Development and Validation of a UAV Based System for Air Pollution Measurements

Monitoring landfill volatile organic compounds emissions by an uncrewed aerial vehicle platform with infrared and visible-light cameras, remote monitoring, and sampling systems

An uncrewed aerial vehicle (UAV) platform equipped with dual imaging cameras, a gas sampling system, and a remote synchronous monitoring system was developed to sample and analyze volatile organic compounds (VOCs) emitted from landfills. The remote synchronous monitoring system provided real-time video to administrators with specific permissions to assist in identifying sampling sites within extensive landfill areas. The sampling system included four kits capable of collecting samples from different locations during a single flight mission. Each kit comprised a 1 L Tedlar bag for measuring landfill VOC concentrations according to the TO-15 method prescribed by the US Environmental Protection Agency. The air sample was introduced into a Tedlar bag via pumping. A known volume of the sample was subsequently concentrated using a solid multisorbent concentrator. Following this, the sample underwent cold trap concentration and thermal desorption. The concentrated sample was then transferred to a chromatography-mass spectrometry system for separation and analysis. Since the anaerobic catabolism of organic waste is exothermic and emits VOCs, this study employed UAV thermal imaging to locate principal emission sources for sampling. Visible-light imaging helped identify newer or older landfill sections, aiding in the selection of appropriate sampling sites, particularly when surfaces were thermally disturbed by solar radiation. Field measurements were conducted under three meteorological conditions: sunny morning, cirrus morning, and thin cloud evening (2 h after sunset), identifying 119, 122, and 111 chemical species respectively. The sequence of total VOC concentrations measured correlated with the meteorological conditions as follows: cirrus morning > thin cloud evening > sunny morning. The results indicated that ambient temperature and global solar radiation significantly influenced daytime gas emissions from landfills. Evening thermal images, unaffected by solar heating, facilitated more accurate identification of major VOC emission points, resulting in higher VOC concentrations compared to those recorded in the sunny morning. VOCs from the landfill were categorized into nine organic groups: alkanes, alkenes, carbonyls, aromatics, alcohols, esters, ethers, organic oxides, and others. The classification was based on carbon-containing compounds (Cn, where the compound contains n carbon atoms). Alkanes were predominant in terms of Cn presence, followed by alcohols and carbonyls. Among the organic groups, organic oxides, particularly 2-heptyl-1,3-dioxolane, exhibited the highest concentrations, succeeded by alkenes. Sampling under cloudy conditions or in the evening is recommended to minimize the effects of surface temperature anomalies caused by solar radiation, which vary due to differences in land composition.

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

Effects of UAS Rotor Wash on Air Quality Measurements

Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85 and for both gas sensor pairs exceeded R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors

Mining is an economic activity that entails the production and displacement of significant amounts of atmospheric particulate matter (PM) during operations involving intense earthcrushing or earthmoving. As high concentrations of PM may have adverse effects on human health, it is necessary to monitor and control the fugitive emissions of this pollutant. This paper presents an innovative methodology for the online monitoring of PM10 concentrations in air using a low-cost sensor (LCS, <300 USD) onboard an unmanned aerial vehicle. After comprehensive calibration, the LCS was horizontally flown over seven different areas of the large Riotinto copper mine (Huelva, Spain) at different heights to study the PM10 distribution at different longitudes and altitudes. The flights covered areas of zero activity, intense mining, drilling, ore loading, waste discharge, open stockpiling, and mineral processing. In the zero-activity area, the resuspension of PM10 was very low, with a weak wind speed (3.6 m/s). In the intense-mining area, unhealthy concentrations of PM10 (>51 μgPM10/m3) could be released, and the PM10 can reach surrounding populations through long-distance transport driven by several processes being performed simultaneously. Strong dilution was also observed at high altitudes (> 50 m). Mean concentrations were found to be 22-89 μgPM10/m3, with peaks ranging from 86 to 284 μgPM10/m3. This study demonstrates the potential applicability of airborne LCSs in the high-resolution online monitoring of PM in mining, thus supporting environmental managers during decision-making against fugitive emissions in a cost-effective manner.

Acceptance of medical drone technology and its determinant factors among public and healthcare personnel in a Malaysian urban environment: knowledge, attitude, and perception

Introduction

Unmanned aerial vehicles (UAVs) are used for commercial, medical, public safety, and scientific research purposes in various countries.

Methods

This study aimed to explore the acceptance of medical delivery drones among medical practitioners as well as the public community in Malaysia using a knowledge, attitude, and perception (KAP) model and statistical analysis to decrease uncertainty. Bivariate and multivariate analyses of the results were performed in SPSS.

Results

A total of 639 respondents took part in the survey, of which 557 complete responses were finally analyzed. The results showed that the overall acceptance rate for medical delivery drones was positive. The acceptance rate was significantly correlated with knowledge, attitude, and perception scores but not with sociodemographic factors.

Discussion

Raising awareness and educating the medical as well as public communities regarding the potential role and benefits of drones are therefore important in garnering support for drone usage for medical purposes.

State of the Art and Future Perspectives of Atmospheric Chemical Sensing Using Unmanned Aerial Vehicles: A Bibliometric Analysis

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor and assess air quality. The interest in the application of UAVs in monitoring air pollutants and greenhouse gases is evidenced by the recent emergence of sensors with the most diverse specifications designed for UAVs or even UAVs designed with integrated sensors. The objective of this study was to conduct a comprehensive review based on bibliometrics to identify dynamics and possible trends in scientific production on UAV-based sensors to monitor air quality. A bibliometric analysis was carried out in the VOSViewer software (version 1.6.17) from the Scopus and Web of Science reference databases in the period between 2012 and 2022. The main countries, journals, scientific organizations, researchers and co-citation networks with greater relevance for the study area were highlighted. The literature, in general, has grown rapidly and has attracted enormous attention in the last 5 years, as indicated by the increase in articles after 2017. It was possible to notice the rapid development of sensors, resulting in smaller and lighter devices, with greater sensitivity and capacity for remote work. Overall, this analysis summarizes the evolution of UAV-based sensors and their applications, providing valuable information to researchers and developers of UAV-based sensors to monitor air pollutants.

