Dietrich Matthew, Shukle John T, Krekeler Mark P S, Wood Leah R, Filippelli Gabriel M
Department of Earth Sciences Indiana University-Purdue University Indianapolis Indianapolis IN USA.
Department of Geology & Environmental Earth Science Miami University Oxford OH USA.
Geohealth. 2022 Feb 20;6(2):e2021GH000525. doi: 10.1029/2021GH000525. eCollection 2022 Feb.
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.
铅（Pb）是一种神经毒性物质，对幼儿危害尤其大。城市环境中土壤和灰尘中的铅含量往往较高，通过吸入和摄入这些受污染介质会带来健康暴露风险。因此，从公共卫生角度更好地了解风险筛查和干预的重点区域至关重要。我们综合了来自美国全国范围内家庭灰尘中铅浓度的大型数据集，这是一项名为“灰尘安全”的社区科学倡议的一部分。利用这些结果，我们开发了一个简单的逻辑回归模型，该模型能在75%的情况下正确预测家庭灰尘中的铅含量是升高（>80 ppm）还是较低（<80 ppm）。此外，我们的模型对铅含量升高的情况估计有18%的假阴性，这表明家庭中铅含量升高被误分类的可能性较低。我们的模型仅使用房屋大致年龄以及房屋内部是否有油漆剥落等变量，这说明如果研究人员提出正确的筛查问题，就能生成一个简单且成功的铅预测模型。扫描电子显微镜显示，在几个铅总浓度升高的灰尘样本中普遍存在含铅油漆，这解释了模型中房屋年龄和油漆剥落的预测能力。该模型还被应用到一个交互式移动应用程序中，旨在提高社区对家庭铅筛查的参与度。该应用程序有望提高对铅风险的认识，并提供一种高效的缓解方法。
