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Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.
Transformational IoT sensing for
air pollution and thermal
Jovan Pantelic
*, Negin Nazarian
, Clayton Miller
Forrest Meggers
, Jason Kai Wei Lee
and Dusan Licina
Well Living Lab, Delos Living LLC, Rochester, MN, United States,
Climate-Resilient Cities Lab, Faculty
of Built Environment, University of New South Wales, Kensington, NSW, Australia,
Building and Urban
Data Science Lab, Department of the Built Environment, College of Design and Engineering, National
University of Singapore, Singapore, Singapore,
CHAOS Lab, School of Architecture and the Andlinger
Center for Energy and the Environment, Princeton University, Princeton, NJ, United States,
Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of
Singapore, Singapore, Singapore,
Human-Oriented Built Environment Lab, School of Architecture,
Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne,
Cities today encounter signicant challenges pertaining to urbanization and
population growth, resource availability, and climate change. Concurrently,
unparalleled datasets are generated through Internet of Things (IoT) sensing
implemented at urban, building, and personal scales that serve as a potential
tool for understanding and overcoming these issues. Focusing on air pollution
and thermal exposure challenges in cities, we reviewed and summarized the
literature on IoT environmental sensing on urban, building, and human scales,
presenting the rst integrated assessment of IoT solutions from the data
convergence perspective on all three scales. We identied that there is a
lack of guidance on what to measure, where to measure, how frequently to
measure, and standards for the acceptable measurement quality on all scales of
application. The current literature review identied a signicant disconnect
between applications on each scale. Currently, the research primarily considers
urban, building, and personal scale in isolation, leading to signicant data
underutilization. We addressed the scientic and technological challenges
and opportunities related to data convergence across scales and detailed
future directions of IoT sensing along with short- and long-term research
and engineering needs. IoT application on a personal scale and integration of
information on all scales opens up the possibility of developing personal
thermal comfort and exposure models. The development of personal
models is a vital promising area that offers signicant advancements in
understanding the relationship between environment and people that
requires signicant further research.
IoT environmental sensing, air quality, indoor air quality, thermal comfort, personal
exposure, personal comfort
Hasim Altan,
Arkin University of Creative Arts and
Design (ARUCAD), Cyprus
Claudio Martani,
Purdue University, United States
Prathap Ramamurthy,
City College of New York (CUNY),
United States
Jovan Pantelic,
This article was submitted to Indoor
Environment, a section of the journal
Frontiers in Built Environment
RECEIVED 17 June 2022
ACCEPTED 10 October 2022
PUBLISHED 24 October 2022
Pantelic J, Nazarian N, Miller C,
Meggers F, Lee JKW and Licina D (2022),
Transformational IoT sensing for air
pollution and thermal exposures.
Front. Built Environ. 8:971523.
doi: 10.3389/fbuil.2022.971523
© 2022 Pantelic, Nazarian, Miller,
Meggers, Lee and Licina. This is an
open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other
forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the
original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution
or reproduction is permitted which does
not comply with these terms.
Frontiers in Built Environment frontiersin.org01
TYPE Review
PUBLISHED 24 October 2022
DOI 10.3389/fbuil.2022.971523
1 Introduction
The United Nations projects that 68% of the population by
2050 will live in the cities (UN, 2018). Numerous challenges arise
for the cities that will need to provide a livable environment for
such a large portion of the human population housed in a high-
density environment. The global increase in air pollution
represents one of the worlds growing concerns. WHO states
almost all of the global population (99%) breathe air that
exceeds air quality limits (WHO, 2021).Air pollution
impacts cardiovascular health (Mills et al., 2009), pulmonary
health (Pope, 2000), and cognitive performance (Peters et al.,
2015). Sources of indoor pollution are cooking, cleaning, candle/
incense burning, smoking (Habre et al., 2014), and building
materials (Liu et al., 2013) while ambient sources include
combustion products, photochemical reaction products, and
metals (Habre et al., 2014). A combination of ambient and
indoor air pollution exposure is associated with (Liu et al.,
2013) million premature deaths annually (Air pollution,
2022), highlighting the signicance and magnitude of the
problem. On the other hand, cities face signicant challenges
of urban overheating driven by global climate change and urban
development (Nazarian et al., 2021a). These compounding
effects represent a threat to human thermal comfort in
outdoor spaces as well as indoor environments where
buildings have to provide comfort under warmer summer
conditions utilizing low-energy design strategies (Holmes and
Hacker, 2007). One of the critical problems in alleviating the air
pollution exposures and thermal comfort is understanding the
complete set of contributing factors that impact the entire
ecosystem and the effectiveness of potential mitigation
strategies. The effectiveness of mitigation strategies needs to
be quantied to implement solutions based on the knowledge.
This requires the ability to quantify various aspects of the urban
environment, motivating integrated and multi-scale
environmental sensing in cities. The Internet of Things (IoT)
technologies are the paradigm that emphasizes such ubiquitous
sensing installed using modern wireless communications
enabling quantication of a full set of environmental
parameters that affect air pollution exposure and thermal
comfort. It is a novel approach to monitoring, assessing, and
ultimately addressing challenges related to air pollution exposure
and thermal stress and comfort (Grimm et al., 2008;Bibri, 2018).
Compared to conventional sensing, IoT sensing offers increased
environmental information with higher spatial granularity and
reduced and less centralized resources. These sensor networks
focus on data volume and the potential to train machine learning
models to allow informed decision-making.
The emergence of new green certication schemes for
buildings and communities, businesses that offer IoT
environmental sensing, analytics, and building management
system integration, alongside wearable tech ecosystems
focusing on human wellness, increased concerns about the
indoor air quality in the COVID-19 pandemic, represent the
driving forces behind widespread IoT adaptation (Parkinson
et al., 2019a). These new trends, however, do not come
without challenges. At the moment, there is still a set of
technological, scientic, and legal challenges regarding data
acquisition and analytics that provides critical information
necessary to solve environmental challenges on the human,
building, and urban scales.
