Development of UAV-Based PM2.5 Monitoring System

Characterizing nighttime vertical profiles of atmospheric particulate matter and ozone in a megacity of south China using unmanned aerial vehicle measurements

Daytime atmospheric pollution has received wide attention, while the vertical structures of atmospheric pollutants at night play a crucial role in the photochemical process on the following day, which is still less reported. Focusing on Guangzhou, a megacity of South China, we established an unmanned aerial vehicle (UAV) equipped with micro detectors to collect consecutive high-resolution samples of fine particle (PM2.5), submicron particle (PM1.0), black carbon (BC) and ozone (O3) concentrations in the atmosphere, as well as the air temperature (AT) and relative humidity (RH) within a 500 m altitude during nighttime from Oct. 24th to Nov. 6th, 2018. The measurements showed that PM2.5, PM1.0, and BC decreased with altitude and were influenced by the nighttime shallow planetary boundary layer (PBL) where BC was more accumulated and fluctuated. In contrast, O3 was positively correlated with altitude. Backward trajectory clustering and Pasquill stability classification showed that advection and convection significantly influenced the vertical distribution of all pollutants, particularly particulate matter. External air masses carrying high concentrations of pollutants increased PM1.0 and PM2.5 levels by 145% and 455%, respectively, compared to unaffected periods. The ratio of BC to PM2.5 indicated that local emissions had a minor role in nighttime particulate matter. Vertical transport caused by atmospheric instability reduced the differences in pollutant concentrations at various heights. Geodetector and generalized additive model showed that RH and BC accumulation in the PBL were significant factors influencing vertical changes of the secondary aerosol intensity as indicated by the ratio of PM1.0 to PM2.5. The joint explanation of RH and atmospheric stability with other variables such as BC is essential to understand the generation of secondary aerosols. These findings provide insights into regional and local measures to prevent and control night-time particulate matter pollution.

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.

The Gas Sensing Drone with the Lowered and Lifted Measurement Platform

A serious factor that limits the environmental applications of drones is the disturbance of the air pollution concentration field by the drone propulsion system. This work presents a gas-sensing drone offering measurements that are unaffected by this phenomenon. The novel development was based on the idea that, during measurements, the sensing device should be spatially separated from a zone influenced by the drone's rotors. To attain this goal, special equipment was designed that allows one to undock and lower the sensing device for measurement, lift it and dock for flight. The field experiments demonstrated the full functionality of the developed system and its superiority compared to a sensing platform mounted at the bottom of the drone. Higher measurement sensitivity and resolution were attained by lowering the sensing platform to the measurement point. This solution minimizes the rotor flow effect, ground effect, and pollution concentration field flattening. The test in real conditions confirmed that the designed construction assures drone stability. The presented technology may be an important step in developing effective mobile measurement tools that allow one to reach poorly accessible or dangerous places and perform measurements at a low cost and with high efficiency.

UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components-sensors, means of communication, processing, and presentation-in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

Powering UAV with Deep Q-Network for Air Quality Tracking

Tracking the source of air pollution plumes and monitoring the air quality during emergency events in real-time is crucial to support decision-makers in making an appropriate evacuation plan. Internet of Things (IoT) based air quality tracking and monitoring platforms have used stationary sensors around the environment. However, fixed IoT sensors may not be enough to monitor the air quality in a vast area during emergency situations. Therefore, many applications consider utilizing Unmanned Aerial Vehicles (UAVs) to monitor the air pollution plumes environment. However, finding an unhealthy location in a vast area requires a long navigation time. For time efficiency, we employ deep reinforcement learning (Deep RL) to assist UAVs to find air pollution plumes in an equal-sized grid space. The proposed Deep Q-network (DQN) based UAV Pollution Tracking (DUPT) is utilized to guide the multi-navigation direction of the UAV to find the pollution plumes' location in a vast area within a short duration of time. Indeed, we deployed a long short-term memory (LSTM) combined with Q-network to suggest a particular navigation pattern producing minimal time consumption. The proposed DUPT is evaluated and validated using an air pollution environment generated by a well-known Gaussian distribution and kriging interpolation. The evaluation and comparison results are carefully presented and analyzed. The experiment results show that our proposed DUPT solution can rapidly identify the unhealthy polluted area and spends around 28% of the total time of the existing solution.

Direct Measuring Particulate Matters in Smoke Plumes from Chimneys in a Textile Dyeing Industrial Park by a Self-Developed PM Detector on an UAV in Yangtze River Delta of China

Directly measuring particulate matters (PM) from chimneys in an industrial park is difficult due to it being hard to reach the peak heights. A self-developed PM detector on an unmanned aerial vehicle (UAV) had been deployed to directly measure the PM emissions in smoke plumes from chimneys in a textile dyeing industrial park. Compared with a commercial PM device (LD-5R, SIBATA, Kyoto, Japan), the self-developed detector showed similar performance with a good correlation (R² varying from 0.911 to 0.951) in simultaneously vertical PM measurements on UAV. The PM emissions from chimneys after different textile treating processes, including pigment printing, dyeing process, and digital printing, were investigated. PM mass concentrations and particle number concentrations (PNC) in different sizes were found to be significantly higher in pigment printing than those in dyeing process and digital printing by 2 or 3 times after electrostatic precipitation. The activated carbon adsorption and electrostatic precipitation were the major PM controlling techniques in the park. The PM mass concentrations and PNC were the highest in the process of dyeing after activated carbon adsorption with the concentrations of PM₁ (1000 μg·m^-3), PM_2.5 (1600 μg·m^-3), and PM₁₀ (2000 μg·m^-3), respectively. According to the results of PM and PNC, PM_2.5 was found to be the dominant particles accounting for 99% of the PM emissions. It may be due to the high temperature in thermo-fixing machine, which is beneficial to the PM_2.5 generation. This study revealed PM_2.5 was the dominant particles to be reduced in textile dyeing enterprises to mitigate PM pollution.

