Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research.

An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.

Autonomous Gas-Sensitive Microdrone: Wind Vector Estimation and Gas Distribution Mapping

This paper presents an approach for a quadrocoper-based micro unmanned aerial vehicle (UAV) that estimates the wind vector (speed and direction) in real-time based on measurement data of its on-board sensors only. This method does not need any additional airspeed sensor or dedicated anemometer, and thus the micro UAV's valuable payload remains free for other sensors. Wind tunnel and field tests were used to evaluate the performance of the approach. In order to quantify its accuracy, experiments are presented where data was collected with an anemometer placed in an open field with the micro UAV in flight following a predefined trajectory around the anemometer and hovering at a defined position close to it.

Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit

Sampling the atmospheric boundary layer with small unmanned aircraft is a difficult task requiring informed selection of sensors and algorithms that are suited to the particular platform and mission Many factors must be considered during the design process to ensure the desired measurement accuracy and resolution is achieved, as is demonstrated through an examination of previous and current efforts A taxonomy is developed from these approaches and is used to guide a review of the systems that have been employed to make in situ wind and thermodynamic measurements, along with the campaigns that have employed them Details about the airframe parameters, estimation algorithms, sensors, and calibration methods are given

Overview of Small Fixed-Wing Unmanned Aircraft for Meteorological Sampling

Computing turbulent fluxes near the surface: Needed improvements

The meteorological mini unmanned aerial vehicle (M2AV) was used for measuring the meteorological wind. The wind is the vector difference between the aircraft speed relative to the earth (inertial velocity) and relative to the airflow (true airspeed). The latter was computed from five-hole-probe pressure measurements in combination with calibration–coefficient polynomials obtained during wind tunnel calibration. The aircraft inertial velocity, position, and attitude were calculated using a Kalman filter that combined data from a global positioning system (GPS) and an inertial navigation system (INS). The temporal (and spatial) resolution of the M2AV wind measurement is remarkably fine. An inertial subrange of locally isotropic turbulence can be measured up to 40 Hz (or 0.55 m at 22 m s−1 airspeed). The first M2AV wind estimation showed some systematic deviations compared to the expected values (like a constant mean wind in every flight direction). Therefore, an in-flight wind calibration technique...

Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV

Scintillometry has been increasingly used over the last decade for the experimental determination of area-averaged turbulent fluxes at a horizontal scale of a few kilometres. Nevertheless, a number of assumptions in the scintillometer data processing and interpretation still call for a thorough evaluation, in particular over heterogeneous terrain. Moreover, a validation of the path-averaged structure parameters derived from scintillometer data (and forming the basis for the flux calculations) by independent measurements is still missing. To achieve this, the LITFASS-2009 field campaign has been performed around the Meteorological Observatory Lindenberg – Richard-Asmann-Observatory of the German Meteorological Service (DWD) in July 2009. The experiment combined tower-based in-situ turbulence measurements, field-scale laser scintillometers, long-range optical (large-aperture) and microwave scintillometers, and airborne turbulence measurements using an automatically operating unmanned aircraft. The paper describes the project design and strategy, and discusses first results. Daytime near-surface values of the temperature structure parameter, $${C_{T}^{2}}$$
 , over different types of farmland differ by more than one order of magnitude in their dependence on the type and status of the vegetation. Considerable spatial variability in $${C_{T}^{2}}$$
 was also found along the flight legs at heights between 50 and 100 m. However, it appeared difficult to separate the effects of heterogeneity from the temporal variability of the turbulence fields. Aircraft measurements and scintillometer data agreed in magnitude with respect to the temporal variation of the path-averaged $${C_{T}^{2}}$$
 values during the diurnal cycle. The decrease of $${C_{T}^{2}}$$
 with height found from the scintillometer measurements close to the surface and at 43 m under daytime convective conditions corresponds to free-convection scaling, whereas the aircraft measurements at 54 and 83 m suggest a different behaviour.

Towards a Validation of Scintillometer Measurements: The LITFASS-2009 Experiment

[1] Airborne measurements in the Arctic stable boundary layer were performed using the helicopter-borne turbulence measurement system Helipod. The observations were carried out during the ARK-XII summer expedition of the research icebreaker Polarstern in the Arctic ocean. The boundary layer over closed sea ice exhibited mainly weakly stable stratification. Only flights in continuous turbulence were chosen for the analysis. The measurements of sensible heat flux H and latent heat flux λE were made 200 km away from land to reduce the influence of any captured mesoscale flux. Nevertheless, the analysis of multiresolution (MR) cospectra yield some small mesoscale flux contribution, visible at the larger timescales. A MR gap timescale τgap was defined to separate the turbulent flux from the mesoscale flux and used for the recalculation of the turbulent fluxes after high-pass filtering. This procedure also reduced the statistical error of the fluxes significantly. For comparison, wavelet transformations were performed that led to wavelet gap scales of the fluxes in good agreement with the MR gap scale. Additionally, the wavelet covariance gave a good representation of the homogeneity of the kinematic heat flux w′θ′ and the kinematic moisture flux w′m′ in time.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006JD007819

Turbulent flux calculation in the polar stable boundary layer: Multiresolution flux decomposition and wavelet analysis

Infrared measurements of boundary-layer transition on an inclined cone at Mach 6

In general a turbulent flow can be divided into scale regimes. The large scales, contain most of the energy and are responsible for the transport of heat, momentum and mass. The small scales include the dissipation range and the inertial range (Sreenivasan and Antonia 1997). Knowledge of small scale turbulence is mostly based on Kolmogorov’s theory (Kolmogorov 1941), but no global theory of turbulence is present. Within the stable boundary layer the turbulent fluxes are small and therefore easily affected by mesoscale fluxes. To estimate this mesoscale flux correctly, long averaging times are needed. Small scale turbulence has completely different characteristics compared to the mesoscales. As small scale turbulence is related to wind shear and temperature stratification, the mesoscales are not. For the proper investigation of small scale turbulence, especially in the (very) stable boundary layer, any contamination of mesoscale fluxes in the records should be expelled (Mahrt and Vickers 2006).

Aline C. van den Kroonenberg

Papers

Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV

Towards a Validation of Scintillometer Measurements: The LITFASS-2009 Experiment

Turbulent flux calculation in the polar stable boundary layer: Multiresolution flux decomposition and wavelet analysis

Infrared measurements of boundary-layer transition on an inclined cone at Mach 6

The turbulent structure of the polar stable boundary layer: Wavelet transformation and multiresolution flux decomposition