1.1 Systematic review
Existing reviews focus on either urban scale (Jovašević-
Stojanovićet al., 2015;Muller et al., 2015;Kumar et al., 2016;
Morawska et al., 2018), intended to address outdoor air quality
and comfort, building scale (Schieweck et al., 2018;Parkinson
et al., 2019a;Cahill et al., 2019) that deals with the indoor air
quality and comfort, or personal scale (Ghahramani et al., 2019a;
Nazarian and Lee, 2021). This separation between scales of the
application keeps the research areas completely isolated. Several
existing reviews summarized the conceptual application of IoT
technology, sensor accuracy, challenges of eld calibration, data
acquisition, and storage (Morawska et al., 2018;Ullo and Sinha,
2020;Hajjaji et al., 2021). Moreover, despite the ongoing
expansion of knowledge, challenges regarding data analytics
and convergence perspectives across various spatial and
temporal scales, which enable the conversion of data into
knowledge, are yet to be addressed.
This paper lls the knowledge gap by synthesizing research
and applying IoT sensing for air quality and thermal comfort
indoors and outdoors. The presented research evidence is based
on a critical synthesis of the existing literature drawn from
broadly used databases such as Web of Science, Scopus, and
Google Scholar. The selection process intended to identify
relevant, high-quality papers on which the authors could base
their conclusions. Each section of the current review covers a
fairly distinctive area of research. Therefore, each section had a
different and unique literature search that covered the
application of IoT sensing on air quality and thermal comfort.
The selection of papers was generally done in two steps: by
systematic bibliographic search and supplementary literature
additions. The systematic bibliographic search relied on a
combination of the following keywords: IoT sensing, low-cost
sensing, indoor air quality, thermal comfort, urban heat,
exposure, sensors, MRT, and proxy sensing. Based on the
authors expert knowledge in each area of the current review,
we included supplementary literature additions. The search
included papers published between 2008 and March 2022, but
more recent papers received substantial emphasis given the
novelty and recent research and application development in
IoT sensing. Since this review covers interdisciplinary
research, where each discipline has a unique publishing
culture, we need to emphasize that supplementary literature
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additions were crucial to cover all relevant papers in the eld. We
were extra careful to cite previous review papers in each of the
disciplines covered in this review.
Criteria for exclusion from the review included papers that
only presented frameworks without demonstrating
implementation and precise data collection and analysis
methods. In order to give recommendations that could be
relevant to building monitoring and benchmarking and
uptake of IoT sensing implementation, the present authors
decided to go beyond the simple synthesis of the otherwise
sparse and broadly archival literature. Instead, the study also
relied on the greyliterature (e.g., government reports, white
papers, and other documentation, etc.) and on the authors
collective long-term practical experience in the eld globally.
1.2 Objectives
To bridge the knowledge gaps and respond to the fast-
emerging trends, this review aims to interpret and synthesize
the current state-of-the-art research on developing, employing,
and assessing IoT technologies from the data convergence
perspective for better assessment and response to issues
related to human health and comfort. The current review
aims to highlight the key scientic and technological questions
that form an integrated vision for future directions in the eld.
We categorize three spatial scales for assessments: urban,
building, and human scales (Figure 1). Based on the published
literature for each scale, we evaluated the level of development of
the eld. In assessing air quality and thermal comfort in the
urban (Section 2) and building context (Section 3), we focus on
mapping the environment that causes an exposureof a
population subgroup. Centered on assessing personal comfort
and exposure directly at the point of contact with a specic
individual, Section 4 addresses the emergence of the Human-
CentricIoT sensing that overcomes the one-size-ts-all
solutions promoted by traditional guidelines and measurement
approaches. Section 5 addresses challenges related to data,
technology, academic, and industry silos, privacy security, and
human and data interaction. Although we mention issues
involving privacy detail review of this complex legal topic is
outside the scope of this review. Technological and scientic
needs, challenges, and opportunities for cross-disciplinary
advancement in short- and long-term future research efforts
are further discussed in Section 6. We summarized our
conclusions in Section 7.
Schematic of IoT sensing applied to the built environment at the urban, building and human scales. The three scales encompass variations of
environmental parameters, while the human scale also includes biometric (physiological) and behavioral data.
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2 IoT sensing applied to urban scale
Urban scale sensing network in the city-state of Singapore
covers 142 km
(Haze - The National Environment Agency,
About Singapore), with the singleair quality measurement
stations, the San Francisco Bay Area air quality network
covers 47.6 km
(Wikipedia contributors 2022, BAAQMD -
Air Quality Data)per station while the state of Nebraska air
quality monitoring network covers 10543.7 km
(NDEE home)
with the single station. These examples depict challanges in
precisely dening urban scale as a particular surface area or
Motivated by pressing environmental issues such as air
pollution and thermal stress, this section discusses the
emerging IoT technologies applied to air quality and thermal
comfort sensing at the urban scale. In addition to summarizing
the current state-of-the-art knowledge, issues related to the
granularity of sensing, eld calibration of sensors, existing
methods for the IoT-based description of the urban
environment, and the utility of collected information to
improve air quality pollutants and heat exposure are reviewed.
Lastly, we discuss the development of air quality exposure and
thermal comfort models while highlighting the need for future
2.1 Outdoor IoT sensing of air quality
Increasingly frequent episodes of elevated air pollution across
different cities have evoked new questions related to the
concentrations of urban air pollutants and their spatio-
temporal patterns. Traditionally, information about human
environmental exposure outdoors has been derived from the
data gathered from governmental or environmental weather
stations (Baxter et al., 2013;Özkaynak et al., 2013). Such
information typically comes from gravimetric techniques and
high-grade optical and chemical analyzers capable of integrated
or longitudinal detection of a bouquet of ambient air pollutants.
Collectively, this approach has offered important insights into
ambient air quality that have been used to regulate air pollutant
emissions, to propose control measures, and evaluate their
efciencies. Also, such monitoring stations have been long
utilized as a benchmark for epidemiologists and public health
authorities to produce formal guidelines for population
exposures to specic air pollutants (Ott and Roberts, 1998;
Xie et al., 2017;Dias and Tchepel, 2018;Caplin et al., 2019).
