Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM_2.5 Pollution

• Main Authors: James Ming Chen, Mira Zovko, Nika Šimurina, Vatroslav Zovko
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: air pollution; particulate matter; PM_2.5; public health; environmental Kuznets curve; machine learning
• Online Access: https://www.mdpi.com/1660-4601/18/16/8688
• ISSN: 1661-7827; 1660-4601

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM_2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM_2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM_2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM_2.5 pollution.