Solar-powered shape-changing origami microfliers

Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We designed origami microfliers using bistable leaf-out structures and uncovered an important property: A simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, a Bluetooth radio, a solar power-harvesting circuit, a pressure sensor to estimate altitude, and a temperature sensor. Outdoor evaluations show that our 414-milligram origami microfliers were able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun.

AirQuality Lab-on-a-Drone: A Low-Cost 3D-Printed Analytical IoT Platform for Vertical Monitoring of Gaseous H2S

The measurement of gaseous compounds in the atmosphere is a multichallenging task due to their low concentration range, long and latitudinal concentration variations, and the presence of sample interferents. Herein, we present a quadcopter drone deployed with a fully integrated 3D-printed analytical laboratory for H2S monitoring. Also, the analytical system makes part of the Internet of Things approach. The analytical method applied was based on the reaction between fluorescein mercuric acetate and H2S that led to fluorescence quenching. A 5 V micropump at a constant airflow of 50 mL min-1 was employed to deliver constant air into a flask containing 800 μL of the reagent. The analytical signal was obtained using a light-emitting diode and a miniaturized digital light detector. The method enabled the detection of H2S in the range from 15 to 200 ppbv, with a reproducibility of 5% for a sampling time of 10 min and an limit of detection of 9 ppbv. All devices were controlled using an Arduino powered by a small power bank, and the results were transmitted to a smartphone via Bluetooth. The proposed device resulted in a weight of 300 g and an overall cost of ∼50 USD. The platform was used to monitor the concentration of H2S in different intervals next to a wastewater treatment plant at ground and vertical levels. The ability to perform all analytical steps in the same device, the low-energy requirements, the low weight, and the attachment of data transmission modules offer new possibilities for drone-based analytical systems for air pollution monitoring.

Air pollution forecasting based on wireless communications: review

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model

In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized.

A bibliometric analysis of scientific research trends in monitoring systems for measuring ship emissions

The maritime sector plays a key role in transportation in the world, and over 90% of world trade is carried by ocean shipping. However, ships are large contributors to global emissions. Hence, a vast majority of research publications have focused on different emission monitoring techniques, which are essential to establishing required policies and regulations that reduce maritime transport emissions. Various documents have been published on monitoring maritime transport emissions affecting air quality since 1977. This paper presents a bibliometric analysis to explore evolution trends, gaps, challenges, and productive countries, as well as the most cited publications with high scholarly impacts. The annual growth of 9.64% in publications demonstrates an increasing interest in reducing maritime vessel emissions. Journal articles constitute 69% of publications, followed by conference papers (25%). China and the USA play a leading role in this field of research. Regarding active resources, the "Atmospheric Environment" journal accounts for the highest relevant publications, H-index, and total citations. Eventually, the temporal evolution of keywords shows the increasing trend towards sustainable maritime transport.

Multidimensional spatial monitoring of open pit mine dust dispersion by unmanned aerial vehicle

Dust pollution is one of the most severe environmental issues in open pit mines, hindering green mining development. Open pit mine dust has characteristics of multiple dust-generating points, is irregular, influenced by climatic conditions, and has a high degree of distribution with a wide dispersion range in three dimensions. Consequently, evaluating the quantity of dust dispersion and controlling environmental pollution are crucial for supporting green mining. In this paper, dust monitoring above the open pit mine was carried out with an unmanned aerial vehicle (UAV) on board. The dust distribution patterns above the open pit mine were studied in different vertical and horizontal directions at different heights. The results show that the temperature changes less in the morning and more at noon in winter. At the same time, the isothermal layer becomes thinner and thinner as the temperature rises, which makes it easy for dust to spread. The horizontal dust is mainly concentrated at 1300 and 1550 elevations. The dust concentration is polarized at 1350-1450 elevation. The most serious exceedance is at 1400 elevation, with TSP (the concentration of total suspended particulate), PM10 (particulates with aerodynamic diameter < 10 μm), and PM2.5 (particulates with aerodynamic diameter < 2.5 μm) accounting for 188.8%, 139.5%, and 113.8%, respectively. The height is 1350-1450 elevation. Dust monitoring technology carried out by UAV can be applied to the study of dust distribution in the mining field, and the research results can provide reference for other open pit mines. It can also provide a basis for law enforcement part to carry out law enforcement, which has expanded and wide practical application value.

UAV-Based Wildland Fire Air Toxics Data Collection and Analysis

Smoke plumes emitted from wildland-urban interface (WUI) wildfires contain toxic chemical substances that are harmful to human health, mainly due to the burning of synthetic components. Accurate measurement of these air toxics is necessary for understanding their impacts on human health. However, air pollution is typically measured using ground-based sensors, manned airplanes, or satellites, which all provide low-resolution data. Unmanned Aerial Vehicles (UAVs) have the potential to provide high-resolution spatial and temporal data due to their ability to hover in specific locations and maneuver with precise trajectories in 3-D space. This study investigates the use of an octocopter UAV, equipped with a customized air quality sensor package and a volatile organic compound (VOC) air sampler, for the purposes of collecting and analyzing air toxics data from wildfire plumes. The UAV prototype developed has been successfully tested during several prescribed fires conducted by the California Department of Forestry and Fire Protection (CAL FIRE). Data from these experiments were analyzed with emphasis on the relationship between the air toxics measured and the different types of vegetation/fuel burnt. BTEX compounds were found to be more abundant for hardwood burning compared to grassland burning, as expected.