However, traditional monitoring of urban air quality faces
several shortcomings. First and foremost, the conventional
monitoring of urban air is stationary, sparse, and typically
remote from human activities, and as such, it poorly
resembles air inhaled by people (Miller et al., 2007;McKone
et al., 2009;Goldman et al., 2010;Harrison et al., 2015;Pearce
et al., 2016). This has been conrmed in studies that deployed
wearable monitors and samplers for measuring PM
et al., 1996;Scapellato et al., 2009;Broich et al., 2011), PM
(Andresen et al., 2005;Crist et al., 2008;Steinle et al., 2015), PM
(Williams et al., 2000;Johannesson et al., 2007;Velasco and Tan,
2016), CO
(Gall et al., 2016), CO (Huang et al., 2012), NO
et al., 2017) and various volatile organic compounds (Rotko et al.,
2000;OConnell et al., 2014;Manzano et al., 2018). Failure of
traditional approaches in representing the air pollution gradients
at the urban, neighborhood, and parcel of land level points to a
need for increased measurement granularity. Second, the high
cost of conventional monitoring equipment (Clemitshaw, 2004),
which includes costs related to instrument maintenance, data
collection, and processing impedes their ubiquitous deployment.
Finally, the information acquired is typically used by experts only
and there is a lack of accessibility and interpretation of the
information that supports citizen activities.
With the recent emergence of low-cost IoT sensors applied to
urban scale, an opportunity has opened up to make use of
affordable, accessible, robust, and non-expert friendly
solutions. Dozens of projects have investigated the application
of IoT sensing in urban environments, pointing toward the need
for a paradigm shift in air quality monitoring that creates new
information services tackling human needs, management of
urban spaces, and environmental policies (Kumar et al., 2015;
Castell et al., 2017;Schneider et al., 2017;Morawska et al., 2018).
The specic goals are set at rened spatio-temporal air pollutant
mapping of urban spaces in order to 1) pinpoint localized air
pollution hotspots, 2) improve source apportionment, 3)
enhance assessment and predictive models for air pollutant
exposures. In addition, easy-to-use ubiquitous IoT sensing
technologies can raise citizen awareness and enable both
professionals and non-experts to acquire insights.
To date, the air quality IoT sensing deployment at the urban
scale has been mostly based on the high placement density of
stationary IoT sensors in areas commonly occupied by humans
(Gao et al., 2015;Moltchanov et al., 2015;Castell et al., 2017;
Zikova et al., 2017;Ahangar et al., 2019;Bulot et al., 2019;
Johnston et al., 2019). These studies conclude that optimal
granularity of low-cost IoT sensors can drastically improve 2D
and 3D air quality mapping, which is essential information to be
translated to urban planners, building designers, the general
public, and other stakeholders. Without optimal sensing
granularity that results in considerable spatial data gaps, it is
impossible to accurately map the urban air qualitya challenge
that can be overcome by combining the crowdsourced
measurements with model data with comprehensive spatial
coverage (Schneider et al., 2017). Other deployment methods
include IoT sensor installation on mobile sensing platforms such
as bicycles, cars, buses, and trams that can improve spatial
coverage (Devarakonda et al., 2013;Mead et al., 2013;Castell
et al., 2015;Hasenfratz et al., 2015;Lim et al., 2019). While
economically more appealing due to the signicantly reduced
number of sensors, the mobility of the platform in conjunction
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with a prolonged response time of typical sensors can cause large
signal distortionan issue that can be overcome through the
application of active sampling that employs pumps/fans as
actuators (Arre et al., 2016). Finally, urban air pollution
mapping has also been conducted by means of wearable IoT
sensors and smartphones (see Section 4.2)(Zhang et al., 2017;
Nyarku et al., 2018;Ueberham and Schlink, 2018).
Despite the rapid progress in method development for IoT
sensors in an urban context, several challenges persist. First, the
identication of air pollution hotspots is limited as both scientic
literature and regulations lack approaches to provide optimal
spatial sensor granularity and placement across different urban
typologies (Castell et al., 2017;Schneider et al., 2017). In addition,
a disconnection between urban and building scales impedes our
ability to better design and operate buildings. For example,
building ventilation standards such as ASHRAE 62.1 requires
a two-step investigation of outdoor air quality1) compliance
verication with the national outdoor air quality standards, and
2) an observational survey of the building site (American Society
of Heating and Engineers, 2016). But for 1) the verication is
based on measurements only at remote governmental stations
and for 2) the building site survey lacks guidance on the type,
duration, and location of measurements. Additionally, once the
IoT-based information is acquired at the urban scale, it is
necessary to trigger the right chain of actions, either via a
feedback loop to encourage human actions and/or through an
IoT gateway that links a cloud platform with automated
controllers and devices. At present, methodologies on how to
utilize that information are sparse, resulting in enormous data
sets that are heavily underutilized (Zanella et al., 2014). The other
challenges include a need for a framework to integrate different
types of existing air quality sensors into a single monitoring
network and a lack of standardized protocols that specify the
required sensor robustness and data quality (Morawska et al.,
2018). Lastly, the spectrum of air pollutants that should be
monitored by IoT sensors is relatively vague with regard to
environmental health knowledge. While it is neither practical
nor technically feasible to measure all the air pollutants relevant
to humans, future research should examine how to optimize the
types of sensors to capture relevant air quality indicators.
2.2 Outdoor IoT sensing of the thermal
Similar to air quality monitoring in urban areas, the
information regarding outdoor thermal environments is often
derived from the stationary and at times remote, weather
stations, neglecting the signicant intra-urban variability of
temperature, humidity, wind and radiation exposure (Fenner
et al., 2017a). The advantages of IoT-based sensing in addressing
such shortcomings are indisputable: crowd-sourced
measurements 1) provide real-time data from various cities
around the globe (inter-urban variability in urban temperature
(Wörner et al., 2014), 2) span a wider range of spatial and
temporal distributions within the built environment (intra-
urban variability in thermal environment (Meier et al., 2017),
3) overcome the high and centralized costs for installation and
maintenance over time (low-cost sensing and/or Web
2.0 technologies (Dayan and Hartley, 2013;Young et al.,
2014), and 4) provide dynamic information regarding heat
exposure and the population impact of thermal comfort
(Grasso et al., 2017).
