PDF(Pubmed)
Abstract：
UNASSIGNED: Heavy metals, pesticides and a host of contaminants found in dust and soil pose a health risk to young children through ingestion. Dust/soil ingestion rates for young children can be estimated using micro-level activity time series (MLATS) as model inputs. MLATS allow for the generation of frequency and duration of children\'s contact activities, along with sequential contact patterns. Models using MLATS consider contact types, and transfer dynamics to assign mechanisms of contact and appropriate exposure factors for cumulative estimates of ingestion rates.
UNASSIGNED: The objective of this study is to describe field implementation, data needs, advanced field collection, laboratory methodologies, and challenges for integrating into and updating a previously validated physical-stochastic MLATS-based model framework called the Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) model. The manuscript focuses on describing the methods implemented in the current study.
UNASSIGNED: This current multidisciplinary study (Dust Ingestion childRen sTudy [DIRT]) was implemented across three US regions: Tucson, Arizona; Miami, Florida and Greensboro, North Carolina. Four hundred and fifty participants were recruited between August 2021 to June 2023 to complete a 4-part household survey, of which 100 also participated in a field study.
UNASSIGNED: The field study focused on videotaping children\'s natural play using advanced unattended 360° cameras mounted for participants\' tracking and ultimately conversion to MLATS. Additionally, children\'s hand rinses were collected before and after recording, along with indoor dust and outdoor soil, followed by advanced mass analysis. The gathered data will be used to quantify dust/soil ingestion by region, sociodemographic variables, age groups (from 6 months to 6 years), and other variables for indoor/outdoor settings within an adapted version of the CACHED model framework.
UNASSIGNED: New innovative approaches for the estimation of dust/soil ingestion rates can potentially improve modeling and quantification of children\'s risks to contaminants from dust exposure.
UNASSIGNED: The objective of this study is to describe field implementation, data needs, advanced field collection, laboratory methodologies, and challenges for integrating into and updating a previously validated physical-stochastic MLATS-based model framework called the Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) model. The manuscript focuses on describing the methods implemented in the current study.
UNASSIGNED: This current multidisciplinary study (Dust Ingestion childRen sTudy [DIRT]) was implemented across three US regions: Tucson, Arizona; Miami, Florida and Greensboro, North Carolina. Four hundred and fifty participants were recruited between August 2021 to June 2023 to complete a 4-part household survey, of which 100 also participated in a field study.
UNASSIGNED: The field study focused on videotaping children\'s natural play using advanced unattended 360° cameras mounted for participants\' tracking and ultimately conversion to MLATS. Additionally, children\'s hand rinses were collected before and after recording, along with indoor dust and outdoor soil, followed by advanced mass analysis. The gathered data will be used to quantify dust/soil ingestion by region, sociodemographic variables, age groups (from 6 months to 6 years), and other variables for indoor/outdoor settings within an adapted version of the CACHED model framework.
UNASSIGNED: New innovative approaches for the estimation of dust/soil ingestion rates can potentially improve modeling and quantification of children\'s risks to contaminants from dust exposure.
摘要：
■重金属，农药和灰尘和土壤中的大量污染物通过摄入对幼儿的健康风险。可以使用微观活动时间序列（MLATS）作为模型输入来估算幼儿的灰尘/土壤摄取率。MLATS允许产生儿童接触活动的频率和持续时间，以及顺序接触模式。使用MLATS的模型考虑接触类型，和转移动力学，以分配接触机制和适当的暴露因素，以累积估计摄取率。
■本研究的目的是描述现场实施，数据需求,高级现场集合，实验室方法，以及整合和更新先前经过验证的基于物理随机MLATS的模型框架（称为儿童特定的总体累积人体暴露和剂量（CACHED）模型）的挑战。手稿着重于描述当前研究中实施的方法。
■这项当前的多学科研究（粉尘摄入儿童[DIRT]）在美国三个地区实施：图森，亚利桑那州；迈阿密，佛罗里达和格林斯伯勒,北卡罗来纳州。在2021年8月至2023年6月之间招募了450名参与者，以完成由4部分组成的家庭调查。其中100人还参加了实地研究。
■现场研究的重点是使用先进的无人值守的360°摄像机对儿童的自然游戏进行录像，以跟踪参与者并最终转换为MLATS。此外,儿童的手冲洗液收集之前和之后的记录，以及室内灰尘和室外土壤，其次是先进的质量分析。收集的数据将用于按地区量化灰尘/土壤摄入，社会人口统计学变量，年龄组（从6个月到6岁），以及适用于CACHED模型框架的自适应版本内的室内/室外设置的其他变量。
■估算灰尘/土壤摄入率的新的创新方法可能会改善儿童暴露于灰尘污染物的风险建模和量化。
■本研究的目的是描述现场实施，数据需求,高级现场集合，实验室方法，以及整合和更新先前经过验证的基于物理随机MLATS的模型框架（称为儿童特定的总体累积人体暴露和剂量（CACHED）模型）的挑战。手稿着重于描述当前研究中实施的方法。
■这项当前的多学科研究（粉尘摄入儿童[DIRT]）在美国三个地区实施：图森，亚利桑那州；迈阿密，佛罗里达和格林斯伯勒,北卡罗来纳州。在2021年8月至2023年6月之间招募了450名参与者，以完成由4部分组成的家庭调查。其中100人还参加了实地研究。
■现场研究的重点是使用先进的无人值守的360°摄像机对儿童的自然游戏进行录像，以跟踪参与者并最终转换为MLATS。此外,儿童的手冲洗液收集之前和之后的记录，以及室内灰尘和室外土壤，其次是先进的质量分析。收集的数据将用于按地区量化灰尘/土壤摄入，社会人口统计学变量，年龄组（从6个月到6岁），以及适用于CACHED模型框架的自适应版本内的室内/室外设置的其他变量。
■估算灰尘/土壤摄入率的新的创新方法可能会改善儿童暴露于灰尘污染物的风险建模和量化。
