Department of Built Environment, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.
Environmental Health and Disease Laboratory, North Carolina A&T State University, Greensboro, North Carolina 27411, United States.
Environ Sci Technol. 2021 Mar 16;55(6):3696-3705. doi: 10.1021/acs.est.0c07317. Epub 2021 Feb 24.
This study characterizes potential soil lead (Pb) exposure risk at the household scale in Greensboro, North Carolina, using an innovative combination of field sampling, statistical analysis, and machine-learning techniques. Soil samples were collected at the dripline, yard, and street side at 462 households (total sample size = 2310). Samples were analyzed for Pb and then combined with publicly available data on potential historic Pb sources, soil properties, and household and neighborhood demographic characteristics. This curated data set was then analyzed with statistical and machine-learning techniques to identify the drivers of potential soil Pb exposure risks and to build predictive models. Among all samples, 43% exceeded current guidelines for Pb in residential gardens. There were significant racial disparities in potential soil Pb exposure risk; soil Pb at the dripline increased by 19% for every 25% increase in the neighborhood population identifying as Black. A machine-learned Bayesian network model was able to classify residential parcels by risk of exceeding residential gardening standards with excellent reproducibility in cross validation. These findings underscore the need for targeted outreach programs to prevent Pb exposure in residential areas and demonstrate an approach for prioritizing outreach locations.
本研究采用创新性的现场采样、统计分析和机器学习技术，对北卡罗来纳州格林斯伯勒的家庭土壤铅（Pb）暴露风险进行了特征描述。在 462 户家庭（总样本量为 2310 户）的滴灌线、院子和街道旁采集了土壤样本。对样本进行了 Pb 分析，然后将其与潜在历史 Pb 源、土壤特性以及家庭和社区人口特征的公开可用数据相结合。然后，使用统计和机器学习技术对这些经过精心整理的数据进行分析，以确定潜在土壤 Pb 暴露风险的驱动因素并建立预测模型。在所有样本中，43%的样本超过了住宅花园中 Pb 的现行标准。潜在土壤 Pb 暴露风险存在显著的种族差异；邻里中黑人居民比例每增加 25%，滴灌线的土壤 Pb 含量就会增加 19%。一个基于机器学习的贝叶斯网络模型能够以极高的交叉验证可重复性，根据超过住宅园艺标准的风险对住宅地块进行分类。这些发现强调了在居民区开展有针对性的外展计划以预防 Pb 暴露的必要性，并展示了一种确定外展地点优先级的方法。