Accordingly, in the last few years, several studies have taken
crucial steps in tackling the challenges in IoT thermal sensing in
the built environment. Previous research was mainly focused on
monitoring of environmental parameters primarily temperature
and humidity and largely divided into the employment of
stationary or mobile sensors, with only a few studies
combining the two to span a larger spatial and temporal
distribution to improve accuracy (Muller et al., 2015;Shi
et al., 2021). Among stationary amateur thermal sensors
applied outdoors, there has been the emergence of low-cost
citizen weather stations with connectivity to smartphones,
local Wi-Fi networks and cloud in real time. Such units have
become mainstream consumer peripherals in recent years with
tens of thousands now deployed in cities across the world,
including the United Kingdom (Chapman et al., 2017;
Chapman and Bell, 2018), Germany (Meier et al., 2015;
Fenner et al., 2017b;Meier et al., 2017), France (Napoly et al.,
2018), Russia (Varentsov et al., 2021), and the Netherlands (de
Vos et al., 2017). The crowdsourced datasets have also been
combined with high-resolution data on urban form and fabric to
provide more in-depth analyses of urban design impacts on
thermal environments (Potgieter et al., 2021); or compared
with remotely-sensed measurements of urban thermal
environments globally (Venter et al., 2021) demonstrating the
shortcomings of satellite data for urban heat risk assessments.
Although the low cost and ease of use of citizen weather
stations have promoted their widespread use, they are largely
focused on urban temperature and humidity as a proxy for
thermal comfort, while other environmental parameters (such
as radiation and wind speed), as well as physiological and
behavioral factors, are often neglected. Several efforts are
emerging to crowdsource urban wind (Droste et al., 2020;
Chen et al., 2021), focusing on quantifying the uncertainty in
wind speed data based on the realistic application of sensors in
the built environment. Overall, such quantication of bias
appears to be one of the key challenges of using citizen
weather stations. Bell et al. (2015) provided a detailed
assessment of data accuracy and concluded that any
application of such low-cost sensing monitoring stations will
require a quality-control system capable of removing gross
errors, correcting instrument bias, and providing an
uncertainty estimate, which is collectively needed to ensure
effective outdoor IoT sensing of the thermal environment.
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Responding to this need, more holistic quality-control measures
for crowdsourced air temperature observations are recently
developed (Fenner et al., 2021), enabling a more consistent
use of citizen weather station data in worldwide applications.
IoT platformsreduced size and cost further enable
distributed and mobile deployments at the urban scale.
Several mobile sensing methodologies deployed to date can be
categorized in four major formats: 1) portable amateur weather
stations, 2) smartphones, 3) vehicle-based sensors, and 4)
wearable devices (Section 4). It is worth noting that compared
to the IoT air quality measurements (Section 2.1), the portable
and smartphone sensors for thermal comfort are distinguished
from wearables, as the latter can provide physiological
parameters, such as heart rate and skin temperature,
corresponding to the thermal environment.
In the rst category, the National Science Experiment in
Singapore (low-cost wireless SENSg devices (Wilhelm et al.,
2016) is arguably the largest deployment of portable amateur
weather stations with 50,000 sensors employed in Singapore for
the assessment of thermal comfort among various other
objectives (Monnot et al., 2016a;Happle et al., 2017).
However, the data regarding the thermal environment has not
been extensively utilized, perhaps due to measures required for
data quality controls in dynamic use. Second, temperature and
pressure obtained from smartphone batteries are proposed to
obtain a spatial and temporal map of air temperature in cities
(Overeem et al., 2013;Pape et al., 2015;Droste et al., 2017;
Martilli et al., 2017). Although this methodology provides an
unprecedented dataset on city-scale thermal environment, the
uncertainties of data collection are of concern and increase in
cities with more extreme weather and higher precipitation when
the smartphone is most likely to be held enclosed (Martilli et al.,
2017). A closer comparison of mobile measurements with
scientic-grade sensors reveals that, even when smartphones
are exposed to ambient air, uncertainties are increased with
higher humidity (where the smartphone hygrometer is
saturated) and bias corrections are needed for Sun exposure
and high wind speeds (Cabrera et al., 2021), which is yet to be
developed for realistic and dynamic data measurements. In the
third category, mobile sensors mounted on vehicles, bicycles, and
public transit systems have been used to make strategic transects
through cities to observe thermal comfort variables, providing
valuable real-time data, particularly in the face of extreme events
in cities (Heusinkveld et al., 2014;Castell et al., 2015;
Anjomshoaa et al., 2018). Lastly, vehicle-based data, while still
understudied, are more comprehensive in obtaining behavioral
patterns and can be used to develop personal comfort models
given a rich dataset (Mahoney and OSullivan, 2013;Fugiglando,
2018). Overall, it is important to consider that if the spatial
distribution of thermal exposure in urban areas is of interest, data
collected using mobile sensors require post-processing in the
form of time-detrending and sensor lag correction (Häb et al.,
2015;Middel and Krayenhoff, 2019).
In addition to stationary and mobile IoT environmental
sensing, a few novel data-driven analyses can be noted which,
in combination with previously described methods, represent a
paradigm shift in thermal comfort assessments at the urban scale.
First, IoT technologies have enabled the assessment of thermal
comfort on human activity and behavior in the built
environment. For instance, in addition to environmental
measurements, Wi-Fi connectivity data in outdoor spaces
have been used to obtain real-time occupancy data and to
explain outside dwelling patterns modied by thermal
environments (Reinhart et al., 2017;Dhariwal et al., 2019).
Using Wi-scanners in public courtyards, Reinhart et al.
(2017) recorded occupancy patterns over 10-month in
addition to measurements of air temperature, relative
humidity, wind speed and direction, and solar radiation. The
thermal environment (characterized by biometeorological
thermal comfort indices) was found to strongly correlate with
lunch-time activity by regular users. These correlations suggest
that IoT measurements of the thermal environment outdoors can
be used by designers and planners to predict the spatiotemporally
differentiated use of outdoor areas. Second, in the last decade,
additional proxy datasets, such as social media data on Facebook
and Twitter have introduced an alternative approach to
crowdsensing urban heatwaves and the consequent impact on
human activity (Grasso et al., 2017). Lastly, methodologies in
urban climate informatics (Middel et al., 2022), such as
crowdsourcing urban form information in the WUDAPT
project (Bechtel et al., 2019) and crowdsourcing the Street
View Imagery (Juhász and Hochmair, 2016;Middel et al.,
2019) have enabled the city-scale calculation of thermal
comfort parameters such as sky view factor and mean radiant
temperature (MRT), which can complement low-cost IoT
sensing of air temperature and humidity implemented across
cities to achieve a comprehensive analysis of thermal comfort
Despite a great deal of progress, the assessment of thermal
comfort has been slow in harnessing crowdsourcing and IoT-
based technologies for data collection (Chapman et al., 2017).
