Robust environmental exposure pattern recognition and outlier detection with the open-source R package pcpr

Pattern recognition in high-dimensional mixture data is of increasing interest in environmental health (EH), as researchers often aim to identify sources or behaviors leading to potentially harmful exposures. Principal component pursuit (PCP)-a robust dimensionality reduction technique-has been successfully utilized for pattern recognition in a number of EH studies. PCP decomposes an exposure matrix into a low-rank matrix encoding consistent exposure patterns and a sparse matrix isolating outlying exposure events. However, PCP's application has been hindered by a lack of available software tailored specifically to EH research.

We introduce an open-source R package, pcpr, enabling easy PCP deployment in EH research. The package provides functions to fit and fine-tune PCP models with three EH-specific extensions: (1) a non-negativity constraint on the low-rank matrix, enhancing interpretability; (2) procedures to accommodate missingness; and (3) a specialized penalty for observations below the analytic limit of detection. We illustrate core functionality by conducting a fully reproducible source-apportionment analysis of an air pollution mixture of 26 PM_2.5 constituents measured every few days in Queens, New York City (2015-2021).

The functions in pcpr autonomously extracted four consistent exposure patterns (secondary signal and tailpipe emissions, traffic, crustal dust, and salt) and 211 outlying exposure events (including July Fourth firework-related spikes) from the Queens PM_2.5 mixture. If found to be associated with adverse health outcomes in downstream health models, these exposure patterns and extreme events can support targeted regulatory action.

The pcpr package facilitates robust, reproducible, and accessible exposure pattern recognition tailored to environmental epidemiology.