Air Quality Monitoring in Urban Areas Using in-Situ and Satellite Data Within Era-Planet Project

Shelestov, Andrii; Kolotii, Andrii; Lavreniuk, Mykola; Medyanovskyi, Kyrylo; Vasiliev, V. N.; Bulanaya, Тatyana; Gomilko, Igor

doi:10.1109/igarss.2018.8518368

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…In this manuscript the authors have attempted to critically report the major findings and limitations of the current research on SEM. Soil monitoring (SM) [14,15,36], ocean environment monitoring (OEM), marine environment monitoring (MEM), air quality monitoring (AQM) [10,11,37,38], water quality monitoring (WQM) [14,39], and radiation monitoring (RM) [1,36] have been covered, by offering a wide analysis of different application fields of SEM.…”

Section: Related Research and Studymentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

411

109

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Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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Section: Related Research and Studymentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

411

109

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“…There is an increasing interest in modelling air quality based on sensor data. For instance, the ERA-PLANET project aimed at estimating the air quality at both city and regional levels, by crossing satellite and in-situ data gathered from fixed reference stations [3]. As an alternative to fixed stations, projects are now deploying low-cost air quality monitoring sensors in vehicles, such as trams [17] and buses [1,4,5].…”

Section: Related Workmentioning

confidence: 99%

“…To monitor air quality in cities, fixed and costly air sampling stations are often planted in key locations. To improve spatiotemporal resolution of the data generated, affordable mobile sensor networks are a promising alternative that is being tested in several cities worldwide [1][2][3][4][5]. The data gathered by these sensor networks are typically made available to citizens via traditional visualisation techniques based on colour-coded plots (e.g., [6,7]) representing pollutants concentrations and air quality indexes (e.g., [8]).…”

Section: Introductionmentioning

confidence: 99%

Game-Like 3D Visualisation of Air Quality Data

Teles

Mariano

Santana

2020

MTI

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The data produced by sensor networks for urban air quality monitoring is becoming a valuable asset for informed health-aware human activity planning. However, in order to properly explore and exploit these data, citizens need intuitive and effective ways of interacting with it. This paper presents CityOnStats, a visualisation tool developed to provide users, mainly adults and young adults, with a game-like 3D environment populated with air quality sensing data, as an alternative to the traditionally passive visualisation techniques. CityOnStats provides several visual cues of pollution presence with the purpose of meeting each user’s preferences. Usability tests with a sample of 30 participants have shown the value of air quality 3D game-based visualisation and have provided empirical support for which visual cues are most adequate for the task at hand.

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“…Shelestov et al [20] have done a detailed study of various satellite products for monitoring air quality in Kyiv smart city. ERA-PLANET project is aimed to develop a screening model for assessing the quality of air in urban areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Framework for Analyzing Air Quality using MatLab

Radhika¹,

Padmapriya*²

2019

IJRTE

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Nature of the air in city and urban regions is the most significant factor that legitimately impacts the frequency of infections and diminishes the personal satisfaction. Taking suitable choices in an opportune period relies upon the estimation and examination of the parameters of the ongoing air quality checking. On the other hand air contamination is ecological and a social issue. Air contamination is one of the biggest natural wellbeing dangers in the world today. The utilization of multi-parameter air quality observing frameworks makes it conceivable to do an itemized level investigation of real poisons and their sources. These air quality observing frameworks are significant segments in many shrewd city ventures for checking air quality and for controlling the primary poison fixations in urban zones. In this research work a methodology for practical estimation of air quality is proposed. This application has been tried in the city and the estimation was contrasted and the yield information of the neighborhood ecological control expert stations. The results of the performance analysis demonstrate that this methodology can be utilized as an affordable option in contrast to the expert evaluation frameworks. In this research work an investigation on the contamination by traffic framework utilizing dataset with grouping strategy through MatLab.

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Air Quality Monitoring in Urban Areas Using in-Situ and Satellite Data Within Era-Planet Project

Cited by 10 publications

References 11 publications

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Game-Like 3D Visualisation of Air Quality Data

Novel Framework for Analyzing Air Quality using MatLab

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