Several challenges contribute to this fact. The rst challenge
pertains to the extent of the parameters that need to be
monitored: an accurate thermal comfort assessment requires a
range of environmental, behavioral, and physiological (Section 4)
parameters which are complex to include in one sensing unit. Air
temperature and humidity are far easier to obtain than radiation
exposure and wind speed. Additionally, thermal comfort is
dened as the state of mind(ASHRAE Standard 55) in
response to the thermal environment, and therefore, the heat
transfer in the environment cannot sufciently describe the
comfort for all(Nikolopoulou et al., 2001). The
characteristics of urban spaces and the mode of activity of the
occupants dramatically inuence the thermal comfort
perception, increasing the need for comprehensive, yet
accurate, data collection mechanisms that include subjective
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feedback. The second challenge relates to the ease of use for
widespread adoption of sensing units. The size of the devices,
power (cordless vs. corded) and the lifetime of the devices in the
eld, and more importantly, the data communication method
(wired vs. wireless) are crucial factors for long-term and scalable
employment. Ideally, the IoT-based thermal comfort sensor that
can record all relevant parameters is 1) of a size that is
employable in a non-intrusive way 2) able to log data with
sufcient storage for the long term, and 3) able to
communicate the data wirelessly with a sufcient frequency.
Lastly, even when all the above-mentioned conditions, the
cost must not be prohibitive. Large city-scale deployment of
IoT sensing for thermal comfort still faces substantial price
barriers, particularly in research.
3 IoT sensing applied to building scale
Since people spend around 90% of their time indoors
(Klepeis et al., 2001), building scale thermal environments and
air quality have proportionally the highest inuence on
occupantsthermal comfort and one if not the most
important inuences on air pollution exposure. Therefore, it is
absolutely critical to characterize the indoor environment. IoT
environmental sensing has the goal to enable an understanding of
the dynamics of building operations. This section summarizes
the capabilities of IoT sensing to characterize the thermal
environment and indoor air quality, sensing methodologies
for longitudinal evaluation of buildings operations, and the
eld of proxy sensing.
3.1 State-of-the-art continuous
measurement capabilities indoors
Several IoT platforms for monitoring air quality and thermal
environment have been deployed in residential and commercial
buildings (Edirisinghe et al., 2012;Abraham and Li, 2014;Kim
et al., 2014;Salamone et al., 2015;Ali et al., 2016;Scarpa et al.,
2017;Pantelic et al., 2018a;Carre and Williamson, 2018;
Coleman and Meggers, 2018;Idrees et al., 2018;Martín-Garín
et al., 2018;Parkinson et al., 2019a), educational facilities
(Palacios Temprano et al., 2020;Ulpiani et al., 2021),
industrial settings (Li et al., 2018), occupant exposure
assessments (Jackson-Morris et al., 2016;Kelly et al., 2017;
Curto et al., 2018;Pantelic et al., 2020) and on mobile
platforms (Jin et al., 2018a).
With regard to assessing thermal environments, indoor IoT
sensing platforms typically measure air temperature and relative
humidity and often lack the capabilities to measure
comprehensive sets of thermal environment indicators that
include air velocity or mean radiant temperature (MRT).
Monitoring air velocity is particularly important in spaces that
use elevated air movement as a means to achieve thermal comfort
(Zhai et al., 2015;Schiavon et al., 2017). Advancements in air
velocity measurements have been made with the development of
low-cost ultrasonic velocity sensors (Ghahramani et al., 2019b)
and the integration and deployment of 2D hot-wire
anemometers in indoor sensing platforms13. With the
increasing popularity of high-rise glass towers, radiative heat
transfer can make up approximately half of the heat transfer
between occupants and the surrounding indoor environment
(Teitelbaum et al., 2019), making MRT measurements critical for
indoor thermal comfort assessments. The black globe
thermometer provides a reasonable and affordable solution for
MRT measurements (Thorsson et al., 2007;Parkinson et al.,
2019a) but recent works have shown that black globes cannot
accurately measure MRT due to the signicant impact of airspeed
on the measurement (Guo et al., 2018;Teitelbaum et al., 2020a;
Teitelbaum et al., 2020b;Teitelbaum et al., 2022). Uncertainty of
MRT measurement is proportional to globe diameter and
depends on the convection regime (Teitelbaum et al., 2022).
When ambient air velocity is lower than 0.2 m/s, the black globe
sensor used for MRT measurement is in the mixed convection
regime that is challenging to account for using the correction
factor (Teitelbaum et al., 2022). Accurate measurement of MRT
on a building scale can be achieved with infrared thermal
scanning and mapping of the physical environment with
LiDAR (Light Detection and Ranging) technology (Houchois
et al., 2019).
With regards to air quality, the common air quality indices at
the building scale include CO, CO
, and radon. The current IoT sensors for PM
reasonable accuracy, within a factor of 2 from a reference, which
suggests their suitability for indoor air quality management;
however, their accuracy can be compromised if the dominant
source includes particles of the ultrane size range (Wang et al.,
2015;Manikonda et al., 2016;Singer and Delp, 2018;Wang et al.,
2020;Demanega et al., 2021). A more recent study showed very
good agreement between low-cost IoT PM
sensors with
scientic-grade instruments (Hegde et al., 2020;He et al.,
2021) especially after applying RH corrections (Tagle et al.,
2020;Zou et al., 2021). The CO
sensors generally have good
agreement with reference monitors (Demanega et al., 2021).
Measurement of PM
in most proposed platforms is based
on models that derive concentration from the same dataset as
(Kim and Oh, 2018), and not from direct measurement.
