Urban Air Pollution Monitoring Using Wireless Sensor Networks:  A Comprehensive Review

Pavani, Movva; Rao, P.Trinatha

doi:10.17762/ijcnis.v9i3.2708

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Additionally, this study relies solely on NDWI averages and does not integrate tidal data to assess coastal changes. However, this research can serve as a reference for dynamic observations related to renewable energy potential (Bender et al, 2017;Ihsan et al, 2022Ihsan et al, , 2021, air pollution (Pavani and Rao, 2017;A.D. Sakti et al, 2023), hazard (Das et al, 2006;Virtriana et al, 2023), and food security (Gommes et al, 2016;Virtriana et al, 2022) in a particular region.…”

Section: Prediction Change In Futurementioning

confidence: 99%

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Ihsan,

Harto,

Sakti

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The coastal area is an area that has a dense population with a lot of human activities that occur there. Due to environmental changes and human activities, changes often occur in coastal areas ranging from erosion and sedimentation. Changes must continuously be monitored to plan countermeasures due to the occurring phenomena. This study aims to create a website-based application to monitor coastal areas. This study will use Landsat data 5,7,8, and 9 to see changes in coastal areas. The analysis can be provided from 1985 until recent data by integrating four Landsat satellites. The NDWI index (Normalized Difference Wetness Index) analyzes changes occurring in coastal areas and differentiates between water and land area. The analysis is not only in the form of changes that occur in coastal areas but also in time series analysis, and trends that occur at a point can be analyzed using land trend analysis. The resulting website based on Cloud Computation in Google Earth Engine can be seen at the link https://bit.ly/MonitoringPesisir. This website can automatically update, and users can choose the location to monitor. This research is expected to be used by policymakers to monitor and plan the development and regulation of coastal areas.

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Section: Prediction Change In Futurementioning

confidence: 99%

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Ihsan,

Harto,

Sakti

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…[33] The paper provides a comparative analysis of machine learning-based urban air quality prediction techniques. [34] This paper reviews indoor and outdoor air pollution monitoring using wireless sensor networks. [35] The paper reviews multiple papers, reports, white papers, and various websites on the role of urban computing in air quality management.…”

Section: Contributions Of the Papermentioning

confidence: 99%

Urban Air Quality Measurements: A Survey

Usama¹

2022

Preprint

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Urban air quality is increasingly becoming a cause for concern for the health of the human population. The poor air quality is already wreaking havoc in major cities of the world, where serious health issues and reduction of average human life by a factor of years are reported. The air quality in developing countries can become worse as they undergo development. The urban air quality varies non-linearly depending upon the various factors such as land use, industrialization, waste disposal, traffic volume, etc. To address this problem, it is necessary to look at the plethora of available literature from multiple perspectives such as types and sources of pollutants, meteorology, urban mobility, urban planning and development, health care, economics, etc. In this paper, we provide a comprehensive survey of the state-of-the-art in urban air quality. We first review the fundamental background on air quality and present the emerging landscape of urban air quality. We then explore the available literature from multiple urban air quality measurement projects and provides the insights uncovered in them. We then take a look at the sources that are significantly contributing to polluting the air quality. Finally, we highlight open issues and research challenges in dealing with urban air pollution.

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“…Another example of a gas sensor is the electrochemical one, in which a chemical reaction takes place between two electrodes made from catalytic metals. These sensors can be further classified in two types: potentiometric and amperometric [30].…”

Section: Aqi Sensorsmentioning

confidence: 99%

Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities

Popa

Dobrescu

Silvestru

et al. 2021

Sensors

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Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.

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Urban Air Pollution Monitoring Using Wireless Sensor Networks: A Comprehensive Review

Cited by 7 publications

References 20 publications

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

Urban Air Quality Measurements: A Survey

Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities

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