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- Li, Longxiang, et al.
(författare)
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Ratios between Radon Concentrations in Upstairs and Basements : A Study in the Northeastern and Midwestern United States
- 2022
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Ingår i: Environmental Science and Technology Letters. - : American Chemical Society (ACS). - 2328-8930. ; 9:2, s. 191-197
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Tidskriftsartikel (refereegranskat)abstract
- Temporally resolved assessment of residential exposure to radon is essential for investigating radon's acute health effects. Recent studies have used large numbers of short-term radon measurements to model the spatiotemporal variations in radon concentrations. However, most short-term radon measurements in the northeastern and midwestern United States were conducted in the basements, which were less frequently occupied and had higher average radon concentrations than the upstairs spaces. Disproportionate usage of basement radon measurements in exposure assessment potentially introduces misclassifications. In an effort to mitigate the issue, we investigated the spatiotemporal gradients in ratios between the radon concentrations in the upstairs and basements (hereafter upstairs/basement ratio). Building-specific ratios were calculated on the basis of 10774 pairs of simultaneous short-term measurements and then aggregated by state and season. We found that upstairs/basement ratios of northeastern states are generally lower than those of Midwestern states, a pattern also found in 3508 pairs of simultaneous long-term radon measurements. Ratios in winter are higher than those in other seasons. Our results, in conjunction with behavior data, can improve the assessment of short-term residential exposure to radon and therefore facilitate future studies regarding the acute health effects of radon.
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| 2. |
- Li, Longxiang, et al.
(författare)
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A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States
- 2021
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Ingår i: Environment International. - : Elsevier BV. - 0160-4120. ; 156
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Tidskriftsartikel (refereegranskat)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/m3). Our model provided an accurate estimation of particulate radioactivity, thus can be used in future health studies.
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