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In: Unmanned Aerial Vehicles ISBN: 978-1-53618-900-1
Editor: Nicholas Barrera © 2021 Nova Science Publishers, Inc.
Chapter 2
ATMOSPHERIC CHEMICAL SENSING BY
UNMANNED AERIAL VEHICLES
M. P. Stewart* and S. T. Martin
School of Engineering and Applied Sciences & Department of Earth
and Planetary Sciences, Harvard University, Cambridge,
Massachusetts, 02138, US
ABSTRACT
Over the past decade, unmanned aerial vehicles (UAVs) have become
increasingly prevalent in commercial and consumer markets due to the
wide array of capabilities and possible applications. One particular niche
of this market is the use of UAVs for performing in situ chemical sensing.
This possibility has resulted in the emergence of a new subfield of
atmospheric research. In particular, rotary-wing UAVs have high
maneuverability and ability to perform stationary airborne measurements
at low altitudes within the planetary boundary layer. These capabilities
have afforded scientists and industry professionals new opportunities, such
as the ability to sample close to forest canopies, the ocean surface, and
indoors. Further opportunities in existing areas have also resulted from this
new technology, such as increased flexibility in studying vertical profiles
* Corresponding Author’s E-mail: matthew_stewart@g.harvard.edu.
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of chemical concentration and in sampling of fixed emission sources. This
chapter introduces to the capability of using UAVs in performing
atmospheric chemical measurements, the design of sensor and sampling
payloads, and a review of recent trends.
1. INTRODUCTION
Earth’s atmosphere consists of a gaseous mixture predominantly
composed of nitrogen (78.1%), oxygen (20.9%), and argon (0.934%). In
combination, these chemical species make up over 99.9% of the atmosphere
and have little temporal and spatial atmospheric variation. The remaining
0.066% of the atmosphere consists of trace gases. These gases are
disproportionately responsible for much of the reactivity and chemical
cycling in the atmosphere. Trace gases can also have important impacts on
climate. Examples of these impacts include global warming via absorption
of infrared radiation by CO2, the development of the ozone hole in the
Antarctic due to catalytic cycling of halogen radical species, and many air
quality-related ailments and deaths attributable to the atmospheric pollutants
such as NOx, O3, SOx, CO, and reactive organic species. Additionally, certain
trace gases act as precursors, which upon oxidation can form secondary
inorganic or organic aerosol particles as well as affects ozone and nitrogen
oxide concentrations. The disproportional impact of trace gases at low
mixing ratios illustrates the importance of developing methods capable of
studying the origin, dynamics, kinetics, and distribution of these gases in the
atmosphere.
The lowest region of the troposphere accessible to many UAV
technologies is commonly referred to as the planetary boundary layer (PBL)
(Figure 1). This layer can extend several kilometers from the ground. Flow
in the PBL is strongly influenced by surface interactions, responding to
surface forcings at a time scale of 1 h or less [1]. There are many types of
emission sources, each releasing a diverse arrays of chemical species. When
coupled with the effects of wind transport, diffusion, turbulence, surface
influences, and chemical reactions, this leads to complex distributions of
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chemicals within the PBL. Additional complexity occurs near to emission
sources and at land-atmosphere and water-atmosphere interfaces.
Concentrations of trace gases typically range from the parts per million,
such as carbon dioxide, to the parts per billion, such as ozone and nitrogen
oxide. Highly reactive, low-yield products are present at lower
concentrations. The dynamic and complex interactions of trace gases in the
lower atmosphere, coupled with low mixing ratios, presents many scientific
and engineering challenges to atmosphere scientists. These challenges are
present not only for trace gases but also for aerosol particles. Despite an
active history of aircraft measurements of aerosol particle number and mass
distributions, significant uncertainties still exist in the understanding of the
vertical distribution of aerosol particles near the surface and the
spatiotemporal variability in particle concentrations [2].
Figure 1. Depiction of daily time evolution of the planetary boundary layer. The
Federal Aviation Administration’s Part 107 rule height limitations are in overlay.
Atmospheric sampling techniques based on small unmanned aerial
vehicles (UAVs, <25 kg, regulatory breakpoint) might possibly
revolutionize many aspects in the coming years. For context, large UAVs of
>25 kg are capable of flying long distances and carrying heavy payloads.
However, such large UAVs are not practical in many cases for atmospheric
sampling at low altitudes. Large UAVs typically employ fossil fuel
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propulsion engines to operate and thus produce exhaust gases, which can
significantly impact ambient measurements. In contrast, small UAVs are
sufficiently light and low power that they can be operated on batteries, and
thus they can avoid producing exhaust gases. Consequently, small
combustion-free UAVs are preferred for performing in situ chemical
measurements.
Advances in fabrication, navigation, remote control capabilities, and
power storage systems have made possible the development of a wide range
of UAVs. Many UAVs can be utilized in various situations where the
presence of humans is difficult, impossible, or dangerous [3]. Previously,
the ability to predict how changes in emissions may impact gas
concentrations and aerosol particle number and mass distributions has been
limited by the spatial and temporal resolution of existing measurements
within the lower troposphere [2, 4, 5]. By attaching compact and lightweight
sensor payloads to easily portable UAVs, scientists can now perform in situ
atmospheric sampling of trace gases and aerosol particles in the planetary
boundary layer with an intermediate range of several kilometers horizontally
and several hundred meters vertically at resolutions of around 0.5 m
vertically and 1m horizontally [6, 7]. Small UAVs are highly maneuverable
and capable of hovering, effectively acting as airborne measurement
platforms, allowing monitoring of emission sources, adverse locations, and
low altitudes. Within the PBL, the vertical concentration profiles of aerosol
particles and trace gases can be complex due to air turbulence caused by
wind shear and temperature gradients [8]. Presently, there are few
measurements of aerosol particle and trace gas concentration gradients
within the lowest 300 m of the PBL. Accurate assessment of the degree to
which surface measurements represent the entire PBL is required to validate
remote sensing techniques for surface-air-quality predictions [9].
