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

Yuny Fikriyah Shofy; Masita Dwi Mandini Manessa; Adi Wibowo

doi:10.1117/12.3009641

29 January 2024 Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java

Yuny Fikriyah Shofy, Masita Dwi Mandini Manessa, Adi Wibowo

Author Affiliations +

Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129770P (2024) https://doi.org/10.1117/12.3009641
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yuny Fikriyah Shofy, Masita Dwi Mandini Manessa, and Adi Wibowo "Spatial and temporal distribution model of carbon monoxide (CO) and particulate matter (PM) emission around PLTU Pelabuhan Ratu, Sukabumi, West Java", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129770P (29 January 2024); https://doi.org/10.1117/12.3009641

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KEYWORDS

Carbon monoxide

Data modeling

Modeling

Atmospheric modeling

Satellites

Interpolation

Landsat

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