Signal-to-Noise Ratio Analysis for the Voltage-Mode Read-Out of Quartz Tuning Forks in QEPAS Applications

Quartz tuning forks (QTFs) are employed as sensitive elements for gas sensing applications implementing quartz-enhanced photoacoustic spectroscopy. Therefore, proper design of the QTF read-out electronics is required to optimize the signal-to-noise ratio (SNR), and in turn, the minimum detection limit of the gas concentration. In this work, we present a theoretical study of the SNR trend in a voltage-mode read-out of QTFs, mainly focusing on the effects of (i) the noise contributions of both the QTF-equivalent resistor and the input bias resistor R_L of the preamplifier, (ii) the operating frequency, and (iii) the bandwidth (BW) of the lock-in amplifier low-pass filter. A MATLAB model for the main noise contributions was retrieved and then validated by means of SPICE simulations. When the bandwidth of the lock-in filter is sufficiently narrow (BW = 0.5 Hz), the SNR values do not strongly depend on both the operating frequency and R_L values. On the other hand, when a wider low-pass filter bandwidth is employed (BW = 5 Hz), a sharp SNR peak close to the QTF parallel-resonant frequency is found for large values of R_L (R_L > 2 MΩ), whereas for small values of R_L (R_L < 2 MΩ), the SNR exhibits a peak around the QTF series-resonant frequency.

Odor source localization of multi-robots with swarm intelligence algorithms: A review

The use of robot swarms for odor source localization (OSL) can better adapt to the reality of unstable turbulence and find chemical contamination or hazard sources faster. Inspired by the collective behavior in nature, swarm intelligence (SI) is recognized as an appropriate algorithm framework for multi-robot system due to its parallelism, scalability and robustness. Applications of SI-based multi-robots for OSL problems have attracted great interest over the last two decades. In this review, we firstly summarize the trending issues in general robot OSL field through comparing some basic counterpart concepts, and then provide a detailed survey of various representative SI algorithms in multi-robot system for odor source localization. The research field originates from the first introduction of the standard particle swarm optimization (PSO) and flourishes in applying ever-increasing quantity of its variants as modified PSOs and hybrid PSOs. Moreover, other nature-inspired SI algorithms have also demonstrated the diversity and exploration of this field. The computer simulations and real-world applications reported in the literatures show that those algorithms could well solve the main problems of odor source localization but still retain the potential for further development. Lastly, we provide an outlook on possible future research directions.

Toward UAV-based methane emission mapping of Arctic terrestrial ecosystems

Methane is an important greenhouse gas, and emissions are expected to rise in Arctic wetland ecosystems when temperatures increase due to climate change. However, current emission estimates are associated with large uncertainties because methane shows high spatial variability. A central problem is that existing methods are often spatially restricted due to limitations in access, cost, power availability, and in need of high maintenance levels. Our study explores how a setup consisting of an unmanned aerial vehicle and a high-precision trace gas analyzer can complement well-established methods, like mobile flux chambers and eddy covariance towers, by providing independent maps of spatial variability in emissions at the landscape scale. In Zackenberg Valley, Northeast Greenland, we mapped concentration measurements from a high-precision trace gas analyzer with a reported precision of 0.6 parts per billion in a high-Arctic tundra fen ecosystem. We connected the analyzer via a long tube to a consumer-grade quadcopter, finding that the combined setup could differentiate near-surface methane concentrations of less than 5 parts per billion within a few meters under favorable weather conditions. Five of ten campaigns showed that relative methane concentration hot spots and cold spots significantly correlated with areas showing relatively high and low emissions (ranging from 1.40 to 7.4 mg m^-2 h^-1) during study campaigns in previous years. Concurrent measurements in a stationary automated chamber setup showed comparatively low methane emissions (~0.1 to 3.9 mg m^-2 h^-1) compared to previous years, indicating that a further improved UAV-analyzer setup could demonstrate clear differences in an ecosystem where methane emissions are generally higher. Calm conditions with some degree of air mixing near the surface were best suited for the mapping. Windy and wet conditions should be avoided, both for the reliability of the mapping and for safely navigating the unmanned aerial vehicle.

Concept of Using an Unmanned Aerial Vehicle (UAV) for 3D Investigation of Air Quality in the Atmosphere—Example of Measurements near a Roadside

A substantial amount of air pollution is emitted from urban sources. Hence, investigating air pollutant dispersion from urban sources is of great importance. The mechanisms influencing air pollutant dispersion also need to be studied thoroughly. Unmanned Aerial Vehicle (UAV)-based systems offer great potential for mobile exploration of air pollutants in the lower atmosphere due to the high maneuverability of multi-rotor UAVs. The aim of this study was to develop an effective UAV system that can perform high-resolution three-dimensional profiling of pollutants, such as particulate matter (PM), ultrafine particles (UFP), black carbon (BC), as well as meteorological parameters, including air temperature, relative humidity, pressure, wind speed, and wind direction. Different experiments were performed to finalize the positioning of the instruments on the UAV platform so as not to destabilize the drone during flight, even when the wind speed is high and during turbulent flight conditions. Another very crucial question is where to place the air inlet of the measurement devices. In addition, field tests were conducted to evaluate the stability of the UAV platform and the in-flight performance of the sensors. This UAV platform was deployed to perform vertical profiles at the University campus in Stuttgart-Vaihingen and in an area near the campus, close to the federal highway B14. The measurement campaign was performed on three days in February 2021, with a maximum flight height of 120 m above ground. The vertical profiles showed that concentrations were higher on the ground due to the proximity to the source and that high wind speeds assisted pollutant dispersion. The horizontal profiles showed that the pollutant concentrations were higher at the roadside and decreased with increasing distance from the road. In conclusion, this UAV platform represented a low-cost, practical, and reliable method for studying the three-dimensional distribution of pollutants near the source.

