Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention

Matthew Dietrich*, Cynthia F. Barlow, Jane Entwistle, Diana Meza-Figueroa, Chenyin Dong, Peggy Gunkel-Grillon, Khadija Jabeen, Lindsay Bramwell, John T. Shukle, Leah R. Wood, Ravi Naidu, Kara L. Fry, Mark Patrick Taylor, Gabriel Michael Filippelli

*Corresponding author for this work

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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.
Original languageEnglish
Article number121039
JournalEnvironmental Pollution
Volume319
Early online date7 Jan 2023
DOIs
Publication statusE-pub ahead of print - 7 Jan 2023

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