Accurate PM 2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

Autor: Rakholia R; Ireland's National Centre for Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland., Le Q; Ireland's National Centre for Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland. Electronic address: quan.le@ucd.ie., Vu K; Institute for Environment and Resources (IER), Ho Chi Minh City, 700000, Viet Nam., Ho BQ; Institute for Environment and Resources (IER), Ho Chi Minh City, 700000, Viet Nam; Department of Science and Technology, Vietnam National University, Ho Chi Minh City, 700000, Viet Nam., Carbajo RS; Ireland's National Centre for Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
Jazyk: angličtina
Zdroj: Chemosphere [Chemosphere] 2024 Sep; Vol. 364, pp. 143097. Date of Electronic Publication: 2024 Aug 16.
DOI: 10.1016/j.chemosphere.2024.143097
Abstrakt: Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM 2.5 ), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM 2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM 2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM 2.5 concentrations, considering meteorological conditions, the rolling mean of PM 2.5 time series, and temporal features. PM 2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM 2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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