This chapter provides a survey of current knowledge related to
atmospheric measurements using UAVs. Topics include design criteria for
coupled sensing systems, best practices, relevant applications, and emerging
trends. This chapter draws upon and updates previous review articles such
as those of Schuyler and Guzman [10] and Villa et al. [11] with the latest
insights. A particular focus of this chapter is on the design, development,
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and applications, of UAVs for atmospheric research involving in situ
chemical sampling. There is emphasis on in situ chemical measurements,
although the measurement of meteorological variables is also discussed
because of the closing coupling to chemical kinetics and transport properties.
As this review outlines, application of UAVs in industry and academic
research is a burgeoning field. It has expanded rapidly over the past decade,
particularly in the relation to environmental science, ecology, and forestry.
Given the utility of UAVs for performing in situ atmospheric measurements
and the potentially countless applications, interest in this field is expected to
grow steadily in the coming years.
2. BACKGROUND CONCEPTS
Identifying and quantifying gas flux exchange between the biosphere
and atmosphere is necessary to improve the understanding of the impacts
these gases can have on human health and environment processes. For
example, quantifying the impact of gases like CO2, CH4, NOx, and NH3,
which are key contributors to global warming, can provide useful
information for developing climate change mitigation strategies. Similarly,
assessing the contribution of O3 to crop damage can help to improve crop
yields [12]. Other important applications for trace gas measurement include
examining gas pipeline leaks [13], indoor and outdoor air quality [14, 15],
greenhouse gas emissions from agricultural production [16], livestock
emissions [17], arson investigations [18], and fugitive emission monitoring
[19]. The topic of trace gas measurements by UAVs has drawn increasing
interest in recent years. Cost reductions in sensing technology along with
new and improved measurement techniques has reduced the barriers to entry
for performing gas measurements. Furthermore, increased awareness of
anthropogenic climate change and human influences on the environment has
led to increased scrutiny by the public and regulatory bodies towards
industrial pollution [20, 21].
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2.1.
In Situ
vs. Remote Sensing
Both remote sensing and in situ chemical techniques have been
developed as methods for performing gas measurements from UAV
platforms [22, 23]. Remote sensing techniques leverage the use of optical
sensors operating at specific frequencies, usually within the infrared
spectrum for chemical detection [24]. Active systems contain are equipped
with a light source and measure the amount of that light that is reflected or
backscattered from a target region. Passive systems make use of ambient
infrared emissions or sunlight to detect emitted or reflected light from a
target region. Light frequencies used by these devices are selected such that
they preferentially interact with the specific gas of interest. This preferential
selection helps to minimize interference from other atmospheric gases that
may have limited but non-zero absorption at these wavelengths. Aerosol
particles can also be studied by similar optical techniques [25].
In situ measurements tend to produce more accurate and reliable
measurements compared to remote sensing techniques but present additional
logistical and technical challenges [26-28]. They are also limited to the point
of measurement whereas remote sensing techniques can provide an average
result across a larger region. In situ gas sensors can be classified according
to the sensing mechanism [29]. The two main classifications are chemical
sensors (e.g., semiconductor, catalytic, electrochemical) and optical sensors
(e.g., photoionization, infrared).
2.2. Measurement Platforms
There is a long history of in situ atmospheric measurements using
analytical instruments placed on tethered and non-tethered balloons [26-28,
30], fixed towers [31-33], and research aircraft [34, 35]. Each approach has
advantages and disadvantages. Aerostats such as balloons and zeppelins
provide increased flexibility and can reach high altitudes, but they are
limited in resolution and maneuverability and can be a challenge to transport
or recover. Fixed towers are suitable for highly time-resolved gas monitoring
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at a fixed location but are expensive and sparsely located, thus inherently
lacking spatial resolution. They are also limited by tower height in the
capability to obtain altitude-resolved data. Furthermore, upscaling
inferences from tower measurements may not be representative due to
statistical bias arising as a result of suitable construction locations [36].
Flights on research aircraft provide greater spatial resolution but have poor
temporal resolution due to the high aircraft speed necessary to remain
airborne. In addition, they are prohibitively expensive for most
investigations and are often limited to a small operating window of a limited
number of total flights. Since aircraft must travel at high-speed and at
relatively high altitudes to comply with legal flight regulations, sampling
fixed locations or lower regions of the PBL is typically not feasible. Gas and
aerosol particle characterization with remote sensing via satellites has also
been achieved [37-39], but the spatiotemporal resolution is typically too low
to be of use below the large-urban scale and is limited by satellite orbits. A
comparison of measurement platforms is shown in Table 1.
Table 1. Comparison of chemical sensing measurements
Method
Horizontal
Resolution
Vertical
Resolution
Degree of
Availability
Payload
Capacity
Operating
Cost
Tower
Fixed
location
< 1 m (up
to tower
height)
Limited
Large
Medium
UAV
< 1 m (up
to 5 km)a
< 1 m (up
to 500 m)b
High
Limited
Low
Aerostat
N/A
30 m (up to
30 km)
High
Limited
Low
Aircraft
0.1 - 1 km
0.1 km
(limited)c
Moderate
Moderate
Medium
aDepends upon regional airspace regulations (e.g., line-of-sight requirement) and limits of the
UAV-controller communication link. bVaries according to regional airspace regulations and
source code restrictions of commercial UAV vendors. cLimited by airspace regulations about
flight altitudes. Table is adapted from ref [156].
Small UAVs operate at an intermediary scale with respect to both
temporal and spatial scales of these other established platforms (Figure 2).
UAVs can simultaneously operate several kilometers from a ground-based
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controller and monitor fixed locations up to the maximum flight time of the
UAV. Both fixed-wing and rotary-wing UAVs are capable of surveying
environments at lower altitudes than research aircraft. Sensing systems of
many types can be coupled to UAVs to perform atmospheric measurements.
