Airborne Particulate Matter Monitoring Using UAVs for Smart Cities and Urban Areas

Mayuga, Gian Paolo T.; Favila, C. M.; Oppus, Carlos; Macatulad, E. G.; Lim, Len Herald V.

doi:10.1109/tencon.2018.8650293

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Their field test showed that the UAV with a LoRa-based PM2.5 sensor could successfully perform data collection and transmit the data to the server in real-time with minimum human intervention. In another study, Mayuga et al [28] tested their aerial sensor system (which consists of a UAV with PM, temperature, and humidity sensors) to measure the PM in different elevations in high and light traffic conditions. Hu et al [29] implemented an IoT-based 3D system to measure and monitor air quality using a combination of aerial and ground sensing.…”

Section: B Environmentmentioning

confidence: 99%

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

et al. 2022

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Drones, or Unmanned Aerial Vehicles (UAVs), are among the most beneficial and emerging technologies, with a wide range of applications that can support the sustainability concerns of smart cities and ultimately improve citizens' quality of life. The goals of this systematic review were to explore the involvement of surveillance drones in smart cities in terms of application status, application areas, proposed models, and characteristics of drones. We conducted this systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We systematically searched the Web of Science and Scopus for journal articles and conference papers written in English and published up to August 2021. Of the 323 records identified, 43 met the inclusion criteria. Findings showed that surveillance drones were used in seven distinct research fields (transportation, environment, infrastructure, object or people detection, disaster management, data collection, and other applications). Air pollution and traffic monitoring were the dominant application areas. The majority of reviewed models were based on the application of rotary-wing single-drones with the camera as the aerial sensor. Reviewed models showed that the adoption of a single or multiple UAVs, either as a stand-alone technology or integrated with other technologies (e.g., internet of things, wireless sensor networks, convolutional neural networks, artificial intelligence, machine learning, computer vision, cloud computing, web applications), can offer efficient and sustainable solutions compared to conventional surveillance methods. This review can benefit academic researchers and practitioners.

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Section: B Environmentmentioning

confidence: 99%

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

et al. 2022

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“…photos, captured video frames, and thermal images, can be collected through different image-sensing technologies [11]. Weather monitoring has also been implemented in smart cities and urban areas through UAVs and sensors, examining air quality in small urban areas and showing trends in Particulate Matter (PM) concentrations about altitude and traf􀅫ic conditions [12].…”

Section: Uav Use For Urban Climate Measurementmentioning

confidence: 99%

Interdisciplinary Collaborative Learning with Modular Programming and Information Visualization of Urban Smart Spaces

Wang¹,

Wu²

2022

J. adv. tec. eng. res.

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The purpose of this research is for interdisciplinary students, with the theme of smart city space, how to conduct collaborative learning on thinking, identi􀅫ication, and strategies for programming and visualization issues, to develop an intelligent interactive interface between users and environmental data. We have set up an interdisciplinary course on "Interactive Smart Space Design", which combines the 􀅫ields of interactive design, engineering, electrical machinery, and electronics, trying to introduce unmanned aerial vehicle simulation path, modular programming, and information visualization modules into the course to conduct programming and computational thinking learning, guiding students to conceive and design, and in-depth development of programs and development practices. Finally, analyze and evaluate the learning effect through the work report and Expert assessment. The results show that the modular programming method is more suitable for students with low information ability, and the information visualization performs better in the later stage of the project, re􀅫lecting the actual staged division of labour in the industry. The interdisciplinary collaborative learning process can re􀅫lect and revise problem points in the process of thematic decision-making, but coordination and division of labour impact team cooperation.

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“…The proposed solution was verified on the campus of Mascara University in Algeria. The article [12] presented measurements carried out in the city during periods of heavy traffic with the use of a quadrocopter robot. A simple recorder based on the Arduino platform equipped with temperature, humidity, and particulate matter sensors was installed under the robot.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones

2022

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Polluted air causes enormous damage to human health. There is a high demand to find a solution for locating the places of illegal waste incineration due to the persistent smog problem. The use of multi-rotor drones for that purpose has now become one of the important research topics. The aim of the work was to check the possibility of using simple algorithms to search for the source of pollution. The algorithms that require low computing power, which may be part of the robot’s measurement and the control system’s internal software, were considered. The focus was on building a system based on a single robot that independently searches an area of a certain size. The simulation of the accuracy and scalability of the three different search algorithms was analysed for areas up to 200 m × 200 m. Two multi-rotor robots were prepared for the fieldwork. The validation of the two selected algorithms was carried out in outdoor environmental conditions. The fieldwork tests were carried out in areas with a maximum size of 100 m × 100 m. The obtained results were different, in particular on the wind speed and direction and the intensity of the pollution source. The random influence of these factors can verify the operation of the proposed system in practical applications. The difference between the true and the position of the source indicated by the robot was up to 15 m. That difference depended on the mutual arrangement of the measurement points and the pollution source location.

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Airborne Particulate Matter Monitoring Using UAVs for Smart Cities and Urban Areas

Cited by 14 publications

References 19 publications

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

Interdisciplinary Collaborative Learning with Modular Programming and Information Visualization of Urban Smart Spaces

Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones

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