Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

Abstract

This study evaluates a wide range of epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 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. Critically, these 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 PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and epidemiological lines. A nonlinear environmental Kuznets curve may describe the fuller relation-ship between economic activity and premature death from PM2.5 exposure. The political alliance of the EU-27 countries should bridge the historical, cultural, and economic gaps that impair the European response to health issues attributable to PM2.5 pollution.

Keywords: air pollution, particulate matter, PM2.5, public health, environmental Kuznets curve, machine learning, supervised learning, unsupervised learning, clustering, manifold learning, dimension-ality reduction, principal component analysis, European Union