study meanmean
collected hourly tem-
hourly
perature datadata
temperature on 27
onMay
27 May (Figure 7) and
(Figure integrated
7) and themthem
integrated withwith
thosethose
provided by the
provided byCen-
the
tral Weather
Central Bureau
Weather Bureau (code:
(code:CWT)
CWT)for forcomparison.
comparison.The Theroof
roof temperature
temperature was was slightly
slightly
higher than
higher than the
the one
one measured
measured by by the
the Bureau
Bureau because
because of of the
the direct
direct sunlight
sunlight onon the
the roof,
roof,
but the trends in the air temperature data on the roof and those provided
but the trends in the air temperature data on the roof and those provided by the Bureau by the Bureau
weresimilar.
were similar.As
AsFigure
Figure88reveals,
reveals,the
theambient
ambienttemperature
temperaturebegan begantotorise
risebetween
between8:00 8:00and
and
9:00in
9:00 inthe
themorning,
morning,andandthe thetemperature
temperaturewas washighest
highestbetween
between12:00
12:00and
and13:00,
13:00,when
whenthe the
ecological terrace had the
ecological the maximum
maximumcoolingcoolingeffect
effect(a(atemperature
temperature drop
dropof of
9.24 °C)◦ C)
9.24 on on
the
space
the
Sensors 2022, 22, x FOR PEER REVIEW spacebeneath. TheThe
beneath. ambient temperature
ambient temperaturedecreased
decreasedto approximately
to approximately ◦ C between
30 °C30between 1020:00
of 15
and 21:00,
20:00 and the
and 21:00, andecological terrace
the ecological beganbegan
terrace to exhibit an insulation
to exhibit effect effect
an insulation on theonspace
the
beneath.
space beneath.
6/01 27.21 30.81 1.73

Table 7. Ambient temperatures of the ecological terrace during 12:00–13:00.

Date Roof AT (°C) Room AT (°C) Top Cooling Effect (°C)

5/25 36.17 31.33 4.84
5/26 39.04 32.61 6.43
5/27 42.86 33.62 9.24
5/28 36.40 32.65 3.75
5/29 38.61 33.07 5.54
5/30 39.01 32.82 6.19
5/31 37.15 31.95 5.20
6/01 29.78 29.67 0.11
Figure7.7.Ambient
Figure Ambienttemperature
temperaturetrends
trendsof
ofthe
theecological
ecologicalterrace
terraceon
on27
27May.
May.

Tables 6 and 7 present comparisons of the temperatures measured at all test points
in the ecological terrace from 25 May to 1 June. The results indicate that, during the lower
temperature period, the ecological terrace had the lowest temperature, followed by the air
temperature on the roof. The highest temperature was that of the classroom, followed by
the temperature on the roof itself, as displayed in Figure 8a. During the higher tempera-
ture period, the highest temperature was measured in the overall ecological terrace (green
roof) environment, followed by the temperature on the roof itself; the lowest temperature
occurred in the classroom, followed by the air temperature on the roof, as can be observed
in Figure 8b.

Table 6. Ambient temperatures of the ecological terrace during 04:00–05:00.

