Source apportionment of air pollution in urban areas: a review of the most suitable source-oriented models

Author:

Coelho S.ORCID,Ferreira J.,Lopes M.

Abstract

AbstractNotwithstanding the improvements already achieved in recent decades through regional and urban scale actions implemented across Europe, air pollution is still a major environment and health concern for Europeans. The quantitative assessment of the different sources of air pollution in regional/urban areas is crucial to support the design of accurate air quality plans. Source apportionment techniques are capable to relate air pollutant concentrations to existing emission sources activities and regions. The selection of the appropriate source apportionment technique to apply to a given area should take into account the ultimate goal of the study. Despite the growing number of studies that include source apportionment techniques, there is still a lack of works that summarise information on this topic in a systematic way. In this work, a literature review of studies applying SA techniques, published between 2010 and 2021, was performed. Additionally, this review summarizes the differences among the different source apportionment techniques, with focus on source-oriented models, highlighting their purpose and their advantages and disadvantages. Results shows that the number of studies using source apportionment source-oriented models has been increasing across the years, with 59% using tagged species methods, 28% brute force methods, and 13% other methods. This source-oriented models have been mostly applied for PM2.5, to assess the causes of air pollution levels.

Funder

Ministério da Ciência, Tecnologia e Ensino Superior

Universidade de Aveiro

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparison of Air Quality Prediction using Random Forest and Gradient Boosting Tree;2023 Eighth International Conference on Informatics and Computing (ICIC);2023-12-08

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