A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States. (November 2021)

• Record Type: Journal Article
• Authors: Li, Longxiang; Blomberg, Annelise J.; Lawrence, Joy; Réquia, Weeberb J.; Wei, Yaguang; Liu, Man; Peralta, Adjani A.; Koutrakis, Petros
• Is Part Of: Environment international. Volume 156(2021)
• Publication Date: 2021-11
• Subjects: Particulate radioactivity -- Spatiotemporal ensemble learning -- Statistical learning -- Geographically and temporally weighted regression; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106643
• Languages: English
• ISSNs: 0160-4120

Abstract

Graphical abstract: Highlights: Exposure to elevated particulate radioactivity was linked to negative health outcomes. We developed a model to predict particulate radioactivity across the contiguous U.S. A new ensemble learning method was developed to achieve better prediction performance. Our predictions can be used to investigate the health effects of particulate matter. Abstract: Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32 km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models that provide different predictions. In stage two, we used a non-negative geographically and temporally weighted regression method to aggregate the selected base learner predictions based on their local performance. The results of block cross-validation analysis suggested that the non-negative geographically and temporally weighted regression ensemble learning model outperformed all base learning model with the smallest rooted mean square error (0.094 mBq/m 3 ). Our model provided an accurate estimation of particulate radioactivity, thus can be used in future health studies.