(a) (b)
Date Roof AT (°C) Room AT (°C) Top Cooling Effect (°C)
Figure 8.
Figure5/25 Environmental effects of the ecological terrace
26.99 of the ecological terrace
8. Environmental effects 30.27during (a) 04:00–05:00
during (a) 04:00–05:00 andand1.95
(b)(b)12:00–13:00.
12:00–13:00.
5/26 27.72 31.01 1.88
3.2.2. Shading
Tables 6 Effect
and 7 of Ecological
present comparisonsTerraceof (Green
the Roof) on the
temperatures Building
measured at all test points in
5/27 28.03 31.53 2.00
the ecological
Green terrace
roofs fromadvantages,
possess 25 May to 1including
June. Thebeautifying
results indicate that,
cities, during
cooling thethe
toplower
floors
5/28 29.36 32.19 0.99
temperature
of buildings,period, the ecological
and reducing terraceeffects.
heat island had the lowest
This temperature,
study analyzed the followed
shading byeffect
the airof
5/29 28.90 31.96 1.25
temperature on the roof.
an undermaintained The highest
ecological terracetemperature was that
on the top floor of the classroom,
of a building followed
during higher by
(12:00–
the 5/30
temperature on the 27.78
roof itself, as displayed in 31.49
Figure 8a. During the 2.82
higher temperature
13:00) and lower (04:00–05:00) temperature periods.
5/31
period,
Fromthe 25
highest
May to 127.56
temperature
June, the was measured
ambient 31.11
in the overall
temperatures roof and in3.02
on the ecological terrace (green
the classroom
roof) environment,
beneath followed
demonstrated that,by
in the
the temperature on the roof
lower temperature itself;
period, thethe
airlowest temperature
temperature in the
occurred
classroomin was
the classroom,
higher thanfollowed bygreen
that of the the airroof
temperature onOn
(Figure 9a). the27roof,
May,asduring
can be the
observed
higher
intemperature
Figure 8b. period, the air temperature in the classroom was lower than the air temper-
ature on the green roof (Figure 9b).
 Sensors 2022, 22, x FOR PEER REVIEW 10 of 15
Sensors 2022, 22, 6521 10 of 15

6/01 27.21 30.81 1.73

Table 6. Ambient temperatures of the ecological terrace during 04:00–05:00.
Table 7. Ambient temperatures of the ecological terrace during 12:00–13:00.
Date Roof AT (◦ C) Room AT (◦ C) Top Cooling Effect (◦ C)
Date
5/25 Roof AT26.99
(°C) Room AT (°C)
30.27 Top Cooling
1.95 Effect (°C)
5/25 36.17 31.33 4.84
5/26 27.72 31.01 1.88
5/26 39.04 32.61 6.43
5/27
5/27 42.8628.03 31.53
33.62 2.00
9.24
5/28
5/28 36.4029.36 32.19
32.65 0.99
3.75
5/29
5/29 38.6128.90 33.07
31.96 5.54
1.25
5/30 39.01 32.82 6.19
5/30 27.78 31.49 2.82
5/31 37.15 31.95 5.20
5/31
6/01 29.7827.56 31.11
29.67 3.02
0.11
6/01 27.21 30.81 1.73

Table 7. Ambient temperatures of the ecological terrace during 12:00–13:00.

Date Roof AT (◦ C) Room AT (◦ C) Top Cooling Effect (◦ C)

5/25 36.17 31.33 4.84
5/26 39.04 32.61 6.43
5/27 42.86 33.62 9.24
5/28 36.40 32.65 3.75
5/29 38.61 33.07 5.54
5/30 39.01 32.82 6.19
(a) 5/31 37.15 31.95
(b) 5.20
Figure6/01
8. Environmental effects
29.78of the ecological terrace
29.67 during (a) 04:00–05:00 and
0.11 (b) 12:00–13:00.

3.2.2. Shading Effect of Ecological Terrace (Green Roof) on the Building

3.2.2. Shading Effect of Ecological Terrace (Green Roof) on the Building
Green roofs possess advantages, including beautifying cities, cooling the top floors
Green roofs possess advantages, including beautifying cities, cooling the top floors
of buildings, and reducing heat island effects. This study analyzed the shading effect of
of buildings, and reducing heat island effects. This study analyzed the shading effect
an undermaintained ecological terrace on the top floor of a building during higher (12:00–
of an undermaintained ecological terrace on the top floor of a building during higher
13:00) and lower (04:00–05:00) temperature periods.
(12:00–13:00) and lower (04:00–05:00) temperature periods.
From 25 May to 1 June, the ambient temperatures on the roof and in the classroom
From 25 May to 1 June, the ambient temperatures on the roof and in the classroom
beneath demonstratedthat,
beneath demonstrated that,ininthe
thelower
lowertemperature
temperatureperiod,
period,the
theairairtemperature
temperatureininthe
the
classroom was higher than that of the green roof (Figure 9a). On 27 May,
classroom was higher than that of the green roof (Figure 9a). On 27 May, during the during the higher
temperature
higher period,
temperature the airthe
period, temperature in the in
air temperature classroom was lower
the classroom was thanlowerthe air the
than temper-
air
ature on the green roof (Figure 9b).
temperature on the green roof (Figure 9b).