This chapter has a focus on rotary-wing UAVs for chemical sensing in
reflection of author familiarity, but both fixed- and rotary-wing UAVs offer
important capabilities. Rotary-wing UAVs have the benefit of high
maneuverability and the capability of hovering. Rotary-wing UAVs can also
survey areas that are difficult or danger to reach for humans, such as within
the canopy of a forest, at the outlet of power plant smokestacks, or close to
the surface of a volcano. Rotary-wing UAVs in common with balloons and
tall towers can obtain vertical profiles at fixed locations, which is difficult
for fixed-wing platforms.
Figure 2. Comparison of fixed tower, UAV, and aircraft/remote sensing measurements
in relation to relevant spatial and temporal scales.
Many countries have adopted regulations for UAV flights. These rules
typically cover UAV classification, predominantly based on weight,
airspace and payload restrictions, operator licensing, and UAV registration
[40]. Motivations behind these restrictions include the safety of people on
the ground and aloft and security against the misuse of UAVs.
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3. DESIGN CRITERIA FOR PAYLOADS
Major engineering challenges presented by UAVs are limits in flight
time, flight range, and environmental robustness. Consequently, there are
three major trends in UAV development: (1) increased autonomy of
navigation and task, (2) increased flight time and distance, and (3) increased
size and power. These factors are strongly influenced by the payload
characteristics, such as weight, size, power requirement, environmental
robustness, and radio communication. Thus, for engineers and scientists
planning to use UAVs to conduct research or atmospheric analysis, care
should be taken when designing sensing payloads to maximize performance
within the constraints of a UAV platform. These design challenges can be
addressed in both the hardware and software to improve UAV
characteristics.
3.1. Establishing Design Requirements
Before a detailed design can be developed, the sensor or sampler (i.e.,
“payload”) requirements must be identified. Typical questions for
consideration are as follows:
(i) How long should the payload operate (e.g., full UAV flight
duration)? Does the payload need to operate prior to flight and, if
so, for how long?
(ii) How can the payload be mounted to the UAV? Should payload
removal be easy?
(iii) What are the environmental requirements of the payload (e.g.,
temperature, humidity, or wind)?
(iv) How can the electrical and vibration requirements of the payload be
met on the UAV? Are there concerns about radio or electrical
interference from other systems on the UAV?
(v) If the payload is not autonomous, what radio bands can be used for
control and data communications?
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(vi) Does the payload require any onboard computation related to the
measurement (e.g., Kalman filtering, machine learning, and so on)?
Once the functional requirements of the payload are clarified, the
constraints of a UAV platform must be kept in mind during development of
the payload. Payload components should be small, lightweight, and low
power so that the payload does not exceed the capabilities of the UAV
platform. Conversely, a UAV can be selected that is able to meet all of the
payload requirements. Custom-built UAVs may be required for specialized
applications in the case that the payload requirements cannot be met by
commercially available UAVs. Specific requirements for sensor and
sampling payloads are in the following sections.
3.2. Payload Design
The sensor payload must be designed to function adequately in the
anticipated operating environment. Factors include aerial-specific
constraints, environment-specific constraints, and platform-specific
constraints [41-43]. Aerial-specific factors are the same as any aerial vehicle
and include weight, power, and compactness [40]. Environment-specific
factors include meteorological variables such as temperature, humidity,
wind, and pressure. Platform-specific factors refer to those directly
associated with the UAV platform, such as vibrations and UAV-induced
changes to the wind field. Inadequate consideration of these factors when
designing a payload may reduce data quality by introducing electrical noise
(e.g., from vibrations or an unsteady power supply) or measurement bias
(e.g., from pressure and velocity distortions caused by the UAV-induced
wind field).
Aerial-specific constraints of a UAV are discussed in Stewart and
Martin [40]. Environment-specific and platform-specific of a payload are
presented in the following two sections.
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3.2.1. Environment-Specific Constraints
A sensor payload must be designed with the measurement environment
in mind. A sensor transduces a physical quantity into a measurable voltage.
Ideally, a sensor responds only to the targeted analyte. In practice, however,
if the measurement environment changes (e.g., temperature or humidity,
among other factors), the sensor response to a target analyte can change. A
goal of sensor development or selection is to minimize the impact of
environmental variables on sensor response. Even so, some influence must
be planned for in the protocol of payload data analysis.
For atmospheric applications, temperature can be an important influence
on sensor response. Humidity is also an important parameter that can affect
not only the intrinsic response of a sensor but also the electronics associated
with sensor operation. Pressure can influence sensors that respond to
volumetric flow as opposed to mass flow. Unlike a laboratory setting in
which these factors can be constant during an extended and controlled period
of time for measurements, these factors can change greatly on the timescale
of 10’s of seconds for UAV flight in the PBL. Unlike temperature, humidity,
and pressure, the surrounding wind field is typically of less importance on
sensor response. Since a UAV generates a strong local wind field, external
winds typically do not present strongly affect sensor response except at
relatively high wind speeds.
3.2.1.1. Temperature
Many types of sensors are sensitive to temperature changes [44-46]. The
effect of temperature on a sensor can be complex and multifaceted, and
sensor calibration under payload conditions should be done over the entire
expected range of UAV operating temperatures. This complexity stems from
the impact that temperature has on the transducer itself as well as on the
onboard electronics. A transducer’s specifications sheet typically states the
maximum error that can be expected upon variation of temperature. In
addition, the temperature range over which the sensor was designed to
operate is typically stated, and operation outside of this temperature can
damage the sensor. The temperature range is often linked to the stability of
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sensor materials, particularly the stability of the active element of the
sensing.
One of the most common effects of temperature on sensor performance
is thermal expansion [47, 48]. Many materials expand as they warm at a rate
proportional to the material’s coefficient of thermal expansion. A typical
response is on the order of 10-6 per°C. While this magnitude can appear
small, shifts in voltage output from the sensor can still be significant.
