Integrating Explainable Machine Learning and Environmental Epidemiology to Predict Acute Pulmonary Function Decline Associated with Ambient Particle Gamma Radiation Exposure

Abstract

Ambient particulate matter serves as a vector for a range of hazardous constituents, including radon decay progeny that emit ionizing gamma radiation. Short-term elevations in particle-bound gamma activity have been associated with acute decrements in lung function, particularly among individuals with pre-existing respiratory disease. While environmental epidemiology has identified these associations, the translation of such evidence into operational early warning and clinical decision support systems remains nascent. This paper presents a systems-level framework that integrates explainable machine learning with environmental epidemiology to predict acute pulmonary function decline attributable to ambient particle gamma radiation exposure. We examine the architecture of a distributed monitoring and modeling infrastructure, emphasizing trade-offs between sensor fidelity, data granularity, and computational scalability. The design of interpretable predictive models is discussed, focusing on the tension between complex deep learning architectures and inherently transparent models, and on post hoc explanation techniques such as SHAP and LIME that can bridge the gap for clinical and regulatory acceptability. Governance challenges are analyzed with regard to fairness across heterogeneous populations, privacy-preserving health data linkage, and accountability in automated public health alerts. The paper further addresses deployment robustness in the face of concept drift, sensor attrition, and evolving emission profiles, along with sustainability considerations in model training and inference. Policy implications for air quality standards for particulate radioactivity, integration into clinical guidelines for chronic obstructive pulmonary disease management, and the institutional arrangements required to sustain such a system are explored. By synthesizing perspectives from exposure science, machine learning, and socio-technical governance, we delineate a pathway toward a transparent, equitable, and resilient predictive public health infrastructure.