(a) (b)
Figure 9. Comparison of the shading effect of the ecological terrace on the building during (a) 04:00–
Figure 9. Comparison of the shading effect of the ecological terrace on the building during (a) 04:00–
05:00 and (b) 12:00–13:00.
05:00 and (b) 12:00–13:00.
Sensors 2022, 22, 6521 11 of 15

Sensors 2022, 22, x FOR PEER REVIEW 11 of 15

Sensors 2022, 22, x FOR PEER REVIEW 11 of 15
3.3. Benefit Analyses of the Cooling Effect of Pervious Roads
During the monitoring period, ambient temperature and humidity, as well as the
3.3. Benefit Analyses of the Cooling Effect of Pervious Roads
temperature
3.3. Benefit and humidity on and under the ground,
Roads on a campus pervious road were
DuringAnalyses of the Cooling
the monitoring Effect
period, of Pervious
ambient temperature and humidity, as well as the
measured and
During thethe data were collected every 15 min. This study conducted the examination
temperature andmonitoring
humidity on period, ambient
and under the temperature
ground, on aand humidity,
campus as well
pervious roadaswere
the
from January
temperature to
andMarch, with relatively stable monitored and transmitted data for analyses.
measured and thehumidity
data wereoncollected
and under the15ground,
every on study
min. This a campus pervious
conducted theroad were
examina-
The ambient
measured
tion temperature
and the data
from January and humidity
were collected
to March, data
every 15
with relatively for consecutive
min.monitored
stable This studyanddays were
conducted gathered,
the examina-
transmitted data for and the
mean hourly
tion fromThe
analyses. temperature
January and
to March,
ambient humidity in
with relatively
temperature the highest
stabledata
and humidity (12:00–13:00)
monitored and lowest
and transmitted
for consecutive days were (04:00–05:00)
data for
gath-
temperature
analyses.
ered, and Theperiods
the ambient
mean were
hourly analyzed
temperature (Table
and
temperature and8).
humidity The daily
data
humidity for highest and
consecutive
in the highest lowest
days weretemperature
(12:00–13:00) gath-
and
ered, and
periods
lowest were the mean temperature
determined
(04:00–05:00) hourly temperature
based on meanand
periods humidity
hourly
were in the highest
temperatures
analyzed (Table 8).in (12:00–13:00)
March.
The daily and
highest and
lowest (04:00–05:00)
lowest temperature temperature
periods wereperiods were analyzed
determined based on(Table
mean 8). The daily
hourly highest and
temperatures in
lowest
Table 8.
March. temperature
Analysis period periods
of were
pervious determined
road data. based on mean hourly temperatures in
March.
Month
Table 8. Analysis period of pervious road data. January February March
Table 8. Analysis period of pervious road data.
Number of days
Month 31 days
January 28 days
February 31 days
March
Data collection rateNumber Month
of days
in the low-temperature period January
31 days
90% February
28 64%
days March
31 days
100%
Data collection Number
rate of days 3190%
days 2864%
days 31 days
Data collection rate in in
thethe low-temperature
high-temperature period
period 87% 61% 100%100%
Datacollection
Data collectionrate
rateininthe
thehigh-temperature
low-temperature period
period 90%
87% 64%
61% 100%
100%
Data collection rate in the high-temperature period 87% 61% 100%
The
The ambient
ambienttemperature
temperature on onthetheroad
roadwaswas compared
compared withwith the temperature
the temperature underunder
the the
road. In
TheFigure
ambient10a, positive
temperature values
on the refer
road to
wassituations
compared when
with the
the air temperature
temperature
road. In Figure 10a, positive values refer to situations when the air temperature was lower under was
the lower
than thethe
road.
than soil
In temperature,
Figure
soil temperature, and
10a, positive negative
values
and refer values
negative to signify
situations
values signify the the
when
the opposite. AsFigure
Figure
air temperature
opposite. As was10b
10b indicates,
lower
indi-
than the
during
cates,the soil temperature,
lower
during the temperature and period,
lower temperaturenegative values
the
period, signifytemperatures
ambient
the ambient the opposite. of
temperatures As Figure
of87%
87% ofof 10b
the indi-
days from
the days
cates,January
January
from during
to Marchthe lower
to March temperature
werewere
lower
lowerthan period,
thanthe thetemperature
thesoil
soil ambient temperatures
temperature of 87%
underground.
underground. As of the Figure
As
Figure days
11a 11a
from January
discloses,
discloses, during to March
during the were temperature
the higher
higher lower than the
temperature soil temperature
period,
period, thethe
ambient underground.
temperatures
ambient As
temperatures Figure
of 71% 11a
ofof71%
the of the
discloses,
days from during
January the
to higher
March temperature
were lower period,
than the
days from January to March were lower than the soil temperature. the
soil ambient temperatures
temperature. of 71% of the
days from January to March were lower than the soil temperature.

