Dietrich, Matthew, Barlow, Cynthia F., Entwistle, Jane, Meza-Figueroa, Diana, Dong, Chenyin, Gunkel-Grillon, Peggy, Jabeen, Khadija, Bramwell, Lindsay, Shukle, John T., Wood, Leah R., Naidu, Ravi, Fry, Kara, Taylor, Mark Patrick and Filippelli, Gabriel M. (2023) Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention. Environmental Pollution, 319. p. 121039. ISSN 0269-7491
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Abstract
Lead (Pb) contamination continues to contribute to world-wide morbidity in all countries, particularly low- and middle-income countries. Despite its continued widespread adverse effects on global populations, particularly children, accurate prediction of elevated household dust Pb and the potential implications of simple, low-cost household interventions at national and global scales have been lacking. A global dataset (∼40 countries, n = 1951) of community sourced household dust samples were used to predict whether indoor dust was elevated in Pb, expanding on recent work in the United States (U.S.). Binned housing age category alone was a significant (p < 0.01) predictor of elevated dust Pb, but only generated effective predictive accuracy for England and Australia (sensitivity of ∼80%), similar to previous results in the U.S. This likely reflects comparable Pb pollution legacies between these three countries, particularly with residential Pb paint. The heterogeneity associated with Pb pollution at a global scale complicates the predictive accuracy of our model, which is lower for countries outside England, the U.S., and Australia. This is likely due to differing environmental Pb regulations, sources, and the paucity of dust samples available outside of these three countries. In England, the U.S., and Australia, simple, low-cost household intervention strategies such as vacuuming and wet mopping could conservatively save 70 billion USD within a four-year period based on our model. Globally, up to 1.68 trillion USD could be saved with improved predictive modeling and primary intervention to reduce harmful exposure to Pb dust sources.
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| Additional Information: | Funding information: We are deeply grateful to those who provided dust samples and the lab techs who helped process samples. Support for this work to M.D. was from the U.S. National Science Foundation (NSF-EAR-PF Award #2052589); to G.M.F. from the Environmental Resilience Institute, funded by Indiana University’s Prepared for Environmental Change Grand Challenge Initiative, the U.S. National Science Foundation (NSF-ICER Award #1701132), and the U.S. Housing and Urban Development Agency; to J.E. from the Natural Environment Research Council (Research Grant NE/T004401/1, U.K. For the purpose of open access, the authors have applied a creative commons attribution (CC BY) licence (where permitted by UKRI, ‘open government licence’ or ‘creative commons attribution no-derivatives (CC BY-ND) licence’); and to M.P.T. from the Australian Government Citizen Science Grant, CSG55984. Lastly, we acknowledge the four anonymous reviewers and the editor for their helpful, constructive comments. |
| Uncontrolled Keywords: | Community science, Pb pollution, Indoor dust, Predictive modeling, Pb screening |
| Subjects: | F800 Physical and Terrestrial Geographical and Environmental Sciences |
| Department: | Faculties > Engineering and Environment > Geography and Environmental Sciences |
| Depositing User: | Elena Carlaw |
| Date Deposited: | 12 Jan 2023 16:23 |
| Last Modified: | 17 Jan 2023 14:14 |
| URI: | https://nrl.northumbria.ac.uk/id/eprint/51148 |
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