Using Community Science to Better Understand Lead Exposure Risks

Lead (Pb) is a neurotoxicant that particularly harms young children. Urban environments are often plagued with elevated Pb in soils and dusts, posing a health exposure risk from inhalation and ingestion of these contaminated media. Thus, a better understanding of where to prioritize risk screening and intervention is paramount from a public health perspective. We have synthesized a large national data set of Pb concentrations in household dusts from across the United States (U.S.), part of a community science initiative called DustSafe. Using these results, we have developed a straightforward logistic regression model that correctly predicts whether Pb is elevated (>80 ppm) or low (<80 ppm) in household dusts 75% of the time. Additionally, our model estimated 18% false negatives for elevated Pb, displaying that there was a low probability of elevated Pb in homes being misclassified. Our model uses only variables of approximate housing age and whether there is peeling paint in the interior of the home, illustrating how a simple and successful Pb predictive model can be generated if researchers ask the right screening questions. Scanning electron microscopy supports a common presence of Pb paint in several dust samples with elevated bulk Pb concentrations, which explains the predictive power of housing age and peeling paint in the model. This model was also implemented into an interactive mobile app that aims to increase community-wide participation with Pb household screening. The app will hopefully provide greater awareness of Pb risks and a highly efficient way to begin mitigation. Plain Language Summary Community science has been gaining traction in many locales throughout the United States, particularly in the field of urban pollution. While this has helped with science education and informing communities of potential hazards and mitigation tools, little has been done to effectively assimilate this information in a useful way to help people in other communities throughout the country. Thus, we utilized a large data set of household dust samples provided by community scientists across the United States to build a simple predictive model that lets users know if their dust is likely to be high in a toxic metal, lead. Additionally, we built this model into an interactive mobile app that we plan to use as a recruitment tool for usage of lead screening kits. Ultimately, we plan to assess whether this mobile app improves user knowledge of household lead risks and increases participation from start to finish for free lead screening services.

Automated summary

Lead is a neurotoxicant that harms young children. This study developed a predictive model using household dust samples to determine the presence of lead. An interactive mobile app was also developed to increase awareness of lead risks and encourage lead screening.