The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform

Nowadays, we observe a great interest in air pollution, including exhaust fumes. This interest is manifested in both the development of technologies enabling the limiting of the emission of harmful gases and the development of measures to detect excessive emissions. The latter includes IoT systems, the spread of which has become possible thanks to the use of low-cost sensors. This paper presents the development and field testing of a prototype pollution monitoring system, allowing for both online and off-line analyses of environmental parameters. The system was built on a UAV and WebRTC-based platform, which was the subject of our previous paper. The platform was retrofitted with a set of low-cost environmental sensors, including a gas sensor able to measure the concentration of exhaust fumes. Data coming from sensors, video metadata captured from 4K camera, and spatiotemporal metadata are put in one situational context, which is transmitted to the ground. Data and metadata are received by the ground station, processed (if needed), and visualized on a dashboard retrieving situational context. Field studies carried out in a parking lot show that our system provides the monitoring operator with sufficient situational awareness to easily detect exhaust emissions online, and delivers enough information to enable easy detection during offline analyses as well.

Continuous and near real-time measurements of gaseous elemental mercury (GEM) from an Unmanned Aerial Vehicle: A new approach to investigate the 3D distribution of GEM in the lower atmosphere

We present the first real attempt to directly and continuously measure GEM through a Lumex RA-915 M, designed for real-time detection of mercury vapor, mounted on an UAV (Unmanned Aerial Vehicle, namely a heavy-lift octocopter), inside and outside the former Hg-mining area of Abbadia San Salvatore (Mt. Amiata, Italy), known as a GEM source. We tested the effectiveness of the UAV-Lumex combination at different heights in selected sites pertaining to both mining facilities and surrounding urban zones, shedding light on the GEM spatial distribution and concentration variability. The Lumex great sensitivity and the octocopter optimal versatility and maneuverability, both horizontally and vertically, allowed to depict the GEM distribution in the atmosphere up to 60 m above the ground. The acquisition system was further optimized by: i) synchronizing Lumex and UAV GPS data by means of a stand-alone GPS that was previously synchronized with Lumex; ii) using a vertical sampling tube (1.20 m high) connected to the Lumex inlet to overcome the rotors strong airflows and turbulence that would have affected GEM measurements; iii) supplying the octocopter with batteries for power supply to avoid the release of exhaust gases; iv) taking the advantage of the UAV ability to land in small spaces and stop at selected altitudes. The resulting dot-map graphical representations, providing a realistic 3D picture of GEM vertical profiling during the flights in near real-time, were useful to verify whether the guideline concentrations indicated by competent authorities were exceeded. The results showed that the GEM concentrations in the urban area, located a few hundred meters from the mining structures, and close to already reclaimed areas remained at relatively low values. Contrarily, GEM contents showed significant variations and the highest concentrations above the facilities containing the old furnaces, where increasing GEM concentrations were recorded at decreasing heights or downwind.

Quantitative analysis and spatial and temporal distribution of volatile organic compounds in atmospheric air by utilizing drone with miniaturized samplers

Our second generation air sampling drone system, allowing the simultaneous use of four solid phase microextraction (SPME) Arrow and four in-tube extraction (ITEX) units, was employed for collection of atmospheric air samples at different spatial and temporal dimensions. SPME Arrow coated with two types of materials and ITEX with 10% polyacrylonitrile as sorbent were used to give a more comprehensive chemical characterization of the collected air samples. Before field sampling, miniaturized samplers went through quality control and assurance in terms of reproducibility (RSD ≤14.1%, N = 4), equilibrium time (≥10 min), breakthrough volume (1.8 L) and storage time (up to 48 h). 128 air samples were collected under optimal sampling conditions from July to September 2019 at the SMEAR II station and Qvidja farm, Finland. 347 VOCs were identified in the air samples either on-site or in the laboratory by thermal desorption gas chromatography - mass spectrometry, and they were quantified/semiquantified using Partial Least Squares Regression models. Individual models were developed for the different coatings and packing materials using gas phase standards obtained by an automatic permeation system. Average gas phase VOC concentrations ranged from 0.1 (toluene, the SMEAR II station) to 680 ng L^-1 (acetone, Qvidja farm). Average VOC concentrations in aerosols ranged from 0.1 (1,4-cyclohexadiene, the SMEAR II station) to 2287 ng L^-1 (megastigma-4,6,8-triene, Qvidja farm). Clear differences in results were seen for samples collected at the SMEAR II station and Qvidja farm, between VOC compositions in gas phase and aerosols, and between the sampling site and height.

Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution

This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM2.5 sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM2.5 trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.

Sensors and systems for air quality assessment monitoring and management: A review

Air quality (AQ) is a global concern for human health management. Therefore, air quality monitoring (AQM) and its management is a must-needed activity for the current world environment. A systematic review of various sensors and systems for AQ management may strengthen our understanding of the monitoring and management of AQ. Thus, the current review presents details on sensors/systems available for AQ assessment, monitoring, and management. First, we had gone through the published literature based on special keywords including AQM, Particulate Matter (PM), Carbon Mono-oxide (CO), Sulfur di-Oxide (SO2), and Nitrogen di-Oxide (NO2) among others, and identified the current scenario of research in AQ management. We discussed various sensors/systems available for the AQ management based on self-conceptualised five major categories including, ground-based AQS (wet chemistry) systems, ground-based digital sensors systems, aerial sensors systems, satellite-based sensors systems, and integrated systems. The prospects in the field of AQ assessment and management (AQA&M) were then discussed in detail. We concluded that the AQA&M can be better achieved by coupling new technologies like ground-based smart sensors, satellite remote sensing sensors, Geospatial technologies, and computational technologies like machine learning, Artificial intelligence, and Internet of Things (IoT). The current work may lead to a junction of information for connecting these sensors/systems, which is expected to be beneficial in future AQ research and management.