Mismatch in the thermal expansion coefficients of the different materials in
a sensor as well as in its packaging can induce mechanical stresses when the
materials are bonded together in the device structure. Such thermal and
mechanical stresses can lead to cracks in the device and delamination,
impacting sensor performance. Other temperature-induced effects
influencing sensor performance can include solder joint fatigue, bond-wire
fatigue, and electrical overstress. For small changes in temperature (i.e.,
within the stated range of the operating temperatures), deformations of all
types are predominantly elastic, and no plastic deformation occurs. The
implications are that a change in sensor response is reversible when the
temperature returns to the baseline value, and reliable sensor calibrations to
temperature are thus possible. For large changes in temperature, however,
heat stress can induce plastic deformation, resulting in a permanent shift in
sensor response even when the temperature returns to the baseline value.
To avoid these effects, in some cases an active temperature-controlled
sensor system can be desirable [49, 50]. This configuration requires the
operation of a heater or a cooler and possibly a fan within a feedback control
loop, increasing the complexity of the sensor payload. This configuration
also increases the weight and power requirements of the payload. When a
weak cooling requirement must be met, one strategy particularly favorable
to rotary-wing UAVs can be to divert wind from the rotor blades to increase
heat transfer from the payload to the surrounding air. The effectiveness of
this method, however, depends on the number or speed of the rotors, and
cooling is thus not independent of flight operations, which is an undesirable
complication. Another more straightforward but less reliable method to
protect a sensor payload from temperature increases due to solar radiation is
to install the payload on the underside of the UAV platform.
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3.2.1.2. Humidity
Humidity affects many sensing devices when performing gas
measurements [51-53]. Often, sensors have non-linearities regarding
relative humidity for low (<5% RH) and high values (>95% RH), which
should be taken into account during calibration or avoided during operation.
Some sensors undergo permanent drift or damage at extreme RH values.
One approach for UAV applications is to measure humidity onboard the
payload and make use of a sensor-specific calibration during data analysis
to adjust for changing humidity during a UAV flight. For some sensors,
however, this approach does not work well because the time response of the
sensor to humidity can be comparable to the time variation of humidity
during a flight, and the deconvolution for the effects of humidity on the
sensor can have significant uncertainty in these cases.
An alternative approach is to condition the humidity of the air that is
sampled before it reaches the humidity. Although this approach is
straightforward in a controlled laboratory settings, accomplishing humidity
adjustment for a payload onboard a UAV faces challenges of extra weight,
power, and volume. One effective approach can be to cool the incoming air
below its dew point, such that condensation removes a large proportion of
the humidity. The air is subsequently heated before reaching the sensor,
thereby significantly reducing the relative humidity compared to ambient
air. The main disadvantage of this method is the requirement of a cooling
system, which increases complexity, weight, and power required by the
payload. Another effective scheme can be the use of a passive filter or dryer.
One example is silica gel, which is a commonly used desiccant to keep
humidity low in packaging. A silica gel dryer typically consists of a
cylindrical tube through which a smaller cylindrical metal mesh runs down
its axial coordinate. The inside volume of the mesh allows air to pass through
unimpeded, and the outside volume is filled with desiccant. As air passes
down the axis through the dryer, water molecules diffuse laterally and are
trapped by the silica gel, reducing the humidity of the passing flow. Different
desiccants can be used, and they can influence what chemical species can
reach the sensing system. For example, most hydrophilic species present in
the flow are lost in a silica gel dryer. For more weight-constrained payloads,
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a membrane filter may be preferable. Membrane filters are thin, permeable
layers that allow targeted substances to diffuse through. Longer sampling
times, however, are often needed in this case.
3.2.2. Platform-Specific Constraints
UAV-induced vibrations as well as induced local winds lead to several
engineering challenges in payload design.
3.2.2.1. UAV-Induced Vibration
Many payload applications require high flight stability and low vibration
levels, including aerial imaging, electro-optical sensor measurements, and
wind measurements. Optical sensors of long effective pathlengths are
particularly affected by onboard vibrations [54]. Mechanical vibrations have
a negative impact on sensing systems and the inertial measurement unit
(IMU), which influences the operation of the entire UAV. Vibration differs
from shock mainly by its periodic and typically harmonic nature. The force
amplitudes of vibrations are also lower than those of shock. Sensitive
equipment may be able to withstand moderate forces during a brief shock
impulse but are not usually expected to continue regular operation while
experiencing this shock. Vibration forces are usually orders of magnitude
smaller in amplitude and longer in pulse duration. The requirement that the
electronic equipment operate appropriately in the presence of these
vibrations is a primary challenge. Vibrations for UAVs operated by batteries
are typically smaller than those powered by combustion engines [55].
The two main approaches for compensating for shock and vibrations are
passive isolation or active elimination via feedback. In the case of a rotary-
wing UAV, passive isolation involves a degree of mechanical decoupling
the payload from the UAV platform such that vibrations are sufficiently
dampened below a critical level for payload operation. Passive isolation
makes use of materials and mechanical linkages that absorb and dampen
mechanical waves. The isolation can be achieved by adding shock absorbers
to protect against mechanical shock and isolators or dampers to protect
against vibration.
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Active isolation utilizes sensors and actuators that produce disruptive
interference to cancel out or dampen incoming vibration, making use of a
feedback circuit that consists of a sensor (example e.g., a piezoelectric
accelerometer), a controller, and an actuator. A control circuit processes the
signal from the piezoelectric accelerometer and subsequently feeds a
compensatory control signal to an electromagnetic actuator. In this way,
stronger suppression of vibrations can be achieved than by passive isolation.
A disadvantage, however, is that the payload complexity increases (i.e.,
additional sensing devices), and as a result the payload weight and power
requirements also increase, both of which constrained for UAV platforms.
3.2.2.2. UAV-Induced Local Winds
For rotary-wing UAVs, a concern for a payload that makes in situ
measurements is the possible influence of the UAV-induced wind field on
the representativeness of localized measurements [56]. Species
concentrations can be locally mixed as well as diluted or enriched by
surrounding gases. Studies on this topic include computational fluid
dynamics [57-66], particle image velocimetry [67-70], testing using wind
sensors [56, 71, 72], flow tracing [73-76], and mathematical modeling [77].