Although studies indicated that low-cost TVOC sensors can be
deployed indoors despite their poor quantitative agreement
(Moreno-Rangel et al., 2018;Demanega et al., 2021), the
common issue is that they cannot differentiate
physicochemical properties of hundreds of individual organic
compounds (Jackson-Morris et al., 2016;Kelly et al., 2017;Curto
et al., 2018) some of which are strongly linked to health effects
(Schieweck et al., 2018). O
sensors have been utilized in several
studies (Firdhous et al., 2017;Zhang et al., 2021). Low-cost O
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sensors can be calibrated to measure concentrations encountered
in indoor environments (Pang et al., 2017) with recent studies
addressing machine learning-based learning-based calibration
improvements (Ferrer-Cid et al., 2020). A review of low-cost
IoT sensor operation by the Air Quality Sensor Performance
Evaluation Center suggested that O
sensors are able to provide
useful qualitative and quantitative measurements when used off
the shelf (Collier-Oxandale et al., 2020). Measurement of indoor
radon with IoT environmental sensing has been recently
proposed (Blanco-Novoa et al., 2018;Pereira et al., 2020)CO
sensor detection range is a topic that needs further research
(Nandi et al., 2019) and both metal-oxide and optical-based
sensors need improvement on the lower end of detection limit
(Nandy et al., 2018). At the moment a low-cost IoT CH
O sensor
capable of measuring CH
O is under development (van den
Broek et al., 2020). NO
was previously measured (Coleman and
Meggers, 2018;Collier-Oxandale et al., 2020), but general
methods for calibration are still missing for these sensors
deployed on a building scale. Based on scientic evidence it
can be argued that IoT sensing of indoor chemistry is still in its
early stages.
Existing IoT sensing platforms can measure the basic
parameters of the indoor air quality matrix (Pantelic et al.,
2018b). Various studies have used different evaluation methods
to assess IoT environmental sensorsaccuracy and reliability.
The sensing drift of low-cost sensors over time is yet another
unknown that requires further investigation (Chojer et al.,
2020). Nonetheless, the quality and variety of the low-cost
sensors have been increasing over the years, but the
development of standards and guidelines for testing
methods, sensing network accuracy, repeatability, and
reliability should be considered a priority (Schieweck et al.,
2018;Chojer et al., 2020). Further research efforts are necessary
to understand the relationships between single point sensor
accuracy (Rackes et al., 2018) and the whole network accuracy
(Parkinson et al., 2019a).
3.2 Characterization of indoor air quality
and thermal comfort
Monitoring air quality and thermal environment enable
characterization of buildings performance, which is useful for
management and benchmarking. Measurement with
conventional methods and instruments can provide a
temporal snapshot of building operation. Similar to the urban
scale, limitations of conventional methods include cost, the time
necessary to measure a large number of points in time and space
(Parkinson et al., 2019a;Parkinson et al., 2019b), size and noise of
equipment, and accessibility of sampling locations (Kumar et al.,
2016). The low-cost IoT sensing has the potential to overcome
some of these limitations and provide a longitudinal evaluation of
building performance with rened spatio-temporal
representation. Certication schemes offer credits for the
implementation of continuous monitoring (RESET, 2022;
WELL, 2022) and represent one of the key drivers of the
widespread application of IoT sensing (Parkinson et al.,
2019a;Parkinson et al., 2019b). The development of
continuous monitoring guidelines for temporal and spatial
sensing resolution has become one of the critical research
priorities (Heinzerling et al., 2013;Parkinson et al., 2019b;
Licina and Bhangar, 2019). At the moment the set of
indicators required by standards is not covered by available
IoT sensing platforms which present another area that needs
Some of the most used current indoor air quality continuous
monitoring guidelines/standards are summarized in Table 1.
These consider air pollutants that need to be measured, the
accuracy of equipment, sensor placement, measurement time,
and analytical tools. Guidelines summarized in Table 1 describe
different approaches, data collection methodologies, instrument
requirements, and various analytical performance indicators.
The development of these guidelines is largely expert-based
and usually not scientically validated. Regarding sensor
density and placement, several research studies explored
sensing densities from ~9 m
up to ~100 m
per sensor (Ali
et al., 2016;Pantelic et al., 2018a;Li et al., 2018;Parkinson et al.,
2019b;Clements et al., 2019). Although some of these studies
demonstrated signicant spatial variation of measured
parameters, their results cannot be used for the validation of
existing continuous monitoring guidelines/standards due to
signicant differences in objectives and sensor placement
methodologies. The failure of the well-mixed assumption in
some environments (Pantelic and Tham, 2013) brings
additional potential complexities to the sensor granularity and
placement problem. Other location recommendations for
example to place a sensor 1 m away from doors, windows,
and diffusers are simply practice-based, and not grounded in
research. The upscaling sensor deployment sensors have to be
named systematically so that data collected in multiple buildings
can be correctly understood and interpreted. Naming schemas
are a critical aspect of integration and are addressed later in
Section 5. Measurement frequency is another variable where the
recommendation of 1 min or 10 min sampling frequency is not
well-grounded in research. From the certication perspective as
discussed earlier the current IoT technology cannot capture all
relevant parameters. As a result, for proper characterization of
the indoor environment, we still need to couple continuous
monitoring with conventional industrial-hygienist practices
like measuring individual VOCs. This also extends to the
thermal environment and the necessity for the maturation of
measurement technology. Considering the speed with which the
industry adopts green building certication schemes and the
impact they have on the built environment, the development of
science-based validated building benchmarking should be made
a top priority.
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Thermal environment and thermal comfort studies that
address the application of IoT sensing on the building scale
can be classied into two groups. In the rst group of studies, the
objective of continuous monitoring of the thermal environment
is to provide information as an input parameter to estimate
thermal comfort (Parkinson et al., 2019b;Valinejadshoubi et al.,
2021). Although the effectiveness of PMV/PPD models to
estimate occupant satisfaction in the eld using thermal
environment measurement can be questioned (Cheung et al.,
2019), this approach is used to assess building operation
compliance with certication schemes and standards (Revel
et al., 2014a;Parkinson et al., 2019b;Wang et al., 2019). The
second group of studies surveys individuals alongside thermal
environment monitoring to obtain assessments of thermal
TABLE 1 Summary of guidelines/standards available for deployment of air quality sensors for continuous monitoring in buildings.
