Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models

Soyoung Park, Sanghun Son, Jaegu Bae, Doi Lee, Jae-Jin Kim and Jinsoo Kim
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Soyoung Park: LX Education Institute, 182, Yeonsudanji-Gil, Sagok-Myeon, Gongju-Si 32522, Korea
Sanghun Son: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Jaegu Bae: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Doi Lee: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Jae-Jin Kim: Department of Environmental Atmospheric Sciences, Pukyong National University, 45 Yongso-Ro, Busan 48513, Korea
Jinsoo Kim: Department of Spatial Information Engineering, Pukyong National University, 45 Yongso-Ro, Busan 48513, Korea

Sustainability, 2021, vol. 13, issue 24, 1-15

Abstract: Particulate matter (PM) as an air pollutant is harmful to the human body as well as to the ecosystem. It is crucial to understand the spatiotemporal PM distribution in order to effectively implement reduction methods. However, ground-based air quality monitoring sites are limited in providing reliable concentration values owing to their patchy distribution. Here, we aimed to predict daily PM 10 concentrations using boosting algorithms such as gradient boosting machine (GBM), extreme gradient boost (XGB), and light gradient boosting machine (LightGBM). The three models performed well in estimating the spatial contrasts and temporal variability in daily PM 10 concentrations. In particular, the LightGBM model outperformed the GBM and XGM models, with an adjusted R 2 of 0.84, a root mean squared error of 12.108 μg/m 2 , a mean absolute error of 8.543 μg/m 2 , and a mean absolute percentage error of 16%. Despite having high performance, the LightGBM model showed low spatial prediction accuracy near the southwest part of the study area. Additionally, temporal differences were found between the observed and predicted values at high concentrations. These outcomes indicate that such methods can provide intuitive and reliable PM 10 concentration values for the management, prevention, and mitigation of air pollution. In the future, performance accuracy could be improved through consideration of different variables related to spatial and seasonal characteristics.

Keywords: particulate matter; air pollutant; gradient boosting machine; extreme gradient boost; light gradient boosting machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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