As a rule of thumb, a UAV introduces local mixing turbulence of several
times its length, so that spatial resolution can be compromised at this scale.
For quantities that are homogeneous on a longer length scale, the local
mixing is not a concern.
Two approaches are typically used to minimize artifacts associated with
the UAV-induced wind field. (1) The first approach is placement of
sampling inlets far enough from the UAV that they are not influenced by the
UAV-induced wind field. In practice, this simple solution can be an
engineering challenge because the sampling inlet must be sufficiently
lightweight and preferably retractable such that it does not impact takeoff,
landing, or flight time. Furthermore, the changed in the weight distribution
can impact the UAV flight stability by influencing the moment of inertia.
(2) The second approach is intelligent placement in a minimally impacted
region within the UAV-induced wind field. Such placement may include
manipulating the wind field aerodynamics to generate minimally impacted
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regions. This goal is difficult to accomplish, however, because the
interactions among the chassis, payload, propulsion system, and
environmental variables are complex and multifaceted.
As an example, Figure 3 shows downward wind velocities that are
simulated for a quadcopter UAV configured either with single rotors or
coaxial dual rotors. Flow velocities and consequently Reynolds numbers
below the rotors are larger for the coaxial rotors. There is a greater influence
on the flow region directly below the UAV. Large Reynolds numbers are
associated with greater turbulence. Configurations that can achieve small
Reynolds number are typically favorable for atmospheric sampling. Even
so, Shukla and Komerath [67] demonstrated that rotor-rotor vortex
interactions can increase for smaller Reynolds numbers (Figure 4). Overall,
a design based on a small number of evenly spaced rotors typically achieves
superior atmospheric sampling because this configuration maximizes inter-
rotor distances, decreases the Reynolds number, and minimizes rotor-rotor
interactions.
Figure 3. (left) Simulated downward velocity profile of a quadcopter. (right) Simulated
downward velocity profile of a coaxial dual rotor quadcopter. Adapted from ref [57].
Yoon et al. [58] examined the effects of propeller speed, propeller pitch,
and rotor blade position (i.e., overmount, undermount, and off-body) on
local mixing and representative sampling. Simulations were carried out of
the unsteady Navier-Stokes equations for a quadcopter configuration.
Higher rotor speeds resulted in increased turbulence, increasing the
magnitudes of velocity and vorticity. Importantly, there was typically a
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confined, relatively stationary region of the wind field (i.e., representative
of local air) directly above and below the UAV. Many sampling systems
thus utilize this location. Several studies performing simulations for rotary-
wing UAVs have produced similar findings [61, 63, 78-82]. Correction
factors have also been proposed as a method to remove the influence of the
induced wind field UAV sampling [63, 83].
Figure 4. Impact of rotor spacing on wake interactions. (a) Large rotor spacing and
high Reynolds number flow. Inter-rotor interactions are negligible. (b) Small rotor
spacing and low Reynolds number. Inter-rotor interactions are considerable [67].
Creative Commons BY 4.0 license. Copyright (2018), reproduced with permission
from the Multidisciplinary Digital Publishing Institute.
An important application of UAVs is the monitoring of pollutant plumes
from industrial emissions, and assessing the impact of the induced wind field
has become an important consideration [84]. Local mixing in the presence
of strong spatial gradients, such as those characteristic of stack emissions
can introduce significant errors in estimates of concentrations and hence the
flux of factory emissions [73, 74]. Villa et al. [56] examined the influence
of the UAV gas inlet location on the nominal concentration when measuring
a reference gas concentration. The nominal concentration had significant
error when the propellers were operational.
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The error arose from the dispersion of the gas plume, and the optimal
sampling location for minimum error was a horizontal distance of 1-1.2 m
from the UAV (~ 8 kg). Sato et al. [85] developed a plume monitoring
system that utilized two UAVs to hold a rod or string upon which a gas
sensor was mounted. The aim of the system was to balance the impact of the
wind field a single UAV with an equal and opposite impact of a second UAV
(Figure 5). A challenge with this approach, however, was that UAV flight
destabilization could occur if the UAV wind fields were too proximate or if
the coupling rod was too rigid.
Figure 5. (a) Diagram of a quadcopter UAV-induced wind field and its influence on a
localized emission source. (b) Diagram of UAV-induced wind field for two connected
quadcopters and the influence on the dispersion of a localized emission source [85].
Creative Commons BY 4.0 license. Copyright (2018), reproduced with permission
from the Multidisciplinary Digital Publishing Institute.
3.3. Payload Mounting
Once a payload has been designed to accommodate aerial-specific
constraints (e.g., weight, power, and compactness), environment-specific
constraints (e.g., temperature and humidity), and platform-specific
constraints (e.g., vibration and induced wind field), the payload must be
securely mounted to the UAV. Many mounting locations and methods are
possible (Figure 6). Some mounting locations may minimize interference by
an induced wind field. Other mounting locations may maximize UAV
stability or provide shielding from solar radiation, among other possibilities.
The optimal mounting location depends on application and flight conditions.
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Figure 6. Various forms of payload integration for gas sensing by UAVs. (a)
Protruding boom payload mounting. (b) Sensor payload with pumped horizontal air
intake. (c) Sensor payload with vertical air intake. (d-h) Bottom-mounted sensor
payloads. (i-j) Top-mounted sensor payloads. (k) Front-mounted sensor payloads.
Adapted from ref [155]. Copyright (2020), reproduced with permission from Elsevier.