Non-cooling 10
Non-cooling
days (13%)10
days (13%)

Cooling 67 days
Cooling 67 days
(87%)
(87%)
Cooling 67 days (87%) Non-cooling 10 days (13%)
Cooling 67 days (87%) Non-cooling 10 days (13%)

(a) (b)
(a) (b)
Figure 10. (a) Temperature distribution and (b) ratio of cooling days on the pervious road during
Figure 10. (a)
Figure 10.from
4:00–5:00
Temperature
(a) Temperature distribution
distribution and (b) ratio of cooling daysdays
January to March.
and (b) ratio of cooling onpervious
on the the pervious road during
road during
4:00–5:00 from January to March.
4:00–5:00 from January to March.

Non-cooling 22
Non-cooling
days (29%)22
days (29%)
Cooling 53 days
Cooling 53 days
(71%)
(71%)
Cooling 53 days (71%) Non-cooling 22 days (29%)
Cooling 53 days (71%) Non-cooling 22 days (29%)

(a) (b)
(a) (b)

Figure 11. (a) Temperature distribution and (b) ratio of cooling days on the pervious road during
12:00–13:00 from January to March.
Sensors 2022, 22, x FOR PEER REVIEW 12 of 15

Sensors 2022, 22, 6521 Figure 11. (a) Temperature distribution and (b) ratio of cooling days on the pervious road 12 of 15
during
12:00–13:00 from January to March.

4. Results
4. Results and
and Discussion
Discussion
The benefits
The benefits of
of the
the photovoltaic
photovoltaicroof,
roof,ecological
ecologicalterrace,
terrace,and
andpervious
perviousroadroadforfor
envi-
en-
ronmental greening were compared using higher (12:00–13:00) and lower (4:00–5:00)
vironmental greening were compared using higher (12:00–13:00) and lower (4:00–5:00) tem-
perature periods.
temperature As Figure
periods. 12 suggests,
As Figure the the
12 suggests, environmental cooling
environmental benefit
cooling of the
benefit pervious
of the pervi-
road was strongest, based on the results of both ambient and ground temperatures.
ous road was strongest, based on the results of both ambient and ground temperatures.

(a) (b)
Figure 12.
Figure 12. Comparison
Comparison of
of overall
overall environments:
environments: (a)
(a) air
air temperature
temperature (b)
(b) ground
ground temperature.
temperature.