Wake Propagation and Characteristics of a Multi-Rotor Unmanned Vehicle in Forward Flight

In this study, experimental investigations are used to explore the wake propagation and characteristics of a multi-rotor unmanned air vehicle (UAV) in a forward flight mission. Qualitative smoke visualization is used first to gain a qualitative understanding of wake characteristics above and below the body of the multi-rotor UAV which is used as guidance for quantitative particle image velocimetry (PIV) experiments which better resolve the region in the vicinity of the multi-rotor UAV body. The experimental results over a wide range of advance ratios show that as the advance ratio increases, achieved by either lower rotor speeds or higher flight speeds, the distance by which the wake propagates below the UAV is reduced. While above the UAV, the flow returns to the freestream flow closer to the body as the advance ratio increases. Therefore, this study concludes that proximity effects are reduced as the advance ratio increases. Findings from this study can be used to inform in situ sensor placement so that sensor readings are minimally affected by the wake from the multi-rotor UAV. Velocity measurement corrections are provided for sensors mounted above the UAV which can be used to improve sensor data reliability in forward flight. These results can advance autonomous sensing and increase the utility of multi-rotor UAV observations while providing designers and users further guidance to avoid proximity effects.

Development of Fixed-Wing UAV 3D Coverage Paths for Urban Air Quality Profiling

Due to the ever-increasing industrial activity, humans and the environment suffer from deteriorating air quality, making the long-term monitoring of air particle indicators essential. The advances in unmanned aerial vehicles (UAVs) offer the potential to utilize UAVs for various forms of monitoring, of which air quality data acquisition is one. Nevertheless, most current UAV-based air monitoring suffers from a low payload, short endurance, and limited range, as they are primarily dependent on rotary aerial vehicles. In contrast, a fixed-wing UAV may be a better alternative. Additionally, one of the most critical modules for 3D profiling of a UAV system is path planning, as it directly impacts the final results of the spatial coverage and temporal efficiency. Therefore, this work focused on developing 3D coverage path planning based upon current commercial ground control software, where the method mainly depends on the Boustrophedon and Dubins paths. Furthermore, a user interface was also designed for easy accessibility, which provides a generalized tool module that links up the proposed algorithm, the ground control software, and the flight controller. Simulations were conducted to assess the proposed methods. The result showed that the proposed methods outperformed the existing coverage paths generated by ground control software, as it showed a better coverage rate with a sampling density of 50 m.

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.

Analysis of Air Pollution around a CHP Plant: Real Measurements vs. Computer Simulations

This study examines the concentrations of air pollution in the vicinity of a combined heat and power plant (CHP) and a communication route, using computer modeling of pollutant dispersion and spatial analysis based on real measurements in the city of Łódź, Poland, Europe. The research takes into account the concentrations of particulate matter (PM10, PM2.5, PM1.0) and gaseous pollutants (SO2 and VOC) in winter and summer. The spatial distribution of pollutants is discussed, including the presence of areas with increased accumulations of pollutants. Because atmospheric air has no natural boundaries, when analyzing any location, not only local sources of pollution, but also background pollution, should be analyzed. A clear difference was observed between the concentrations of pollutants in the summer and winter seasons, with significantly higher concentrations in the winter (heating) period. The impacts of road transport, individual heating systems, and combined heat and power plants were also assessed. Computer calculations confirmed that road transport accounted for the largest share of both PM and SO2 emissions. The CHP plant was responsible for the smallest percentage of dust emissions and was the next largest producer of SO2 emissions. The share of the total emissions from the individual sources were compared with the results of detailed field tests. The numerical analysis of selected pollution sources in combination with the field analysis shows that the identified pollution sources included in the analysis represent only a part of the total observed pollutant concentrations (suggesting that other background sources account for the rest).

Modeling Pollutant Emissions: Influence of Two Heat and Power Plants on Urban Air Quality

Large industrial plants, power plants, and combined heat and power plants are popularly believed to be the main sources of point emissions, affecting both local and global air quality. This is because these installations emit significant amounts of pollutants at high altitudes every year. In this study, we investigate the impact of two solid fuel (hard coal)-fired CHP plants located within the urban agglomeration on the air quality of the city of Lodz in Poland (Europe). We used an OPA03 computer software to model the spatial distribution of pollutants. The results show that the annual average concentrations of pollutants were highest at an altitude of 25 m above ground level and decreased at lower measurement heights. The concentrations did not exceed permissible levels, reaching only 4% of national and international regulatory limits. We also made field measurements during the winter heating period, using an unmanned aerial vehicle (UAV) equipped with sensors to map the distributions of dust and gas pollutants in the areas with the highest concentrations of emissions from the two heat and power plants. Overall, the field measurements confirmed that it is not high-altitude emissions that have the greatest impact on local air quality.