4. CURRENT APPLICATIONS
Current applications of UAVs for atmospheric chemical sensing using
UAV-based payloads include emissions monitoring of point and fugitive
sources, gas plume tracking, and the monitoring of local and regional
atmospheric composition and air quality [11]. Such applications involve the
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use of horizontal and vertical profiling measurements as well as near-surface
sampling over forests, oceans, and other landscapes. Common targets for
sampling are gas pollutants and aerosol particles. UAVs can be used to
acquire concentrations of regulated air pollutants such as such as SOx, NOx,
CO, and PM that affect human health and greenhouse gases such as CO2 and
CH4 [6, 11, 86-105]. The flights can be automated for monitoring [106], and
there is the potential for a monitoring UAV to alert individuals, industry
professionals, or authorities when pollutant concentrations exceed
regulatory levels. There is also the potential for UAVs equipped with gas
sensors to monitor pollutants from forest fires [107], including early
detection purposes [108]. Several novel and unconventional applications
exist and include the use of UAVs for pollution abatement and sampling of
insect populations [109].
4.1. Gas Source Monitoring
Source monitoring involves the detection and assessment of emissions,
chiefly pollutants, from point and non-point sources. The monitoring of such
sources provides valuable information for quantifying the emission fluxes.
Emissions monitoring can be done using both in situ and remote sensing
techniques depending on the gas species and concentration. Examples of
commonly monitored emission sources include landfills, industrial
processing facilities, power plant smokestacks, forests, and transportation
corridors.
Remote sensing methods have been to map atmospheric emissions of
human-associated methane [110, 111] and carbon dioxide [112]. Several
studies have also used remote sensing for multi-species detection [89, 113,
114]. Emran et al. [111] detected methane to assess emissions in the
concentration range of upto 1000 ppm using a UAV to traverse a preset path
above a landfill. The relatively high concentrations allowed the remote
sensing, but the approach is not easily transferrable to lower-concentration
gases. Detection limits as low as 15 ppb, however, have been obtained for
some species using optical techniques [54], and further improvements could
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imply that in the future the detection limit might become less of a barrier for
remote sensing.
Monitoring volcanic emissions is another important use for UAV-based
chemical sensing. Volcanoes are a natural emission source for large
quantities of sulfur- and halogen-containing compounds, as well as
significant amounts of carbon dioxide. The use of fixed-wing UAVs for
measuring volcanic gas plumes can be traced to the 1970s [115]. More recent
studies have utilized commercial UAV platforms [116, 117]. The studies
have largely focused on measuring gas emissions directly within volcanic
plumes and have involved the use of multi-species sensor payloads. Mori et
al. [117] used a UAV sensor payload consisting of SO2 and H2S
electrochemical sensors, a H2 semiconductor sensor, an infrared CO2 sensor,
a miniature ultraviolet spectrophotometer, and humidity and temperature
sensors. The payload also included a passive dust sampler for offline
sampling. Diaz et al. [116] equipped a UAV with a lightweight and compact
aerial mass spectrometer. These results were also combined with remote
sensing data from satellites, demonstrating the synergy of in situ and remote
sensing data for validating results. In the future, the possibility of combining
in situ, bottom-up measurements with satellite-based, top-down
measurements may produce increasingly accurate and precise measurements
of atmospheric gases.
Rüdiger et al. [118] began to use rotary-wing UAVs within volcanic
plumes. Over three different volcanoes, the UAV measured the
concentrations of CO2, SO2, and halogen species (including bromine,
chlorine, and iodine species) within the plume. The use of a rotary-wing
UAV sensor improved the time resolution because of the ability to hover
within the plume. A challenge, however, was that the UAV-induced wind
field degraded the data quality in plumes having high concentration
gradients across short distances.
Flux quantification of volatile organic compounds (VOCs) from forests
are a further example of source monitoring. Batista et al. [36] conducted
UAV measurements in the central Amazon using a sorbent cartridge payload
to sample biogenic VOCs from forest emissions. Specifically, several
samples were collected within 30-min flights to obtain averaged isoprene
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and α-pinene concentrations at fixed altitudes over two different forest
subtypes located 800 m apart. The sorbent tube samples were analyzed
offline by gas chromatography. Different levels of isoprene and α-pinene
concentrations were observed over the two sites, indicating the existence of
intermediate-scale heterogeneity in forest emissions. The study
demonstrated the utility that UAVs can provide to atmospheric scientists for
studies at previously unavailable scales.
Emissions of anthropogenic VOCs can also be assessed via source
monitoring. Using a similar approach to Batista et al., Chen et al. [119]
demonstrated the ability to measure benzene, toluene, ethylbenzene, and
xylene (BTEX) concentrations at various anthropogenic sources and
altitudes in the mid-south USA. These anthropogenic sources consisted of a
municipal landfill, a petroleum refinery, and a coal-fired power plant. Chang
et al. [120] likewise obtained UAV-based samples over the exhaust shaft of
a roadway tunnel. The UAV was equipped with a 2-L stainless steel
sampling canister and flow system for VOCs, as well as temperature,
humidity, and pressure sensors (plus, particle black carbon and CO2
sensing). From the canister, 109 different VOCs were analyzed by gas
chromatography. This study demonstrated fast and accurate measurement of
detailed chemical composition from UAV-based samples.
4.2. Gas Plume Tracking
Plume tracking entails detecting and monitoring the downwind
concentrations of a point source of emissions. Monitoring the trajectory of a
gas plume is important for assessing the downstream impacts of pollution
sources. The ability of UAVs to survey an environment in real-time makes
them a suitable candidate for plume tracking applications. In a seminal
paper, Neumann et al. [121] developed a UAV-based gas source localization
technique that tracked an emission plume of CO2 in a 20 m3 test chamber to
its source. Rohi et al. [106] developed a UAV to fly to a predetermined
height every hour and measure the concentrations of CO2, CO, NH3, SO2,
PM, O3, and NO2.
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In addition to an online sensing payload, in situ plume tracking by a
UAV typically requires accurate measurements of wind speed and direction.
