Source apportionment of PM10 particles in the urban atmosphere using PMF and LPO-XGBoost
Year: 2025
Type: article
Abstract: = 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.
Source: Environmental Research
Authors: Ying Liu, Bowen Jin, Xun Zhang, Xiansheng Liu, Tao Wang, Vy Ngoc Thuy Dinh, Jean-Luc Jaffrezo, Gaëlle Uzu, Pamela Dominutti, Sophie Darfeuil, Olivier Favez, Sébastien Conil, Nicolas Marchand, Sonia Castillo, Jesús D. de la Rosa, Stuart Grange, Christoph Hueglin, Konstantinos Eleftheriadis, Evangelia Diapouli, Manousos-Ioannis Manousakas, Maria Gini, Giulia Calzolai, Célia Alves, Marta Monge, Cristina Reche, Roy M. Harrison, Philip K. Hopke, Andrés Alastuey, Xavier Querol
Institutions: Beijing Technology and Business University, Beijing Municipal Education Commission, State Key Laboratory of Resources and Environmental Information System, Xinjiang Hetian College, Guangdong University of Technology, Institute of Environmental Assessment and Water Research, Fudan University, Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut des Géosciences de l'Environnement, Institut de Recherche pour le Développement, Institut national de l'environnement industriel et des risques, Agence Nationale pour la Gestion des Déchets Radioactifs, Aix-Marseille Université, Universidad de Granada, Consejo Superior de Investigaciones Científicas, Universidad de Huelva, Swiss Federal Laboratories for Materials Science and Technology, National Centre of Scientific Research "Demokritos", Istituto Nazionale di Fisica Nucleare, Sezione di Firenze, University of Florence, Centro de Estudos do Ambiente e do Mar, University of Aveiro, University of Birmingham, Clarkson University, University of Rochester Medicine, University of Rochester
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,880
Funders: Universidade de Aveiro, Université Grenoble Alpes, European Commission, National Natural Science Foundation of China, Ministry of Environment, Science Fund for Creative Research Groups, Ministerio de Ciencia e Innovación, Universidad de Granada, State Key Laboratory of Resources and Environmental Information System, Région Normandie, Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro, Fundação para a Ciência e a Tecnologia, Bundesamt für Umwelt, Horizon 2020, Chunhui Project Foundation of the Education Department of China
Awards: G2411503435, G2656030440, G2763078649, Centro de Estudos do Ambiente e do Mar, G3441463414, G3693810898, G3714572211, G3819876658, G4090066734, G434337789, G5805502524, G6564996764, Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS (RI-URBANS), G8349692305, G8697456707, G8871150867
OpenAlex ID: W4409686182