Building Shading
4.1. The Benefit of the Solar Photovoltaic Roof for Building Shading
(a) Roof shading
(a) Roof shading benefits
benefits of solar panels
During the
During thecold
coldperiod
periodfrom
from 4 to
4 to 5 in5 the
in the morning,
morning, thetemperature
the air air temperature underunder the
the solar
solar panel
panel was lower
was lower than than the ambient
the ambient air temperature
air temperature on theon the
top top
floor.floor.
When Whenthethe ambi-
ambient
ent temperature
temperature at night
at night was was
lower lower 25 ◦ C,
thanthan 25 the
°C, ground
the ground below below the solar
the solar panel panel generally
generally had
ahad a thermal
thermal insulation
insulation effect,
effect, and theand the maximum
maximum temperature temperature
was 2.14 was◦ 2.14
C; if it was°C; if it than
higher was
25 ◦ C, the
higher than 25 °C,below
ground the ground
the solarbelow
panel the solar panel
generally had generally had a cooling
a cooling effect, and the effect,
maximum and
the maximum
temperature drop was 3.7 ◦ C.
temperature drop was 3.7 °C.
During the the hot
hotperiod
periodfromfrom1212toto 1313noon on on
noon thethe
solar photovoltaic
solar photovoltaic roof,roof,
the airthetem-
air
temperature
perature under underthethe solar
solar panel
panel waswas generallylower
generally lowerthanthanthetheambient
ambientair air temperature
temperature on
the top
top floor,
floor,and
andthethemaximum
maximumtemperature
temperature drop
dropwas was2 ◦2C.°C.
WhenWhen thetheambient
ambient temperature
tempera-
during the day ◦
ture during the was
day higher than than
was higher 25 C, 25the
°C,ground
the groundtemperature
temperatureunder the solar
under panelpanel
the solar had
ahad
cooling effect,
a cooling andand
effect, the the
maximum
maximum temperature
temperature drop
drop waswas 5.77◦ C;
5.77 °C;ififititwas
waslower
lower thanthan
25 ◦°C,
C, the ground below the solar panel generally had a thermal insulation effect, and the
maximum temperature ◦ C.
temperature was was 2.4
2.4 °C.
According to Dominguez,
Dominguez,Kleissl,
Kleissl,and andLuvall
Luvall(2011),
(2011), inclined
inclined solar
solar panels
panels areare con-
condu-
ducive
cive to air flow below and do not easily collect heat, so the temperature of the air andand
to air flow below and do not easily collect heat, so the temperature of the air the
the ground
ground under
under an an inclined
inclined panel
panel is is lowerthan
lower thanthat
thatofofthetheflat-mounted
flat-mounted solar solar panel.
panel. TheThe
environmental
environmental benefits
benefits ofof this
this case
case are
are similar
similar toto their
their effects.
effects.
4.2. The Environmental Benefits of Green Roofs on the Top Floor and the Shading Effects of Green
4.2. The Environmental Benefits of Green Roofs on the Top Floor and the Shading Effects of
Roofs on Buildings
Green Roofs on Buildings
(a) The highest temperature day in May
(a) The highest temperature day in May
Comparing the daily temperature data of the Central Weather Bureau with the campus
Comparing the daily temperature data of the Central Weather Bureau with the cam-
environment monitoring system, it can be seen that the temperature trend monitored
pus environment monitoring system, it can be seen that the temperature trend monitored
in this study was similar to the temperature data trend of the Central Meteorological
in this study was similar to the temperature data trend of the Central Meteorological Ad-
Administration. They were all slightly higher than the temperature data of the Central
ministration. They were all slightly higher than the temperature data of the Central Mete-
Meteorological Bureau, and the climate temperature on that day was similar to summer
orological Bureau, and the climate temperature on that day was similar to summer tem-
temperatures in Taiwan, which can be used as the benefit of green roofs affecting the top
peratures in Taiwan, which can be used as the benefit of green roofs affecting the top floor
floor environment in summer. From the statistical results, it is known that green roofs are
environment
built in summer.
in environments From
with high the statistical
ambient results, it is known
temperatures. It may that greenfor
be better roofs are built
cooling the
space under buildings.
(b) Shading effect of green roofs on buildings
Sensors 2022, 22, 6521 13 of 15