Several techniques have been developed to obtain UAV-based wind
measurements, despite the presence of a locally perturbed wind field. These
techniques include the use of onboard anemometers as well as wind field
estimates based upon telemetry from the onboard inertial measurement unit
(IMU). Neumann and Bartholmai [122] demonstrated that measurements of
wind speed and direction could be accurately obtained using data from the
UAVs onboard IMU without the need for external sensors. To validate this
approach, they performed wind tunnel and field tests, comparing the wind
measurements from an anemometer with those inferred from the IMU. The
anemometer was positioned such that it was not influenced by the UAV-
induced wind field, and the IMU-based measurement matched the
anemometer measurements. The wind vector was thus obtained without the
use of specialized wind-sensing devices. In recent years, this method of wind
estimation has continued. There are multiple kinematic methods [123, 124],
point-mass models [125-127], and rigid-body models [128, 129] to
characterize how a quadrotor UAV responds to the wind vector. This
knowledge is then used to determine the wind vector from the IMU data.
Neumann et al. [130] proposed three bio-inspired and sequential Monte
Carlo-based algorithms that could be used for the purpose of plume tracking
and source detection. Outdoor tests to evaluate the algorithms were carried
out using a gas-sensing payload. A methane cylinder was used as the
emission source, and a fan was used to spread the analyte. The results
showed that changing winds and turbulence have a substantial impact on the
ability of a UAV to find an emission source. Even so, the experiments were
largely successful, demonstrating the potential for UAV-based plume
tracking outdoors. More recently, Neumann et al. [131] tackled the same
problem using a UAV-based spectroscopic instrument coupled to a 3-axis
gimbal to apply the principles of tomography for plume visualization.
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4.3. Aerosol Particle Monitoring
Characterization and sampling of non-gaseous materials, such as
pollution and natural aerosol particles and bioaerosol particles, by UAVs is
common. Bieber et al. [132] developed a UAV-based aerosol particle
sampler to measure atmospheric ice nuclei in Austria. Two payloads were
developed and mounted onto two separate drones. The first payload
consisted of a cascade impactor, which collected and size-separated aerosol
particles into four size bins. The particles were analyzed offline by
fluorescence microscopy. The second payload consisted of an impinger for
aerosol particle collection. The particles were analyzed offline by cryo-
microscopy. PM2.5 and PM10 were also monitored during UAV flights by an
optical particle counter. This study demonstrated the capability of both
online and offline analyses to characterize aerosol particles using UAV-
based payloads. Brady et al. [6] showcased the use of UAVs for studying
sea surface aerosol particles close to the ocean surface (e.g., produced by
wave breaking) as well as vertical profiles from surface to 100 m. Previous
studies of sea surface aerosol particles were constrained to measurements on
fixed platforms (e.g., towers [133] and boats [134]) and consequently were
unable to determine vertical profiles.
Aerobiological sampling is an important application for UAVs.
Terrestrial ecosystems emit many types of biological aerosol particles,
including pollen grains and fungal spores. Maldonado-Ramírez [135] and
Maldonado-Ramírez et al. [136] used remote-controlled airplanes during the
early 2000s to examine the concentration of low-altitude spores over wheat
fields. Techy et al. [137, 138] and Schmale et al. [139] likewise used fixed-
wing UAV flights to study pathogen transport over farmlands. Lin et al.
[140] used a fixed-wing UAV for fungal spore collection to study the
seasonality and atmospheric transport of the spores. Jimenez-Sanches et al.
[141] collected airborne bacteria by UAV and studied the ice nucleation
activity. The most recent studies for aerobiological sampling have employed
rotary-wing UAVs [132, 142, 143]. Aerobiological particles are
significantly larger than most pollution aerosol particles, and hence they
respond differently to the UAV-induced wind field. A concern is that
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measurements of aerobiological particle concentration for rotary-wing
UAVs might have downward bias.
4.4. Other Unique Applications
UAVs have been applied to several other unique chemical sensing
problems. Koparan et al. [144] used a UAV for autonomous lake and river
water sampling. A hexacopter UAV with a waterproof payload was floated
on the water body, and the submerged payload measured several features
related to water quality. The study demonstrated simultaneous in situ surface
water and surface air characterization away from shore, thus allowing
scientists to more easily study interactions between these two compartments,
especially for locations that either are not easily accessible or are perturbed
by other historical approaches.
Another novel water-related application is the use of a so-called
“snotbot” for aerobiological sampling of whale DNA. The UAV device
equipped with a sampling payload hovers 10 m above the sea surface and
collects blowhole-ejection products from whales, such as mucus cells [145].
The DNA of an individual whale is instead obtained by analysis of the
mucus. This method represents a non-invasive alternative to the traditional
method of using biopsy darts for tracking whales [146].
The development of a UAV payload for catching insects is important to
several different fields of research. Kim et al. [109] used a two-net payload
on a single rotary-wing UAV to catch insects over rice fields. Over the
course of 21 flights of 5 min duration and altitudes of 5, 10, 50, and 100 m
altitudes, 251 insects were caught. The payload consisted of two 28 × 32 cm
double-layered nets axially aligned with the UAV direction of movement
and attached to the chassis via a wooden rod. In the future, this method may
allow faster sampling of insect populations at lower cost, more
autonomously, and more spatially representative than traditional techniques.
The ability to sample insect populations can be useful for studying disease
transmission via insects as well as monitoring pests around agricultural
lands.
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5. EMERGING TRENDS
Several trends are emerging with the potential to transform the way in
which UAVs are used for in situ chemical measurements. These trends
include the use of artificial intelligence for various control and data
analytics, UAV swarms in combination with wireless sensor networks, and
miniaturized payloads for indoor measurements.
