Development of Cloud Based Air Pollution Information System Using Visualization

Han, Sang-Il; Seo, Jungyeon; Kim, Dae-Young; Kim, SeokHoon; Lee, HwaMin

doi:10.32604/cmc.2019.06071

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Open-source code software like R and the RStudio application enable cost-effective and appealing data visualization. RStudio generates script-format commands, featuring diverse modules that can be expanded for varied statistical calculations [21].…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java

Shofy,

Manessa,

Wibowo

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The rising demand for electricity, driven primarily by coal-fired power plants, has escalated concerns over hazardous gas emissions and their impact on air quality and human health. This study focuses on the Pelabuhan Ratu region, where there is a notable gap in understanding the spatial and temporal distribution of particulate matter (PM) and carbon monoxide (CO). To address this, we conducted a ground survey to measure concentrations of CO, PM2.5, and PM10 at various points. Additionally, we utilized Landsat 8 satellite imagery to predict the spatial distribution of these aerosols, while also developing a one-year temporal model. Pelabuhan Ratu's unique geomorphology, encompassing both mountains and coasts, significantly influences pollutant concentrations, which vary with elevation and proximity to the power plant. Employing the Random Forest machine learning algorithm, we predicted concentrations of CO, PM2.5, and PM10 by integrating ground-level gas concentrations with satellite-derived vegetation indices, ambient temperature, altitude, land use, wind direction, and humidity data. Our findings reveal varied predictive accuracies: the CO model exhibited a low correlation value (0.32) and a Root Mean Square Error (RMSE) of 136 ppm, suggesting a less reliable prediction. In contrast, the PM2.5 model showed a moderate correlation (0.474) with an RMSE of 18.4 µg/m³. The PM10 model performed slightly better, achieving a correlation of 0.56 and an RMSE of 55.4 µg/m³. These results underscore the challenges and potential of using integrated ground and satellite data for predicting air pollutant concentrations in complex geographic settings.

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Section: Methodsmentioning

confidence: 99%

Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java

Shofy,

Manessa,

Wibowo

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

View full text Add to dashboard Cite

show abstract

“…Deep learning, neural networks, and convolutional neural networks (CNNs) have been used for the classification and prediction of clouds, wind and precipitation [21][22][23][24]. Within a radar processing context, approaches for radar quantitative precision estimation (QPE) and computer vision have been proposed [25][26][27]. Aguilar et al [28] shows the decision tree as an analytical method that is suitable for making decisions in the automation of natural ventilation in a nonindustrial closed environment.…”

Section: Related Workmentioning

confidence: 99%

FACT: An Air-Ground Communication Framework for Seeding Quality Control of Aircraft

Li¹,

Li²,

Zhou³

et al. 2022

Computer Systems Science and Engineering

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A new type of air-ground communication application framework named FACT (framework for air-ground communication technology with weather-modification aircraft) is presented to track and command weather-modification aircraft to perform ideal cloud seeding. FACT provides a set of solutions from three perspectives, namely, onboard, onground and air-to-ground, with the core purpose of solving the problems of the rapid exchange of information, contract analysis and identifying potential seeding areas when flight plans and meteorological conditions change. On board, the observed data are processed centrally and transmitted downward through air-to-ground communication. The real-time application and sharing of aircraft detection data are strengthened on the ground, and potential areas of operation are automatically identified based on ground data. The communication between the air and the ground achieves a technical breakthrough by realizing double satellite links, adaptive data transmission and VPN channel encryption. Additionally, an application based on FACT is designed and implemented for the real-time command of weather-modified aircraft. This approach has become the key air-to-ground communication system support for more than 40 Chinese aircraft and the big data service support center of airborne data to ensure improved operation of weather-modification aircraft in China.

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Design and Implementation of Heterogeneous Identity Alliance Risk Assessment System

Gan

Sheng

Zhang

et al. 2020

Lecture Notes in Computer Science

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Development of Cloud Based Air Pollution Information System Using Visualization

Cited by 7 publications

References 4 publications

Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java

Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java

FACT: An Air-Ground Communication Framework for Seeding Quality Control of Aircraft

Design and Implementation of Heterogeneous Identity Alliance Risk Assessment System

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