Because it was not easy to monitor and compare the air temperature and humidity
of the classrooms under the ungreen roof, the roof air temperature and humidity data
were used for comparison. The statistical results show that the green roof contained a soil
layer during the colder period from 4 to 5 in the morning, which had a thermal insulation
effect on the space below, and the thermal insulation effect was about 1~3 ◦ C. The average
temperature was 1.95 ◦ C, and the green roof received most of the heat during the hot period
from 12:00 to 13:00, which had a cooling effect on the space below. The cooling effect was
about 0.5~9 ◦ C, and the average temperature drop was 5.16 ◦ C.

4.3. Comparison of the Greening Benefits of the Campus Environment and the Ratio of the Number
of Days That the Permeable Pavement Soil Absorbs Heat
(a) Comparison of campus greening benefits
The data was collected for a total of five days. It can be clearly seen that the benefits
of the greening sites regarding the cooling of the ambient air and the ground are in the
order of permeable pavement > solar photovoltaic roof > ecological terrace (green roof). It
may be because the surrounding environment of the permeable pavement was open and
planted with flowers, plants, and trees to increase the cooling effect of the surrounding
environment, and the cooling part of the ground may be because the sensor was buried
about 5~10 cm below the soil of the permeable pavement, so the cooling effect better.
(b) Ratio of days in which pervious pavement soil absorbed heat
A permeable pavement made of grass-planted bricks on campus has been monitored
for about a year since last year. Due to the testing of communication and power supply
in the first half of the year, the data collection was incomplete and was not included in
the comparison. This analysis compared the soil air temperature under the permeable
pavement and the above-ground air temperature. The results show that the cooling days
during the colder period from 4 to 5 a.m. in the morning from January to March accounted
for 89% of the cooling days compared with 71% in the warmer period from 12 to 13 a.m.
The reason was that the ambient temperature in the daytime was too high and the cooling
effect was not significant. From January to March, the maximum cooling effect of the hours
during the day was 4.75 ◦ C, the average maximum temperature drop in the month was
2.32 ◦ C, and the maximum cooling effect was 4.33◦ from 4 to 5 in the morning. The monthly
average maximum temperature drop reached 2.22 ◦ C.

5. Conclusions
This study established a wireless environmental monitoring management system
based on IoT technology and the smart city concept to monitor and quantify the long-term
benefits of green engineering on campus environments. Three greening engineering areas,
namely a photovoltaic roof, an ecological terrace (green roof), and a pervious road (grass
pavers), were analyzed. The results may be summarized as follows.
1. Feasibility of the wireless environmental monitoring management system: This study
built a wireless environmental sensing management system for the greening envi-
ronment on a campus and successfully monitored physical quantities in the green
engineering environments. These quantities could be applied in other green engi-
neering areas and the sensing data could be uploaded to a cloud system for data
management. By using a visual monitoring interface to display the environmental
monitoring situations, managers could develop system functions such as instant
data display, historical trend figure establishment, and report queries and outputs
according to their needs. This study can be an index for Taiwan’s EEWH.
2. Shading effect of the photovoltaic roof during spring (March–May): The air tempera-
tures under the fixed solar panels were generally lower than the ambient temperature
whether during 4:00–5:00 or 12:00–13:00, and the roof temperature under the panels
was affected by the ambient temperature. When the ambient temperature was below
25 ◦ C, the solar panels had an insulation effect on the roof of the building during
Sensors 2022, 22, 6521 14 of 15

