Short-term prediction of particulate matter (PM 10 and PM 2.5 ) in Seoul, South Korea using tree-based machine learning algorithms

Atmospheric Pollution Research

article

Bu-Yo Kim¹  Yun-Kyu Lim¹  Joo Wan Cha¹ 

[1] Research Applications Department, National Institute of Meteorological Sciences

关键词: Particulate matter prediction;    PM 10;    PM 2.5;    Tree-based machine learning;    Air quality monitoring;    Light gradient boosting algorithm;   

DOI  :  10.1016/j.apr.2022.101547

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

In this study, highly accurate particulate matter (PM 10 and PM 2.5 ) predictions were obtained using meteorological prediction data from the local data assimilation and prediction system (LDAPS) and tree-based machine learning (ML). The study area was Seoul, South Korea, and data from July 2018 to June 2021 as well as LDAPS 36-h predictions with 1-h intervals 4 times a day were used. The predicted PM values were then compared with the observed PM measurements to evaluate the prediction accuracy. The PM prediction performance of the Community Multi-Scale Air Quality (CMAQ)-based chemical transport model (CTM) was compared with that reported by this study. The experimental results report that, among tree-based ML algorithms, light gradient boosting (LGB) is the most suitable for PM prediction. The PM prediction results of the LGB algorithm for the hourly test data were: bias = −0.10 μg/m 3 , root mean square error (RMSE) = 13.15 μg/m 3 , and R 2  = 0.86 for PM 10 and bias = −0.02 μg/m 3 , RMSE = 7.48 μg/m 3 , and R 2  = 0.83 for PM 2.5 , and for daily mean were: RMSE ≤1.16 μg/m 3 and R 2  = 0.996. The relative RMSE (%RMSE) is 21% lower than the results of the CTM model, and R 2 is 0.20 higher. Even in the high PM concentration case prediction results, the algorithm showed good predictive performance with %RMSE = 8.91%–20.43% and R 2  = 0.89–0.97. Therefore, in addition to the CTM, high-accuracy PM prediction results using ML can also be used for air quality monitoring and improvement.

【 授权许可】

CC BY