Browsing by Author "Wood, Leah R."

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    Contributory science reveals insights into metal pollution trends across different households and environmental media
    (IOP, 2023-02-17) Dietrich, Matthew; Wood, Leah R.; Shukle, John T.; Herrmann, Angela; Filippelli, Gabriel M.; Earth and Environmental Sciences, School of Science
    Heavy metals are prevalent in urban settings due to many legacy and modern pollution sources, and are essential to quantify because of the 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 (n = 162 households), we measured Pb and other heavy metal concentrations in soil, paint, and dust. Community scientists completed sampling with screening kits and samples were analyzed in the laboratory via x-ray fluorescence by researchers to quantify heavy metal concentrations, with Pb hazards reported back to participants. Results point to renters being significantly (p ≤ 0.05) 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. Housing 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 metal pollution exposure in Indianapolis, with housing age being reflective of both past and present Zn and Pb pollution at the household scale in dust and soil. Thus, future environmental pollution work examining renters versus homeowners, as well as other household data such as home condition and resident race/ethnicity, is imperative for better understanding environmental disparities surrounding not just Pb, but other heavy metals in environmental media as well.
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    Melting Himalayan Glaciers Threaten Domestic Water Resources in the Mount Everest Region, Nepal
    (Frontiers, 2020-04) Wood, Leah R.; Neumann, Klaus; Nicholson, Kirsten N.; Bird, Broxton W.; Dowling, Carolyn B.; Sharma, Subodh; Earth Sciences, School of Science
    Retreating glaciers and snowpack loss threaten high-altitude communities that rely upon seasonal melt for domestic water resources. But the extent to which such communities are vulnerable is not yet understood, largely because melt contribution to water supplies is rarely quantified at the catchment scale. The Khumbu Valley, Nepal is a highly glaciated catchment with elevations ranging from 2,000 to 8,848 m above sea level, where more than 80% of annual precipitation falls during the summer monsoon from June to September. Samples were collected from the rivers, tributaries, springs, and taps along the major trekking route between Lukla and Everest Base Camp in the pre-monsoon seasons of 2016–2017. Sources were chosen based upon their use by the communities for drinking, cooking, bathing, and washing, so the sample suite is representative of the local domestic water supply. In addition, meltwater samples were collected directly from the base of the Khumbu Glacier, and several rain samples were collected throughout the study site. Meltwater contribution was estimated from δ18O isotopic data using a two-component mixing model with the Khumbu glacial melt and pre-monsoon rain as endmembers. Results indicate between 34 and 90% of water comes from melt during the dry, pre-monsoon season, with an average meltwater contribution of 65%. With as much as two-thirds of the dry-season domestic water supply at risk, the communities of the Khumbu Valley are extremely vulnerable to the effects of climate change as glaciers retreat and snowpack declines.
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    Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention
    (Elsevier, 2023) Dietrich, Matthew; Barlow, Cynthia F.; Entwistle, Jane A.; 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; Filippelli, Gabriel M.; Earth and Environmental Sciences, School of Science
    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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    Screening for Lead: Predictive Modeling of Indoor Dust Lead Concentrations and Possible Effects of Intervention
    (Elsevier, 2022) Dietrich, Matthew; Isley, Cynthia F.; Entwistle, Jane A.; 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; Filippelli, Gabriel M.; Earth and Environmental Sciences, School of Science
    Lead (Pb) pollution 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 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 the U.K. 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. We also find that the heterogeneity associated with Pb pollution at a global scale can complicate the predictive accuracy of our model, which is lower for countries outside the U.K., 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 the U.K., U.S., and Australia, straightforward household intervention could conservatively save $70 billion USD within a four-year period, and as much as $1.68 trillion USD globally with universal household remediation based on our predictive results.
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    Using Community Science to Better Understand Lead Exposure Risks
    (AGU, 2022-02) Dietrich, Matthew; Shukle, John T.; Krekeler, Mark P. S.; Wood, Leah R.; Filippelli, Gabriel M.; Earth Sciences, School of Science
    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.