In Situ, Rotor-Based Drone Measurement of Wind Vector and Aerosol Concentration in Volcanic Areas

Unmanned aerial vehicles (UAVs), represented by rotor-based drones, are suitable for volcanic observations owing to the advantages of mobility and safety. In this study, vertical profiles of wind and aerosol concentrations at altitudes up to 1000 m around Mt. Sakurajima, one of the most active volcanoes in Japan, were measured in situ using a drone equipped with an ultrasonic anemometer and aerosol sensor. The drone-measured wind profiles were compared with Doppler LiDAR data and analysis values derived from a meteorological model. Drone-measured vertical profiles collected at a vertical speed of 1 m·s−1 (upward and downward) showed strong agreement with the LiDAR observations, as did the averaged values of hovering drone measurements. Obvious vertical wind shear was found by the drone in the vicinity of Mt. Sakurajima. An aerosol sensor was installed on the drone with the capability to measure fine (PM2.5) and coarse particles (PM10) simultaneously; in this manner, volcanic ash and aerosol pollutants around the volcano could be distinguished. Thus, it was proven that drones could be applied to investigate wind conditions and aerosols in situ, even at dangerous locations near active volcanoes.

Estimation of radiative forcing and heating rate based on vertical observation of black carbon in Nanjing, China

Owing to a lack of vertical observations, the impacts of black carbon (BC) on radiative forcing (RF) have typically been analyzed using ground observations and assumed profiles. In this study, a UAV platform was used to measure high-resolution in-situ vertical profiles of BC, fine particles (PM_2.5), and relevant meteorological parameters in the boundary layer (BL). Further, a series of calculations using actual vertical profiles of BC were conducted to determine its impact on RF and heating rate (HR). The results show that the vertical distributions of BC were strongly affected by atmospheric thermodynamics and transport. Moreover. Three main types of profiles were revealed: Type I, Type II, Type III, which correspond to homogenous profiles (HO), negative gradient profiles (NG), and positive gradient profiles (PG), respectively. Types I and II were related to the diurnal evolution of the BL, and Type III was caused by surrounding emissions from high stacks and regional transport. There were no obvious differences in RF calculated for HO profiles and corresponding surface BC concentrations, unlike for NG and PG profiles. RF values calculated using surface BC concentrations led to an overestimate of 13.2 W m^-2 (27.5%, surface) and 18.2 W m^-2 (33.4%, atmosphere) compared to those calculated using actual NG profiles, and an underestimate of approximately 15.4 W m^-2 (35.0%, surface) and 16.1 W m^-2 (29.9%, atmosphere) compared to those calculated using actual PG profiles. In addition, the vertical distributions of BC HR exhibited clear sensitivity to BC profile types. Daytime PG profiles resulted in a positive vertical gradient of HR, which may strengthen temperature inversion at high altitudes. These findings indicate that calculations that use BC surface concentrations and ignore the vertical distribution of BC will lead to substantial uncertainties in the effects of BC on RF and HR.

Use of an unmanned aircraft system to quantify NO x emissions from a natural gas boiler

Aerial emission sampling of four natural gas boiler stack plumes was conducted using an unmanned aerial system (UAS) equipped with a lightweight sensor-sampling system (the "Kolibri") for measurement of nitrogen oxide (NO), and nitrogen dioxide (NO₂), carbon dioxide (CO₂), and carbon monoxide (CO). Flights (n=22) ranged from 11 to 24min in duration at two different sites. The UAS was maneuvered into the plumes with the aid of real-time CO₂ telemetry to the ground operators and, at one location, a second UAS equipped with an infrared-visible camera. Concentrations were collected and recorded at 1Hz. The maximum CO₂, CO, NO, and NO₂ concentrations in the plume measured were 10000, 7, 27, and 1.5 ppm, respectively. Comparison of the NO _x emissions between the stack continuous emission monitoring systems and the UAS-Kolibri for three boiler sets showed an average of 5.6% and 3.5% relative difference for the run-weighted and carbon-weighted average emissions, respectively. To our knowledge, this is the first evidence of the accuracy performance of UAS-based emission factors against a source of known strength.

Use of an unmanned aircraft system to quantify NO x emissions from a natural gas boiler

Aerial emission sampling of four natural gas boiler stack plumes was conducted using an unmanned aerial system (UAS) equipped with a lightweight sensor-sampling system (the "Kolibri") for measurement of nitrogen oxide (NO), and nitrogen dioxide (NO₂), carbon dioxide (CO₂), and carbon monoxide (CO). Flights (n=22) ranged from 11 to 24min in duration at two different sites. The UAS was maneuvered into the plumes with the aid of real-time CO₂ telemetry to the ground operators and, at one location, a second UAS equipped with an infrared-visible camera. Concentrations were collected and recorded at 1Hz. The maximum CO₂, CO, NO, and NO₂ concentrations in the plume measured were 10000, 7, 27, and 1.5 ppm, respectively. Comparison of the NO _x emissions between the stack continuous emission monitoring systems and the UAS-Kolibri for three boiler sets showed an average of 5.6% and 3.5% relative difference for the run-weighted and carbon-weighted average emissions, respectively. To our knowledge, this is the first evidence of the accuracy performance of UAS-based emission factors against a source of known strength.

Calibration of Electrochemical Sensors for Nitrogen Dioxide Gas Detection Using Unmanned Aerial Vehicles

For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO₂. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO₂ measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO₂ measurements in future outdoor experiments that include electrochemical sensor integration with UAV’s.

Vertical Profiles of Atmospheric Species Concentrations and Nighttime Boundary Layer Structure in the Dry Season over an Urban Environment in Central Amazon Collected by an Unmanned Aerial Vehicle

Nighttime vertical profiles of ozone, PM2.5 and PM10 particulate matter, carbon monoxide, temperature, and humidity were collected by a copter-type unmanned aerial vehicle (UAV) over the city of Manaus, Brazil, in central Amazon during the dry season of 2018. The vertical profiles were analyzed to understand the structure of the urban nighttime boundary layer (NBL) and pollution within it. The ozone concentration, temperature, and humidity had an inflection between 225 and 350 m on most nights, representing the top of the urban NBL. The profile of carbon monoxide concentration correlated well with the local evening vehicular congestion of a modern transportation fleet, providing insight into the surface-atmosphere dynamics. In contrast, events of elevated PM2.5 and PM10 concentrations were not explained well by local urban emissions, but rather by back trajectories that intersected regional biomass burning. These results highlight the potential of the emerging technologies of sensor payloads on UAVs to provide new constraints and insights for understanding the pollution dynamics in nighttime boundary layers in urban regions.

