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 dataset 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.

Heavy metals are often prevalent in urban settings due to many possible legacy and modern pollution sources, and are essential to quantify because of the potential adverse health effects associated with them. Of particular importance is lead (Pb), because there is no safe level of exposure, and it especially harms children. Through our partnership with community scientists in the Marion County (Indiana, United States) area, we measured Pb and other heavy metal concentrations in various household media. Community scientists completed screening kits that were then analyzed in the laboratory via X-Ray fluorescence (XRF) to quantify heavy metal concentrations in dust, soil, and paint to determine potential hazards in individual homes. Early results point to renters being significantly more likely to contain higher concentrations of Pb, zinc (Zn), and copper (Cu) in their soil versus homeowners, irrespective of soil sampling location at the home, and home age was significantly negatively correlated with Pb and Zn in soil and Pb in dust across all homes. Analysis of paired soil, dust, and paint samples revealed several important relationships such as significant positive correlations between indoor vacuum dust Pb, dust wipe Pb, and outdoor soil Pb. Our collective results point to rental status being an important determinant of possible legacy metal pollution exposure in Indianapolis, and housing age being reflective of both past and possibly current Zn and Pb pollution at the household scale in dust and soil. Thus, future environmental pollution work examining rental status versus home ownership, as well as other household data such as home condition and resident race/ethnicity, is imperative for better understanding environmental justice issues surrounding not just Pb, but other heavy metals in environmental media as well.