TY - JOUR
T1 - Predictive modeling of indoor dust lead concentrations
T2 - Sources, risks, and benefits of intervention
AU - Dietrich, Matthew
AU - Barlow, Cynthia F.
AU - Entwistle, Jane
AU - Meza-Figueroa, Diana
AU - Dong, Chenyin
AU - Gunkel-Grillon, Peggy
AU - Jabeen, Khadija
AU - Bramwell, Lindsay
AU - Shukle, John T.
AU - Wood, Leah R.
AU - Naidu, Ravi
AU - Fry, Kara L.
AU - Taylor, Mark Patrick
AU - Filippelli, Gabriel Michael
N1 - 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.
PY - 2023/2/15
Y1 - 2023/2/15
N2 - 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.
AB - 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.
KW - Community science
KW - Indoor dust
KW - Pb pollution
KW - Pb screening
KW - Predictive modeling
UR - http://www.scopus.com/inward/record.url?scp=85146091766&partnerID=8YFLogxK
U2 - 10.1016/j.envpol.2023.121039
DO - 10.1016/j.envpol.2023.121039
M3 - Article
SN - 0269-7491
VL - 319
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 121039
ER -