5.1. Artificial Intelligence
Although UAVs have already begun to be used with artificial
intelligence, these applications mainly involve the use of machine learning
algorithms on data extracted from UAV flights. This trend could continue as
sensors become smaller, more accurate, and more reliable. One exciting
direction of machine learning for gas-sensing UAVs is the paradigm of
reinforcement learning. Several papers have expressed and demonstrated the
possibility of reinforcement learning within UAVs [147, 148]. This
paradigm could allow a UAV to navigate based on sensor inputs and can be
used for autonomous flight in performing tasks like gas source localization
or plume tracking. Recent developments in tiny machine learning, meaning
the ability to run machine learning algorithms on memory- and compute-
constrained microcontrollers, have demonstrated that such reinforcement
learning algorithms can be utilized on devices as small as 10 cm × 10 cm
[149]. Such algorithms on a small-form-factor UAV could create new
potential for performing indoor gas source detection.
5.2. Atmospheric Sensing Networks
Techniques of multi-agent systems could allow the creation of
coordinated UAV swarms. These swarms can be used to survey aerial
environments. They can be coupled to wireless sensor networks, involving
ground-based sensors or other airborne payloads, to obtain high-resolution
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and detailed real-time atmospheric observations. Due to the rapid
development of the “Internet of Things” (IoT) in the past few years, sensor
networks could become increasingly common.
Silic and Mohseni [150] present an approach of using a flock of fixed-
wing UAVs to perform plume monitoring. The study demonstrates the
potential for using multi-agent systems to perform plume monitoring.
Experimental validation using a real gas plume, however, remains to be
done. A complication could be that the use of many UAVs at once may
exacerbate any influence from the UAV-induced wind field, even when
using fixed-wing UAVs.
Rohi et al. [106] demonstrated the capability of using multiple UAVs as
part of an airborne sensor network to produce a local map of the air quality
health index. The method could be used in the future for automated detection
of atmospheric pollution. This method could be an attractive approach for
monitoring emissions in city environments where emissions are often
localized to individual small structures and thus challenging to detect and
resolve in a timely manner.
5.3. Flapping Wing UAVs
Flapping wing UAVs, colloquially known as ornithopters, are a topic of
increasing interest in the UAV community. The trend towards low-cost,
lightweight, and low-power sensors is making them increasingly attractive
in the atmospheric chemistry community. Development of small UAVs and
lightweight sensor payloads could lead to the further development of
airborne sensor networks capable of studying intermediate scale
atmospheric characteristics. Such networks could provide highly accurate
assessments of ambient air quality in cities in support of emissions
compliance and emissions monitoring. Although ornithopters tend to be
more robust than larger UAVs in the presence of wind gusts, there is
increased vibration compared to rotary-wing or fixed-wing UAVs. Vibration
can negatively impact the performance of some sensors (section 3.2.2.1).
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5.4. Indoor Air Quality Measurements
The high maneuverability of small rotary-wing UAVs suggests
developments for use indoors. Several models are already available as toys,
and the company Amazon intends to market an indoor UAV to provide home
security. Indoor UAV applications for chemical sensing may provide a new
method to search for gas leaks as well as to study indoor air quality related
to “sick building syndrome” in large- and small-scale buildings [151].
Chemical sensing and emission localization by UAVs in factories,
warehouses, and other spaces can also be possible for particular health and
safety concerns regarding exposure to pollutants. Even so, overly small
UAVs could pose a respiratory hazard. Although indoor environments do
not provide a wind hazard to UAVs, an enclosed environment can result in
air recirculation patterns as a long-range interaction that pose different kinds
of challenges to UAV flight [56].
Figure 7. Crazyflie 2.0 nano-quadcopter prototype coupled to a gas-sensitive metal
oxide semiconductor [152]. Copyright (2017), reproduced with permission from the
Institute of Electrical and Electronics Engineers.
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Ross et al. [152] developed a small-form-factor gas-sensing UAV out of
a commercial 10 × 10 cm Crazyflie 2.0 nano-quadcopter. Metal oxide
semiconductor (MOS) sensors were mounted as the UAV payload (Figure
7). The UAV-induced wind field had a substantial impact on the
measurements, and several different configurations were tried to resolve the
issue. These configurations involved distancing the sensing device from the
UAV vortex field, which in turn led to stabilization challenges. Ultimately,
the UAV was operated in a “butterfly” mode, meaning that the UAV landed
on a surface and subsequently sampled air once the rotors had stopped.
Fahad et al. [153] extended this work by attaching a chemically sensitive,
field-effect transistor to a Crazyflie 2.0. The sensor measured hydrogen gas.
The UAV was placed in a fume hood, and hydrogen was released in an upper
corner of the hood. Upon gas release, the UAV ascended and hovered at
approximately 60 cm where the gas concentration was measured.
Burgués et al. [154] extended this result further to include not only
hovering but also active gas sampling during flight. A nano-quadcopter of a
mass of 27 g and a wingspan of 10 cm was equipped with two MOS sensors.
A source of ethanol was placed at different locations of an 11 m × 18 m
room. The UAV made predetermined flight paths around the room while
simultaneously sampling. The results demonstrated the possibility of
obtaining approximate gas measurements on-the-fly using a small-form-
factor UAV.
CONCLUSION
UAVs are increasingly utilized for atmospheric chemical sensing, and
capabilities continue to improve in line with better microcontrollers and
sensor technologies. While important challenges remain, substantial
progress has been made over the past decade in the development of UAV-
based chemical sensing payloads. Public interest in the environment, public
health, and climate change can be expected to continue to drive these
developments. The development of more lightweight and energy-efficient
UAV propulsion and sensing payloads can improve flight time, decrease
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costs, and reduce hazards. Moreover, airborne chemical measurements can
play important roles in emissions monitoring and compliance. In outlook, as
UAVs proceed along a trajectory of greater integration into society,
government regulatory decision-making could either restrict or enable such
measurements. The next decade could see an increasing push toward the use
of miniaturized and flapping-wing UAVs for gas-sensing purposes, more
emphasis on autonomous data-driven UAV technology, and the adoption of
UAVs for indoor air quality monitoring.
ACKNOWLEDGMENTS
Support from the Division of Atmospheric and Geospace Sciences
(AGS) of the USA National Science Foundation (AGS-1829025) is
gratefully acknowledged.
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