Arc scoru ASHRAE/
RESET v2.0 WELL v2 LEED v4
Quantities to
COutdoors: None COutdoors: EPA
COutdoors: None COutdoors*: PM
, CO, NO
COutdoors: PM
CIndoors: CO
, VOC CIndoors: PM
CIndoors: PM
CIndoors**: PM
, CO,
CIndoors: PM
CT, RH - suggested (not
Data Collection CPhysical measurement CContinuous
C3 levels accuracy CAccuracy dened for each
CPhysical measurement
COccupant satisfaction survey
min 25% response rate
CData lossnot
CCloud Interface; Data
Structure; Resolution;
C10 min minimal collection COccupant satisfaction
satisfaction survey
C3 levels of data loss
density and
CIn locations representative of
all occupied spaces
locations in space
(not specied)
C80% of occupants
C325 m
per sensor; or one
sensor per oor at
1.11.7 m height (if oor is
CNot specied
CWithin the breathing zone CDucts CAll space types
CDuring occupied hours C500 m
per sensor
CUnder typical minimum
ventilation conditions
CSpecied measurement
CNot specied C7 days minimum CContinuous CContinuous CNot specied
Analytics tools CArc performance score:
Perceived occupant
satisfaction and
data to occupants -
not encouraged
CCommunicating data
to occupants via
CCommunicating data to
occupants via display
measurement against
different levels of
thresholds during
occupied hours
CVariance CO
and VOC
concentration >95% of the
time normalized for oor area
and occupancy
62.1 compliance
CCalculating daily
averages from IAQ
monitors during
occupied hours
measurement against
thresholds at 3 levels
during occupied hours
CConsider outdoor air
quality (2 levels of
indoor conditions)
CTime weighted
CConsider data
loss (>20%)
CAnnual Report
CExceedance CThresholds standard/
high performing
Outdoor*: WELL, does not explicitly ask for continuous monitoring on the building, but does require continuous information from the weather station within a 4 km radius; WELL**
requires monitoring of 3 of listed pollutants.
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comfort and develop personal comfort models (Kim et al., 2018a;
Kim et al., 2018b;Kim et al., 2019). Utilization of this approach
demonstrated that personal comfort devices can increase
occupantssatisfaction by over 95% (Kim et al., 2019). The
use of infrared thermography for real-time monitoring of
personal thermal comfort (Revel et al., 2012;Revel et al.,
2014b) represents another opportunity for human-centric
assessments of comfort at the building scale, but so far has
mainly relied on a single parameter (e.g., skin temperature)
tracking of thermal environment and is yet to be integrated
with comprehensive IoT thermal environment monitoring.
Published studies point towards the development of
personalized thermal comfort models, but actual use in the
building operation of these models is still unclear. The
combination of thermal environment mapping and survey of
occupant satisfaction is an important research area that can
improve the operation of existing and new buildings by
providing optimal thermal comfort setpoints.
3.3 Proxy sensing
When several environmental parameters are measured in
space and that information is used to infer knowledge about
some other property or process, that can be considered proxy
sensing or context-aware computing. Prior research suggests that
humans have a unique environmental footprint, including CO
(Persily and de Jonge, 2017), particulate matter and bioaerosols
(Bhangar et al., 2016;Licina et al., 2016), various volatile organic
compounds (Tang et al., 2016), as well as sound (Li et al., 1991;
Sabatier and Ekimov, 2008), that can form a basis for the use of
context-aware computing to detect occupants and their activities
in the space. Occupants proxy sensing was explored using a
combination of CO
, Lux, T and RH (Candanedo and Feldheim,
2016), passive infrared, TVOC, and CO
(Pedersen et al., 2017),
only CO
(Dong et al., 2010;Jin et al., 2018b), CO
, energy
consumption of lighting, and energy consumption of appliances
(Ryu and Moon, 2016), CO
, RH, T and pressure (Chen et al.,
2016), passive infrared, CO
and RH (Han et al., 2012), PM
, T and RH (Jeon et al., 2018). The number of occupants in a
building is an important parameter for understanding space
utilization and energy use patterns. The presence of people
can be measured directly with the surveys, based on radio
frequency signals, passive infrared, ultrasonic, video cameras
or global positioning information, Bluetooth, wireless local
area network signals (Yang et al., 2016), but all these methods
require signicant labor and are usually very costly to perform,
especially at the large scale. The use of proxy sensing with
environmental IoT sensors has the potential to signicantly
reduce the cost of these processes. Besides the detection of the
number of occupants in the space, Ghahramani et al. (2018) used
environmental sensing to detect occupantsworkplace social
interactions. The challenge remains to demonstrate that proxy
detection of the number of occupants and type of social
interactions can be generalized and upscaled.
4 IoT sensing applied at the human
scaleWearable IoT sensing
Unlike stationary IoT sensing which often aims to assess
exposures of a population subgroup, wearable devices,
i.e., smart electronic devices that can be worn or be integrated
into clothing (i.e., standoff sensors), are at the heart of new
capabilities that pervasive connectivity can bring at the human
scale. Currently, various health- and tness-oriented wearables are
commercially available which can sense, store, and track the
temporal variability in human activities (such as steps and
locations) and biometric data (including heart rate, perspiration
levels, and oxygen levels in the bloodstream). The potential of
wearables in promoting positive health outcomes and enhancing
human comfort and well-being is indisputable and scientic
research has been able to evaluate and validate various wearable
technologies. This section aims to review such applications in
wearable IoT sensing, particularly focused on air pollution
exposure, physiological monitoring, and thermal comfort.
