UAVs Based Particulate Matter Pollution Monitoring

Marinov, Marin B.; Topalov, Ivan; Ganev, Borislav; Gieva, Elitsa; Galabov, Vasil

doi:10.1109/et.2019.8878586

Cited by 10 publications

(11 citation statements)

References 4 publications

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“…Often measured are also oxides of nitrogen [13,18,19] and volatile organic compounds (VOCs) [11,20]. Some IoT systems measure general air quality parameters, expressed by the Indoor Air Quality (IAQ) indicator [11] or the Air Quality Index (AQI) [21]. For measurements of the concentration of gaseous air pollutants, low-cost sensors often are used [22], such as the Waveshare MQ-135 [12,20,23] and other sensors of the Waveshare MQ family [23,24], the Bosch BME680 [21,24], the Bosch BME688 [11], and the Sensirion SGP30 [11].…”

Section: Pollutantsmentioning

confidence: 99%

“…Some IoT systems measure general air quality parameters, expressed by the Indoor Air Quality (IAQ) indicator [11] or the Air Quality Index (AQI) [21]. For measurements of the concentration of gaseous air pollutants, low-cost sensors often are used [22], such as the Waveshare MQ-135 [12,20,23] and other sensors of the Waveshare MQ family [23,24], the Bosch BME680 [21,24], the Bosch BME688 [11], and the Sensirion SGP30 [11]. In the case of particulate matter, the PM2.5 is usually measured [18,19,21,[25][26][27], although some systems also measure PM10 [18,19,27,28] and PM1 [27].…”

Section: Pollutantsmentioning

confidence: 99%

“…For measurements of the concentration of gaseous air pollutants, low-cost sensors often are used [22], such as the Waveshare MQ-135 [12,20,23] and other sensors of the Waveshare MQ family [23,24], the Bosch BME680 [21,24], the Bosch BME688 [11], and the Sensirion SGP30 [11]. In the case of particulate matter, the PM2.5 is usually measured [18,19,21,[25][26][27], although some systems also measure PM10 [18,19,27,28] and PM1 [27]. For particulate matter measurements, such low-cost sensors as the Alphasense OPC-N2 [27], the Alphasense OPC-N3 [21], and the PMS7003 [28] are used.…”

Section: Pollutantsmentioning

confidence: 99%

“…In the case of particulate matter, the PM2.5 is usually measured [18,19,21,[25][26][27], although some systems also measure PM10 [18,19,27,28] and PM1 [27]. For particulate matter measurements, such low-cost sensors as the Alphasense OPC-N2 [27], the Alphasense OPC-N3 [21], and the PMS7003 [28] are used. Some solutions also use expensive, customized sensors, such as the Dylos DC1700 [25,26].…”

Section: Pollutantsmentioning

confidence: 99%

See 3 more Smart Citations

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

Chodorek

Yastrebov

2022

Sensors

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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.

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Section: Pollutantsmentioning

confidence: 99%

Section: Pollutantsmentioning

confidence: 99%

Section: Pollutantsmentioning

confidence: 99%

Section: Pollutantsmentioning

confidence: 99%

See 2 more Smart Citations

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

Chodorek

Yastrebov

2022

Sensors

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“…With the development of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), unmanned vehicles progressively take the place of human-operated vehicles to conduct complex or dangerous missions such as specific area searching and surveillance [1][2][3][4][5][6], farming [7][8][9][10], package delivery [11][12][13][14], and disaster relief [15][16][17][18]. In these applications, due to the high space utilization and multi-terrain adaptation property, UAVs can play a more important role than UGVs.…”

Section: Introductionmentioning

confidence: 99%

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

Ming

Huang

2021

Drones

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In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

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