@article{10272/28000,
year = {2025},
url = {https://hdl.handle.net/10272/28000},
abstract = {Atmospheric particulate matter (PM), as a leading part of air pollution, affects health in many ways. Thus, identifying and quantifying the contribution of atmospheric particulate matter sources of PM is vital for developing effective air quality management strategies. Positive Matrix Factorization (PMF) is one of the most common methods for source apportionment. However, PMF has some limitations, particularly its assumption that each source contributes linearly. In reality, some sources may exhibit nonlinear behaviors, which can compromise the accuracy of source apportionment. This study introduces a Lung Performance Optimization-based XGBoost (LPO-XGBoost) model, which leverages adaptive optimization principles inspired by lung function to enhance classic PM source apportionment. We demonstrate the potential for efficient, real-time application of the LPO-XGBoost model across 21 monitoring sites in 6 European countries. Trained and validated on extensive environmental datasets, the model is capable of predicting major pollution sources, including road traffic, biomass burning, crustal, industrial, nitrate-rich particles, sulfate-rich particles, heavy fuel oil, and sea salt. It outperforms other machine learning models with an overall predictive coefficient of determination (r2 = 0.88). Notably, the model performs exceptionally well in predicting sources such as sea salt (r2 = 0.97) and biomass burning (r2 = 0.89), but shows lower accuracy for the sulfate-rich particles source (r2 = 0.75). Comparative analyses with models including Random Forest (RF), Support Vector Machine (SVM), and their LPO-enhanced variants confirm that LPO-XGBoost provides the most reliable performance in estimating pollution source contributions, offering scalability and robustness ideal for high-time-resolution observational data. This model has significant potential to support targeted air quality management strategies. Future research should focus on expanding key species measurements at monitoring sites, ensuring consistent  temporal coverage, and optimizing the model for improved mixed-source predictions to strengthen its applicability in comprehensive urban air quality assessments.},
organization = {This study has been supported and been done by RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial Areas, European Union's Horizon 2020 research and innovation program, Green Deal, European Commission, contract 101036245). Furthermore, additional support has been received from State Key Laboratory of Resources and Environmental Information System, the National Natural Science Foundation of China (42407566, 42205099, 72441001), and the Chunhui Project Foundation of the Education Department of China (HZKY20220053), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (72221002) that carried out the ML implementation. Meanwhile, samples in France were collected within many research and Air Quality assessment programs, including the programs CARA (funded by the Ministry of Environment within the LCSQA), DECOMBIO, CAMERA, and QAMECS (all funded by Ademe), ACME and MIAI-Airquality (funded by University Grenoble Alpes), OPE – Andra (funded by Andra), and multiple fundings by Atmo AuRA, Atmo Sud, Atmo Grand Est, Atmo Haut de France, Atmo Normandie, for the sampling and analyses. We would like to express our deep thanks to many people in the AASQA France for the sampling of all these samples, and to people in several laboratories in France, including IGE, for the analyses of these samples. The University of Aveiro thanks the Foundation for Science and Technology (FCT) for funding CESAM (UID Centro de Estudos do Ambiente e do Mar + LAP/0094/2020). The University of Granada acknowledges the financial support of the Spanish Ministry of Science and Innovation through the project ELPIS PID2020-120015RB-I00. Samples in Switzerland were collected by the Swiss National Air Pollution Monitoring Network NABEL (BAFU/Empa).},
publisher = {Elsevier},
title = {Source apportionment of PM10 particles in the urban atmosphere using PMF and LPO-XGBoost},
doi = {10.1016/j.envres.2025.121659},
author = {Liu, Ying and Rosa Díaz, Jesús de la and Querol, X.},
}