EPA Particulate Matter Data -- Analyses using Local Control Strategy

TL;DR

This paper demonstrates the application of a nonparametric, unsupervised statistical learning approach to analyze US environmental data, exploring the relationship between biogenic particulate matter in PM2.5 and mortality rates.

Contribution

It introduces the NU Learning methodology for analyzing large environmental datasets and encourages collaborative research to understand PM2.5 health effects.

Findings

Regions with high biogenic PM2.5 tend to have higher mortality rates.

The NU Learning approach effectively analyzes complex environmental data.

Open data availability promotes broader research efforts.

Abstract

Statistical Learning methodology for analysis of large collections of cross-sectional observational data can be most effective when the approach used is both Nonparametric and Unsupervised. We illustrate use of our NU Learning approach on 2016 US environmental epidemiology data that we have made freely available. We encourage other researchers to download these data, apply whatever methodology they wish, and contribute to development of a broad-based ``consensus view'' of potential effects of Secondary Organic Aerosols (volatile organic compounds of predominantly biogenic or anthropogenic origin) within PM2.5 particulate matter on circulatory and/or respiratory mortality. Our analyses here focus on the question: ``Are regions with relatively high air-borne biogenic particulate matter also expected to have relatively high circulatory and/or respiratory mortality?''