4.1 Physiological and health monitoring
It is likely that the impending increase in air pollution and
urban overheating in many cities around the globe will
discourage outdoor physical activity and therefore impact the
health of individuals (Chan and Ryan, 2009;Paulin and Hansel,
2016;Nazarian et al., 2021b;Romanello et al., 2021). Generic
recommendations and guidelines have been developed to address
these concerns, but given the fact that environmental health
impacts vary greatly for individuals, the adoption of general
guidelines often does not optimize the health and performance of
every individual and even can at times induce negative health
implications (Tan et al., 2015). As there is extensive variability
intolerance to a given absolute level of stress, individual
monitoring of physiological status has the potential to
optimize health and performance (Piwek et al., 2016;Notley
et al., 2019). The development of individualized guidelines for
environmental health through IoT sensing at a human scale that
accounts for intrinsic and extrinsic factors is envisaged.
In recent years, the use of smart devices is becoming more
prevalent to provide users with personalized data for self-
diagnosis and behavior alterations to optimize health and
performance (Piwek et al., 2016). In occupational settings,
smart devices can be used to monitor workers in real-time
and therefore protect them from excessive heat strain (Notley
et al., 2018), which is particularly useful for high-risk workers.
These smart devices offer unprecedented opportunities to collect
rich sources of data to guide interventional strategies.
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While the proliferation of smart devices at times serves as
motivational tools to initiate health and performance programs,
accuracy can be lacking, and more importantly, there is an
absence of validated models to translate the raw data to
actionable advisory for the users. Dias and Paulo Silva Cunha
(Dias and Cunha, 2018) reviewed important aspects of smart
devices for health, listing the state-of-the-art wearable vital signs
sensing technologies, including their system architectures and
specications. There are also a number of concerns about the
safety, reliability, and security of using consumer wearables in
healthcare (Piwek et al., 2016), which are similarly applicable to
the use of wearables for environmental monitoring at the human
scale. To this end, the Electronic Patient-Reported Outcome
Consortium proposed a framework by listing a set of
recommendations in relation to the selection of and
evidentiary considerations for wearable devices to ensure
adequate precision, accuracy, and reliability of data collected
(Byrom et al., 2018), which to date are not widely used in
wearable environmental sensing.
Prospectively, as smart devices become more accurate, more
sophisticated modeling techniques will also be harnessed. For
example, an articial intelligence platform solely based on patient
data is used to prospectively guide drug dosage to the patient with
prostate cancer, resulting in durable response and no disease
progression (Pantuck et al., 2018). Instead of using population-
based algorithms, this platform only harnesses data from the
individual of interest to enable small data set-driven
optimization. This could be particularly amenable to human
performance and health optimization, considering the fact that
human responses to a given stressor are varied. In addition, the
prole of an individual is expected to shift during the course of
time. The ability to dynamically modulate inputs to affect
optimal outcomes therefore will drive a powerful and unique
ability to truly personalize health and performance in a sustained
4.2 Wearable sensors for air quality
Direct assessment of personal level exposure accounting for
the dynamic nature of human activities has been identied as a
central area in air pollutant exposure research (Steinle et al., 2013;
Health Effects Institute, 2015;Marćet al., 2015;Borghi et al.,
2017;Spinelle et al., 2017). Portable air quality monitors with
local data storage have been previously used in air quality studies
to quantify individual exposures (Steinle et al., 2013;Marćet al.,
2015;Borghi et al., 2017;Spinelle et al., 2017). The use of portable
air monitors with local data storage is associated with a limited
number of study participants, laborious manual work, frequent
data download, cataloging, and keeping of activity diaries which
prevents real-time use of the data (Steinle et al., 2013;Marćet al.,
2015;Borghi et al., 2017;Spinelle et al., 2017). On the contrary,
wearable IoT sensors represent the progression of technology for
personal exposure estimation and offer a promise for automation
of environmental data collection, simultaneous GPS tracking
(Predićet al., 2013;Antonićet al., 2014;Wong et al., 2014;
Zhuang et al., 2015;Tian et al., 2016;Zhang et al., 2017;Yang
et al., 2018), and upscaling to citizen scientist data collection
strategies (Piedrahita et al., 2014;Nikzad et al., 2012;Jerrett et al.,
2017;von Schneidemesser et al., 2019). Improvements in
personal exposure assessment were suggested by combining
(wearable/portable) behavioral sensors with wearable sensors
that capture environmental and biometric data (Fletcher et al.,
2014;Hu et al., 2014) as illustrated in Figure 1. A recent survey of
occupant needs rated wearable PM2.5 sensors just behind
wearable air cleaners (Wang et al., 2021) indicating an
increase in population awareness of the importance of
environmental impacts on health.
There has been signicant development of wearable IoT
environmental sensors in recent years. Deng et al. (2016)
developed a portable wireless VOC sensing kit and
demonstrated its application for measuring personal exposure.
Zhang et al. (2017) show the application of wearable IoT sensors
for PM
monitoring integrated with location and temperature
data and demonstrated how they can be used for IAQ mapping of
underground stations. Hojaiji et al. (2017) develop wireless
wearable PM and N
sensors for personal exposure
measurement. Xu et al. (2019) developed a wearable PM
sensor and demonstrated how crowdsourcing can be used to
detect large variations of air pollution in the city. Zhong et al.
(2020) used wearable CO
sensors for monitoring and
forecasting the indoor environment. Cureau et al. (2022)
developed and implemented a wearable wi-connected
system for mapping urban environments from the pedestrians
perspective as an aid to city planners. Kane et al. (2022)
developed wearable IoT PM sensors for personal exposure
measurement and modication of peoples behavior based on
the results of communication. Kortoçi et al. (2022) showed how
wearable air quality monitors provide insights into personal
pollution exposure and the micro-climates of the city
measuring PM, CO, NO
, and O
. Besides insight into
personal exposure through crowdsourcing wearable air quality
sensors is a useful tool in mapping air pollution hot spots on the
urban scale (Buehler et al., 2021;Hernández-Gordillo et al., 2021;
Cureau et al., 2022).
In a recent systematic review of the environmental
monitoring with the wearable sensor application, Salamone
et al. (2021) included 39 papers that address air quality
monitoring among them 5 addressed wearable IoT sensors
and pointed out that air quality monitoring calibration is an
issue and not often reported. Hernández-Gordillo et al. (2021)
reviewed recent advancements in low-cost IoT wearable sensors
and pointed out that sensors still have challenges related to
sensitivity, selectivity, measurement accuracy, and drift.
Authors also concluded that further development of models
for data processing in addition to lack of community
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