both 4:00–5:00 and 12:00–13:00, with an optimal insulation effect of 2.45 ◦ C. When the
ambient temperature was above 25 ◦ C, the panels had a cooling effect on the roof of
the building whether during 4:00–5:00 or 12:00–13:00, with an optimal cooling effect
of 5.77 ◦ C.
3. Shading effect of the ecological terrace (green roof) on the building: During the lower
temperature period (4:00–5:00), the ecological terrace had an insulation effect on
the space beneath, with an effect of approximately 1–3 ◦ C and a mean insulation of
1.95 ◦ C. During the higher temperature period (12:00–13:00), it presented a cooling
effect on the space beneath, with an effect of approximately 0.5–9 ◦ C and a mean
cooling temperature of 5.16 ◦ C. The green roof cooled and insulated the space beneath
during the day and at night, respectively.
4. Environmental cooling effect of the pervious road: The cooling effect of the three
greening areas on the air and ground temperatures decreased in the following or-
der: pervious road > photovoltaic roof > ecological terrace. The pervious road pre-
sented the optimal cooling effect of the three. During a higher temperature period
(12:00–13:00), the pervious road had a maximum cooling temperature of 4.75 ◦ C, with
a monthly mean minimum cooling temperature of 2.32 ◦ C. During a lower temper-
ature period (4:00–5:00), it had a maximum cooling temperature of 4.33 ◦ C and a
monthly mean cooling temperature of 2.22 ◦ C.

Author Contributions: Project administration, L.-H.C.; resources, K.-H.T. and L.-H.C.; supervision,
K.-H.T. and L.-H.C.; funding acquisition, N/A; data curation, M.-Y.W.; formal analysis, M.-Y.W.;
methodology, L.-H.C. and M.-Y.W.; validation, M.-Y.C.; writing—original draft, M.-Y.W. and M.-Y.C.;
writing—review and editing, M.-Y.C. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available within the article.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Burke, M.; Davis, W.M.; Diffenbaugh, N. Large potential reduction in economic damages under UN mitigation targets. Nature
2018, 557, 549–553. [CrossRef] [PubMed]
2. Steffen, W.; Rockström, J.; Richardson, K.; Lenton, T.M.; Folke, C.; Liverman, D.; Summerhayes, C.P.; Barnosky, A.D.; Cornell,
S.E.; Crucifix, M.; et al. Trajectories of the earth system in the anthropocene. Proc. Natl. Acad. Sci. USA 2018, 115, 8252–8259.
[CrossRef] [PubMed]
3. Bianco, L.; Serra, V.; Larcher, F.; Perino, M. Thermal behaviour assessment of a novel vertical greenery module system: First
results of a long-term monitoring campaign in an outdoor test cell. Energy Effic. 2016, 10, 625–638. [CrossRef]
4. Hendel, M.; Royon, L. The effect of pavement-watering on subsurface pavement temperatures. Urban Clim. 2015, 14, 650–654.
[CrossRef]
5. Poulek, V.; Matuška, T.; Libra, M.; Kachalouski, E.; Sedláček, J. Influence of increased temperature on energy production of roof
integrated PV panels. Energy Build. 2018, 166, 418–425. [CrossRef]
6. Yu, Q.; Xiong, F.; Wang, Y. Integration of wireless sensor network and IoT for smart environment monitoring system. J. Interconnect.
Networks 2021, 2143010. [CrossRef]
7. Carter, T.; Keeler, A. Life-cycle cost–benefit analysis of extensive vegetated roof systems. J. Environ. Manag. 2008, 87, 350–363.
[CrossRef]
8. Yan, L.; Jia, W.; Zhao, S. The Cooling Effect of Urban Green Spaces in Metacities: A Case Study of Beijing, China’s Capital. Remote
Sens. 2021, 13, 4601. [CrossRef]
9. Tseng, K.-H.; Chung, M.-Y.; Chen, L.-H.; Chang, P.-Y. Green smart campus monitoring and detection using LoRa. Sensors 2021,
21, 6582. [CrossRef]
10. Chen, C. Multipoint Ecological Environment Monitoring System with Internet of Things. Math. Probl. Eng. 2022, 2022, 1481931.
[CrossRef]
11. Jaffal, I.; Ouldboukhitine, S.-E.; Belarbi, R. A comprehensive study of the impact of green roofs on building energy performance.
Renew. Energy 2012, 43, 157–164. [CrossRef]
Sensors 2022, 22, 6521 15 of 15