Protocol development for real-time ship fuel sulfur content determination using drone based plume sniffing microsensor system

Pollutants from navigation sector are key contributors to emission inventories of most coastal cities with heavy port activities. The use of high fuel sulfur content (FSC) bunker oil by ocean going vessels (OGVs) has been identified as a major source of sulfur dioxide (SO₂). Government authorities all over the world, including Hong Kong government, have implemented air pollution control regulations to cap FSC of fuel used by OGVs to 0.5%, from the existing 3.5%, to reduce SO₂ emissions. However, the lack of efficient screening tools to identify non-compliant OGVs has prevented effective enforcement. This study developed and evaluated an unmanned aerial vehicle (UAV)-borne lightweight (750 g) microsensor system (MSS), which is capable of measuring ship plume SO₂, NO₂, NO, CO₂, CO, and particulate matter in real-time. Extensive experiments were conducted on the sensor system to evaluate its performance during laboratory and field operations. The effects of cross-sensitivity and meteorological conditions were studied and incorporated to account for the measurement conditions in dispersed ship plumes. The SO₂ to CO₂ concentration ratio-based FSC expression was formulated as per the 2016 European Union Directive and Regulations. Furthermore, the impact of plume dilution on the accuracy of FSC measurement was investigated at different stages using the MSS, with and without the UAV in both simulated conditions and real-world scenarios, maintaining a safe distance from the OGV exhaust stacks. The study demonstrates the robustness of using UAV-borne sensor system for ship emission sniffing and FSC determination. The results will assist in development of a technological framework for effective enforcement of ship emission control regulations.

Performance Assessment of Portable Optical Particle Spectrometer (POPS)

Accurate representation of atmospheric aerosol properties is a long-standing problem in atmospheric research. Modern pilotless aerial systems provide a new platform for atmospheric in situ measurement. However, small airborne platforms require miniaturized instrumentation due to apparent size, power, and weight limitations. A Portable Optical Particle Spectrometer (POPS) is an emerged instrument to measure ambient aerosol size distribution with high time and size resolution, designed for deployment on a small unmanned aerial system (UAS) or tethered balloon system (TBS) platforms. This study evaluates the performance of a POPS with an upgraded laser heater and additional temperature sensors in the aerosol pathway. POPS maintains its performance under different environmental conditions as long as the laser temperature remains above 25 °C and the aerosol flow temperature inside the optical chamber is 15 °C higher than the ambient temperature. The comparison between POPS and an Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) suggests that the coincidence error is less than 25% when the number concentration is less than 4000 cm⁻³. The size distributions measured by both of them remained unaffected up to 15,000 cm⁻³. While both instruments’ sizing accuracy is affected by the aerosol chemical composition and morphology, the influence is more profound on the POPS.

Daytime Evolution of Lower Atmospheric Boundary Layer Structure: Comparative Observations between a 307-m Meteorological Tower and a Rotary-Wing UAV

A 307-m tall meteorological tower was used to evaluate meteorological observation data obtained using a rotary-wing unmanned aerial vehicle (UAV). A comparative study between the tower and UAV observations was conducted during the daytime (06:00 to 19:00 local time (LT)) in the summer of 2017 (16–18th August). Hourly vertical profiles of air temperature, relative humidity, black carbon (BC), and ozone (O3) concentrations were obtained for up to 300 m height. Statistical metrics for evaluating the accuracy of UAV observations against the tower observation showed positive (potential temperature) and negative (relative humidity) biases, which were within acceptable ranges. The daytime evolution of the lower atmospheric boundary layer (ABL) was successfully captured by the hourly UAV observations. During the early morning, a large vertical slope of potential temperature was observed between 100 and 140 m, corresponding to the stable ABL height. The large vertical slope coincided with the large differences in BC and O3 concentrations between altitudes below and above the height. The transition from stable to convective ABL was observed at 10–11 LT, indicated by the ABL height higher than 300 m in the convective ABL. Finally, we provide several recommendations to reduce uncertainties of UAV observation.

Post-movement stabilization time for the downwash region of a 6-rotor UAV for remote gas monitoring

Unmanned aerial vehicles (UAV) have been used to monitor gas emissions for research projects, though downwash, the airflow produced by the UAV rotors, is potentially capable of artificially altering gas concentration measurements. Anemometers, placed at ten different distances below a 6-rotor UAV, measured air speeds in the downwash region. The collected data was used in combination with UAV rotor speed data to determine the stabilization time of the downwash region after the UAV has returned to a stable hovering position. The stabilization time will determine the amount of time after UAV movement until reliable concentration readings can be obtained within the downwash region. This paper presents stabilization times after vertical upward and rotational UAV movement.

An optimized multicopter UAV sounding technique (MUST) for probing comprehensive atmospheric variables

The unique maneuverability, ease of deployment, simplicity in logistics, and relatively low costs of multicopters render them effective vehicles for low atmospheric research. While many efforts have contributed to the fundamental success of atmospheric applications of multicopters in the past, several challenges remain, including limited measurable variables, possible response-delay in real-time observations, insufficient measurement accuracy, endurance of harsh conditions and tolerance towards interferences. To address these challenges and further fortify the applicability in diversified research disciplines, this study developed an optimized multicopter UAV sounding technique (MUST). The MUST serves as an integrated platform by combining self-developed algorithms, optimized working environments for sensors/monitors, and retrofitted sampling devices to probe a comprehensive set of atmospheric variables. These variables of interest include meteorological parameters (temperature, relative humidity, pressure, wind direction and speed), the chemical composition (speciated VOCs, CO, CO₂, CH₄, CO₂ isotopologues, O₃, PM2.5, and black carbon), and the radiation flux, as well as visible and thermal images. The aim of this study is to achieve the following objectives: 1. to easily probe a comprehensive set of near-surface atmospheric variables; 2. to improve data quality by correcting for sensors' delay in real-time observations and minimizing environmental interferences; and 3. to enhance the versatility and applicability of aerial measurements by incorporating necessary hardware and software. Field launching cases from the surface to a maximum height of 1000 m were conducted to validate the robustness of the integrated MUST platform with sufficient speed, accuracy and resolution for the target variables.

Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor

The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO₂. Field tests were conducted in a 100 m² area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.

Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China

An unmanned aerial vehicle (UAV) equipped with miniature monitors was used to study the vertical profiles of PM_2.5 (particulate matter with a ≤2.5-μm diameter) and black carbon (BC) in Macau, China, from the surface to 500 m above ground level (AGL). Twelve- and 11-day measurements were conducted during February and March 2018, respectively. In total, 46 flights were conducted between 05:00 and 06:00 AM Local Time (LT). The average concentrations of PM_2.5 and BC were significantly lower in March (40.1 ± 17.9 and 2.3 ± 2.0 μg m^-3, respectively) when easterly winds prevailed, compared with those in February (69.8 ± 35.7 and 3.6 ± 2.0 μg m^-3, respectively) when northerly winds dominated. In general, PM_2.5 concentrations decreased with height, with a vertical decrement of 0.2 μg m^-3 per 10 m. BC concentrations exhibited diverse vertical profiles with an overall vertical decrement of 0.1 μg m^-3 per 10 m. Meteorological analyses including back-trajectory analysis and atmospheric stability categorization revealed that both advection and convection transports may have notable influences on the vertical profiles of PM pollutants. The concentration of PM pollutants above the boundary layer was lower than that within the layer, thus exhibiting a sigmoid profile in some cases. In addition, the lighting of firecrackers and fireworks on February 16 (first day of the Chinese New Year) resulted in the elevated concentrations of PM_2.5 and BC within 150 m AGL. The takeoff of a civil flight on February 10 may have resulted in a substantial increase in the PM_2.5 concentrations from 80.8 (±2.1) μg m^-3 at the ground level to 119.2 (±9.3) μg m^-3 at a height of 330 m. Although the results are confined to a height of 500 m AGL, the current study provides a useful dataset for PM vertical distributions, complementing the spatiotemporal variations by ground-based measurements.

An Unmanned Aerial Vehicle-Based Gas Sampling System for Analyzing CO2 and Atmospheric Particulate Matter in Laboratory

We developed and tested an unmanned aerial vehicle-based gas sampling system (UGSS) for collecting gases and atmospheric particulate matter (PM). The system applies an alternative way of collecting both vertical and horizontal transects of trace gases in order to analyze them in the laboratory. To identify the best position of the UGSS intake port, aerodynamic flow simulations and experimental verifications of propeller airflow were conducted with an unmanned aerial vehicle (UAV) in hover mode. The UGSS will automatically replace the original gas in the system with gas from a target location to avoid the original gas being stored in the air bags. Experimental results show that the UGSS needs 5 s to replace the system’s own original gas using its pump. CO₂ and PM2.5/10 above the corn field are used as the test species to validate the accuracy of the CO₂ gas and PM concentrations collected by UGSS. Deming regression analyses showed good agreement between the measurements from the UGSS and the ground sampling station (y = 1.027x – 11.239, Pearson’s correlation coefficient of 0.98 for CO₂; y = 0.992x + 0.704, Pearson’s correlation coefficient of 0.99 for PM).The UGSS provides a measuring method that actively collects gases and PM for manual analyses in the laboratory.

Vertical and Horizontal Profiles of Particulate Matter and Black Carbon Near Elevated Highways Based on Unmanned Aerial Vehicle Monitoring

Highways passing through cities cause additional pollution inside the city. However, most of the current studies are using ground-based monitoring technologies, which make it difficult to capture the dispersion patterns of pollutants near elevated highways or transportation interchanges. The purpose of this study is to discover short-term three-dimensional variations in traffic-related pollutants based on unmanned aerial vehicles. The monitoring locations are at suburban elevated highway and transportation interchanges. The monitoring parameters include the particle number concentration (PN), particle mass concentration (PM), and black carbon (BC). The vertical profiles showed that most air pollutants increased significantly with the height of the elevated highways. Compared with the ground level, PNs increased by 54%–248% and BC increased by 201%. The decline rate of particle concentrations decreased with the increase of height and remained stable after 120 m. Furthermore, the R2 heatmap for regressions between each altitude showed that the linear relationship between 0–120 m was higher than that of other altitudes. In horizontal profiles, PNs spread to 100 m and then began to decline, BC began to decay rapidly after 50 m, but PMs varied less. After crossing another highway, PNs increased by 69–289%, PMs by 7–28%, and BC by 101%. Furthermore, the formation of new particles was observed at both locations as PN3 increased with distance within 100 m from the highway. This paper fills in the void of three-dimensional in situ monitoring near elevated highways, and can help develop and refine a three-dimensional traffic-related air pollution dispersion model and assess the impacts of transportation facilities on the urban environment.

A Drone-Based Bioaerosol Sampling System to Monitor Ice Nucleation Particles in the Lower Atmosphere

Terrestrial ecosystems can influence atmospheric processes by contributing a huge variety of biological aerosols (bioaerosols) to the environment. Several types of biological particles, such as pollen grains, fungal spores, and bacteria cells, trigger freezing processes in super-cooled cloud droplets, and as such can contribute to the hydrological cycle. Even though biogenic particles are known as the most active form of ice nucleation particles (INPs), the transport to high tropospheric altitudes, as well as the occurrence in clouds, remains understudied. Thus, transport processes from the land surface into the atmosphere need to be investigated to estimate weather phenomena and climate trends. To help fill this knowledge gap, we developed a drone-based aerosol particles sampling impinger/impactor (DAPSI) system for field studies to investigate sources and near surface transport of biological INPs. DAPSI was designed to attach to commercial rotary-wing drones to collect biological particles within about 100 m of the Earth’s surface. DAPSI provides information on particulate matter concentrations (PM10 & PM2.5), temperature, relative humidity, and air pressure at about 0.5 Hz, by controlling electrical sensors with an onboard computer (Raspberry Pi 3). Two remote-operated sampling systems (impinging and impacting) were integrated into DAPSI. Laboratory tests of the impinging system showed a 96% sampling efficiency for standardized aerosol particles (2 µm polystyrene latex spheres) and 84% for an aerosol containing biological INPs (Betula pendula). A series of sampling missions (12 flights) were performed using two Phantom 4 quadcopters with DAPSI onboard at a remote sampling site near Gosau, Austria. Fluorescence microscopy of impactor foils showed a significant number of auto-fluorescent particles < 0.5 µm at an excitation of 465–495 nm and an emission of 515–555 nm. A slight increase in ice nucleation activity (onset temperature between −27 °C and −31 °C) of sampled aerosol was measured by applying freezing experiments with a microscopic cooling technique. There are a number of unique opportunities for DAPSI to be used to study the transport of bioaerosols, particularly for investigations of biological INP emissions from natural sources such as birch or pine forests.

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.

Vertical Distribution of Particulates within the Near-Surface Layer of Dry Bulk Port and Influence Mechanism: A Case Study in China

Knowing the vertical distribution of ambient particulate matter (PM) will help port authorities choose the optimal dust-suppression measures to reduce PM concentrations. In this study, we used an unmanned aerial vehicle (UAV) to assess the vertical distribution (0–120 m altitude) of PM in a dry bulk port along the Yangtze River, China. Total suspended particulates (TSP), PM10, and PM2.5 concentrations at different altitudes were measured at seven sites representing different cargo-handling sites and a background site. Variations in results across sites make it not suitable to characterize the vertical distribution of PM concentration at this port using simple representative distributions. Bulk cargo particle size, fog cannon use, and porous fence all affected the vertical distribution of TSP concentrations but had only minor impacts on PM10 and PM2.5 concentrations. Optimizing porous fence layout according to weather conditions and cargo demand at port have the most potential for mitigating PM pollution related to port operation. As ground-based stations cannot fully measure vertical PM distributions, our methods and results represent an advance in assessing the impact of port activities on air quality and can be used to determine optimal dust-suppression measures for dry bulk ports.

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.

UAV-Based Air Pollutant Source Localization Using Combined Metaheuristic and Probabilistic Methods

Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.

Design and Evaluation of Sensor Housing for Boundary Layer Profiling Using Multirotors

Traditional configurations for mounting Temperature–Humidity (TH) sensors on multirotor Unmanned Aerial Systems (UASs) often suffer from insufficient radiation shielding, exposure to mixed and turbulent air from propellers, and inconsistent aspiration while situated in the wake of the UAS. Descent profiles using traditional methods are unreliable (when compared to an ascent profile) due to the turbulent mixing of air by the UAS while descending into that flow field. Consequently, atmospheric boundary layer profiles that rely on such configurations are bias-prone and unreliable in certain flight patterns (such as descent). This article describes and evaluates a novel sensor housing designed to shield airborne sensors from artificial heat sources and artificial wet-bulbing while pulling air from outside the rotor wash influence. The housing is mounted above the propellers to exploit the rotor-induced pressure deficits that passively induce a high-speed laminar airflow to aspirate the sensor consistently. Our design is modular, accommodates a variety of other sensors, and would be compatible with a wide range of commercially available multirotors. Extensive flight tests conducted at altitudes up to 500 m Above Ground Level (AGL) show that the housing facilitates reliable measurements of the boundary layer phenomena and is invariant in orientation to the ambient wind, even at high vertical/horizontal speeds (up to 5 m/s) for the UAS. A low standard deviation of errors shows a good agreement between the ascent and descent profiles and proves our unique design is reliable for various UAS missions.

Aerial drone as a carrier for miniaturized air sampling systems

The applicability of an aerial drone as a carrier for new passive and active miniaturized air sampling systems, including solid phase microextration Arrow (SPME Arrow) and in-tube extraction (ITEX), was studied in this research. Thermal desorption, gas chromatography and mass spectrometry were used for the determination of volatile organic compounds (VOCs) collected by the sampling systems. The direct comparison of the profiles of VOCs, simultaneously sampled in air by SPME Arrow system including four different coatings, allowed the elucidation of their adsorption selectivity. A more complex experimental design, involving 20 samples (10 flights) and non-supervised pattern recognition techniques, was needed for the clarification of the same sampling parameters in the case of five ITEX sorbent materials. In addition, ITEX sampling accessories, such as particle, water and ozone traps, were evaluated by comparing the results obtained for air samples simultaneously collected by two ITEX systems, packed with the same sorbent and furnished or not with sampling accessories. The effect of the aerial drone horizontal displacement (HD) on the sampling efficiency was clear in the case of SPME Arrow. The number of detected compounds and their relative peak area values (RPA) revealed a clear increase (4 and 43%, respectively) in comparison with samples collected without drone HD. However, just minor differences were observed in the case of ITEX (2 compounds and 9% of the ∑RPA). In addition, the system was able to provide almost simultaneous passive (SPME Arrow) and active (ITEX) samplings at different altitudes (5 and 50 m), being a good tool for low cost vertical profiling studies (∑RPA decreased over 35% for the samples collected at 50 m). Finally, the successful simultaneous air sampling by SPME Arrow and ITEX systems in two difficult access places, such as boreal forest and wetlands, was demonstrated, resulting in 21 and 31 detected compounds in forest and wetlands by SPME Arrow, and 27 and 39 compounds by ITEX.