12. Jin, J.; Gubbi, J.; Marusic, S.; Palaniswami, M. An information framework for creating a smart city through internet of things.
IEEE Internet Things J. 2014, 1, 112–121. [CrossRef]
13. Van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.-F.;
et al. The representative concentration pathways: An overview. Clim. Change 2011, 109, 5–31. [CrossRef]
14. Matasov, V.; Marchesini, L.B.; Yaroslavtsev, A.; Sala, G.; Fareeva, O.; Seregin, I.; Castaldi, S.; Vasenev, V.; Valentini, R. IoT
monitoring of urban tree ecosystem services: Possibilities and challenges. Forests 2020, 11, 775. [CrossRef]
15. Naphade, M.; Banavar, G.; Harrison, C.; Paraszczak, J.; Morris, R. Smarter cities and their innovation challenges. Computer 2011,
44, 32–39. [CrossRef]
16. Augustin, A.; Yi, J.; Clausen, T.; Townsley, W.M. A study of LoRa: Long range & low power networks for the internet of things.
Sensors 2016, 16, 1466.
17. He, Y.; Yu, H.; Ozaki, A.; Dong, N. Thermal and energy performance of green roof and cool roof: A comparison study in Shanghai
area. J. Clean. Prod. 2020, 267, 122205. [CrossRef]
18. Pratesi, M.; Cinelli, F.; Santi, G. Evaluation of Multifunctional Aspects of a Green Roof in Mitigating the Negative Effects of Urban-
ization in Mediterranean Environment. In Green Buildings and Renewable Energy; Springer: Cham, Switzerland, 2020; pp. 371–382.
19. Rao, Y.; Zhang, J.; Yang, T.; Yang, B. Effect of bottleneck-causing clogging on infiltration-runoff of pervious concrete pavement
system. Road Mater. Pavement Des. 2021, 1–14. [CrossRef]
20. Alshayeb, M.J.; Chang, J.D. Variations of PV Panel Performance Installed over a Vegetated Roof and a Conventional Black Roof.
Energies 2018, 11, 1110. [CrossRef]
21. Dominguez, A.; Kleissl, J.; Luvall, J.C. Effects of solar photovoltaic panels on roof heat transfer. Sol. Energy 2011, 85, 2244–2255.
[CrossRef]
22. Green, M.A.; Hishikawa, Y.; Warta, W.; Dunlop, E.D.; Levi, D.H.; Hohl-Ebinger, J.; Ho-Baillie, A.W. Solar cell efficiency tables
(version 51). Prog. Photovolt. Res. Appl. 2017, 26, 3–12. [CrossRef]
23. Schindler, B.Y.; Blank, L.; Levy, S.; Kadas, G.; Pearlmutter, D.; Blaustein, L. Integration of photovoltaic panels and green roofs:
Review and predictions of effects on electricity production and plant communities. Isr. J. Ecol. Evol. 2016, 62, 68–73. [CrossRef]
24. Aronescu, A.; Appelbaum, J. Shading on photovoltaic collectors on rooftops. Appl. Sci. 2020, 10, 2977. [CrossRef]
25. Zhang, X.; Shen, L.; Tam, V.W.; Lee, W.W.Y. Barriers to implement extensive green roof systems: A Hong Kong study. Renew.
Sustain. Energy Rev. 2012, 16, 314–319. [CrossRef]
26. Wilkinson, S.; Feitosa, R.C. Technical and engineering issues in green roof retrofit. In Green Roof Retrofit: Building Urban Resilience;
John Wiley & Sons: Hoboken, NJ, USA, 2016; pp. 14–36.
27. Dwivedi, A.; Mohan, B.K. Impact of green roof on micro climate to reduce Urban Heat Island. Remote Sens. Appl. Soc. Environ.
2018, 10, 56–69. [CrossRef]